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Modelling cell state transitions in differentiating embryonic stem cells


Project Description

Cellular behaviour in development, regeneration and cancer is often classified by defining various cell states, which may for example describe the propensity of cells to divide or differentiate, or to assume different modes of motility. In many cases, we know little about how cells integrate complex queues to regulate their states. To address this, in silico mathematical modelling can be used to formulate hypotheses on cell state control, which can then be tested by comparing with experimental data on varying culture conditions in vitro. An abundance of data exists in the field, comprising snapshots of cell populations at single-cell resolution, yet there are few quantitative predictive models of cell states and their regulatory networks. Integrating such models with data will enable us to optimise experiments to produce the most informative data and accelerate the testing of differentiation protocols in cell culture.
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 statistical modelling of cell state transitions. Models will be calibrated against population and single-cell data to quantify the cell state transition rates, and how these change under different culturing conditions. We have developed a preliminary modelling approach (extending on [2]) and statistical analysis pipeline to existing data [1].

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 computational tools for modelling and analysis of cell state transitions. The student will experimentally test predictions from the model. Depending on the student’s interests, the project can then be taken into several further directions, such as working with human cell lines, scaling the method to work with transcriptomics data, or quantifying spatial variation of gene expression in stem cell colonies using RNAscope.

This project is a great opportunity for students with previous experience in mathematics or statistics and interest 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.

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 & St. Andrews.

Application:
For instructions on how to apply for an EASTBIO PhD studentship please refer to http://www.eastscotbiodtp.ac.uk/how-apply-0

All applicants must download the EASTBIO application form (from http://www.eastscotbiodtp.ac.uk/how-apply-0) and submit the completed document and their academic transcript to .

Please ensure that referees send their completed reference forms (also available from http://www.eastscotbiodtp.ac.uk/how-apply-0) to .


Funding Notes

This 4 year PhD project is part of a competition funded by EASTBIO BBSRC Doctoral Training Partnership View Website. This opportunity is only open to UK nationals (or EU students who have been resident in the UK for 3+ years immediately prior to the programme start date) due to restrictions imposed by the funding body.

References

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

How good is research at University of Edinburgh in Biological Sciences?

FTE Category A staff submitted: 109.70

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