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 2026-27 entry
- Professor Anil Bharath
- Dr Jun Ishihara
- Dr David Labonte
- Dr Guang Yang
- Dr Amy Howard
- Dr Chris Rowlands
- Dr Claire Stanley
- Dr Joseph van Batenburg-Sherwood
- Dr Kaushik Jayaram
- Dr Laki Pantazis
- Dr Nic Newell
- Dr Spyros Masouros
- Dr Sylvain Ladame
- Dr Tom Ouldridge
- Dr Yap
- Dr Hayriye Cagnan
- Dr James Choi
- Professor Aldo Faisal
- Professor Mengxing Tang
- Dr Sonja Billerbeck
- Dr Pedro Ballester
- Dr Sam Au
- Professor Rylie Green
- Dr Sophie Morse
- Professor Martyn Boutelle
- Professor Danny O'Hare
- Professor Darryl Overby
- Professor Firat Guder
- Professor Holger Krapp
- Professor Manos Drakakis
- Professor Pantelis Georgiou
- Professor Reiko Tanaka
- Professor Rodrigo Ledesma Amaro
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. |
Dr Jun Ishihara
Profile: https://profiles.imperial.ac.uk/j.ishihara
Contact details: j.ishihara@imperial.ac.uk
| Project title | Description |
| Enhancing the effect of COVID-19 vaccine by prolonged skin tissue retention of antigen | Lab-based project for those with synthetic biology or biomaterials experience Our new laboratory focuses on a protein engineering based translational immunology for drug development. We have recently developed a new method to engineer protein that can retain at the skin tissue through fusing extracellular matrix binding domain to the protein. Skin tissue has many antigen-presenting cells, including dendritic cells. In theory, the prolonged tissue retention of the COVID-19 antigen in the skin tissue would increase the vaccination effect. We will design the protein, produce, and purify. We will characterise the protein and compare with non-tissue retaining COVID-19 antigen. We take protein engineering approaches without biomaterials to make advanced vaccines for the future use. To be clear, you will not be exposed to the virus. We want to offer 1 lab-based project for dedicated students who can do full days in the lab to work in this area. |
| 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. |
| 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. |
Dr David Labonte
Profile: https://profiles.imperial.ac.uk/d.labonte
Contact details: d.labonte@imperial.ac.uk
| Project title | Description |
| Computational models of muscle activation | The vast repertoire of human and animal movements is made possible by converting neural signals into a muscular responsea process called activation dynamics. While a wide range of activation models exist”from simplified pulse-based formulations to detailed biophysical cross-bridge models”we dont yet know how well they can match real muscle behaviour across realistic control and task conditions. This project offers an exciting opportunity to systematically evaluate various muscle activation models using numerical simulations. You will write and run simulation code, fit models to biological data, assess their functionality, and analyze the computational cost-performance trade-offs associated with each approach. The results will contribute to a deeper understanding of muscle function and provide critical insights for applications in neuromuscular diseases, rehabilitation engineering, and bioinspired robotics. This project is particularly well-suited for students interested in biomechanics, computational physiology, and neural control. Relevant literature: Otazu, G. H., Futami, R., & Hoshimiya, N. (2001). A muscle activation model of variable stimulation frequency response and stimulation history, based on positive feedback in calcium dynamics. Biological cybernetics, 84(3), 193-206. RamÃrez, A., Grasa, J., Alonso, A., Soteras, F., Osta, R., Muñoz, M. J., & Calvo, B. (2010). Active response of skeletal muscle: in vivo experimental results and model formulation. Journal of theoretical biology, 267(4), 546-553. |
| 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. |
| Mechanical properties of insect tendons. | Movement is integral for bilateral animals, and driven by a highly conserved molecular motor: muscle. A muscle's mechanical output is limited by the force it can exert across different shortening lengths and velocities, and these physiological and mechanical limitations constrain animal performance, behaviour, and evolution. Muscle action is transmitted to the environment via skeletal elements, but rarely do muscle and skeleton connect directly. Instead, muscle action is mediated via tendons, aponeuroses (in vertebrates), or apodemes (in invertebrates). Although these elements are non-contractile---and thus can neither generate force nor do mechanical work---their presence is thought to bring great functional benefits to musculoskeletal performance. During running, energy that would otherwise be lost as heat can be transiently stored in form of elastic strain energy in stretched tendons, reducing the need for muscle work, and so presumably the expense of metabolic energy; upon impulsive loading, tendons may act as shock absorbers that protect muscle fibres from critical damage; tendons can significantly enhance muscle mechanical output during explosive movements, because the speed of elastic recoil is not restricted by the otherwise limiting force-velocity relationship of muscle; and the rapid elastic response of tendons can passively stabilise movements against unpredictable perturbations on time scales much faster than achievable via the nervous system, and simplify necessary control strategies. Without doubt, placing appropriately tuned elastic elements in-series with muscle can have diverse and significant benefits. The mechanical properties of \emph{vertebrate} tendons have consequently been the subject of a substantial body of work. Perhaps surprisingly, much less is known about the mechanical properties of the invertebrate analogue: apodemes. The literature contains tendon data for upwards of 40 vertebrate species, covering a large range of body sizes, clades and ecological niches. In sharp contrast, only two reports on the mechanical properties of invertebrate apodemes exist to the best of the authors knowledge---both on the same species, and both published about half a century ago. In this project, you will further develop a novel method based on beam resonance to extract the mechanical properties of tiny insect apodemes. Successful project completion may enable you to present your work at an international conference, and, if luck permits, perhaps even write a publication. |
| 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. |
Dr Guang Yang
Profile: https://profiles.imperial.ac.uk/g.yang
Contact details: g.yang@imperial.ac.uk
| Project title | Description |
| Super-Resolved 3D Late Gadolinium Enhanced Cardiovascular MRI | Aim: To develop novel AI powered data driven and MR physics guided super-resolution and blind denoising methods for cutting-edge cardiovascular MR techniques, potentially producing step-changes in routine clinical CMR. The AI models will be validated on prospectively scanned low-resolution data with degraded image quality. Background: Cardiovascular disease (CVD) the major cause of mortality globally with an estimated 17.9 million people died from CVD in 2019, representing 32% of all global deaths. Cardiovascular MRI diagnosis enabled by artificial intelligence (AI) has a promising future. However, the limited spatial resolution and inherent noise in MR data could affect the AI assisted diagnosis and analysis. Main goal: Cardiovascular MR (CMR) is one of the major clinical tools for diagnosis, prognosis, risk stratification, treatment planning and follow-up. 3D late gadolinium enhanced (LGE) CMR plays an important role in scar tissue detection in patients with atrial fibrillation (AF). However, LGE CMR technique suffers from limited spatial resolution and low signal-to-noise ratio (SNR). Increasing the acquired spatial resolution of LGE CMR is a major challenge and generally not recommended in practice, because it is expensive and time-consuming. Thus, super-resolution (SR) based post-processing becomes a promising option to increase the spatial resolution without increasing LGE CMR data time. In this study, we will design novel data and MR physics driven deep learning methods to boost the spatial resolution of the LGE CMR images while blindly suppressing image noise by utilising recently proposed Noise2Noise models. Retrospectively acquired data will firstly be used to train a general super-resolution model and prospectively acquired data will then be incorporated to fine tune the developed model for the super-resolution of really acquired low-resolution data with lower SNR. Experimental approach: Establish AI based strategies to i) super-resolve and denoise retrospectively acquired data, ii) perform trained model(s) on prospectively acquired data, iii) transfer learning using information from prospective data, and iv) validation against clinical quantifications (e.g., atrial scar). Outcome: Developing data driven and MR physics driven super-resolution and blind denoising method(s) for LGE CMR and comparing and quantifying clinical estimations using the developed AI powered method, e.g., estimation of atrial scar percentages before and after using our super-resolution methods. |
| Explainable prognostic models for progressive pulmonary fibrosis | Fibrotic lung diseases (FLD) refer to a group of diseases that cause collagen-based scar tissue formation in the lungs, which can lead to progressive loss of lung function. Among these patients, there is a subset that progresses inexorably despite conventional therapy, known as progressive pulmonary fibrosis (PPF). Early identification of PPF is crucial for prompt initiation of antifibrotic therapy, only licensed in patients with progressive disease. It is also important for the accurate stratification of patients in clinical treatment trials. Currently no reliable means for predicting the progression of fibrosis using baseline data, meaning that patients must undergo a period of functional decline before they qualify for antifibrotic therapy. This is one of the most urgent unmet needs in the management of patients with fibrotic lung disease. In this project, we will first develop a DL-based model which makes predictions directly from the whole baseline scan followed by developing a regression model which fits the DL output using a number of identified representative CT patches that are learned automatically by an unsupervised clustering method as the explanation method. The milestone is the successful development of the prognostic model with its explanation method. |
| Novel biomarker identification for progressive pulmonary fibrosis | The objective of this project is to identify novel phenotypic biomarkers for progressive pulmonary fibrosis using CT images. To achieve this, we will develop novel explainable AI (XAI) methods specifically designed for our deep learning-based prediction model. Our XAI methods will incorporate both attribution-based techniques and counterfactual explanation methods. Firstly, we will assess existing XAI methods to determine which attribution methods are reliable and capable of identifying the semantic features used by the prediction model. This assessment will help us select the most suitable attribution method for our specific application. Following that, we will develop generative-based models to generate counterfactual explanations. These explanations will shed light on the reasoning behind the model's predictions by generating hypothetical scenarios that could result in different outcomes. The combination of attribution-based methods and counterfactual explanations will enhance the interpretability of DL-based prediction models. The developed XAI methods will be validated on our in-house fibrotic lung disease datasets and be used for identifying the novel phenotypic features for progressive pulmonary fibrosis. |
| 3D HRCT Synthesis Enhancing Pulmonary Disease Diagnosis | High-Resolution Computed Tomography (HRCT) scans play a critical role in diagnosing pulmonary diseases such as interstitial lung disease. However, acquiring HRCT scans can be costly and involves radiation exposure. In this project, we will develop a deep generative model-based approach to generate synthetic HRCT images from lower-resolution CT scans or other imaging modalities, e.g., MRI. The generated HRCT images should accurately represent the structural details of the lungs and abnormalities associated with various pulmonary diseases. The performance of the synthetic HRCT images can be evaluated by comparing the diagnostic accuracy of radiologists using both real and synthetic images. The outcome of this project will lead to potential conference presentations/publications and a further PhD project. |
| Non-Imaging Data Synthesis for Clinical Decision Support | Non-imaging clinical data, such as electronic healthcare records (EHR), wearable device data, and laboratory results, play a crucial role in clinical decision-making. However, the availability and privacy concerns surrounding these data pose challenges for research and analysis. This project aims to develop a generative model for synthesizing realistic longitudinal non-imaging medical data, addressing missing data and privacy breaches while preserving the statistical properties and dependencies found in real-world patient records. In this project, we will focus on synthesizing non-imaging medical data, such as patient demographics, vital signs, laboratory test results, and medical histories. You will develop a generative model that can generate realistic longitudinal synthetic patient records, preserving the statistical properties and dependencies present in real-world data. The quality of the synthesized data can be evaluated by comparing its distribution and statistical characteristics with real patient data. We will also develop downstream tasks, including disease identification, to further verify the utility of the synthetic patient records. |
| Unsupervised Learning for Airway Image Segmentation | With the rapid accumulations of large-scale datasets, supervised deep learning methods spread out various medical image analysis domains like disease diagnosis and organ segmentation. However, expert annotation is time-consuming and highly subjective, especially for labeling large-scale 3D medical datasets. Meanwhile, fully supervised learning may restrict the model performance in some cases, for instance, noisy label scenes. To tackle the problems, unsupervised learning has gradually attracted much attention in the community of medical image analysis. For instance, innovative agent tasks of Cube Reconstruction and Masked Image Modeling (MIM) have been developed. In this project, we propose an efficient and effective airway image segmentation method called parallel multi-order joint learning network (PMJL), which integrates multi-order unsupervised and supervised learning in parallel to alleviate the scarcity of labeled airway image datasets. In the branch of multi-order unsupervised learning, since (1) the normalization of different order exponential operation of raw image can enhance the contrast attribute, and (2) the features of oriented gradients can strengthen the texture feature representations in 2D space, we extend both of them to 3D space with multi-order normalized responses as targets, which is beneficial for airway image segmentation. For the supervised learning branch, it directly predicts the full segmentation map by random masked input rather than full input, which not only omits the pretrain and finetune steps but also reduces the risk of over-fitting. |
| Border Rendering Feature Orthogonal Network for Blood Vessel Segmentation | Blood vessel segmentation of the lungs can be very beneficial for identifying important pulmonary diseases: it helps to delineate lung nodules, detect pulmonary emboli and evaluate the lungs vasculature in pulmonary hypertension. The patient’s individual values can be compared with normative values to detect and assess the occurrence, the extent and the clinical evolution of the disease. In this project, we develop an effective blood vessel segmentation method called Border Rendering Feature Orthogonal (BRFO) network. Unlike previous cutting-edge methods that operate on all regular dense points, our BRFO decouples and depicts the medical image regions as a cube-tree. This approach focuses only on recycle-sampling vulnerable border points, rendering the serious discontinuity as well as false-negative/positive small-size vessels with two novel Multi-momentum Global-Local Fusion (MGLF) and Sparse Patched Feature Orthogonal (SPFO) modules. Furthermore, we develop a Multi-stage Self-Knowledge Distillation (MSKD) module to improve the model performance and robustness, which can also accelerate the convergence of model training. We validate the performance of our method by training on normal lung disease datasets and testing on lung cancer and COVID-19 datasets. The outcome of this project will lead to potential conference presentations/publications and a further PhD project. |
| Image retrieval for computational data harmonisation | Data harmonisation refers to the process of integrating data from multiple sources to facilitate analysis and comparison. Collecting data using different methods, storing it in different formats, or measuring it on different scales can make it challenging to integrate and compare data across sources. To overcome these challenges, data harmonisation methods are used to standardise, match, transform, aggregate, or clean the data. In this project, participants will focus on developing novel image retrieval systems for multi-modality medical images. Students are encouraged to develop novel prompt-based retrieval systems to address the existing challenges. Specifically, using digital image processing techniques to extract highly efficient features as extern prompts, and integrated them with carefully designed encoding networks. The outcome of this project will lead to potential conference presentations/publications and a further PhD project. |
| Synthetic Data Enhanced Medical Image Segmentation | Multi-modal structural Magnetic Resonance Image (MRI) provides additional information and has been widely utilised for brain tumour diagnosis and treatment planning. While machine and deep learning are now widely used to interpret and evaluate MRI images, most available methods are dependent on entire sets of multi-modality data that are expensive and, in some cases, difficult to obtain in real clinical circumstances. In this project, we will develop novel deep generative models for multi-modality glioma MRI synthesis in order to solve the problems of incomplete multi-modal MRI acquisitions. The proposed medical datasetGAN method, in particular, will use an encoder-decoder architecture to map the input modalities into a common feature space, from which (1) the missing target modality(-ies) can be synthesised by the decoder, and (2) the jointly performed glioma segmentation can help the synthesis task to better focus on the tumour regions. The synthesis and segmentation tasks use the same common feature space, and multi-task learning improves both. Besides tumour and non-tumour regions will be synthesised separately to disentangle the confounding issues when GAN-based models may get confused. The validation will be performed against tumour sub-regions segmentation and tumour grading/classification accuracies. The outcome of this project will lead to potential conference presentations/publications and a further PhD project. |
| Quantitative Analysis of Cell Populations | Understanding the distribution and proportion of different cell types in a sample is crucial for many biological and medical studies, such as cancer research and immunology. In this project, the student will implement image processing techniques to segment cells in immunofluorescence images, followed by identification based on specific fluorescence markers. Quantify cell populations and perform statistical analysis. The target is to accurately quantify and compare different cell populations under various conditions to draw meaningful biological conclusions. |
| Machine Learning for Predictive Biomarker Discovery | Identifying predictive biomarkers from images can aid in early diagnosis and personalized treatment strategies. In this project, the student need to extract relevant features from immunofluorescence images, train Convolutional Neural Networks based predictive models using clinical outcome data, and validate biomarkers. The target of this project is to discover and validate predictive biomarkers that can be used to forecast disease progression and treatment response. |
| 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. |
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. |
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. |
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 Joseph van Batenburg-Sherwood
Profile: https://profiles.imperial.ac.uk/jvbsherwood
Contact details: jvbsherwood@imperial.ac.uk
| Project title | Description |
| 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 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. A recent MRes student developed new components towards developing the next generation prototype: such as a custom PCB for ventilator control and power management. A remaining challenge is to optimise the pneumatic manifold and build these components into a portable unit. A second challenge is to modify the pneumatics system to enable use without a compressed air supply, in mobile settings such as ambulances. A third 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. 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. Please get in touch if you have any questions. Visit vbslab.uk for more details on the research team |
| Development of a microfluidic blood vessel model for diabetes research | SUMMARY This is a microfluidics project about studying blood flow in microvascular disease. You will build and test a microfluidic device and develop interdisciplinary skills in microfabrication, cell work and microscopy. 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, which is made up of approximately equal portions of plasma and red blood cells (RBCs). These specialist cells have fascinating behaviours: they are highly deformable which makes them move away from vessel/channel walls, and they aggregate (stick together) in regions of low shear stress. Previous work in the vBS lab has shown that these behaviours change how blood flows, and in turn affects microvessel function. To study how this process might be affected by diabetes, we need a model of a blood vessel that replicates its structure, function, and interaction with red blood cells. PROJECT DETAILS This project will build upon existing work and involve creating a microfluidic chip that models a small blood vessel within a hydrogel and that can be perfused with healthy and diabetic-like red blood cells. You will learn how to run biological assays to examine the effect of blood flow on vascular cells. You will build skills in microfabrication, cell culture, and fluid mechanics, and have the chance to contribute to our research. Please get in touch if you have any questions. Visit vbslab.uk for more details on the research team |
| A microfluidic device to find patient-specific optimal treatment for diabetic macular edema | 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 AND AIMS 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, a very indirect measurement of hydraulic conductivity. 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 programmed, optimised and tested. Our clinical collaborators and King 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. Then we will incorporate cells into the device and test with substances known to change barrier function. Please get in touch if you have any questions Visit vbslab.uk for more details on the research team |
| A new technique for measuring the role of red blood cells in vascular regulation | SUMMARY This is a wet lab project about studying blood flow through vessels and analysing biochemical effects. You will work with microfluidic devices and develop interdisciplinary skills in engineering, chemistry and microscopy. BACKGROUND In the body, 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, which is made up of approximately equal portions of plasma and red blood cells (RBCs). These specialist cells have fascinating behaviours: they are highly deformable which makes them move away from vessel/channel walls, and they aggregate (stick together) in regions of low shear stress. Previous work in the vBS lab has shown that these behaviours change how blood flows, and in turn affects microvessel function. An important signalling molecule called nitric oxide (NO) could be the key that helps us understand this process. PROJECT DETAILS This project will involve using human blood samples to perfuse artificial, microfluidic blood vessels in the lab. Then, you will use spectrophotometry, chemistry and microscopy to assess how the vascular cells release NO in response to flow and how it binds to the haemoglobin inside the red blood cells. You will learn how to run biochemical assays, building skills in microfabrication, cell culture, fluid mechanics, and spectrometry. Your work will be an invaluable addition to our research. Please get in touch if you have any questions. 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. |
Dr Laki Pantazis
Profile: https://profiles.imperial.ac.uk/p.pantazis
Contact details: p.pantazis@imperial.ac.uk
| Project title | Description |
| The very first oncogenic hit: watching a single-cell mutation hijack a normal intestinal crypt in real time to initiate colorectal cancer | Colorectal tumours often begin when a single stem cell in the intestinal lining picks up a driver mutation—typically knocking out a gene like Apc, a key gatekeeper in the Wnt signalling pathway. But what happens next has never been directly observed. Does that one cell grow faster than its neighbours? Or does it reprogram the local environment to suppress competition? These are not just questions of cancer biology—they’re questions about how cells compete, cooperate, and take over structured tissues. This project gives you the opportunity to answer those questions using a cutting-edge genome editing system with unprecedented control. You’ll use mouse intestinal organoids—synthetic, miniaturised tissues grown in 3D culture—as your model of the crypt, the basic unit of intestinal self-renewal. These structures are ideal for studying stem-cell behaviour in a setting that closely mimics the native tissue. At the core of the platform is a precision-engineered CRISPR system designed for subcellular, real-time control. It remains completely inactive until illuminated by two intersecting laser beams—a method called Primed Conversion, originally developed in the Pantazis lab. Only where the two light paths meet is genome editing activated. That means you can mutate exactly one stem cell inside a live organoid at exactly the moment you choose—before a division, during stress, or mid-way through a regenerative cycle. All other cells remain untouched. This kind of temporal and spatial precision has never been possible in genome editing before. Existing systems affect whole tissues or entire cell populations, masking the earliest dynamics of cell competition. Here, by targeting a single cell, you can dissect the very first steps of tumour initiation—from the first mutation to potential clonal dominance—in real time, inside a living structure. Practical student experience Over nine months you will: • design and build CRISPR constructs using Gibson cloning, • optimise DNA delivery into 3D organoids using high-efficiency Neon electroporation, • perform single-cell editing using subcellular photo-activation on a Leica Stellaris 8 confocal and a custom-built light-sheet microscope, • acquire and analyse long-term 3D movies of clonal dynamics, • use Python to extract lineage and competition data from image stacks. This project gives you hands-on exposure to synthetic tissue models, programmable gene control, advanced microscopy, and quantitative bioanalysis—a complete pipeline from molecular design to live-tissue dynamics. |
| The birth of an organiser: watching a single engineered cell ignite a Wnt gradient to break symmetry | Embryonic development often begins when a tiny cluster of cells acts as an organiser, secreting morphogens like Wnt3a to polarise an otherwise uniform tissue. But what happens when a single cell takes on this role? Can one cell alone create a true morphogen field and induce organised patterning in its neighbours? This fundamental question — whether one cell is sufficient to break symmetry — has never been directly answered. Doing so requires precise control over both the birth of the organiser and the ability to track its influence over time. This project gives you the opportunity to dissect these events using a cutting-edge synthetic biology platform that tightly couples optogenetic control, cell–cell contact logging, and endogenous fate induction. You’ll use mouse embryonic stem cell aggregates—3D synthetic tissues that mimic the early embryo—as your model system for symmetry breaking. These structures are ideal because they retain responsiveness to morphogen gradients but start from a naive, isotropic state. At the core of the platform is a light-gated genetic switch combined with SynNotch synthetic receptors. A dual beam illumination technique called Primed Conversion triggers one cell to simultaneously begin secreting wild-type Wnt3a and display a membrane-bound GFP ligand. Direct neighbours are permanently logged by SynNotch activation (via mCherry expression), while Wnt target gene expression (T/Brachyury, Axin2) is detected by immunostaining. This separation between physical contact history and Wnt gradient response allows you to map, cell-by-cell, how far secreted Wnt propagates beyond immediate neighbours—and whether true patterning emerges. This kind of temporal and spatial precision—over both the organiser’s birth and its domain of influence—has never been achieved before. Existing methods activate whole populations or introduce global ligands, masking the early emergence of gradients. Here, by controlling a single cell, you can dissect the very first steps of organiser formation: the birth of asymmetry itself, inside a living structure. Practical student experience Over nine months you will: • design and assemble the light-gated organiser constructs using Gibson cloning, • generate stable mouse ESC lines using PiggyBac transposition, • induce single-cell organiser activation using subcellular photo-activation on a Leica Stellaris 8 confocal or custom-built light-sheet microscope, • fix and immunostain aggregates for key readouts (mCherry for contact history, HA for Wnt secretion, T/Brachyury and Axin2 for Wnt response), • acquire and analyse 3D confocal image stacks to extract spatial patterns of fate induction, • use Python to segment cells, map distance-dependent signalling, and quantify gradient spread. This project gives you hands-on exposure to synthetic developmental biology, programmable cell signalling, live-tissue optogenetics, advanced microscopy, and spatial data analysis—a complete pipeline from molecular design to real-time morphogen mapping. |
| 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. Practical student experience Over 9 months you will: • design and model SHG-active peptide candidates for expression in mammalian cells, • co-express peptides with encapsulin shells and a TEV-protease under inducible control, • validate encapsulation and proteolytic release by western blot, microscopy, and SHG polarimetry, • culture human and/or mouse cells to assess intracellular assembly efficiency and compatibility, • 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. |
| 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 developing zebrafish embryos. Practical student experience Over 9 months, you’ll: • design and engineer the ChemiGenEPi sensor using HaloTag chemistry and advanced dye-ligands, • test its performance in live-cell imaging assays (brightness, photostability, dynamic range), • push it further into in vivo models to see mechanosensation unfold in real biological contexts. 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. |
| PhOTO-Bow: Painting Cell Lineages with Light | Every organism begins as a single cell, yet by adulthood becomes an intricate mosaic of billions. How do individual cells decide who they will become, where they will go, and how their descendants contribute to health or disease? To answer that, we need to see the entire history of each cell— who it divides into, how it moves, and when it changes fate. Traditional lineage-tracing tools are powerful but blunt: they label too many cells at once or rely on stochastic recombination without spatial or temporal control. PhOTO-Bow changes the game. It merges two powerful technologies - primed conversion single cell labelling and Cre/lox rainbow recombination - to create a system where you decide when and where every colour appears. By combining the pinpoint accuracy of light-activated primed conversion with the stochastic diversity of the Brainbow system, PhOTO-Bow allows you to illuminate the story of tissue development and disease in living organisms. Each cell’s colour becomes a barcode of identity, history, and fate—recorded directly in its fluorescence. Practical student experience You will: Test PhOTO-Bow constructs to integrate precise light control and randomised recombination. Perform primed conversion using dual-beam illumination to trigger spatially confined activation at the single-cell level. Induce rainbow recombination through light-controlled Cre activity, permanently marking clones with unique spectral fingerprints. Track clonal expansion and migration across development using Leica light-sheet microscopy. Quantify lineage dynamics with advanced 3D image segmentation and Python-based tree reconstruction tools. This project gives you hands-on exposure to synthetic gene circuit design, live-cell imaging and computational lineage analysis - a complete pipeline from molecular construction to visualising multicellular history in living tissue. Why It Matters PhOTO-Bow is not just another lineage tool - it is a cinematic recorder of biology in motion. It lets you watch, in real time, how a single cell’s decision ripples through development or disease. By integrating primed conversion precision with rainbow recombination diversity, this system can resolve cellular ancestry with unprecedented fidelity—mapping how clonal mosaics emerge during organogenesis, tissue repair, or tumour evolution. The resulting colour-coded trees will provide insight into how fate decisions propagate through space and time. |
Dr Nic Newell
Profile: https://profiles.imperial.ac.uk/n.newell09
Contact details: n.newell09@imperial.ac.uk
| Project title | Description |
| Understanding thresholds of loading that result in spinal disc herniations | Spinal disc herniations (commonly called œslipped discs) are one of the most common causes of lower back pain. In a disc herniation, the gel-like central region of the disc (the nucleus) is forced through the tough annulus fibrosus that surrounds it. This results in either material being completely extruded, or a bulge being created on the outside of the disc, both of which can result in compression of nearby nerves, inducing pain. The exact mechanism of disc herniation is not well understood but improving our knowledge in this area would be valuable clinically to allow surgeons to assess the risk of herniation, or to improve treatments. To better our understanding it is important to understand the type of loading, and the thresholds of that loading, that result in disc herniations. Recently, we have developed a rig in our group that allows complex loads to be applied to spinal disc specimens. This project will utilise this rig to carry out experiments with the following aims: -Determine which multi-axial loads are important to ensure disc herniations can be induced in an ex vivo animal model. -Determine whether rate of loading affected the risk of herniation. -Determine the thresholds of loading that result in disc herniation. This project will be mainly lab-based and utilise experimental testing on animal models (sheep/cows). There may be scope for experimentation using human cadaveric models. There is also the possibility to run FE models alongside the experiments, which may help better understand how multi-axis loads are distributed through intervertebral discs. In the past 5 years I have supervised two project students who have won departmental prizes for their thesis (best in cohort) and 3 students who have published work conducted as part of their project in academic journals. This project could be equally as impactful. |
Dr 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 |
Dr Sylvain Ladame
Profile: https://profiles.imperial.ac.uk/s.ladame
Contact details: s.ladame@imperial.ac.uk
| Project title | Description |
| Engineering denaturing hydrogel for efficient and automated recovery of RNA biomarkers from blood | Circulating cell-free nucleic acids (cfNAs) in blood have recently emerged as clinically useful and minimally invasive diagnostic and predictive tools for a broad range of pathologies, including cancer and prenatal disorders. Among them, non-coding microRNAs (or miRNAs) are frequently found to be upregulated or down-regulated in body fluids and have great potential as novel blood-based fingerprints, e.g. for the early detection of cancer. However, because of their complexity and cost, current gold-standards for microRNA detection (e.g RT-qPCR) are unsuitable for point-of-care testing and can only be carried out by trained professionals in equipped laboratories. One of their main shortcomings is their inability to provide clear answers from whole blood without heavy processing which, in the absence of standardised procedures, is a major source of errors and variation between analyses. Herein, we are proposing to engineer and test a broad range of denaturing hydrogels capable of efficiently extracting miRNAs from whole blood, focusing on their ability to i) release miRNAs bounds to proteins and/or trapped in exosomes and ii) isolate those small RNA oligonucleotides by size exclusion. Engineered hydrogels covering a broad range of viscosities and mesh-sizes (e.g. through chemical modification and variation in concentration of the monomers/fibres) will be tested. |
| Paper-based lateral flow assay for early prediction of preterm birth | 15 million preterm babies are born every year, with preterm birth (PTB) the largest cause of death of children under five worldwide. Birth before 26 weeks associates with 80% mortality, 25% severe handicap, and 75% overall morbidity in survivors. The strongest risk factor for PTB is previous PTB.[2] However the majority of PTBs are to women with no identifiable risk factors and occur in first pregnancies. So an urgent need exists to develop a simple, economic test which identifies preterm labour (PTL) risk in all settings and particularly in low-risk women early in pregnancy. Current predictive tests include cervical length scans and fetal fibronectin tests, which are usually conducted during late 2nd or 3rd trimester, at which point it becomes difficult to prevent PTB.[3] Timely medical interventions such as progesterone and steroids could help prevent PTB and reduce the risk of health issues in new-borns, justifying the need for a new, non-invasive and low-cost test based on the detection of highly specific blood biomarkers for earlier prediction of PTB and point-of-care monitoring of response to treatment. Having already demonstrated that probes engineered in-house can detect endogenous concentrations of miRNA in solution, we will next carry out similar sensing experiments on lateral flow (LF) paper strips, using an optical readout. Briefly, a lateral flow nitrocellulose membrane will be used as a low-cost platform for both sensing and detection. It will be functionalised in-house with a test line of biotinylated PNA catch-probe. Sequence-specific immobilisation of the only miRNA of interest followed by on-chip reaction between the catch probe and the reporter probe will result in the formation of a fluorescent dye that can be visualised optically. |
| 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-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 |
| Exploring Sequence Orthogonality in Primer Exchange Reaction Systems through Nucleotide Composition and Design Constraint | The Primer Exchange Reaction (PER) is a catalytic DNA-based mechanism that enables autonomous, isothermal synthesis of sequence-defined strands [1]. As its use expands in applications such as molecular recording, sensing, and programmable self-assembly [2], there is growing interest in designing multiple PER hairpin systems that can operate in parallel without unintended cross-reactivity [3]. Achieving this functionality requires careful control over sequence orthogonality – the ability for each PER unit to function independently and selectively. This project will investigate how the design of PER hairpins and primers can be tuned to maximise orthogonality across a multi-component system. It will explore how restricting nucleotide composition (e.g. using only three out of the four bases: A, T, C, G, as is helpful for a single PER system) affects the potential to generate distinct, non-interacting PER reactions within the same environment. The aim is to better understand the sequence design limits that govern orthogonality and inform future applications involving complex PER networks. Aims and Objectives: · To explore how sequence design and nucleotide composition influence the orthogonality of PER hairpin systems. · To assess the compatibility of multiple PER systems operating in parallel without interference. · To evaluate whether constrained base alphabets can support distinct and separable product strands. Approach: The project will make use of computational tools for DNA sequence design and analysis. Sequence candidates will be generated and tested for potential cross-reactivity using thermodynamic models. The number and characteristics of compatible PER systems will be assessed under different design constraints, such as restricted base usage or structural layout. Where appropriate, tools such as NUPACK4 will be used to assess secondary structure formation, base-pairing energies, and unintended hybridisation. Broader exploratory strategies will help to evaluate orthogonality across multiple sequence combinations and identify limits to system scalability. When a suitable set of candidate sequences have been identified, they will be tested in wet-lab experiments. Expected Outcomes: The project will provide insights into how many PER hairpin systems can coexist without interference and how sequence design rules can be adapted to increase system capacity. It will contribute to ongoing efforts to develop scalable, multi-step DNA reaction networks and inform future experimental implementations. Relevance to Research Context: This project aligns with broader goals in DNA nanotechnology and molecular systems engineering, particularly the design of dynamic, programmable networks that operate under non-equilibrium conditions. Understanding orthogonality in PER systems supports the development of more complex, reliable tools for synthetic biology, diagnostics, and information processing. 1. Kishi, J. Y., Schaus, T. E., Gopalkrishnan, N., Xuan, F. & Yin, P. Programmable autonomous synthesis of single-stranded DNA. Nat Chem 10, 155–164 (2018). 2. Zhang, M. et al. A DNA Circuit That Records Molecular Events. Sci. Adv vol. 10 https://www.science.org (2024). 3. Sheth, R. U. & Wang, H. H. DNA-based memory devices for recording cellular events. Nature Reviews Genetics vol. 19 718–732 Preprint at https://doi.org/10.1038/s41576-018-0052-8 (2018). 4. Zadeh, J. N. et al. NUPACK: Analysis and design of nucleic acid systems. J Comput Chem 32, (2011). |
| Leak reactions in a novel motif in DNA nanotechnology: menace or opportunity? | Owing to DNA’s unique ability to form predictable and programmable base-pairing interactions, DNA nanotechnology has been applied in molecular computation, biosensing, drug delivery, and the construction of molecular machines [1]. One particularly important mechanism in this field is toehold-mediated strand displacement (TMSD), which enables enzyme-free reaction networks with precise kinetic control and has become a foundational tool for building programmable molecular systems [2]. As an advancement of this technology, we recently developed handhold-mediated strand displacement (HMSD), which introduces an additional binding domain, referred to as the handhold [3,4]. This mechanism enables transient interactions between the strands and facilitates the production of out-of-equilibrium reaction products. However, experimental observations revealed that the HMSD mechanism can sometimes still be triggered even with low complementarity in the handhold region. We hypothesize that partial binding between the complementary handhold on one strand strand and a separate part of the invading strand could act as a templating interaction, facilitating strand displacement. This unintended templating effect can cause unwanted reactions in certain system designs, but may also be an alternative strategy for deliberately accelerating strand displacement if considered during the design process. To investigate this hypothesis, the student will employ the computational tool NUPACK to design DNA systems that test whether this reaction mechanism does indeed explain the results, and explore its properties. These systems will then be experimentally tested using a fluorescence plate reader to measure real-time reaction kinetics. [1] Seeman, N. C.; Sleiman, H. F. DNA Nanotechnology. Nat. Rev. Mater. 2018, 3, 17068. [2] Zhang, D. Y.; Turberfield, A. J.; Yurke, B.; Winfree, E. Engineering Entropy-Driven Reactions and Networks Catalyzed by DNA. Science 2007, 318 (5853), 1121–1125. [3] Cabello-Garcia, J.; Bae, W.; Stan, G.-B. V.; Ouldridge, T. E. Handhold-Mediated Strand Displacement: A Nucleic Acid Based Mechanism for Generating Far-from-Equilibrium Assemblies through Templated Reactions. ACS Nano 2021, 15 (2), 3272–3283. [4] Cabello Garcia, J.; Mukherjee, R.; Bae, W.; Stan, G.-B. V.; Ouldridge, T. E. Information Propagation through Enzyme-Free Catalytic Templating of DNA Dimerization with Weak Product Inhibition. Nat. Chem. https://www.nature.com/articles/s41557-025-01831-x |
| 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)' |
| Differentiable Virtual Move Monte Carlo for Biomolecular Design | Automatic differentiation (AD) is a powerful computational technique enabling the use of gradient-based optimization for complex functions. It is a cornerstone of modern machine learning and has recently become much more accessible due to high-performance AD libraries such as JAX [1], which allow automatic and efficient computation of gradients even for complicated numerical programs. Researchers in synthetic biology and molecular programming have begun applying AD to problems in system design, optimization, and simulation [2-5]. However, many commonly used algorithms in this space are not naturally differentiable due to their discrete or stochastic nature. For example, the well-known Gillespie algorithm (used in modelling stochastic chemical kinetics [6]) is discontinuous and thus not directly compatible with AD. Recent efforts have successfully developed continuous, differentiable approximations to such algorithms, enabling their use in differentiable programming frameworks [2]. The Principles of Biomolecular Systems group works at the interface of DNA nanotechnology and theoretical biomolecular systems, developing both experimental platforms and mathematical models for understanding and engineering molecular processes. In the context of this project, we are particularly interested in Monte Carlo methods used to simulate and optimize self-assembling biomolecular systems. One such method, the Virtual Move Monte Carlo (VMMC) algorithm [7], is used to simulate systems where interactions between particles lead to complex collective motions. Our aim is to develop a differentiable version of VMMC that can be incorporated into optimization pipelines for biomolecular system design. This would enable us to apply gradient-based techniques to optimize the structure or behaviour of molecular assemblies, including models like oxDNA [8], a coarse-grained simulation platform for DNA nanotechnology. We are seeking a motivated student to work on: - Designing and implementing a differentiable approximation of the VMMC algorithm using JAX or similar tools. - Testing the new algorithm on simplified biomolecular systems to demonstrate its ability to optimize structural properties. The project will involve a combination of programming, algorithm design, and application to biophysical models. It provides an opportunity to contribute to a novel research direction at the intersection of machine learning, simulation, and synthetic biology. Tools & Techniques: - Programming with Python and JAX - Concepts from numerical modelling and stochastic simulation This project would suit students with interests in computational biology, optimization, or molecular systems. References: [1] J. Bradbury et al., "JAX: Autograd and XLA", https://github.com/google/jax [2] Rijal, Krishna, and Pankaj Mehta. ‘A Differentiable Gillespie Algorithm for Simulating Chemical Kinetics, Parameter Estimation, and Designing Synthetic Biological Circuits’. eLife, vol. 14, Mar. 2025, p. RP103877. DOI.org (Crossref), https://doi.org/10.7554/eLife.103877.3. [3] Greener, Joe G. ‘Differentiable Simulation to Develop Molecular Dynamics Force Fields for Disordered Proteins’. Chemical Science, vol. 15, no. 13, 2024, pp. 4897–909. DOI.org (Crossref), https://doi.org/10.1039/D3SC05230C. [4] Krueger, Ryan K., et al. ‘Tuning Colloidal Reactions’. Physical Review Letters, vol. 133, no. 22, Nov. 2024, p. 228201. DOI.org (Crossref), https://doi.org/10.1103/PhysRevLett.133.228201. [5] Goodrich, Carl P., et al. ‘Designing Self-Assembling Kinetics with Differentiable Statistical Physics Models’. Proceedings of the National Academy of Sciences, vol. 118, no. 10, Mar. 2021, p. e2024083118. DOI.org (Crossref), https://doi.org/10.1073/pnas.2024083118. [6] Gillespie, Daniel T. ‘Exact Stochastic Simulation of Coupled Chemical Reactions’. The Journal of Physical Chemistry, vol. 81, no. 25, Dec. 1977, pp. 2340–61. DOI.org (Crossref), https://doi.org/10.1021/j100540a008. [7] Whitelam, Stephen, et al. ‘The Role of Collective Motion in Examples of Coarsening and Self-Assembly’. Soft Matter, vol. 5, no. 6, Mar. 2009, pp. 1251–62. pubs.rsc.org, https://doi.org/10.1039/B810031D. [8] Ouldridge, Thomas E., et al. ‘Structural, Mechanical, and Thermodynamic Properties of a Coarse-Grained DNA Model’. The Journal of Chemical Physics, vol. 134, no. 8, Feb. 2011, p. 085101. DOI.org (Crossref), https://doi.org/10.1063/1.3552946. |
Dr Yap
Profile: https://profiles.imperial.ac.uk/c.yap
Contact details: c.yap@imperial.ac.uk
| Project title | Description |
| Deep Learning Assessment of Brain Aneurysm Shape and Fluid Mechanics to Predict Outcomes | 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. To date, there are some evidence that the aneurysm shape and the blood flow forces in it can determine whether an aneurysm will progress and rupture, but it remains difficult to predict which case will worsen and which will be stable. Making this prediction is important, because interventions to treat aneurysms are very invasive and carry risks, and they are expensive, and so we can't just offer them to everyone before we determine that they are most likely going to worsen. Our project aims to develop neural networks that can extract shape and fluid dynamics features of aneurysms from MR images, so that in future, we can use these features in another network to accurately predict outcomes and risks of disease progression. Doctors can then use our tools to make predictions quickly and inexpensively, without needing to have expertise in advanced biomechanics simulations typically needed to flow results. To date, there is no statistial shape model to desribe the aneurysm geometry, and no neural network that can produce flow results using shape features of the aneurysm. We will use a Graph Fourier Basis approach we recently developed to fit a 3D mesh to the aeurysm image, and to extract the shape latent features. We will further develop a neural network take shape features and output flow charactersitics (eg., wall shear stress and pressures), which will be supervised by mass CFD simultions. We will work with Dr. Dylan Roi from Imperial NHS, an expert in aneurysms, to retain clinical relevance of the project. |
| Foundation Model for Fetal Echocardiography to Improve Cardiac Malformation Detection | Echocardiography screening of fetal hearts is a standard of care, and is routinely performed during a pregnancy. However, mass screening programmes only detect 50% of congenital heart malformations. Being surprised at birth with cardiac malformation result in less time for clinicians to plan for and provide treatment, resulting in higher mortality risks, and in greater risks for neurological maldevelopment, due to prolonged duration where the malformation is not treated and continue to compromise brain oxygen transport. Foundation models in image processing has made waves. Trained with a large dataset, they can be used to improve performance of any specific image processing task (segmentation, motion tracking, disease detection, etc) while requiring a much less sample size in these tasks. In our lab, we have a collection of large dataset of fetal heart echo images in 4D. Here, we will develop the first foundation model for fetal echo images, adapting architecture known to have good performance in 2D camera and medical images (DINO, SAM, and SIMCLR) models. We will subsequently use the foundation model to train a network for segmentation, and show that the foundation model improves the performance of this subsequent task, and require a much smaller dataset for it. The student will work with a research team, and stand to gain AI image processing techniques, as well as real-world applications of such techniques. They can also interact with fetal cardiologists collaborators in Europe for a well-rounded exposure. |
| Deep Learning Cardiac Biomechanics for Disease Diagnosis | Finite Element computational simulations of myocardial tissue biomechanics has been very useful in the past to understand the heart's function during health and disease, and to predict how the heart will remodel in disease. However, the simulatinos are slow and clinicians find it hard to adopt the technique as they don't have the engineering skills. Deep learning algorithms to calculate cardiac biomechanics can achieve very fast computations within seconds, and does not require any skills to use, thus enabling clinical translation. We developed one such algorithm, a physics informed neural network (PINN) cardiac finite element algorithm. It relies on deep learning for segmentation and motion tracking, and subsequently, motion modes are extracted for input into the PINN model. The model takes in medical heart images (ultrasound or MRI), and computes the stiffness and contractilty of the heart, as well as it's motion and stresses over the heartbeat. Here, we ask the student apply this model to a substantial number of cardiac MRI images, to see if the computed biomechanics parameter can be used to determine whether a heart has diseases. For example, does weak contractility point out hearts with cardiomyopathy, and can the stiffness prediction evaluate the size of scarred tissue on the heart? Student will work with a PhD student researcher, and Dr. James Howard, an MRI Physician-Scientist at Imperial NHS, to receive a well-rounded exposure |
| 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 to get an echo scan. Someone rushed to the emergency room may actually have to wait many hours to get one. This is obviously dangerous. 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 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 help the data collection of the echo images to validate the accuracy of our device, working with a PhD student and a team of cardiologists and songraphers at Imperial NHS (Dr. Amit Kaura and Prof. Jamil Mayet and their team). We will also interface with collaborators in Sub-Saharan African countries, who are interested to adopt the system. These efforts will help with our plans to commercialize the system. For MDDE, we will develop a unique business plan where we will try to use revenue from high income zones to subsidize distribution in low income zones for inclusivity. |
| Virtual-Reality Deep-Learning System for Fetal Echocardiography Visualization | Fetal echocardiograph is the primary tool used for mass screening to detect congenital heart malformations, and for evaluation of fetal cardiac health to determine if interventions are needed (eg., placenta ablation during twin-to-twin transfusion or valvuloplasty for valve stenosis). However, detection rate for malformations is only 50% accurate, and if undetected, plans for surgery after birth are not prepared well, leading to death and poorer outcomes. Further, fetal echo measurements are notoriously imprecise, preventing advancements. The norm for fetal echo is still 2D, despite 3D+time imaging being widely available, due to difficulty with visualization of 3D images. We aim to develop a Virtual Reality (VR) system to display echo images, and coupled it with Deep-Learning segmentation and motion tracking algorithms that we are developing, so that clinicians can better visualize the heart and its motion, and can obtain in real time shape, size and deformation measurements to aid with detection of abnormalities and evaluation of disease severity. We believe this will be the next paradigm in this field. This project thus task the student to bring our VR system forward, to test various display approach to find the one that allows the easiest interpretation of the images, and that gives the highest chance of detecting abnormalities, and to work towards incorporating our deep learning algorithms with the system. You will work in a team of engineers and clinicians on this project to facilitate your work and gain suitable exposure. |
| Can the Shape and Motion of the Heart Predict the Risk of Death? | Cardiac diseases is the number one killer in the world. When diseased, the heart can adapt a different shape, for example, it becomes thicker and rounder with obtructive heart failure, and it becomes elongated with valve regurgitation. During disease, it also moves differently, for example, during cardiomyopathy, contractile motions are reduced. So far, there has been partial success at using machine learning to detect diseases from cardiac images such as echo and MRI (diagnosis), but there's been only 1 recent paper in a top journal showing that deep learning processing of cardiac motion predict outcomes of the patient better than doctors can (better prognosis). We ask the question, whether we can combine the shape and the motion of the heart to better predict its outcomes? We will first refine deep learning tools in the lab to create a neural network algorithm to extract the shape and motion of the heart from MRI images, using echocardiography images from a large database from Imperial NHS. We will use a graph fourier approach to describe the 3D shape and motion of the heart. We will then create a network to use shape and motion features to predict all-cause-mortality from the large database. Being able to predict risks of poor outcomes can help doctors figure out which patients to single out for better monitoring and care, and this can potentially reduce deaths and improve outcomes. |
| 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 recently developed an ML algorithm that calculates whether a patient really have heart attack, based on their medical history and their initial blood test (troponin and others) results. It 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 attention-based ML model does better than our existing algorithm, second, use imputative deep learning models to address cases where there are some missing data (common in the clinic). 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, 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 Parkinsons Disease and Essential Tremor, affecting around one million people in the UK. This project aims to leverage plasticity brains 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, KÃnig 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 Parkinsons 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 Parkinsons 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 persons 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 Parkinsons 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. |
| Optical hand tracking using machine learning | Purpose. Implement optical hand tracking using machine learning, analyse speed and bottlenecks, and explore ways of improving existing methods. Motivation. Optical hand tracking is a method used in virtual reality, augmented reality, and human-machine interfacing as it allows the user to interact with virtual environments and communicate with robots and machines in a natural way. However, optical hand tracking has not been able to achieve widespread adoption due to limitations in speed and precision, and a constant breaking of the immersive experience. The purpose of this project is to analyse existing optical hand tracking methods and quantify their speed, precision, and failure rates; and explore ways of improving the optical hand tracking performance. Work. The student will setup their own optical hand tracking setup using a camera (e.g., a webcam) and write his/her own optical hand tracking method from scratch using python and PyTorch. The student will then improve the algorithm using the state-of-the-art published algorithms and quantify the speed, precision, and failure rates of all of these methods. The student will evaluate the bottlenecks in each of these categories. For example, what is the physical, hardware, or computational reasons for these limitations. Certainly the speed of light is fast and so is not constraining the speed of calculations. Perhaps it's the two-step process of identifying where the hand is in the image and the subsequent steps of identifying where the hand joints are located? Is the constraint due to the hardware's calculation speeds? We will explore these questions and many others. The students will learn how to approach a common machine learning problem with the deep analytical abilities of an academic researcher. Work. |
| 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). |
| Microfluidic Devices for Engineering Advanced Microparticles for Noninvasive Surgery | Purpose: To develop microfluidic devices to engineer advanced microparticles that can be controlled noninvasively with focused ultrasound devices. Motivation. The vision for noninvasive surgery is to manipulate and probe tissue deep in the body without having to cut open the body. Dr. Choi's laboratory develop noninvasive ultrasound devices that emit and receive sound from the patient's surface. We are working with Dr. Au's laboratory to create particles that our devices could manipulate. Here, we ask the student to develop a microfluidic platform to create advanced microparticles that our noninvasive devices could manipulate. In particular, we would like to design microbubbles to address one of the greatest medical challenges of our time - treating brain disorder. Drugs developed to treat brain disorders, such as Alzheimer's disease are untreatable, not because great drugs aren't available, but because those drugs cannot cross the brain's blood vessels, which is lined by a blood-brain barrier. Using engineered microbubbles remotely controlled by ultrasound, we can open the blood-brain barrier, finally allowing drugs to enter the brain. The work. Build microfluidic devices. This includes working in a cleanroom. You also may be exposed to working with ultrasound devices, so strength in engineering and physics would be helpful. |
| 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. |
Professor Aldo Faisal
Profile: https://profiles.imperial.ac.uk/a.faisal
Contact details: aldo.faisal@imperial.ac.uk
| Project title | Description |
| A Human-AI Collaborative Framework for Enhanced BCI Training | Brain-computer interfaces (BCIs) represent the cutting-edge fusion of neuroscience and computer engineering and offer promising unparalleled control of machines directly by human thoughts [1]. A cornerstone of this technology is the synergy between the user's brain and the decoding machine [1,2,3,4]. While considerable progress has been made in refining machine decoders, guiding the brain to generate optimal signals for these decoders remains a challenge [4]. In an optimal setting, the brain learns to produce signals, and the machine learns to interpret them. Establishing a training framework that facilitates this mutual learning process is pivotal for effective BCI control [4]. ims and Objectives: The central goal of this project is to develop a Human-AI Joint Training Framework tailored for enhancing BCI training. The project's objectives include: 1. Collection and analysis of brain data from participants to establish a rich dataset for developing and testing the framework. 2. Designing a joint training framework that promotes a symbiotic relationship between the human user's brain signals and the decoder. 3. Crafting innovative feedback methods that guide human participants in generating brain signals that align with the optimal distribution estimated by the decoder. 4. Evaluating the efficacy of the joint training framework in real-world scenarios, ensuring both the user and machine benefit from the mutual training process. 5. Adapting the framework to cater to individual differences, ensuring broad applicability and user-specific optimization. Skills Required for the Project: 1. Human Interaction and Ethical Considerations: Skills in participant interaction, ensuring ethical data collection, and maintaining participant well-being throughout the process. 2. Programming: Expertise in Python and signal processing libraries such as MNE. Familiar with UI design. 3. Machine Learning & AI: Familiarity with developing machine decoders for BCIs, alongside knowledge of feedback loop mechanisms in AI systems. 4. Neuroscience and Data Collection: Experience in EEG or other neural data collection techniques, alongside a good understanding of neuroscience principles guiding brain signal generation. [1] Millán, J. D. R. (2015). Brain-machine interfaces: the perception-action closed loop: a two-learner system. IEEE Systems, Man, and Cybernetics Magazine, 1(1), 6-8. [2] Perdikis, S., & Millan, J. D. R. (2020). Brain-machine interfaces: a tale of two learners. IEEE Systems, Man, and Cybernetics Magazine, 6(3), 12-19. [3] Vidaurre, C., Sannelli, C., Müller, K. R., & Blankertz, B. (2011). Co-adaptive calibration to improve BCI efficiency. Journal of neural engineering, 8(2), 025009. [4] Wang, H., Qi, Y., Yao, L., Wang, Y., Farina, D., & Pan, G. (2023). A Human–Machine Joint Learning Framework to Boost Endogenous BCI Training. IEEE Transactions on Neural Networks and Learning Systems. Please contact Jinpei Han <j.han20@imperial.ac.uk> for day-to-day project running questions. If you are interested in the project please sign-up here, we will contact you then about project meetings: https://docs.google.com/spreadsheets/d/1KID0MMebuOAbl_WztvlB7nyzFLAfMIUSmP5ItZl2TvQ/edit?usp=sharing |
| Active adjustment of exoskeleton movements towards Healthy Human gait using offline reinforcement learning |
This research project is aimed at developing machine learning methods for robotic (ultimately exoskeleton control). and its focus on investigating the feasibility of employing an offline reinforcement learning (OffRL) approach for neuromuscular gait modelling, which could be particularly beneficial in rehabilitation. By optimizing the reward policies, we will try to train the model to establish sensory-motor mappings (control policy), enabling it to generate human-like walking patterns. The training process incorporated essential factors such as human motion capture data, muscle activation patterns, and metabolic cost estimation within the reward function. Our goal is to demonstrate the model's ability to faithfully reproduce human kinematics and ground reaction forces during walking and generate human-like walking behavior at different speeds, with a focus on improving walking movements in neurological patients during rehabilitation. Please signup here https://docs.google.com/spreadsheets/d/1KID0MMebuOAbl_WztvlB7nyzFLAfMIUSmP5ItZl2TvQ/edit?usp=sharing and if you are interested in the project, please contact Dr. Jyotindra Narayan at jnarayan@ic.ac.uk |
| CYBATHLON: Self-driving Wheelchair control for Assistive Robot Race. | 0The Cybathlon (https://cybathlon.ethz.ch/en/event/disciplines/rob) is a unique competition where individuals with physical disabilities compete in various tasks using advanced assistive devices and technologies. It's designed not only as a competition but also as a platform for developing technologies that can be used in everyday life to assist people with disabilities. These bionic olympics games happen only every four year, now our lab - Team Imperial (https://www.imperial.ac.uk/engineering/news-and-events/cybathlon/team-imperial/) has repeatedly won medals against 65 competing teams from 5 continents wants to aprticpate again. Core to our experience is that we work with end-users and students form day one. This project aims to innovate and refine the current gaze-controlled wheelchair controls [1, 2], aligning them for seamless integration with the existing gaze-controlled assistive robotic arm controls [3,4]. Your primary task will be to evaluate and enhance the capability of simultaneously managing both the wheelchair and the robotic arm through gaze control. This involves a detailed assessment of the combined system's feasibility, followed by necessary adjustments to ensure that the controls are intuitive, responsive, and efficient. In this endeavor, you'll be working closely with other members of Team Imperial, leveraging collective expertise to achieve a harmonious and effective integration of these two advanced assistive technologies. Please sign up here https://docs.google.com/spreadsheets/d/1KID0MMebuOAbl_WztvlB7nyzFLAfMIUSmP5ItZl2TvQ/edit?usp=sharing thereafter you can contact Dr. Bukeikhan Omarali b.omarali@imperial.ac.uk for a pre-chat [1] M. Subramanian, N. Songur, D. Adjei, P. Orlov and A. A. Faisal, "A.Eye Drive: Gaze-based semi-autonomous wheelchair interface," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 5967-5970, doi: 10.1109/EMBC.2019.8856608. [2] M. Subramanian, S. Park, P. Orlov, A. Shafti and A. A. Faisal, "Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform," 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), Italy, 2021, pp. 335-338, doi: 10.1109/NER49283.2021.9441218. [3] A. Shafti and A. A. Faisal, "Non-invasive Cognitive-level Human Interfacing for the Robotic Restoration of Reaching & Grasping," 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), Italy, 2021, pp. 872-875, doi: 10.1109/NER49283.2021.9441453. [4] A. Shafti, P. Orlov and A. A. Faisal, "Gaze-based, Context-aware Robotic System for Assisted Reaching and Grasping," 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2019, pp. 863-869, doi: 10.1109/ICRA.2019.8793804. |
CYBATHLON: Fusing Robotic restoration of reaching & grasping with Autonomous Wheelchair Systems |
The Cybathlon (https://cybathlon.ethz.ch/en/event/disciplines/rob) is a unique competition where individuals with physical disabilities compete in various tasks using advanced assistive devices and technologies. It's designed not only as a competition but also as a platform for developing technologies that can be used in everyday life to assist people with disabilities. These bionic olympics games happen only every four year, now our lab - Team Imperial (https://www.imperial.ac.uk/engineering/news-and-events/cybathlon/team-imperial/) has repeatedly won medals against 65 competing teams from 5 continents wants to aprticpate again. Core to our experience is that we work with end-users and students form day one. This project centers on the hardware integration of an existing gaze-controlled robotic arm with a gaze-controlled wheelchair, based on insights from previous studies [1-4]. As part of this task, you will take on several interconnected responsibilities. Initially, you'll design and assemble a mechanical mount to attach the robotic arm securely to the wheelchair. This involves ensuring the mount is both robust and flexible, providing ease of movement and stability for the arm. Additionally, you'll work on integrating the electronic systems of the wheelchair with those of the robotic arm. This step is crucial to ensure that the two systems operate in harmony, allowing for smooth and efficient control. Your expertise will be essential in ensuring that the electronic interfaces of both the wheelchair and the robotic arm are seamlessly integrated. Another significant aspect of your role will be to collaborate closely with other members of Team Imperial to merge the control systems of the wheelchair and the robotic arm. This will involve combining the individual control mechanisms into a single, cohesive system that can be operated through gaze control. Please sign up here https://docs.google.com/spreadsheets/d/1KID0MMebuOAbl_WztvlB7nyzFLAfMIUSmP5ItZl2TvQ/edit?usp=sharing thereafter you can contact Dr. Bukeikhan Omarali b.omarali@imperial.ac.uk for a pre-chat |
| CYBATHLON: Gaze control for a Wheelchair-mounted robotic arm for Cybathlon Assitance Robot Race | The Cybathlon (https://cybathlon.ethz.ch/en/event/disciplines/rob) is a unique competition where individuals with physical disabilities compete in various tasks using advanced assistive devices and technologies. It's designed not only as a competition but also as a platform for developing technologies that can be used in everyday life to assist people with disabilities. These bionic olympics games happen only every four year, now our lab who has repeatedly won medals against 65 competing teams from 5 continents wants to aprticpate again. Core to our experience is that we work with end-users and students form day one. The project focuses on enhancing a wheelchair-mounted robotic arm with an improved gaze control interface, inspired by previous research [1-4]. This development aims to adapt the robotic arm, originally designed for kitchen and dining environments, for the specific challenges of the Cybathlon competition, Assistance Robot Race track . The key innovation lies in enabling gaze-controlled operation of the robotic arm to assist individuals with paralysis or partial paralysis. This will involve reimagining the robotic arm's functionalities to perform a variety of tasks, such as opening doors and manipulating objects, which are different from its initial capabilities. The project's foundation is based on significant prior studies in the field, ranging from gaze-based control systems for assistive robotics to cognitive-level human interfacing for robotic restoration of mobility functions. Please sign up here https://docs.google.com/spreadsheets/d/1KID0MMebuOAbl_WztvlB7nyzFLAfMIUSmP5ItZl2TvQ/edit?usp=sharing thereafter you can contact Dr. Bukeikhan Omarali b.omarali@imperial.ac.uk for a pre-chat. [1] A. Shafti, P. Orlov and A. A. Faisal, "Gaze-based, Context-aware Robotic System for Assisted Reaching and Grasping," 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2019, pp. 863-869, doi: 10.1109/ICRA.2019.8793804. [2] M. Subramanian, N. Songur, D. Adjei, P. Orlov and A. A. Faisal, "A.Eye Drive: Gaze-based semi-autonomous wheelchair interface," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 5967-5970, doi: 10.1109/EMBC.2019.8856608. [3] A. Shafti and A. A. Faisal, "Non-invasive Cognitive-level Human Interfacing for the Robotic Restoration of Reaching & Grasping," 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), Italy, 2021, pp. 872-875, doi: 10.1109/NER49283.2021.9441453. [4] M. Subramanian, S. Park, P. Orlov, A. Shafti and A. A. Faisal, "Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform," 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), Italy, 2021, pp. 335-338, doi: 10.1109/NER49283.2021.9441218. |
Professor Mengxing Tang
Profile: https://profiles.imperial.ac.uk/mengxing.tang
Contact details: mengxing.tang@imperial.ac.uk
| Project title | Description |
| Quantitative analysis of microvascular images | Background/Rationale Microvascular flow is closely related to tissue function and pathology including cancer and cardiovascular disease. For example, as tumors grow, they eventually outgrow their blood supply and angiogenesis, the formation of new blood vessels, is induced. The resulting vasculature can be different from those in healthy tissue. Therefore, the microvasculature can offer valuable information for detection and diagnosis of pathology. Imaging techniques to gather information on the microvasculature and to characterize a lesion as benign or malignant includes dynamic contrast-enhanced MRI, contrast enhanced CT, and ultrasound. However, they lack the sensitivity and resolution for imaging microcirculation. Recet advance in super-resolution ultrasound offers very high resolution imaging (tens of microns) of microvasculature in human, which is also a more affordable bed side technology. Aims/Objectives: • To develop image analysis algorithms and an analysis software to quantify a range of features in acquired the microvascular images. Example tasks include segmentation of vessels from the image, and calculating features of these vascular geometry including e.g. size, torturosity will be calculated. Experiences: • signal/image processing and programming skills |
| Mapping the Speed of Sound in Tissue: A New Approach Using Passive–Active Crossing Ultrasound Imaging | Ultrasound is a powerful and widely used imaging modality, offering real-time, non-invasive views inside the body. Conventional ultrasound focuses on differences in how tissues reflect sound, but this only tells part of the story. Another, often overlooked parameter is the speed of sound within tissue, which varies with structure, composition, and pathology. Measuring this directly could unlock an entirely new dimension of diagnostic imaging. This project explores a novel method to estimate local tissue sound speed using a combination of passive and active ultrasound imaging. These two modes interact with tissue differently: in passive beamforming, point sources (like microbubbles) appear farther away at higher sound speeds, while in active imaging, the same sources appear closer. By identifying the speed of sound at which a point aligns in both modes, we can estimate the true speed of sound at that location. We hypothesise that by introducing sparse microbubbles into the field of view and imaging them with both active and passive techniques, a full speed-of-sound map of the tissue can be reconstructed. This map could reveal subtle changes in tissue composition, with potential applications in cancer detection, fibrosis assessment, and beyond. The student will simulate this approach, develop algorithms to generate these speed of sound maps, and validate the method experimentally. This project spans signal processing, simulations, and hands-on lab work. Opinging the door to a fundamentally new imaging capability. |
| Learning based blood flow estimation in the brain using 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. |
| Robotic-assisted multi-view ultrasound for volumetric blood flow imaging in human brain | Background Transcranial blood flow imaging using ultrasound such as transcranial doppler is a non-invasive technique for cerebrovascular diseases diagnosis and monitoring. Conventional TCD devices provide either 1D blood flow spectra or 2D slices which contains limited spatial information and could be highly operator-dependence [3]. 3D ultrasonic imaging has been developed through i.g., the mechanical scanning of 2D probe, matrix array, and multi-element array, enabling the comprehensive visualisation of tissue structures [4]. In this work, we propose a robotic-assisted multi-view ultrasound imaging method aiming for improved imaging quality and larger field of view by combing data from multiple views using advance computational methods. Aims and methods: A robotic-assisted multi-view ultrasound imaging system will be developed, incorporating a robotic arm with an integrated control system to enable transcranial ultrasound scanning from multiple angles. Meanwhile, image reconstruction and enhancement algorithms will be developed to generate volumetric transcranial blood flow images at high quality. the performance of the proposed imaging system will be validated using phantoms, ex vivo skull models and in vivo healthy human volunteers. Skills Knowledge of robotics and ultrasound is preferable not mandatory. References: [3] J. Naqvi, K. H. Yap, G. Ahmad, and J. Ghosh, “Transcranial Doppler Ultrasound: A Review of the Physical Principles and Major Applications in Critical Care,†International Journal of Vascular Medicine, vol. 2013, no. 1, p. 629378, 2013, doi: 10.1155/2013/629378. [4] H. Favre, “Transcranial 3D ultrasound localization microscopy using a large element matrix array with a multi-lens diffracting layer: an in vitro study,†Phys. Med. Biol., 2023. |
| Acoustoelectric Imaging of the heart – A Pilot Project | Background: Ventricular tachycardias (VT) are life-threatening arrhythmias that may manifest with syncope, cardiogenic shock and/or cardiac arrest. Catheter ablation for VT has emerged as an important complementary treatment option to reduce VT recurrences and ICD shocks. Invasive electro-anatomical contact mapping is considered the gold standard to define ablation targets, yet several fundamental limitations exist with current approaches notably the restriction to surface measurements alone. This implies that substantial parts of the ventricular myocardium (=the intramural space) cannot be accessed and eludes an assessment in the procedure. Novel mapping technologies are needed to allow the clinicians to assess the arrhythmogenic substrate in its entirety including the full thickness of the myocardial wall. This would offer a more precise identification of ablation targets to prevent recurrences of ventricular arrhythmias. Cardiac Acoustoelectrical Imaging (AEI) is a technology that exploits the interaction of an ultrasonic pressure wave and the resistivity of tissue to map current densities. AEI allows for in-vivo mapping and characterisation of these biological current densities beyond the tissue surface and with high spatio-temporal resolution. Only limited preclinical research for its application for cardiac mapping is available. The ability to map transmurally across all layers of the myocardium distinguishes it from other available approaches and would address one of the most fundamental limitations in contemporary clinical cardiac mapping. Aim: In this pilot project we aim to evaluate if cardiac AEI allows to record transmural electrical currents with high temporal and spatial resolution and can differentiate between endo-, midmyocardial and epicardial impulse origins. This is the first cardiac AEI project at this institution and involves establishing a new setup for cardiac AEI, test and optimise the workflow in an ex vivo phantom model and then langendorff perfused heart model to establish feasibility, review safety of ultrasound parameters and assess practicality for 2D and 3D mapping. If confirmed, in a second step spatial accuracy for transmural mapping and differentiating normal from abnormal propagation characteristics will be evaluated using intramural plunge needles as validation to gather pilot data for a subsequent translational project. In this pilot study an experimental rig will be set up to enable AEI measurement, which including acoustic and electrical sensors working in a water tank, and generate some initial pilot data for validate the feasibiliy of the technology in a simplified lab environment. Ultrasound engineers should ideally have a previous experience / basic understanding of acoustoelectric imaging. Knowledge of cardiac electrophysiology and mapping is desirable. This is a joined project working with a clinical cardiac electrophysiologists. |
| Cracking the Bubble: How Gas Vesicle Collapse Affects Engineered Cells | Gas vesicles are an exciting breakthrough in ultrasound imaging. They are tiny, genetically encoded nanostructures that allow cells to become visible under ultrasound, offering a powerful new approach to molecularly specific imaging. Recent research has successfully engineered cells to express these structures, making it possible to track gene expression, cell location, and biological activity in real time using sound. But there's a catch: gas vesicles are fragile. When exposed to high-intensity ultrasound, they collapse. While this destruction is useful in some contexts, the biological impact on the host cells remains largely unknown. This project investigates what happens to cells after their gas vesicles are destroyed. How do they recover? Does the collapse affect their viability, metabolism, proliferation, migration, or gene expression? Could repeated destruction stress the cells or alter their behaviour over time? The student will design and perform experiments to track the biological response of gas-vesicle-producing cells before and after ultrasound exposure. This may include assays for cell health, microscopy for structural changes, and gene expression profiling to uncover deeper biological effects. Understanding these responses is essential for safely and effectively using gas vesicle-expressing cells in diagnostics, synthetic biology, and therapeutic delivery, and may reveal new insights into how genetically engineered cells handle mechanical stress. |
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. |
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 |
| The impact of high-dimensional datasets on AI for precision oncology | Introduction: Precision oncology aims at prescribing the most suitable anti-cancer treatment to a patient to ensure maximum efficacy and minimum side effect. By analysing pharmaco-omics data, we can identify individual variability in drug response by studying the molecular profiles of the patient's tumours. Furthermore, clinical features such as age, co-morbidities and the like can influence how patients respond to treatment. Artificial Intelligence (AI), and more concretely its most developed subarea Machine Learning (ML), holds tremendous promise for precision oncology. Recently published ML studies show that, at least in some cases, it is possible to predict which patients are resistant, or responsive, to treatment from their molecular and/or clinical features. However, a major challenge is the very large number of features describing each patient. About the supervisor: Dr Pedro Ballester has a 10-year track record on this research topic. His later paper on this topic was on the application of AI to patient response prediction to drug treatments from multi-omics tumour profiles (https://onlinelibrary.wiley.com/doi/10.1002/advs.202201501). His group has hence developed infrastructure around the data sources I will employ. For example, the Genomic Data Commons (GDC) from the US National Cancer Institute (NCI). Research plan: The first stage will be to master scripts to integrate clinical and omics data from the GDC. Here we already combined the clinical responses annotated with the RECIST standard as two types, namely “Responder†(including “Complete Response†and “Partial Responseâ€) and “Non-Responder†(including “Stable Disease†and “Clinical Progressive Diseaseâ€). Then we will select problem instances with various dimensionalities as case studies. That is, cohorts of patients with the same cancer type, whose tumours were profiled with the same omics technology and administered the same drug. Tumours were comprehensively profiled (CNA, DNA methylation, miRNA and mRNA) Lastly, we will investigate the application of ML techniques to identify the most predictive features among the hundreds to thousands of them. Among the techniques to be assessed, we will look at integrating ML algorithms with feature selection schemes such as OMC, Boruta or RFE. Particular attention will be paid to rigorously estimate classification performance, some methods for this are already implemented here: https://cran.r-project.org/web/packages/MXM/ |
| 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. |
| A webserver to predict the molecular targets of small organic molecules | Knowing all the macromolecular targets bound by a drug molecule is needed to understand its mechanism of action (MoA) in treating a particular disease. Consequently, predicting the targets of a molecule is a key problem in drug design. This is exacerbated by the growing realisation that this knowledge is crucial to understand the efficacy and side-effects of drug as well as the resurgence of phenotypic screening boosting the number of effective drug candidates without known targets. The student will learn how to use existing ligand-centric computational methods for target prediction and update those developed by the Ballester lab (https://www.frontiersin.org/articles/10.3389/fchem.2016.00015/full, https://www.nature.com/articles/s41598-017-04264-w). These methods, which use molecular similarity techniques to relate targets by their known binding molecules, have already led to the discovery of tightly-bound targets of clinical drugs (https://pubmed.ncbi.nlm.nih.gov/33021050/, https://pubmed.ncbi.nlm.nih.gov/33227945/). Existing webserver, SQL and Python code will be employed to update a webserver implementing this target prediction method (https://onlinelibrary.wiley.com/doi/abs/10.1111/cbdd.13516). There will be also opportunities to investigate whether the latest molecular similarity methods improve target prediction. New functionality may also be incorporated, such as available tools for MoA hypothesis generation (e.g.https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-0409-9). |
| 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 |
| Searching for molecules with potent and selective PLK activity using machine learning | Polo-like kinases (PLKs) are a family of serine/threonine protein kinases involved in multiple functions in eukaryotic cell division. There are 5 members of this family identified in humans (PLK1-PLK5). Given their proven and potential roles as drug targets, there is interest in identifying molecules that selectively inhibit specific members of this family. This project will consider the data available for each member, that is the set of molecules that has already been tested on each of them and investigate models able to predict their activity. These models will be constructed using machine-learning approaches, which constitute a form of artificial intelligence that can ameliorate virtual screening performance through a process of automatic and direct learning from the data. Following intensive validation and prospective application of make-on-demand compound libraries, our collaborators will test in vitro selected molecules to have potent and selective PLK activity. |
| Machine learning to predict the activities of molecules on cultures of pathogenic bacteria | This methodology research project aims to investigate the development of machine learning models to predict how molecules inhibit pathogenic bacteria growth. The project will exploit recent antimicrobial and toxicity data in novel ways. The student will investigate which supervised learning algorithm leads to the most predictive models under realistic scenarios (distribution shifts). The best models will be employed to screen ultraÂlarge compound libraries to identify those potential antibiotics unlikely to be toxic for humans. There will be opportunities to validate these predictions in vitro via existing collaborations. |
| A toxicity prediction toolbox based on the Therapeutic Data Commons benchmarks | Drug leads can induce many forms of toxicity in humans, which ultimately can result in abandoning that lead molecule or even the therapeutic target all together. It is therefore important to have count with computational models able to predict each known toxicity endpoint for a given molecule. The Therapeutic Data Commons proposes a suite of toxicity benchmarks (https://tdcommons.ai/single_pred_tasks/tox/) along with leaderboards pointing out the most artificial intelligence (AI) models to date (e.g. https://tdcommons.ai/benchmark/admet_group/20herg/). For each of these problems, the student will review the literature to introduce the problem and its most predictive model in a clear manner. The student will also evaluate the model with other performance metrics and apply it to other sets of molecules provided by the supervisor (thus, independency in running code such as python scripts is required). This project is an opportunity to be introduce 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/s41589-022-01131-2 https://arxiv.org/abs/2102.09548 |
| 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 |
| 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. |
| 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. |
Dr Sam Au
Profile: https://profiles.imperial.ac.uk/s.au
Contact details: s.au@imperial.ac.uk
| Project title | Description |
| Multicancer Diagnosis of BRCA-associated Breast and Ovarian Cancer from Blood by Microfluidic Isolation of Extracellular Vesicles | Extracellular vesicles (EVs) are nanometer-scale lipid bilayer sacs generated from cells containing genetic and protein payload. EVs play very important roles in cancer and metastasis including promoting the adhesion, migration, invasion and growth of metastastising tumour cells. EVs are commonly found in blood and therefore may be very useful for minimally invasive early diagnosis of cancer. EVs contain distinct surface markers and payload which may be used one day to determine what type of cancer an individual has and help select which therapeutic approach is best for an individual patient. To one day achieve a "pan-cancer" method of cancer diagnosis from blood, we wish to first explore a "multicancer" approach to cancer diagnosis. BRCA is a gene which drives a much higher risk of breast and ovarian cancer when women inherent mutations to this gene. Some women with BRCA mutations can have both breast and ovarian cancers at the same time. This makes it an excellent initial model for multicancer diagnosis because blood samples can be taken from the subset of women known to carry BRCA mutations for routine screening. The challenge however, is how to best isolate these small particles from the blood of cancer patients and then subsequently use these particles for simultaneous breast and ovarian cancer diagnosis? The goal of this ambitious project is to microfluidic device that a) isolates EVs from whole blood and b) determine if these EVs are derived from breast and/or ovarian cancer. While many methods of EV isolation are currently under investigation, an inertial based method that uses wall lift effects is particularly suited for the physical isolation of EVs in ways that do not modify their characteristics: https://www.science.org/doi/10.1126/sciadv.adi5296 . Isolated EVs will then be combined with techniques such as immunohistochemistry or mass spectrometry to classify EVs to move us towards a blood-based screen for the diagnosis of multiple cancer types at once. Students will gain valuable skills in microfluidic device design, CAD, microfabrication, cancer cell culture, molecular biology and imaging. |
| Mechanogenetic Control of Shearosome Production for Tailored Immune Activation | Project Description: Extracellular vesicles (EVs) are lipid bilayer-bound particles secreted by cells that carry bioactive cargo such as proteins, RNAs, and lipids (EL Andaloussi, 2013). As natural delivery vehicles, EVs offer a promising alternative to synthetic or viral vectors (Di Ianni, 2025). Unlike mRNA-loaded lipid nanoparticles (LNPs), EVs are highly stable and easily stored or transported without requiring ultra-cold conditions. Compared to viral vectors, they are biocompatible, non-replicative, and less likely to provoke acute immune responses—unless intentionally engineered to do so (Di Ianni, 2025). However, conventional EVs like exosomes face key translational limitations, especially around inefficient cargo loading, labor-intensive isolation, and low yields (Lener, 2015). To address these issues, our group has developed a novel microfluidic platform using capillary bifurcations, which exploits mechanical forces to induce membrane shedding. Through this, we discovered a new class of large EVs called shearosomes (Angelos Vrynas, 2025). Shearosomes are generated in a cell type-agnostic, mechanically driven manner and have demonstrated potent interaction with innate immune cells such as macrophages and monocytes—making them strong candidates for use in therapeutic cancer vaccines. Problem Statement: To fully harness shearosomes as precision immunotherapy agents, two major technical questions remain: 1. How can we control shearosome formation? Shearosome generation relies on membrane fluidity and intracellular lipid dynamics of the host cell. Our goal is to dissect and manipulate the molecular mechanisms that influence their production: • Enhancement strategies include increasing external cholesterol availability in culture media, pre-incubating cells with cholesterol, and upregulating endosomal escape regulators (e.g., CD63) that redistribute cholesterol to the plasma membrane. • Downregulation approaches include genetic silencing of these same factors or cholesterol modulation to explore how mechanical stress and membrane composition limit vesicle formation. This line of investigation will improve our understanding of how cells physically respond to microfluidic shear and how to tune this response for consistent vesicle output. 2. How can we generate tailored shearosomes that direct specific immune responses? To make shearosomes therapeutically actionable, they must carry not just random cellular contents, but precisely selected antigens or immunomodulatory proteins, and potentially display targeting ligands that guide them to desired immune cell populations. Our approach includes: • Plasmid design optimization to ensure efficient expression and packaging of cargo proteins into shearosomes. • Engineering of surface markers or receptors via genetic engineering techniques to control downstream immune cell targeting. • Exploration of intracellular trafficking routes that influence selective cargo loading during vesicle shedding. Together, these aims support the overarching goal of producing customized, cell-free cancer vaccines that can be manufactured on demand using a patient's own cells. Student Involvement & Skills Gained: MEng students joining this project will engage in: • Mammalian cell culture and vesicle harvesting • CRISPR/Cas9 and transfection-based gene modulation • Microfluidic design and shear stress manipulation • EV characterization techniques (NTA, DLS, electron microscopy, Western blotting) • Molecular cloning and plasmid engineering This project is ideal for students interested in the intersection of immunoengineering, synthetic biology, and translational oncology. Bibliography EL Andaloussi, S. M. (2013). Extracellular vesicles: biology and emerging therapeutic opportunities. 12(347–357). Di Ianni, E. O. (2025). Extracellular vesicles for the delivery of gene therapy. Nature Reviews Bioengineering. Lener, T. G. (2015). Applying extracellular vesicles based therapeutics in clinical trials - An ISEV position paper. ournal of Extracellular Vesicles. Angelos Vrynas, S. B.-S.-P. (2025). Circulating Tumor Cells Shed Shearosome Extracellular Vesicles in Capillary Bifurcations That Activate Endothelial and Immune Cells. Advanced Science. |
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 |
| Non-invasive manipulation and imaging of the brains immune system | Our brain has its own dedicated immune system and rapid response team: microglia. These cells actively survey the brain, clearing away toxins and pathogens. The ability to temporarily stimulate microglia has generated much excitement, due to its potential to treat brain diseases. For example, stimulating microglia can help clear away the amyloid-beta plaques that build up in Alzheimers disease. Focused ultrasound is a non-invasive and targeted technology that can stimulate microglia in any region of the brain. However, how ultrasound is stimulating these crucially important cells is unknown. This project aims to visualise whether focused ultrasound stimulates PIEZO1 mechanically sensitive ion channels in microglia to better understand the mechanism of this stimulation (expertise in Dr Morses group). A genetically-encoded fluorescent reporter based on PIEZO1, GenEPi, developed in Dr Pantaziss group, will be used to visualise whether ultrasound is stimulating these ion channels, that play multiple roles in the activation of microglia. The student will design a setup to simultaneously image the activity of PIEZO1 with confocal microscopy while performing ultrasound stimulation, which will be tested in a microglial cell line. These results will provide invaluable insight into the mechanism of how focused ultrasound stimulates microglia, allowing ultrasound treatments to be optimised to achieve improved beneficial therapeutic effects for the treatment of neurological disorders, such as Alzheimers disease. |
| 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 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 same technology can be used to delay age-related cognitive decline, as well as restore cognition in 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 and fluorescence microscopy. |
| Can ultrasound help prevent organ transplant rejection? | Immune cells triggering inflammatory responses can lead to the rejection of organ transplants. Recently, ultrasound has been shown to have an anti-inflammatory effect on immune cells, such as macrophages. We here propose to investigate how ultrasound can be used best to lead to the release of anti-inflammatory cytokines. CD4+/CD8+ T cells, purified T-regulatory cells and macrophages will be cultured in vitro and multiplexed cytokine assays will be performed following ultrasound treatment. These findings will be translated and tested on organ transplants of hearts, livers and lungs currently being done at the Technical University of Munich (TUM). This project is in collaboration with Dr Konrad Fischer from TUM. Cell culture skills are preferable, any experience with cytokine assays desirable. |
| 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. |
| The effects of focused ultrasound on neural activity and cognition in young and aged mice | 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 same technology can be used to delay age-related cognitive decline, as well as restore cognition in 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 and fluorescence microscopy. |
Professor Martyn Boutelle
Profile: https://profiles.imperial.ac.uk/m.boutelle
Contact details: m.boutelle@imperial.ac.uk
| Project title | Description |
| Real-time monitoring of traumatic brain injury patients | Background: Were funded by the Wellcome Trust / Department of Health to build a clinical instrument to monitor traumatic brain injury patients during the 5 day stay they have in the intensive care unit. We monitor brain pressure, brain electrical activity and levels of metabolic markers such as potassium, glucose and lactate to understand the state of the injured brain tissue. We have now build a prototype instrument and will be using it in the intensive care unit of King's College Hospital. Aim: . To Analyse the data for new patterns of changes indicative of 'secondary insults to the brain'. Possibility to work alongside our monitoring team in the intensive care unit to collect this vital data. Methods: You will analyse this clinical data to find new patterns of changes across the measured variables indicating transient worsening of the brain tissue state. If hospital access is possible you will have the opportunity to learn how to operate the new clinical instrument in a clinical environment. This will include characterising microfluidic biosensors. You will then work with our programmers to embed this as an adverse 'event' in our data analysis software. |
| Wearable sensors for detection of ALS | This project iis an extension of an EPSRC funded project to detect the progression of ALS (motor neurone disease) in patients. It is a Collaboration between the Boutelle group, the Drakakis Group and Prof Chris Shaw (Maurice Wahl Clinical Research Centre, King;s College Hospital). ALS is a devastating disease that is characterised by rapid deterioration in motor function leading typically to death within a few years of diagnosis. Development of therapies is hampered by the lack of a reliable method of determining the progression of the disease. Typically clinical function assessments are used, but the scale is too corse and too subjective to allow evaluation of drug therapies that might for example slow the rate of disease progression. We have been following a different approach, where we use multiple skin contact to record EMG's from pairs of major muscle groups in the arms or legs of a patient. We are looking for complex fasciculation that seem to be characteristic of ALS. There are two stands of the project; the first is to develop wearable clothing that can reliably hold the contact onto the skin to allow longer recording to be made outside of the clinical consultation. The second is to develop pattern recognition algorithms to group ALS fasciculation potentials to allow processing of large volumes of data. this is particularly important as the project now aims for patient self monitoring at home. The project will be supported by MGB group and MRC Fellow James Bashford at KCH. |
| Tracking neuronal activity in the human brain | This project custom designed project comes from a long term collaboration with Prof Anthony Strong and his team at King;s College Hospital NHS Trust. In the past we have demonstrated the importance of Spreading depolarisations (SDs)in the development of secondary brain injury in patients who have had a severe traumatic brain injury. This project involves working with the field potential data streams we obtain from out patients. We are looking to examine the role of electrical events in our clinical data sets. In particular we would like to know how such events change the stage of the brain tissue. The project will involve visits to the Hospital to see the clinical team together with working with project members in MGB group and Prof Strongs team. In particular Sharon Jewel, a PhD student from the mgb group and expert in neurophysiology. The aim will be to work with Sharon and a state of the art human neurophysiological instrument, and to program this instrument to monitor electrophysiological events and see how these parameters are changed by interaction with the events. The work will be supported by Boutelle group PhD student Sharon Jewel |
| Tracking neuronal injury in the human brain - using direct cortical responses (DCR) | This project comes is a variant of MGB 1911. Both come from a long term collaboration with Prof Anthony Strong and his team at King;s College Hospital NHS Trust. In the past we have demonstrated the importance of Spreading depolarisations (SDs)in the development of secondary brain injury in patients who have had a severe traumatic brain injury. This project involves working with the field potential data streams we obtain from out patients. The distinction between this project and MGB1911 is that in this project work focusses on what are called direct cortical responses (DCR). If we send out a single stimulus pulse of sufficient power it will cause neighbouring neurones to respond by firing back once. the power required reflects the resting state of those neurons. In the brain of a TBI patient this is perhaps the only way we can get an unbiased measurement of how destabilised this brain tissue is by the injury. Preliminary data indicates that we can track the level of tissue injury in real-time whist patients are in a drug induced coma. The project has the possibility of visits to the Hospital (if allowed) to see the clinical team together with working with project members in MGB group . In particular Sharon Jewel, a n MRC training fellowt from the MGB group and expert in neurophysiology. The aim will be to work with Sharon and a state of the art human neurophysiological instrument (Neurolinx), and to monitor DCR events and see how are changed by interaction with the pathophysiology in the injured human brain. The work will be supported by Boutelle group MRC fellow Sharon Jewel |
| Real-time detection of exposure to acetylcholine esterase inhibitors such as pesticides | This project comes from an Government agency sponsored PhD project within the MGB group. Many toxic pesticides are toxic via their effects on acetylcholine esterase. This prevents the enzyme from breaking down acetylcholine, the main neurotransmitter at the nerve muscular junction. At high doses the leaves all muscles (eg heart, diaphragm) in permenant contraction, resultiung is death. A low doses the effect is more subtle, often giving very non specific symptoms of feeling unwell. This project is to develop a blood test that will enable low exposure of such agents to be detected. Our approach is to use microdialysis sampling of the blood coupled to microfluidic biosensors to detect the free levels of acetylcholine esterase associated with the haemoglobin of the blood. The project will work on optimising this microfludic biosensor system in vitro (ie not requiring blood). Students will learn how to make microfluidic devices and use computer controlled micrfluidic systems to build biosensors. The project will be supported by PhD student Georgia Smith |
| Medical biosensors for human tissue with integrated calibration microfluidics | We have been designing a new class of biosensor for use in human tissue in a project in collaboration with the Stevens group and Paulina anekeve (MIT) In addition to micro electrode sensing elements these devices also incorporate microfluidic channels to allow (a) in tissue calibration (b) local administration of stimulating or blocking chemicals to the tissue (see reference at end for idea from a proof of concept device). This project takes these idea to develop the device concept further, establishing new methods of operation in tissue, and new device forms for different types of tissue or organ monitoring. There is also the potential within this project for another student who is interested in modelling to use FEA of the concentration profiles of these devices to optimise designs. The project will be supported by the MGB group including PDRA Dr Sally Gowers paper:Booth MA, Gowers SAN, Hersey M, Samper IC, Park S, Anikeeva P, Hashemi P, Stevens MM, Boutelle MG. 2021. Fiber-Based Electrochemical Biosensors for Monitoring pH and Transient Neurometabolic Lactate. Analytical Chemistry 93: 6646-55 Selected as Journal Cover. |
| Imaging ions in solution using an ISFET array ion concentration detector. | We have developed in collaboration with Prof Pantellis Georgiou (EEE) a platform that can 'image' ion concentrations at the surface of a CMOS fabricated detector. Each device has more than 2500 sensing pixels in an array (2 x2 mm total area). The surface of each pixel is effectively the gate of an FET transistor, so responds to the surface change. As manufactured such devices are inherently selective to pH. However, if we coat with an ion selective polymer membrane we can make the pixels sensitive to the concentrations of different ions in in solution - making an Ion Selective Electrode (ISE). Hence across an array we can detect the main ions present in physiological fluids or tissue extracellular fluids - this is particularly useful when studying injury processes in the human brain or human kidney. In this lab-based project you will learn how to use the ISFET array and then characterise the spatial selectivity of the device. This will involve using a new Bioplotter facility (BioDOT Omnia - https://www.biodot.com/technology ) just delivered to the department. You will use the device to precisely control when polymer is dispensed, then see how it responds to small additions of test sample. the project will be supported by MGB Group PHD student Chiara Cicatiello Referance about prototype device: 1. Moser N, Leong CL, Hu Y, Cicatiello C, Gowers S, Boutelle M, Georgiou P. 2020. Complementary Metal–Oxide–Semiconductor Potentiometric Field-Effect Transistor Array Platform Using Sensor Learning for Multi-ion Imaging. Analytical Chemistry 92: 5276-85 |
| Engineering a new class of chemical sensor for human tissue monitoring using FET arrays | We have developed in collaboration with Prof Pantellis Georgiou (EEE) a platform that can 'image' concentrations the solution at the surface of a CMOS fabricated detector. Each device has more than 2500 sensing pixels in an array (2 x2 mm total area). The surface of each pixel is effectively the gate of an FET transistor, so responds to the surface charge. As manufactured such devices are inherently selective to pH. However, we are investigating how we can change this chemical selectivity by immobilising a specific recognition sites such as an antibody or aptamers to the sensing surface. Specific target include inflammatory cytokines such as IL6w which are raised in injured tissue, including the brain. In this lab-based project you will learn how to use the ISFET array and learn how to immobilise antibodies onto a surface. The surface can then be characterised using XPS, a fluorescent microscope and FTIR spectroscopy. One the device is optimised you will characterise is performance in biologically relevant fluids. the project will be supported by MGB Group PHD student Chiara Cicatiello Referance about prototype device: 1. Moser N, Leong CL, Hu Y, Cicatiello C, Gowers S, Boutelle M, Georgiou P. 2020. Complementary Metal–Oxide–Semiconductor Potentiometric Field-Effect Transistor Array Platform Using Sensor Learning for Multi-ion Imaging. Analytical Chemistry 92: 5276-85 |
| Microfluidic technologies 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. This project is related to develop transdermal sampling technologies based on microfludics. We will target glucose and lactate (as a marker of ischaemia and sepsis). The work will be supported by MGB PDRA Sally Gowers (Sensors)and a PhD student (Microfludics). 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 |
| Real-time neurochemical monitoring of Traumatic Brain injury patients | This project is based on a long collaboration with Professor Anthony Strong from King's College Hospital. We have been using multimodal monitoring patients in the intensive care unit who have sever traumatic brain injury. We have used monitoring of the brain electrophysiology (see other projects) and real-time neurochemical monitoring using the sampling technique microdialysis. My group has develop many microfluidic based system to enable this monitoring, and we have shown that monitoring of brain energy metabolism via glucose and lactate measurement gives vital information as to how the injured brain copes with the dynamic events such as spreading depolarisations that are responsible for much of the secondary brain injury that occurs during the 5-10 days patients are in the intensive care unit. This project is to evaluate a new clinical instrument the Loke system by M Dialysis. (https://www.mdialysis.com/product/md-system-1-0-loke/) As world leaders in this field we are being provided with 2 Loke systems . The student will initially characterise the performance of the Lake system using the equipment based in the Boutelle group lab. They will then support the use of these systems to monitor patients in the intensive care unit at KCH. This project will be supported by the Boutelle group and MRC Clinical Fellow Sharon Jewell. Paper: Rogers ML, Leong C, Gowers S, Samper I, Jewell SL, Khan A, McCarthy L, Pahl C, Tolias CM, Walsh DC, Strong AJ, Boutelle MG. 2017. Simultaneous monitoring of potassium, glucose and lactate during spreading depolarisation in the injured human brain – proof of principle of a novel real-time neurochemical analysis system, continuous online microdialysis (coMD). J Cerebral Blood Flow and Metab 37: 1883 - 95 |
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. |
Professor Firat Guder
Profile: https://profiles.imperial.ac.uk/f.guder
Contact details: f.guder@imperial.ac.uk
| Project title | Description |
| Embroidered textile sensors | In this project, we will produce textile-based sensors by use of computerized embroidery for the purposes of measuring toxic gases or for the purposes of biosensing using sweat. Some of the tasks withing this project include synthesis of chemically functionalized threads, design and fabrication of electronics and integration of embroidered sensors and electronics onto clothing. All necessary training will be provided over the course of the project. If you would like to discuss the details of this project, please drop me a line at guder@imperial.ac.uk |
| Plant-on-a-Chip: Development of a low-cost and simple growth and monitoring environment for plants | Although plant scientists use sophisticated genomic and biochemical methods to characterize tissues of plants, they depend mostly on visual analysis to characterize performance and vigor. In addition, they use highly complex sub-optimum environments for culturing, studying or screening plants for various features. In this project, we will develop low-cost and simple millifluidic growth environments for precise monitoring, controlling and studying development of plants from seed, to seedling and later on, to young plants. This project will consist of several different parts all of which can be broken into sub-projects or can be unified into a single (large) project. The project is highly interdisciplinary and involves: 1.materials and sensors fabrication 2. systems design and integration 3. Mixed signal systems development and programming. If you would like to find out more about this exciting project, please drop me a line (guder@imperial.ac.uk) or catch me in the hallways. |
| Diagnostics: Silicon-based Molecular Diagnostics | Electrochemical identification of most analytes require well characterized, high surface area conductive electrodes to accurately measure presence (or absence) of a molecule of interest. In terms of integration into high-performance, compact and portable devices, silicon is potentially one of the best materials out there today. This project will explore the use of metal coated, catalytically etched porous silicon electrodes for the detection of target DNA molecules in solution. The eventual goal of this project is to develop a the pen-side diagnostic technology for the detection of infectious animal diseases. Such technologies are particularly important for infectious diseases with no cure. This project will involve fabrication of the electrodes, immobilization of DNA, electrochemical analysis of DNA spiked samples and (possibly) construction of a portable electrochemical reader. If you're interested on a single aspect of the proposed project, that may be arranged. If you would like to work on this project as a group, that could also be arranged. Please contact me at guder@imperial.ac.uk for more information. |
| Functionalized paper-based gas sensors | Cellulose paper, though a low-cost and ubiquitous material, hasnt been brought to its full potential in chemical sensing applications. Many use paper as a substrate to support more complex sensing mechanisms but few have explored the intrinsic properties of the cellulose material itself. The vast interconnected network of cellulose fibres inside paper constantly adsorbs water from the surrounding atmosphere allowing fast and easy conductometric measurements of water soluble gases, like ammonia, carbon dioxide or nitrogen dioxide. This project targets the fact that the response of one single paper sensor cannot be assigned to a specific water soluble gas but could even be just a change in relative humidity level. To tackle this problem an array of sensors is proposed, each individual will have a characteristic treatment and response to different gases. The project is highly interdisciplinary and may involve: Materials and sensor fabrication on/in cellulose paper Electronic circuit design/optimisation and integration Signal processing (programming) |
| Design and Implementation of a 2D-prototyping Wax Plotter | Wax printers have become more and more popular for several laboratory applications. They are used to print microfluidic channels on paper substrates, stencils for metal etching or hydrophobic barriers in liquid flow applications. With the currently available wax printer technology one is limited in size, thickness and surface condition of the substrate material. In this project we aim to develop a highly adoptable 2D wax plotting machine for the use on all kinds of different substrates. It will comprise of a wax reservoir and print head, both attached to an x-y-axis controllable system. The wax heating/melting and deposition rate have to be closely monitored, controlled and optimized. This includes the design of electronic circuits (heating element, temperature sensor) as well as the interfacing between computer, microcontroller and wax plotter. To summarize, this project will require engineering skills such as designing mechanical parts and circuits, optimisation of the interplay of all the different parts from software to print head and programming for a smooth user interface to be adjustable for different substrates and conditions. |
| Autonomous control of interfacial contact pressures in pressurized wearable devices | Correct distribution of interfacial contact pressures plays a crucial role in all engineering applications whilst it is even more critical in wearable medical devices such as prosthetics . Medical devices causing pressure points or incorrect dissipation of the load lead to many painful skin problems such as skin breaks, (compression) ulcers, and blisters. We will be developing an intelligent autonomous system that will monitor pressure changes on a surface, learn the variation patterns, and take action by manipulating the surface to provide an even load distribution. This project will involve the following activities: i. Computational Methods ii. Fabrication of Elastic polymers (3D Printing, silicone moulding) If you would like to find out more about this exciting project, please drop me a line (guder@imperial.ac.uk) or catch me in the hallways. |
| Plug-n-Play chemical sensors for smartphones | The number of sensors in the world has increased 1000 times in the last 5 years and, mainly due to the proliferation of smartphones, there are now more sensors than humans in the world. Chemical sensing, while probably the most useful and extensive area of sensing, is still completely inaccessible to everyday users. Ordinary people have no way of measuring their water quality, air pollution, soil content, food freshness etc., all of which could be enabled by smartphone enabled chemical sensing. This project will build the interface between chemical sensing and smartphones. Some of the first wireless, batteryless and low-cost sensors are being developed by the group already, and we will interface these with the smartphones capability to exchange data and energy wirelessly via NFC. Using smartphones connected to wireless sensors, this project could lead to chemical sensing being part of ordinary peoples daily life. This project involves: 1. Low-power electronics 2. Wireless energy and data transfer 3. Mobile app development If you would like to find out more about this exciting project, please drop me a line (guder@imperial.ac.uk) or catch me in the hallways |
| Implants for Plants: Measuring chemical markers from within live plants | In this project, we will develop a set of electrical sensors which can be implanted into live plants for monitoring chemical processes within plants. The chemical markers can help us with the early detection of diseases or help measure plant health and identify presence of other biotic and abiotic stressors. This project will consist of several different parts all of which can be broken into sub-projects or can be unified into a single (large) project. The project is highly interdisciplinary and involves: 1.materials and sensors fabrication 2. systems design and integration 3. Mixed signal systems development and programming. If you would like to find out more about this exciting project, please drop me a line (guder@imperial.ac.uk). |
| (BIOLOGICAL,CHEMICAL, PHYSICAL) SENSORS FOR SPACE APPLICATIONS | As humans expand outside the earth, the requirements for biosensors will change given the enormous costs involved in transporting cargo into the space (low earth orbit and beyond). Currently, many specimens from the international space station (ISS) are shipped back to the earth for further analysis after freezing. Although this is possible at the ISS, returning specimens from Mars or the moon may not be as easy. This project will consist of two parts. In part I, we will investigate the needs for (bio)sensor to be used in space. In the part II, we will select an application and prototype/model a sensor in the lab as a proof-of-concept. This project is particularly suitable for someone who is an independent and free-thinker and willing to investigate this rather unusual topic. |
| Cooling Systems for Microfluidic Medical Applications | There are many devices requiring a certain temperature to function. Nevertheless, this time we are approaching a heat problem of an unusual device: Cooling systems for prosthetic legs. Currently, amputees encounter excess sweat problem which makes the prosthetic legs slippery and affects their daily lives. In this multidisciplinary project, we will find an innovative solution to prevent excess heating of the leg. The alternative methods can be inspired by existing cooling systems such as astronaut suits, but will be majorly shaped by your imagination and creativity. During the project you will be using: i. Elastic Polymer Designs (3D Printing & silicone molding) ii. Thermodynamics Principles iii. Understanding of If you would like to find out more about this exciting project, please drop me a line (guder@imperial.ac.uk) or catch me in the hallways. |
| NFC Enabled Biosensors | This project will focus on NFC enabled biosensors for food applications |
| Materials for Wearables: Fabrication of Stretchable Conductors for Wearable Devices | Flexible electronics, that can conform to the shape of the human body, allows seamless integration of various diagnostic, energy storage, and computational devices over the body. Since the human skin is also elastic, various research groups explored the use of rubbery, low modulus materials for the construction of wearable devices that can stretch in addition to bend (hence the term stretchable electronics). The fabrication of stretchable electronics involves mounting or embedding of electronic components into/onto an elastomeric substrate in which the components are electrically connected with stretchable conductors. The current strategies for the fabrication of stretchable conductors involve highly complex methods of fabrication that limit adaptation of this technology to high volume processes and drive up the cost of manufacturing. In this project, we will explore the use of commonly available methods, which are used in the textile manufacturing industry, to design and manufacture stretchable devices that can be worn over various parts of the body, especially the joints. Stretchable devices are an exciting area of research with an enormous potential. As part of this project, the student will acquire skills in the development of materials, composites, electronics and software. |
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 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 students 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. |
Professor Pantelis Georgiou
Profile: https://profiles.imperial.ac.uk/pantelis
Contact details: pantelis@imperial.ac.uk
| Project title | Description |
| Design of large-scale chemical sensing platform for diagnostics | At the Centre for Bio-Inspired Technology, we have developed Lacewing, a handheld platform able to perform nucleic acid detection in under half an hour. Our approach relies on Lab-on-Chip technology, combining microfluidics, molecular biology and microchip technology to form a smart cartridge. The microchip is implemented in standard semiconductor technology and integrates thousands of label-free electrochemical sensors which detect the release of protons associated with nucleic acid amplification. The sensors suffer from non-idealities including noise and temporal drift which compromise the readout and therefore the accuracy. The molecular assay is based on loop-mediated isothermal amplification (LAMP) which is an equivalent reaction to PCR offering rapid time-to-result and higher specificity. In this project, the student will focus on implementing an efficient temperature regulation method based on a PID controller to maintain the reaction temperature at 63oC during the nucleic amplification. The student will apply to the new methods and data processing algorithms to a new very large array of 59,000 sensors (on a 8 mm x 12 mm microchip called TITAN), considering the limitations in terms of data bandwidth. If the student is interested they will have the opportunity to run DNA experiments in the laboratory. The project is novel and a good outcome will lead to a conference publication. We expect the student to have basic knowledge in hardware design and programming (python and Matlab). The student will be joining a multi-disciplinary team of enthusiastic researchers and will be encouraged to get involved with several fields and learn new skills. This project is suitable for students wanting to do lab based testing of biochemical reactions (DNA based), characterization of sensors and development of lab-on-chip platforms. Required expertise: Instrumentation, Matlab programming, wet-lab skills, understanding of DNA. Skills on small volume testing is desirable. |
| Multiplex Lab-on-Chip diagnostic technology | Lab-on-Chip technology is ideally suited to bring the diagnostic capabilities of a laboratory into a point-of-care device. State-of-the-art microchip technology developed at the Centre for Bio-Inspired technology has enabled the development of Lacewing, a portable test which was validated with a wide range of infectious diseases including dengue, malaria and SARS-CoV-2. As the prototype is transitioned to a sample-to-result workflow, this project will focus on developing methods for diagnostic reliability. The student is expected to carry out lab-based experimentation cycles involving microchip assembly and device characterisation towards a validation of the device on host gene transcripts. Successful outcomes will lead to publication in an internal conference or journal. |
Professor Reiko Tanaka
Profile: https://profiles.imperial.ac.uk/r.tanaka
Contact details: r.tanaka@imperial.ac.uk
| Project title | Description |
| Systems biology approach for mechanistic understanding of paediatric asthma exacerbations | Asthma is the most common chronic disease of childhood, affecting up to 10% of children in Westernised societies and 200,000,000 individuals worldwide. Many factors indicate the importance of the microbiome in asthma. Asthma is rare in rural societies, and its prevalence has been increasing markedly in the developing world as populations become urbanised. Exacerbations of asthma are often precipitated by otherwise trivial viral infections. Our studies have shown that the normal human airways contain a characteristic microbiome that is altered in children and adults with the illness. Asthmatic airways contain an excess of pathogens (which may damage the airways) and also lack particular commensal species that may be necessary for normal airway functions. This project will take a systems biology approach, by combining experiments with primary bronchial epithelial cells, in silico modelling, and clinical data analysis, to elucidate the effects of the airway bacterial microbiome in asthma, and the role of epithelium barrier integrity in disease initiation and control. We already have - a preliminary mathematical model that will be used to quantify the dynamic interactions among pathogen, commensals at the airway surface, the airway barrier and the immune system, - preliminary data from in vitro experiments, and - clinical data to be analysed. The student(s) will conduct several computational methods to identify the model structures and model parameters, using Matlab. |
| Uncovering dynamical interactions in the altered microbiota of atopic dermatitis skin “ towards designing live therapeutics | Atopic dermatitis/eczema (AD) is a devastating and very common chronic skin disease affecting 15-30% of children worldwide. The ultimate aim of this project is to design live therapeutics for AD. Healthy skin is habitated by a rich, balanced diversity of microbes, which help protect our body from invading pathogens and infections. However, this balance is thrown off on AD skin “ with a microbiome dominated by staphylococci, primarily opportunistic pathogen, S. aureus (SA). SA release peptides (via Agr quorum sensing (QS) system) that kill competitor microbes and damage the skin barrier, exacerbating AD symptoms. Other friendly staphylococci, such as S. epidermidis (SE) and S. hominis (SH), are an integral part of the healthy skin microbiome and appear to co-exist with SA on AD skin, although they are also armed through their own Agr QS systems. How does SA win the battle against SE and SH? Can we find a way to stop SA winning the battle and improve AD symptoms? This project aims to answer these two questions. The student will develop a simple mathematical model of the interspecies interactions [2], and fit the model to the experimental data to unveil the key interactions between SA, SE and SH. |
| Integrating multi-study data to identify key microbes driving skin health and disease | The skin microbiome plays an important role in maintaining skin health and contributing to disease1,2. An imbalanced skin microbiome, known as dysbiosis, is associated with conditions ranging from eczema3 to acne4. Despite the growing body of skin microbiome data deposited in public databases5, identifying the key microbes driving skin health and disease remains a challenge due to the high variability in skin microbiome compositions across studies6. This project aims to integrate data from multiple studies to identify the key microbes driving skin health and disease (e.g., eczema and psoriasis) by using statistical and machine learning approaches to untangle complex relationships within the skin microbiome. Identifying the key microbes driving skin health and disease will advance the development of data-informed in silico models of the skin microbiome. The aim of this project is to identify the key microbes in driving skin health and disease. |
| Development of in silico human skin microbiome models using 16S rRNA data | This project is suitable for students who want to gain following skills: 1) Empirical data processing and its pipeline development 2) Mathematical modelling of microbial communities and ecological dynamics 3) Computational modelling, parameter fitting, and data visualisation With recent advancements in next-generation sequencing (NGS) technologies, genetic information can now be analysed in a high-throughput and cost-effective manner. This increased availability of genomic data has since then fuelled the expansion of its research applications, such as 16S rRNA data in microbiome analysis. However, challenges exist in interfacing such data with mathematical models of microbiome dynamics. The model development process generally requires the availability of absolute abundance data to correctly infer both qualitative (e.g. whether microbes are facilitating or inhibiting each other growth) and quantitative features (e.g. intrinsic growth rates, strength of microbial interactions). However, absolute abundance data are not always available in 16S data sets, and even when the total microbial load is determined through quantitative PCR (qPCR), estimates obtained are susceptible to large coefficients of variation across technical replicates. Biological noise arising from variations of 16S gene copy number across different species also poses another hurdle between the conversion from gene counts to absolute abundances. This project aims to address the above challenges in applying 16S rRNA data to human skin microbiome modelling. We will first develop a data processing pipeline to convert 16S rRNA data into absolute abundance data. Next, we will use the obtained absolute abundances to model the human skin microbiome, by fitting to a simple generalised Lotka-Volterra (gLV) model. Finally, we will perform a critical evaluation of our data processing pipeline, by comparing the obtained models to those generated from the same data using other computational methods. |
| 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 unique context, lifestyle, and symptom trajectory. |
| Improving Atopic Dermatitis Severity Forecasting with Transformer Models and Synthetic Time Series Augmentation | Atopic Dermatitis (AD), commonly known as eczema, is a chronic inflammatory skin disease characterized by intense itching and recurrent eczematous lesions. AD characterised by complex, patient-specific symptom dynamics. Accurate short-term prediction of severity (e.g., PO-SCORAD scores) is critical for timely interventions and personalised treatment. Our group previously developed EczemaPred, a Bayesian model for forecasting AD severity, and explored LSTM-based models, including Time-aware LSTM (T-LSTM). While these models showed promise, they were constrained by limited data availability and irregular sampling intervals. Recently, transformer architectures have emerged as state-of-the-art in time series forecasting. Unlike LSTMs, transformers can model long-range dependencies and handle variable-length sequences without requiring consistent time intervals. However, their performance is often constrained by data scarcity, as transformers are known to be data-hungry models. This project will explore synthetic time series generation based on existing patient trajectories to augment the training dataset. This may involve models such as variational autoencoders (VAEs), generative adversarial networks (GANs), or diffusion models adapted to time series, with the goal of improving downstream forecasting performance. |
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 |
| Synthetic protein design for food applications | Our current food systems is not sustainable and there is a clear need to produce alternative sources of proteins for human nutrition. This project aims to use synthetic biology and computational tools to explore the potential of designed proteins to become the food of the future. This project will have a main computational component that can be followed by an experimental validation if the student wants. This project can be open to more than one student with different objectives. |
| 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 |