Postgraduate Projects in Genetics
The projects listed below are funded through the main funding competition. The application deadline is 8 December 2026 for students intending to begin their studies in October 2027.
Doctoral Projects
Project title: Developmental genetics and evolution of early head patterning in flies
Project outline
The body plan of the model fruit fly, Drosophila melanogaster, is established early in development, while the embryo is a syncytial blastoderm. The anterior third of the blastoderm gives rise to the head and brain, while the remainder of the blastoderm gives rise to the segmented trunk. The patterning of the trunk is much better studied than the patterning of head, being both conceptually simpler and more conducive to genetic analysis. Whereas the patterning of the trunk by gap genes and pair-rule genes is essentially one-dimensional and maps clearly onto the segmented pattern of the larval cuticle, the patterning of the head is two-dimensional at the blastoderm stage and is later distorted by complex morphogenetic rearrangements. As a consequence, the patterning of the head remains poorly understood, although it is clear that the maternal Bicoid gradient plays an important role in activating and coordinating domains of gene expression. Interestingly, the bicoid gene evolved relatively recently, within Diptera (flies), and has subsequently been lost multiple times. Head patterning appears to be quite evolutionarily flexible, contributing to ecological adaptation and insect biodiversity.
The main aim of this project is to characterise the gene regulatory network controlling early head patterning in Drosophila melanogaster. The student will use advanced 10-plex confocal microscopy and computational image analysis to map patterns of gene expression in the anterior blastoderm, and quantify how they are affected by mutations and other genetic perturbations. This data will be used to construct a 2D dynamical model of head patterning, in collaboration with another lab member. A second aim is to expand this analysis to fly species which do not use a Bicoid gradient to pattern their anteroposterior axis. Together, these results will provide new insights into the development of the insect head and brain, and how these processes vary across evolution.
Principal Supervisor
Email address: ec491@cam.ac.uk
Department / Partner Institute: Genetics
Project title: Global genomic viral surveillance with pandemic-scale phylodynamics
Project outline
Viral genomics is a transformative tool for epidemiology and public health. By comparing viral genomes from different hosts we can reconstruct patterns of transmission and evolution with remarkable precision. During the COVID-19 pandemic, this enabled the identification of variants of concern and the reconstruction of viral spread within and between countries. Current computational methods are however not sufficiently scalable or robust to analyse the thousands to millions of viral genomes now available, leaving much of their potential unrealised.
Building on phylogenetic algorithms and models developed by the De Maio Lab, this project will address current methodological limitations, enabling the real-time, large-scale reconstruction and prediction of virus evolution and transmission, using adenovirus as an initial case study.
Methodology
- Enable scalable reconstruction of pathogen recombination. We will extend pandemic-scale phylogenetic techniques (De Maio et al. 2023, 2025, 2026) to infer mosaic ancestry arising from recombination within massive ancestral recombination graphs of up to millions of genomes.
- Investigate the evolution and immune escape of adenoviruses. These viruses have high divergence and recombination rates, challenging existing phylogenetic approaches. We will analyse thousands of human and primate adenovirus genomes from outbreaks, community, and wildlife samples using the developed computational methods.
- Maximise impact and community adoption. The PhD student will collaborate with leaders in the field to integrate methodological advances into widely used phylogenetic software packages (e.g. IQ-TREE), ensuring rapid dissemination and adoption by the wider community.
This project will enable the study of pathogen recombination at unprecedented scale and accuracy.
Supervisors
Dr Nicola De Maio and Dr Charlotte Houldcroft
Email address: nd597@cam.ac.uk
Department / Partner Institute: Genetics
Website: https://bioinfotraining.bio.cam.ac.uk/staff/nicola-de-maio
Project title: Decoding stochastic silencing of mammalian transposons using epigenome editing
Project outline
Tissue-specific gene regulation is essential for normal development and lifelong health. Transposable elements, which constitute 40-50% of human and mouse genomes, are typically silenced by epigenetic mechanisms to restrict their expression and transposition. However, incomplete or stochastic silencing can restore their regulatory potential, influencing nearby gene expression.
Epigenetic silencing is reset across the genome in early embryonic development as DNA methylation is erased and re-established. At newer transposable elements, re-establishment of silencing can vary between genetically-identical individuals, generating epigenetically variable elements (epialleles) that are maintained into adulthood. Variation likely reflects stochastic competition for DNA binding between transcription factors and epigenome modifiers. We will manipulate DNA binding sites and epigenetic marks during this crucial re-establishment period to define the order of events in epigenetic silencing.
This project will investigate the role of the methylation-sensitive transcription factor CTCF in the establishment and maintenance of epialleles. CTCF binding sites in epialleles will be removed by genome editing in vitro and in vivo, and inheritance of stochastic silencing will be tracked through mouse development. Targeted epigenome editing in the Agouti viable yellow mouse model will test the contribution of histone modifications to the inheritance of epigenetic states, in collaboration with international partners.
The findings from this project will provide mechanistic insights into the establishment of active and repressive epigenetic states. Tackling these underexplored regions of the genome will uncover how certain transposable elements escape silencing and influence gene regulation, informing transgene design in synthetic biology applications and interpretation of novel transposable element insertions in clinical genetics.
Principal Supervisor
Email address: cae28@cam.ac.uk
Department / Partner Institute: Genetics
Project title: Systematic study of cell cycle disruptors across the virosphere
Project outline
Viruses cannot reproduce without the help of a host cell. They therefore manipulate the processes through which cells grow and divide, collectively known as the cell cycle, to obtain the materials required to make new copies of themselves. Each virus family has evolved its own way of achieving this, although some families have independently homed in on the same mechanisms—a process called convergent evolution. For many viruses, although we know that they must manipulate the cell cycle, we do not yet know how they do so, which viral genes are involved, or which host-cell proteins are targeted.
This PhD will provide the first systematic survey of viral manipulators of the cell cycle. The student will use methods from systems biology and virology to gain fundamental insights into virus–host evolution across the tree of life.
Supervisors
Dr Antoine Hocher and Dr Charlotte Houldcroft
Email address: ah2368@cam.ac.uk
Department / Partner Institute: Genetics
Website: https://mimiclab.uk
Project title: Viral Counter-Defence in Living Cells: From Molecular Mechanism to Infection Outcome
Project outline
Bacteria and the viruses that infect them are locked in a continual evolutionary race: bacteria evolve defence systems, while phages evolve counter-defences that disable them. Yet identifying a defence and its inhibitor does not tell us whether infection will succeed inside a living cell, where the timing and abundance of both systems, and the physiological state of the host, can vary dramatically.
This project will ask what determines whether a viral counter-defence wins this race against bacterial immunity. Using DNA-mimicking phage proteins such as T7 Ocr and related counter-defence systems as tractable models, the student will combine genetics, molecular and computational approaches with quantitative single-cell infection experiments. Natural and engineered counter-defences will be used to test how their molecular properties, expression timing and abundance interact with bacterial defence strength and host physiology to determine infection outcome.
Mathematical and computational analysis will identify combinations of defence, counter-defence and cellular state that allow infection to proceed, while experiments will test whether the same molecular mechanism succeeds equally well across heterogeneous bacterial populations. The project will then ask how these single-cell outcomes translate into phage fitness and shape the evolution of counter-defence strategies.
By connecting molecular mechanisms of viral counter-defence to infection dynamics in living cells, the project aims to uncover general principles governing host–virus races. Ultimately, understanding what makes counter-defences robust across physiological states could help guide the selection or engineering of phages that remain effective against defended bacterial populations.
Principal Supervisor
Email address: ah2368@cam.ac.uk
Department / Partner Institute: Genetics
Project title: What happens when a virus meets a new host? A 76-year natural experiment
Project outline
In 1950 myxoma virus was released into rabbit populatio as a biocontrol agent. It killed 99.8% of the rabbits it infected, one of the most lethal epidemics ever recorded. Within three years the virus had become less deadly, and over the following decades it evolved again — this time gaining the ability to potently suppres the rabbit immune system. Rabbits, meanwhile, evolved resistance. Because the release was deliberate and documented, and because virologists have collected the virus ever since, we have something no other system offers: a collection of viruses spanning 76 years of evolution, many with the disease caused measured.
This project asks what actually changed. Viruses evolve at the level of DNA, but what matters for disease is what their proteins do inside the host cell, and that is invisible in a genome sequence. You will infect cells with viruses from across the archive and use mass spectrometry to measure how each remodels the rabbit proteome. The central question is whether viruses evolving independently in Britain and Australia arrived at the same solution, and whether we can identify the viral genes responsible.
You will then use reverse genetics to build viruses carrying particular ancestral or evolved versions of candidate genes, and ask whether swapping a single gene reproduces the change seen in nature.
The rules that emerge should apply whenever a virus jumps into a new host, a problem that matters across human, livestock and wildlife disease.
The project combines evolutionary biology with cutting-edge proteomics and you will be trained in both. You will be based in the Department of Genetics (Frank Jiggins) and Cambridge Institute for Medical Research (Mike Weekes). No previous experience of proteomics is expected.
Principal Supervisor
Email address: fmj1001@cam.ac.uk
Department / Partner Institute: Genetics
Website: https://www.gen.cam.ac.uk/research-groups/research-groups/jiggins
Project title: Mechanisms safeguarding genome integrity
Project outline
Our lab studies the germline, the immortal cell lineage that provides the continuity of life. Using Drosophila as a model, we combine developmental, genetic, microscopy, and genomic analyses (small RNA-seq, RNA-seq, Ribo-seq, ATAC-seq, Hi-C, long-read DNA- and RNA-seq, etc.) to build a systematic and unbiased understanding of the diverse aspects that govern germline biology in vivo. We are interested in dissecting the mechanisms protecting the genome against naturally occurring mutations as well as selfish DNA modules such as transposons (Teixeira et al, Nature 2017; Gebert et al, Mol Cell 2021; Jansen et al, Genes & Dev 2024), and in using germline stem cells as a model for understanding stem cell self-renewal, growth, and differentiation in vivo (Sanchez et al, Cell Stem Cell 2016; Gui et al, Cell Rep 2023; Samuels et al, EMBO J 2024; Grobicki et al, PNAS 2025). For instance, accumulation of unrepaired damage can lead to infertility and tumour development. Given that, we aim to understand, at the single-cell level, how germ cells assess, control, and respond to DNA damage during development; how this compares with somatic cells' responses to damage; the mechanisms that regulate DNA damage responses; and how these affect genome function and stability (both within and across generations). Current projects involve using genetics to dissect molecular mechanisms, developmental biology, single-molecule microscopy, as well as single-cell, NGS, and long-read sequencing approaches (notably Nano-seq). This project will be conducted in collaboration with Prof Alex Cagan (Dept of Genetics, Pathology, and Veterinary Medicine).
Supervisors
Professor Felipe Karam Teixeira and Professor Alex Cagan
Email address: fk319@cam.ac.uk
Department / Partner Institute: Genetics
Project title: Regulation of stem cell differentiation in vivo
Project outline
Our lab studies the germline, the immortal cell lineage that provides the continuity of life. Using Drosophila as a model, we combine developmental, genetic, microscopy, and genomic analyses (small RNA-seq, RNA-seq, Ribo-seq, ATAC-seq, Hi-C, long-read DNA- and RNA-seq) to build a systematic and unbiased understanding of the diverse aspects that govern germline biology in vivo. We are interested in dissecting the mechanisms protecting the genome against naturally occurring mutations as well as selfish DNA modules such as transposons (Gebert et al, Mol Cell 2021; Jansen et al, Genes & Dev 2024), and in using germline stem cells as a model for understanding stem cell self-renewal, growth, and differentiation in vivo (Gui et al, Cell Rep 2023; Grobicki et al, PNAS 2025; Samuels et al, Dev 2025). For the latter, we have developed a transgenic system to synchronise germline stem cell differentiation in vivo, which allowed us to uncover the transcriptional and translational programmes that underlie germline differentiation into oocytes (Samuels et al, EMBO J 2024). Surprisingly, gene expression changes did not accumulate linearly throughout the differentiation programme, but rather occurred in two distinct, developmentally sequential waves. While the first wave is characterised by the erasure of stem cell identity, the second wave, involving the rewiring of hundreds of genes, drives terminal differentiation, oocyte determination and entry into meiosis. This project aims to uncover the master regulators driving the second wave, as well as the mechanisms by which this is achieved. Our preliminary analysis suggests that the activation of the second wave mainly involves transcriptional changes, alternative transcriptional start site (TSS) usage, and epigenetic regulation. We will use genetic tools, genomics (ATAC-seq, ChIP-seq, Cut-&-Run, Hi-C), and microscopy to build a refined molecular understanding of how germline stem cells differentiate into oocytes in vivo.
Supervisors
Professor Felipe Karam Teixeira and Professor Julie Ahringer
Email address: fk319@cam.ac.uk
Department / Partner Institute: Genetics
Project title: Demography and population genetics in ancient and historical landscapes
Project outline
Genetic studies typically consider abstract populations, but real populations evolve on landscapes in two (or even three) dimensions. The effects of spatial dynamics on demography and ancestry can now be investigated using efficient computational simulation tools, and the high-dimensional parameter spaces involved can be explored with statistical learning methods. Furthermore, a growing quantity of ancient genomic and archaeological datasets are available for ancient and historical populations, particularly from northern, western and central Europe. These developments provide an opportunity to investigate the detailed demographic, social and cultural factors in ancient and historical populations in these regions.
This project will develop a simulation and inference framework for spatial population demography and apply it to data from prehistoric and medieval Britain. It will investigate the effects of spatial demographic processes and parameters such as migration rates, individual range, mate choice and landscape features on genealogies ancestry distributions. These will be compared with observed genetic and archaeological data from the Iron Age and medieval periods, including measures of genetic kinship such as identity by descent. It will address scientific questions, such as:
- Given present-day and ancient genetic data, with what resolution can we infer spatial aspects of past populations, such as their distributions of ancestry and the movements of individuals and groups?
- To what extent can we use archaeogenetic evidence to infer social and cultural factors such as population stratification, sex biases in mating and movement, past mortality from epidemics, and life expectancy or reproductive behaviour?
The methods and approaches developed will also have application to other species, as data become available for them, and indeed to other high-dimensional and data-driven inference problems.
Principal Supervisor
Email address: aos21@cam.ac.uk
Department / Partner Institute: Genetics
Project title: Quantitative imaging of matrix-guided collective cell invasion in gastruloids
Project outline
How are cell movements and cell fate decisions coordinated to drive collective cell behaviour? This project will investigate how extracellular matrix (ECM) environments guide collective cell invasion and lineage commitment in mouse gastruloids, a 3D stem cell model of early embryonic development. It will build on recent working showing how embedding gastruloids in fibrin promotes collective invasion and biases differentiation towards cardiac and vascular lineages. It will first combine multi-view light-sheet microscopy, spinning disk imaging, and high-throughput imaging pipelines to resolve invasion dynamics, ECM organisation, and reporter expression in space and time. Existing fluorescent ECM reporters and lineage reporters will be used to determine how fibronectin and laminin remodelling relates to cell movement and the emergence of cardiovascular progenitors. Image analysis will include 3D cell tracking, morphometric analysis, and quantitative comparison of ECM organisation across conditions. It will then investigate how invasion is linked to signalling and fate. Hyper-plexed HCR and immunostaining will be used to map pathway activity and lineage marker expression in embedded gastruloids, then used to test whether defined extracellular environments can be used to modulate invasion mode and cell fate. Alongside fibrin, a focused set of defined matrix conditions will be explored to determine how matrix composition and physical properties influence invasion behaviour, tissue morphology, and lineage balance. This will provide a direct test of the idea that the acellular environment is not merely permissive but can guide self-organisation in organoids. The student will be embedded in an inter-disciplinary team spanning developmental biology, advanced microscopy, image analysis, and bioengineering. As such, the project provides an opportunity to bridge embryology with quantitative and biophysical approaches to tissue self-organisation.
Principal Supervisor
Email address: bjs57@cam.ac.uk
Department / Partner Institute: Genetics
Website: https://steventonlab.wordpress.com/
Project title: Decoding regulatory programmes that build an animal
Project outline
Starting from a single cell, an animal develops through differential interpretation of its genome sequence. Across development, regulatory programmes comprising transcription factors (TFs), regulatory elements and target genes control cell fate and state. While we are beginning to understand programmes in differentiated cell types, we know much less about the regulatory decisions that drive transitions between cell states and fates.
Single-cell multiomics can address this by measuring gene expression and chromatin accessibility across development. However, profiling is destructive. Because daughter cells cannot be assayed, regulatory transitions across individual cell divisions cannot be made.
C. elegans solves this probelem, because it develops through an invariant lineage. We can profile a mother cell from one embryo and its daughters from another, then connect their profiles through the lineage tree. We have generated the first lineage-resolved multiomic single-cell map in a developing animal and used it to predict regulatory programmes controlling early developmental transitions. These models predict which TFs act in particular cells, the regulatory elements they control and their target genes. The critical next step is to test whether these relationships are causal.
The student will select predicted regulators associated with cell-fate decisions and test their functions in vivo. Using genetic perturbation and single-cell multiomic profiling, they will determine which genes and regulatory regions respond when a TF is removed and whether affected cells retain their identity, adopt an alternative fate or enter a new state. Computational analyses will reconstruct altered developmental trajectories and refine the regulatory models. Selected regulatory elements will also be tested in vivo by mutating predicted TF binding sites in transgenic reporters.
Principal Supervisor
Email address: ja219@cam.ac.uk
Department / Partner Institute: Gurdon Institute
Project title: Dissecting MLL3/4-mediated enhancer regulation during early human development
Project outline
During early human development, the pluripotent epiblast gives rise to all embryonic lineages through tightly regulated changes in gene expression. The establishment of these developmental programmes largely relies on epigenetic regulation at gene regulatory elements called enhancers, yet the underlying molecular mechanisms are poorly understood.
During development, enhancers transition between repressed, primed, and active states. The histone methyltransferases MLL3 and MLL4 bind active and primed enhancers and deposit the histone H3 lysine 4 monomethylation (H3K4me1) mark. Moreover, accumulating evidence suggests that MLL3/4 have functions beyond histone modification. However, the functional importance of H3K4me1 and the non-catalytic roles of MLL3/4 remain unresolved.
This PhD project will define the role of MLL3/4 during human development. Human pluripotent stem cells will be used to model the earliest cell fate decisions of the human embryo. MLL3/4 function will be perturbed using genetic knockouts, inducible degrons and catalytic mutants. Inducible degrons will define when MLL3/4 are required during development. Catalytic mutants will reveal how they regulate enhancer function by separating the roles of H3K4me1 from the non-catalytic activities of MLL3/4.
The consequences of MLL3/4 perturbations will be investigated at both the cellular and molecular levels. Functional differentiation assays will determine how the loss of MLL3/4 activities affects developmental potential, while genome-wide profiling of epigenetic changes and gene expression programmes will uncover the molecular mechanisms underlying these phenotypes.
This project will uncover how MLL3/4 regulate human cell fate decisions, providing fundamental insights into the epigenetic mechanisms that control early human development. This experience will train the student in both stem cell biology and computational genomics, launching their research career in a supportive environment.
Supervisors
Dr Maria Rostovskaya and Professor Ben Steventon
Email address: mr631@cam.ac.uk
Department / Partner Institute: Gurdon Institute
Website: https://www.gurdon.cam.ac.uk/people/maria-rostovskaya/
Project title: Dissecting epithelial polarity using acute protein depletion
Project outline
Most animal tissues are composed of epithelial cells that form sheets that act as barriers between compartments. The formation of epithelial monolayers depends on the apical-basal polarisation of each cell, and this polarity underlies all aspects of epithelial biology. We know most factors that generate apical-basal polarity in epithelia, but their functions are not well understood, because null mutations cause a complete loss of polarity, which is uninformative. Furthermore, protein levels only decay slowly after the induction of a mutant clone or RNAi, making it hard to distinguish the direct effects of protein loss from indirect effects or compensatory mechanisms. These limitations can be overcome by acutely inhibiting a polarity factor and studying which effects occur first and are direct responses and which take longer and are secondary.
The goal of the project is to develop a general method for acute protein knock down in Drosophila, in which the protein of interest is rapidly sequestered at the mitochondrial surface using the Catchfire technique. Once the approach has been proven to work on a test target (aPKC), it will be applied to the lateral polarity factors and tumour suppressors, Discs large (Dlg) and Scribble, whose functions are largely unknown. Making movies of tagged polarity factors after the treatment should reveal which are affected first and how the knock-down alters epithelial cell mechanics and cell shape. We will also perform super-resolution microscopy to examine how the nanoscale organisation of cytoskeleton changes over time and how the stoichiometry and localisation of polarity protein clusters is affected. These experiments should reveal the functions of Scribble and Dlg and distinguish between two competing models for how antagonism between apical and lateral polarity factors occurs. If the Catchfire approach is not effective, other options include using the auxin-dependent degron system or the traditional knocked sideways approach.
Principal Supervisor
Email address: ds139@cam.ac.uk
Department / Partner Institute: Gurdon Institute
Website: https://www.gurdon.cam.ac.uk/people/daniel-st-johnston/
Project title: Developing large language models to understand how DNA replication is shaped by evolution
Project outline
DNA replication is one of the most fundamental processes in cell biology, yet we still understand remarkably little about where it begins. Replication starts at discrete genomic sites called origins of replication, but in humans and most organisms, what determines whether a genomic locus can act as an origin has remained largely unknown. Until recently, the prevailing view was that primary DNA sequence does not govern whether a particular locus acts as a replication origin in the human genome. We overturned this view by developing ORILINX, a large language model that predicts replication origins from primary DNA sequence alone. Although ORILINX was trained on human data, it generalises across mammals (mouse, sheep) and even birds (chicken). This project will explore how and why origin locations change over evolutionary time.
The student will use ORILINX to call replication origins genome-wide across mammalian species spanning major clades (primates, rodents, and laboratory and livestock species). From these maps, they will build phylogenetic trees describing how organisms replicate their DNA and compare these against established sequence-based phylogenies. There will be ample opportunity to build upon, further develop, and expand ORILINX so that it generalises across an increasingly wide range of organisms.
Skills gained through this project will include applying AI models to biological data, high-performance computing, familiarity with bioinformatics file formats, computer programming, managing large datasets, and leading an interdisciplinary research project. Our team of computational scientists and software developers has extensive experience providing supportive mentorship. This project is therefore well-suited to individuals who are new to programming and computational biology but eager to take their first steps in using and building AI models; it can also be made more advanced for students with prior computational biology experience.
Principal Supervisor
Email address: mb915@cam.ac.uk
Department / Partner Institute: Pathology
Website: https://www.boemogroup.org/
Project: Antimicrobial resistance ecology across One Health compartments
Research Area
We study the ecology and evolution of bacterial genes in naturally evolving populations, with a particular focus on antimicrobial resistance (AMR) genes that can make bacteria resistant to drug treatment. Much of our research focuses on bacteria that move between humans, animals, and the environment. We study the movement of bacterial genes across these compartments with a view to understanding the fundamental processes and selection pressures that drive gene flow in bacterial populations.
We are a largely computational biology group that analyses large datasets of microbial genomes using bioinformatic methods. Using a variety of techniques, we draw out the evolutionary processes underlying the emergence of AMR as well as the ecological movement of AMR through bacterial communities. We use a combination of data generated de novo for projects, publicly available data, and genomic surveillance data from governmental public and animal health agencies.
Project Interests
I am particularly interested in developing studies looking at the movement of bacteria and AMR (e.g. Shiga toxigenic E. coli, Salmonella). I am also interested in projects that seek to analyse large scale bacterial genome datasets to understand the ecology of genes in these populations in terms of their adaptation to anthropogenic pressures over the last century. There may also be scope to look at metagenomic datasets from environmental and wastewater surveillance.
Principal Supervisor
Email address: kb827@cam.ac.uk
Department / Partner Institute: Genetics
Project: How parasites and pathogens drive rapid evolution in their hosts, consequences of this for the evolution of genomes, survival of populations and pathogen dynamics.
Research Area
Our research explores the genetic and evolutionary dynamics that govern interactions between hosts and their parasites. The group investigates the molecular basis of natural variation in infection susceptibility and the evolutionary processes that maintain this diversity within populations. Through an integrative approach combining genomics, evolutionary theory, and experimental biology, we aim to advances understanding of how genetic diversity in host defence evolves, informing broader questions in evolutionary biology, epidemiology, and vector-borne disease control.
Working primarily with Drosophila, Aedes aegypti, and rabbit models, we employ genome-wide association analyses, population genomics, and experimental evolution to dissect how natural selection shapes host–pathogen coevolution. Our studies have studies revealed how immune system genes evolve under fluctuating selective pressures and how resistance to infection can impose evolutionary trade-offs.
The laboratory also examines the genetic determinants of vector competence in mosquitoes, elucidating how host factors influence pathogen transmission. Recent work has provided insights into adaptive responses of wild rabbit populations to myxoma virus epidemics, highlighting the rapid pace of host evolutionary change in the face of emerging pathogens.
Project Interests
I am interested in developing projects in three model systems—rabbits, mosquitoes, and Drosophila—to investigate the evolution of host–parasite interactions. In mosquitoes, we aim to understand the population dynamics of Wolbachia infections used in public health programs to suppress dengue transmission, informing improved release strategies. In rabbits, we will analyse genomic data from historical and modern samples to reveal how immune systems evolved through viral pandemics. In Drosophila, long-term experimental populations will dissect the genetic basis of adaptation to parasites. Projects may range from experimental evolution to computational genomics, depending on the student’s interests and expertise.
Principal Supervisor
Email address: fmj1001@cam.ac.uk
Department / Partner Institute: Genetics
Website: https://www.gen.cam.ac.uk/research-groups/research-groups/jiggins
Project: Predicting the outcomes of cross-species hybridisation.
Research Area
With the explosion of whole-genome sequencing, it became clear that species boundaries are more porous than many had imagined, with many “good” species undergoing extensive hybridisation and gene exchange. Between-species hybridisation is also of growing interest in conservation biology, with anthropogenic changes in species range bringing once-isolated groups into secondary contact, and deliberate translocations between isolated populations used as a conservation tool – albeit controversially. These developments are controversial because hybridisation, with its injection of novel genetic variation, can be a “double-edged sword”. If the variation is useful, this can facilitate adaptive evolutionary change, but if the variants prove incompatible, this can harm the admixed population, even leading to “extinction by hybridisation”.
I investigate these questions using the tools of mathematical quantitative- and population- genetics, and the comparative analysis of genome sequence data (the latter in collaboration with empiricists, working on a range of animal and plant systems). The broad goals are to infer the effects of past hybridisations, and predict the outcomes of future hybridisations, to make interventions more timely and effective.
Project Interests
What, exactly, should we measure to predict the outcome of a novel hybridisation? The answer will probably be some measure of genetic interaction, but many are available, and we don’t know which – in any – could give us the predictive power we need.
How might demographic context alter the outcomes of hybridisation? For example, how different is selection in a “hybrid swarm” (where hybrids mate with other hybrids), versus “introgressive hybridisation” (where most hybrids mate with a parental types)? This question can be addressed using both theory and data, e.g. by inferring natural selection from wild-sampled or lab-crossed hybrid genomes.
Principal Supervisor
Email address: jjw23@cam.ac.uk
Department / Partner Institute: Genetics
Project: Arthropod evolutionary developmental biology (evo-devo).
Research Area
We are broadly interested in the evolution and development of arthropods, with a specific focus on the gene regulatory networks that pattern the anteroposterior axis of insects. We are interested in the transcriptional control logic of individual insect segmentation genes (e.g. gap genes, pair-rule genes, segment-polarity genes); how these genes together form a dynamical system that reliably sets up complicated embryonic patterns; and how this patterning system has evolved across species to become adapted to different life history strategies and modes of development.
Most of our research relies on advanced imaging techniques to characterise gene expression patterns within embryos. We use multiplexed confocal imaging to visualise up to 10 gene products simultaneously in each embryo, live imaging of transcripts and proteins to reveal patterning dynamics over time, and computational pipelines to process and quantify all this data. We combine these techniques with genetic perturbations to uncover regulatory interactions and mathematical modelling to test our understanding of the whole system.
Our main lab model is the fruit fly Drosophila melanogaster because of its powerful genetics, but we also work on a variety of other dipteran species and on the beetle Tribolium castaneum.
Project Interests
We are particularly interested in co-developing projects that involve comparative developmental biology across different arthropod species. For example, comparing early embryo patterning across different species of flies, or investigating differences in developmental timing across different species of beetles. We are also generally interested in the robustness and evolvability of embryo patterning systems, and the three-way relationship between ecology, evolution, and development (eco-evo-devo).
Principal Supervisor
Email address: ec491@cam.ac.uk
Department / Partner Institute: Genetics
Project: Genetics and evolution of transmissible cancers in marine environments
Research Area
My group studies the processes by which clonally transmissible cancers emerge, evolve and spread in marine environments. In particular, a family of leukaemia-like contagious cancers known as bivalve transmissible neoplasias (BTNs) have recently been discovered in several species of marine bivalve molluscs, including clams, cockles and mussels. These infectious marine cancers can spread between genetically unrelated hosts through waterborne transfer of cancer cells, sometimes across entire oceans.
BTNs have emerged at least 10 independent times in a variety of bivalve species. Some of these cancers are exceptionally long-lived, having existed as continuously propagating cell lines for hundreds or thousands of years. BTNs thus offer an unparalleled platform for investigating how cancer genomes evolve over very long time-frames, and for understanding the evolutionary consequences of the transition from somatic cell to parasitic unicellular species.
Our research aims to improve understanding of the genetic causes, natural diversity, evolutionary histories, and ecological impacts of BTNs. Our work involves collecting and screening live bivalves with the help of collaborators in several countries; applying advanced technologies such as laser capture microdissection and single-cell sequencing to large numbers of tumour samples; and developing specialised computational methods for evolutionary analyses of cancer genomes.
Project Interests
I am interested in co-developing projects investigating the genomic features of different independent bivalve transmissible cancers in order to find recurrent genetic hallmarks. This would involve laser microdissection of tumour samples and advanced bioinformatic analysis of tumour genomes. Alternatively, I am interested in ideas for studying chromosomal instability in bivalve transmissible cancers, which would involve collecting live specimens in the UK, performing cytogenetic and single-cell sequencing experiments, and analysing the resulting data. The data could be either chromosome imaging data from cell metaphases, or computational copy number data obtained through single-cell sequencing. Any project would involve a substantial computational component.
Principal Supervisor
Email address: ab2324@cam.ac.uk
Department / Partner Institute: Genetics
Project: Mapping bacterial pan-genome function through pooled fitness experiments
Research Area
Our lab studies the evolution of chromosomes across the tree of life. We are interested in DNA as a substrate for information: how genomes are organized, how they vary between organisms, and how this variation affects fitness. Bacterial species are highly diverse, and closely related strains can differ by many accessory genes. This pan-genome diversity is likely to shape competition, environmental adaptation and interactions with phages, but its functional consequences remain difficult to measure systematically.
This project aims to develop high-throughput methods to study pan-genome function at scale. The initial focus would be on natural E. coli isolates, but the broader goal is to build a general framework for studying how bacterial genetic diversity shapes ecological and evolutionary outcomes. The project would combine microbial genetics, phage biology, pooled fitness assays, high-throughput sequencing and computational pan-genome analysis.
Project Interests
I am interested in projects asking how bacterial pan-genome diversity determines fitness in complex environments. A possible starting point would be to establish pooled competition assays using natural E. coli isolates with well-characterised genomes. These strains could then be competed under different conditions, including nutrient limitation, environmental stress and phage exposure.
The student could ask which accessory genes predict competitive success, how phage pressure reshapes strain composition, and whether pooled competitions can reveal functions for poorly annotated genes. The project would suit a student interested in interdisciplinary biology, with scope to focus on microbial genetics, phage-host interactions, high-throughput sequencing, or quantitative analysis of fitness, pan-genomes and community dynamics.
Principal Supervisor
Email address: ah2368@cam.ac.uk
Department / Partner Institute: Genetics
Project: Human evolutionary genetics and ancestry.
Research Area
I study the genetics of humans and other primates, with a view to understanding the key events of the evolutionary past, and the diversity and ancestry of populations and individuals. This research involves computational and mathematical models of genome evolution, using whole genome sequence data from population-scale datasets, and also a rapidly growing quantity of archeogenetic evidence for ancient and historical populations.
Project Interests
Genetic models typically consider abstract populations with no spatial aspect, but real populations and species evolve on landscapes in two (or even three) dimensions. This adds a considerable degree of complexity, but the effects of spatial dynamics on demography and evolution can now be explored using powerful new computational simulation methods. These devlopments provide an opportunity to investigate demographic, social and cultural factors in ancient populations in these regions. Moreover, the methods and approaches developed will have broader application to other species as similar evidence becomes available for them.
Principal Supervisor
Email address: aos21@cam.ac.uk
Department / Partner Institute: Genetics
Project: Dissecting the forces by which transposable elements drive genome evolution
Research Area
A large fraction of our genome (up to 50%) consists of selfish DNA modules known as transposable elements (TEs) – mobile units that aim to increase in copy number by jumping from one location to the other. Since their discovery, transposable elements have been involved in the organization, functioning, and evolution of genomes, but their uncontrolled activity can be detrimental to the host and must therefore be regulated. On one side, selfish elements aim to increase in copy number by mobilization, while on the other side host mechanisms act to suppress TE activity and its detrimental effects. Often balanced, these two opposed actions are thought to safeguard the functional and structural integrity of the genome while allowing genome evolution.
Together with Richard Durbin's group (Department of Genetics), we are building an interdisciplinary research team that combines large-scale experimental evolution studies, comparative genomics, population genetics, and innovative computational methods to uncover the breadth of transposon-derived genetic variation within populations, across species, and how this impacts the evolution of genomes in invertebrate and vertebrate systems.
Project Interests
Projects can include study of Drosophila (fruit flies) and bat species/ populations, model systems with a rich phylogeny and a wealth of active transposable elements. Using these clades, we could use long-read sequencing technology to assemble the genomes to perform detailed comparative analysis, sequence large number of individuals obtained from lab and wild samples to access inter-species variation, profile RNAs/small RNAs and chromatin modifications from animal gonads to determine the mount defence mechanisms and transposable element activity, or perform large-scale experimental evolution studies in flies to understand how TEs spread in populations. Our ultimate goal is understand the shorter- and the longer-term consequences of TE dynamics for genome evolution.
Supervisors
Professor Felipe Karam Teixeira and Professor Richard Durbin
Email address: fk319@cam.ac.uk
Department / Partner Institute: Genetics
Project: Novel drivers of disease emergence in antimicrobial resistance priority pathogens
The BBSRC GSK TTPS ("tee-tips") Doctoral Programme is a collaboration between world-leading researchers and supervisors at GSK and from the Universities of Oxford, Cambridge, and Southampton, developing novel methods in pharmaceutical sciences.
The programme provides four years of doctoral training leading to a DPhil at Oxford or a PhD at Cambridge (from 2026) or Southampton (from 2027). During the first six months you will be based at the Doctoral Training Centre (DTC) at the University of Oxford and receive training in the fundamentals of pharmaceutical science. Short intensive training modules will equip you to take advantage of cutting edge in silico approaches, including machine learning/AI and new data-generating technologies. Further information on the course structure can be found here: Course Structure | Transformative Technologies for Pharmaceutical Science.
Principal Supervisor
Email address: kb827@cam.ac.uk
Department / Partner Institute: Genetics, GSK
Project title: Using Explainable Artificial Intelligence to Reconstruct Replication Stress States from Cancer Transcriptomes
Project outline
Background
The preceding MRes project will investigate whether artificial intelligence can infer replication stress states from transcriptomic data by learning relationships between gene expression programmes and experimentally measured replication dynamics. If successful, this would transform RNA sequencing into a readout of different types of replication stress.
However, inferring replication stress represents only the first step. A much larger opportunity lies in understanding what AI-derived replication stress states reveal about tumour biology and therapeutic vulnerability. Replication stress is not a single process but arises through diverse mechanisms including oncogene activation, transcription-replication conflicts, deregulated origin firing and defective DNA repair. Different tumours may therefore experience distinct forms of replication stress that require different therapeutic strategies.
This PhD proposes that AI-derived replication stress states can serve as a new class of functional biomarker, capturing dynamic tumour behaviour that is not accessible through genomic alterations alone. By integrating transcriptomic prediction, functional genomics and drug-response data, the project aims to uncover biologically distinct replication stress phenotypes and determine how these influence therapeutic sensitivity.
Aim
To develop and validate AI-derived replication stress states as functional biomarkers for predicting therapeutic vulnerability and guiding precision deployment of replication stress response-targeted therapies.
PhD Experimental Plan (max. 500 words)
Skills Acquired During the PhD
- Artificial intelligence for cancer biology
- Analysis of large-scale transcriptomic and multi-omics datasets
- Good software development and engineering practices
- Biology of DNA replication, replication stress, and genome instability
- Long-read sequencing and replication dynamics analysis
- Scientific communication, project leadership and interdisciplinary collaboration
Principal Supervisor
Email address: mb915@cam.ac.uk
Department / Partner Institute: Genetics, CRUK Cambridge Centre
Website: https://www.boemogroup.org/