MRes Bioengineering students work on their research project throughout the year. You can apply for one of the projects listed below, or contact your preferred supervisor to discuss a different project.
You must name at least one project or potential supervisor in your personal statement when you apply.
Applications will be considered in three rounds. We encourage you to apply in Round 2 or 3 for the best chance to be considered for your preferred project. If you apply in round 4, please consider including a second or third choice project in your application, as some projects may already have been allocated.
Visit the course page for full instructions and deadlines.
Projects available for 2027-28 entry
- Professor Anil Bharath
- Dr Jun Ishihara
- Dr Dandan Zhang
- Dr David Labonte
- Dr Guang Yang
- Dr Adam Celiz
- Dr Amy Howard
- Dr Andriy Kozlov
- Dr Chiu Fan Lee
- Dr Chris Richards
- Dr Chris Rowlands
- Dr Claire Stanley
- Dr Huai-Ti Lin
- Dr Joseph van Batenburg-Sherwood
- Dr Kaushik Jayaram
- Dr Laki Pantazis
- Dr Swathi Padmanabhan
- Dr Sylvain Ladame
- Dr Tom Ouldridge
- Dr Yap
- Dr Hayriye Cagnan
- Dr James Choi
- Professor Jimmy Moore
- Professor Mengxing Tang
- Dr Sonja Billerbeck
- Dr Pedro Ballester
- Dr Sam Au
- Professor Rylie Green
- Dr Sophie Morse
- Professor Martyn Boutelle
- Professor Claire Higgins
- Professor Danny O'Hare
- Professor Darryl Overby
- Professor Holger Krapp
- Professor Julien Vermot
- Professor Manos Drakakis
- Professor Reiko Tanaka
- Professor Rodrigo Ledesma Amaro
- Professor Spyros Masouros
- Professor Tom Ellis
Professor Anil Bharath
Profile: https://profiles.imperial.ac.uk/a.bharath
Contact details: a.bharath@imperial.ac.uk
| Project title | Description |
| Generative Modelling: Simulating Blood Glucose Levels | We are making use of insulin dose response modelling to equip language-based LLMs with an "understanding" of physiological responses to exercise, meal intake and endogenous and exogenous insulin. This allows LLMs to be able to aid the process of interpreting the results of CGM sensing, and identify the timing of certain events in a person's activities that may not have been recorded in another way. Quite apart from the use cases in patient monitoring and clinical management of diabetes, particularly T1DM, there may be privacy implications to releasing CGM traces of known individuals. This project builds not only the underlying simulation, but you will be working with LLM and AI specialists to help build flexible and robust simulations that can inform an AI agent (part LLM, part time-series interpreter), how to read CGM data correctly.This project, unlike many others, has very strong pre-requisites around computational modelling of physiology. Experience with data-driven AI is NOT necessary - there is plenty of that around! What we would suggest is a candidate with a good foundation and strong interest in the use of differential equations and how ODEs and PDEs, with appropriate numeric solvers, can be used to model physiological systems. What you will learn is how to use simulation data to "inform" LLMs. Again, we normally DON'T put prerequisites on our projects, but this project is a clear exception due to the nature of the project. |
| Language Models as Interfaces to Physiological Simulations | Language models are rapidly finding their way into use for laboratory, clinical summarisation and - of course - in software engineering. However, attempts to equip LLMs with time-series encoders or joint time-series/language models are challenging to implement to satisfactory levels of accuracy and reproducibility. In this project the aim will be to equip LLMs with access to well-tested physiological simulators that carefully bound predictions according to physical and physiological laws. We hypothesise that doing so will help bypass the current limitations in joint language/time-series training, and will allow LLMs to harness the capacity of regulatory-approved, widely accepted patient and physiological simulators to improve predictions through "the simulator as a tool". In effect, the LLM becomes the interface to running the simulator. We will explore architectural choices for agentic workflows around design parameters, including in our analysis the fidelity of predictions made about glucose trends through purely language-based interactions, using as baseline token-based or retrieval-based alternatives. 1. Emami, P., Li, Z., Sinha, S., & Nguyen, T. (2025, May). Syscaps: Language interfaces for simulation surrogates of complex systems. In International Conference on Learning Representations (Vol. 2025, pp. 65369-65391). 2. Ghimire, S.; Celik, T.; Gerdes, M.; Omlin, C.W. Personalized Blood Glucose Prediction Using Physiology- Informed Machine Learning. Mach. Learn. Knowl. Extr. 2026, 8, 96. https://doi.org/10.3390/make8040096 |
Dr Jun Ishihara
Profile: https://profiles.imperial.ac.uk/j.ishihara
Contact details: j.ishihara@imperial.ac.uk
| Project title | Description |
| Engineering anti-cancer cytokine for effective cancer immunotherapy | Lab-Based Project for Students with Expertise in Synthetic Biology or Biomaterials. Students with cell culture experience is preferred. Our laboratory is at the forefront of translational immunology and drug development through protein engineering. Recently, we have innovated a method to engineer proteins capable of targeted drug delivery to cancerous tissues. Our current focus is on producing cancer-targeted cytokines, involving the design, production, purification, and characterization of these proteins. This project employs advanced protein engineering techniques to facilitate novel drug development. We are offering a lab-based project for a dedicated student able to commit to full days in the lab, possessing experience in synthetic biology or biomaterials, and eager to contribute to this cutting-edge research. We expect to spend a long time of the day (almost every weekday) |
| Engineering of serum albumin fusion proteins with enhanced therapeutic potential to treat autoimmune diseases | The Ishihara Laboratory has pioneered an innovative cytokine engineering strategy aimed at mitigating inflammation via a novel cytokine. This groundbreaking cytokine will serve as the foundation for developing therapeutic agents targeting autoimmune diseases. The project's focus will encompass extensive cell culture experiments and protein affinity assays, necessitating full engagement in wet lab activities. Consequently, we seek a student with substantial cell culture experience. Given the demanding nature of the work, characterized by prolonged hours and iterative experimental processes, highly motivated individuals with a keen interest in pharmaceutical industry research and drug development are strongly preferred. |
| Investigating tumour-immune cell interactions for cancer immunotherapy using capillary-on-chip models | Lab-Based Project for Students with Expertise in Synthetic Biology or Biomaterials. Students with cell culture experience is preferred. Ishihara and Au laboratories have this shared project. Recently, we have innovated a method to engineer proteins capable of targeted drug delivery to cancerous tissues. Our current focus is on producing cancer-targeted antibodies. Using this new therapeutic antibodies, we will test the cancer cell killing using microfluidics. Therefore, this project involves the design, production, purification, and characterization of proteins and further tests in a cell culture device. This project employs advanced protein engineering techniques to facilitate novel drug development. We are offering a lab-based project for a dedicated student able to commit to full days in the lab, possessing experience in synthetic biology or biomaterials, and eager to contribute to this cutting-edge research. |
| Targeting of rare collagen for tumor immunotherapy | Cancer has high expression of rare collagen types because of an abnormality. We made antibodies for these rare collagens and try to deliver therapeutics specific to the tumours. Students who join this project needs prior cell culture experience and wet lab (molecular biology) experience. We expect to spend a long time of the day (almost every weekday) |
| Engineering Anti-Cancer Bispecific Antibodies for Anti-Cancer Immunotherapy | Cancer has high expression of antigens because of an abnormality. We made antibodies for these tumour antigens and try to deliver T cells specific to the tumours. Students who join this project needs prior cell culture experience and wet lab (molecular biology) experience. We expect to spend a long time of the day (almost every weekday) |
Dr Dandan Zhang
Profile: https://profiles.imperial.ac.uk/d.zhang17
Contact details: d.zhang17@imperial.ac.uk
| Project title | Description |
| Sim-to-Real Transfer Learning for Robotic Manipulation | Project Description: The objective of this MSc project is to leverage the potential of simulation environments for training deep learning models for controlling robotic arms for dexterous manipulation. However, the deep learning models that drive these robotic manipulators necessitate vast amounts of experimental data for training, which can be impractical and economically infeasible. To this end, this project will focus on developing a robust sim-to-real transfer learning framework that allows a pre-trained model in a simulated environment to adapt effectively to a real-world setting. The student will train a deep learning model to accurately identify and manipulate target objects in a virtual domain. This training will be performed using state-of-the-art simulation software that provides a high-fidelity approximation of real-world physics and environmental conditions. The core challenge lies in bridging the gap between the simulated and real environments. This project will not only address the practical challenges of training robotic manipulators but will also contribute to the broader understanding of transfer learning techniques, which are crucial for the deployment of intelligent systems in dynamic real-world scenarios. Project Goals: i) To develop a deep learning model capable of object detection and grasp point selection within a simulated environment. ii) To conduct model training within the simulation platform, utilizing synthetic data generated within the simulated environment and using advanced deep learning techniques that are resilient to the domain shift between simulation and reality. iii) To implement and fine-tune sim-to-real transfer learning algorithms to adapt the simulation-trained model to real-world conditions effectively. iv) To evaluate the performance of the transferred model on a physical robotic manipulator setup and iteratively refine the model through real-world training cycles to enhance accuracy and reliability. Required Skills: i) Proficiency in deep learning frameworks such as PyTorch, TensorFlow, or Keras, with the ability to construct and train deep neural network architectures. ii) Knowledge of computer vision techniques and libraries, particularly OpenCV, for processing and analyzing visual data from both simulated and real-world environments. iii) Experience with robot simulation software, capable of simulating physics, robot kinematics, and environmental interactions to generate synthetic training data. Expected Outcomes: i) A deep learning model trained in a simulated environment for robot manipulation. ii) A tested and validated transfer learning pipeline for adapting simulation-trained models to the physical robot. iii) A comprehensive evaluation report detailing the model's performance, with insights into the challenges of sim-to-real transfer and potential solutions. |
| Robot Learning for Robotic Manipulation Based on Multi-Sensor Fusion | Project Description: To enhance the manipulation abilities of robots, it is crucial to integrate and interpret sensory information from multiple modalities. The principal modalities — vision and tactile — provide distinct yet complementary data streams. Vision provides spatial and contextual understanding, while tactile feedback offers details about contact forces and surface textures. This MSc project aims to explore multimodal representation learning, targeting the development of integrated sensory processing methods that enhance the efficiency and performance of reinforcement learning for dexterous tasks with multimodal perception data as input. By developing a unified representation of visual and tactile data, the project will create a foundation for robotic systems to execute complex manipulation tasks with greater awareness and precision. Project Goals: i) Develop methods for fusing visual and tactile data, ensuring that the resulting representation captures the salient features from both modalities. ii) Utilize deep learning techniques to encode high-dimensional raw sensory data into a lower-dimensional, task-relevant space; and explore different architectures and frameworks for learning a joint multimodal representation that is informative for a contact-rich robotic manipulation task. iii) Implement reinforcement learning algorithms that can efficiently utilize the learned multimodal representations to make informed decisions. iv) Conduct experiments to test and validate the approach on physical robotic systems and evaluate the performance gains achieved by leveraging multimodal data compared to unimodal approaches. Required Skills: i) Proficiency in machine learning frameworks such as PyTorch or TensorFlow, with experience in implementing deep neural networks. ii) Practical understanding of robotic manipulation and the various sensing modalities involved. iii) Basic knowledge of reinforcement learning algorithms and experience with applying them to control tasks. Expected Outcomes: i) A sophisticated learning framework that effectively fuses vision and tactile sensory data for improved robotic manipulation. ii) A series of experiments demonstrating the efficacy of multimodal representation learning in real-world scenarios and an assessment of the impact of multimodal data on reinforcement learning performance, particularly in terms of efficiency and robustness. iii) A final report detailing the methodologies, experiments, results, and an in-depth discussion of the findings, including potential avenues for future research. |
| Design of a Robotic Hand with Tactile Feedback for In-Hand Manipulation | Project Description: In-hand manipulation involves adjusting the position and orientation of an object within the palm and fingers. The aim of this MSc project is to design and prototype an affordable robotic hand that not only mimics the complex motions required for in-hand manipulation but also integrates tactile feedback to enhance manipulation capabilities. This project requires the student to push the boundaries of current robotic hand designs by incorporating tactile sensors and developing algorithms to interpret and respond to tactile data, thus enabling the hand to perform delicate and skillful in-hand manipulations. Project Goals: i) To design a dexterous robotic hand with articulations that reflect the degrees of freedom necessary for in-hand manipulation; employ cost-effective manufacturing processes and materials, potentially including 3D printing, to construct the hand. ii) To integrate affordable tactile sensors that provide critical feedback for in-hand manipulation, such as slip detection and pressure distribution; map sensor placements effectively across the hand, focusing on areas that maximize manipulation control. iii) To leverage open-source electronics and software platforms to manage costs and encourage community collaboration and innovation; enable the construction of a compact actuation and control system that can process sensory input and coordinate the actuation of the hand with precision and responsiveness. iv) To construct a working prototype that embodies the designed features and can be tested for functional capabilities; conduct a comprehensive series of tests that assess the in-hand manipulation skills of the robotic hand, including object reorientation, precision lifting, and controlled release. Required Skills: i) Proficient knowledge of mechanical engineering principles and experience with 3D CAD modeling. ii) Experience with sensor technology and skills in electronic circuit design and familiarity with microcontroller programming and interfacing. iii) Practical experience in prototype building, including familiarity with rapid prototyping techniques. Expected Outcomes: i) A functioning prototype of an affordable robotic hand capable of in-hand manipulation with tactile feedback. ii) A set of test results demonstrating the hand's manipulation capabilities and the effectiveness of the tactile feedback system. iii) A final project report detailing the design process, control algorithms, technical challenges, performance evaluation, and recommendations for future development. |
| Teleoperated Microsurgery with Human-Robot Cooperative Control and Haptic Guidance | Project Description: Robot-assisted microsurgery is a field where the precision and stability of robotic systems can significantly enhance human capabilities. Teleoperation can provide surgeons with advanced tools to perform highly delicate tasks. However, to further improve the efficiency and safety of these surgeries, there is a need for an optimized human-robot cooperative control model that ensures intuitive operation, precision, and appropriate levels of autonomy when needed. This MSc project will focus on the development of a leader-follower control framework that assists the surgeon in a cooperative manner. The proposed system will allow for fine manual control during critical phases of the surgery while offering the option for automated, accelerated movements when transitioning to less delicate tasks. Additionally, the system will integrate haptic feedback, providing tactile cues that guide the operator through the procedure, which is particularly beneficial for training purposes and enhancing the skills of novice surgeons. Project Objectives: i) To design an optimized leader-follower control model that allows for seamless switching between manual and automated control. ii) To incorporate haptic guidance into the control model, giving the operator force feedback corresponding to the surgical environment. iii) To employ learning from expert demonstration algorithms to capture and replicate expert surgeon's techniques and use the learned models to provide predictive haptic guidance, aiding in training and operation consistency. iv) To simulate the teleoperated microsurgical environment and validate the control model and haptic feedback system; conduct experiments within the simulation to refine the control parameters and the machine learning models. Required Skills: i) Basic knowledge of machine learning and artificial intelligence principles. ii) Proficiency in programming languages such as Python and C++, with experience in software development for robotic systems. iii) Familiarity with simulation tools and environments relevant to robotics and teleoperation. Expected Outcomes: i) Develop a functional human-robot cooperative control framework tailored for microsurgery applications; integrate a haptic guidance system that is informed by machine learning models based on expert demonstrations. ii) Demonstrate the effectiveness of the system through simulation, showcasing improved operation efficiency and training potential. iii) Compile a comprehensive report detailing the design process, system architecture, simulation results, and potential for real-world application. |
| High-Fidelity Simulation Platform for Training and Evaluation in Robotic Microsurgery | Project Description: This MSc project is dedicated to the design and implementation of a high-fidelity simulation platform for Robot-assisted microsurgery (RAMS). The platform will serve as an advanced training tool for surgeons and a testing ground for new microsurgical techniques and robotic interventions. High fidelity in surgical simulation is crucial to provide realistic visual and haptic feedback, replicating the tactile, visual, and kinesthetic experiences of actual surgery. This project will encompass the creation of detailed virtual models of surgical anatomy, the development of physics-based simulation for tissue interaction, and the integration of haptic feedback devices to mimic the tactile sensation of surgery. Project Objectives: i) To construct a comprehensive virtual environment with high-resolution, anatomically accurate models for various microsurgical procedures. ii) To implement a physics engine capable of simulating soft tissue behavior, including cutting, suturing, and manipulation. iii) To incorporate haptic devices that can deliver realistic touch-based feedback to the user, simulating the feel of tissues and surgical tools; fine-tune the haptic feedback system to accurately reflect the forces experienced during microsurgical tasks. iv) To simulate the control systems of existing RAMS platforms, allowing users to operate virtual robotic instruments with the same dynamics and responsiveness as their real-world counterparts. Required Skills: i) Basic knowledge in computer graphics and simulation software, with the ability to model and animate complex systems. ii) Experience with haptic technology and force feedback systems. iii) Proficiency in programming, particularly in languages and environments conducive to simulation (e.g., Python, C++, etc.). Expected Outcomes: i) A fully functional high-fidelity simulation platform for RAMS. ii) A set of validated haptic feedback mechanisms for simulated surgical interaction. iii) A detailed report documenting the development process, system capabilities, user feedback, and potential areas for further research. |
| Robotic Tactile Palpation for Tumor Detection | Project Description: Tumor detection through palpation is a critical skill in various surgical disciplines, allowing surgeons to identify abnormal masses within tissue. In robotic surgery, where direct tactile feedback is not available, the ability to detect tumors by feel is significantly compromised. This MSc project seeks to address this limitation by developing an advanced tactile palpation system that can be integrated into surgical robots, providing surgeons with the necessary haptic feedback to detect tumors effectively. The project will involve the design and fabrication of tactile sensors that can mimic the sense of touch a surgeon uses to differentiate between normal and abnormal tissues. The student will work on creating a system that processes the tactile data and converts it into palpable feedback, enhancing the surgeon's ability to detect tumors during minimally invasive robotic procedures. Project Objectives: i) To investigate and develop advanced tactile sensors that can discern differences in tissue stiffness, texture, and other mechanical properties indicative of tumor presence; integrate these sensors into a robotic end-effector suitable for minimally invasive surgery. ii) To create algorithms that can process tactile data in real-time and identify patterns consistent with tumor tissue; utilize machine learning techniques to enhance the sensor system's accuracy and sensitivity in tumor detection. iii) To develop a haptic feedback system that can accurately convey the sensation of tumor tissues to the surgeon's fingertips; fine-tune the feedback system to represent different textures and consistencies as closely as possible to natural palpation. iv) To develop an intuitive user interface that allows surgeons to interpret tactile data efficiently and validate the system's performance through benchtop experiments with tissue phantoms. Required Skills: i) Basic skills in sensor development, particularly with applications in detecting mechanical properties of soft tissues. ii) Proficiency in programming for data processing and machine learning, particularly in Python or similar high-level languages. iii) Basic understanding of robotic systems and integration, including hardware and software aspects. Expected Outcomes: i) A prototype tactile palpation system with tumor detection capabilities. ii) A software suite for processing and pattern recognition of tactile data. iii) A report for introduction of system integration, data analysis, and a set of experimental results demonstrating the system's detection capabilities. |
| Real-Time Tracking and Segmentation of Multiple Microrobots and Biological Objects Using Machine Learning | This project aims to develop an advanced system for the real-time tracking and segmentation of multiple microrobots and biological objects using machine learning techniques. The successful completion of this project will enhance the precision and efficiency of microrobot tracking and biological object segmentation, with potential applications in medical research and minimally invasive surgery. Project Objectives: i) Investigate and develop machine learning algorithms for accurate and efficient real-time tracking of microrobots and biological objects. ii) Develop methods to segment microrobots and biological objects using advanced machine learning algorithms. iii) Develop a user-friendly interface for real-time monitoring and control of the system. iv) Conduct experiments to validate the performance of the system under various conditions. Required Skills: i) Machine Learning and data processing: Basic knowledge of developing and training machine learning models, particularly for tracking and segmentation tasks. ii) Computer Vision and Image Processing: Understanding of object detection, tracking, and segmentation algorithms and image processing techniques. Expected Outcomes: i) Prototype Tracking and Segmentation System: Development of algorithms capable of real-time tracking and segmentation of microrobots and biological objects. ii) Software Suite for Data Processing: Creation of a user interface for real-time processing of image data and visualization of the tracking and segmentation results. iii) Experimental Validation Report: Documenting the system integration, data analysis, and experimental results demonstrating the system's performance in real-time applications. |
| Development of Biomimetic Tactile Sensors Using Magnetic Sensing Technology for Robotics | This project aims to develop biomimetic tactile sensors based on magnetic sensing technology. The successful completion of this project will enhance the tactile perception capabilities of robots, enabling them to perform delicate and precise tasks in various applications, including medical treatment and human-robot interaction. Project Objectives: i) Develop and fabricate tactile sensors that mimic the human sense of touch using magnetic sensors. ii) Create algorithms to process tactile data in real-time and interpret the sensed information. iii) Utilize machine learning techniques to enhance the sensitivity and accuracy of the sensors in detecting various tactile stimuli. iv) Integrate the developed tactile sensors into robotic systems and test their performance in real-world scenarios. v) Optimize the design and functionality of the tactile sensors based on experimental results and feedback. Required Skills: i) Sensor Development and Fabrication: Basic skills in the design and fabrication of sensors, with background knowledge in magnetic sensing technology. ii) Data Processing and Machine Learning: Ability to develop and implement algorithms for real-time data processing and pattern recognition. iii) System Integration: Experience with the integration of sensors into robotic systems and conducting performance evaluations. Expected Outcomes: i) Prototype Tactile Sensors: Development of prototype tactile sensors based on magnetic sensing technology, capable of providing force/torque estimation. ii) Software for Data Processing: Creation of a software for real-time processing and interpretation of tactile data, enhancing the sensors' accuracy and sensitivity. iii) Experimental Validation Report: Documenting the design, development, and testing of the tactile sensors, including experimental results and performance evaluations. |
| Development of a Reconfigurable Magnetic Actuation System for Microrobotic Manipulation | This project aims to develop a reconfigurable magnetic actuation system for precise microrobotic manipulation. The project focuses on designing a versatile and efficient actuation platform that combines the strengths of both permanent magnets and electromagnets to achieve high precision and flexibility in microrobot control. The successful completion of this project will enhance the precision and flexibility of microrobot control, enabling their use in various medical applications, including minimally invasive surgery and targeted drug delivery. Project Objectives: i) Develop a reconfigurable magnetic actuation platform that integrates both permanent magnets and electromagnets to provide a strong and adjustable magnetic field. ii) Construct the actuation system with a camera-based imaging system for real-time control and monitoring of microrobots. ii) Design and fabricate microrobots with magnetic properties suitable for manipulation by the developed actuation platform. iii) Create control algorithms for the real-time manipulation of microrobots using the magnetic actuation platform. iv) Conduct benchtop experiments to validate the performance of the magnetic actuation system and microrobots. Required Skills: i) Basic knowledge of electrical circuits and magnetic field generation using electromagnets and permanent magnets. ii) Proficiency in programming for data processing and control, particularly in Python or similar high-level languages. Expected Outcomes: i) Reconfigurable Magnetic Actuation System: Development of a versatile and efficient magnetic actuation platform capable of precise control of microrobots. ii) Prototype Magnetic Microrobots: Creation of prototype microrobots with magnetic properties suitable for manipulation by the proposed actuation system. |
| Development of a Magnetically Driven Soft Continuum Microrobot for Intravascular Operations | This project aims to develop a magnetically driven soft continuum microrobot for precise intravascular operations. The magnetic microrobot will be designed and fabricated to navigate through complex vascular environments and perform tasks such as clot removal. The project focuses on overcoming the challenges of miniaturization and control to enable effective microrobot performance in medical applications. The successful completion of this project will enhance the capabilities of minimally invasive surgical techniques, potentially improving patient outcomes in vascular therapy and other medical applications. Project Objectives: i) Develop a soft continuum microrobot using polydimethylsiloxane (PDMS) and neodymium-iron-boron (NdFeB) particles for magnetic actuation. ii) Design and build a magnetic actuation system using electromagnets to control the movement and steering of the microrobot. iii) Implement a control algorithm to achieve precise navigation and manipulation within the vascular environment. iv) Set up experiments to test the steering and locomotion capabilities of the microrobot in a microfluidic channel mimicking vascular condition. Required Skills: i) Basic skills in the design and fabrication of micro-scale devices, particularly those involving soft materials and magnetic particles. ii) Proficiency in programming for control algorithms and data processing, particularly in Python or similar high-level languages. Expected Outcomes: i) Prototype Soft Continuum Microrobot: Development of a prototype microrobot with the ability to navigate and perform tasks within simulated vascular environments. ii) Magnetic Actuation System: Creation of a magnetic actuation system capable of precise control of the microrobot’s movement and steering. iii) Simulation and Experimental Data: Collection of experimental data demonstrating the microrobot’s performance in navigating microfluidic channels and manipulating microbeads. iv) Detailed Report: A comprehensive report documenting the design, fabrication, simulation, and experimental validation of the microrobot, including analysis of results and potential improvements. |
| Data Efficient Machine Learning for Tactile Sensor Array-Based Robotic Grasping Perception | Supervisor: Dandan (Dian) Zhang Co-Supervisor: Etienne Burdet, Alexis Devillard Project Description Robotic grasping and manipulation are critical capabilities for automation in various applications. The integration of tactile sensors with machine learning enhances the perception abilities of robotic hands, allowing for precise and adaptive interaction with objects. This project focuses on developing data-efficient machine learning models that leverage tactile sensor array data to improve robotic grasping perception, encompassing object recognition and force distribution estimation with minimal data requirements. This project aims to advance the tactile sensing capabilities of robotic hands through the development of data-efficient machine learning models. By achieving high performance in object recognition and force estimation with minimal data, this research will contribute significantly to the field of robotic manipulation, enabling more efficient and adaptable robotic systems. Methodology The project involves a thorough review of current technologies in tactile sensing, machine learning for tactile data, and data-efficient learning methods. A dataset will be gathered from tactile sensors on various objects and pre-processed for quality enhancement. Machine learning models tailored for data efficiency, such as GNNs, will be developed, utilizing techniques like transfer learning, few-shot learning, and data augmentation to minimize data requirements. Tactile sensor data will be seamlessly integrated into the models, addressing challenges like managing high-dimensional data and maintaining real-time processing. The models will be trained using data-efficient algorithms and optimized through hyperparameter tuning, regularization, and pruning. Performance will be evaluated using metrics such as accuracy, precision, recall, F1 score, MSE, RMSE, etc. Finally, the models will be deployed in real-world grasping and manipulation scenarios to test their effectiveness and adaptability. Expected Outcomes • Development of data-efficient machine learning models for accurate object recognition and force estimation using tactile sensor data. • Enhanced perception capabilities of robotic hands, leading to improved performance in grasping and manipulation tasks. • Comprehensive performance data demonstrating the models' effectiveness in various applications. • Potential for broad applications in fields requiring precise robotic manipulation Required Skills: • Software: Tools for data pre-processing and analysis (e.g., Python libraries) • Machine Learning Frameworks: TensorFlow, PyTorch • Data Collection Tools: Robotic arm operation |
| Human-in-the-Loop Control for a Tactile Sensor-Based Contact-Rich Manipulation Task | Contact-rich manipulation tasks, such as assembling intricate components or handling delicate objects, pose significant challenges for autonomous robotic systems due to the complexity and variability of tactile interactions. This project aims to develop a human-in-the-loop control system for a robotic arm equipped with tactile sensors, enabling precise and adaptive manipulation through real-time human guidance and feedback. This project aims to enhance robotic manipulation in contact-rich tasks through the development of a human-in-the-loop control system with integrated tactile sensors. By leveraging human intuition and tactile feedback, the system will improve the precision and effectiveness of robotic arms in complex manipulation scenarios, with broad applications in various industries. Expected Outcomes • A fully functional teleoperation system with integrated tactile sensors for robotic manipulation. • Enhanced precision and adaptability in contact-rich manipulation tasks through effective human-robot collaboration. • Comprehensive performance data demonstrating the system’s effectiveness in various applications. Required Skills: • Hardware: Robotic arm, haptic feedback devices • Software: Control algorithms, user interface development tools • Sensors: Vision-Based Tactile Sensor |
| Semi-Autonomous Control for Augmented Robotic Hand Using Tactile Feedback | Introduction and Background: Neurotechnology has advanced rapidly, particularly in the development of neuroprosthetics that aim to restore motor and sensory functions for individuals with limb loss or neuromuscular impairments. However, current robotic prosthetics often lack the ability to provide tactile feedback and semi-autonomous control, which are essential for delicate tasks requiring precision and safe interaction with objects. There is a need for prosthetic systems that can autonomously adjust their grip based on sensory input, enhancing both control and user experience. This MSc project will focus on developing semi-autonomous control algorithms for an augmented robotic hand using tactile feedback. The goal is to create a system that not only allows the user to perform high-level tasks such as gripping and releasing objects but also autonomously adjusts the grip force based on real-time sensor input. This would simulate natural reflexes, such as detecting when an object is slipping or being over-gripped. Objectives: 1. Tactile Sensor Integration: Design and integrate tactile sensors into a augmented robotic hand or prosthetic hand to capture sensory data such as pressure, texture, and contact forces. These sensors will provide critical input for detecting grip states like object slipping or over-gripping. 2. Algorithm Development: Develop algorithms to classify sensor data and detect grip states (e.g., slipping, over-gripping) in real time; Implement control logic that adjusts the grip force autonomously based on sensor input, mimicking natural reflexes. This will enable the robotic hand to react to changes in object handling conditions without user intervention. 3. Simulation and Testing: Simulate the system's performance using a robotic hand model and test its functionality in a series of object manipulation tasks, such as gripping objects of different sizes, shapes, and textures. 4. Performance Evaluation: Evaluate the system’s efficiency in improving grip stability, object handling, and user experience. The focus will be on ensuring that the semi-autonomous control algorithms enhance the user’s ability to handle objects with precision and safety. This project lies at the intersection of neurotechnology, robotics, and machine learning. The development of semi-autonomous control systems for robotic prosthetics or augmented robotic hand can significantly enhance user experience by improving grip stability and reducing cognitive load. With a focus on tactile feedback, this project will contribute to the creation of more natural, reflexive prosthetic hands or augmented hand that adapt to real-world conditions, helping to restore functionality for individuals with disabilities. |
| Event-Adaptive Parallel Decoding for World Action Models | This project will develop a World Action Model (WAM) that dynamically adjusts the temporal granularity of parallel decoding according to anticipated interaction events. During predictable free-space motion, the model will decode longer video–action blocks in parallel. Near contact, grasping, release, or uncertain transitions, it will switch to shorter blocks and more frequent refinement. Unlike existing methods that mainly determine how many actions from an already generated chunk should be executed, this project will adapt the decoding block structure itself, using the WAM’s predicted future visual dynamics and contact-related events. The student will first reproduce an existing WAM, such as LingBot-VA or DreamZero, and then implement and evaluate the proposed event-adaptive decoder. Experiments will primarily be conducted in simulation using selected contact-rich tasks from RoboTwin 2.0. Subject to project progress, the method will also be validated on a real robotic platform. Background: World Action Models jointly predict future visual observations and robot actions, enabling action generation to benefit from learned physical dynamics. DreamZero and LingBot-VA demonstrate the potential of this approach for closed-loop robot control. However, fixed-length action blocks create a trade-off between efficiency and reactivity. Long blocks reduce inference frequency but are vulnerable to prediction errors near contact events, while short blocks enable precise feedback control at greater computational cost. Parallel decoding methods such as PD-VLA improve action-generation efficiency but do not explicitly adapt the decoding structure to the predicted physical phase of the task. Recent methods including PACE, DEHP and Mixture of Horizons dynamically select execution horizons or combine predictions across different horizons. This project instead investigates whether the future visual states predicted by a WAM can be used to change the block size and refinement schedule during generation. Key Tasks: • Baseline reproduction: Reproduce LingBot-VA or DreamZero on a selected subset of RoboTwin 2.0 manipulation tasks. • Event prediction: Identify interaction events from predicted video–action trajectories, such as contact onset, object motion, gripper-state changes, abrupt motion transitions or model uncertainty. • Adaptive parallel decoding: Develop a decoder that generates long blocks during predictable motion and short blocks near important or uncertain events. • Evaluation: Compare against fixed short-block, fixed long-block, conventional parallel-decoding and adaptive execution-horizon baselines. Metrics will include task success, inference latency, model evaluations, action smoothness and contact-related failures. • Real-robot extension: Subject to progress, deploy and evaluate the proposed method on a physical robot. Key Contact: Ms. Lan Wei (l.wei24@imperial.ac.uk) |
| Accelerating Vision-Language-Action (VLA) Model with Adaptive Parallel Action Decoding | Project Description: This project investigates efficient action decoding for Vision–Language–Action (VLA) models. The student will first reproduce the action-decoding approach proposed in Discrete Diffusion VLA on a selected robotic manipulation benchmark. Building on this baseline, the student will develop an adaptive parallel decoding method, such as confidence-aware early stopping, dynamic remasking, or computation-budget-aware refinement, to reduce inference latency while preserving control performance. Depending on progress, the proposed method may also be evaluated on an additional VLA backbone. Background: VLA models translate visual observations and language instructions into robot actions. However, autoregressive action generation can introduce substantial latency, particularly when predicting action chunks for closed-loop control. Recent work has shown that parallel decoding and action chunking can significantly improve VLA inference efficiency. Discrete Diffusion VLA represents discretised action chunks using a discrete diffusion process within a unified transformer. Actions are generated through parallel iterative refinement, with uncertain predictions revisited through adaptive decoding and remasking. This provides a promising foundation for developing faster and more adaptive action decoders. Key Tasks: • Reproduce the core Discrete Diffusion VLA pipeline on selected LIBERO tasks. • Benchmark task success, inference latency, decoding steps, memory usage, control frequency, and action consistency. • Develop an adaptive parallel decoding algorithm, for example using confidence-aware early stopping or dynamic remasking. • Conduct ablation studies to analyse the trade-off between decoding speed and policy performance. • Optionally transfer the proposed method to another VLA architecture or action representation. Expected Outcomes: A reproducible implementation and evaluation of Discrete Diffusion VLA. A new adaptive action-decoding method for accelerating VLA inference. A systematic evaluation of the accuracy–efficiency trade-off in parallel action decoding. A final technical report, documented codebase, and potentially a research paper submission. |
Dr David Labonte
Profile: https://profiles.imperial.ac.uk/d.labonte
Contact details: d.labonte@imperial.ac.uk
| Project title | Description |
| Design and building of an experimental environmental chamber | Many experiments need to be conducted in controlled environmental conditions (temperature, humidity, etc). However, control chambers are often prohibitively expensive. The aim of this project is to design and build a simple and cheap system using off-the-shelf parts and microcontrollers, and to document this design such that it can be shared with others - science is best when it is open. The chamber should allow variation between 5-45 C, and between 10-90% relative humidity. |
| Are larger animals more efficient? On the thermodynamics of muscle, the prime biological engine. | Animals large or small must move, and this movement consumes a large share of their daily energy budget - they thus ought to move as efficiently as possible. Curiously, available data has it that animals become more efficient as they grow in size: the ratio between energy output (muscle mechanical work) and system input (metabolic energy) increases in heavier animals. It is clear enough that something is amiss, because extrapolation of this trend suggests that large extinct animals (dinosaurs!) moved with an efficiency larger than unity; but the origin of this paradox remains at large. In this project, you will take a bioengineering perspective on this open problem, and analyse muscle as a thermodynamic machine. You will build on recent thermodynamical and mechanical models of muscle performance to derive a prediction for the efficiency of muscle as a function of animal size, and compare this prediction to existing data. Relevant literature: Alexander, R. M. Models and the scaling of energy costs for locomotion Journal of Experimental Biology. 2005, 208, 1645-1652 Labonte, D. A theory of physiological similarity for muscle-driven motion. PNAS, 2023, 120, e2221217120 Goupil, C.; Ouerdane, H.; Herbert, E.; Goupil, C. & D’Angelo, Y. Thermodynamics of metabolic energy conversion under muscle load. New Journal of Physics. 2019, 21, 023021 |
| Body size and the cost of the neural control of movement | Movement is integral to all animals, and emerges from a coordinated interaction between a nervous control architecture and the musculoskeletal system. Traditionally and today still, these two elements are often considered in isolation. But no muscle will generate a force without being instructed to do so, and brains are embodied and thus useless without muscles to actuate – the two systems must be in tune. The ideal tuning may be reasonably expected to change with animal size: an elephant must worry about gravity much more so than an ant, and it is widely accepted that the musculoskeletal system adapted to meet this and other mechanical constraints. What is much less clear is how size impacts the costs and optimal strategy of neural control, and how both interact with size-specific changes to the physical environment. In this project, you will begin the building of a bridge between the two involved fields – neuroscience and biomechanics. You will tackle two related problems. At first, you will estimate the costs of neuronal computation across animal body sizes, and compare it to the costs involved in muscle-driven locomotion. Are neural control costs significant, and how does their relative importance vary with animal size? Next, you will link established mechanical consequences of changes in body size, such as increased limb cycling periods, with equivalent but understudied changes in the optimal neural control strategy, including demands on nerve action potential velocity, muscle activation times and more. Do mechanical and neuronal constraints vary in synchrony, or are demands on one steeper, so that it takes a dominant role in shaping animal movement across body sizes? Suggested reading Hooper, S. L. (2012). Body size and the neural control of movement. Current Biology, 22(9), R318-R322. More, Heather L., and J. Maxwell Donelan. "Scaling of sensorimotor delays in terrestrial mammals." Proceedings of the Royal Society B 285.1885 (2018): 20180613. Attwell, D., & Laughlin, S. B. (2001). An energy budget for signaling in the grey matter of the brain. Journal of Cerebral Blood Flow & Metabolism, 21(10), 1133-1145. Laughlin, S. B. (2001). Energy as a constraint on the coding and processing of sensory information. Current opinion in neurobiology, 11(4), 475-480. |
| Testing a muscle fibre tracing algorithm to reconstruct 3D muscle architecture from CT-scans | Muscle is the universal biological engine, and its physiology and anatomical arrangement are key to organismal performance. Increased availability and reduced costs of CT-imaging is now making it easier and easier to assess the arrangement of muscle in 3D, but quantitative analysis of key metrics such as fibre length and pennation angles is still extremely time consuming, and typically completed manually only for a small number of muscle fibres. To overcome this limitation, and to so enable large-scale 3D reconstruction of muscle from CT scans, we developed a fibre tracing algorithm, implemented as an imageJ plug-in. In this project, you will first help prepare some CT scans of insect legs for muscle fibre tracing, and then test and help improve the fibre tracing algorithm. Suggested reading: Püffel, Frederik, et al. "Morphological determinants of bite force capacity in insects: a biomechanical analysis of polymorphic leaf-cutter ants." Journal of the Royal Society Interface 18.182 (2021): 20210424. |
| Using robots to study the efficiency of pedestrian locomotion across body sizes | Across species, larger animals appear to use less energy per unit body mass and distance travelled (Heglund & Taylor, 1988), a puzzling observation whose mechanistic basis remains poorly understood. In this project, you will build and use robotic animal models to investigate why. Specifically, you will design, build and test a family of geometrically similar bio-inspired walking robots at different scales, creating a controlled experimental system in which body size can be varied while the underlying design remains constant. You will gain hands-on experience in CAD and additive manufacturing, electronics, microcontrollers, servo actuation, sensor integration, robotic gait programming and experimental data analysis. Building on insect-inspired hexapod locomotion, you will measure energetic cost of transport and investigate which physical and engineering factors determine locomotor efficiency across body sizes. The goal is to use robotics as an experimental tool to uncover fundamental scaling laws of locomotion spanning biology and engineering. |
| A species-agnostic 3D body model to study animal locomotion | Insects range from long, slender forms to short, stocky ones, yet share the same basic body plan. How does this diversity affect locomotion, and what can it teach us about designing six-legged robots? Our lab’s omnidirectional walking platform keeps untethered insects—from fruit flies to large stick insects—within a fixed recording volume by moving the floor beneath them. They can walk, turn and accelerate for thousands of step cycles without human intervention. This project tackles the next challenge: extracting 3D movement data from these recordings without time-consuming manual labelling. Starting with our library of high-resolution 3D insect scans, you will use Holotype (https://github.com/FabianPlum/SMILify), our open-source pipeline, to build a shared, deformable, articulated body model spanning different species. You will then train deep neural networks to fit it to walking-platform recordings, testing how well the approach generalises to species not seen during training—and investigating where and why it succeeds or fails. The project uses Python, PyTorch and Blender. The data, core processing tools and evaluation protocol are established,the rest is yours to own: explore network architectures, single- versus multi-view training and model-fitting strategies. Some experience in machine learning and computer vision—or strong enthusiasm for learning these skills—is required. In return, you will gain practical experience in parametric body modelling, deep learning, 3D kinematics and insect biomechanics, while developing tools to reveal how diverse bodies solve the shared challenge of walking on six legs. |
Dr Guang Yang
Profile: https://profiles.imperial.ac.uk/g.yang
Contact details: g.yang@imperial.ac.uk
| Project title | Description |
| Synthesizing Immunofluorescence Images from Bright Field Images | Immunofluorescence imaging is valuable for visualizing cellular structures but is costly and resource-intensive. Bright field imaging is more accessible, so creating immunofluorescence images from bright field images can make detailed cellular analysis more widely available. The student will need to use a generative adversarial network (GAN) trained on paired bright field and immunofluorescence images to generate synthetic fluorescence images from bright field inputs. The target is to develop a tool that accurately produces immunofluorescence images from bright field images, providing detailed cellular insights without the need for expensive staining techniques. |
| Concept and Visual guided Large Vision Language Model for Medical Report Generation | Different imaging protocols can produce varying image data even for the same type of cells. To address this, the student will develop a generative adversarial-based or diffusion-based model trained on paired images obtained from different cell painting protocols. The goal is to standardize these images, making data from diverse protocols consistent and comparable. This tool will harmonize cell painting images, enhancing the reliability and accuracy of cellular analysis without the need for uniform imaging conditions. |
| Enhancing Cell Image Segmentation through Data Harmonization | Cell image segmentation is crucial for accurate cellular analysis, but variations in imaging protocols can affect segmentation quality. The student will develop a harmonization algorithm that standardizes cell images from different protocols before segmentation. By training a machine learning model on harmonized datasets, the tool will improve segmentation accuracy across various imaging conditions, making it easier to analyze and compare cellular structures. |
| Self-supervised learning for cell imaging foundation model | Cell imaging generates vast amounts of data, but labeled datasets are often limited and costly to produce. Self-supervised learning (SSL) offers a way to leverage large amounts of unlabeled data for pretraining models, which can then be fine-tuned with limited labeled data for specific tasks. This approach can significantly improve the performance of models in various cell imaging applications, such as segmentation, classification, and feature extraction. |
| Interpretability of Multimodal Models for Clinical Hypothesis Generation in Oncology | Abstract: Background: The integration of histopathology images and clinical text through Multimodal Vision-Language Models (VLMs) is a promising frontier for precision oncology. While foundational models like PathChat and CONCH demonstrate powerful predictive capabilities, their "black box" nature limits clinical trust and, critically, their use for generating novel scientific insights. A deeper understanding of a model's internal reasoning is essential to unlock its full potential as a tool for discovery. Aims and Objectives: This project aims to bridge the gap between predictive performance and model understanding. The central objective is to develop and apply a framework of interpretability techniques to dissect a state-of-the-art VLM, transforming it from a prediction tool into a verifiable engine for generating new clinical and biological hypotheses. Proposed Approach: This research will leverage a foundational VLM architecture (e.g., building on LLaVA-Med or CONCH), fine-tuning it on multimodal oncology datasets. The core of the project will be a multi-faceted interpretability investigation. The research can use established post-hoc explainability (XAI) methods, such as saliency mapping, to attribute model outputs to specific features in both the image and text modalities. The project will also explore mechanistic interpretability (MI) techniques. This may involve methods such as concept activation mapping or sparse autoencoder-based feature extraction to reverse-engineer the model's learned algorithms and identify internal circuits corresponding to human-understandable biological concepts. Expected Outcomes & Impact: The primary deliverable will be a validated framework for interpreting multimodal clinical AI. A successful project will not only achieve high predictive accuracy but will also yield data-driven hypotheses about visual biomarkers and their relationship to clinical outcomes, as identified by the model. This work will hopefully have potential to contribute to the field of trustworthy AI in medicine. |
| Foundation Models and Multimodal AI for Computational Paediatric Cardiovascular MRI | Paediatric cardiovascular MRI (CMR) is essential for diagnosis, treatment planning and follow-up, but remains challenging due to small cardiac structures, fast heart rates, respiratory motion, limited breath-holding ability and complex anatomy. This MRes project will develop and evaluate foundation model and multimodal AI methods for fast paediatric cardiovascular MRI by adapting knowledge from large adult CMR datasets to paediatric imaging. The project will explore self-supervised learning, adult-to-paediatric transfer learning, physics-informed reconstruction, and multimodal fusion of CMR images, k-space data where available, acquisition metadata and clinical annotations. Beyond image reconstruction, the project will investigate multi-task adaptation for downstream clinical applications, including segmentation, quantitative phenotyping and disease-related assessment. The aim is to develop a transferable AI framework that improves accelerated paediatric CMR reconstruction while preserving clinically relevant measurements such as ventricular volumes and ejection fraction. The project is suitable for students interested in medical imaging, foundation models, multimodal AI, image reconstruction, and translational cardiovascular AI. |
Dr Adam Celiz
Profile: https://profiles.imperial.ac.uk/a.celiz
Contact details: a.celiz@imperial.ac.uk
| Project title | Description |
| Tissue adhesives hydrogels for regenerative periodontics | Tissue adhesives hydrogels for regenerative periodontics |
| Evaluating Dual Static–Dynamic Crosslinked PEG Hydrogels as Alternatives to Matrigel for Cancer Organoid Culture: A Mechanical and Cytocompatibility Analysis |
Dr Amy Howard
Profile: https://profiles.imperial.ac.uk/a.howard
Contact details: a.howard@imperial.ac.uk
| Project title | Description |
| Mapping Brain Connectivity Via Low-Cost 3D Polarised Light Imaging | Polarised light imaging (PLI) is a powerful microscopy method for ex vivo investigations of brain connectivity ("the structural connectome"). PLI utilises the optical property of birefringence within brain tissue to estimate axonal orientations within brain tissue with micron-scale resolutions. However, most PLI systems can only reliably inform on axon orientations within the 2D microscopy plane. This is problematic as it limits the utility of 2D PLI for imaging the 3D trajectories of axons linking different brain regions. Extracting 3D orientations typically requires bespoke set-ups which are expensive, limiting wide-spread access. This project aims develop a low-cost 3D polarised light imaging system for accessible, high-resolution connectomics. This will include microscope development, data acqusition using postmortem brain samples, image analysis and connectivity mapping. Investigations could consider whole-brain connectomics, or focus on specific structures such as the hippocampus which has key functions in memory and learning, and is implicated in conditions such as Alzheimer's disease. Comparisons with diffusion MRI acquired in the same tissue can be used to validate a drive methods for estimating brain connectivity in vivo. |
| Can Combined MRI-PLI Analysis Provide Reliable Myelin Estimates? | Myelin, the insulating sheath surrounding axons in the brain, is crucial for the efficient transmission of electrical signals in the nervous system, enabling faster communication between neurons and supporting overall brain and spinal cord function. Reliable myelin imaging is therefore essential to the diagnosis of different demyelinating pathologies such as multiple sclerosis. Polarised light imaging (PLI) - a microscopy method sensitive to myelinated axons in the brain - uses the optical property of tissue birefringence to inform on axonal orientations in ex vivo brain tissue samples. One of the signals from PLI, the tissue retardance, is dependent simultaneously on both the 3D orientation of axons and the amount of myelin in the tissue. Without the use of bespoke set-ups, these two signals are difficult to disentangle, making robust analysis of either property (the 3D orientation or amount of myelin) ill-posed. This project will aim to build on the combined analysis of MRI and PLI data to develop a robust method to simultanously estimate both the axons orientation and its degree of myelination. These data can provide insight on the fundamental understanding of how myelin varies across different pathways in the brain, and the preferential dyemyelination of specific white matter bundles in pathological conditions. Comparisons with myelin-sensitive MRI in the same samples will ellicuidate the extent to which demyelinating pathologies can be detected in vivo. |
| Computational Neuroanatomy: Machine Learning for Microscopy in the Brain | The brain’s microstructure is remarkably rich and diverse — from the layered organisation of the cortex to intricate bundles of axons weaving through white matter. These features are vividly captured in high-resolution microscopy images, revealing striking regional differences in cellular and fibre architecture. In this project, you will use image analysis and machine or deep learning techniques to extract meaningful biological information from microscopy data and map microstructural variation across the brain. You will have access to detailed stained histological sections highlighting cell bodies and axonal architecture, as well as polarised light imaging (PLI) data that describes fibre orientation in fine detail. Students are welcome to propose their own ideas, but possible project directions include: - Cell segmentation and classification of cell types - Identification of cortical layers - Multi-modal brain parcellation or region classification - Extraction of spatial gradients across the brain - Comparison with MRI data acquired in the same brains - Cross-species analysis to explore similarities and differences across rodents, monkeys, and humans This project offers an opportunity to apply and expand skills in image analysis, machine learning, and neuroscience. It is well suited to students interested in medical imaging, neuroscience, and machine learning. |
| Tracking Connections: Better Methods for MRI Structural Brain Mapping | Mapping how the brain is structurally connected is central to human neuroscience, yet our abilities to achieve this in living people (via MRI tractography) remains limited in accuracy, particularly in complex regions such as subcortical structures, crossing fibres, and cortical entry zones. This project will explore new tractography methods using multi-modal MRI and/or the incorporation of high-resolution microscopy data (e.g. Nissl, myelin stains, polarised light imaging) for model development or validation. Depending on interests, the project can focus purely on MRI-based approaches, or expand to multimodal analysis combining MRI and microscopy. Possible directions include: - Developing new fibre tracking algorithms using machine or deep learning (e.g. reinforcement learning) - Designing tractography methods specialised for subcortical or superficial white matter regions - Mapping cortical fibre fanning and entry patterns, using microscopy for validation or model inspiration - Creating high-resolution tract reconstructions in bespoke datasets and translating these methods to standard human MRI data Our aim will be to develop and evaluate methods that ultimately benefit in vivo human connectivity mapping by improving orientation estimation, resolving complex white matter configurations, and incorporating biologically informed constraints derived from MRI and/or microscopy. This project is well suited for students interested in medical image analysis, brain connectivity, machine learning, and neuroanatomy. |
| Redundancy or Specialisation? Linking Brain Connectivity to Microstructure | Understanding how different brain regions are connected — and how their microstructure supports these connections — is a key challenge in neuroscience and neuroengineering. This project will explore whether structurally connected brain regions are also microstructurally similar, and how this relationship varies across functional networks in the brain. You will use a unique multimodal dataset that pairs diffusion MRI (which maps structural connections) with high-resolution histology (e.g. Nissl and myelin stains, polarised light imaging) that captures microstructural features such as cell density, layer thickness, and fibre orientation. The aim is to develop a quantitative analysis framework that: - Measures microstructural similarity between brain regions - Links this to connectivity strength and distance - Tests whether similarity is greater within networks (supporting redundancy), or systematically different between networks (supporting computational specialisation) You may also explore whether these patterns follow smooth microstructural gradients across the brain or reflect discrete network boundaries. This project will involve biomedical image analysis, feature extraction from multimodal imaging, and applying computational models to study brain structure–function relationships. It is suited to students interested in neuroimaging and computational neuroscience. |
| High-Resolution Mapping and Modelling of Hippocampal Connectivity | The hippocampus is a critical brain structure involved in memory and navigation, but its internal connectivity is challenging to capture with standard MRI due to its small size and complex organisation. In this project, you will develop methods for high-resolution mapping of hippocampal connectivity, using data from either high-resolution diffusion and structural MRI, and/or microscopy. The aim is to explore how fibre pathways and subfields within the hippocampus can be mapped more accurately at high resolution, and develop a model to translate this detailed information to lower-resolution, standard MRI datasets. This approach aims to make insights gained from specialised datasets applicable to broader populations or clinical settings. Depending on interests, the project may include: - Analysing high-resolution MRI or microscopy data to extract detailed hippocampal connectivity - Combining structural and diffusion MRI for more accurate mapping - Building a model to infer fine-scale connectivity from standard MRI inputs - Testing the method on publicly available human datasets (e.g. HCP) This project is ideal for students interested in neuroimaging, neuroanatomy, data fusion and computational modelling. |
| Building a Toolbox for Robust Quantitative MRI | Quantitative MRI aims to go beyond producing images of the brain and instead estimate properties of the underlying tissue, such as cell sizes, densities and fibre organisation. These properties can be estimated using biophysical models, but their reliability depends strongly on the MRI measurements acquired, the model being fitted, and the methods used to estimate its parameters. This project aims to develop a user-friendly computational toolbox for exploring the reliability of biophysical MRI models. Building on models already implemented within our lab, the toolbox would allow users to specify an MRI acquisition protocol and model, and then investigate how accurately and robustly different tissue parameters can be estimated. There are several possible avenues for extending the project. These include developing more robust parameter-estimation methods, such as simulation-based inference (SBI); comparing alternative fitting approaches; and developing tools for MRI protocol optimisation—identifying which measurements should be acquired to provide the most reliable estimates of tissue properties. The project will provide experience in quantitative MRI, biophysical modelling, parameter estimation, simulation, optimisation and scientific software development. |
Dr Andriy Kozlov
Profile: https://profiles.imperial.ac.uk/a.kozlov
Contact details: a.kozlov@imperial.ac.uk
| Project title | Description |
| Developing new anaesthetic compounds | This project focuses on a new class of anaesthetic drugs which we synthesise (with Dr Sylvain Ladame) and test using patch clamp. The student will need to be interested in doing chemical synthesis & characterisation, cell culture, and electrophysiology (neuropharmacology). |
Dr Chiu Fan Lee
Profile: https://profiles.imperial.ac.uk/c.lee
Contact details: c.lee@imperial.ac.uk
| Project title | Description |
| Modelling tissue regeneration as fluid flow: Implication for wound healing | If you scratch a layer of epithelial tissue, the epithelial cells will have the remarkable ability to proliferate in a coordinated manner to repopulate the scratched area, to the extent that the tissue almost regenerates to the original form. This extraordinary property of regeneration is far from fully understood. Recent advances have revealed that the cells at the edge of the wound are not the only active players in the healing process, rather, cells deep in the bulk exhibit active motility forces [1], and large scale swirling patterns [2]. The highly dynamic nature of tissue under regeneration suggests the possibility of viewing wound healing as a collective movement of a fluid under self-generated stress (due to cell motility) and self-generated pressure (due to cell division). This perspective has recently been investigated using computer simulations which were able to capture some of the salient features of experimental observations [3]. To render the model more realistic, a number of the model assumptions need to be connected to actual biological processes. Further, a comprehensive verification of the model requires derivable predictions that can be validated by experiments. In this project, we will first construct a cell proliferation model by incorporating elements of cell adhesion, motility and proliferation common to epithelial cells. We will then employ simulation methods to study how tissue regeneration would proceed for different types of tissue damages. For instance, we will investigate how the shape of the finger-like protrusions from the edges of the wound varies with the shape of the wound. This project will lead to an in-depth understanding of the biophysical mechanisms behind wound healing, and will equip the student with the computational skills to model cell proliferation in diverse contexts. References 1. Trepat, X., et al., Physical forces during collective cell migration. Nature Physics, 2009. 5(6): p. 426-430. 2. Angelini, T., et al., Glass-like dynamics of collective cell migration. Proceedings of the National Academy of Sciences, 2011. 108(12): p. 4714-4719. 3. Basan, M., et al., Alignment of cellular motility forces with tissue flow as a mechanism for efficient wound healing. Proceedings of the National Academy of Sciences, 2013. 110(7): p. 2452-2459. |
| Biophysical Modelling of Giant Vacuole Dynamics in Endothelial Cells | Cells in physiological conditions are often subjected to external mechanical stresses, such as pressure gradients, to which they typically respond by changing their shape. This ability has been shown fundamental for several important biological functions like lumen formation during sprouting angiogenesis [1] or aqueous humor intraocular pressure regulation [2]. In these processes, endothelial cells are subjected to large hydrodynamic pressure gradients to which they respond by forming fluid-filled membrane invaginations. Around these structures actin filaments and myosin motors are recruited to construct an active contractile shell that sustains the vacuole. At large times the vacuoles are observed to collapse, which is likely caused by the formation of a second pore at the top of the vacuole through which the internal chamber pressure is released. This complex behaviour has been hypothesized to be the characteristic cellular response to large pressure gradients [1], but the biophysical mechanisms underlying it are currently under investigation. In this project, the Candidate will contribute to the formulation of a biophysical model of this phenomenon, called inverse blebbing. Two main research topics are available: (i) understanding the vacuole collapse process through modelling of the membrane pore formation within the framework of active gel theory [3]; (b) understanding the initial invagination dynamics and formulating a suitable stochastic model of it. The Candidate will be required to learn some basics of elasticity theory and to perform both analytical and numerical calculations. [1] Gebala V, Collins R, Geudens I, Phng LK, and Gerhardt H (2016). Nat Cell Biol 18:443. [2] Pedrigi RM, Simon D, Reed A, Stamer WD, and Overby DR (2011). Exp Eye Res 92:57. [3] Prost J, Julicher F, and Joanny JF (2015). Nat Phys 11:111-117. |
| Cytoplasmic organisation through phase separation | Biological cells organise their contents in distinct compartments called organelles, typically enclosed by a lipid membrane that forms a physical barrier and controls molecular exchanges with the surrounding cytosol. Recently an intriguing class of organelles lacking a membrane is being studied intensely. Membrane-less organelles have attracted an intense interest from the biology community as they are present in many organisms from yeast to mammal cells, and are critical for multiple biological functions. For example, P granules are involved in the asymmetric division of the Caenorhabditis elegans embryo, and stress granules assemble during environmental stress and protect cytoplasmic RNA from degradation. Strong experimental evidence indicates that membrane-less organelles are assembled via liquid–liquid phase separation, a common phenomenon in everyday life responsible for example for oil drop formation in water. Under the equilibrium condition phase separation is well understood. However cells are driven away from equilibrium by multiple energy consuming processes such as ATP-driven protein phosphorylation, which can potentially affect the phase-separating behavior of membrane-less constituents. In this project, we will study how these energy-consuming processes affect the dynamics of phase separation in the context of the cell cytoplasm. |
| Biophysical modelling of the pathogenesis of Alzheimer's disease | Many human diseases are characterised by the formation of amyloid fibrils linear aggregates of abnormally folded proteins among which Alzheimers disease (AD) is a prevalent and particularly morbid one that affects us all. A common signature of amyloid-related pathogenesis is the gradual replacement of healthy tissue by aggregates of amyloid fibrils (e.g., in the form of amyloid plaque (AP) in AD), resulting in the degradation of the functioning of the tissue. Amyloid fibrils are insoluble biopolymers that are robust against proteolysis, and their presence in the forms of extracellular AP and intracellular neurofibrillary tangles are the defining histopathologic features of AD. However, mounting evidence has indicated that proteins in the monomeric form and oligomeric form, instead of proteins in the fibrillar form, are predominantly responsible for cell death. This finding thus raises the conundrum of the role of amyloid fibrils in amyloid pathogenesis: Since monomers have a tendency to self-assemble into amyloid fibrils, fibril formation should be a good way to sequester monomers and oligomers in the system by locking them up into the fibrillar form. Unfortunately, evidence points to the contrary, as demonstrated by the facts that injecting fibrils into transgenic mice induces the onset of amyloid pathogenesis and the well documented cases of Creutzfeldt-Jakob disease resulting from ingesting the misfolded form of prion proteins. In other words, there is a disconnect with the histopathologic characterisation of AD and our understanding of the cell-death inducing mechanism at the molecular level. Physically, amyloid fibrilisation and AP formation are well-described by the physics of aggregation [1]. Separately, there are well-developed methods to model toxicity-induced cell death. This project thus aims to combine these two distinct fields to further our understanding of Alzheimers disease pathogenesis. [1] Hong L, Lee C F and Huang Y J 2016 Statistical Mechanics and Kinetics of Amyloid Fibrillation. To appear in Biophysics and biochemistry of protein aggregation, edited by J.-M. Yuan and H.-X. Zhou (World Scientific). E-print: arxiv:1609.01569. |
| Modelling bone marrow stem cell dynamics in mouse | Leukemia cells can be highly motile in the bone marrow environment. However the exact type of motion executed by a Leukemia cell, be it diffusive, sub-diffusive or super-diffusive, remains unclear. In this project, with the help of high-resolution and high-frequency imaging data, we will perform an in-depth study of the motility pattern of Leukemia cells in the bone marrow. We will also incorporate the complex environment into account and investigate how Leukemia cells interact with the diverse components (e.g., bone cells and blood vessels). The knowledge generated in this project will help us understand the dynamical behaviour of Leukemia cells in the bone marrow, and may provide key insight into how blood cancer develops from few initial malignant cells. Activities 1. Image analysis of the trajectories of stem cells in the bone marrow and the changes in cell shapes 2. Statistical analysis of how stem cell movement is affected by the bone marrow environment 3. Statistical analysis of how the stem cell interacts with the diverse cell types in the bone marrow 4. Particle simulation incorporating the movement and interactions of the stem cell as well as the various cell types in the bone marrow environment 5. Mathematical modelling of the system by setting a set of partial differential equations 6. Numerical analysis of the mathematical model |
| Active nematics of zebrafish heart sarcomere organisation | |
| Unravelling the mechanism of elevated enzymatic activity within crowded protein droplets. |
Dr Chris Richards
Profile: https://profiles.imperial.ac.uk/c.richards1
Contact details: c.richards1@imperial.ac.uk
| Project title | Description |
| Computational actuation: Programming a robotic actuator to behave like a muscle | Skeletal muscle is the universal biological actuator. Despite vast behavioural differences across animal taxa, key aspects of muscle function have remained unchanged through evolution. One such property is that muscle force is not directly controlled by the nervous system. Rather, muscle force is the outcome of nonlinear activation dynamics, force-length and force-velocity properties. These properties confer mechanical behaviour which is profoundly different from conventional robotic motors. Specifically, muscle’s mechanical impedance (stiffness and damping) depend on the muscle’s length, velocity and activation level. Crucially, actuator impedance dictates how a muscle interacts with the mechanical environment. The implications of this are not yet fully clear, though recent theory suggests that muscle’s mechanical versatility may arise from muscle’s “tuneable†impedance. In search of an improved design for robust biologically-inspired actuation, this project will develop hardware which incorporates muscle properties. The project will involve adapting a conventional actuator (e.g. a brushless servo) by building a controller which incorporates activation-length-velocity properties of real muscle. A large part of this project will be developing analogue and digital (e.g. microcontroller) circuitry to “program†the motor to behave like a biological muscle. Once a suitable design has been prototyped, it will be tested under a range of loading conditions (e.g. powering a simplified robotic limb model). |
| 3D computational simulation of running behaviour in frogs | Frogs are celebrated for their jumping ability, however several species also run. There is a hidden mystery of these frogs. Paradoxically, jumping requires elongated legs (for power) and a stiff spine (for stability). However walking requires the opposite; balanced arm/leg lengths and a flexible spine to enhance stride length. How do frogs resolve this apparent paradox to be able to run and jump with equal ability? Using our large library of detailed high-speed video recordings, this project will address “the frog paradox†by studying the intricate motions of the legs and arms. The project will involve analysing digital video images towards a 3D digital reconstruction of the animal. The reconstructed 3D coordinates will then be used to create a mathematical representation of the frog; i.e. a "digital frog" with morphable anatomy. This “digital frog†can be manipulated to directly explore how skeletal features confer locomotor versatility. |
Dr Chris Rowlands
Profile: https://profiles.imperial.ac.uk/c.rowlands
Contact details: c.rowlands@imperial.ac.uk
| Project title | Description |
| A new way to simulate optical systems | Optical devices are some of the earliest precision instruments ever made, and underpin developments in many fields, not least of which is biology where they have contributed to our understanding of cells, germ theory, neurology and pathology, to name but a few fields. Designing and developing new microscopy tools is therefore of widespread interest, but techniques for simulating optical propagation are either precise but computationally inefficient, or efficient but crude. Small-scale phenomena can be accurately simulated using high-precision finite element methods, which accurately account for diffraction effects, polarization, and nonlinearily, but simulating a microscope objective using these methods is infeasible. Large-scale simulation methods such as ray-tracing can handle macroscopic features, but these simplify the propagation of light considerably, meaning that the user must exercise good judgement when interpreting the results of the simulation. In many optical systems, the propagation through bulk media, such as glass or air, dominate the computational time of the simulation, and as such dramatic time savings can be made by identifying these large, homogeneous regions and not simulating their contents. The student on this project will work on approaches to segment an arbitrary space into a computationally-tractable volume, as well as approaches to combine the finite element simulation with the free-space propagation simulations. As such, a good programming background is very desirable, but enthusiasm and a problem-solving attitude are also important. |
| A framework for high-throughput imaging using multiple cameras | Bioimaging is a field with a thirst for data; from motion tracking to neuroimaging, hyperspectral imaging to multiphoton microscopy, researchers always want faster images, with more resolution and more sensitivity. Although better-performing cameras are always being developed, they are hampered by the difficulty of transferring large amounts of data to disk, as well as the cost of producing a flawless camera sensor with the desired resolution and speed. Fortunately, many applications are parallelizable; motion tracking can be performed with multiple cameras simultaneously, hyperspectral imaging can capture all wavelengths in parallel, and so on. What is currently missing is therefore a framework for combining these cameras, ensuring that they have compatible imaging parameters, are synchronized, and that their combined data outputs can be visualized by the user. The student taking this project will be responsible for designing an architecture that will scale to up to 64 cameras, capturing 3 megapixel images at 120 frames per second each. After a suitable design is complete, the student will program a model system consisting of just two cameras, which can be scaled out as needed. The ideal student will therefore have a good background in programming, ideally with knowledge of LabVIEW, C++, Java or another language commonly used for hardware development. Enthusiasm and problem-solving abilities are also important. |
| Developing algorithms to sculpt light in 3D | Photolithography (literally 'light stone-writing') is widely used in the semiconductor industry for patterning microchips, but using light to trigger a chemical or physical change has many uses in biology as well. 3D bioprinting, photodynamic therapy, image recording, and optogenetic control of neurons all employ light to induce a change in a biological system. One important limitation of conventional projection-based optics is that the change is induced by a single photon. This has a subtle problem in 3D applications, because if one wishes to confine the photo-response to a particular plane, the regions above and below the plane are also illuminated. This is a particular problem in optogenetics, a cutting-edge technique in which light is used to excite neurons in the brain. Here, it is desirable to excite one neuron but not the ones above or below, yet this is impossible with conventional optical projection. Fortunately, there is a potential solution - one can use high-speed projectors to make holograms that change very rapidly. None of the projected images are intense enough to trigger a neuron on their own, but the sum of many of them is. One can therefore find a series of patterns that trigger the desired cell, but cause the light above and below the desired cell to 'miss' the important regions, thus having no effect. The student for this project will work on developing an algorithm that can, for a given distribution of neurons, find a sequence of holograms that trigger a single cell but don't affect the surrounding cells. They will develop software to control a projector in order to make these patterns, and if everything goes according to plan, it may be possible to test the algorithms in a laboratory setting. The student for this project should have a moderate to strong mathematical background, and some experience in Matlab or another similar programming environment. If necessary they should have, or be able to develop the lab skills necessary to test their software in real life. |
| Analyzing hyperspectral oncological images using cutting-edge data processing | Deep learning techniques have found considerable use in pattern recognition for image analysis, but in medical imaging there are often additional data dimensions which can be exploited for improved diagnosis. This project will involve one such dataset - hyperspectral Raman images taken from tumour resection margins. In this case, the goal is to identify whether any tumor remains in the image, and if so, where it is located. Neural networks and other deep learning techniques will be used to perform this analysis, incorporating spatial and spectral information to make an accurate diagnosis. |
| Biophotonics | The Rowlands lab develops optical systems (microscopes, spectrometers, displays and so on) for use in biology. Anyone who is interested in designing new systems, building instrumentation, simulation or performing image analysis is welcome. |
| Towards a Raman-Activated Cell Sorting system for cancer screening | Nobody needs to be told how much of a threat cancer poses to the population; even worse, certain types of cancer (such as pancreatic cancer, or certain types of ovarian cancer) are so difficult to detect that once they are observable, the prognosis is very poor. A screening method that can detect the limited number of cancer cells circulating in the blood would be of interest in these cases. Fluorescence Activated Cell Sorting, or FACS, is a routinely-used method for sorting cells into different categories based on fluorescence. Unfortunately, cancer cells aren't fluorescent, and finding a good label is arduous and often ineffective. The alternative is to use some form of intrinsic contrast, such as the Raman effect. The Raman effect allows molecules to be identified by the characteristic vibrational frequencies of the bonds in the molecule itself, thus it is very specific and requires no labelling or staining. The goal of this project is to take the first steps towards a combined Raman-Activated Cell Sorting (RACS) and single-cell sequencing instrument that can identify rare circulating tumour cells early. The student on this project will first be responsible for designing, building and programming a Raman microspectrometer, and then using it to analyse different cell populations (some made up of known cancer cells, some not) to see whether the system can distinguish an individual cancer cell from the thousands of other cells also found in the blood. The ideal student will have a background in programming, some CAD skills, and experience building instrumentation, but these are by no means a requirement; the student will be taught anything necessary that they do not already know. |
| Drugs on Demand - towards an automated synthesis platform | Modern drug synthesis occurs in large chemical plants, or at the very least on a lab bench, and requires extremely well-trained researchers, lots of glassware or plant components, and great expense. This project tries to do away with all of those limitations, allowing essentially any synthesis to be performed on a reconfigurable microfluidic chip. Microfluidics has great promise, particularly for small-scale syntheses, in that it can perform reactions more rapidly, under more tightly controlled and uniform conditions, and in an entirely automated manner. Unfortunately, chip designs for one reaction cannot be easily modified or used for another reaction, which limits flexibility. This new microfluidic chip will be able to emulate any other design, changing reaction conditions and configuration rapidly and easily, ushering in a new era of microfluidic drug synthesis. The student on this project will be working with a postdoc to develop the new microfluidic chip. It uses tiny an array of tiny wax motor valves, so first the student will be responsible for designing and characterizing these valves, before scaling up to larger arrays. The ideal student will have some experience in CAD modelling, design of simple electrical circuits, and basic programming, but these are by no means essential - all candidates will be considered, and any required skills can be taught. |
| Detecting bioweapons with stand-off Raman spectroscopy | Bacillus anthracis, commonly known as anthrax, is a potent bioweapon. Having first been used in World War Two, there have been a number of attacks and close calls, ranging from a 1979 accidental release of spores in the former Soviet Union which killed 69 people, several attempts at terrorist attacks by the Aum Shinrikyo cult in Japan in the 1990s, and the 2001 anthrax letter attacks on senators in the United States. Anthrax is a powerful bioweapon not only due to its pathogenicity, but because it can form spores which are extremely difficult to eradicate. These spores are stable for decades, and are resistant to radiation, ultraviolet light, dessication, extreme heat and cold, as well as a number of chemical disinfectants. Identification and detection of these spores is critical to decontamination of an area after a suspected attack, but for obvious reasons, it is not a good idea for a user to get too close to a suspected contamination. Finding a way to detect these spores at ranges of 10m and above would be extremely beneficial for first-responders who wouldn't have to risk their lives to test a suspected release site. One way to perform this detection is using Raman microscopy. The student on this project will be responsible for building a system to perform Raman detection at long distances, without compromising on sensitivity. This system will be able to detect Bacillus subtilis (a benign analog of anthrax) without the need for the user to come near the sample location, and the project will involve some optical engineering, programming, and potentially some electrical engineering. |
| Speedy Spectroscopy - investigating new ways to speed up vibrational spectroscopy | Raman spectroscopy is an analytical technique which provides a wealth of information about a sample, allowing identification of molecules and even diagnosis of diseases (especially cancer). It requires no labelling of the sample, is extremely specific, and applicable to almost any compound imaginable. Given these virtues, it is fair to ask why it is not more ubiquitous in medical diagnosis, and the answer is that it is painfully slow. Spontaneous Raman microscopy takes around a second to collect even a low-quality spectrum, and this is simply too slow as a tool for mapping tissue, or screening cells. Finding a way to speed the process up would be ideal. In this project you will be exploring techniques to speed up Raman microscopy, for example by using parallel excitation, light-sheet imaging, electron-multiplying CCDs, high-power lasers or high-performance signal-processing methods. Some useful skills might include programming hardware devices / signal processing algorithms, optical alignment or precision machining, but these are not required, and the requisite skills can be taught. |
| World's Fastest Video Camera | High-speed imaging requires specialized cameras to capture fleeting events like explosions, hypersonic flow, or even the passage of light. In this project, we are interested in the oscillations of an ultrasound bubble, which occurs at frequencies of a few megahertz. As such, we will need to build a camera that can image at around one hundred million frames per second, for a duration of around one second. These requirements are far beyond even the fastest cameras available today, necessitating a new development program. The student on this project will be building part of the camera, specifically a small piece of the sensor. Using newly-available silicon photomultiplier arrays, we will be constructing a small-scale prototype with the sensitivity and speed necessary to capture data at these incredible speeds. The ideal candidate will have a good background in electrical engineering, and will be designing and testing readout circuitry for the camera. Once this is complete, they will begin testing a small-scale prototype by building the large-scale optical system required to magnify the bubbles enough to be seen by the sensor. This project will also involve a certain amount of programming, in order to reconstruct the data after the experiment is complete. |
| A New Head Mounted Display Concept: Virtual Reality in a Pair of Sunglasses | In order to experience immersive virtual reality, a display must have a large field of view and a high resolution, otherwise the user will feel like they are 'looking at the world through a toilet roll'. Commercially available head-mounted displays like the Occulus Rift, HTC Vive and Playstation VR solve this problem by placing the screen in front of the eyes, but this is clearly an inelegant solution as it involves basically strapping a brick to your face. More recent designs such as the Microsoft Hololens and Magic Leap One use holographic gratings to project light into the eye, but these have a smaller field of view, leading to the 'toilet roll' problem described above. The Rowlands lab is currently developing a new type of holographic display, which can achieve a large field of view along with high resolution, by making the hologram itself active, rather than passive. Instead of projecting the whole image at once, the display scans a beam across the eye at high speeds, producing the illusion of high resolution but without the compromises needed for the Hololens or Magic Leap One. The student on this project will conduct theoretical and experimental studies into the feasibility of this design. They will be using finite-difference time-domain modelling and fabricating electro-optically active waveguides in an attempt to demonstrate a proof of principle, with the goal of producing a device that can project simple patterns into a stationary eye. The ideal student will have a good background in computer modelling, an interest in microfabrication and photolithography, and possibly some electrical engineering expertise. Any necessary skills can be taught however. |
| Building a next-generation scanning microscope | Scanning optical microscopy is a workhorse tool for modern biology - it can see things deeper into tissue, with 3D resolution, and observe fast dynamic events. Recently, Drs Rowlands and Pantazis have been interested in developing a technology called Primed Conversion (https://www.nature.com/articles/nmeth.3405) in order to make it easier to use for researchers around the world. Primed conversion involves optically tagging cells as they develop, allowing us to trace the development of an organism from a single cell all the way up to a complete animal and seeing which cells are destined to form which parts. The missing piece for the widespread use of Primed Conversion is the integration of the system into microscope systems. The student on this project will build an add-on to a microscope which can perform Primed Conversion, aligning two lasers and scanning them in parallel through the sample. The skills required involve programming, electronic engineering, some mechanical design and some optical engineering, but any skills that the student doesn't possess can be taught. The most important thing is an aptitude for learning quickly and hard work. |
| AstroTIRF: Pinning light to a surface | Total Internal Reflection Fluorescence Microscopy is an imaging technique that can take pictures of cells with incredible resolution - it is able to see things that are the thickness of a virus. While this is very important for imaging of complex cell processes, the limitation is that we can only see the surface of the cell - we can't see inside, as we can with a normal microscope. Nevertheless, it might be possible to interfere two illumination patterns together and combine the high resolution of TIRF with the ability to see features hidden inside the cell. The student on this project will be responsible for delivering on this vision. The student will start this project by modelling the system using optical wave propagation software, before moving on to optics experiments in the lab. Initially work will be on a test system, but eventually will be incorporated into a microscope and used to image cells. The ideal student for this project would have a good background in programming, and some experience with building precise mechanical devices, but the student could be taught anything they need to know. |
| Next-Generation Drug Synthesis: Optimizing bioreactors with lasers | A great many modern drugs are manufactured, not in chemical reactors, but in bioreactors: steel or glass vessels housing many litres of cell culture medium and a colony of genetically-modified cells which produce the drug itself. As this mass-manufacturing technology underpins the production of pharmaceuticals worldwide, there is considerable interest in achieving even modest gains in efficiency and yield which, when scaled out over a large-scale manufacturing process, contribute to dramatic cost-savings. Unfortunately, if optimising a chemical reactor is hard (with all the inhomogeneities in temperature, pressure, reagent concentration and so on), optimising a bioreactor is much harder still, because cells are much more sensitive to their local environment. Fortunately, researchers in the Polizzi lab in Chem Eng, and the Rowlands Lab in Bioeng are working on a way to monitor these cells in situ, using optical imaging and fluorescent reporter cells. The student will work on a system to image the fluorescence from a variety of locations within a large (liter-scale) volume using a large number of optical fibers coupled to a microscope. The student will use the system to monitor reactions in the reactor, and try to reconstruct the resulting fluorescence distribution. The student will need some basic precision manufacturing skills and an ability to prototype ideas quickly, but the most important is a willingness and ability to learn quickly. |
| Advanced Microscopy for Everyone | One of the workhorse instruments in a microscopy suite is the confocal microscope. Unlike a normal microscope, it can image objects in three dimensions, which helps explain why modern laboratories use theirs so extensively, in fields as diverse as histopathology, neuroscience and cell biology. Nevertheless, confocal microscopes are very expensive, costing hundreds of thousands of pounds in many cases, despite containing no particularly expensive parts. This enormous price puts the instrument out of reach of researchers in the developing world, and even several laboratories in developed countries as well. This project will seek to redress this balance, by developing a confocal microscope using modern low-cost rapid prototyping facilities, off-the-shelf microcontrollers and careful design, broadening access to this core technology throughout the world. The student on this project will be responsible for building this instrument, based on a modern design known as a 'rescanned confocal'. This will require some work with a CAD package (like Solidworks), some 3D printing or CNC machining (possibly outsourced) and a bit of programming experience. Students should not be put off taking this project if they don't feel they possess these skills though, as they can be taught. Motivation and a willingness to learn is much more important. |
| Seeing the world in hundreds of colours: SERS tags for biology | Fluorescence microscopy is performed by countless labs around the world, labelling their molecules, proteins, membranes or organelles with a bright, fluorescent label which can be seen under a microscope. Unfortunately, there are a limited number of fluorophores that can be seen in the same image - separating them by their colour gets progressively harder because they all emit over a broad range of wavelengths which are difficult to separate. The same is not true for Raman spectra; these contain very sharp spectral features and can be easily identified from their spectral patterns, but the Raman effect is very weak - taking Raman maps of a surface is very laborious. One solution to this problem is to use the Surface Enhanced Raman Scattering (SERS) effect. SERS occurs when an analyte interacts with a gold nanoparticle, which enhances the electric field substantially. Since the Raman effect scales as the fourth power of the electric field, a modest 100x field enhancement results in 10^8 increase in the Raman effect, making it a bright and efficient molecular tag. This project will be to investigate the use of SERS particles as tags, from modelling the electromagnetic properties of these SERS particles, to using them to image tens of different features in a small cell. This project is quite open-ended, and would therefore suit a range of students, from those interested in computational modelling to people interested in microscopy, instrument development, or even wet chemistry. |
| Diagnosing Disease with Speedy-Scanning Raman Readout | Ordinary microscopes can use light to diagnose diseases but they do so using a limited number of colours. If we replace the three colours of the spectrum with thousands of wavelengths in a spectrum, we can learn much more about each pixel in an image. This is the promise of Raman microscopy. Raman microscopy uses highly intense laser light to illuminate a sample. When the laser scatters from the sample, a very small fraction of it changes in wavelength, and this change is unique to a particular chemical bond. By mapping the sample using a spectrometer (which can image and quantify these wavelength changes) we can therefore gain chemical information about the sample, which is sufficient to diagnose a number of diseases, including (most notably) cancer. The downside of Raman microscopy is that it is slow, and while it can be sped up by increasing the illumination intensity, eventually this comes at the cost of damaging the sample. The key to increasing imaging speed is therefore to share the laser out over more pixels, recording from them in parallel. The student on this project will develop a new system that can illuminate dozens of points simultaneously, thus significantly speeding up the imaging process. This will involve working on an existing Raman microscope, modifying it to implement this new scanning process. The work will then progress to methods for rapidly diagnosing cancer using tissue biopsies. The ideal student on this project would have a reasonable background in programming, but all other skills and techniques can be taught. |
| Watching Sound - creating a new technique for stand-off ultrasound imaging | Ultrasound is one of the safest, cheapest and most powerful ways to image deep within the body. Compared to MRI it is fast, easy to use and significantly less onerous on the patient. Nevertheless, there are limitations which we are working to overcome. All current forms of ultrasound imaging require the user to place an ultrasound probe in contact with the skin. This in turn requires a skilled ultrasound technician to apply ultrasound gel and move the probe to image the organ of interest. A more elegant solution would be to use optical imaging to see the acoustic signal (as well as exciting it), thus removing the need for the technician, gel or even for the patient to lie on a bed. The acoustic signal could be simply recorded by imaging the patient's body with a very fast camera. The Rowlands lab is working on developing optical ultrasound detectors based on evanescent wave sensors; these are extremely sensitive to minute changes in the position of an array of nanoparticles, and thus to a passing acoustic wave. The student working on this project will help develop this new type of ultrasound detector, building the nanoparticle suspension, excitation optics and imaging / readout. The ideal student would have a background in the physical sciences or engineering, with a willingness to try new things and learn. The Rowlands lab is highly multidisciplinary, with lots of different researchers studying lots of different things, so new perspectives and approaches are encouraged. The student can be taught most (if not all) of the skills and techniques they will need to know. |
| Dynamic Dichroic Mirrors - making reprogrammable optical filters for stand-off chemical imaging | Hyperspectral imaging is used in applications from chemical weapon detection to cancer diagnosis, from fraud monitoring to industrial quality control. Currently wide-field camera-based hyperspectral imaging systems are based around single filters - you must know exactly what you're looking for in order to select the right filter. The Rowlands Lab is working on a new type of optical filter which can be reprogrammed at will, allowing arbitrary chemicals to be searched for, for example. Currently, the optical components have been assembled, but need to be tested and new materials tried out. The student on this project will be responsible for taking this system from prototype stage to working tool, and will have to develop a number of skills, from instrument development and debugging, to materials development and optimization and finally development of robust testing methods. There is also the potential for publication or intellectual property development, should groundbreaking advances be made. The ideal student on this project would have a willingness to learn, adaptability and some background in the physical sciences, engineering or computer science. That said, talented students from any background will be considered, and the relevant knowledge taught. |
| Making a true 3D camera | When it comes to microscopes, there are no shortage of approaches to imaging a 3D sample: multiphoton microscopy, light-sheet microscopy, confocal and so on. What is notable about these techniques however is that they work by imaging a volume one plane at a time, and thus aren't really imaging in 'true' 3D. This project will change all that, as the student will be working on a system that can really image a volume (animal heart, brain, cancer organoid, tissue sample etc.) in 3D. The system itself is based on a design called a Framing Camera. This uses a mirror to reflect light to a number of cameras, each of which can see a different plane in the sample. The student in this project will be constructing the prototype of this system, which will involve assembling the cameras and the optical system, programming the mirrors, and ultimately building the world's first true 3D microscope. The ideal student for this project will have a good background in mechanical, electronic or software engineering, and a keen interest in picking up new skills. He or she will be ambitious and self-motivated, and a quick learner. There is no specific requirement on skills as these can all be taught. |
| Intelligent Imaging - tagging and tracking cells in 3D | In collaboration with the Pantazis lab, we have created a new type of microscope that can selectively switch a cell expressing a fluorescent protein from green to red. This is useful for a number of cell-tracking tasks, particularly lineage tracing where the cell of interest needs to be tracked along with all its daughter cells. Now we want to take the next step - programming the microscope to track these cells in real time, and "top up" the colour change where necessary. The student on this project will program the microscope to rapidly scan the sample, mapping out its structure and reconstructing the images into a 3D volumetric dataset. The program will then identify regions in which the photoconversion is lacking, return to those locations and photoconverting them specifically. The ideal student would have a decent programming background, an interest in hardware construction / automation and a willingness to learn new skills. That said, the most important thing for this project is self-motivation - the rest can be taught where necessary. |
| Investigating mice with brain cancer by Raman mapping | Raman microscopy is a powerful technique for mapping the distribution of different molecules. Subtle molecular changes can be recorded and used to assess the disease state of a tissue. The Rowlands lab pioneers high-throughput Raman imaging technologies, and in this project the student will be using some of them to investigate the metabolic activity of a mouse brain tumour provided by the Syed lab (Brain Sciences). The student on this project will be responsible for collecting samples, taking both point-by-point Raman measurements as well as light-sheet Raman measurements, and comparing the two. If needed, the student will make modifications to the Raman instrument in order to improve performance. The student on this project should be willing to learn, able to obtain biological samples competently and reliably, possess good attention to detail, and ideally have some programming and / or mechanical engineering expertise. |
| Virtually Microscopic - building a virtual-reality interface to complex microscopic data | The design of a microscope has remained the same for 350 years: the user looks down an eyepiece, moves a stage and focuses the lens to see features of interest in a sample. Nevertheless, the recent availability of low-cost virtual reality systems means that users need no longer be tethered to the instrument; researchers, doctors and students alike can explore the rich datasets that are gathered by modern microscopy, or even guide the microscope in real time, gaining a new perspective which hopefully leads to new insight. As a researcher on this project, you will have good programming skills and some familiarity with complex Software Development Kits (SDKs). You will be programming a head-mounted display to project part of a large microscopic dataset, updating the display as the user moves around the environment. As the project progresses, you will be incorporating control over the microscope as well, rapidly capturing data to allow the user to explore a sample with as much freedom as possible. |
| High-precision LEGO photonics | Optical instruments require the most precise modern precision machining techniques - even a basic biomedical microscope contains components aligned to sub-micron accuracy. This precision alignment comes at a cost, and commercial microscopes can easily cost hundreds of thousands of pounds. To try to reduce this cost, we can turn to one of the planet's foremost experts in low-cost high-precision engineering - the LEGO Group. LEGO is a miracle of modern engineering - each brick is moulded to a tolerance of 20 microns, smaller than the diameter of a white blood cell. By designing optical systems that can accommodate slightly degraded tolerances we can dramatically broaden the ability of researchers worldwide to construct custom optical systems without the need for expensive machined parts and precision alignment methods. The student on this project will be constructing high-precision optical systems for biological applications based on low-cost LEGO parts. These might include beam expanders, automated stages, power controllers, adjustable mirrors, and even full microscopes (complete with automated stages, cameras, autofocus and a variety of illumination sources). The ideal student for this project will be innovative and creative, quick to learn and willing to work hard. Some programming skill may be helpful, but a problem-solving mindset and curiosity are more important. All necessary practical skills (especially traditional optical alignment) can be taught. |
| 3D Print ALL the Things? | Optics laboratories investigate a diverse range of phenomena, such as brain activity, tumour growth, embryonic development and so on, however the fundamental components used in optical setups are similar across labs and experiments. This compatibility allows components to be commercially sourced and easily integrated in a range of set-ups, however these parts are generally expensive and involve international supply chains which can create a barrier-to-entry for many researchers around the world. In this project the student will design a family of optical components that they will manufacture via 3D printing and test in the lab. The end goal is to create an online database of CAD models and assembly instructions which can be freely shared with researchers around the world, creating cheap and fast access to research equipment. The student on this project will be responsible for designing and manufacturing a range of opto-mechanic components. This will involve work in a CAD package to design components, conversion into a printable format, followed by printing and testing the components. Some experience with either CAD or 3D printing would be advantageous. |
| Freezing sound - pioneering high resolution ultrasound through optical tomography | Ultrasound is one of the most widely used medical imaging techniques, yet unlike the camera you're carrying in your pocket, the detector has only a few hundred pixels, severely limiting resolution and field of view. Fortunately, optical cameras can record an ultrasound field with megapixel resolution, substantially boosting medical imaging performance. The student on this project will take the first steps towards tomographic imaging of an ultrasound wave, by observing the sound wave as it passes through a person, changing the refractive index as it compresses and stretches the material it is passing through. This project will start by simulating the ultrasound wave and trying to estimate how sensitive the camera needs to be. The student will then set up an experiment in the lab to see whether measured performance matches simulation. The first reconstructions will be done in 2D but ultimately the system wants to be built in 3D to fully capture any ultrasound wave. Students will ideally have a good background in programming, and have good lab skills; the optics and acoustics knowledge will be taught on the job. |
| SIMaging the future | Optical microscopy is widely used in biomedicine as it is easy to use, safe for almost all samples, fast, and versatile. Unfortunately it has a physical limitation called the diffraction limit which prevents it from observing features smaller than ~100nm or so. Structured Illumination Microscopy (SIM) is a technique for increasing the resolution of an optical microscope. It works by taking nine images of a sample with different interference patterns applied to them, then reconstructing these images, using the Moire effect to work out what the underlying sample distribution looks like. The Rowlands lab has pioneered one of the fastest SIM systems in the world, and is in the process of developing version2 which should be practical an commercializable. The student on this project will be responsible for developing an improved version of the existing instrument that is more robust, compact and easier to use than the original, ultimately so that the instrument can be sold commercially. They should be comfortable handling expensive hardware with care, and ideally have a reasonable programming background (although all necessary skills on this project can be taught). The more important attributes are a willingness to learn and be self-motivated. |
| Turning tissue totally transparent | Within the last year there has been a breakthrough in our ability to control the transparency of live tissues. Typically, light scattering is the main reason why we can't see through things, and scattering is caused by microscale inhomogeneities in refractive index. By exploiting a mathematical relationship called the Kramers Kronig relations, we can now tune the refractive index of an object until it matches that of the rest of the local environment, making it essentially invisible. What the vast majority of people have failed to notice is that rather than just flooding a tissue with another medium to change its bulk refractive index, photopatterning can change the refractive index locally, allowing us to *remove* the refractive index differences, rather than just minimising them. The student will use the multiphoton microscope in the Rowlands lab to selectively photobleach a dye that has been infused in to a tissue, in an attempt to demonstrate that the refractive index can be changed to whatever value the user desires. This will involve wetlab work, potentially some programming and hardware design, and potentially even work with live tissues if the project is that successful. All of these skills can be taught, so the main requirement is a willingness to learn. |
| Edible holography | In the news recently it was announced that a common food dye, tartrazine, could be used to change the refractive index of an object. This is important because being able to structure refractive index on the microscale makes it possible to create true volume holograms, which can be observed in all directions and which are visually indistinguishable from the real object. The student on this project will be using tartrazine infused into Agar gels to create these volume holograms. By photobleaching the tartrazine in a carefully defined pattern, the refractive index could be changed to whatever the user desires, allowing these holograms to be built up one pixel at a time. And because the gels are made out of agar and tartrazine, they are (technically) edible. More seriously, these holograms can act as sensors, probes, vision correctors and so on. On the day-to-day basis the student will be creating gels, testing the photobleaching, designing holograms, and ultimately creating arbitrary three-dimensional holographic objects. This will combine optics, programming, web lab work, and analysis, but as all students in the Rowlands Lab are encouraged to make the project their own, the exact balance of these skills is flexible, and all can be taught. |
| Real laboratory automation | Laboratory automation usually refers to the use of robots to perform experiments and other repetitive tasks without user interaction, but another meaning could be to literally automate a laboratory; to control the lighting, shutters, fans and other noise sources automatically in response to voice commands, interlock conditions, number of users, lab temperature or any other condition. Until recently this will be an extreme expensive endeavour, but the proliferation of the low cost home automation equipment and open source projects such as home assistant mean that this can be explored at comparatively low cost. The goal of this project is therefore to make work in the laboratory safer, easier, more repeatable, and with better logging of conditions than previously has been possible. This will involve significant amounts of programming, a modest amount of hardware design, and possibly some interior decorations skills. Fortunately all of these can be taught, although the interior decoration may be somewhat subjective. More seriously, many accidents happen because users are not paying attention to changing conditions or different environments in the lab. Computers do not suffer from this lack of awareness, and by logging everything that happens in the lab and making sure that conditions are as safe as possible, experimental outcomes can only benefit. |
| FLYdom of movement | The behaviour of many organisms is strongly affected by their movement, which often gets ignored when designing experiments; animals are often fixed in place, suspended on treadmills, balls, or floating platforms, so they can be imaged while "moving", using big, bulky microscopes. This is especially true for flies, which lack the strength or size to carry even a simple imaging system. Nevertheless, these configurations are rarely satisfying as an experimental paradigm; it would be better to move the microscope. This is where we step in. The Rowlands lab is working on a high-performance, lightweight robotic fly-tracking microscope that can move to keep a fly within the field of view as it walks around. The student will be refining this instrument, improving the tracking of flies and ensuring that detailed images may still be taken regardless of how fast the fly moves. Ultimately, the instrument will be used to record neural activity in awake, behaving flies as they interact with their environment. The student who takes this project will have a strong grasp of programming, electronics and an ability to succeed despite challenging the limits of what is possible. |
| Streaming Continuous Optical Nanosecond Events (SCONE) | When high-intensity focussed ultrasound is directed at the brain, in can cause microbubbles injected into the blood stream to break through the blood-brain barrier, allowing drugs and other treatments to reach an organ that is usually carefully protected. Now obviously this has very clear medical utility, but the problem is we have no idea how the microbubble does this, and thus can't optimise the process. SCONE is a project to record an oscillating microbubble at roughly 20 million frames per second, so that when it does break through the blood-brain barrier, we can see what it is doing. SCONE requires computational reconstruction to recover the microbubble data; the student will therefore be employing advanced data recovery and modelling algorithms to address this challenge. A strong background in mathematics and / or programming would therefore be advised. |
| iPhone Imaging Improvements | Colour cameras work by filtering out around 66% of the light hitting them. Researchers in the Rowlands lab have recently developed a method for making those colour filters work with 0% loss, potentially making every camera on the planet three times as sensitive. Currently we need to explore the parameters used to design these lossless colour filters before we start commercializing / selling the design. The student on this project will be working on the design, prototyping and testing of these filters, using advanced simulation tools, nanofabrication and the analytical instruments available in the Rowlands lab. As with anything in the Rowlands lab, no specialized prior knowledge is required and all techniques can be taught. |
| Evading Export Edicts for Illegal Imaging in the Infrared | Infrared cameras can see through fog, paper and bodies, which makes them well suited to clinical microscopy. Unfortunately these cameras are "Export Restricted" - possessing them is illegal if they can image too quickly, or with too much sensitivity, or with high resolution, which makes the ones you can buy pretty much useless for bioimaging... unless you combine them. I want to build an infrared microscope that can see with unprecedented sensitivity, resolution and speed, and that hopefully doesn't get me sent to prison. The student will design and build a testbed to prototype these designs. Any skills necessary can be taught, but the student should possess a can-do attitude, and good problem-solving abilities. |
Dr Claire Stanley
Profile: https://profiles.imperial.ac.uk/claire.stanley
Conatct details: claire.stanley@imperial.ac.uk
| Project title | Description |
| FUNGI-ON-A-CHIP: Investigating the influence of fungal species on the role of water transport through hyphal networks | Preliminary experiments have revealed that fungal hyphae draw liquid films into unsaturated environments. This observation confirms that soil fungi redistribute water along gradients in water potential through their hyphal networks and represents the first account of these events on the microscale. The observed phenomenon of water transport through hyphal networks has important consequences regarding the migration of soil bacteria into new microhabitats. Indeed, we expect this interdisciplinary research study to contribute fundamental knowledge to the current fungal highways dispersion theory, which states that hyphal networks provide a platform for the active movement of bacteria in soil (Kohlmeier et al., ES&T, 2005). We are developing microfluidic or "Lab-on-a-Chip" technologies to probe the interplay between soil-dwelling organisms and roots at the single cell level. This technique has a great potential to provide a unique view of biological events at the level of single organisms and cells by enabling precise environmental control, high-resolution dynamic imaging, the simulation of environmental complexity and affording quantitative information. In this project, you will use a microfluidic platform to direct and constrain the growth of fungal cultures, making it possible to study water transport through hyphal networks at the cellular level. Specifically, the so-called fungal-fungal interaction (FFI) device (Gimeo, Stanley et al., Commun. Biol., 2020, under review) and adaptations thereof will be used to culture fungal hyphae and assess the influence of fungal species on the role of water transport through hyphal networks using novel microfluidic technology. You will gain experience with microfluidic device manufacture, microscopy, microbiology, data analysis, designing experiments, statistical analysis, scientific writing and doing science For more information about the Microbiome-Microscopy and Microfluidics Lab please visit: www.imperial.ac.uk/people/claire.stanley |
| FUNGI-ON-A-CHIP: Investigations on tripartite microbial interactions using a new microfluidic platform | Filamentous fungi are successful inhabitants of soil and play a major role in the decomposition of organic and inorganic matter, regulation of nutrient levels and transport of water. However, little is known about the influence of the physio-chemical environment upon the function of these mycelial networks at the cellular level. We aim to develop microfluidic or "Lab-on-a-Chip" technologies to study the functional role of bacteria and fungi in soil. This technique has a great potential to provide a unique view of biological events at the level of single organisms and cells by enabling precise environmental control, high-resolution dynamic imaging, the simulation of environmental complexity and affording quantitative information. We recently developed the fungal-fungal interaction (FFI) device (Gimeo, Stanley et al., Commun. Biol., 2020, under review) to direct and constrain the growth of fungal cultures and therefore gain experimental access to FFIs at the hyphal level in real-time. We now aim to extend the functionality of this platform to include tripartite interactions. In this project, you will assess the suitability of a new FFFI microfluidic platform and quantitatively investigate tripartite interactions. You will gain experience with microfluidic device manufacture, microscopy, microbiology, data analysis, designing experiments, statistical analysis, scientific writing and doing science. For more information about the Microbiome-Microscopy and Microfluidics Lab please visit: www.imperial.ac.uk/people/claire.stanley |
| FUNGI-ON-A-CHIP: Investigating bacterial transport along the fungal highway using microfluidic technology | Filamentous soil fungi are ubiquitous in nature and play a major role in the decomposition of organic and inorganic matter, the formation of symbiotic associations as well as the transport of water. More recently, we have also discovered that fungi can transport defense signals and nutrients via specialised hyphae. However, despite their importance, a major knowledge gap exists concerning the influence of the physio-chemical environment upon the function of these extensive mycelial networks, especially with regard to water transport processes and bacterial distribution. Moreover, there is little appreciation of how these dynamic processes occur within this black box at the cellular level, as it has not been possible to visualise these interactions nor assess microbiome behaviour in terms of its individual components. We aim to develop microfluidic or "Lab-on-a-Chip" technologies to study the functional role of bacteria and fungi in soil. This technique has a great potential to provide a unique view of biological events at the level of single organisms and cells by enabling precise environmental control, high-resolution dynamic imaging, the simulation of environmental complexity and affording quantitative information. We recently developed the fungal-fungal interaction (FFI) device (Gimeo, Stanley et al., Commun. Biol., 2020, under review) to direct and constrain the growth of fungal cultures and therefore gain experimental access to FFIs at the hyphal level in real-time. We now aim to extend the functionality of this platform to understand the nature of fungus-driven bacterial dispersal at the single cell level. In this project, you will investigate the potential of bacteria to utilise hyphae as a fungal highway using advanced microscopy techniques. You will gain experience with microfluidic device manufacture, microscopy, microbiology, data analysis, designing experiments, statistical analysis, scientific writing and doing science For more information about the Microbiome-Microscopy and Microfluidics Lab please visit: www.imperial.ac.uk/people/claire.stanley |
| Yeast-on-a-Chip: Probing community dynamics at the cellular level using microfluidic technology | Single-cell studies on yeast have provided new opportunities to investigate cellular aging, stochastic gene expression and cellular morphology, for example. However, we currently lack tools to explore the dynamic interaction between cooperating yeast communities with cellular resolution. A poly(dimethylsiloxane) (PDMS) microfluidic platform has been developed recently, in which yeast cells are confined to microchemostats, exposed simultaneously to a variety of micro-environments and stimuli in a controlled manner and imaged with high spatial and temporal resolution and. In this project, you will use this tool to investigate novel questions regarding the growth dynamics of yeast co-cultures at the cellular level. Investigations on such communities will have great importance with respect to the development and understanding of synthetic microbial consortia, e.g. for the bioproduction of commercially desirable products. You will gain experience with image processing, microfluidic device manufacture, microscopy, microbiology, data analysis, statistical analysis, scientific writing and doing science… For more information about the Microbiome-Microscopy and Microfluidics Lab please visit: www.claire-stanley.com |
| Single cell microfluidics imaging of mycobacterial interactions with the major mould pathogen Aspergillus fumigatus | Project Background The pulmonary mould pathogen Aspergillus fumigatus causes life threatening invasive infections in the immunocompromised host. It also causes opportunistic infections in people with pre-existing lung diseases like cystic fibrosis and tuberculosis. In these settings there is a significant co-infection rate with both Mycobacterium tuberculosis and non-tuberculous mycobacteria such as Mycobacterium abscessus. In particular, 10-20% of individuals with pulmonary tuberculosis go on to develop secondary pulmonary aspergillosis. Given the numbers involved it its estimated there are 1-2 million such individuals globally, making this one of the most common forms of aspergillosis. Furthermore, this disease is virtually incurable, and antifungals only stop progression in half of cases. Better understanding is required of how mycobacteria predispose to secondary fungal infection Project Aims We have discovered that M. tuberculosis infection of macrophages impairs fungal killing and is associated with NOD2-dependent hyper-inflammatory responses. Furthermore, we observed that Aspergillus, and its supernatant, were able to inhibit the growth of Mtb. Previous studies suggest that this may be a consequence of toxic secondary metabolites of Aspergillus fumigatus. We now wish to determine the microbial interactions between Af. and mycobacteria and their mechanistic relevance in host immunity. Aims: 1. Exploit recently developed microfluidics chambers capable of visualizing the interaction of Aspergillus fumigatus and Mycobacteria 2. Compare mycobacterial attachment patterns with Af throughout growth stages exploiting the microfluidic chamber platform. 3. Identify biologically active metabolites produced upon two species interaction and model their impact on coinfection outcomes This project is supervised by Dr Claire Stanley (Department of Bioengineering) and Prof. Darius Armstrong-James (Department of Infectious Disease). |
Dr Huai-Ti Lin
Profile: https://profiles.imperial.ac.uk/h.lin
Contact details: h.lin@imperial.ac.uk
| Project title | Description |
| Bioinspired microglider design with micro-robotics fabrication techniques | Biomimetic micro‑gliders are important for understanding insect gliding flight and developing distributed environmental sensing networks. This project focuses on designing and prototyping a tandem‑wing micro‑glider inspired by gliding insects. We will build on prior high‑performance designs from Dr. Lin’s lab and incorporate micro‑robotics fabrication techniques from Dr. Jayaram’s lab to engineer wing‑hinge articulations for in‑flight control. Key elements: Design and prototype tandem‑wing gliders; iterate using lightweight materials and origami‑inspired mechanisms. Integrate articulated hinges for controllability; evaluate stability, glide ratio, and maneuverability. Test in Aerial Robotics Lab (e.g., launch rigs, motion capture); optional wind‑tunnel characterization in Dr Lin's Lab as needed. Who should apply: Students interested in origami/mechanism design and hands‑on micro‑fabrication. You must be comfortable assembling millimetre‑scale parts with tweezers under a microscope. If not, this project is unlikely to be a good fit. The project can accommodate up to two students working in different but complementary fronts (pending total lab capacity). Please email Dr Lin (h.lin@imperial.ac.uk) your CV and research interest with subject title: 2026-27 BioMicroGlider MEng project. |
| Control of flexible wings for oscillating wind energy harvesting | This project investigates structural control of aeroelastic flutter on an oscillating flexible aerofoil in a low‑speed wind tunnel, with applications in structural safety (vibration damping) and energy harvesting (power amplification). Dr Lin’s lab has a research stream on wing sensory system which investigates the neural representation of wing deformation. See paper for context https://www.pnas.org/doi/10.1073/pnas.2518032122 To build a physical model, Lin Lab has developed a flexible‑wing evaluation platform with precise pitch control for wind‑tunnel testing. The system currently operates in open‑loop to induce oscillations. In collaboration with Dr Jayaram’s lab, we will integrate micro‑fabricated strain sensors on the wing and collect data across a range of oscillation conditions. Using these data, we will develop and test control strategies (moving toward closed‑loop operation) to modulate vibration for energy harvesting and quantify device performance. Who is suitable Strong mechanical design skills and interest in fluid dynamics/control, flexible wings, and energy harvesting. Basic electronics and microcontroller experience (e.g., Arduino for sensor‑feedback control). Comfortable assembling millimetre‑scale components under a microscope. This is important. Expected outcomes Instrumented flexible‑wing test article with strain sensing. Datasets across operating regimes, with analysis of flutter onset and response. Prototype control strategies (e.g., feedback damping, energy‑harvesting amplification) and a performance report with recommendations for next steps. Please email Dr Lin (h.lin@imperial.ac.uk) your CV and research interest with subject title: 2026-27 OsciFlexWing MEng project. |
Dr Joseph van Batenburg-Sherwood
Profile: https://profiles.imperial.ac.uk/jvbsherwood
Contact details: jvbsherwood@imperial.ac.uk
| Project title | Description |
| Measuring flow in mouse eyes for glaucoma research | SUMMARY This is a challenging but exciting experimental design project about ways to measure the flow that regulates pressure in the eye. You will design, build and test a system, and learn skills such as microfabrication, flow control and image processing. Lots of people want this technology, so if successful, it could have a significant impact on the field. BACKGROUND AND AIMS The pressure in the eye is regulated by the flow of aqueous humour across resistive tissues. In glaucoma, the pressure in the eye increases and expands the eye, irreversibly damaging the retinal cells. In order to better understand the way in which the pressure is altered, we need to be able to measure both the rate at which the fluid is secreted and the resistance of the tissues. We developed the world-leading technology for the measurement of tissue resistance in mouse eyes, iPerfusion, but there are presently no accurate ways to measure the rate of aqueous humour secretion. We have conceived a novel method to accurately measure this flow rate by cannulating the eye with two needles. We would then exchange a fluorescent marker and evaluate dilution using droplet microfluidics and optical methods. If we can demonstrate the accuracy of the method, the technology will be taken up by labs across the world where we have installed iPerfusion systems, such as Duke, Columbia, University of Utah and Northeastern University. WHAT YOU WILL LEARN The project will use analytical methods, computer-aided design, microfluidics and optics to first develop an in vitro model of the system. The next step will be to build a prototype and test it on real ex vivo eyes. If you're interested, please get in touch and we can arrange a meeting to discuss (jvbsherwood@imperial.ac.uk) Visit vbslab.uk for more details on the research team   |
| A Microfluidic device for patient-specific blood modelling | SUMMARY This is an experimental and computational design project to develop a device to measure the parameters for a new, powerful computational model for blood flow in microvessels. You will design, build and test a device, analyse the results and use the model to evaluate how well the experiments worked. BACKGROUND Blood flows through a branching network, reaching microvessels smaller than a hair in order to deliver oxygen to all cells in the body. In diseases such as diabetes and malaria, the microvessels do not get enough blood or leak, with consequences such as blindness, liver disease and even death. We think the blood itself is playing an important role and have developed a model to help us test this. Proper function of microvessels is dependent on the blood flowing in them. Blood is made up of approximately equal portions of plasma and red blood cells (RBCs). These specialist cells are highly deformable, which causes them to ‘migrate’ away from the blood vessel walls, causing a higher concentration in the vessel centre. RBCs also collide with each other, causing ‘margination’, which counteracts this tendency. These two phenomena result in spatially varying distributions of RBCs that determine how blood flows in microvessels. Our model therefore needs parameters to capture the correct ‘migration’ and ‘margination’ behaviour for a given blood sample. This project will designing a microfluidic device to specifically measure these two characteristics for a given sample of human blood cells. Combining with our CFD model, we will be able to evaluate and optimise the efficacy of the modelling approach. WHAT YOU WILL LEARN You will learn skills such as microfabrication, flow control, image processing and processing of blood samples, and have the opportunity to use Computational Fluid Dynamics (CFD) using OpenFOAM. If you're interested, please get in touch and we can arrange a meeting to discuss (jvbsherwood@imperial.ac.uk) |
| Optimisation and Functional Expansion of the RELAVENT Ventilator | SUMMARY This is a lab-based engineering project about optimising and adding functionality to our ventilator design. The project will involve design, pneumatics and electronics, and can be customised to the student’s interest and expertise. BACKGROUND A ventilator breathes for patients when they don’t have the strength to get in enough breath, or are sedated so cannot breathe. They must control breath volume, rate and oxygen concentration, while protecting the patient from harm. In 2020, there was a projected shortage of 1M ventilators to address COVID 19. Thankfully that turned out to be a major overestimate and 5 years on, the developed world is happy to forget about ventilators, as they have plenty (~500 per million people). In low and middle income countries, the story is very different. With many countries having only 10 ventilators per million (and some as low as 1) the shortage of ventilators was, and remains, a major problem. The origin of the problem is cost: existing ventilators cost $20k-70k each, need replacing every 5-10 years, and are difficult to maintain and repair. What is needed is an affordable, robust ventilator that can perform like a high-end ventilator and work in many environments. In lockdown, the global shortage of ventilators and the specialist parts that are used to build them led us to develop RELAVENT. The unique design eliminates many of the typically used specialist components, and in doing so improves power usage, robustness and cost. We have developed and tested full lab prototypes to ISO standards, but some features still need optimising, and additional functionality needs to be added. PROJECT DETAILS This project is suitable for any students with engineering skills, but the specific remit can be defined based on a preference for design, programming, mechanics or electronics. Our challenge is to test and optimise the ‘spontaneous breathing’ mode, a function for helping to wean patients off ventilators. The mode provides support inversely proportional to the patient effort, by sensing patient breaths and responding accordingly. Another challenge is to create an on-off type exhalation valve that can be rapidly cleaned. You will be directly supervised by Dr van Batenburg-Sherwood, the original inventor of the design, which has potential to help save the lives of millions. If you're interested, please get in touch and we can arrange a meeting to discuss (jvbsherwood@imperial.ac.uk) Visit vbslab.uk for more details on the research team |
| A microfluidic device for measuring hydraulic conductivity for diabetes research | SUMMARY This is a hands-on experimental project about a device to measure hydraulic conductivity of monolayers of cells. Based on an existing design concept, you will build the control, optimise the design and test the system, and learn skills such as microfabrication, flow control and image processing. BACKGROUND Diabetic macular edema is a buildup of fluid in the macula, the region of the retina where the most sensitive parts of vision are located. The fluid leaks from blood vessels due to dysfunction of the blood-retinal barrier, which normally inhibits fluid transport out of the blood vessels. The main treatment for this is regular injections of anti-VEGF, which helps to improve the function of the barrier. The process of having injections in the eye is extremely unpleasant for the patient and burdensome for the healthcare system. Further, a proportion of patients do not respond, or respond poorly to this treatment, requiring more frequent injections. Alternative treatments and/or combinations of them are being developed and can work for some patients, but require a trial-and-error approach to identify what will work best for an individual. It has been demonstrated that induced pluripotent stem cells (iPSCs) can be differentiated into endothelial cells, and used to predict ‘responders’ vs ‘non-responders’. However, their study only measured electrical impedance of the cell layer (transport of ions), a very indirect measurement of hydraulic conductivity (transport of liquid). Inspired by this, we aim to build a screening platform to we can measure hydraulic conductivity of endothelial cell monolayers in a parallelised microfluidic device. We have a well-developed design concept for a previous project (with a different application), but the system needs to be built, programmed, optimised and tested. Our clinical collaborators and King’s College and Central Middlesex Hospital will advise to help the project proceed with the translational application in mind. WHAT YOU WILL LEARN The development phase of the project will use hardware coding, optics, microfluidics, pressure control and image processing. If time allows, after in vitro validation, we will incorporate cells into the device and test with substances known to change barrier function. If you're interested, please get in touch and we can arrange a meeting to discuss (jvbsherwood@imperial.ac.uk) Visit vbslab.uk for more details on the research team   |
| A Microfluidic device for measuring blood viscosity | SUMMARY This is an experimental design project about developing a practical way to measure blood viscosity, which has potential as a biomarker for many conditions. You will design, build and test a device, and learn skills such as microfabrication, flow control, image analysis and processing of blood samples. BACKGROUND Blood flows through a branching network, reaching microvessels smaller than a hair in order to deliver oxygen to all cells in the body. In diseases such as diabetes and malaria, the microvessels do not get enough blood or leak, with consequences such as blindness, liver disease and even death. Proper function of microvessels is dependent on the blood flowing in them. Blood is made up of approximately equal portions of plasma and red blood cells. These specialist cells are highly deformable so that they can fit through capillaries and reversibly aggregate in regions of low shear stress. Together these behaviours give blood complex rheological characteristics. Blood viscosity is dependent on red blood cell concentration and the shear rate, hence to properly characterise a blood sample, we need to measure viscosity as a function of both parameters. We have multiple reasons to want to measure blood viscosity 1. We have a numerical model for microscale blood flow that can predict the flow of blood in microvessels, but it needs the patient-specific blood viscosity as an input 2. We modify healthy blood samples to mimic diseased blood and carry out flow experiments. We want to characterise how these modifications have affected the viscosity. 3. Blood viscosity is not clinically measured, but has promise as a biomarker. An easy-to-use device will make it a more practical proposition for monitoring the blood of patients. Classic rheometers are extremely expensive and time consuming, so are impractical. Existing microfluidic devices are too rudimentary to quantify viscosity as a function of shear rate, and no existing devices can automatically separate blood into different concentrations, requiring additional manual processing and increasing measurement time considerably. The aim of this project is to design an easy-to-use parallelised microfluidic device that can measure blood viscosity as a function of shear and haematocrit. WHAT YOU WILL LEARN This project will involve designing, evaluating and optimising a device to measure blood viscosity, and investigating the sensitivity and repeatability of the measurement compared to a conventional cone-and-plate rheometer. If you're interested, please get in touch and we can arrange a meeting to discuss (jvbsherwood@imperial.ac.uk) Visit vbslab.uk for more details on the research team |
| Simulations of flow in the eye for glaucoma research | SUMMARY This is primarily a computational project with a component experimental design and prototyping if time permits. You will use computer aided design and computational fluid dynamics (CFD) to build a model of the front of the eye, and simulate the transport of drugs via needles inserted into the cornea. If time permits, an extension will be improving the existing design for inserting the needles into the eye. The results may contribute to a paper on this design that has potential to be used by many users of the technology. BACKGROUND AND AIMS The pressure in the eye is regulated by the flow of aqueous humour across resistive tissues. In glaucoma, the pressure in the eye increases and expands the eye, irreversibly damaging the retinal cells. In order to screen potential treatments for glaucoma, we need to be able to measure the resistance of the tissues. We developed the world-leading technology for the measurement of tissue resistance in mouse and rat eyes, iPerfusion, which we have installed in labs across the world as, such as Duke, Columbia and Northwestern Universities. However, we identified an issue with the use of perfusion of rodent eyes through a single cannula when testing the efficacy of potential glaucoma drugs: the turnover of fluid in the eye is too small to know what concentration the drug is being delivered at. The solution for large eyes is trivial, put two needles in the eye and exchange the fluid volume with the drug at the desired concentration. However, for rodent eyes this is highly impractical. We have designed a device to cannulate the eye with two needles simultaneously, and it has been used in numerous studies. We now want to share the rationale behind this design by publishing a paper showing the need and efficacy of the approach. Working in collaboration with Dr Ester Reina-Torres, the project will provide simulations to match experiments. WHAT YOU WILL LEARN The project will involve CAD and numerical simulation of fluid and solute transport. If time allows, the design for the double cannulation system could be improved and prototyped based on lessons from the simulations. If you're interested, please get in touch and we can arrange a meeting to discuss (jvbsherwood@imperial.ac.uk) Visit vbslab.uk for more details on the research team   |
| Microfluidic device for Antimicrobial Resistance | SUMMARY This is an experimental design project using microfluidics to apply controlled concentrations to bacteria on a parallelised microfluidic chip. You will design, build and test a device, and learn skills such as microfabrication, image processing and data analysis. BACKGROUND Antimicrobial resistance (AMR) is a major global health threat, compounded by delays in identifying effective antibiotic therapies. Conventional antimicrobial susceptibility testing (AST) typically relies on static culture methods that require prolonged incubation and may fail to capture the dynamic antibiotic exposures experienced in patients. While advanced systems such as the hollow fibre infection model (HFIM) better replicate in vivo pharmacokinetics, they are expensive, low-throughput and unsuitable for routine diagnostics. Microfluidic systems have the potential to offer a scalable alternative by enabling precise control of antibiotic concentration gradients and temporal dosing at smaller volumes, more closely mimicking physiological conditions. This approach supports shorter culture times and faster readouts while providing more clinically relevant assessment of bacterial responses, with potential to improve diagnostic speed and guide optimised therapy. With colleagues in Bioengineering and the Department of Infectious Disease, we have developed a simple microfluidic device that allows for culture of bacteria and readouts of the response to different antibiotics. The next development step is to use microfluidics to provide physiological transient exposure to drugs based on pharmacokinetics, and to parallelise the device so that multiple concentrations can be analysed simultaneously. This is therefore an experimental microfluidics based project. Device designs will be prototyped and evaluated using dyes. If time allows, final tests of the design with bacteria will be carried out in conjunction with Dr Jenny Lawrence from the Department of Infectious Disease. WHAT YOU WILL LEARN Microfluidics is an increasingly important tool in bioengineering. This project will provide the opportunity to develop a range of valuable skills and, if successful, contribute to helping to resolve one of the worlds most pressing medical needs. If you're interested, please get in touch and we can arrange a meeting to discuss (jvbsherwood@imperial.ac.uk) Visit vbslab.uk for more details on the research team   |
| A novel microfluidic device design with self-filling reservoirs | SUMMARY This is an engineering project about designing a microfluidic platform with self-filling reservoirs under hydrostatic pressure, for the purposes of culturing human cells under blood flow in one direction. You will develop practical skills in engineering, microfabrication and prototyping. BACKGROUND In the body, blood flows through a branching network of vessels to deliver oxygen to all cells in the body. In diseases such as diabetes and malaria, the microvessels do not get enough blood or leak, with consequences such as blindness, liver disease and even death. The vBS lab studies how blood flow affects microvessel function using in vitro models incorporating human vascular cells and blood perfusion systems. PROJECT DETAILS Microfluidic devices with vascular cells require continuous perfusion for a few days at a low flow rate, until the cells reach maturity. This is usually achieved by maintaining a hydrostatic pressure gradient between the inlet and outlet reservoirs, requiring either periodic manual refilling, or a back-and-forth flow that is not representative of in vivo conditions. This is the current standard of practice in microvascular chips, and we would like to improve upon it. This project invites you to create a new microfluidic platform that uses hydrostatic pressure to automatically recirculate the fluid between the inlet and outlet. Two base concepts will be prototyped, one involving passive fluid flow (see example: Wang and Shuler, 2018, Lab Chip, doi:10.1039/c8lc00394g), and another involving Arduino / Raspberry Pi automation. You will develop skills in prototyping, microfabrication, fluid dynamics, and programming hardware. Your work will be invaluable to our research. If you're interested, please get in touch and we can arrange a meeting to discuss (jvbsherwood@imperial.ac.uk) Visit vbslab.uk for more details on the research team. |
Dr Kaushik Jayaram
Contact details: k.jayaram@imperial.ac.uk
| Project title | Description |
| Distributed Tactile Sensing | The project involves creating cockroach inspired antenna with distributed 1D and 2D mechanosensors (inspired by insect campaniforms). We will understand the role of active antenna movements for enhancing sensing and tactile discrimination. These antennae will be integrated on insect-scale robots and demonstrate high-speed tactile SLAM-based navigation (in the dark). Will involve collaborations with Dyson School of Engineering. Expect strong interest in microfabrication, nano-3D printing, laser processing. Experience with clean room procedures and microcontroller programming is an advantage. |
| Modeling of Insect-scale Shape Morphing Robots | The project involves modeling the kinematics and dynamics of insect-scale bioinspired shape morphing robots in IssacGym/ Mujoco to create digital twins. These models will be used for training machine learning algorithms and for developing AI bioinspired controllers for navigating complex terrains. Will involve collaborations with Computer Science Department at Imperial and at ETH Zurich. Prior experience with physics-based modeling softwares in a must. |
| Digital twins of spiders | The project involves modeling the kinematics and dynamics of arthropods in Unity/ Mujoco to create digital twins using high-fidelity tracking data collected (DeeplabCut, Replicant) from spiders moving on a treadmill at varying inclinations (vertical, lateral and upside down). These models will inform the creation of new bioinspired gaits to be deployed on insect scale robots. Involve collaboration with other insect labs in department. Expect a strong background in data processing, programming, AI/ML. Prior experience with computer graphics and modeling is an advantage. |
| Firefly inspired Optical communication for Swarming Drones | The project involves creating a nanoquadrotor (less than 60mm) capable of emulating firefly like communication (flashing and response). The project will involve mechanical design, electronic fabrication of custom controller boards and software development for developing firefly inspired strategies. We expect to field test this system by the end of the project to demonstrate active closed loop communication with fireflies as the first step towards understanding complex signalling. Will involving collaboration with international teams. Looking for background in prior drone design, control and programming. Experience with building custom electronics is an advantage. |
| Plant-sensing insect robots | The project involves creating custom sensors, manipulators and attachment mechanisms for insect-scale robots to sample and deliver biomolecules to plant tissues. Interest in plant science/ environmental monitoring is an advantage. Strong background in flexible electronics design and integration along with software development is preferred. Involves collaboration with other departmental and international teams. |
| Backend and frontend interface for neural recording insect backpack | The project involves creating software, both backend (embedded programming using Zephyr) and frontend (python/java/etc) to log data, perform communication and remote telemetry/control for custom insect backpacks being developed in the group. Participants are expected to be independent and have prior experience with software development using one or more of the tools/packages mentioned above. |
| Backend and frontend interface for microrobot control | The project involves creating software, both backend (embedded programming using Zephyr) and frontend (python/java/etc) to log data, perform communication and remote telemetry/control for custom electronic boards being developed in the group. Participants are expected to be independent and have prior experience with software development using one or more of the tools/packages mentioned above. |
| Software for remote and structural health monitoring using NDE sensors onboard insect robots | The project involves creating software, both backend (embedded programming using Zephyr) and frontend (python/java/etc) to log data, perform communication and remote telemetry/control for custom electronic boards that interface with NDE sensors (eddy current and ultrasonic sensors) being developed in the group. Participants are expected to be independent and have prior experience with software development using one or more of the tools/packages mentioned above. Familiarity with NDE sensors is an advantage. |
| Dung-beetle inspired burrowing robot | The project involves creating 3-4cm autonomous robot inspired by dung-beetles that can dig, burrow and clear narrow tunnels (similar body size as a robot). Strong background in design, small scale manufacturing, flexible electronics design and integration along with software development is preferred. Involves collaboration with other departmental and international teams. |
Dr Laki Pantazis
Profile: https://profiles.imperial.ac.uk/p.pantazis
Contact details: p.pantazis@imperial.ac.uk
| Project title | Description |
| Programming the first cancer cell: engineering spatially confined CRISPR to trigger and track oncogenic mutations in living organoids | Cancer begins when genetic change arises in individual cells embedded within otherwise normal tissue. Yet conventional genome-editing approaches perturb populations simultaneously, making it difficult to observe how one newly mutant cell interacts with its neighbours and reshapes a tissue. Our laboratory is developing an optically controlled CRISPR platform to overcome this limitation. Cas9 is sequestered through the photocleavable protein PhoCl and released by Primed Conversion, in which intersecting laser beams provide highly localised optical activation. Recent work in our lab established proof-of-principle light-dependent Cas9 activity and demonstrated spatially confined Primed Conversion within individual cells in 3D organoids. This project will engineer a next-generation light-gated CRISPR circuit with improved OFF-state repression and high light-induced dynamic range. The student will quantitatively characterise alternative circuit designs using molecular cloning, flow cytometry and live-cell microscopy, then transfer the optimised system into a 3D organoid model to test single-cell-restricted genome editing. Depending on progress and student interests, the project will extend to perturbing a disease-relevant endogenous gene, such as APC, and longitudinally tracking the resulting clone within surrounding wild-type tissue. The project combines synthetic gene-circuit engineering, CRISPR, optogenetics, organoid biology, advanced microscopy and quantitative image analysis. Principal Supervisor Name Periklis (Laki) Pantazis Principal Supervisor Email p.pantazis@imperial.ac.uk Secondary Supervisor Name Professor Mark Isalan Secondary Supervisor Email m.isalan@imperial.ac.uk |
| Towards single-cell precision ultrasound neuromodulation using optically traceable Bioharmonophores | Focused ultrasound provides a powerful non-invasive approach to neuromodulation, but its spatial resolution remains constrained by the acoustic focal volume, which typically encompasses many cells. This project will investigate a different strategy: using ultrasound as a broad, subthreshold energy source while defining the site of neuronal activation through spatially localised piezoelectric nanotransducers. We will test biodegradable peptide-based bioharmonophores as optically traceable nanotransducers. Bioharmonophores are polymer-encapsulated, self-assembling aromatic peptide nanoparticles that generate strong second-harmonic-generation (SHG) signals and biodegrade in biological environments.¹–³ Because ordered diphenylalanine assemblies exhibit pronounced piezoelectric properties,â´â€“ⶠwe hypothesise that suitably engineered bioharmonophores can convert ultrasound-induced mechanical stress into a highly localised electrical stimulus. The central hypothesis is that bioharmonophore-associated neurons can be activated at ultrasound exposures below those required for direct ultrasound neuromodulation, while neighbouring particle-negative neurons exposed to the same acoustic field remain inactive. If achieved, stimulation precision would be determined primarily by nanotransducer localisation rather than by the ultrasound focal volume. |
| Beyond fluorescence: watching genetically-encoded bioharmonophores assemble in live mammalian cells | Fluorescent proteins have shaped modern cell biology, but they come with trade-offs: they bleach, saturate, and blur under high-intensity light. These limitations cap what we can see—especially when tracking fast or subtle dynamics over long periods. What if we could express a label that never fades, never saturates, and produces a clean, quantifiable signal—one that’s visible through even the densest cellular environments? This project gives you the opportunity to build and validate a new class of genetically encoded imaging probes—bioharmonophores—in live mammalian cells. These are not fluorescent proteins. Instead, they are small peptides that self-assemble into non-centrosymmetric nanocrystals inside protein shells, generating second-harmonic generation (SHG) signal: a narrow, photostable, and unbleachable optical readout. At the heart of the system is a synthetic expression circuit: peptides with strong SHG potential are targeted to encapsulin nanocompartments, where they remain inert until released by a genetically encoded TEV protease. This two-part design allows for controlled liberation and local concentration of SHG-active sequences, triggering their self-assembly into crystalline structures that produce strong SHG signal when imaged. This project focuses on the critical proof-of-principle: can we trigger SHG-active peptide self-assembly inside living human or mouse cells? This has never been shown. Success would confirm that bioharmonophores can be expressed, activated, and imaged in standard cell culture, opening the door to genetically programmable, multiplexed SHG imaging in deep tissues. Computational/Practical student experience You will: • quantify SHG signal strength across time and conditions using an existing Zeiss two-photon microscope platform • analyse optical signatures to correlate peptide identity, structure, and signal intensity. This project gives you hands-on experience with synthetic gene circuits, nonlinear optics, mammalian cell engineering, signal quantification, and peptide design—a complete pipeline from molecule to imaging outcome. Outcomes By the end of the project, you will produce the first demonstration that genetically encoded bioharmonophores can self-assemble and generate SHG signal in live mammalian cells. |
| Synchronised to Silent: Watching Piezo1 Signalling Break Down Under Shear in Real Time | Cells in epithelial tissues do more than just survive mechanical stress—they interpret it, synchronise their responses, and adapt. One of the most powerful molecular sensors orchestrating this process is Piezo1, a mechanically activated ion channel that converts shear stress into calcium signals. When activated, Piezo1 channels trigger transient calcium influxes, which can be monitored using live-cell biosensors. But how exactly do individual cells encode mechanical input? How do they coordinate their responses across space and time—and what happens when that coordination fades? This computationally based project gives you the opportunity to answer these questions using a rich, high-quality dataset of time-lapse fluorescence images acquired from GenEPi biosensor-expressing epithelial cells under shear stress. GenEPi is a genetically encoded calcium reporter that lights up when Piezo1 channels open, enabling direct, real-time readouts of mechanosensitive activity in live tissues. Crucially, the data have been collected under defined flow conditions using a custom-designed microfluidic platform, meaning you will be working with dynamic, structured datasets that closely reflect physiological mechanical environments. What makes this project particularly compelling is the discovery that Piezo1 activation is not only force-dependent, but also transient and coordinated across cells—until it isn’t. With continued or repeated stimulation, the system begins to lose its coherence: signal amplitudes decline, synchronisation breaks down, and the tissue shifts from a unified responder to a fragmented, desynchronised field. What governs this transition? Can it be predicted or quantified? These are not just questions of cell signalling—they touch on the principles of collective behaviour, adaptation, and signal fidelity in living systems. Your role in this project will be to explore, quantify, and model these dynamic transitions using image processing and computational analysis. By extracting time-resolved GenEPi fluorescence from single cells and tracking those responses across multiple stimulation cycles, you’ll uncover how shear stress modulates both the strength and structure of Piezo1 activity. You’ll also compare how different flow conditions—low vs. high shear—and cytoskeletal states affect signal propagation, adaptation, and decay. The work will involve building or adapting pipelines for image segmentation, signal extraction, time-series analysis, and population-level synchrony metrics. Computational/Practical student experience You will: - process and analyse large-scale time-lapse imaging datasets of GenEPi-expressing cells under controlled shear stress, - use Python, MATLAB or ImageJ/Fiji to extract ΔF/F₀ calcium signals from individual cells and clusters, - quantify signal dynamics over time: amplitude, rise time, duration, and frequency, - develop computational tools to measure intercellular synchronisation, using pairwise correlation, signal entropy, or phase-locking indices, - perform comparative analyses across shear conditions and treatment states to assess signal adaptation and coordination loss. This is a fully desk-based, computational project requiring no lab work or cell culture. You will work with already-acquired, publication-quality datasets and develop analytical methods that directly contribute to an active manuscript. The project offers deep training in image analysis, time-series data handling, and biological signal interpretation—skills with wide relevance in both academic and biotech contexts. Outcomes By the end of the project, you will have generated a series of analytical outputs—plots, heatmaps, and coordination metrics—that reveal how Piezo1 responses change over time and under stress. Your work will illuminate how force signals propagate through epithelial tissues, how collective behaviour is initiated and lost, and how cellular coordination is shaped by both mechanical inputs and intracellular architecture. These results will not only strengthen the current understanding of Piezo1 function but also provide a general framework for analysing other dynamic biosensor systems in mechanically active contexts. |
| Advancing Mechanobiology with ChemiGenEPi Biosensors | Cells are constantly pushed, stretched, and squeezed—and they feel it. Mechanobiology is the science of how cells sense and respond to forces, a process that drives development, organ function, and disease. At the centre of this force-sensing machinery is Piezo1, a pressure-sensitive ion channel. Our lab has already introduced GenEPi, the first genetically encoded Piezo1 activity reporter, but we want to go further. What if you could watch Piezo1 at work in real time, in living tissue, with sensors that are brighter, more stable, and tunable across colours? This project gives you the chance to build exactly that: ChemiGenEPi1.0, a brand-new chemigenetic biosensor. By combining the genetic precision of GenEPi with the dye-based flexibility of WHaloCaMP, ChemiGenEPi will let us see Piezo1 activity with unprecedented clarity—from single cells in culture to organs/organoids and whole animal models. Computational&Practical student experience You will: • test its performance in live-cell imaging assays (brightness, photostability, dynamic range), This project is hands-on at the interface of molecular engineering, synthetic biology, and cutting-edge imaging. You’ll gain experience in biosensor design, live-cell fluorescence and FLIM microscopy, and in vivo validation. Outcomes By the end, you’ll have helped create a next-generation tool for watching mechanosensitive signalling in action—one that could reshape how we study force in biology and even inspire future drug discovery. |
Dr Swathi Padmanabhan
Profile: https://profiles.imperial.ac.uk/s.padmanabhan
Contact details: s.padmanabhan@imperial.ac.uk
| Project title | Description |
| Developing Polarisation-Sensitive Complex Tissue-Mimicking Phantoms For Photoacoustic Sensing | As light travels through tissue, its polarisation changes. This is shaped by properties like birefringence and dichroism arising from structures such as aligned collagen and muscle fibres. Polarisation-sensitive sensing systems, including polarisation-enhanced photoacoustics, depend on these effects. This project develops complex, realistic phantoms that reproduce scattering, birefringence, dichroism and chirality independently, using materials such as aligned fibres, stretched polymers and chiral solutes. The work includes fabrication and characterisation against optical and photoacoustic measurement, establishing a standard for the photoacoustic instrumentation ahead of in-vivo work. If there are any questions, please reach out to Swathi Padmanabhan (s.padmanabhan@imperial.ac.uk). |
| Development Of A Differential Model For Chiral Biomolecular Sensing Using Photoacoustics | Chiral molecules like glucose rotate the plane of polarised light in a way that depends on their structure, a measurable optical fingerprint. In photoacoustics, ultrashort laser pulses are sent into tissue and the resulting sound is detected with an ultrasound transducer. This project implements an AI-driven, digitally closed-loop photoacoustic system: a founding step toward sensing instrumentation that tunes itself. You will develop a differential model of the sensing instrumentation for molecules like glucose, covering light–tissue interaction, the resulting initial pressure distribution, and the feedback that adjusts system parameters. Because the model propagates gradients end to end, it supports an autonomous optimisation agent that decides which parameter to change and by how much, rather than relying on manual tuning or blind search. If there are any questions, please reach out to Swathi Padmanabhan (s.padmanabhan@imperial.ac.uk). |
| Electronics System Design For A Miniaturised Closed-Loop Non-Invasive Photoacoustic Sensor | Today's photoacoustic systems fill an optical bench; a usable diagnostic device needs to be compact and efficient. This project designs the electronics which includes data acquisition, signal conditioning, timing and feedback control from AI agent for a miniaturised instrument, working within real constraints of cost, power budget and noise. A core part of the work is deploying a reinforcement-learning agent directly on the embedded hardware, so the device adjusts its own acquisition and control parameters in real time rather than running on fixed settings. This is a hands-on introduction to edge AI and real-time control. Skills that might be useful for this project: basic electronics system design and FPGA programming; prior exposure to embedded systems or control theory would help. If there are any questions, please reach out to Swathi Padmanabhan (s.padmanabhan@imperial.ac.uk). |
| Computational Design Of Induced Chiroptical Probes For Disease Biomarker Detection | Many biologically important molecules, including glutamate and lactate, are key markers of metabolism but do not have strong, useful optical signatures. This project will explore a new strategy: designing molecular probes that become optically active when they bind to these target molecules. The key concept is induced chiroptical response, where binding to a chiral biomolecule produces a measurable optical signature in the probe–target complex. You will combine molecular modelling and machine learning to predict these chiroptical responses and guide the design of new probes. The project will first establish a computational framework for predicting induced-CD responses in probe–target complexes. Building on this framework, you will investigate and design new chiral probes for biomolecules such as glutamate and lactate, opening up new possibilities in imaging and drug delivery. If there are any questions, please reach out to Swathi Padmanabhan (s.padmanabhan@imperial.ac.uk). |
| Development Of Single-Shot Polarimetric Photoacoustic Sensing For Deep Tissues | Measuring how tissue responds to different polarisation states currently takes many laser pulses fired in sequence. This project develops a single-shot method: illuminating with multiple polarisation states simultaneously via structured illumination, to recover morphological and structural information from tissues such as brain, skin and tumour without compromising depth. You will build the linking polarised illumination to the tissue properties to be recovered, establishing whether different tissues show distinct contrast under polarised light, how many independent polarisation states survive a single pulse, and what acquisition rate and detector layout the resulting hardware needs. Alongside the physics model, you will train a machine learning decoder that recovers tissue properties directly from the recorded signals, and compare it against conventional analytical inversion to establish where learning helps and where it doesn't. |
| Development of a Single-Element Wearable Photoacoustic Patch For Glucose Sensing | Non-invasive wearable sensing needs efficient light delivery and acoustic detection with minimal dependence on benchtop equipment. This project will involve designing a wearable patch integrating the light source and acoustic detector on a common substrate, then fabricating and characterising the device for glucose sensing. If there are any questions, please reach out to Swathi Padmanabhan (s.padmanabhan@imperial.ac.uk). |
| A Foundation Model For Image-Based Diagnosis Of Instrumentation Faults | Reconstructed images from photoacoustic and ultrasound systems often contain artifacts, but tracing an artifact back to its actual instrument-level cause, such as a faulty transducer element, timing error or miscalibration, is currently a slow, expert-driven process. This project focuses on whether that diagnosis can be learned directly from the image itself. You will build a foundation model trained across a wide range of simulated instrument faults and their resulting image artifacts, learning the mapping from what an image looks like to what is actually wrong with the system that produced it. Skills that might be useful for this project: Python, machine learning, deep learning and signal processing or instrumentation would help. If there are any questions, please reach out to Swathi Padmanabhan (s.padmanabhan@imperial.ac.uk). |
Dr Sylvain Ladame
Profile: https://profiles.imperial.ac.uk/s.ladame
Contact details: s.ladame@imperial.ac.uk
| Project title | Description |
| Hydrogel-coated microneedle arrays for early diagnosis of skin cancer | Minimally-invasive technologies that can sample and detect cell-free nucleic acid biomarkers from liquid biopsies have recently emerged as clinically useful for early diagnosis of a broad range of pathologies, including cancer. Although blood has been so far the most commonly interrogated body fluid, skin interstitial fluid has been mostly overlooked despite containing the same broad variety of molecular biomarkers originating from cells and surrounding blood capillaries. Minimally-invasive technologies have emerged as a method to sample this fluid in a pain-free manner and often take the form of microneedle patches. Herein, we will develop microneedles that are coated with an alginate-peptide nucleic acid hybrid material for sequence-specific sampling, isolation and detection of nucleic acid biomarkers from skin interstitial fluid. This platform technology will also enable for the first time the detection of specific nucleic acid biomarkers either on the patch itself or in solution after light-triggered release from the hydrogel. Considering the emergence of cell-free nucleic acids in bodily fluids as clinically informative biomarkers, platform technologies that can detect them in an automated and minimally invasive fashion have great potential for personalized diagnosis and longitudinal monitoring of patient-specific disease progression. |
| Selective capturing and detection of circulating cell-free nucleic acid from bodily fluids using nanoparticles and magnetic microbeads | Circulating cell-free nucleic acids recently emerged as clinically useful, minimally invasive tools. Among them, microRNAs (miRNAs), non-coding RNAs 19-25 nucleotides in length, are frequently found to be dysregulated in cancer patients. Their potential to inform about disease processes combined with miRNAs’ stability and accessibility in liquid biopsies make them ideal biomarkers, but miRNAs are challenging to detect accurately, and the relative abundance and form in which they exist in blood are two critical remaining questions, due to lack of suitable analytical tools. In this project, you will develop a range of magnetic beads and chemically functionalise them with peptide nucleic acid (PNA) or DNA hybridisation probes for fishing out specific nucleic acid biomarkers (single-stranded microRNAs or doubles-stranded DNA) from various types of bodily fluids. Strategies will also be developed for optimising the detection of detection of the captured biomarkers either on beads on in solution once released from the beads. |
Dr Tom Ouldridge
Profile: https://profiles.imperial.ac.uk/t.ouldridge
Contact details: t.ouldridge@imperial.ac.uk
| Project title | Description |
| Simulation of DNA under stress | Both in its natural role within cells, and increasingly in complex artificial nanostructures and devices, DNA is subject to a range of stresses [1,2]. These stresses provide physical mechanisms for regulation of transcription, and strongly influence the functioning of nanotechnological systems. Recent work from the Rueda group suggests that supercoiling stress in particular can cause off-target CRISPR activity [3]. We have recently developed a coarse-grained model of DNA which is designed to capture the fundamental thermodynamic, structural and mechanical properties of the molecule [4]. This combination of features makes the model ideal for exploring the rich stress-response behaviour of DNA [5,6]. This project will involve simulating the model to probe one of several possible systems, including a design for a molecular force sensor or the Rueda system [3], providing understanding of the basic biophysics and linking the observed phenomena to other contexts. An eagerness to learn basic coding in languages such as c++/python is necessary for this project. [1] http://www.pnas.org/content/106/52/22564.long [2] http://science.sciencemag.org/content/325/5941/725 [3] https://www.nature.com/articles/s41586-026-10255-7 [4] http://scitation.aip.org/content/aip/journal/jcp/142/23/10.1063/1.4921957 [5] http://www.nature.com/articles/srep07655.pdf [6] http://pubs.acs.org/doi/abs/10.1021/acsnano.5b04726 |
| Simulation of DNA-based systems (theory/simulation) | DNA is a crucial molecule in both nature and synthetic nanotechnological systems. We have developed a coarse-grained model of DNA which is designed to capture the fundamental thermodynamic, structural and mechanical properties of the molecule [1]. This combination of features makes the model ideal for exploring the rich stress-response behaviour of DNA [2,3], and for describing reaction kinetics of crucial processes like Toehold-Mediated Strand Displacement [4,5]. This project will involve simulating the model to probe one of several possible systems, including a design for a molecular force sensor, and a strand displacement system showing anomalous kinetics, providing understanding of the basic biophysics and linking the observed phenomena to other contexts. An eagerness to learn basic coding in languages such as c++/python is necessary for this project. [1] http://scitation.aip.org/content/aip/journal/jcp/142/23/10.1063/1.4921957 [2] http://www.nature.com/articles/srep07655.pdf [3] http://pubs.acs.org/doi/abs/10.1021/acsnano.5b04726 [4] https://pubmed.ncbi.nlm.nih.gov/24019238/ [5] https://pubmed.ncbi.nlm.nih.gov/25382214// |
| Principles of molecular systems (theory/simulation) | Placeholder for a general theory/simulation project within my group, modelling either DNA nanotechnology, molecular neural networks or the biophysics of molecular information processing |
| Engineering of molecular systems (lab-based) | Placeholder for a generic experimental project within my group, designing and building information processing systems using DNA nanotechnology. |
| Can simple models of gene regulation be used to understand complex gene control circuits? | Enhancers are regions of DNA that play a crucial role in switching genes on and off in the right cell, developmental stage and environmental condition [1]. Notably, although enhancers can be separated from the genes they control by millions of base pairs, they are often found in close proximity in the cell due to 3D chromosomal contacts. The strength of enhancer-gene interactions is driven by topological and geometrical considerations, and multiple enhancers often control the same gene. Recent experimental breakthroughs provide ever more quantitative data on how changes in enhancer activity or genomic location affect gene expression [2,3,4] , but the underlying mechanisms remain unclear [5]. At a deeper level, it is not clear what additional biological advantage is obtained by having transcriptional regulation function in this way. In this project, the student will take simple mathematical models [3,6] developed for an engineered locus, where a single enhancer controls a reporter gene, and extend them to the more typical scenarios, where genes are controlled by multiple enhancers. The student will apply basic simulation and theory to understand the possible behaviours of such models, and then explore whether these behaviours are presented in experimentally derived datasets [3,4]. They will look to identify biologically beneficial modes of action in the observed behaviour. This project is well-suited to students interested in mathematical and computational modelling, simulation and the analysis of large datasets. The project will be performed in collaboration with Mikhail Spivakov from the MRC Laboratory of Medical Sciences (MRC LMS) at the Hammersmith Hospital campus. [1] https://link.springer.com/article/10.1007/s00018-021-03903-w [2] https://www.nature.com/articles/s41588-019-0538-0 [3] https://www.nature.com/articles/s41586-022-04570-y [4] https://www.sciencedirect.com/science/article/pii/S009286741831554X [5] https://www.sciencedirect.com/science/article/pii/S0959437X23000321 [6] https://www.biorxiv.org/content/10.1101/2025.03.26.645410v1 |
| Are larger animals more efficient? On the thermodynamics of muscle, the prime biological engine. | Animals large or small must move, and this movement consumes a large share of their daily energy budget - they thus ought to move as efficiently as possible. Curiously, available data has it that animals become more efficient as they grow in size: the ratio between energy output (muscle mechanical work) and system input (metabolic energy) increases in heavier animals. It is clear enough that something is amiss, because extrapolation of this trend suggests that large extinct animals (dinosaurs!) moved with an efficiency larger than unity; but the origin of this paradox remains at large. In this project, you will take a bioengineering perspective on this open problem, and analyse muscle as a thermodynamic machine. You will build on recent thermodynamical and mechanical models of muscle performance to derive a prediction for the efficiency of muscle as a function of animal size, and compare this prediction to existing data. Relevant literature: Alexander, R. M. Models and the scaling of energy costs for locomotion Journal of Experimental Biology. 2005, 208, 1645-1652 Labonte, D. A theory of physiological similarity for muscle-driven motion. PNAS, 2023, 120, e2221217120 Goupil, C.; Ouerdane, H.; Herbert, E.; Goupil, C. & D’Angelo, Y. Thermodynamics of metabolic energy conversion under muscle load. New Journal of Physics. 2019, 21, 023021 |
| Modular Design of DNA Strand Displacement Networks with Arbitrary Kinetics | Molecular programming is a rapidly growing field focused on designing, constructing, and testing chemical systems that execute algorithms traditionally run on electronic hardware. These molecular systems perform computations using molecules and chemical reactions, enabling information processing and computation in environments where electronics are impractical. A key mechanism in this field is toehold-mediated strand displacement (TMSD), which underlies many breakthroughs in DNA-based nanotechnology [1]. By combining large numbers of DNA "gates" that rely on TMSD, researchers have built chemical systems capable of implementing digital logic [2], neural network algorithms [3], and temporal information processing [4]. The logic of these molecular programs is governed by the reaction rate of the TMSD process. This reaction rate can be tuned by modifying the DNA sequence in a specific region called the toehold. However, predicting the reaction rate in advance, by designing toehold sequences with desired kinetics, remains a major challenge. Current biophysical models and machine learning techniques have not yet provided accurate solutions to this problem. This lack of ability to reliably fine-tune kinetics limits the application of DNA strand displacement to non-equilibrium systems that are governed by reaction kinetics. To overcome this problem, we propose a modular design in which all toeholds are variants on a single sequence, with different numbers of mismatched base pairs used to control reaction kinetics [5]. To this end, we aim develop a database of partially mismatched sequences with experimentally verified reaction rates to be used in general molecular programming applications. The project involves refining and experimentally testing the DNA strand displacement designs, using both: NuPack: a computational tool for DNA sequence design [6,7], and Laboratory techniques for measuring DNA strand displacement kinetics. [1] D. Soloveichik, G. Seelig, and E. Winfree, “DNA as a universal substrate for chemical kinetics,†Proceedings of the National Academy of Sciences, vol. 107, pp. 5393–5398, 2010. [2] L. Qian and E. Winfree, “Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades,†Science, vol. 332, pp. 1196–1201, 2011 [3] L. Qian, E. Winfree, and J. Bruck, “Neural network computation with DNA strand displacement cascades,†Nature, vol. 475, pp. 368–372, 2011. [4] A. P. Lapteva, N. Sarraf, and L. Qian, “DNA Strand-Displacement Temporal Logic Circuits,†Journal of the American Chemical Society, vol. 144, pp. 12443–12449, July 2022. [5] Machinek, Robert R. F., et al. ‘Programmable Energy Landscapes for Kinetic Control of DNA Strand Displacement’. Nature Communications, vol. 5, no. 1, Nov. 2014, p. 5324. www.nature.com, https://doi.org/10.1038/ncomms6324. [6] M. E. Fornace, J. Huang, C. T. Newman, N. J. Porubsky, M. B. Pierce, N. A. Pierce. ‘NUPACK: analysis and design of nucleic acid structures, devices, and systems ChemRxiv., 2022. [7] J. N. Zadeh, C. D. Steenberg, J. S. Bois, B. R. Wolfe, M. B. Pierce, A. R. Khan, R. M. Dirks, N. A. Pierce. NUPACK: analysis and design of nucleic acid systems. J Comput Chem, 32:170–173, 2011. (pdf)' |
| In situ detection of double-stranded DNA using nucleic acid nanotechnology | Double stranded (ds)DNA is a key biomarker, indicating the presence of contaminants, pathogens and tumours in different contexts. The polymerase chain reaction (PCR) is a standard method to amplify and detect specific sequences of dsDNA, but it is poorly suited to applications in the field. An alternative approach to detecting DNA, nucleic acid nanotechnology [1], can operate autonomously and isothermally in a broad range of conditions. In such systems, cascades of strand displacement interactions, driven by the formation of ever-increasing numbers of base pairs, can be designed to implement essentially arbitrary operations, including parallel logic, thresholding and controlled amplification, in response to a set of input signals. This functionality is underpinned by the logic of Watson-Crick-Franklin base pairing, which makes nucleic acid nanotechnological systems uniquely engineerable. Unfortunately, nucleic acid nanotechnology typically requires one or more single-stranded nucleic acids as inputs, and therefore current paradigms cannot be triggered by dsDNA. This project will explore a Cas9-based strategy for triggering nucleic acid nanotechnological circuits using double-stranded DNA. The student will first demonstrate that target dsDNA can be recognised, then optimise over the parameters of the system, and finally demonstrate that optimised systems can detect multiple sequences in parallel, processing the signal for the purposes of amplification and thresholding. [1] https://www.nature.com/articles/natrevmats201768 |
| Usage of information in protein folding | Cells assemble arbitrary and incredibly complex proteins using only 20 basic building blocks - the amino acids. This feat is only possible because the amino acids are first assembled into a polymer sequence via molecular templating process (transcription and translation) [1]; this sequence then directs folding into a functional structure. An open question is how efficiently the information in the template sequence is used. Cells produce peptide sequences that are extraordinarily accurate copies of the DNA sequence (via an RNA intermediate). But is that accuracy really necessary? And if so, why? The student will use alphafold3 [1] to explore how randomly introduced copy errors translate into variations in the structure of a protein, asking how much information is actually needed to drive the formation of a specific stucture. We hypothesise that high information is not needed when folding a single protein, but is required if the same copying method is used to fold multiple different proteins. The project will involve extensive computer-based simulation and theoretical/numerical analyses of models of biochemical reaction networks. An eagerness to learn basic coding in languages such as c++/python/matlab is necessary. This project would be enjoyable for students who were particularly interested by the 2nd part of the Modelling in Biology course from 3rd year. [1] https://www.nature.com/articles/s41586-024-07487-w |
| Characterising the design space of the Primer Exchange Reaction | The Primer Exchange Reaction (PER) is a method for producing single-stranded DNA of programmable sequence using a strand-displacing polymerase and a set of catalytic hairpin templates [Kishi et al., 2018]. A short primer binds the single-stranded toehold of a hairpin and is extended along the hairpin stem by the polymerase, until a stop sequence (typically a GGG clamp, with dGTP omitted from the reaction) halts further extension. The newly extended primer then dissociates from the hairpin and can bind a second hairpin to be extended again. By chaining multiple hairpin templates together, PER produces an output transcript whose sequence is set by the cascade design rather than by any pre-existing template strand. The reaction is isothermal, runs at modest temperatures, and uses commercially available reagents. PER has been used as a primitive for molecular recording (writing temporal information into DNA), in situ signal amplification, conditional logic in DNA circuits, and the synthesis of long structured DNA without an oligonucleotide template. Each of these applications relies on running PER reliably in a defined sequence context, often with multiple PER systems operating in parallel. Despite this growing use, the design rules that govern PER are not well established. It is not known what the minimum toehold length is for reliable polymerase initiation, how tolerant PER is to mismatches in the toehold or stem, or how restricting the nucleotide alphabet (for instance, to the three-letter sets implied by PER’s G-stop mechanism) affects the size of the accessible orthogonal sequence space. Without these design rules, scaling PER to multi-component systems is largely trial and error, and the limits of what is possible are unknown. This project would close that gap, giving the field a design reference for PER systems and producing a sequence-design pipeline that anyone running multi-component PER could use directly. What the project would involve Computational (DNA system design): use NUPACK [Zadeh et al., 2011] and related thermodynamic sequence-design tools to generate candidate sets of PER hairpins under varying constraints — toehold length, stem composition, alphabet restriction — and predict which sets should remain orthogonal in a single pot. The pipeline would output a ranked set of candidate sequences, predicted cross-reactivity values, and design rules linking sequence parameters to predicted independence. Experimental (wet lab): test the predictions on a small set of designed PER hairpins, running them individually and in combination in single-pot reactions and assaying extension efficiency and cross-talk by denaturing PAGE. Specific experiments would include a toehold-length titration to establish the minimum reliable initiation length, a mismatch series to characterise polymerase tolerance, and a cross-reactivity matrix on a designed orthogonal set. The two halves of the project feed into each other directly: the experimental measurements refine the design pipeline, and the predicted limits set what gets tested next. The relative emphasis on both parts would be up to the student. Skills a student would gain Computational DNA system design using NUPACK and related thermodynamic tools, including programmatic sequence generation and constraint-based design. Wet-lab oligonucleotide handling, PER reaction setup, plate reader based experiments and PAGE analysis. Quantitative gel analysis (band quantification, kinetic interpretation). Experience translating between predicted thermodynamic behaviour and observed reaction outcomes, which is a core skill for anyone working in DNA nanotechnology. References: Jocelyn Y. Kishi, Thomas E. Schaus, Nikhil Gopalkrishnan, Feng Xuan, and Peng Yin. Programmable autonomous synthesis of single-stranded DNA. Nature Chemistry, 10(2):155–164, 2018. doi: 10.1038/nchem.2872. Joseph N. Zadeh, Conrad D. Steenberg, Justin S. Bois, Brian R. Wolfe, Marshall B. Pierce, Asif R. Khan, Robert M. Dirks, and Niles A. Pierce. NUPACK: Analysis and design of nucleic acid systems. Journal of Computational Chemistry, 32(1):170–173, 2011. doi: 10.1002/jcc.21596. |
| Probing a minimal self-replicating system | How self-replicating systems might emerge in abiotic contexts is one of the key questions in origins of life research. A major landmark in this field was the development of a small, self-replicating RNA ribozyme within the Joyce group [1], whose efficiency was subsequently improved over the following years [2]. This ribozyme can catalyze the covalent bonding of two halves of a complementary ribozyme, which in turn can catalyze the formation of the original ribozyme from its components, thus completing a cross-catalytic cycle. A key challenge faced by self-replicators is product inhibition - the tendency of the product to adhere extremely tightly to the template which catalysed its formation. The Joyce replicator appears to have high turnover and suffer from remarkably little product inhibition, but it's not clear why. This project will probe whether active strand displacement of the product by the next pair of monomer units helps catalytic turnover, with potential for collaboration with the Nghe group in Paris who are interested in this question. The project has scope for either experiments or theoretical modelling, particularly using the oxRNA simulation package [3]. If selected as a group project, both will be performed. [1] https://www.science.org/doi/10.1126/science.1167856 [2] https://www.sciencedirect.com/science/article/pii/S1074552113004262 [3] https://pubs.aip.org/aip/jcp/article/140/23/235102/73414/A-nucleotide-level-coarse-grained-model-of-RNA |
| Triggering strand displacement cascades with protein aptamers | Nucleic acid nanotechnology [1] allows the engineering of complex molecular information-processing systems that operate isothermally in a broad range of conditions. In such systems, cascades of "strand displacement" reactions, driven by the formation of ever-increasing numbers of base pairs, can be designed to implement essentially arbitrary operations, including parallel logic, thresholding and controlled amplification, in response to a set of input signals. This functionality is underpinned by the logic of Watson-Crick-Franklin base pairing, which makes nucleic acid nanotechnological systems uniquely engineerable. Similar circuitry is much harder to engineer in proteins, but they are much better suited to aptameric recognition of small molecules - particularly with the advances in protein design brought about by machine learning [2]. Such proteins can undergo large conformational changes, including dimerization, in response to small molecule signals [3]. The aim of this project is to combine the strengths of both systems by attaching DNA strands to a protein that undergoes conformational change upon binding to a small molecule. This conformational change will induce binding between the attached DNA strands, which will then be used to trigger information-processing strand displacement circuits. The work will be performed in collaboration with the Murray group in Life Sciences The project has scope for both experimental and computational work, particularly if performed by a group. [1] https://www.nature.com/articles/natrevmats201768 [2] https://www.biorxiv.org/content/10.1101/2025.04.22.649862v1.abstract [3] https://pubs.acs.org/asbcd6/article/15/5/2054/5106785/Engineering-Rapamycin-Induced-Dimerization-for |
| High-level control of nucleic acid nanotechnology with junction binders | Nucleic acid nanotechnology [1] allows the engineering of complex molecular information-processing systems that operate isothermally in a broad range of conditions. In such systems, cascades of "strand displacement" reactions, driven by the formation of ever-increasing numbers of base pairs, can be designed to implement essentially arbitrary operations, including parallel logic, thresholding and controlled amplification, in response to a set of input signals. This functionality is underpinned by the logic of Watson-Crick-Franklin base pairing, which makes nucleic acid nanotechnological systems uniquely engineerable. Recent progress in organic chemistry has led to the development of organic molecules that bind very stably to multi-stranded junctions and bulges in nucleic acids [2], both of which are relevant motifs during strand displacement. Designed primarily as drugs that target replication and transcription processes, the influence of these molecules on nanotechnological strand displacement is currently unknown. We hypothesise that they maybe able to suddenly freeze or reverse strand displacement processes by stabilizing certain configuration - if so, they would represent a unique tool for applying global control with nanotechnology. This project will test the effect of junction binders on DNA strand displacement, in collaboration with Michael Hannon's group at the University of Birmingham. If performed as part of a group project, there is scope for both experimental and modelling work. [1] https://www.nature.com/articles/natrevmats201768 [2] https://link.springer.com/article/10.1007/s00775-025-02123-y |
Dr Yap
Profile: https://profiles.imperial.ac.uk/c.yap
Contact details: c.yap@imperial.ac.uk
| Project title | Description |
| Deep Learning Extraction of Brain Aneurysm Shape and Fluid Mechanics to Predict Risks | Cerebral aneurysms are vascular diseases of the brain, where weaknesses in the vascular wall led to a bulge, which overtime may progress in size and rupture, leading to dangerous stroke. Some aneurysms remain stable for a long time, but others can quickly grow and rupture, leading to catastrophic events, but it remains very difficult to predict which case will worsen and require intervention and which will be stable. Making this prediction is important to clinical management of patients, and the need for better prediction of deterioration risks has prompted large scale national research projects such as the ROAR study. In the literature, many studies conclude that blood flow forces (consequent to aneurysm geometry) determines whether the aneurysm will progress and rupture, or remain stable. Computing flow forces may thus be one way to address this unmet need. However, computational fluid mechanics simulations based on MRI or CT images required to compute this are very slow, taking many days to properly process each case. This prevents clinical trials from being conducted at a meaningful scale to properly assess if flow forces can indeed be good predictors of risks. We addressed this by developing a deep learning algorithm that can extract the shape of the anuerysm based on MRI scans, and then to quickly compute fluid mechanics stresses in the aneurysm. This network produces results within seconds, and can thus support large scale clinical trials. Further, doctors can use our algorithm to quickly and inexpensively derive fluid stresses details without specialized skills in performing such simulations. In this project, we will fine-tune this model, and potentially test it in a large national dataset. For fine-tuning, we will (1) train competing networks to quickly locate and extract the shape of the anuerysm from 3D MRI images, such as using the VGGT network or Visual Transformers, (2) perform mass fluid mechanics simulations to build a larger dataset, and (3) fine-tune and re-train our flow predictor network for better performances, through having larger datasets or testing alternatives such as mesh graph net. If time allows, we will test our algorithm on the ROAR dataset through collaborators in Southampton. Students will work with with a secondary mentor, a PhD student, and collaborate with Dr. Dylan Roi from Imperial NHS, an expert in aneurysms, as well as potentially physicians from Southampton. |
| Importance of the Forces of the Heart During Embryonic Heart Development | Congenital Heart Malformations affects about 1% of pregnancies, and can be devastating. There are evidence that abnormal biomechanical forces and subsequent abnormal mechano-biology expressions could be responsible for the malformations. However, to date, there has not been many studies computing the mechanics of embryonic hearts, and our understanding of it is poor. In the embryonic heart, the myocardium has a actin-myosin fiber network that appears to have a specific pattern with specific changes over developmental stages, and suffer from specific changes when the heart's contractions are disturbed, but no one knows why it develops in this way. We hypothesize that the fiber orientations develop in a way that either (1) provides maximum structural support to counter tissue stresses, and (2) provide the heart with the optimal means to contract and generate flow. We will test this here. Specifically, we will perform analysis of 4D microscopy images of zebrafish embryonic hearts, and use a finite element model to characterize the stresses and strains of the myocardium. We will use in house deep learning tools to speed up the geometric extraction, and motion quantification to support the simulations. We will study normal embryonic hearts from 2-4 days post fertilization, as well as those that has been stopped for a certain period of time (which causes changes to the fiber patterns), and test the hypothesis above using both normal and abnromal scenarios The project will be co-advised by Dr. Yap on the mechanics part, and Dr. Vermot on the biology part, the project will be guided by a PhD student secondary mentor, and there are opporunities to work with biophysics experts in Glasgow who are developing molecular reportes of myocardial stresses. |
| Developing AI Tools for Fetal Cardiologists to Help Fetal Heart Intervention Planning | Congenital Heart Disease has limited prevalence, but has a massive economic cost, and causes substantial fetal and neonatal deaths and lifelong disability. We are working with a team of fetal cardiologists in Austria, Poland and Sweden, who uses catheters to intervene on fetal hearts before birth, so that they can avoid developing into malfoprmations at birth. Specifically, we target cases with aortic stenosis, perform balloon expansion of the blockage, so that these hearts will not grow into single ventricle defects at birth. The goal here is to develop more accurate ways to choose the right patients for the intervention, because the current clinical selection criteria is inaccurate. Studies are showing that some of them fitting the selection criteria do not actually need it and will grow towards birth just fine, while others fitting the criteria actually does not respond to the intervention and will still be malformed at birth. Specifically, we will (1) fine-tune existing deep learning fetal echocardiography segmentation and motion tracking tools in the lab into packages that our cardiologist partners can use, (2) adapt an existing network that computes the mechanics stresses of the adult heart from images, so that it can be used on fetal hearts as well, and (3) test if the cardiac shape, motion, and mechanics can predict if the patient is suitable for the aortic balloon valvuloplasty intervention. We have a sizeable a prospective cohort data, but is also collecting prospective data through the FASS-prosp clinical study, which this study can use. Student will work with a postdoc as secondary mentor, and can collaborate with the clinical teams in Austria, Sweden and Poland. |
| An Echocardiography System that Anyone can Use | Echocardiography is very important tool to evaluate heart diseases, but it's not easy to get one..! In the UK, most patients have to wait 6 weeks or more to get an echo scan. Someone rushed to the emergency room may actually have to wait many hours to get one. This is dangerous for heart failure, as many are known to deteriorate during this waiting period. The reason is that to perform echocardiography scan, a sonographer need years of training, and even then, if they are inexperienced, they have cause large measurement errors and unusable data. Insufficient supply of these sonographers caused this long wait time. In low-resource countries, this is even worst, as skilled sonographers are even fewer. We have developed and patented a set of AI algorithms that reduces the skill needed to perform echo scans. Instead of requiring the sonographer position the transducer very accurately at specific positions, we let them do scans at various random positions while recording the position of the transducer via a position sensor. Subsequently, we reconstruct the 3D shape of the heart from very few 2D echo scans using our novel shape modelling algorithm. The recontructed 3D shape over the heartbeat can then automatically give us measurements of the cardiac size, dimensions, volumes, volume changes, and myocardial contractions, stuff that doctors usually need to evaluate the heart. By not requiring skilful positioning of the transducer, we remove the requirement that the sonographer needs to be highly skilled, and by automating the measurement process, we become more precise and the the sonographer does not need to know a lot about cardiology to make quantifications from images. The result, we believe is that anyone can now perform echocardiography, and this will greatly enhance the availability of echocardiography for patients. Further, we plan to implement this in the new form of echo machines, pocket-sized, handheld echo machines. This will make it very cheap to easy to perform echo, greatly enhancing access to it for better care all over the world. It is especially beneficial in low-resource countries where skilled sonographers are dificult to find, and where traditional echo systems are not affordable. In this project, we will develop the prototype of our low-skill echocardiography system, to get ready for clinical trials. This includes (1) collecting echo images acquired by skilled and unskilled sonographers to verify that they are equivalent, and (2) validating that our AI algorithm can make quantifications from echo images at the same accuracy as MRI images. We will work with medical images already in Imperial NHS Trust (through Dr. Amit Kaura and Prof. Jamil Mayet)m, and also with medical images that are being collected in Zimbabwe (through collaborator, Prof. Anoop Shah from London School). We will aim to publicly disseminate a software package while we are working towards regulatory approval so that we can encourage early adoption. For MRes MDDE Programme, we will also develop a business plan where we will try to use revenue from high income zones to subsidize distribution in low income zones for inclusivity. |
| Machine Learning to Figure Out if There's Been a Heart Attack | Over 1 million patients go to the Emergency Department every year in the UK, thinking they have heart attack, but only 20% of them actually do have one. The others are often kept in the hospital for a day or more, going through various tests several times, as doctors want to be very sure before they let them go home. This is because being wrong about letting them can be potentially dangerous, if they really have a heart attack and we send them home wrongly, they can die. However, keeping all these people who are not really sick around takes up a lot of hospital resources, waste a lot of money the hospital doesn't have, and hogs onto hospital beds and doctors/nurses' time when they really should be caring for really sick people. We developed an ML algorithm using the large unique multi-centre HIC dataset that calculates whether a patient really have heart attack, based on their medical history and their initial blood test (troponin and others) results. The algorithm can safely rule out 50% more patients compared to current clinical practice that follows guidelines, leading to early discharge of these patients back home, and in the ruled-out cohort, there is a lower error rate. Our algorithm won an award at the British Cardiovascular Society Conference, and now we are planning for clinical trials and commercialization. Here, the project for the student is to refine the model: First, testing if a foundation model or an attention-based ML model does better than our existing algorithm, second, develop strategies that mitigate minor but significant differences in measurement data between different hospitals that is causing prediction inaccuracies, and third, assess if the large dataset can be fully encoded in a generative model that can avoid cumbersome setup to protect patient identity during research. Student will work with a PhD student and a team of Cardiologists (Dr. Amit Kaura, and Prof. Jamil Mayet) on this. They can also interface with a Malaysian team also trying to test and roll out our algorithm in Malaysia. Given the clear interest from cardiologists and funders, we are currently working towards prospective clinical trials. Our preliminary estimate is that we can enable the NHS to save £50-80 millions per year in the UK. for MDDE MRes programme, the student will refine this estimate and derive a more detailed business plan. |
Dr Hayriye Cagnan
Profile: https://profiles.imperial.ac.uk/h.cagnan
Contact details: h.cagnan@imperial.ac.uk
| Project title | Description |
| Dual-site transcranial alternating current stimulation for tremor control | Involuntary shaking is a common symptom of Parkinson’s Disease and Essential Tremor, affecting around one million people in the UK. This project aims to leverage plasticity—brain’s ability to adapt and change—for therapeutic purposes by delivering well-timed electrical inputs to key regions across the tremor network. Based in the Cagnan lab, the focus will be on piloting dual-site stimulation of the motor cortex and cerebellum to achieve longer-lasting therapeutic benefits for tremor patients. Your role will include (1) modelling the volume of tissue activated during dual-site stimulation, (2) developing and testing closed-loop control algorithms and (3) developing approaches for efficient optimisation of stimulation parameters. We are looking for a student with strong skills in engineering, instrumentation, and programming, along with a background in neuroscience. 1. Schwab BC, König P, Engel AK. Spike-timing-dependent plasticity can account for connectivity aftereffects of dual-site transcranial alternating current stimulation. NeuroImage. 2021;237:118179. doi:10.1016/j.neuroimage.2021.118179 2. Schwab BC, Misselhorn J, Engel AK. Modulation of large-scale cortical coupling by transcranial alternating current stimulation. Brain Stimulation. 2019;12(5):1187-1196. doi:10.1016/j.brs.2019.04.013 3. Saturnino GB, Madsen KH, Siebner HR, Thielscher A. How to target inter-regional phase synchronization with dual-site Transcranial Alternating Current Stimulation. NeuroImage. 2017;163:68-80. doi:10.1016/j.neuroimage.2017.09.024 4. Fleming JE, Sanchis IP, Lemmens O, et al. From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation. PLOS Computational Biology. 2023;19(12):e1011674. doi:10.1371/journal.pcbi.1011674 5. Cagnan H, Pedrosa D, Little S, et al. Stimulating at the right time: phase-specific deep brain stimulation. Brain. 2017;140(1):132-145. doi:10.1093/brain/aww286 |
| Modulatory role of transcranial stimulation on cognitive control | Everyday decision-making depends on our ability to adapt and sometimes stop actions unexpectedly. This skill can range from something as simple as resisting a tempting slice of cake to something as critical as hitting the brakes in an emergency. Cognitive control can be compromised in a range of neuropsychiatric disorders and remains difficult to restore using invasive and non-invasive brain stimulation techniques. We previously targeted the medial prefrontal cortex, a key brain region involved in response inhibition, using transcranial electrical stimulation to modulate neural rhythms and associated behaviors. This project, based in the Cagnan lab, will focus on (1) data analysis of electrophysiological and behavioral responses, (2) stimulation artifact removal, and (3) modeling behavioral and electrophysiological data. We are looking for a student with strong signal processing skills and a background in neuroscience. Tuning the brakes – Modulatory role of transcranial random noise stimulation on inhibition Mandali, Alekhya Torrecillos, Flavie ... Cagnan, Hayriye et al. Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, Volume 17, Issue 2, 392 - 394 |
| Phase Transitions in Circadian Tremor Patterns | Involuntary shaking is a common symptom of Parkinson’s Disease (PD), affecting approximately 150,000 people in the UK. Tremor in PD can be triggered or influenced by various factors throughout the day (e.g., stress or medication intake), making it crucial to identify trends that are key to effective clinical management. The project will take place in the Cagnan Lab and will involve analysing a unique dataset consisting of long-term (2 years) recordings of PD patients collected via wearable sensors in free-living conditions. Tremor events in this dataset have already been identified through machine learning algorithms. With this project, we will explore the circadian dynamics of tremor in PD over several days using recurrence quantification analysis (RQA), a nonlinear data-driven technique that provides objective markers for regularity, trends, and phase transitions in time series data. A particular focus will be on the impact of patients’ medication schedule changes on these dynamics. We are seeking a motivated student with programming and signal processing skills who is eager to deepen their understanding of the circadian progression in neurodegenerative disorders. |
| Sleep Fragmentation in Parkinson’s Disease and its impact on tremor | Sleep fragmentation, characterised by frequent awakenings or disruptions, has a significant impact on daytime functioning, leading to increased fatigue, reduced motor control, cognitive decline, and heightened stress and anxiety. In Parkinson’s disease (PD), sleep fragmentation can diminish a person’s ability to manage and compensate for daily tremors, worsening their symptoms' severity and duration. While recent evidence supports this connection, a systematic and comprehensive study with a representative PD cohort and long-term follow-up is still lacking. This project aims to investigate sleep fragmentation from multiple angles and assess how it affects the severity and duration of daily tremors in PD patients. The research will be conducted in the Cagnan Lab, utilising a unique dataset containing two years of long-term recordings from PD patients in free-living conditions, with tremor events already identified by machine learning algorithms. We are looking for a motivated student with strong programming and signal processing skills who is eager to better understand the relationship between sleep fragmentation and Parkinson’s disease symptoms. |
Dr James Choi
Profile: https://profiles.imperial.ac.uk/j.choi
Contact details: j.choi@imperial.ac.uk
| Project title | Description |
| Visualising Sound using Machine Learning and/or Signal Processing Algorithms | Purpose. The purpose of this project is to develop a deep neural network or beam forming algorithms that can reconstruct the location of acoustic sources using multiple microphones. Motivation. In therapeutic ultrasound, a focused ultrasound transducer is used to concentrate energy to a point in the body, allowing us to noninvasively and locally manipulate tissue (tumour ablation, drug release from acoustically-active particles, etc). Our laboratory developed therapeutic ultrasound devices for delivering drugs to the brain (across the blood-brain barrier) for the treatment of brain cancers, neurodegenerative diseases, and other neurological conditions. However, the success or failure of the technique has been difficult to track as clinicians are unable to directly observe what is happening within the body. An emerging way of monitoring this procedure is with the use of microphones located around the focused ultrasound transducer. Sound generated during the procedure are captured by the microphones. We then reconstruct an image of the treated area using passive beamforming algorithms. The reconstruction of a signal source based on multiple sensor signals is broadly known as beamforming. In addition to medical imaging, it is used in underwater acoustics, astronomy, and other disciplines. The problem with many existing passive beamforming algorithms is the poor spatial resolution in the reconstruction of the sound sources. This means we can't precisely locate where the source is coming from. The purpose of this project is to develop a deep neural network and/or signal processing methods that can reconstruct an image of the treated region with better accuracy and spatial resolution. Work description. This work will involve generating training data using computer simulations on a Matlab toolbox known as k-wave. We will then train the deep neural network on PyTorch or develop fundamental signal processing algorithms. We will explore conventional neural networks such as convolutional neural networks, recurrent neural networks, and others; and, potentially, more advanced techniques, such as transformers and physics-inspired neural networks. |
| Fast Tracking of Physical Objects in Virtual Reality (VR) and Augmented Reality (AR) | Purpose. Create a physical object in the real world that can be tracked in the virtual world with speed and accuracy Motivation. Virtual reality (VR) and augmented reality (AR) provide an infinite world that users can interact with. In many applications, we would like to project our real-world body (hands, legs, etc.) and objects (a ball, stick, cup, etc.) into the virtual space. However, current methods still remain slow, clunky, and inaccurate, breaking the immersive experience that we hope for and creating a disconcerting feeling when using VR and AR. What are the current methods for tracking objects and what are the physical, hardware, and computational limitations? How can we improve upon these limitations? Work. You are tasked with building a physical object that includes trackable sensors: infrared light-emitting diodes (LEDs) and inertial measurement units (IMUs). The IMU provides very fast positional tracking (1,000's of Hz), but suffers from poor accuracy over time. The infrared LEDs provide highly accurate tracking, but is slow (10's of Hz). We will therefore provide fast tracking with IMU that is corrected by the infrared LEDs over time. This combined IMU + infrared LED tracking provides a rapid and accurate method of tracking objects. The object's position will be recreated in a virtual world using Unity. The speed and accuracy will be quantified. You will then explore ways of improving upon key limitations with this method, such as tracking out of the field of view, or adding capabilities, such as user feedback with haptic vibration motors. |
| Ultra-High-Speed Video Camera at 10 Million Frames per Second | Purpose: Develop an ultra-high-speed video camera at 50 million frames per second. Motivation: Certain phenomena, such as ultrasound imaging and therapy with microbubbles (contrast agents) operate in the MHz rate. Such fast dynamics cannot be captured using traditional cameras, which operate at around 60 Hz. Commercially available cameras can reach 1 million frames per second, which is still not enough. And while a 10 million frames per second camera is available on the market for £200k, that camera cannot capture more than 256 frames (25.6µs of data). We propose a new video camera concept that could reach up to 50 million frames per second, capable of capturing nearly unlimited amounts of frames. By creating this device, we would be able to observe phenomena in biological tissue that no one has been able to observe. Outside of the domain of biomedical engineering, this camera could be used to image plasma in fusion reactors, high-speed objects in space, and other high-speed phenomena that requires incredibly high frame rates. Work: This project requires electrical engineering skills. The students would be asked to make circuits that are connected to a unique sensor array. If the analog circuit is successful, we would then require some digital electronics skillsets and optics (physics). |
| Visualising Sound - A Neural Network for Passive Acoustic Beamforming | Visualising Sound - A Neural Network for Passive Acoustic Beamforming |
| An Ultra-High-Speed Depth Camera | We propose an ultra-high-speed depth camera. This camera would be able to not only capture optical images, but also see how far away those objects are. What makes this camera unique is that it would be ultra-fast. It would be able to track depth of very fast moving objects; or track depth when the camera is on a very fast moving object (eg, a drone). The camera would be very versatile, being able to track people, survey lands, and more. There is also the potential for this camera to be adapted for medical imaging. We are still in the very early stages of sensor development. The first task would be to work with a single optical sensor and then progressively work to a 2 by 2 array, 4 by 4 array and so forth. Required skillset: Electronics Engineering. You'll need a basic understanding of how to build analogue circuits. |
| Microbubble generation and characterisation for drug delivery using therapeutic ultrasound | Motivation: Focused ultrasound and microbubbles can be used to alter the permeability of the blood-brain barrier. However, variability in the microbubble design can lead to variability in the procedure itself. This includes unpredictable safety and performance profiles. Purpose: The purpose of this project is to characterise microbubbles using acoustic and/or optical methods. Work: Students with different skillsets can work on this project. This includes chemists, physicists, computer scientists, and engineers. |
| Hydrogel-based microchannels for studying acoustic cavitation | Create hydrogel-based capillary channels down to 5-10 µm in diameter. See https://onlinelibrary.wiley.com/doi/full/10.1002/smll.202301163 |
| Optimising of an ex vivo brain slice platform for studying acoustic cavitation-mediated blood-brain barrier opening | Develop methods and devices for studying mechanical phenomena in ex vivo brain slices. This may include many goals depending on the student's skillsets. For example, improve the health of the tissue and ensure that it is as close to physiological relevance as possible. Other examples include improving methods for interrogating the brain slices, such as injection of molecules directly into vessels, and increasing the throughput of these experiments. |
| Serum biomarkers of blood-brain barrier disruption induced by focused ultrasound and microbubbles. | Analyse blood samples for evidence of a change to blood-brain barrier permeability. |
| Scalable Digitisation and Readout Architecture for Large SiPM Arrays | This is electrical engineering work to create a camera using an array of SiPM sensors that can count individual photons. You'll be building the hardware the converts analog electrical signals into digital outputs. This platform can be used for high frame rate imaging (millions per second), depth imaging, and other applications. |
Professor Jimmy Moore
Profile: https://profiles.imperial.ac.uk/james.moore.jr
Contact details: james.moore.jr@imperial.ac.uk
| Project title | Description |
| Lymph node physiological mass transport | Many important immune processes occur in lymph nodes, but we actually know little about their structure. This is important because it determines patterns of mass transport which assure that antigens are presented to the appropriate cells. Based on a combination of high-resolution imaging protocols that quantify the structure of human lymph nodes, we will construct models of mass transport of chemokines and antigens in collagen bundle conduits, parenchyma and blood vessels. |
| MRI phantom for testing lymphatic vessel imaging sequences | There are no means for measuring pressure, flow or diameter reliably in any lymphatic vessel without surgical exposure. While the vessel diameters are on the order of MRI spatial resolution, their unique flow dynamics profile offers opportunities to develop diffusion-based imaging sequences to distinguish them from blood vessels and interstitial fluid movement. We aim to develop a phantom that reproduces these flow patterns to aid in the development of better imaging sequences. |
| Stem Cell Injection Device for Minimising Shear-Induced Cell Lysis | While there is great potential in using cells as part of therapeutic strategies for many diseases, these strategies are limited by the survivability of cells during the injection process. We have designed a combination hydrogel and syringe injection system that aims to maximise cell viability. The project will involve making different hydrogel formulations and testing their mechanical properties. This information will be used to determine the details of the syringe design, which will be tested computationally and experimentally. |
| Lymph flow disruptions in networks of connected lymph nodes after surgical removal | Cancer patients often have lymph nodes removed as part of surgical treatment. This often leads to lymphoedema in neighbouring tissues. This project aims to analyse flow patterns in lymph node networks for the purpose of identifying patients who are more susceptible to oedema development. |
Professor Mengxing Tang
Profile: https://profiles.imperial.ac.uk/mengxing.tang
Contact details: mengxing.tang@imperial.ac.uk
| Project title | Description |
| High Frame Rate Cardiac Imaging using ultrasound | Increasing the frame rate of medical ultrasound imaging is of high benefit for functional analysis of cardiac function, where High Frame Rate Ultrasound (HFRUS) should be required to facilitate the examination of the temporal behavior of fast changing cardiac anatomy and blood dynamics. Current clinical echocardiography typically achieve a standard frame rate (40-80Hz), which is still below the clinical needs in rapid cardiac events observed such as stress echocardiography and fetal echocardiography. Plane/diverging wave ultrasound is a typical method for HFRUS but has low Signal to Noise Ratio (SNR) due to motion artefacts. On the other hand, phase array HFRUS implemented simultaneously by Multi-Line Acquisition (MLA) and Multi-Line Transmission (MLT) has a potential of imaging at >400 Hz, while keeping a relatively high SNR. MLA transmits a wide beam and receive several beams simultaneously and usually can facilitate fast spatial scan. MLT transmits short excitation pulses during a short interval and receives/constructs multiple beams at the same time. Combining MLA and MLT could provide up to 4-6 times higher frame rate while the image quality remains not compromised comparing to traditional cardiac imaging. The aim of this research is to verify these methods in simulation and in experiments to demonstrate the feasibility of ultrafast phase array cardiac imaging to achieve up to ~400Hz frame rate without significantly compromising image quality. Proposal 2: Mapping cardiac electrical activity using Ultrasound Electromechanical Wave Imaging Cardiac electrical activation mapping is clinically recommended for diagnosis of patients with heart failure and arrhythmia. It often provides anatomical structures combined with cardiac functional information, and can provide more information than electrocardiogram (ECG) and thus being potentially more accurate. However current cardiac electrical activity is usually obtained with intracardiac electroanatomic mapping, which is invasive, costly, time-consuming with high-risk. Electromechanical Wave Imaging (EWI) is a novel ultrasound-based technique that can noninvasively map myocardial activation of all four chambers at high temporal resolution (500-2000 frames per second). EWI can be implemented with transthoracic ultrasound with the potential of single-heartbeat diagnosis. Based on previous clinical trials, the accuracy of EWI in predicting arrhythmia location is higher than that of standard ECG, thus leading to reduced ablation time, costs and the need of repeat procedure in the future. This proposal aims at obtaining high quality 2D EWI results with transthoracic ultrasound using the open ultrasound platform, and to show the clinical potential of this method in animals or human. |
| Depth-Resolved Blood Flow Velocity Estimation for Transcranial Doppler Ultrasound | Background Accurate estimation of cerebral blood flow velocity is critical for diagnosing cerebrovascular conditions. Transcranial doppler (TCD) uses ultrasound waves to measure cerebral blood flow velocity in the major intracranial arteries, which can be an indicator of increased stroke risk for children with sickle cell disease [1]. TCD provides blood flow spectra based on a single element transducer or color-coded blood flow based on a phase/linear probe. Both requires three-dimensional knowledge of cerebrovascular anatomy and its variations to localise targeted arteries as well as precise probe positioning for tracking/monitoring. Besides, the interpretation of the blood flow spectra for diagnosis and treatment demands professional training and practical experience, making the TCD study highly operator dependent. Recently, volumetric ultrasound imaging has been developed and explored for transcranial blood flow imaging [2], enabling comprehensive visualisation of intracranial arteries with minimised manual adjustments. In this project, a deep learning framework is proposed to directly estimate blood flow velocity from volumetric doppler ultrasound data (in human). Aims and methods: A deep learning model will be trained to estimate blood flow velocity using doppler signals. Different data preprocessing including IQ, phase, and radiofrequency will be explored. The proposed method will be compared against conventional doppler processing (FFT-based and autocorrelation methods). The proposed framework will be validated using simulation, flow experiments, and in vivo human volunteer data. Skills: Knowledge of ultrasound simulation software such as Field II and k-Wave; practical experience of deep learning (Keras, PyTorch) is preferable but not mandatory References: [1] R. Adams et al., “The Use of Transcranial Ultrasonography to Predict Stroke in Sickle Cell Disease,” New England Journal of Medicine, vol. 326, no. 9, pp. 605–610, Feb. 1992, doi: 10.1056/NEJM199202273260905. [2] P. Xing et al., “3D ultrasound localization microscopy of the nonhuman primate brain,” eBioMedicine, vol. 111, Jan. 2025, doi: 10.1016/j.ebiom.2024.105457. |
Dr Sonja Billerbeck
Profile: https://profiles.imperial.ac.uk/s.billerbeck
Contact details: s.billerbeck@imperial.ac.uk
| Project title | Description |
| Fungal GPCRs: deorphanization and drug discovery | This project aims to establish a yeast-based autocrine screening platform for GPCR-ligand interactions, by engineering yeast cells that co-express a GPCR and a cognate surface-displayed ligand. By activating their own GPCR-downstream signalling pathway, these self-contained cells would generate a built-in fluorescent readout upon receptor-ligand binding. The goal is to show that this setup can be used to screen and identify functional GPCR-ligand pairs in a high-throughput, genotype-linked manner. Over the course of the project, the system will be designed, built, and tested as a proof of concept that could support future efforts in GPCR deorphanization and drug discovery. |
| Hydrogen-oxidizing bacteria for food production from air | Currently, almost all globally consumed food is produced by traditional agriculture. This includes growing crops and growing feed for livestock. Traditional agriculture contributes 37% of global greenhouse gas emissions and places significant burden on the environment as vast amounts of arable land and fresh water are used. To mitigate this looming crisis, microbial fermentations using bacteria, yeast or fungi have emerged as a more sustainable way for food production. Hydrogen-oxidizing bacteria (HOBs) are specifically interesting for food production as they can use carbon dioxide from air as carbon feedstock. Thus, any food production process based on these gram-negative bacteria would not rely on agriculture. However, to use these organisms for bioprocessing, significant knowledge gaps in physiology, culturing and genetic engineering need to be addressed. This project aims to study growth requirements and genetic engineering capacities of several Xanthobacter species. |
| Development of a CRISPRi-based growth selection method | This project develops CRISPRi-based growth selections as a tool to uncover the modes of action of antifungal proteins. |
| Food from Air: Metabolic engineering of hydrogen-oxidising bacteria for high-yield food protein production. | Currently, almost all globally consumed food is produced by traditional agriculture. This includes growing crops and growing feed for livestock. Traditional agriculture contributes 37% of global greenhouse gas emissions and places a significant burden on the environment, as vast amounts of arable land and fresh water are used. To mitigate this looming crisis, microbial fermentations using bacteria, yeast, or fungi have emerged as a more sustainable way for food production. Here, gas fermentation offers a disruptive solution with the potential to completely uncouple food production from agriculture, enabling the production of foods from gases present in the “airâ€. Gas fermentation is based on bacteria that can convert gases such as atmospheric COâ‚‚ and green Hâ‚‚ into biomass. One class of bacteria able to feed on CO2 and hydrogen are gram-negative hydrogen-oxidising bacteria of the Xanthobacter clade. Gas fermentation has recently been commercialised at scale for biomass fermentations with non-modified microorganisms, proving the technical feasibility of its scalability. However, to diversify this air-fed food chain to high-value products beyond biomass, the next frontier is to move toward using engineered hydrogen-oxidising bacteria able to produce high yields of a given food protein (e.g. milk protein). We recently developed genetic engineering tools for different Xanthobacter species, and this project will use a combination of these tools to enhance the yields of food protein production. Skills that will be learned: culturing Xanthobacter on various growth media, Golden Gate cloning, metabolic engineering, heterologous protein production, and data analysis. Required background: basic skills in molecular cloning and biochemistry are advantageous. Further reading: 10.1016/j.tibtech.2025.08.003 External Link |
| Food from Air: Decoupling biomass production from product formation in hydrogen-oxidising bacteria. | A second frontier in gas precision fermentation (see project above for background) is achieving precise genetic control over when a food molecule is produced during the process. Because producing food molecules places a significant metabolic burden on cells, effective fermentation strategies typically operate in two stages. First, cells grow and accumulate biomass without producing the target food molecule. This is followed by a dedicated production phase, in which synthesis is switched on using an external trigger. This project focuses on characterising and iteratively improving a range of genetic control switches in Xanthobacter species and evaluating which induction triggers are most compatible with food-industry requirements. Skills that will be learned: culturing Xanthobacter on various growth media, Golden Gate cloning, metabolic engineering, heterologous protein production, and data analysis. Required background: basic skills in molecular cloning and biochemistry are advantageous. Further reading: 10.1016/j.tibtech.2025.08.003 External Link |
| Food from Air: Developing enabling technologies for metabolic engineering of hydrogen-oxidising bacteria | To fully harness the capacities of hydrogen-oxidising bacteria for bioproduction, advanced genetic and genomic engineering tools are required. This project involves surveying the literature for the newest and most efficient genome editing tools, as well as implementing and optimising them for performance in Xanthobacter. Skills that will be learned: culturing Xanthobacter on various growth media, Golden Gate cloning, recombineering, CRISPR/Cas-based genome engineering, and data analysis. Required background: basic skills in molecular cloning and biochemistry are advantageous. |
| Food from Air: Engineering effective protein secretion from hydrogen-oxidising bacteria. | The secretion of protein from the cells is a way to avoid costly downstream processing in gas-based precision fermentations. Life cycle analysis and techno-economic analysis show that downstream processing has a huge impact on cost and sustainability. Protein secretion inherently separates the target protein from biomass, which simplifies downstream processing towards a pure protein product. This project engineers the secretion machinery of hydrogen-oxidising bacteria for enhanced secretion. Skills that will be learned: culturing Xanthobacter on various growth media, Golden Gate cloning, metabolic engineering, heterologous protein production, and data analysis. Required background: basic skills in molecular cloning and biochemistry are advantageous. |
| Simplifying downstream processing in precision fermentation: Engineering genetically programmable product-release from yeast cells. | Precision fermentation involves the controlled cultivation of engineered microorganisms to biosynthesise specific target compounds. These systems enable the production of food-relevant molecules, such as proteins, lipids, carbohydrates, and secondary metabolites, that are traditionally sourced from animals or plants, and are subsequently recovered and purified from the fermentation broth or biomass. Life cycle and techno-economic analyses indicate that downstream processing is a major contributor to both cost and environmental impact. A critical first step in downstream processing is cell lysis to release the product, which is typically achieved using mechanical methods that require costly, energy-intensive equipment. In this project, we aim to develop a genetic system for programmable cell lysis, enabling engineered cells to undergo autolysis and release the product at the end of fermentation. This will be accomplished by testing a range of yeast virus–derived autolysis modules available in our laboratory. The project will also involve developing and validating assays to quantitatively measure the release of heterologous proteins. Skills that will be learned: Yeast molecular biology, Golden Gate cloning, assay development, data analysis, metabolic engineering. Required background: basic skills in molecular cloning are advantageous. Further reading: 10.1016/j.celrep.2024.114449 |
| Waste-to-value: Towards diverse feedstock usage using wild yeast varieties. | Precision fermentation is a biotechnological process in which microorganisms are engineered to produce specific target molecules. It enables the manufacturing of food ingredients traditionally derived from plants or animals, such as proteins, lipids, carbohydrates, and other metabolites. In a waste-to-value precision fermentation framework, low-value or waste streams (e.g., food processing residues, agricultural by-products, lignocellulosic hydrolysates, or industrial effluents) are repurposed as feedstocks for these microbial precision fermentations. As such, waste can be turned into food ingredients. One challenge for this framework is identifying suitable microorganisms that can use diverse waste streams as feedstock and that could be employed in future precision fermentation processes. This project will start developing a structured approach to identify new microorganisms and microbial communities with useful feedstock-usage profiles. It involves 1. measuring carbon-source-usage capacities of a large set of natural yeast varieties using commercially available carbon-source arrays and micro-scale biomass fermentations, 2. Performing a literature survey to identify carbon sources common and specific to waste streams, and 3. Based on the survey outcomes, developing waste-stream-relevant carbon-source arrays for microbial testing. Skills that will be learned: Yeast microbiology, high-throughput assay development, data analysis, waste-to-value precision fermentation. Required background: basic skills in microbiology are advantageous. |
| Yeast-based crop biocontrol for a pesticide-free plant-based food production chain. | Enabling the scaling of a plant-based food production chain increases demand for crop cultivation worldwide. Crop diseases caused by crop pathogens already pose a significant threat to global food security. Fungi are responsible for 80% of crop diseases, leading to over 20% loss in crop productivity annually. Although chemical fungicides have long been effective, their overuse along with crop monoculturing, climate change, and global trade has accelerated disease spread and resistance development. Moreover, broad-spectrum fungicides harm biodiversity and kill the “good fungi†in soil, which are required for effective plant growth. There is an urgent need for new, targeted, and functionally diverse biocontrol strategies to sustainably protect crops from fungal pathogens. Environmental yeasts offer a diverse, yet underexplored potential for fungal biocontrol. They naturally inhibit fungal pathogens through secreted antifungal molecules such as siderophores, antifungal proteins and peptides (ribosomal and non-ribosomal), volatiles, and biosurfactants. Many act selectively against a narrow pathogen range, enabling targeted control that preserves beneficial microbiota. This project develops a yeast formulation based on genetically modified yeast that expresses a cocktail of antifungal molecules. It involves combining multiple biosynthetic pathways and heterologous antifungal proteins in one yeast (or a yeast community), optimizing expression levels and testing antifungal capacity. Skills that will be learned: Yeast molecular biology, Golden Gate cloning, assay development, data analysis, metabolic engineering. Required background: basic skills in molecular cloning are advantageous. Further reading: https://www.sciencedirect.com/science/article/pii/S0167779924000672 |
Dr Pedro Ballester
Profile: https://profiles.imperial.ac.uk/p.ballester
Contact details: p.ballester@imperial.ac.uk
Ballester group page - https://ballestergroup.github.io/
| Project title | Description |
| Optimal design of virtual screening benchmarks from in vitro screening data | Introduction: Virtual screening (VS) has become an important source of small-molecule drug leads. A benchmark is needed to identify the VS method/s that will perform best prospectively for that therapeutic target. Benchmarks are also needed to find the optimal settings for the selected VS method/model. A VS benchmark is a library with two classes of molecules: those whose activity for the target is above a given threshold - actives as the positive class- and those with weaker or no activity at all - inactives as the negative class. Among them, a screened library is one whose molecules have been screened in the same centre and using same assay/s, e.g. the results of high-throughput screening (HTS) a compound library against the considered target. Unfortunately, HTS data have been used for VS benchmarking in a convenient yet unrealistic manner (e.g. generating benchmarks with much smaller chemical diversity than HTS). The question is how useful are these HTS-derived datasets as VS benchmarks with respect to the ground truth represented by the unadulterated HTS datasets. About the supervisor: Dr Pedro Ballester has over 17 years of experience in this research area. His last papers in this area have shown the potential of Artificial Intelligence (AI) for structure-based drug design: https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wcms.1478 https://academic.oup.com/bib/article-abstract/22/3/bbaa095/5855396 Research plan: The student will start by learning about these data types as well as existing VS benchmarks (e.g. MUV) and VS methods (e.g. USR, Smina). Then, s/he will be applying each VS method to rank the HTS-derived benchmark molecules in order to assess its performance on the associated target. This process will also be carried out for other VS methods, unadulterated HTS benchmarks and targets. The results will be employed to investigate to which extent the filters used to select molecules for a VS benchmark make it unrealistic. This is crucial for the development and selection of VS methods. About the candidate: This project is suitable for a student who is keen to learn about molecular modelling in the context of early drug design. Python programming is required. Contact: p.ballester@imperial.ac.uk |
| Optimal design of simulated-library benchmarks for virtual screening | Introduction: Virtual screening (VS) has become an important source of small-molecule drug leads. A benchmark is needed to identify the VS method/s that will perform best prospectively for that therapeutic target. Benchmarks are also needed to find the optimal settings for the selected VS method/model. A VS benchmark is a library with two classes of molecules: those whose activity for the target is above a given threshold - actives as the positive class- and those with weaker or no activity at all - inactives as the negative class. Among them, a screened library is one whose molecules have been screened in the same centre and using same assay/s, e.g. the results of high-throughput screening (HTS) a compound library against the considered target. In the absence of publicly-available HTS data, one has to resort to define a simulated-library (SimL) benchmark. Here one gathers actives from the literature and generates decoys from each of them so that these molecules collectively simulate a screened HTS library. This is the most common type of VS benchmark, where decoys are often required to have dissimilar chemical structures to that of their active to make them even less likely to be also active (activity is a rare event and hence randomly-chosen molecules are already unlikely to be active). Furthermore, an active and their property-matched decoys have similar physico-chemical properties so that they cannot be trivially discriminated by one of these properties, as this does not happen prospectively either. One popular example of such benchmarks is DUD-E (http://dude.docking.org/). The question is how useful are these SimL benchmarks for VS with respect to the ground truth represented by the entire HTS datasets (in the targets for which these are available). We expect this study to reveal which are active-to-inactive ratios and decoy schemes best anticipate performance on HTS datasets. This will be useful to decide best SimL benchmarking in absence of HTS data. About the supervisor: Dr Pedro Ballester has over 17 years of experience in this research area. His last papers in this area have shown the potential of Artificial Intelligence (AI) for structure-based drug design: https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wcms.1478 https://academic.oup.com/bib/article-abstract/22/3/bbaa095/5855396 Research plan: The student will start by learning about these data types as well as existing VS benchmarks (e.g. MUV) and VS methods (e.g. USR, Smina). Then, s/he will be applying each VS method to rank the DUD-E benchmark molecules in order to assess its performance on the associated target. This process will also be carried out for other VS methods, unadulterated HTS benchmarks and targets. The results will be employed to investigate to which extent the design factors for a retrospective VS benchmark make it unrealistic. This is crucial for the development and selection of VS methods. About the candidate: This project is suitable for a student who is keen to learn about molecular modelling in the context of early drug design. Python programming is required. Contact: p.ballester@imperial.ac.uk |
| Integrating multi-omics data from cancer cell lines with chemical structure data from molecules for enhanced pharmaco-omic modelling | Predictive models built with artificial intelligence (AI) methods are powerful tools to predict molecule-induced growth inhibition of cancer cell lines. Despite important successes, there are major challenges limiting the potential of such AI models. Some are specific to this problem (e.g. how to augment pharmaco-omic datasets in a way that improves the performance of these models). Other challenges are also found in other supervised learning problems (e.g. accurately delimiting the applicability domain of the model). This project will take a multi-task learning approach to this problem by integrating multi-omics data from cancer cell lines with chemical structure data from molecules. This will require care in acquiring and preparing omics datasets such as miRNA or mRNA expression, among others. Some introductory readings: https://doi.org/10.1371/journal.pone.0061318 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681992/ https://doi.org/10.1093/bib/bbab312 Contact: p.ballester@imperial.ac.uk |
| A toxicity prediction toolbox based on the Therapeutic Data Commons benchmarks | Drug candidate molecules can induce many forms of toxicity in humans, often leading to the failure of a drug development programme or even the abandonment of an otherwise promising therapeutic target. Reliable computational methods capable of predicting toxicity endpoints at early stages of drug discovery are therefore of considerable importance. Recent advances in Artificial Intelligence (AI) have led to the development of highly predictive models for a wide range of toxicity-related tasks. The Therapeutic Data Commons (TDC) Toxicity Benchmarks (https://tdcommons.ai/single_pred_tasks/tox) provide a collection of standardised datasets and benchmark tasks covering multiple toxicity endpoints, alongside public leaderboards highlighting state-of-the-art AI approaches. Examples include benchmark tasks such as hERG Toxicity Prediction (https://tdcommons.ai/benchmark/admet_group/20herg), which focuses on predicting cardiotoxicity risks associated with drug molecules. This project is for students with some background in AI and Python programming. The student will: - Review the scientific literature surrounding selected toxicity prediction problems; - Investigate and explain the leading AI models used for these tasks in a clear and accessible manner; - Reproduce and evaluate published models using additional performance metrics beyond those reported in benchmark leaderboards; - Apply selected models to external molecular datasets provided by the supervisor; - Develop practical skills in running and adapting computational workflows, including Python scripts and machine learning pipelines. The project provides an opportunity to gain hands-on experience in a highly relevant area of AI-driven drug discovery while strengthening programming and computational research skills. Students will become familiar with modern toxicity prediction benchmarks, molecular datasets, model evaluation methodologies and the practical challenges associated with deploying AI models in cheminformatics research. Contact: p.ballester@imperial.ac.uk |
| Optimal selection of structure-based AI models to search for drugs leads against a target | 3D atomic-resolution model of a therapeutic target (e.g. a crystal structure of PARP1 as a target for breast cancer) can be leveraged to search for small-molecule drug leads. There is a plethora of structure-based AI models designed for this application. However, typically few of them release code that can be used on other sets of molecules. More importantly, we currently do not have any way to anticipate which of the available models to employ for a given target. The supervisor will provide targets with their structures and sets of molecules known to bind to those targets (i.e. binding molecules or binders). For each of the targets, the student will review the literature to introduce the target and find structure-based AI models for virtual screening (i.e. to discriminate between binders and non-binders in a given set of molecules). The student will evaluate the models to find out which is the most effective for the target. Some strategies to anticipate which is the best model will be evaluated (e.g. calculating the similarities between the considered targets and those included in the training set of that model). Thus, independence in running code such as Python scripts is required. This project is an opportunity to be introduced to an important drug discovery problem, the best AI models to tackle this problem and hone your programming skills. References: https://www.nature.com/articles/s41596-023-00885-w https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wcms.1478 Contact: p.ballester@imperial.ac.uk |
| Machine Learning for Predicting Drug Resistance in Mycobacterium tuberculosis | Mycobacterium tuberculosis (Mtb), the bacterium causing tuberculosis, is becoming harder to treat to due antimicrobial resistance. To make things worse, it is challenging to predict if emerging Mtb strains will diminish the effectiveness of existing drug therapies. Predictive models, built with machine learning algorithms on increasingly large and well-curated datasets, are powerful tools to predict drug-induced growth inhibition of Mtb strains from their genomic profiles. This project will: 1) review the literature on predicting drug resistance in this human pathogen, 2) compile and curate pharmaco-omic datasets, 3) build and evaluate machine learning models using these datasets. This is an example of the datasets that are relevant for this project: https://academic.oup.com/bib/article/22/6/bbab313/6347947 Note that the label to predict, drug-induced growth inhibition, has a more generic name: antimicrobial susceptibility testing or AST. Contact: p.ballester@imperial.ac.uk |
| Machine Learning for Predicting Drug Resistance in Plasmodium falciparum | Plasmodium falciparum (Pf), the key parasite causing malaria, is becoming harder to treat to due antimicrobial resistance. To make things worse, it is challenging to predict if emerging Pf strains will diminish the effectiveness of existing drug therapies. Predictive models, built with machine learning algorithms on increasingly large and well-curated datasets, are powerful tools to predict drug-induced growth inhibition of Pf strains from their genomic profiles. This project will: 1) review the literature on predicting drug resistance in this human pathogen, 2) compile and curate pharmaco-omic datasets, 3) build and evaluate machine learning models using these datasets. This is an example of the datasets that are relevant for this project: https://academic.oup.com/bib/article/22/6/bbab313/6347947 Note that the label to predict, drug-induced growth inhibition, has a more generic name: antimicrobial susceptibility testing or AST. Contact: p.ballester@imperial.ac.uk |
| Understanding the impact of data partition biases on structure-based binding affinity prediction | Test set performance is strongly dependent on how data is partitioned into a training set and test set, regardless of the machine learning algorithm and representation employed to build and evaluate prediction models on that data partition. Protein-ligand complexes can be partition by how similar the structures or sequences of their proteins are, the chemical structures of their ligands or the intermolecular interactions holding the complex together. This project will investigate how these partitioning strategies affect test set performance and which ones offer a more realistic evaluation of models for structure-based binding affinity prediction. Contact: p.ballester@imperial.ac.uk |
| Benchmarking Generic Machine-Learning Scoring Functions for Structure-Based Virtual Screening | Generic scoring functions are designed to distinguish binding from non-binding molecules across diverse therapeutic targets. However, their virtual screening performance often varies substantially between targets, and even across different datasets of molecules for the same target. Thus, this project aims to systematically evaluate which scoring functions perform best for each specific target by benchmarking multiple scoring functions on each of the considered virtual screening benchmarks. The resulting insights will guide the selection of the most appropriate scoring functions for future prospective virtual screening campaigns. Contact: p.ballester@imperial.ac.uk |
| Survey and Benchmarking of Computational Toxicity Prediction Tools for Drug Discovery | Drug candidate molecules can induce many forms of toxicity in humans, often resulting in the failure of a drug development programme or even the abandonment of a therapeutic target altogether. Reliable computational methods capable of predicting toxicity risks at early stages of drug discovery are therefore of considerable importance. Over the past decade, a large number of computational approaches, including both Artificial Intelligence (AI)-based and non-AI-based cheminformatics methods, have been developed to predict specific toxicity endpoints. However, for many toxicity problems it remains unclear: - which predictive tools are currently available, - how well these methods generalise to new molecules outside their original training datasets, and - how computationally efficient they are when applied to large molecular libraries. This project is suited for students with an interest in cheminformatics, AI and computational drug discovery. Some experience with Python programming or data analysis tools would be beneficial. The student will conduct a broad literature review to identify major drug-relevant toxicity endpoints and the computational studies associated with them. For each endpoint, the student will identify publicly available prediction tools and models,determine whether these tools can be applied to user-supplied molecules, assess their accessibility, reproducibility, and usability, critically evaluate their reported predictive performance and potential limitations and compare their computational efficiency where possible. The project will make use of several important community resources and repositories, including: - Therapeutic Data Commons (TDC) for datasets, benchmarks, and leaderboards (Therapeutic Data Commons). - the associated TDC publication in Nature Chemical Biology. - ADMET-AI (ADMET-AI publication). - OCHEM toxicity modelling competitions (OCHEM Challenges). - the Polaris/Auroris benchmark repository (Auroris Benchmark Repository). - Papers With Code for identifying relevant machine learning studies and leaderboards (Papers With Code). - the Computer-Aided Drug Design (CADD) Vault (CADD Vault). - and the VLS3D directory of computational chemistry tools and databases (VLS3D Directory). This project provides an opportunity to gain experience in a highly relevant area of modern drug discovery while developing skills in literature analysis, benchmarking methodology, cheminformatics software and scientific programming. Students will become familiar with both AI and non-AI approaches for molecular toxicity prediction and gain practical experience in evaluating and running computational research tools. Contact: p.ballester@imperial.ac.uk |
| Benchmarking Tools for Molecular Property Prediction in Drug Discovery | In modern drug discovery, molecular properties such as absorption, distribution, metabolism, excretion and toxicity (ADMET), quantitative estimate of drug-likeness (QED) and synthetic accessibility (SA) are critical for prioritising promising drug leads. Numerous computational tools have been developed to predict these properties. However, it remains unclear which predictors provide the best balance between performance indicators (e.g. generalisability to unseen molecules, reproducibility and computational efficiency at large scale). This project will investigate and benchmark existing predictive tools and datasets used for molecular property prediction. The student will conduct a systematic survey of publicly available resources and frameworks, including Polaris (https://polaris-hub.github.io/polaris), TDC (https://tdcommons.ai) and MoleculeNet (https://moleculenet.org), with a focus on methods that allow investigating the best balance between performance indicators. The project will evaluate: - How well existing predictors generalise to molecules outside their original training sets. - The reproducibility of published benchmarking results. - The computational cost and scalability of these methods when applied to very large molecular libraries (potentially trillions of compounds). The student will gain experience in cheminformatics, machine learning evaluation, benchmarking methodology and high-throughput computational screening. Depending on progress and background, there may also be opportunities to implement automated benchmarking pipelines and investigate trade-offs between predictive accuracy and inference speed. Contact: p.ballester@imperial.ac.uk |
| Survey and Evaluation of Computational Methods for Molecular Target Prediction | Understanding all the macromolecular targets bound by a drug molecule is essential for elucidating its mechanism of action (MoA) in the treatment of disease. Predicting the targets of small molecules is therefore a key challenge in modern drug discovery. This problem has become increasingly important due to the growing recognition that drug efficacy, off-target effects and adverse side effects are often linked to interactions with multiple biological targets. In parallel, the resurgence of phenotypic screening approaches has led to the discovery of many active compounds whose molecular targets remain unknown. A large number of computational methods for molecular target prediction have been developed in recent years. Typically, these are ligand-based, structure-based, network-based and/or machine learning approaches. Examples of relevant studies and methodologies include: * https://pubs.rsc.org/en/content/articlehtml/2025/dd/d5dd00199d * https://pubs.acs.org/doi/10.1021/acs.jcim.6c00299 * https://pubmed.ncbi.nlm.nih.gov/31220208 Despite the large number of published methods, it remains unclear: * which tools are publicly available and operational for predicting targets of user-supplied molecules, * how these methods compare in terms of predictive performance, applicability and usability, * what their main strengths and limitations are, * and how reproducible their published retrospective validation studies are. This project is suitable for students with an interest in cheminformatics, computational biology, machine learning or drug discovery. Some background in Python programming, data analysis or computational workflows is required. The student will conduct a broad literature survey of computational target prediction methods and systematically analyse the available tools and resources. The project may involve: * identifying and categorising target prediction methodologies, * investigating publicly accessible software and web servers, * evaluating the reproducibility of published benchmarking studies, * comparing methods according to predictive performance, scalability, and ease of use, * and testing selected tools on additional molecules provided by the supervisor. This project offers an opportunity to gain experience in an important and rapidly evolving research area, while developing skills in scientific literature analysis, cheminformatics software evaluation and computational research workflows. Contact: p.ballester@imperial.ac.uk |
Dr Sam Au
Profile: https://profiles.imperial.ac.uk/s.au
Contact details: s.au@imperial.ac.uk
| Project title | Description |
| Microfluidic Avidity Based Selection for Autoimmune Disease | We have developed a new microfluidic laboratory tool that can probe how strongly immune cells bind to tumour cells (avidity), which gives us an indication of how promising the interactions are https://advanced.onlinelibrary.wiley.com/doi/10.1002/adhm.202200169 . This device has been demonstrated to reliably predict the in vitro and in vivo efficacy CAR-iNKT cells, TCR T cells, and NK cell candidates based on the formation of immunological synapse. However, we have not used this technique to study if it would work for selecting immune interventions for autoimmune diseases. This project will explore whether our microfluidic avidity device can be used to help biotech and pharmaceutical companies select the best and safest biomolecules for treating autoimmune diseases like type 1 diabetes. |
| Accelerating Preclinical Development of Cell Engagers using Microfluidic Avidity | We have developed a new microfluidic laboratory tool that can probe how strongly immune cells bind to tumour cells (avidity), which gives us an indication of how promising the interactions are https://advanced.onlinelibrary.wiley.com/doi/10.1002/adhm.202200169 . This device has been demonstrated to reliably predict the in vitro and in vivo efficacy CAR-iNKT cells, TCR T cells, and NK cell candidates based on the formation of immunological synapse. However, we have not used this technique to study if it would work for selecting the cell engagers. This project will explore whether our microfluidic avidity device can be used to help biotech and pharmaceutical companies select the best and safest cell engagers for treating cancers like Ewing Sarcoma before they are tested in patients. |
Professor Rylie Green
Profile: https://profiles.imperial.ac.uk/rylie.green
Contact details: rylie.green@imperial.ac.uk
| Project title | Description |
| A bioelectronic implant for cancer treatment | This project revolves around aiding in the development of a device for the selective delivery of chemotherapy directly to the site of non-operable brain tumors (glioblastoma multiforme). This device consists of a conductive polymer-based material that can used as an electrically controlled drug delivery system. The goal of this project is to evaluate the drug release profiles for multiple different molecules that are analogs to those commonly used in chemotherapy. Parameters such as molecule size, charge, and stability will be investigated. Characterization of the drug release profiles will be accomplished through chemical, electrochemical, and spectroscopic techniques. |
| Living Bionics: Stimulation to drive neural network development | Electrical stimulation has been demonstrated to induce directional neurite growth in various cell types, both human and non-human using biphasic stimulation. This research project aims to evaluate a range of sinusoid stimulation frequencies to drive activity, growth and release of neurotransmitters of developing neurons using a cell stimulation rig made in house. |
| Spinal cord bridge | Nerve regeneration in an injured spinal cord is often restricted. One possible reason may be the lack of topographical signals from the material constructs to provide contact guidance to invading cells or re-growing axons. This research project aims to evaluate electroactive scaffolds and study device topographical effects on neural and glial cell behavior. |
| Injectable electrodes: Colloidal systems for conductive nanoelectronics | This project is about biomaterials development to make injectable systems that are electrically addressable |
| Implanted device development for targeted in-tumour delivery of chemotherapeutics | Design and prototyping of device for delivery of electronic chemotherapy. Understanding fabrication and drug loading interactions and subsequent impact on delivery into brain tumour tissues. |
Dr Sophie Morse
Profile: https://profiles.imperial.ac.uk/sophie.morse11
Contact details: sophie.morse11@imperial.ac.uk
| Project title | Description |
| Can focused ultrasound delay Alzheimer's disease? | Focused ultrasound is a technology that has very recently shown to restore cognitive function in Alzheimer's disease patients. This is a non-invasive technology that can be focused onto specific regions of the brain. One theory is that this technology can restore cognition by stimulating the innate immune cells of the brain as well as neuronal function and health. In this project you will explore 1) whether this same technology can be used to delay Alzheimer's as well as restore cognition and 2) delve into exploring the mechanisms behind why these effects are observed. This will involve working with mouse brain tissue, sectioning, imaging, staining and fluorescence microscopy. |
| Can focused ultrasound produce an anti-tumour effect in the brain? | Focused ultrasound and microbubbles is the only technique that can non-invasively, locally and temporarily open the blood-brain barrier to allow drugs into the brain. The way this technology works is by first injecting clinically-approved microbubbles and drugs into the bloodstream. When the microbubbles reach the area where the ultrasound is targeted in the brain, these bubbles oscillate, mechanically stimulating the blood vessels, allowing the barrier to open so that drugs can get reach the brain. This technology has generated a lot of excitement as the blood-brain barrier currently prevents over 98% of drugs from entering the brain. Therefore, this technique can allow drugs that have previously failed clinical trials due to this barrier, to be tested again. This project will involve testing a range of drugs against low-grade glioma brain tumours to see which might be most effective in vitro. We will then select the best drug combinations to test in vivo in a mouse model of the disease. In this project, you will explore with cell culture, assays, sectioning, staining and fluorescence microscopy the effect of different drugs against low-grade glioma cells and tumours. Focused ultrasound will be used to deliver these drugs in the in vivo models. |
| Can focused ultrasound delay brain ageing? | Focused ultrasound is a technology that has very recently shown to restore cognitive function in Alzheimer’s disease mice and patients. This is a non-invasive technology that can be focused onto specific regions of the brain. One theory is that this technology can restore cognition by stimulating the innate immune cells of the brain as well as neuronal function and health. In this project you will explore 1) whether this technology can be used to delay age-related cognitive decline, as well as restore cognition in aged mice and Alzheimer’s disease, and 2) delve into exploring the mechanisms behind why these effects are observed. This will involve working with mouse brain tissue, sectioning, imaging, staining, fluorescence microscopy and molecular techniques such qPRC and ELISA. |
| Neuromodulation of Memory Circuits in Mice | We seek an MRes student to support investigating into how focused ultrasound can modulate memory-relevant neural circuits in mice. This role combines behavioral neuroscience, surgical technique, and neuroimaging—ideal for someone interested in translational neuromodulation research. Memory formation depends on astrocyte signalling in the hippocampus. We use two-photon calcium imaging during spatial memory tasks to monitor astrocyte activity, focused ultrasound neuromodulation (PhoCUS system/iTBUS) to selectively modulate circuits, and behavioral analysis to link neural dynamics to learning. The MRes student will help conduct stereotactic surgeries, run behavioral experiments on the T-maze with trained mice, support ultrasound neuromodulation sessions, and contribute to experimental design and data interpretation. We seek candidates with strong interest in neuroscience or neuroengineering, manual dexterity for surgical work, and a collaborative, problem-solving approach to research. Pre-requisite: Personal Animal License |
| Developing an algorithm to quantify how therapeutic ultrasound can modulate the brain's immune cells | Glia are essential for the brain to function properly and they are also heavily involved when the brain does not function properly. These glial cells are involved in the immune response of the brain, clearing unwanted substances and regulating neuronal activity. We are investigating how therapeutic ultrasound can be used as a tool to modulate the behaviour of these cells with the potential to help treat brain diseases, such as brain tumours, Alzheimer’s disease and Parkinson’s disease. We have developed an algorithm to quantify changes in the morphology of these glial cells in an automated way - which is saving so many hours of work. In this project, we want to take it a step further. Can we also quantify images of human brain tissue? What about multiple markers of glial cell activity at the same time? Can we automate the segmentation of white and grey matter regions? This project will be on the computation image processing side of the work, however, you can also shadow and/or get experience with the actual staining and imaging of mouse brain tissue to see how the images are acquired. Ultimately, with this project you will enabling the powerful ability of ultrasound, as a therapeutic technology to modulate cellular activity in the brain, to be taken a step further. Good programming/image processing skills needed. |
Professor Martyn Boutelle
Profile: https://profiles.imperial.ac.uk/m.boutelle
Contact details: m.boutelle@imperial.ac.uk
| Project title | Description |
| Novel microfluidic devices for organ on/in a chip monitoring. | The MGB group has an expertise in-tissue monitoring using microfluidics sampling devices coupled to electrochemical sensors and Biosensors for measurement the real-time concentrations of key ions, metabolites and neurotransmitters. This lab-based project is part of an effort within the group to adapt our microfabricated sampling technology to engineer a sampling component of an organ on/in a chip device. Such devices offer the exciting possibility of complete control of the tissue environment allowing study of organ function mechanisms, and testing of drugs. In the project the student will learn how to make 2D microfluidic devices using laser cutters, paralene film coaters and a range of other microfabrication technologies. They will help design a new device for use within an organ on/in a chip microfluidics device and test performance. Applications of the devices are wide and include organs, muscle and cells. Successful devices will be tested in collaboration with Professor Overby's group. the project will be supported by MGB and POSTDOC Dr Sally Gowers |
| Microfabrication of flexible devices for monitoring of premature babies | The project is a the result of a collaboration between the MGB group and Dr Jay Bannerjee, Neonatalconsultant at Queen Charlotte's and Chelsea Hospital, Part of Imperial College. Small for gestational age (SGA) babies cn have impaired ability to handle glucose load in their body. These babies requre special feeding regimes, however, their poor glucose handling can lead to very high levels of blood glucose sometimes up to 15-20 millimolar (compared to 5-6 mM normally), which require insulin infusions to reduce osmotic diuresis and dehydration, and if not managed well can lead to intraventricular haemorrhage in tiny babies. Yet such babies have very small blood volumes that conventional needle pricks prevent blood measurement on a reasonable time scale. We have developed novel transdermal sampling technologies based on microfludics using biosensors to monitor skin glucose and lactate (as a marker of ischaemia and sepsis). The project will involve fabrication new flexible microfludic devices and flexible biosensors that are suitable to be skin mounted. The work will be supported by MGB PDRA Sally Gowers (Sensors)and a PhD student Vanessa Wang This is a lab based project however There is a possibility of generating a modelling project on the microfluidic design if a student is interested |
| Microfabrication of flexible biosensor devices for monitoring of premature babies | The project is a the result of a collaboration between the MGB group and Dr Jay Bannerjee, Neonatalconsultant at Queen Charlotte's and Chelsea Hospital, Part of Imperial College. Small for gestational age (SGA) babies cn have impaired ability to handle glucose load in their body. These babies requre special feeding regimes, however, their poor glucose handling can lead to very high levels of blood glucose sometimes up to 15-20 millimolar (compared to 5-6 mM normally), which require insulin infusions to reduce osmotic diuresis and dehydration, and if not managed well can lead to intraventricular haemorrhage in tiny babies. Yet such babies have very small blood volumes that conventional needle pricks prevent blood measurement on a reasonable time scale. We have developed novel transdermal sampling technologies based on microfludics using biosensors to monitor skin glucose and lactate (as a marker of ischaemia and sepsis). The project will involve fabrication new flexible biosensors using the femptosecond laser system top create micro electrodes followed by (1) use of the BioDOT liquid handling robot to deposit conductive inks or biosensor components or (2) Sputter coating of gold / platinum electrode surfaces The work will be supported by MGB PDRA Sally Gowers (Sensors)and a PhD student Vanessa Wang This is a lab based project. |
| Development of a cortisol sensor for monitoring stress in premature babies | The project is a the result of a collaboration between the MGB group and Dr Jay Bannerjee, Neonatalconsultant at Queen Charlotte's and Chelsea Hospital, Part of Imperial College. Small for gestational age (SGA) babies cn have impaired ability to handle glucose load in their body. These babies requre special feeding regimes, however, their poor glucose handling can lead to very high levels of blood glucose sometimes up to 15-20 millimolar (compared to 5-6 mM normally), which require insulin infusions to reduce osmotic diuresis and dehydration, and if not managed well can lead to intraventricular haemorrhage in tiny babies. Yet such babies have very small blood volumes that conventional needle pricks prevent blood measurement on a reasonable time scale. Such needle prick are likely to be stressful for the infant leading to increases in blood cortisol. We have developed novel transdermal sampling technologies based on microfludics using biosensors to monitor skin glucose and lactate (as a marker of ischaemia and sepsis). This project will involve fabrication new biosensors suitable for monitoring cortisol as a marker of stress. Tow approaches are available for cortisol. (1) the use of aptamers - binding of cortisol results in folding of the aptamer to give an electrochemical signal. (2) we have potentially the opportunity to test a nano-particle molecularly imprinted polymer (MIP). Her a synthetic polymer selectively bind cortisol. Electrochemical techniques are then used to detect the binding. the project will involve making sensors and optimising their performance for use with neonatal samples. The work will be supported by MGB PDRA Sally Gowers (Sensors)and a PhD student Jing Lou. |
Professor Claire Higgins
Profile: https://profiles.imperial.ac.uk/c.higgins
Contact details: c.higgins@imperial.ac.uk
| Project title | Description |
| Mapping out the hair follicle epithelium in the context of its' macroenvironment | In this project the student will combine transcriptomic dataset analysis with wholemount immunofluorescence to map out the distinct regions of the hair follicle, and the regions which are connected to macroenvironmental regulators. This includes imaging lymphatic vessels, blood vessels and neurons that wrap around the follicle. The dataset to be analysed was previously published by another group, and our initial analysis of it shows a neuronal signature in the upper follicle, which we also believe is the area where neurons innervate the follicle. The student will interrogate the dataset for lymph and blood vessel signatures, and correlate these with the location of vessels around the follicle. The staining protocol is already established in my group and allows high resolution imaging of whole human hair follicles (1x3mm) and their surrounding environment. |
Professor Danny O'Hare
Profile: https://profiles.imperial.ac.uk/d.ohare
Contact details: d.ohare@imperial.ac.uk
| Project title | Description |
| Non-linear time series analysis of biosensor data | Electrochemical sensors offer low-cost, local and real time measurements but selectivity remains a problem. Mainstream data analysis tools such as the Fourier transform are not valid since the signals are non-linear. There is a particular difficulty measuring neurotransmitters- many monoamines have similar redox potentials. We have developed numerical models of the sensors that allow extraction of the physico-chemical properties (redox potential, rate constant, diffusion coefficient etc) to enable more positive identification. For further information see: C.Anastassiou, K.H. Parker & D. OHare Determination of Kinetic and Thermodynamic Parameters of Surface Confined Species through ac Voltammetry and a Nonstationary Signal Processing Technique: the Hilbert Transform Anal. Chem. 77 (2005) 3357-3364. C.A. Anastassiou, B.A. Patel, K.H. Parker & D. OHare, Characterisation of AC Voltammetric Reaction - Diffusion Dynamics: From Patterns to Physical Parameters, Anal. Chem. 78 (2006) 4383-4389. C. A. Anastassiou, B. A. Patel, M. Arundell, M. S. Yeoman, K. H. Parker and D. OHare, Novel Subsecond Voltammetric Separation between Dopamine and Serotonin in the Presence of Ascorbate Anal. Chem. 78(19) (2006) 6990-6998. CA Anastassiou, , Parker, KH, O'Hare, D, Scaling in nonstationary voltammetry representations, J Phys Chem A, , 111, 13053 13060 (2007). |
| Electrochemical detection of microRNA biomarkers | This project is a proof-of-concept investigation to study the feasibility of using chemically modified electrodes as the detection element of a novel bioanalytical scheme of broad potential applicability. The search for non-invasive tools for the diagnosis and management of cancer has led to great interest in the field of circulating nucleic acids in plasma, serum and urine. Among the potential biomarker candidates, tumour-derived micro RNAs (miRNAs), a class of non-coding RNAs of 19-25 nucleotides in length, are emerging as novel fingerprints for the diagnosis and prognosis of human cancers. Because of the lack of sensitivity and/or reliably of the existing optical methods for the profiling of these miRNAs (e.g. qPCR), we are aiming to develop an ultrasensitive and minimally invasive electrochemical technology for the screening of multiple miRNAs in serum, plasma, saliva or urine. Ladame has demonstrated a novel fluorescence based detection strategy for sensing miRNAs in various biological fluids. It uses a combination of two complementary hybridization probes based on a synthetic peptide nucleic acid (PNA) scaffold. PNAs show higher binding affinity, specificity and chemical robustness than more commonly used DNA or RNA probes making them attractive for application in diagnostics. Simultaneous binding of both probes (Figure 1) leads to a Michael-type addition of a thiol from one probe to the quenched coumarin on the second probe, leading to restoration of the fluorescence signal. In this project we propose to investigate extension of this technology to electrochemical detection. Electrochemical methods are low cost, easily implemented in portable point-of-care devices and scale well to extreme miniaturization. Core to our proposal is the immobilization of electrochemically active quinone-labelled PNAs on the surface of BDD. Binding of the miRNA target followed by the second thiol-labelled probe will facilitate a 1,4 addition reaction at the quinone which can be detected from the change in redox properties. The principle of this scheme is shown in Figure 2. Potential advantages of this wholly novel approach include the ability to switch off the reaction by reducing the quinone to the hydroquinone, enabling background drift corrections to be implemented prior to measurement. Different electrode materials ranging from boron-doped diamond to graphite inks will be assessed for suitability and electrochemical properties of quinoid moieties and kinetic parameters of the reacting base will inform development of a prototype device. In addition to the electrochemical and chemical optimisation, possibly by numerical modelling, signal acquisition and data processing could form part of this project. |
| Microneedle Arrays for Biomedical Sensing Applications | The outcome of the project is the preparation of microneedle arrays and their application as diagnostic tools for in-vivo and ex-vivo experiments. These substrates can assess the interstitial fluid compartment thus probing non-invasively the levels of physiologically relevant biochemicals and we are currently applying this technology to the measurement of antibiotics in order to gain more precise control over dosing and reduce the risk of resistance. We are also interested in lactate as a marker for sepsis. There are multiple aspects that can be investigated. The precise mechanism of the sensor remains unclear in important aspects and we need key information about enzyme loadings to optimise the design and fabrication protocols. There are also other transduction mechanisms to investigate- we are currently using a beta-lactamase enzyme coupled to an iridium oxide pH sensor. Other technology may be more robust. Calibration in media comparable to skin is essential. Preliminary work using microdialysis fibres embedded in hydrogels shows promise and modelling and design of such rigs would be useful in characterising the sensors and in developing the perfusion pump controllers. |
| MRes project: developing the manufacturing of solid microneedle biosensors |
Two MRes projects covering: Developing scalable manufacturing processes for solid microneedle arrays. We are currently developing and using solid microneedle arrays to measure antimicrobial concentrations in patients. These sensors are key components in a system of closed-loop control for drug delivery to optimise and monitor drug delivery, improve patient outcomes and minimise the development of resistance. (i) Metalisation of injection-moulded polycarbonate is being developed with Torr Scientific Limited. Development of quality control tests to evaluate process development is required. (ii) Insulation of the microneedles is using a soluble laquer. This is labour-intensive and not intrinscially scaleable (iii) Post-processing of the biological recognition chemistry and deposition of hydrogels needs to be automated. |
| Nanostructured interfaces for biocompatible biosensors | A major problem with implantable devices is the adsorption of proteins on the sensor surface which adversely affects the performance at the molecular level. New approaches to producing nanometre scale pores by dynamic templating seems to offer improved resistance to adsorption by providing a large internal area which is accessible to small target analyte molecules but which exclude the large proteins which are responsible for degrading sensor performance. The project will involve mastering the techniques of preparing nanoporous metal structures, characterising their properties and applying these novel structures to the measurement of pH, glucose, dissolved oxygen and lactate in cell culture. |
| Biosensors for breath analysis | Breath condensate can contain markers of inflammation such as hydrogen peroxide, nitric oxide and peroxynitrous acid. These markers appear to rise in advance of asthmatic patients being aware of an impending crisis and could therefore have therapeutic uses in indicating when patients should take their medication. In addition, Scheller's group have been able to detect lactic acid in breath condensate which correlates to some degree with blood lactate levels. The collection of most biological samples is necessarily invasive and the high protein content interferes with-most analytical techniques, so if therapeutic or bioanalytically useful data can be obtained from breath condensate, this is an attractive proposition. We propose a project for 1-2 students to design and build a portable instrument for collecting and analysing breath condensate and aerosols. There are at least two parts: (i)sample collection and handling to ensure reproducible volumes and (ii) development of electrochemical sensors for analysing the samples and comparison with chromatographic analysis. A very hands-on lab an workshop based project. |
Professor Darryl Overby
Profile: https://profiles.imperial.ac.uk/d.overby
Contact details: d.overby@imperial.ac.uk
| Project title | Description |
| A novel perfusion microdevice for studying the cellular biomechanics of glaucoma | Glaucoma is a blinding eye disease that robs people of precious vision mostly during their later years in life, preventing them from enjoying reading, driving or seeing the faces of their grandchildren. The key to treating glaucoma is to lower pressure in the eye, but we do not fully understand the pressure-regulating machinery. The relevant tissue is the trabecular meshwork and Schlemms canal (TM/SC), located around the periphery of the iris. Aqueous humour (fluid that fills the eye) continually drains through the TM/SC and the resistance to flow through these tissues is what determines eye pressure. Our laboratory aims to understand the mechanics of fluid flow through the TM/SC, so as to understand how it generates hydraulic resistance to control eye pressure. Our past research has shown that this mechanism is closely linked to cellular biomechanics. This project will develop a perfusion system which will allow us (for the first time) to directly visualise fluid flow through the TM/SC and the biomechanical response of SC and TM cells. There at least two separate approaches for this project . One aspect will focus on using mouse eyes, which our laboratory has shown mimics important aspects of the human TM/SC. Transgenic mice are available that encode fluorescent protein within SC, allowing in principle microscopic visualisation of SC during flow. The aim would be to develop a perfusion system to mount the mouse eye onto a microscope stage to allow SC to be visualised during flow, with fluorescent tracer nanoparticles used to assess flow patterns. The second aspect of the project will use strips of TM/SC tissue isolated from human eyes (provided by a collaborator in the US). The goal is to develop a microfluidics platform to mount and perfuse these tissue strips (~ 1 mm x 0.1 mm) on a microscope stage to directly visualise SC and TM behaviour under flow. We have a preliminary design for both aspects of the project to help students get started. Both aspects of the work will feature centre stage in future glaucoma research, meaning that the projects will have full access to laboratory personnel and support and an opportunity to publish the work in peer reviewed journals. |
| Brain-On-Chip: A Novel Explant Perfusion Model for Ex Vivo Studies of Living Brain Tissue | The neuroscience community aims to study neural and glial cells in their native microenvironment. Typically, this requires intravital techniques, such as implantation of intracranial windows, or advanced 3D models such as organoids. For example, the blood-brain barrier is a key factor regulating transport into brain tissue, for example for chemotherapeutics or large molecules used to treat cancer or neurodegenerative disease. In this project, we aim to develop a device to preserve the viability and function of murine brain explants for ex vivo analysis of neural function. The project builds on a successful design already applied to liver explants. Briefly, liver tissue placed in our perfusion device exhibits flow through blood vessels, recreating blood flow as occurs in vivo. We now wish to extend the applications to brain tissue. This project will interface with a team of researchers to adapt the current explant-in-chip design to murine brain. We aim to measure viability markers as a function of perfusion time, assess flow through the microvasculature, and measure neural cell function using voltage-sensitive dyes. |
| cellular therapy for blindness | In this project, the student will work on a Fight for Sight-funded project to develop a cellular therapy to treat glaucoma, a blinding eye disease associated with elevated eye pressure. The student will work with a senior post-doctoral research fellow with over 10 years of experience in the field. The project aims to engineer macrophages to express a molecular product that has been shown to lower eye pressure. By implanting these cells in the eye, we aim to provide a continuous of pressure-lowering drug to reduce eye pressure. The cells will be engineered in vitro using adenoviral vectors, applying biochemical assays to confirm production, followed by implantation into mouse eyes to confirm effects in vivo. The student will work with a diverse team of engineers, biomedical scientists and entrepreneurs to push this research towards clinical translation. |
| LiverTox-Chip: A Perfusable Liver-on-Chip Platform for Hepatotoxicity Screening | Drug-induced liver injury is a leading cause of clinical trial failures and post-market drug withdrawals, costing pharmaceutical companies billions annually. Current hepatotoxicity models using 2D cell cultures or animal studies fail to recapitulate human-specific responses, while existing organ-on-chip approaches lack the complexity of native tissue architecture. We have developed a breakthrough explant-in-chip platform that achieves true intravascular perfusion through fresh liver tissue slices obtained from animal or human sources. The USP of our technology is a custom design that creates a microfluidic seal around the tissue that forces culture medium to flow through the native vasculature within the tissue slice rather than around the slice. This biomimetic approach delivers oxygen and nutrients directly via blood vessels, maintaining hepatocyte viability and function for several days. Further, intravascular perfusion enables physiologically relevant drug delivery, allowing compounds to reach hepatocytes through the same routes as occurs in vivo. This preserves drug metabolism patterns and toxicity responses that are lost in simplified models. The platform supports repeated dosing regimens and real-time monitoring of liver function biomarkers. This project will assess the market demand and commercial potential for a LiverTox-Chip in preclinical drug development, focusing on pharmaceutical industry needs, competitive positioning, regulatory pathways, and scalability for widespread adoption in hepatotoxicity screening. |
| Next-Generation Explant-in-Chip: Continuous Long-Term Perfusion and Metabolic Monitoring of Tissue Explants | Maintaining viable tissue explants ex vivo requires preserving the native microenvironment, particularly the physiological delivery of oxygen and nutrients through the vascular network. We have developed a novel explant-in-chip platform that achieves intravascular perfusion, forcing flow through native blood vessels to deliver oxygen and nutrients directly to cells via their natural vascular route, thereby maintaining tissue viability and function. However, current implementations are limited by finite reservoir capacity, restricting experiments to several hours. Extending perfusion duration to multiple days would enable longitudinal studies of tissue metabolism, drug pharmacokinetics, and therapeutic responses while continuously monitoring metabolic status to verify tissue health. This project offers multiple engineering challenges. One challenge involves developing a continuous recirculation system to enable multi-day perfusion. The current perfusion design uses supply and waste reservoirs that deplete within hours. Building on preliminary flow rectifier modules developed in the laboratory, students will optimise a "microfluidic diode" circuit analogous to an AC/DC electrical bridge rectifier that converts bidirectional flow into unidirectional perfusion across the explant. Importantly, this design will enable indefinite unidirectional perfusion through explant vasculature despite bidirectional reservoir exchange. A second challenge involves integrating electrochemical biosensors for real-time metabolic monitoring. Collaborating with Prof. Martyn Boutelle's laboratory, who have pioneered rapid-response glucose and lactate biosensors, students will incorporate these sensors into the perfusion loop. This integration will provide continuous readouts of tissue metabolic activity, enabling immediate detection of viability changes, optimisation of perfusion conditions, and real-time assessment of drug-induced metabolic perturbations. Preliminary data demonstrate stable sensor performance in perfusion media, with rapid response times suitable for closed-loop control. These engineering advances will enable unprecedented long-term maintenance of tissue explants with real-time metabolic feedback, advancing applications in drug testing, disease modelling, and personalised medicine. Success will be measured by demonstrating multi-day tissue viability through continuous perfusion and/or real-time glucose and lactate monitoring in liver explants. |
| Is transcellular pore formation a universal response to stretch in endothelial cells? | Endothelial cells comprise the lining of every blood and lymphatic vessel in the body, and how they control the passage of fluid and cells across the vessel wall is central to inflammation, oedema, drug delivery and tumour spread. One striking route is the transcellular pore or micron-scale hole through the body of a single cell. Our laboratory discovered that pores form in the endothelium of Schlemm's canal when exposed to stretch, and these pores carry the fluid drainage that sets pressure inside the eye, which become dysfunctional in glaucoma. These cells experience an unusual mechanical load, and pore formation has long been assumed to be a specialisation of this tissue. Recent work in our laboratory has now shown that stretch also triggers pores in a highly plastic endothelial precursor cell type. This raises the possibility that pore formation is a latent capacity of endothelium in general, triggered by mechanical strain. If true, this has fundamental implications for how vascular and lymphatic walls regulate endothelial transport. The project aims to (i) establish whether stretch-induced pore formation occurs across endothelial cells of different vascular and lymphatic origins, (ii) determine how pore density and size scale with applied strain in each cell type, and (iii) identify what distinguishes cells that readily form pores from those that do not. The student will work with human aortic, umbilical vein and dermal microvascular endothelial cells, comparing vascular against lymphatic endothelium, using cell lines already available within the laboratory or available commercially. Cells will be cultured on flexible membranes and subjected to controlled stretch in a purpose-built device. The laboratory has developed a fluorescence assay in which the cell body and the underlying substrate are labelled in separate fluorescent channels, so that a pore appears as a discrete opening through the cell where the substrate signal shows through. High-resolution imaging, combined with existing machine learning technique, allows pores to be counted and sized across large numbers of cells. Alongside pore measurements, the student will also characterise cell shape and and cytoskeletal architecture to examine the morphological factors that regulate pore formation. The student will gain training in endothelial cell culture, mechanical stimulation of living cells, and quantitative fluorescence microscopy and image analysis. They will join a team working at the interface of cell mechanics and vascular biology, on a question with fundamental importance to physiology and disease. |
| Connecting living and engineered systems via the vasculature using microfluidics | There is growing demand for new approach methodologies (NAMs) using human-relevant models to reduce reliance on animal testing and better predict how patients respond to drugs. Tissue slices keep the native architecture and cell diversity of the native tissue, but without blood flow they starve beyond their outer layers. Our laboratory has developed a novel design that drives flow through the tissue's own vasculature. This allows living tissue to be connected by flow to engineered systems that support its viability and function and allow drug responses to be tested. The project forms part of INTREPID, a multi-partner programme developing live human tumour explants as alternatives to animal models. The project aims to (i) engineer the tissue–device interface for optimal perfusion through the tissue vasculature, (ii) demonstrate robust preservation of tissue viability over time, and (iii) demonstrate a measurable drug response. Work will focus on murine tissue, with access to patient-derived explants through INTREPID. The project can take either of two directions, suited to one or more students. Experimental: The student will fabricate candidate designs using a high-resolution 3D printer and test them on tissue slices. Readouts include perfusion flow rate, hydraulic resistance through the tissue, tracer delivery and tissue viability. Through our collaboration with the Institute of Cancer Research, microbubble ultrasound imaging will show which vessels are perfused. Computational: The student will build finite element models of the tissue–device interface under perfusion. These will identify the design features that best seal the tissue while keeping its vessels open, guiding the next generation of devices. The student will gain hands-on training in device design, precision 3D printing, perfusion and tissue biology, or in computational mechanics. They will work within a team spanning engineering, cancer biology and imaging, on a platform heading towards clinical and industrial use. |
| A bio-inspired approach to interface living tissue slices with microfluidics and microscopy | There is growing demand for new approach methodologies (NAMs) that use human-relevant models that reduce reliance on animal testing and better predict how patients respond to drugs. Perfusing a living tissue specimen, rather than bathing it in medium, is what keeps it alive and allows drugs and tracers to be delivered the way they arrive in the body. The challenge is sealing. Fluid will follow take the easiest path, so unless the edges of the specimen are well sealed, medium simply escapes around the tissue instead of passing through it. Existing seals typically clamp or glue the specimen in place, which damages delicate tissue, are irreversible which limits further analysis, and obstructs the optical access needed to watch what happens inside. This project will develop an alternative inspired by the iris of the eye. Namely, a flexible aperture that closes gently around a thin tissue specimen to grip and seal its edge and can be reopened to release the tissue unharmed. The project aims to (i) develop an aperture-based seal that accommodates specimens of varying size, shape and stiffness, (ii) demonstrate that it directs flow through the tissue rather than around it, and (iii) demonstrate that the sealed specimen can be imaged live under flow. The design will be tested on thin-cut murine tissue, such as liver, tumours or the outflow tissues of the eye as cases of particular interest. The student will design and fabricate prototype apertures and housing devices in flexible polymers using soft lithography and high-resolution 3D printing, exploring how aperture geometry, membrane thickness and actuation determine the grip on the tissue and the associated mechanical stresses. Performance will be assessed by measuring flow rate and hydraulic resistance through the specimen, by quantifying leak around the seal using fluorescent tracers, and by confirming that the tissue remains viable and undamaged after release. By applying microfluidic approaches, the housing can be made thin enough to be compatible with light microscopy. The student will gain training in device design, microfabrication, soft material mechanics, perfusion measurement and live tissue imaging. They will join a team developing perfusion technologies for tissue explants, on a device intended for use across many tissue types. |
| Microvascular Plumbing: Linking vascular architecture to flow resistance in engineered microvascular networks | There is growing demand for new approach methodologies (NAMs) that use human-relevant models to reduce reliance on animal testing and better predict how patients respond to drugs. A major advance has been the ability to grow living, perfusable blood vessel networks inside microfluidic chips, where endothelial cells suspended in a hydrogel self-assemble into a branching network that can be connected to flow. These vascularised models are increasingly used to study drug delivery, tumour invasion and vascular disease. What is almost never measured, however, is how well they actually work as plumbing. Hydraulic resistance is the property that determines how much flow a network carries and how much pressure the tissue sees, and it is set by the architecture of the network. Namely, how many vessels there are, how wide, how well connected, and how leaky. Our laboratory grows vascular networks within microfluidic devices and has built a measurement platform, iPerfusion, capable of resolving these resistances precisely. This project will examine the relationship between the structure of engineered microvascular networks to their function. The project aims to (i) establish reliable measurement of hydraulic resistance in engineered microvascular networks, (ii) determine how resistance relates to the architecture of the network as seen by microscopy, and (iii) determine how supporting mural cells influence the structure-function relationship. The student will culture human umbilical vein endothelial cells in fibrin hydrogels within three-channel microfluidic chips, a widely used format in which networks form over several days and connect to flanking channels for perfusion. Supporting cells, such as mesenchymal stromal cells, fibroblasts or smooth muscle cells, will be co-cultured in the gel to promote vessel maturation, and the student will compare networks grown with and without support cells. Resistance will be measured using iPerfusion, and the same chips imaged by confocal microscopy to extract vessel density, diameter, branching and connectivity. Relating the two will reveal which architectural features most strongly influence flow, and whether resistance can serve as a rapid functional readout of network quality in place of imaging. If time allows, the platform opens onto questions the laboratory is keen to pursue. For example, whether resistance changes with perfusion pressure (e.g., flow homeostasis via shear-induced nitric oxide signalling) and how the resistance responds to vasoactive or anti-angiogenic drugs. The student will gain training in microfluidic cell culture, organ-on-chip approaches, vascular biology, precision flow and pressure measurement, confocal imaging and quantitative image analysis. They will join a team developing perfusion technologies for living tissue, on a model system with broad relevance across vascular research and drug testing. |
| Keeping Tissues Alive ex vivo: Engineering a multi-well supra-fusion platform for drug testing on living tumour slices | There is growing demand for new approach methodologies (NAMs) using human-relevant models that reduce reliance on animal testing and better predict how patients respond to drugs. Precision-cut tissue slices retain the native architecture, cell diversity and immune content of the organ they came from, making them a powerful testbed for drug responses. The limitation is viability. In static culture, the viability of tissue slices falls away within two to three days as the tissue depletes the medium around it and metabolic waste accumulates. Continuous flow of medium over the slice, known as supra-fusion, can sustain the tissue for longer. Existing perfusion systems, however, depend on pumps and tubing networks for each sample, which are cumbersome, hard to sterilise, and incompatible with the multi-well plates that laboratories already use. The project aims to (i) develop a scalable supra-fusion system compatible with standard multi-well plate formats, (ii) demonstrate preservation of tissue slice viability over extended culture relative to static controls, and (iii) demonstrate a measurable drug response in perfused tissue. Work will begin with murine liver slices, a well-characterised and robust starting material, and build towards tumour slices, with access to patient-derived tissue through INTREPID, a multi-partner programme developing live human tumour explants as alternatives to animal models. Several routes to delivering supra-fusion are open and exploring them is central to the project. One approach is a microfabricated "smart lid" with pneumatically actuated channels embedded in the plate lid, driving flow between wells without external connections. Others include direct inline tubing, gravity-driven or rocking arrangements, and hybrid designs. The student will evaluate these against flow rate stability and uniformity between wells, sterility over days of culture, biocompatibility, and robustness in routine handling and incubation. The design must work for slices submerged under medium or at an air-liquid interface, as different tissue types favour different configurations. The student will use CAD, 3D printing and soft lithography to build prototypes, with fluid dynamic analysis guiding design iterations before testing. Viability will be assessed by ATP and metabolic assays alongside histology, comparing perfused against static culture over three to seven days, before moving to drug challenge. The student will gain training in microfluidic design and fabrication, flow control and characterisation, tissue slice culture and viability assessment. They will work within a team spanning engineering and cancer biology, on a platform with applications across organoid and spheroid culture and high-throughput drug screening. |
Professor Holger Krapp
Profile: https://profiles.imperial.ac.uk/h.g.krapp
Contact details: h.g.krapp@imperial.ac.uk
| Project title | Description |
| Electrophysiological characterization of optic flow processing interneurons in flying insects. | The visual system in many animals and humans contributes to state change estimations by analysing panoramic retinal image shifts known as optic flow. Earlier studies in blowflies have revealed the underlying neuronal mechanism which is believed to complement state change estimation based on mechanosensory/inertial systems. This project aims to support the idea that optic flow processing in the visual system of flying insects is tuned to control species-specific natural modes of motion which are determined by the animal's flight dynamics. Experimental evidence in support for the mode sensing hypothesis requires a comparative study of the receptive field properties of motion sensitive interneurons in the visual systems of other than dipteran flies, e.g. species belonging to the order of orthopteran, odonata and lepidoptera. Hoverflies, which show distinctly different flight patterns than blowflies and also perform differently in behavioural gaze stabilization experiments, would be an ideal candidate species. Preliminary experiments have shown that hoverflies, too, employ motion sensitive interneurons in their visual system. But only one such interneuron has been studied so far. This project requires the dissection of flying insect species for extracellular recordings upon visual motion stimuli. The neuronal responses will be analysed using customized MATLAB/Python programmes which reveal the cell's receptive field organization. From the electrophysiological results the preference of individually identifiable interneurons for specific self-motion components (state changes) will be derived. |
Professor Julien Vermot
Profile: https://profiles.imperial.ac.uk/j.vermot
Contact details: j.vermot@imperial.ac.uk
| Project title | Description |
| Machine learning for 3D segmentation of large datasets to detect normal and pathological hearts | Cardiovascular diseases (CVDs) take a huge toll on the world population. An estimated 19 million people died from CVDs in 2010, representing 30% of all global deaths. This project aims at developing a data analysis pipeline allowing to assess 3D tissue shape changes associated with pathological hearts. The student will develop an image analysis method allowing to quantify 3D heart shape robustly using large 3D imaging datasets collected in the Vermot lab. The goal is to develop and train a segmentation network using deep learning in order to achieve 3D image segmentation of large image stacks. A model based approach will be used to quantify the 3D tissue deformation and predict pathological hearts. The project is shared between the Vermot (in vivo cardiovascular imaging) and Bharath labs (3D segmentation and machine learning). |
| Modelling cell shape changes in complex morphogenetic processes | Cardiac morphogenesis is a process that is often perturbed in Congenital Heart Diseases. We use zebrafish as a model organism to study morphogenesis of the cardiovascular system at cellular resolution. This project will focus on the initial steps of cardiac valve formation. The student will study the cellular behaviours associated with the formation of site of development of the cardiac valve called the atrioventricular canal. It is a tube-like structure that delimitates the ventricle and the atrium of the heart. The cellular behaviours, such as cell shape and volume changes, during the developmental process are crucial to the canal formation but are not well understood. In this project, we will cell-based computational model to study quantitatively the impact of cell morphology and dynamics during atrioventricular canal formation. |
| Scaling up expansion microscopy pipelines for tissue diagnostics | Expansion microscopy (ExM) is a recent and innovative imaging method based on the physical expansion of biological samples, with the aim to reach super-resolution precision with conventional light-microscopes available in most biology laboratories. The principle is that the specimen of interest (isolated organelles, cells, pieces of tissue, …) is embedded in a swellable hydrogel that will physically expand upon water addition. The physical expansion therefore increases the distance between molecules physiologically close to each other, that cannot be distinguished with regular light microscopes. ExM has been showed to be efficient on mouse-brain and now constitutes a new tool to address fundamental biological questions through the imaging of protein complexes at unprecedented scales. The applications of ExM are enormous and remains to be optimized for tissue expansion and diagnostics. A major set-back for the technology to scale up is its low throughput. This project will aim at developing a pipeline allowing to automate tissue embedding and expansion in a go. The proof of concept will be established with our model of interest, the zebrafish embryo. Automatic hydrogel embedding of single embryo will be tackled using microfluidics devices that will need to be developed. With such type of device, we will aim to scale up our tissue preparation for expansion microscopy with high efficiency and high reproducibility. If successful, such technical development will constitute the base for cheap, high-resolution, tissue diagnostics. |
| Lowering costs of super-resolution imaging by developing expansion microscopy | Primary cilia are essential cellular organelles involved in cardiovascular development and cardiovascular diseases. Primary cilia ultrastructure remains difficult to assess even though it is clear that pathological cilia function stem from abnormal organisation. Super resolution imaging is a key method for imaging primary cilia ultrastructure. Nevertheless it requires complex labelling methods and expensive microscopes. Expansion microscopy (ExM) is a recent and innovative imaging method based on the physical expansion of biological samples, with the aim to reach super-resolution precision with conventional light-microscopes available in most biology laboratories. The principle is that the specimen of interest (isolated organelles, cells, pieces of tissue, …) is embedded in a swellable hydrogel that will physically expand upon water addition. The physical expansion therefore increases the distance between molecules physiologically close to each other, that cannot be distinguished with regular light microscopes. The applications of ExM in cell and cilia biology are growing exponentially even though a number of technical pittfalls are limiting the use of ExM. The aim of this project will be to develop a novel visualization approach based on expansion light-microscopy to address the function of key cilia-related proteins associated with Congenital Heart Diseases. The student will investigate the ultrastructure and the molecular composition of primary cilia in vitro in endothelial cell culture and in situ on endocardial cells quantitatively and with unprecedented precision, using zebrafish (ZF) as a model in both physiological and pathological contexts. I will map at nanometric resolution the localization of proteins shuttling between the cilium and the cytoplasm at different stages of cardiovascular system maturation using expansion light-microscopy. |
Professor Manos Drakakis
Profile: https://profiles.imperial.ac.uk/e.drakakis
Contact details: e.drakakis@imperial.ac.uk
| Project title | Description |
| Reciprocity properties of networks of memristors | MATHEMATICAL (MATLAB) PROJECT - Memristors are novel nano-elements theorised in 1976 but only recently (2008) fabricated by Hewlett Packard Labs. Qualitatively speaking memristors' operation and dynamics resemble naturally encountered synapses. The fabrication though of memristors is still in its infancy. The project investigates the reciprocity properties of networks of memristors and how/whether these can be reliably used in encryption. The student will investigate in detail the reciprocity properties of simple networks of ideal identical memristors and, ideally, the use of such networks for the encryption of biomedical signals. Ideally the student will be using Mathematica code but Matlab is also acceptable. Intended for a good YR4 student; or for MSc students with a strong EEE degree. (Keywords: mathematica/matlab/simulations) |
| Revisiting low-power CMOS Hodgkin-Huxley dynamics realisation | TRANSISTOR LEVEL/MATHEMATICAL PROJECT -Tough non-linear transistor-level design focusing on the celebrated and Nobel-prize winning Hodgkin-Huxley dynamics. Do not choose this project unless you love realising Matlab by means of transistors! This project is based on rich previous work and can include both transistor-level design and matlab. It provides the student with the opportunity to master transistor-level design and to familiarise oneself with the use of industrial level software. The scope of the project is to unlock the exact synthesis of the Hodgkin-Huxley dynamics by revisiting both the transistor-level design and the mathematical approximations of the original non-linear dynamics.The project is intended for a student who has excelled in EE-1, EE-2, Bioinstrumentation and Signals & Systems courses; or for MRes students with a strong EEE degree. (Keywords: mathematics/transistors/cadence) |
| Tau 20 Atrial Fibrilation Stimulator Testing | ELECTRONICS TESTING PROJECT - This project involves collaboration with Cardiologists at Imperial. They are developing a method for the treatment of AF (atrial fibrillation). This involves detecting sites of the heart with strong autonomic innervation and then ablating (cauterising) the heart at that location. The method requires a new type of stimulator the aim of this project is to study, design and build a prototype. The stimulator will be attached to electrodes that are placed inside the heart. The stimulator is required to be able to detect cardiac activity and then pace with 10ms pulses of up to 20mA at 1-40Hz. Ideally, the stimulator will be able to detect positive responses from measured signals and then automatically terminate the pacing manoeuvre. There are already stimulator and recording prototypes. We would like to focus on the conduction of experiments which would confirm the functionality f the instruments and we would particularly wish to test closed-loop scenarios. The project is suitable for v.strong YR4 students of the EE stream or for v.strong MRes/MSc students with EEE background. |
| Realisation and testing of an ECG front-end | Realisation of an ECG front-end |
| Study and Simulation of a full Cochlear Implant Processor and Stimulator | This is a transistor-level project which allows the student to master Cadence. You will be given certain already designed blocks and you will be asked to put together a full cochlear processor and stimulator. You will understand transistor-level design choices and trade-offs, architectural level constraints and you will be testing/evaluating the architecture that you designed by importing and processing in cadence different audio files. A project relying heavily on simulation. At the end of this project you will be known for your Cadence skills. |
| 4R Biosensor Longevity Strategy: An Electronics-Enabled Strategy for the Acquisition ofLong-Term High-Quality Electrochemical (Bio)Sensor Data | PRACTICAL ELECTRONICS/PROJECT: Electrochemical sensors and biosensors are powerful tools for in-situ monitoring.On the other hand long term operation of sensors in high cell density/high protein environments is beset by problems collectively referred to as biofouling. They originate by the absorption of proteins and cells to the sensor surface and results in the build-up of a layer that reduces mass transport rates, causes changes in the local environment and passivates the sensor surface. In operational use this is experienced as falling sensor sensitivity which ultimately limits longevity.Whilst there are strategies in the literature that mitigate against these effects none offer sufficiently robust or long-lived solutions. This project aims at investigating/realising a solution which can be codified as Recalibrate, Regenerate, Reconfigure and Replace (4R strategy) miniaturised biosensors. The 4R longevity strategy is realisable my means of appropriate tailor-made electronics. |
| Investigating ECG/EMG acquisition without an Analog Front-End | This project compares the acquisition of ECG/EMG with and without an Analog Front End. Simulation- and PCB-based project. |
| An Energy efficient F0 estimator | (A software desk based project (100% MATLAB)) At the cutting edge of Cochlear Implant research is a massive ongoing effort in improving their effectiveness. Several Stimulation Strategies (SS) are employed to convey relevant information to individuals with hearing loss. In traditional SS (such as CIS and CA) users find it difficult to understand and appreciate tonal based languages and speech in noise. There exist promising results showing that speech formants (especially F0) provide considerable comprehension to tonal based languages by including the F0 contours in the SS. In this project you are to compare current existing methods of F0 extraction (e.g. Autocorrelation, cepstrum ..) evaluate their energy efficiency as well as its hardware implementation feasibility, you are also encouraged to propose a novel technique in estimating/extracting F0 from signals we can currently extract (Instentaneous frequency, envelope slow/fast). Project route: 1. Survey existing F0 algorithms 2. Implement the algorithms in MATLAB /SIMULINK 3. Compare their accuracy/complexity/efficiency (Major Milestone) 4. Either: a. shortlist algorithms implementable in hardware (This is a safety net) b. propose a new/hybrid method prioritising efficiency/complexity 5. Extra: if student has enough time left to propose hardware blocks for said implementation Advantages: • From the student’s perspective this project is very achievable in software(MATLAB) • Gives a taste of research-based projects • There are areas where a student can express creativity (proposing a hybrid F0 estimator) • This is a mostly signal processing project (many students take advanced signal processing in other Depts) • There are ‘safety nets’ in the project if the student does not manage to propose anything new as they would be able to achieve a thorough comparison of existing F0 estimators. |
| Investigation and implementation of novel artefact suppression methodologies in bidirectional electrophysiological interfaces. | Deep Brain Stimulation involves both stimulating by means of strong pulses and recording very weak brain signals while stimulating. However, the strong stimulating pulses generate (stimulation) artefacts which contaminate or even destroy the recording of the weak signals. You will be involved in the design of innovative analogue, digital, or mixed-mode approaches for achieving real-time artefact-free biopotential recording, directly from the stimulation site. Experimental testing results using different electrode arrays, along with head-to-head comparison results (with existing artefact elimination techniques) are expected to be delivered. The project is suitable for students who wish to master (micro)electronics by testing their ability to design and test very high-performance circuits. Publication possible. |
| Design and delivery of a miniaturised, wearable, and high channel-count biopotential acquisition device. | You will be involved in the design of multichannel and high-performance prototype devices that have the potential to be used in a noisy clinical environment. Design trade-offs have to be taken into consideration and an optimal solution must be identified for the successful delivery of a noise-robust instrument that can provide low-noise recordings from multiple neuronal targets. You will gain experience of the full design cycle of a device and you will master PCB design. Such skills are snatched by well-paying companies/employers. |
| Design and delivery of a complete stimulation and recording system for DBS and SCS applications. (DBS=Deep Brain Stimulation; SCS=Spinal Cord Stimulation) |
You will be involved in the design of a state-of-the-art instrument that aims at providing both stimulation and recording capabilities from neural targets within the human brain and spinal cord. Various artefact suppression methodologies that require timing indicators from the stimulator, along with their real-time capabilities, will be investigated. A demanding project leading at the mastering of PCB design, stimulation topologies and recording limitations. A project suitable for ambitious candidates aiming at conducting research later or being employed in the field of medical devices. Do not take this project unless you enjoy the thrill and the challenge of producing a useful and robust instrument. |
| Micro-instrument for Traumatic Brain Injury (TBI) Monitoring | Design and realisation of a micro-instrument for the neuroelectrochemical monitoring of the injured human brain. |
| Modular Open platform for novel ultrasound imaging techniques | The development and commercialization of novel ultrasound device require research instruments that allow the user full control of the transmission sequences and access to the recorded echoes at the different stages of the receiver pipeline. You will be involved in the design of a modular ultrasound instrument for low-cost ultrasound imaging. The instrument is being used to develop novel diagnostic tools. This is a demanding project for those who wish to have a career in the medical device field. You will be designing and testing modules for a low-cost imager. The project carries humanitarian value. SKILLS: PCB design, FPGAs. The ambitious student who will thrive in this project will be ready for both research and industry. |
| Investigation of biosignal acquisition architectures with and without analog front-ends | Typical signal readout topologies incorporate a customised for the targeted biosignal front-end. In this project the student will investigate the possibility of realising simpler data acquisition architectures which do not benefit from the presence such a customised front-end. Such a bold move: a) leads to simpler and lower power overall designs, a fact which supports wearability, but b) comes at the expense of less good quality of the recorded signal; however, the quality of the interfaced signal can be restored by post-processing in the digital domain. Design and performance trade-offs between architectures with and without front-ends will also be investigated. Skills: mastering simulation tools; design and physical layout of pcbs. |
| Design of Capacitorless High-performance EMG and EEG Application Specific Integrated Circuit (ASIC) in 180nm technology | Recently we have shown that it is possible to realise very large time-constants without the use of very large in area standard integrated capacitors. More specifically, it has been shown that by combining MOS-based capacitors (MOSCAPs) and large-value pseudo-resistors, it is possible to create such large time-constants needed for band-limiting the EEG and EMG biosignals. The very high capacitive density (capacitance per unit area) of MOSCAPs leads to very significant capacitor area reductions. We have already built 64/128 channel 24-bit conversion ASICs-based nodes in 0.35um AMS technology. This project is about investigating the migration of the above basic techniques and designs onto newer 180nm technologies; comparative performance trade-offs will also be investigated. Skills: mastering Cadence simulation tools and ASICs design. |
| Experimental evaluation and optimisation of a new Simultaneous Impedance-Electromyography Recorder (SIER) instrument | Recently we have been working on the realisation of a new multi-channel skin stimulation and EMG recording instrument (concurrent stimulation and recording). SIER instruments can offer unique highly sought after real-time impedance and sEMG information which in turn can be used for feeding learning models and/or robotic/prosthetic devices. The project will involve the conduction of electrode-skin contact impedance (ESCI) and sEMG recording experiments by means of an already existing SIER instrument, the optimisation of the SIER instrument and the study of techniques for the post-processing of ESCI and SEMG data. Skills: mastering lab-based/application testing skills and matlab-based post-processing skills. |
| Study of the “High-Pass Pole Shifting†technique for Bidirectional Electrophysiological Interfaces | In general, closed-loop neurostimulation setups underpin the scheme: (1)stimulation > (2)readout of the stimulation response > (3)making sense of the read out response to the stimulation > (4)control/adjustment of the stimulation chacteristics > (1) delivery of adjusted stimulation. Such general schemes have been under investigation for conditions such as Parkinsons, tremor etc. The “High-Pass Pole Shifting†technique focuses on step (2) of the closed-loop scheme described above and involves timely and clever switching of carefully chosen and adjustable analog-front-end blocks which facilitate the readout stage (3) without sacrificing quality of the target biosignal (which, typically, is much weaker than the stimulation artefacts) by minimising the impact of stimulation artefacts during recording. This project is primarily a simulation project which will investigate the incorporation of fractional order transfer functions in the switched blocks of the front-end and in particular their effect in the recovery time of the targeted weak biosignal in the presence of strong stimulation artefacts. Skills: mastering simulation tools, fractional order systems and matlab-based processing and analysis tools. |
| Migration of the “High-Pass Pole Shifting†technique for Bidirectional Electrophysiological Interfaces on silicon | In general, closed-loop neurostimulation setups underpin the scheme: (1)stimulation > (2)readout of the stimulation response > (3)making sense of the read out response to the stimulation > (4)control/adjustment of the stimulation chacteristics > (1) delivery of adjusted stimulation. Such general schemes have been under investigation for conditions such Parkinsons, tremor etc. The “High-Pass Pole Shifting†technique focuses on step (2) of the closed-loop scheme described above and involves timely and clever switching of carefully chosen and adjustable analog-front-end blocks which facilitate the readout stage (3) without sacrificing quality of the target biosignal (which, typically, is much weaker than stimulation artefacts) by minimising the impact of stimulation artefacts during recording. The new technique has been confirmed by pcb-level prototypes. This project aims at investigating the migration and realisation of the “High-Pass Pole Shifting†technique at silicon/ Application Specific Integrated Circuit (ASIC) level. Skills: mastering Cadence simulation tools; depending on time constraints opportunities for layout as well. |
| Design and Evaluation of a Compact, Capacitor-less AC-Coupling and SAR ADC for Biosignal Acquisition System |
Professor Reiko Tanaka
Profile: https://profiles.imperial.ac.uk/r.tanaka
Contact details: r.tanaka@imperial.ac.uk
| Project title | Description |
| Mathematical modelling of microbial evolution and competition in eczema development | This project is suitable for student who want to gain following skills: 1) Mathematical modelling of biological systems. 2) Application of evolutionary theory to study host-microbe interactions. 3) Critical thinking and translation of biological data into a mathematical framework. 4) Computational modelling, data visualisation, and code organisation. Eczema is a chronic inflammatory skin disease that typically begins in infancy and is characterised by skin damage, inflammation, and microbial dysbiosis. Preventative treatments remain limited, partly due to a poor understanding of the mechanisms underpinning disease development. A key contributor to eczema development during infancy is Staphylococcus aureus (SA) – a pathogen that exacerbates disease symptoms by producing proinflammatory toxins through its accessory gene regulator (agr) system. A longitudinal study by Nakamura et al. found that infants who developed eczema maintained stable SA colonisation on their skin, while those who remained healthy showed a decline in SA populations. Intriguingly, many SA strains from healthy infants acquired mutations in the agr system. These agr-deficient strains were less able to colonise the skin and induce inflammation in murine models, yet they outcompeted more virulent SA strains in healthy infants. In this project, we investigate how SA strains evolve and compete on infant skin to uncover mechanisms that could guide the development of preventative treatment strategies for eczema. This project aims to develop and analyse a mathematical model that can explain how SA strains evolve and compete on infant skin to drive eczema development. This aim will be achieved through the following objectives: 1) Build an ordinary differential equation (ODE) model of SA strain dynamics by adapting an existing mathematical framework that captures microbial mutations. 2) Explore how host-microbe and microbe-microbe interactions affect SA strain composition. 3) Interpret results based on findings from Nakamura et al. and the wider literature. |
| Mathematical modelling of human microbiome using relative abundance data | This project is suitable for students who want to gain the following skills: 1) Mathematical modelling of ecological dynamics. 2) Experimental design using simulation studies. 3) Computational modelling, data visualisation, and code organisation. The human microbiome plays a critical role in maintaining health by regulating immunity, metabolism, and resistance to pathogens. Disruptions to the microbiome have been linked to a wide range of diseases. However, our mechanistic understanding of how microbes interact to drive community composition and function remains poor, hindering our ability to design treatment interventions targeting the microbiome. Mathematical models, particularly generalised Lotka-Volterra (gLV) models, are promising for inferring microbial interactions. By capturing how microbes grow and influence one another over time, gLV models can reveal whether microbes cooperate or compete with one another, provided that absolute microbial abundance data (e.g., microbial biomass per species) are available. However, most microbiome datasets are of relative microbial abundances, which capture microbial proportions rather than absolute counts. This compositional nature can misrepresent inferred microbial interaction networks. For example, Joseph et al. (2020) showed that variations in total microbial biomass can lead to qualitatively incorrect inferences, such as misclassifying cooperation as competition. Conversely, Remien et al. (2021) found that if total microbial biomass remains constant, relative abundance data may still recover qualitatively correct inferences. However, how biomass variation impacts the accuracy of gLV-inferred interactions from relative abundance data remains poorly understood. This project aims to develop gLV models to investigate microbial interactions within the human microbiome, using both relative and absolute microbial abundance data. We will first explore how total biomass fluctuations influence the accuracy of inferred interaction networks. Then, we will propose strategies for integrating targeted biomass measurements into experimental workflows to strengthen the reliability of inferred interactions from relative abundance data. To address this aim, we propose the following objectives: 1) Simulate synthetic absolute abundance data under biologically realistic biomass trajectories (e.g., antibiotic-induced biomass change, recolonisation, oscillatory variations, and environmental fluctuations). 2) Investigate inference error when fitting gLV models to relative vs absolute abundance data, focusing on qualitative accuracy (e.g., misclassifying cooperation as competition). 3) Identify conditions where inference reliability breaks down. 4) Investigate how collecting sparse biomass measurements at specific time points (e.g., at perturbation events) can help strengthen the reliability of inferred interactions when combined with relative abundance data. 5) Propose guidelines for when and how to sample biomass measurements in future experiments. |
| Extracting Patient-Centered Environmental and Treatment Factors from Text using Large Language Models for Eczema Causal Forecasting | Atopic Dermatitis (AD), commonly known as eczema, is a chronic inflammatory skin disease characterised by intense itching and recurrent eczematous lesions. Its pathogenesis is multifactorial, involving complex interactions between genetic predispositions, environmental triggers, and adherence to treatment regimens. A significant portion of patient experiences, particularly regarding daily activities, symptom fluctuations, and specific treatment responses, continues to be documented by clinicians or patients in tables or text formats such as diaries, messages, questionnaires, and interviews. These qualitative narratives contain rich, context-specific insights about symptom triggers and treatment efficacy that are often overlooked in conventional data analysis pipelines. Recent advancements in Large Language Models (LLMs), exemplified by models like GPT-4 and LLaMA-3, have revolutionised natural language processing by demonstrating capabilities in contextual understanding and structured information extraction from diverse text sources. These models present an opportunity to automatically extract structured, causally relevant features from raw patient narratives and reports. When synergistically combined with existing predictive models, such as EczemaPred, this approach could facilitate the development of a patient-centric system that can forecast how eczema symptoms may change over time. This project aims to enable causal forecasting of eczema symptoms to identify which treatments work best for individual patients and which environmental factors most strongly influence their symptoms. By doing so, we can move beyond population-level associations and toward personalised care, where interventions are tailored to each patient’s unique context, lifestyle, and symptom trajectory. |
Professor Rodrigo Ledesma Amaro
Profile: https://profiles.imperial.ac.uk/r.ledesma-amaro
Lab: www.rlalab.org
Contact details: r.ledesma-amaro@imperial.ac.uk
| Project title | Description |
| Bioengineering yeast for the production of fuels, vitamins, antioxidants and colorants | This project aims at hacking the metabolism of a yeast cell in order to produce commercially relevant compounds, specifically carotenoids. Carotenoids are used as colorants, antioxidants and as vitamin precursors and they are normally extracted from plants in a low efficient process that makes the products very expensive. During this project, cutting edge synthetic biology techniques (such as Golden Gate DNA assembly or CRISPR-Cas9) will be used for engineering the cells in a reliable and efficient manner towards the production of the desired compounds. The development of novel, more efficient bioprocesses will help us to move towards more environmentally friendly industrial setups. Please, do not hesitate to ask Rodrigo Ledesma-Amaro (r.ledesma-amaro@imperial.ac.uk) for further information. Skills: The student will develop skills and knowledge in molecular biology, genetic engineering, bioprocesses, metabolic engineering, analytical techniques, microbiology, synthetic biology, etc. Recommended for students with interests in synthetic biology and cellular engineering, especially for those who want to develop a career either in academia or in industry. |
| Development of a novel CRISPR-based synthetic biology method to control metabolism | CRISPR is the genome engineering technique that has recently revolutionized synthetic biology and medicine. This project aims at creating a novel method based on CRISPR technology to achieve metabolic control. The control of metabolism is an essential bioengineering problem that applies not only to microbial cell engineering for the biotechnological production of industrially relevant compounds, such as fuels and chemicals, but also to tackle diseases related to unbalanced metabolic states. The proposed method will be developed in microbial cells (yeast) and will be applied to relevant metabolic pathways for industry and health. Please, do not hesitate to ask Rodrigo Ledesma-Amaro (r.ledesma-amaro@imperial.ac.uk) for further information. Skills: The student will develop skills and knowledge in molecular biology, genetic engineering, bioprocesses, metabolic engineering, analytical techniques, microbiology, synthetic biology, etc. Recommended for students with interests in synthetic biology and cellular engineering, especially for those who want to develop a career either in academia or in industry. |
| Synthetic biology and metabolic engineering for microbial biotechnology and bioengineering | Microorganisms are important for both industrial bioprocesses and biomedicine (i.e. gut or skin microbiota). The lab is interesting in a wide array of organisms, from yeast (S. cerevisiea and Y. lipolytica), fungus (A. gossypii) and bacteria (E. coli and Acetobacter) to complex microbial consortia (human and industrial microbiota). The manipulation and optimization of microbial metabolic pathways are the keys to biotechnology and a bio-based economy. we are highly interested in hacking metabolism using synthetic biology tools to create new properties and enhanced behaviors in microbial cells. The engineering strategies are not only designed to produce new high-value products or higher amount of pre-existing products but also to facilitate the downstream and upstream parts of the bioprocesses. |
| Creating novel promoters for synthetic biology appliciations using machine learning | The project is about the analysis of databases containing eukaryotic promoters sequences and the use of machine learning algorithms in order to identify important regulatory elements encoded in these DNA regions. Ultimately this will lead to the creation of synthetic promoters with desired capabilities for synthetic biology. |
| Deciphering the codon usage code and its role in metabolism with applications in synthetic biology | The DNA codes for all the heritable information required to form life. Due to the degeneration of the genetic code, different DNAs can code for the same proteins, this is possible because several codons (groups of 3 nucleotides) can be translated into the same amino acid. This property emerges in all living systems and there are many theories that justify this mechanism. One of these theories, yet to be explored, is that the codons represent an additional level of regulation. This project will explore variations in codon usages in specific metabolic pathways or conditions in order to identify novel regulatory elements. This project could lead to important biological insights that can be used to understand diseasese and to improve synthetic biology approaches. |
| Investigating Anaerobic Growth in Strict Aerobe Yarrowia lipolytica | Engineering Yarrowia lipolytica to make it able to grow in anaerobic or microaerobic conditions |
| Engineerig yeast to prouce colorants (I): Overexpressing an heterologous pathway for sustainable biotechnology | In this project, the organism Yarrowia lipolytica will be engineered using cutting-edge synthetic biology tools to produce colourants in order to move away from petroleum-based chemistry and allow a more sustainable and eco-friendly industrial bioproduction. Please contact r.ledesma-amarao@imperial.ac.uk for further information |
| Engineering Y. lipolytica filamentation pattern | Engineering yeast morphology to mimic meat texture |
| market research and feasibility for biotechnological products of interest made by engineering biology | This project will research the technoeconomics of a variety of products that can be made using engineering biology |
| Exploring the commercialisation of sustainable bioproducts manufactured with synethtic biology | This project will explore the viability of different engineered microbial strains in be used industrially. The project will explore the potential to create startups or license technologies. |
| Assesing the sustainability of microbial biotechnology related products | This project will use LCA analysis to assess the sustainability of biotech processes as opposed to alternative methods such as chemical synthesis or plant extraction. |
| Re-inventing Quorn - healthy meat alternatives for a more sustainable planet | Quorn is a populat meat alternative food product made by the microorganisms Fusarium. In this project the student will use synthetic biology tools to improve the nutritional properties, taste and texture of Fusarium, making the next generation of meat alternatives to help people and the planet. |
| Designing the next generation of fermented food with synthetic biology | The student will use synthetic biology to engineer microorganisms that can be used as nutritious and sustainable sources of food. This project will develop new microbial strains to produce food and food ingredients |
| The food startup database - Bezos Centre for Sustainable Protein | This project would develop a database of startups in the UK and Europe working on future foods such as alternative proteins, precision fermentation, cultivated meat, etc. The student will work closely with startups and academic groups. The project will also map the ingredients that are currently being made using synthetic biology and biotechnology. The database will have a large impact and the student will work closely with the managers of the Bezos Centre |
| The global potential of mycoprotein to overcome malnutrition: fortified tempeh from fungal bacterial co-cultures | In this work engineering biology will be used to produce Tempeh 2.0, with improved nutrition and sensory quality |
| Synthetic Biology of Fusarium Venenatum for Meat Alternative Applications | In this work, the student will develop a toolbox to improve the nutritional and sensory properties of Fusarium venantum to manufacture affordable and delicious meat alternatives |
| Metabolic Engineering in Yarrowia lipolytica to produce functional lipids for food applications | In this project, the oleaginous yeast Yarrowia lipolytica will be used to produce specific fats with high relevance in food and nutrition. Y. lipolytica can naturally produce a high amount of lipids. By applying state-of-the-art genetic engineering tools, the metabolism can be engineered to produce even more lipids and to alter the lipid composition for specific applications. You will be introduced to the full toolbox of genetic engineering tools available for this unconventional yeast and apply them to produce specific lipids. This project is great if you are interested in applied synthetic biology and metabolic engineering. What you will learn - Metabolic engineering in Y. lipolytica (knock-out, overexpression, promoter exchange…) - Cultivation of Y. lipolytica on different scales - Media optimisation for increased lipid production and alternative carbon sources - Analytics for different metabolites (citrate, lipids, biomass, …) - Microscopy to verify lipid-enrichment in cells |
| Engineering Robust Rhizopus–Based Solid State Fermentation for Tempeh and Alternative Protein Production | Solidstate fermentation (SSF) using Rhizopus species underpins the production of tempeh and a growing range of fungalbased alternative proteins. Yet, despite centuries of empirical practice, SSF remains difficult to scale and standardise due to its inherently heterogeneous microenvironment. Gradients in oxygen, moisture, nutrients and heat drive pronounced spatial variability in fungal physiology, leading to inconsistent product texture, flavour development and nutritional profiles. Building on emerging tools for genetic tractability and quantitative phenotyping in Rhizopus microsporus, this project aims to treat biological stability as an engineering design parameter, rather than an empirical outcome. Students will characterise how Rhizopus reorganises hyphal architecture, interaction with bacteria and metabolic activity across defined SSF gradients. Additionally, 3D microscopy and AI‑assisted segmentation pipelines will allow to reconstruct fungal networks, growth kinetics and micro‑scale interaction dynamics within co‑cultures with bacteria. Using controlled SSF, imagingdriven biomass quantification, and in situ process parameter determination (e.g. dissolved oxygen and temperature), the project will identify the parameters that stabilise fungal performance during tempehlike fermentation. These insights will then be incorporated into engineering strategies for improving robustness, such as tailored substrate architecture, microenvironmental modulation or targeted genetic perturbations. The project provides interdisciplinary training in fungal biology, fermentation engineering, quantitative imaging and sustainable food biotechnology, and contributes to developing predictable, scalable fungalbased protein production systems. |
| From Genetic Tools to Designer Tempeh: Programmatic Control of Rhizopus Physiology for Next Generation Alternative Proteins | Following the recent on a transformation system, promoter panel, and selectable chassis for Rhizopus microsporus, the next frontier is to use these tools to program fungal physiology during solidstate fermentation (SSF). Unlike submerged fermentation systems, SSF creates sharp gradients in moisture, oxygen and nutrients that drive complex morphological states, variable enzyme secretion, and heterogeneous flavour development. This project aims to convert R. microsporus into a genetically addressable SSF chassis capable of producing designer sensory and nutritional traits for nextgeneration tempeh and fungalbased foods. The student will construct and test modular expression units controlling hyphal morphology and ironfortification proteins (e.g., ferritin or leghemoglobin). Building on the tested promoter panel, the project will deploy tunable gene expression during different SSF phases. Readouts, including fluorescent reporters, imaging, and quantitative assays in soybeanbased SSF, will link genetic design to spatially resolved fermentation outcomes. These data will enable modelling of genotype–microenvironment–phenotype relationships and identify engineering principles for stabilising fungal performance under SSF heterogeneity. This project trains students at the interface of synthetic biology, fungal physiology, and alternativeprotein bioprocessing. It delivers the first demonstration of programmed fungal behaviour in a foodgrade SSF system and establishes a foundation for precision fungal fermentation, flavour optimisation and nutritionenhanced tempeh. |
Professor Spyros Masouros
Profile: https://profiles.imperial.ac.uk/s.masouros04
Contact details: s.masouros04@imperial.ac.uk
| Project title | Description |
| Bone failure at high loading rates | This project will be mostly lab based. It involves using existing machines in the lab to quantify failure of small testing samples from bone at physiological loading rates (ie load not slowly, but quickly). You will cut samples of different shapes and from different types of bone (long vs pelvic), to accommodate a range of local stress states ahead of failure. The data will be used to develop material models of skeletal damage, which are critical for the prediction of injury in widely used (mostly by the automotive injury) current human body models. Having taken the advanced stress analysis and FEA module (BACSA) in Y3 would be preferable but not a prerequisite. |
| Project on injury biomechanics / orthopaedic and trauma surgery / optimal rehabilitation post injury | Generic title. Please contact me during office hours to discuss options |
Professor Tom Ellis
Profile: https://profiles.imperial.ac.uk/t.ellis
Contact details: t.ellis@imperial.ac.uk
| Project title | Description |
| Flexible-length DNA sequence design with bidirectional context | Many existing DNA design methods either generate sequences left-to-right or infill a fixed-length missing region. For genomic engineering applications, it is often more useful to design edits in context: the model should see the flanking DNA on both sides and be capable of making variable-length insertions, deletions or substitutions. This project will develop and benchmark a computational model or analysis pipeline for flexible-length DNA sequence design in the context of yeast regulatory sequences. The student will benchmark an existing fill-in-the-middle DNA design model for yeast promoter or regulatory sequence design, adapt variable-length editing ideas from protein engineering (such as edit-flow) to regulatory DNA, and condition designs on desired expression outputs using Yorzoi/Shorkie-style predictors or measured reporter data. Where feasible within project constraints, candidate designs will be nominated for experimental validation. The expected output is a proof-of-concept flexible-length DNA design model for yeast promoters or regulatory elements, together with in silico validation and a shortlist of candidate designs for further testing. Key words: DNA design, regulatory sequence engineering, fill-in-the-middle, yeast promoters, generative models, synthetic biology, bioinformatics. Student applicability: Good knowledge of the following is required – Python programming, bioinformatics and ideally machine learning. This project is primarily computational. Suitable for either a Master of Research student or a fourth-year Bioengineering undergraduate; scope can be adjusted by limiting datasets, model complexity and experimental validation. |
| Building a better Yorzoi: multimodal sequence-to-expression modelling in yeast | Yorzoi is a yeast sequence-to-expression model that predicts strand-specific RNA-sequencing coverage from a 5-kilobase DNA window. While powerful, it is currently limited to a single output modality and does not incorporate complementary genomic measurements such as chromatin state, nucleosome positioning or ribosome occupancy. This project asks whether Yorzoi can be extended into a more general yeast sequence-to-function model, closer in spirit to broader multimodal models such as AlphaGenome, while remaining focused on yeast and synthetic biology applications. The student will develop and benchmark a computational model or analysis pipeline that extends the Yorzoi framework to additional modalities, evaluates whether transformer-based architectures improve performance and interpretability, and tests generalisation to synthetic, rearranged and exogenous yeast sequence contexts. The project will also explore broader fungal or yeast-family pretraining strategies inspired by Shorkie-style masked DNA language modelling. The expected output is a Yorzoi-v2 prototype benchmarked against the current Yorzoi model and relevant Shorkie/ExoShorkie-style baselines on native, synthetic and exogenous yeast sequence tasks. Key words: Yorzoi, sequence-to-expression model, multimodal genomics, deep learning, yeast, synthetic biology, transformer models. Student applicability: Good knowledge of the following is required – Python programming, bioinformatics and ideally machine learning or deep learning. This project is primarily computational. Suitable for either a Master of Research student or a fourth-year Bioengineering undergraduate; scope can be adjusted by limiting datasets, model complexity and experimental validation. |
| Barcoded combinatorial plasmid library for studying the effect of transcriptional neighbourhoods on gene expression | Gene expression is influenced not only by the regulatory elements directly upstream of a gene but also by the broader transcriptional neighbourhood in which it resides. How the arrangement of neighbouring genes affects expression levels is incompletely understood, particularly in engineered or synthetic contexts. This project will construct a barcoded combinatorial plasmid library in which the positions of three to four genes are systematically varied, allowing the effect of different transcriptional arrangements to be assessed in a high-throughput manner. By coupling the library with sequencing-based readouts, the project will develop a simple model to identify enrichments associated with particular gene arrangements and begin to uncover the rules governing transcriptional context effects on gene expression. Key words: transcriptional neighbourhood, combinatorial library, gene expression, barcoded sequencing, synthetic biology, gene context. Student applicability: Good knowledge of the following is required – molecular cloning, DNA assembly methods and basic microbiology. Experience with sequencing-based assays or computational analysis of sequencing data is advantageous. |
| Improving inter-kingdom conjugation for enhanced DNA delivery to eukaryotic cells | Bacterial conjugation is an ancient and highly efficient mechanism for transferring DNA directly between cells through physical contact. While conjugation is well-established among prokaryotes, its application to eukaryotic recipient cells remains comparatively inefficient and poorly understood. This represents a significant missed opportunity: unlike conventional DNA delivery methods such as lipofection or electroporation, conjugation can transfer very large DNA payloads (>500 kb) without the shearing forces that typically fragment large DNA molecules. This project aims to improve the efficiency of bacterial-to-eukaryotic (inter-kingdom) conjugation by rationally engineering key components of the conjugative machinery to better interface with eukaryotic biology. The project will focus on two interconnected engineering challenges: first, improving cell-to-cell contact by engineering surface-displayed proteins on the bacterial donor to promote stable physical interaction with eukaryotic recipient cells; and second, improving cytoplasmic DNA trafficking by engineering and co-delivering DNA-binding proteins to direct conjugated DNA towards the eukaryotic nucleus. Together, these strategies aim to unlock the potential of conjugation as a next-generation tool for large-payload DNA delivery to eukaryotic systems. Key words: bacterial conjugation, inter-kingdom DNA transfer, synthetic biology, protein engineering, mammalian cell biology, DNA delivery. Student applicability: Good knowledge of the following is required – molecular cloning techniques, PCR and sequencing, and basic microbiology. Experience with mammalian or yeast cell culture is desirable. Students will gain broad experience in rational protein design, molecular cloning, DNA isolation and diagnostic analysis, bacterial conjugation assays, and mammalian and/or yeast cell culture techniques. |