Please see below for our list of available projects for 2022 entry. Further information on how to apply can be found here.


Combining in vitro peptide display and in vivo directed evolution to search for entirely de novo folds of DNA-binding domains

  • Lead supervisor: Prof. Mark Isalan (Imperial College London)
  • Co-supervisor: Prof. Geoff Baldwin (Imperial College London)

This project will take recent advances in protein evolution methods to develop entirely novel DNA binding domains as a basis for new synthetic transcription factors in genetic regulation. The development of new-to-nature protein functionality remains a significant challenge even given recent computational advances in protein structure determination. To address this we will take an experimental approach that combines CIS peptide display for the in vitro expression and selection of massive libraries (Patel et al. Protein Engineering, Design & Selection 26, 307–315, 2013), with phage-based in vivo selection (Brodel et al. Nature Communications 7, 13858, 2016), which gives exponential amplification of genetically selectable traits.

At 66 amino acids, lambda cro is perhaps the smallest known transcription factor that functions in E. coli, and it can be converted into an activator (Brodel et al. Science Advances 6: eaba2728, 2020). Therefore, providing an unstructured gene sequence coding for 70 - 100 aa, could in principle provide a reasonable starting point for de novo evolution of a transcription factor. The ~1091 - 10130 amino acid combinations seem daunting, but that is also why it is a fascinating question to ask whether the compounding advantages of evolution, which we will employ, will lead to an exponential selection trajectory.

The project will take a staged approached to ensure viable progression and development of the techniques during the project. Initially, we will screen large libraries of structured but non-DNA binding sequences based on familiar DNA regulatory elements (e.g. helix-turn-helix), using in vitro CIS display. Subsequently, this will be expanded to create massive libraries of unstructured proteins to screen for novel DNA binding scaffolds. DNA binding domains from these studies will then be developed as transcription factors using further protein engineering and in vivo accelerated evolution using a phage-based microbial gene drive as well as a novel technique for accelerating protein evolution through gene-directed DNA damage. The outcomes will be further analysed through biochemical, computational and structural approaches.

This project will be based at Imperial College London.

Programming synthetic cells as new therapeutic vectors

  • Lead supervisor: Dr Yuval Elani (Imperial College London)
  • Co-supervisor: Dr Francesca Ceroni (Imperial College London)

Synthetic cells are non-living entities assembled from biomolecular building blocks that mimic the cellular behaviours that are the hallmarks of life (decision making, metabolism, replication, self-repair, communication etc). The motivation behind most current synthetic cell research revolves around their use as simplified models with which to study cell biology in simplified environments. Their potential as micromachines deployed in clinical applications, has been largely neglected due to various technological bottlenecks: a damaging oversight. In this project we will undertake a series of pioneering feasibility studies aimed at unlocking an engineering rulebook for the design and construction of therapeutic synthetic cell microdevices. We will exploit synthetic biology tools and concepts for the construction of synthetic cells with smart logic circuits able to respond to a host of stimuli on-demand via logic computation.

Our aim is to adopt a rational design strategy to engineer soft microscale machines able to sense physico-chemical cues present in a tumour microenvironment and respond via the on-site synthesis and release of an anti-cancer peptide.  The technologies developed will lay the foundation for an entirely novel class of smart therapeutic agents. In our modular approach, sensing, decision making, and response will be intertwined, to create synthetic cells that approach the sophistication of live-cell therapies. The fact they are non-living and designed de-novo brings it a wealth of advantages, meaning this will be a powerful new therapeutic modality. Our platforms will be enabled by fusing together liposome biotechnology, microfluidics, gene circuit design and cell free protein expression technologies, hence transcending traditional disciplinary boundaries.  Bypassing the limitations of re-engineering living cells for therapeutics, and instead leveraging the power of biomimetic synthetic cells micromachines will open up unchartered frontier research areas in biodesign and be a step change on current approaches.

This project will be based at Imperial College London.

Developing a CRISPR toolbox for efficient gene editing in crops

  • Lead supervisor: Dr Karen Polizzi (Imperial College London)
  • Co-supervisor: Dr Jose Jimenez (Imperial College London)
  • Co-supervisor: Dr Cleo Kontoravdi (Imperial College London)

Gene editing technologies provide a rapid and reliable method for engineering new properties into living organisms to help address societal challenges.  In agriculture, they can be used to improve crop nutritional content, stress resistance, and shelf-life with potential impacts on sustainability, human nutrition, and resource utilisation.  In contrast to traditional genetic engineering and plant breeding, gene editing makes precise alterations, allowing more targeted changes to traits than previously possible.  However, efficient gene editing in plants remains a challenge, due to the complexity of the experimental system (genome size, polyploidy) and the molecular properties of the tools used to make the modifications.  This project aims to develop better gene editing tools for crop plants using enhanced molecular and computational tools.   These will be prototyped in E. coli as a more feasible test platform for rapid implementation of the design-build-test-learn cycle. We will use directed evolution and machine learning to improve gene editing nucleases with a focus on stability, activity, and gene expression burden in host cells.

The objectives of this project are:

1. Develop a screening and selection platform to identify nuclease variants with improved stability and activity

2.Use combinatorial mutagenesis to introduce random mutations into the nuclease gene and screen for stability/activity and smaller (truncated) variants

3. Build a machine learning model to design highly stable and active nuclease variants based on the data generated in Objective 2

4. Deploy nuclease variants in gene editing experiments in various chassis

The student will be trained in cutting-edge techniques in machine learning, protein evolution, and gene editing and undertake a placement with the industrial partner, Phytoform labs, where they will learn industrial gene editing workflows.

Overall, this project will contribute improved gene editing tools that are host-agnostic and can be used to gene editing in virtually any chassis of interest.

This project will be based at Imperial College London.

Application of synthetic gene circuits to novel NK cell immunotherapy

  • Lead supervisor: Prof. Hugh Brady (Imperial College London)
  • Co-supervisor: Prof. Geoff Baldwin (Imperial College London)

This project will utilise synthetic biology principles to develop a next-generation therapeutic approach for cancer, in particular carcinomas and focussing on Ovarian Cancer (OC) for proof-of-principle work. We aim to develop a universal platform for cancer immunotherapy based on Natural Killer (NK) cells. We will adapt a gene circuit model currently developed in our labs as a novel immunotherapy via NK cell activation leading to direct killing of tumour cells.  The circuit will trigger tumour cell-specific expression of molecules that can enhance the activation of endogenous NK cells within the tumour or attract more NK cells into the tumour.

We will develop a genetic logic circuit where the key component is a Boolean AND gate that generates an output signal only if more than one input signal is present. The genetic circuit is transferred by viral vector into cancer cells, initially, ovarian.  Only a small proportion of the cancer cells within a tumour would need to be transduced to have significant therapeutic effect by producing potent output molecules such as cytokines or receptor ligands to activate nearby NK cells.

The PhD project will consist of:

  1. Developing a modular system to test multiple outputs to find the most efficient combination for NK cell activation in different cancers. This will employ the Biopart Assembly Standard for Idempotent Cloning (BASIC) DNA assembly platform developed in the Baldwin lab (Storch et al., 2015).
  2. Testing the system with primary human NK cells.  NK cell biology and immune-oncology experiments will be done in the Brady lab and in collaboration with the Imperial College spin-out company NK:IO Ltd that specializes in NK cell immunotherapy. 
  3. Transfer the gene circuit to a viral delivery system suitable for a prospective clinical study.  Initially Adeno Associated Virus 2 (AAV2) and Herpes Simplex Virus 1 (HSV-1) will be tested.

This project will be based at Imperial College London.

Application of Process Raman Spectroscopy for bioprocessing using gene edited production hosts and cell-free systems

  • Lead supervisor: Prof. Paul Freemont (Imperial College London)
  • Co-supervisor: Prof. Sergei Kazarian (Imperial College London)
  • Co-supervisor: Prof. Geoff Baldwin (Imperial College London)

Raman spectroscopy (RS) is a non-destructive vibrational spectroscopy technique that provides sharp spectral features that correlate to a sample’s chemical or molecular structure in aqueous solution. Raman measurements of biological molecules, cells and tissues have been well established with many biomedical applications focused on disease detection and in vivo glucose monitoring. In parallel there has been substantial developments in the use of RS to monitor fermentation processes. In situ, simultaneous measurement of nutrients, metabolites or by-products, cell density (or biomass), have made RS an important Process Analytical Technology in industrial bioprocessing. A number of global biomanufacturers including for example Lonza, GSK, Merck Novartis Hoffman La Roche have already implemented online Raman Spectroscopy for process control and optimisation of biomanufacturing.

 Whilst genome editing has opened up opportunities in gene and cell therapy applications, it can also be used in bioprocessing to improve cell growth characteristics; cell productivity and product titre and optimise and/or improve product quality; improve cell free extracts as production hosts. An alternative to cell culture for bioprocessing is the use of cell-free protein expression (CPE) systems based on cell lysates. Recently CPE has been demonstrated as a potential production host for a variety of biologics, vaccines and antimicrobials. The advantages of CPE systems for bioprocessing it that they are non-living and not prone to contamination, have semi-defined components and an assay format that offer flexibility in adjusting the reaction mix and expression process.

The overall aim of the proposed project is to explore the application of RS into new areas such as genome edited cell cultures and cell-free systems with an industrial collaborator Kaiser (Endres-Hauser group) who design and manufacture Raman probes. The proposed project brings together the expertise of Freemont group in CPE and cell culture, the Kazarian group in RS and London Biofoundry in synthetic biology tool development.

This project will be based at Imperial College London.

A molecular device for tuneable evolution

  • Lead supervisor: Dr Jose Jimenez (Imperial College London)
  • Co-supervisor: Prof. Guy-Bart Stan (Imperial College London)

Evolution is a unique property of biological systems and a challenge for Engineering Biology. It can play a double role: On one hand it can be undesirable, as evolution may lead to mutations inactivating an engineered function. On the other hand, evolution creates variation which can lead to improving functions of interest or create new ones. In this project we will exploit this dual role to create a molecular tool that can operate in a cell to both prevent mutations resulting in loss of function and generate diversity for directed evolution on demand. This will be achieved by using retrons. Retrons are DNA elements that encode for a reverse transcriptase and a single-stranded DNA/RNA hybrid (msDNA). Once the retron is transcribed, the reverse transcriptase generates the msDNA and a fragment of it gets integrated back into the lag strand of the bacterial chromosome by recombination.

In this project we aim to develop tuneable evolution through these aims:

Aim 1: Calibration of a retron system for genome editing. We will analyse factors contributing to the efficiency of gene editing such as the DNA mismatch machinery and the expression levels of the retron.

Aim 2: Development of a retron system for continuous directed evolution of a target gene. The retrons used in the previous task are not expected to evolve. In this aim, however, we will allow the msDNA to evolve by using an error-prone RNA polymerase and/or error-prone reverse transcriptase.

Aim 3: Implementation of a pathway with tuneable evolution. We will generate a system of one or more retrons that we will use to tune the evolution of a biosynthetic pathway of interest on demand.

Ideal candidates should have a passion for Engineering Biology with strong molecular biology expertise and not be intimidated by quantitative approaches including mathematical modelling and bioinformatics.

This project will be based at Imperial College London

Engineering synthetic disease resistance genes to tackle plant pathogens

  • Lead supervisor: Dr Tolga Bozkurt (Imperial College London)
  • Co-supervisor: Dr Nikolai Windbichler (Imperial College London)

Plant pathogens hamper agricultural productivity and pose a clear and present danger to our food systems. Plant diseases alone account for 10-80% of global crop losses, enough to feed several billion people. Breeding broad-spectrum disease resistance is regarded as sustainable way of managing crop diseases. Plants rely on immune receptors encoded by the resistance (R) genes that can sense and eliminate pathogens. Due to their high potency in plant protection, R genes have been bred into virtually every crop from various germplasms.

The problem is that R gene mediated resistance is often defeated by the rapid evolution of pathogen populations. Therefore, timely identification of new R genes that are effective against emerging pathogen races is a major challenge. Here we aim to generate synthetic immune receptors (R genes) with new resistance specificities to keep up with rapidly evolving pathogens. R genes carry leucine-reach repeat (LRR) domains comprised of individual repeat modules implicated in ligand sensing. We will exploit the modular nature of these proteins and focus on engineering the LRR domains to generate new ligand binding interfaces. We will generate a synthetic LRR library by using a novel recombination-based approach that efficiently allows the shuffling of LRRs that are present in existing R genes. We will screen the synthetic LRR library with ligands encoded by the pathogens to identify new LRR-ligand partners by using a well-established proteomics approach.

The LRRs capable of binding pathogen ligands will be incorporated into various available R gene scaffolds (“synthetic R genes”) to identify the most suitable synthetic R gene–Ligand pairs that can mount appropriate immune responses. At the completion of this work, we expect to design synthetic disease resistance genes that confer new specificities or broad-spectrum resistance against agronomically important plant pathogens.

This project will be based at Imperial College London.

Engineered phage for expanded host range and increased payload capacity

  • Lead supervisor: Prof. Joanne Santini (University College London)
  • Co-supervisor: Prof. Saul Purton (University College London)

The rapid rise of antimicrobial resistance has resulted in renewed interest in alternative strategies to control the spread of pathogens including multidrug resistant ones. Phage (virus that infects and kills bacteria) therapy has gathered renewed interest as a viable alternative to antibiotics but selection and production of phages for biomedical applications to prevent or treat bacterial infections in humans and animals remains a challenging task. Current solutions can involve a complex cocktail of different phages to produce efficient results. Consequently, genetic engineering approaches are now being explored to optimize phage anti-microbial activity, e.g. by use of phages as CRISPR-Cas delivery vehicles, and address problems such as the narrow host range of individual phages that limit broader use as effective therapeutic interventions.

The project aims to create novel, engineered bacteriophages that will improve or add desired traits for therapeutic use by combining computational (i.e., artificial intelligence) with molecular genetic/synthetic biology approaches. Mining large, publicly available datasets using computational/bioinformatic tools will identify phage receptor variants and phage anti-defence systems that can be screened and assessed for functionality. Individual elements displaying favourable characteristics will be used to rationally design and genetically assemble novel phages with enhanced properties such as improved host range and infectious potential. Integration of additional modules encoding genetic payloads such as CRISPR-Cas systems with the potential to further increase efficacy of the modified phages will require the construction of minimal phage genomes able to accommodate the added genetic material. The selected phage(s) will be subjected to several rounds of genome reduction to determine the minimal viable genome(s) that will be used to re-assemble modified phages integrating the payload module.

This project will be based at University College London.

Computational biofilms: analogue spatial computing with bacterial colonies

  • Lead supervisor: Prof. Chris Barnes (Unversity College London)
  • Co-supervisor: Dr Karen Polizzi (Imperial College London)
  • Co-supervisor: Prof. Alexey Zaikin (University College London)

Our aim in this project is to engineer arbitrary analogue computation into bacterial biofilms. To explore this, we will implement a type of artificial neural network (ANN) using engineered bacterial populations arranged spatially and linked by intercellular signals.

Previous work on computation in biological systems has mostly tried to emulate the digital logic capabilities of electronic circuits. This is a potentially limiting constraint on the naturally analogue processes that occur in biological systems. In comparison, ANNs are not constrained by these limitations, meaning they could be a useful framework for building artificial biological computers.

We will engineer E. coli strains with intercellular signalling (quorum sensing) systems, where spatial separation of communicating colonies form a neural network structure. There are three objectives:

  1. Develop an in silico design process for computational biofilms
  2. Create engineered bacterial neurons
  3. Demonstrate computational capabilities of spatial biofilms

Our approach will open up new ways to perform biological computation, with applications in synthetic biology, bioengineering and biosensing. Ultimately, these neural-network-inspired bacterial communities will help us explore information processing in natural biological systems.

This project will be based at University College London.

Engineering bacterial traps to understand and inspire next-generation antibiotics

  • Lead supervisor: Prof. Bart Hoogenboom (University College London)
  • Co-supervisor: Prof. Guillaume Charras (University College London)

Antimicrobial resistance is rising whereas the development of new antibiotics has stalled: infections that were routinely cured in the past are now becoming life-endangering. This emerging and major health threat calls for radically new ideas and inspiration for next-generation antibiotics. With this project, we aim to provide such new ideas and inspiration based on a deeper understanding of how membrane-targeting antibiotics can kill bacteria and how bacteria can develop resistance against such antibiotics.

Such understanding is still surprisingly scarce, largely due to various technical limitations and due to the complex nature of bacterial cell envelopes. We will overcome some of these limitations and tackle this complexity by making use of our recently developed tools to probe live bacterial surfaces at molecular-scale resolution, using atomic force microscopy (Benn et al., PNAS 2021). To enable such microscopy approaches on live, growing and dividing cells in real time as they are under attack by antibiotics, we will engineer micropatterned surfaces that capture bacteria in suitably functionalised, microfluidic traps within which they are sufficiently immobilised to facilitate nanoscale microscopy, yet not so immobilised that it prevents them from growing and dividing.

Different bacterial strains (sensitive and resistant) will next be imaged at nanometre resolution in such traps as they are exposed to varying doses of antibiotics, and machine-learning based image analyses will used to quantitatively assess bacterial damage throughout the cell cycle and to correlate this with bacterial cell death as measured by fluorescence microscopy.

This project will be based at University College London.

Artificial intelligence driven platform to aid experimental design of optimised plasmid DNA for in vivo expression of biologics

  • Lead supervisor: Dr Stephen Goldrick (University College London)
  • Co-supervisor: Dr Francesca Ceroni (Imperial College London)

DNA-based in vivo expression of therapeutic molecules is an emerging platform aiming to deliver biologic compounds to the patient via administration of viral or non-viral DNA.  It is now known that the presence of particular sequences (such as cruciform, hairpins, palindromes, micro-RNA targets, cryptic splice sites) within the plasmid DNA can have a strong impact on the structure of the DNA, can affect the quality of the DNA produced, and the performance of expression elements. Additionally, there is a complex relationship between the presence of particular DNA elements and downstream effects on structure, expression levels, immunostimulatory effects and plasmid production. Artificial intelligence-driven bioinformatics can aid the development of novel computation tools for the automated analysis of DNA elements and, combined with literature, used to guide the design of optimised plasmid systems. Synthetic biology can offer a route for characterisation of novel designs and their output for incorporation of structure considerations into nucleic acid-based therapeutics. 

In this project we will optimise plasmid-based therapeutic design by adopting iterative experimental and computational approaches in addition to leveraging advanced machine learning algorithms. Following this systematic design approach our aim is to understand how to successfully transfer industrially-relevant therapeutic systems design within in vitro and, possibly, in vivo studies. Achieving an understanding of how to translate motif design rules into mammalian cell lines and in vivo systems will be a major achievement enabling us to revolutionise therapeutic production in the future.

This project will be based at University College London.

Developing a pipeline for rapid optimization of transcriptional expression regulation

  • Lead supervisor: Dr Mato Lagator (University of Manchester
  • Co-supervisor: Dr Neil Dixon (University of Manchester
  • Co-supervisor: Prof. Michael Brockhurst (University of Manchester)

A key challenge in engineering biological systems is the development of re-usable, precise and tunable gene expression control elements. Currently, developing these systems requires expensive and time-consuming experiments based on trial-and-error. This is particularly problematic for applications requiring complex systems with multiple co-regulated components or systems that use components obtained from non-model microorganisms.

This project will develop tools to systematically characterise transcriptional control elements, uncovering their design rules to enable re-use of these control elements in alternative hosts and genetic contexts.

Transcriptional regulation in prokaryotes is achieved primarily through the binding of transcription factors (TFs) to a promoter. Each TF has a specific binding preference, which determines its function (activator or repressor) and the location of its binding site(s) within a promoter. Knowing the biophysical binding preferences of TFs, hence, enables computational prediction of the regulatory logic and the transcriptional activity of any given promoter.  Currently, however, the biophysical binding preferences are known for only a handful of TFs, meaning that we cannot easily exploit heterologous TFs in bioengineering.

In this interdisciplinary project, the student will utilize a range of molecular/synthetic biology, microbiology, and biophysical approaches to: (i) develop an experimental and data analysis pipeline that can determine the binding preferences of any given TF; (ii) unravel design rules of activators; (iii) use the pipeline to study the evolutionary rules governing how TFs adapt to heterologous hosts. As such the student will acquire skills that are highly relevant for future careers in the molecular biology and microbiology, biotechnology, biopharmaceutical, industrial biotechnology and related industries.

Achieving these objectives will enable researchers to rapidly characterize the function (i.e. binding preference) of any desired TF, incorporate that TF into any desired regulatory network, and computationally predict promoter sequences that would contain a desired regulatory logic and result in desired transcriptional expression levels.

This project will be based at the University of Manchester.

Designing and Engineering Gamma-Butyrolactone Signalling for Synthetic Biology

  • Lead supervisor: Prof. Eriko Takano (University of Manchester)
  • Co-supervisor: Prof. Rainer Breitling (University of Manchester)

Gamma-butyrolactones are signalling molecules acting as “bacterial hormones” and regulating the production of antibiotics through well-studied signalling systems. This project will use these natural systems to develop a versatile toolkit of engineered butyrolactone circuits for broader application in synthetic biology.

Design and engineer diverse butyrolactone producing E. coli strains (“signalling devices”). [Pathway modification and assembly, LS-MS analytics] We will modify both the chemical nature of the signals and the kinetics of their production. The diversity of the butyrolactone chemistry will be expanded and engineered through plug-and-play to create novel chemistry using sensitive bioassays established previously.

Design and engineer diverse butyrolactone-responsive E. coli strains (“receiver devices”). [CRISPR-based protein engineering] Receptor and biosynthesis enzymes pairs will be characterised and mutated to create receiver variants with a broad range of response characteristics.

Characterise the signalling interaction between signalling and receiver devices. [computational modelling] Liquid co-culture and plate bioassays will be used to obtain data to parameterise an existing ensemble model of the kinetics of gamma-butryolactone signalling to predict the behaviour of integrated circuits. Based on this modelling, selected combinations of signalling and receiver devices will be combined in a single E. coli strain to create integrated circuits with a spectrum of different predicted behaviours (bistable switch, growth-phase specific toggle switch).

We will further apply these designer “signalling devices” to awaken sleeping antibiotic gene clusters. Mixed cultures of E. coli signalling devices and an Actinomyces native receiver strain from the industrial partner will be grown and analysed for induction of antimicrobial compound production.

This project provides comprehensive interdisciplinary training in methods pioneered by the supervisory team, across a range of key disciplines at the interface of biology, biosystems engineering, analytics, and bioinformatics, as well as training in an industrial setting at Odyssey Therapeutics, which are all essential for the next generation of synthetic biologists.

This project will be based at the University of Manchester.

Engineering of human artificial chromosomes to decipher the mechanisms of chromosome instability-driven prostate cancer progression

  • Lead supervisor: Prof. Patrick Cai (University of Manchester)
  • Co-supervisor: Prof. Robert Bristow (University of Manchester)

Prostate cancer (PCa) is the most commonly diagnosed cancer in the UK and Europe, and responsible for close to 110,000 male deaths annually.1 Up to 20% of PCa cases present hallmarks of genomic instability characterized by chromosomal amplifications in the right arm of chromosome 8 (Chr.8q) —commonly associated with treatment resistance and poor survival rates.2 The Chr.8q region harbours the c-Myc oncogene whose copy gain was thought to be a strong candidate to explain the poor prognosis.3 To note, c-Myc copy number alterations (CNA) in minimally gained 8q regions can involve the co-amplification of up to 40-50 other genes, some acting as oncogenes involved in prostate, ovarian and breast cancer.4

Given the genetic complexity of chromosomal gains, the functional consequences of these amplified loci remain unknown due to the lack of cell models that can recapitulate the full genetic sequence, and order of that sequence, for use in cell models, both in vitro and in vivo. Therefore, this proposal aims to implement and engineer human artificial chromosomes (HAC) to reproduce the observed genetic aberrations in patients to decipher the mechanisms of chromosome amplifications-driven increased metastatic capability of cancer cells. This project has the potential to create a brand-new gold standard approach to model, characterise and screen for new drug candidate against genomic instability-related diseases.


  1. Implementation of human artificial chromosomes mimicking Chr-8a amplification;
  2. Computational analysis, assembly and incorporation of the Chr-8q amplified regions;
  3. Deciphering the impact of copy gain on cancer progression

This project will be based at the University of Manchester.


  1. IARC. World Cancer Report: Cancer Research for Cancer Prevention.  (2020).
  2. Kou, F., Wu, L., Ren, X. & Yang, L. Chromosome abnormalities: new insights into their clinical significance in cancer. Molecular Therapy-Oncolytics 17, 562-570 (2020).
  3. Matejcic, M. et al. Pathogenic variants in cancer predisposition genes and prostate cancer risk in men of African ancestry. JCO precision oncology 4, 32-43 (2020).
  4. Weaver, B. A. & Cleveland, D. W. The aneuploidy paradox in cell growth and tumorigenesis. Cancer cell 14, 431-433 (2008).

Biodesign and Engineering of Functionalized Spider Silk Variants

  • Lead supervisor: Prof. Rainer Breitling (University of Manchester)
  • Co-supervisor: Prof. Eriko Takano (University of Manchester)

Spider silk is a high-performance biomaterial with exciting applications ranging from personal protective clothing to surface coatings for medical implants and guides for neuronal regeneration. Harvesting silk from spiders is tedious and economically not viable on a large scale. Consequently, the heterologous production of silk in a wide range of host organisms (from bacteria to goats) has been the focus of intense synthetic biology research. In this project, we intend to build on these advances to create libraries of functionalised spider silk derivatives for various applications.

Design: The first part of the project will exploit recently available spider genome and transcriptome sequences to characterize the full spectrum of natural spider silk sequence motifs. The aim is to identify design patterns of silk sequences that correlate with biophysical properties of the resulting silk.

Build: The second part of the project will test the inferred design patterns, using DNA synthesis and automated DNA assembly pipelines to create diverse libraries of silk proteins, for expression and characterization. We will use Escherichia coli, as well as biotechnologically promising alternative host species, to produce and isolate the engineered silk proteins.

Test: To facilitate the high-throughput testing of these libraries, a third part of the project will focus on the development of suitable tags for the expressed silks, which will allow rapid determination of expression levels and correlation with key properties of the resulting spun material using a collection of biophysical and materials sciences assays.

Learn: The fourth part of the project uses machine learning methods to predict improved sequence designs, which will be tested in several iterations of the design cycle.

This project provides comprehensive interdisciplinary training in methods pioneered by the supervisory team, across a range of key disciplines at the interface of biology and engineering, essential for the next generation of biotechnology scientists.

This project will be based at the University of Manchester.