The CDT in BioDesign Engineering now invites applications for the following projects:

1. Machine learning approaches to biosynthetic pathway optimisation

  • Lead supervisor: Dr G. Baldwin (Imperial College London)
  • Co-supervisors: Dr P. Jones (Imperial College London), Prof S. Muggleton (Imperial College London)

We will test the hypothesis that machine learning can provide an effective and efficient method to find optimal solutions to complex biological design problems. We have developed a framework for the design and build of biological systems based our BASIC DNA assembly process. Automation of this has led to a step-change in our ability to rapidly prototype new biological designs: the vastly reduced timescale and cost afforded by this development therefore presents an opportunity to address new approaches to biological optimisation. The BASIC framework provides a conceptual design space for the construction of operons and their control through regulatory elements: promoter, 5’UTR, RBS, gene order, copy number. This design space also provides the framework for learning, since the DNA modules assembled also represent the parameter space for data analysis. This enables closed loop learning and iterative optimisation.

Here we will apply this automation and assembly framework to address the problem of biosynthetic pathway optimisation. Due to the intricacies of a biosynthetic pathway, the output will be contingent on a number of important, but unknown, factors. The optimum design to maximise yield of a biosynthetic product thus cannot be predicted, because the design rules for any particular biosynthetic pathway cannot be known in advance. Here we seek to develop a semi-autonomous approach that will use inductive logic programming to develop and test hypotheses by guiding the experimental plan.

The successful candidate will be closely involved with developing new aspects of the platform analytics and automation and the application of ML approaches. Some experience in coding and computational approaches, combined with wet lab skills would be an advantage.

2. Design and engineering of biosensing by blood cells

  • Lead supervisor: Dr T. Ellis (Imperial College London)
  • Co-supervisor: Dr F. Ceroni (Imperial College London)

Cell-based therapies are the new technology of choice in biomedicine, and synthetic biology offers a rational design route towards engineered cells that sense in the human body and record and respond as required. In this project we will use synthetic biology rules to design and build modular genetic systems for programmable sensing in model lymphocyte cell lines. This will involve genome engineering to rewire GPCR-based biosensors to sense small molecules such as melatonin and serotonin, and in parallel using RNA-based detection to sense miRNAs that are the hallmarks of cancer. The project will involve the use of CRISPR and Golden Gate assembly systems to integrate multigene constructs into lymphocyte cell lines and testing the resulting cells in lab-based assays. Collaboration is planned between signalling modelling teams and cancer research experts who can assess cell performance in mouse models.

3. Engineering systems with emergent patterns in synthetic biofilms

  • Lead supervisor: Prof M. Isalan (Imperial College London)
  • Co-supervisor: Dr Robert Endres (Imperial College London)

 The project will develop computational approaches to guide engineering of synthetic biology gene circuits for spatial pattering.

The self-organisation of spatial patterns and structures in developmental biology is a fundamental problem that promises to underpin our future efforts in tissue engineering and regenerative medicine. Computational Systems Biologists and Synthetic Biologists can together provide a bridge from theory towards practical applications, by building working prototype systems.

In our groups, we have already built simple synthetic patterns using cells, and are working towards our dream of engineering self-organising tissues and organs at will. Although several patterning mechanisms have been proposed in classical developmental biology, the Turing pattern mechanism is unique in terms of self-correction, and economy of design, and regular patterns have never been constructed ab initio using defined biological components. It is therefore a tantalising engineering and modelling target, and we have chosen to engineer these systems in bacterial 'lawns' or biofilms. In this theory project in the Endres group, we will implement a biophysical reaction-advection diffusion model for such a growing biofilm, considering gene expression, as well as fluid- and cell-mechanical aspects. Theoretical predictions will be tested in the nearby Isalan lab.

4. Engineering synthetic microbial consortia for next-generation biotechnology

  • Lead supervisor: Dr R. Ledesma-Amaro (Imperial College London)
  • Co-supervisor: Dr G-B Stan (Imperial College London)

 Natural ecosystems are composed of cohorts of microorganisms where the ensemble genetic biodiversity allows efficient utilisation of habitat resources. In contrast, industrial bioprocessing applications currently employ microbial cultures inoculated with a single microbial species, which limits processing of raw materials and end products to the properties conferred by the genetic material of one particular species. With the advent of Synthetic Biology and the availability of low-cost chemical synthesis of DNA, synthetic microbial consortia can be designed with engineered microbial species to capture the beneficial properties of genetic diversity and division of labour in a similar way to what is happening in nature.

In this project, we aim to build the tools necessary for the rational design of synthetic microbial consortia for use in industrial bioprocess applications. As part of an integrated design process, we will extract cell-cell communication systems found in nature. In particular, we will use bioinformatics tools to “mine” such systems from publicly available sequence databases with a novel approach for the identification of systems that exhibit no/minimal signal cross-talk. We will then use synthetic biology technologies to produce cell-to-cell communication devices from the bioinformatics data. High-throughput, fluorescence-based gene expression assays will be used for the assessment of functionality. Once the communication devices are established, we will identify metabolic pathways that are susceptible to benefit from a division of labour. Such pathways will be transferred using advanced DNA assembly methods and novel CRISPR tools to maximise the bioproduction of high-value molecules such as fuels, chemicals or nutraceuticals.

The ability to rationally design microbial consortia for industrial bioprocessing applications can aid efforts for a more sustainable future. This can be achieved through the use of bio-based processes in the manufacturing of chemicals and other molecules where synthetic microbial consortia rich in genetic content will allow the re-cycling of raw resources such as wastes back into the productive processes of society rather than environmentally harmful disposal alternatives such as incineration or burying in landfills.

5. Developing a framework for population-level cellular modelling employing agent-scale resource constraints

  • Lead supervisor: Dr G-B. Stan (Imperial College London)
  • Co-supervisor: Dr Tom Ellis (Imperial College London)

Reliably predicting the behaviour of engineered cells remains an underexplored problem in synthetic biology and is of high importance to the biotech industry. This necessitates the construction of mathematical/computational models which are detailed enough to describe the intricacies of underlying biological systems, but simple enough to be computationally feasible and interpretable in a manner that can aid their design.

In this project, we propose the development of an agent-based population-level modelling framework, with nutrient concentrations and other environmental conditions explicitly modelled and treated as both spatial and temporal variables. The individual agents will employ principles of whole-cell modelling to couple their internal processes through shared cellular resource constraints, and allow modelling of phase transitions. Agents will be constructed from a coarse-grained perspective, and in a modular manner to allow for the iterative addition of fine-grained modules when required. Modularity and a coarse-grained approach will facilitate straightforward transition from microbial to multicellular (higher eukaryotic) systems. This modelling framework will be iteratively improved through selected experimental testing using the wet lab facilities available to the Stan and Ellis group.

The modelling framework proposed here has the potential to provide insight into the behaviour of both isogenic and non-isogenic population in a way that has previously not been possible, which would lead to an increased efficiency across the scope of the biochemical industry. Through exploring variable environmental conditions, whilst coupling cell growth to nutrient availability and the growth of other cells in the population, there is also potential to elucidate new behaviours at a cellular level that emerge as a result of their integration into the wider environment. The proposed framework lends itself towards both investigation of failure modes and mutation propagation within homogeneous populations, and dynamics and interactions in heterogeneous cultures, considering a range of competitive, communicative and symbiotic relationships. Both have significant implications for the efficiency and scope of experiments across the entire biotech industry.

6. Rational design of DNA Nanostructures for disease diagnostics and prognostics

  1. Lead supervisor: Prof M. Stevens (Imperial College London)
  2. Co-supervisors: Dr T. Ouldridge (Imperial College London), Dr C. Barnes (University College London)

As the “omics” revolution has made abundantly clear, diseases manifest at the cellular level through myriad subtle changes in behaviour. For example, immune cells alter transcription patterns after encountering pathogens, as do cancerous cells in response to chemotherapeutics. These patterns have allowed the identification of a broad set of expression signatures with diagnostic potential for conditions ranging from infectious disease and diabetes to cancer and drug resistance. However, the technology to quickly and accurately recognize these characteristic signatures has yet to reach fruition. Hybridization-based technologies have excelled at ultraspecific recognition of numerous RNA sequences in parallel yet exhibit poor sensitivity; conversely, enzymatic reactions have achieved rapid detection of trace target concentrations yet lack the resolution to discern the subtle changes in evident in these expression signatures. The goal of this project will be to combine these two approaches, leveraging the specificity of rationally-designed hybridization systems with the sensitivity of enzymatic systems. The student will become skilled in the simulation-guided design of DNA nanostructures, modelling DNA interactions at hierarchical scales to engineer the desired sequence, domain, and whole-reaction dynamics. The student will apply these BioDesign principles to the diagnosis and prognosis of disease.

7. Synbio production of monoterpenes through computationally-guided enzyme engineering

  • Lead supervisor: Prof N. Scrutton (University of Manchester)
  • Co-supervisor: Dr S. Hay (University of Manchester)

Monoterpenes (C10 isoprenoids) are a large group of structurally diverse natural compounds that are attractive to industry as flavours, fragrances and fuels. Monoterpenes are produced from a single linear substrate, geranyl diphosphate, by a group of enzymes called the monoterpene cyclases/synthases (mTC/Ss) that catalyse high-energy cyclisation reactions involving unstable carbocation intermediates. Efforts towards producing monoterpenes via biocatalysis or metabolic engineering often result in the formation of multiple products due to the nature of the highly branched reaction mechanism of mTC/Ss. We have established platform microbial strains for the production of these compounds using automated synthetic biology strain bioengineering approaches in the Manchester Synthetic Biology Research Complex (SYNBIOCHEM; ChemistrySelect. 2016, 1, 1893-) and have discovered and characterised a range of new mTC/S enzymes using a multidisciplinary approach including enzyme biochemistry, structural biology and computational chemistry (ACS Catal. 2017, 7, 6268-).

This project will focus on the optimisation of mTC/S enzymes and pathways for the in vivo (bacterial) biosynthesis of specific compounds of interest; initially, pinene, cineol and linalool with scope to expand this selection after consultation with additional industry partners. An iterative approach using computational methods to design variants that are then characterised experimentally using our established mTC/S assays. Initial work will focus on an existing large, and ever-growing library of mTC/S sequences and product profiles collated in Manchester, which will be used for benchmarking computational predictions and to train machine-learning algorithms.

8. Design/Build/Test/Learn: novel antibiotic discovery using a plug-and-play robotic platform

  • Lead supervisor: Prof E. Takano (University of Manchester)
  • Co-supervisors: Dr R Breitling (University of Manchester), Prof P Freemont (Imperial College London)

The project combines research areas pioneered by the supervising team, in a unique interdisciplinary approach: advanced genome editing; cell-free production systems; predictive computational modelling; and statistical learning strategies. It exploits capabilities in high-throughput robotics, large-scale screening and machine learning available in the participating groups.

The application case targeted in this project focuses on creating an optimized Escherichia coli host for the production of polyketide drugs (antimicrobials and anticancer agents). The work will have an integrated approach based on an iterative design – build – test – learn cycle in several rounds during the lifetime of the project. Key approaches will be:

  1. Identify and express several minimal units required to produce core type II polyketide structures in E. coli identified computationally from genome sequences and analyse the obtained chemical structure.
  2. Model-driven disruption using genome editing using genome-scale models of E. coli metabolism to predict a set of enzyme-coding genes as targets for disruption or overexpression.
  3. Statistical learning to optimize genome editing strategies.

9. Building novel functional fibres

  • Lead supervisor: Prof J. Ward (University College London)
  • Co-supervisor: Dr Helen Hailes (University College London)

The ability to design functions into materials is becoming a tangible goal. Functions such as electron transfer, conversion of energy (light to e-) or enzyme reactions would be possible to design and build into biologically produced fibrous material. The properties gained by such materials could have many applications in diagnostics, optics and biocatalysis.

Many biological fibre forming systems are known based on carbohydrates and proteins e.g. bacterial cellulose or dextran and protein fibres such as collagen or poly-glutamate. In this PhD project we will use the properties of filamentous phages such as M13 which are long, 900 nm x 10 nm, naturally occurring molecules that self-assemble as they are secreted from E. coli. When M13 phage are concentrated they can further assemble together to form fibrous and liquid crystal domains. A cysteine containing phage will be used to build metal containing fibres and test these for electron transfer. We will use enzymes such as tyrosinase to functionalise surface tyrosines and transaminase to target lysine residues to enable cross-linking of phage with each other and with other filamentous proteins. Further functionalisation with enzymes arrayed on the surface will generate fibres with enzymic proerties that could be used in novel formats such as hybrid metal/enzyme catalysts.