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PhD in Applied Mathematics: Stochastic Cell Dynamics

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  • Full or part time
    Dr Philipp Thomas
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

We have an exciting opportunity for a well-funded PhD student working at the interface of mathematics, biophysics and quantitative biology. You will develop mathematical methods to analyse agent-based models of cell populations. As part of the Department of Mathematics (P Thomas), you will be based in South Kensington at the heart of London’s cultural and scientific quarter.

Student Background: Mathematics/Statistics, Theoretical Physics, Engineering (Biological knowledge not required)

Description: Cells decide when to divide based on the dynamics of intracellular reaction networks. This behaviour often varies drastically from cell to cell because their biochemical reactions are stochastic. Our group develops theoretical methods using a mix of stochastic processes and nonequilibrium physics to understand the cell division dynamics of bacterial and mammalian cells. We are particularly interested in developing theory for agent-based models, a novel approach that allows tracking every individual in a growing and dividing cell population at the experimental single-cell resolution. Such approaches are now becoming increasingly important in cell biology, medicine and biotechnology.

The aim of this project is to understand how stochasticity in gene regulatory networks affects cellular behaviour. To this end, you will extend the common Chemical Master Equation approach to include cell growth and division. You will develop cutting-edge analytical methods to approximate agent-based models. Using these methods, we will investigate cell-to-cell heterogeneity in lineage trees, which can be measured in experiments. Possible applications to experimental data will explore how cells respond to stress or drug treatment. The project provides first-hand, state-of-the-art training in stochastic processes, biomathematics and computational biology.

This is a well funded 3.5-year position for UK or EU nationals. We are seeking applications from highly motivated theorists with excellent intuition, strong analytical skills and a background in a quantitative discipline (mathematics, physics, engineering, computational biology). Expertise in stochastic processes, Markov chains and/or analysis of the Chemical Master Equation will be beneficial. As a member of the Biomathematics group you will benefit from the thriving environment at the Department of Mathematics, the EPSRC Centre for Precision Healthcare and a range of world-leading experimental collaborators (Dr J Locke, University of Cambridge; Dr A Barr, MRC London Institute of Medical Sciences). For more information about the group, please visit www.imperial.ac.uk/people/p.thomas. For information about the PhD programme, please visit http://www.imperial.ac.uk/mathematics/postgraduate/research-phd-in-mathematics/

Applications should be sent informally to [Email Address Removed] including a brief motivation, CV and a transcript of records.

We are also interested to hear from exceptional international students regarding several competitively funded opportunities.

Funding Notes

Fully funded PhD position for EU/UK nationals.

References

Making sense of snapshot data: ergodic principle for clonal cell populations. Thomas P (2017) Journal of The Royal Society Interface 14:20170467. https://doi.org/10.1098/rsif.2017.0467

Sources, propagation and consequences of stochasticity in cellular growth. P Thomas, G Terradot, V Danos, AY Weiße. Nature Communications 9, 4528. https://doi.org/10.1038/s41467-018-06912-9

Analysis of cell size homeostasis at the single-cell and population level. Thomas P (2018) Frontiers in Physics 6:64.
https://doi.org/10.3389/fphy.2018.00064

Intrinsic and extrinsic noise of gene expression in lineage trees. Thomas P (2019) Scientific Reports 9, 474. https://doi.org/10.1038/s41598-018-35927-x

How good is research at Imperial College London in Mathematical Sciences?

FTE Category A staff submitted: 100.31

Research output data provided by the Research Excellence Framework (REF)

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