PhD Studentship: Decoding the rules of human gene interaction networks using combinatorial functional genomics

Large-scale sequencing initiatives have identified thousands of genetic variants associated with human traits and disease. However, understanding how these variants influence biology remains a major challenge because genes do not act independently but within interconnected networks. Existing approaches, including CRISPR screens, have transformed gene discovery but remain poorly suited to measuring dose-dependent and higher-order genetic interactions. This limits our ability to understand how gene networks generate cellular phenotypes and how genetic variation contributes to disease.

This project addresses a fundamental question in functional genomics: how do combinations of genes interact to determine cell behaviour? The student will develop and apply innovative technologies to map genetic interactions in human cells, generating new insights into how gene networks influence health and disease. The project builds on our recently developed multiplexed Variable Dose Analysis (mVDA) technology, supported by preliminary data demonstrating robust detection of genetic interactions and integration with single-cell sequencing in Drosophila cells. mVDA enables simultaneous perturbation of multiple genes across a range of knockdown efficiencies, providing greater sensitivity and higher throughput than conventional screening approaches.

Adapting mVDA for human cells represents a significant technical advance with the potential to transform genetic interaction mapping.

The project has three objectives. First, the student will adapt and optimise mVDA in human cells, using established control interactions. Second, they will integrate this approach with either live-cell microscopy or single-cell sequencing, depending on interest. Live imaging provides sensitive phenotypic measurements, while single-cell sequencing offers scalability for large screens. Third, the student will apply the platform as a proof-of-concept to investigate genetic interactions involving the tumour suppressor NF1. As NF1-associated tumours have proven difficult to treat with single agents, they provide an ideal test case for a screening platform designed to identify interacting genes and potential combination therapies.

The project is highly interdisciplinary, integrating functional genomics, molecular genetics, imaging, bioinformatics, statistics and data science. Training will include genetic perturbation technologies, advanced microscopy, single-cell genomics, computational analysis, statistical modelling and reproducible research practices. The project is intentionally flexible, allowing the student to shape its direction as their interests and expertise develop. For example, there are opportunities to focus on technology development, computational methods, network analysis or biological applications depending on their interests and expertise.

The student will join a collaborative research environment with access to state-of-the-art facilities for high-throughput screening, live-cell imaging, single-cell sequencing and computational methods. The project will be supported by expertise within the Functional Genomics Screening Laboratory and the Exeter Living Systems Institute, providing opportunities to work across disciplines and engage with national functional genomics networks.

Expected outcomes include validation of mVDA for genetic interaction mapping in human cells, development of analytical approaches for interpreting combinatorial perturbation data, and new understanding of NF1-associated gene networks. The project will help overcome a major barrier in functional genomics by enabling systematic exploration of how gene interactions generate cellular phenotypes. The student will develop a unique combination of experimental, computational and transferable skills highly relevant to careers in academia and industry. Responsible research practices, including reproducibility, FAIR data stewardship and research integrity will underpin all aspects of the project.

Funded by: EPSRC Engineering and Physical Sciences Research Council 

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