Biologists increasingly use the concept of cell states to classify cellular behaviour in development, regeneration, and cancer. This is driven in part by an abundance of data comprising snapshots of cell populations at single-cell resolution. Yet quantitative predictive models of cell states and their regulatory networks remain lacking. Such models are required for an integrative mechanistic understanding of the structure of cell populations and their plasticity, and could help, for example, optimise differentiation protocols in vitro.
This project will investigate early cell fate decisions in stem cell populations resembling early embryonic progenitors, using a combination of quantitative analysis of lineage marker expression and data-driven mathematical modelling of cell state transitions. Models will be calibrated against population and single-cell data using a Bayesian inference approach to quantify the cell state transition rates and their uncertainties, and how these change under different culturing conditions.
The lab of VW has pioneered the culture of lineage-biased pluripotent cells. LS’s research group focuses on the computational modelling of cell populations and their self-regulation, and how single-cell data can be used to inform such models. LS has developed a preliminary modelling approach (unpublished, similar in its approach to ) and Bayesian inference pipeline (see  for a review) to VW’s existing data .
The student will receive training in relevant ‘wet lab’ techniques, such as cell culture and microscopy/image analysis, while working with existing preliminary data and developing the computational tools for modelling and analysis, such as Markov models of cell state transitions and parameter inference. Utilizing this skills development the student will experimentally test predictions from the model in the second half of the PhD. Depending on the student’s skills and interests, the project can then be taken into several further directions, such as working with human cell lines, scaling the method to work with transcriptomic data, or exploring different theories of cell state.
LS and VW will have an active supervisory role and hold regular progress meetings with the student. Training in professional and research skills will be tailored to the individual student’s background and training needs. The student’s critical understanding of primary data and research literature will be advanced through regular group meetings and journal clubs at the Centre for Regenerative Medicine. The student will also have the opportunity to engage with the mathematical and systems biology research community at other departments in Edinburgh.
This project is a great opportunity for students with a background in mathematics, statistics, physics, computer science, engineering, or similar, or from a biological background with existing skills and interests in computer programming. The student will benefit from integration in an active biomedical research environment at the Centre for Regenerative Medicine and interaction with a cross-institutional network of collaborators.
1. Tsakiridis, A., Huang, Y., Blin, G., Skylaki, S., Wymeersch, F., Osorno, R., … Wilson, V. (2014). Distinct Wnt-driven primitive streak-like populations reflect in vivo lineage precursors. Development, 141(6), 1209–1221. https://doi.org/10.1242/dev.101014
2. Gupta, P. B., Fillmore, C. M., Jiang, G., Shapira, S. D., Tao, K., Kuperwasser, C., & Lander, E. S. (2011). Stochastic State Transitions Give Rise to Phenotypic Equilibrium in Populations of Cancer Cells. Cell, 146(4), 633–644. https://doi.org/10.1016/j.cell.2011.07.026
3. Beaumont, M. A. (2010). Approximate Bayesian Computation in Evolution and Ecology. Annual Review of Ecology, Evolution, and Systematics, 41(1), 379–406. https://doi.org/10.1146/annurev-ecolsys-102209-144621.