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Modelling stem cell population dynamics: from cell interaction networks to tissue regeneration

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  • Full or part time
    Dr Linus Schumacher
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Tissue regeneration is an emergent phenomenon at the scale of cell populations. The requirement for robust tissue homeostasis without over-proliferation poses a constraint on the possible cellular dynamics to build and maintain tissues, as only some sets of microscopic mechanisms, or “rules”, will enable regeneration after injury. In healthy tissues, cell populations also have to self-regulate so as not to over-proliferate and grow in an unregulated, or malignant, manner.

Despite a rich history of insights from developmental biology, quantitative & predictive understanding of regeneration and repair remains elusive. Our group uses mathematical and computational modelling to predict outcomes of hypothesised regulatory mechanisms in development and regeneration. By developing theoretical models we also come up with new perspectives on how to interrogate experimental data. We work closely with experimental collaborators with the long-term aim to formulate principles that apply to multiple biological systems, gain insight into misregulation in disease, and inform improvements to regenerative therapy.

We are looking for an ambitious and motivated postgraduate candidate to join the new computational biology group, led by Linus Schumacher, at the MRC Centre for Regenerative Medicine in Edinburgh. The group is using expertise in mathematical modelling of cellular processes in development [1,2] and collective behaviour in biological systems [3], with a recent focus on stem cells and regeneration.

Recent work suggests that the stability and robustness of cell populations depends on the structure of their interactions [4,5], more so than on the detailed molecular identity of the mediators of these interactions. This project will pursue an integrative understanding of which patterns of cellular interactions enable tissue-level behaviours, using tools of dynamical systems, statistical physics/stochastic processes, and inference methods. A particular focus will lie on extracting predictions as characteristic features of different model classes, to guide experimental measurements to distinguish between plausible mechanisms of cell population self-regulation. Successful students may be given the option to validate their theoretical work in collaboration with experimental labs.

Throughout the PhD, the student will acquire skills in mathematical methods and computational tools, gain an understanding of the broader research field, and learn to communicate with “wet lab” biologists. The student will have some freedom to define their own project in the context of the group’s research interests, and this is expected to increase as they progress through their PhD towards becoming an independent researcher.

This is an opportunity to conduct your PhD research on mathematical and computational biology, embedded in a world-leading centre for multidisciplinary research in mammalian stem cell biology and regenerative medicine.

The student will benefit from active supervision with regular progress meetings, and from the active community at the Centre for Regenerative Medicine, with regular seminars by internal and invited speakers, as well as journal clubs. Training in professional and research skills will be tailored to the individual student’s background and training needs. The student will also have the opportunity to engage with the mathematical and systems biology research community at other departments in Edinburgh, to which the group maintains active connections.

Edinburgh is a vibrant city consistently ranked amongst the top places to live in the UK.
You can find information about the University’s position on Brexit here: https://www.ed.ac.uk/news/eu

Applicants should have a strong academic track record with a first or upper second class undergraduate degree (or equivalent), or a Master’s degree, in one of the following: mathematics, physics, computer science, engineering, or similar. A relevant postgraduate (Master’s or similar) degree is desirable. Graduates from a biological biomedical degree will be considered if they have strong skills and interest in quantitative approaches and relevant scientific programming experience.

Applicants should submit the following documents to our e-mail address [Email Address Removed] by December 10th, 2018: (1) Personal statement about your research interests and reasons for applying; (2) CV; (3) References; (4) Marks for degree

Funding Notes

Funding includes stipend, fees (UK/EU/overseas), and travel/research expenses.

References

1. Blanchard, Fletcher, Schumacher (2018). The devil in in the mesoscale: Mechanical and behavioural heterogeneity in collective cell movement. Seminars in Cell & Developmental Biology, (in press) https://doi.org/10.1016/j.semcdb.2018.06.003
2. Schumacher, Maini, Baker (2017). Semblance of Heterogeneity in Collective Cell Migration. Cell Systems, 5(2), 119–127.e1. https://doi.org/10.1016/j.cels.2017.06.006
3. Ding, Schumacher, Javer, Endres, Brown (2018). Common behavioural mechanisms underlie C. elegans aggregation and swarming. bioRxiv https://doi.org/10.1101/398370
4. Kunche, Yan, Calof, Lowengrub, Lander (2016). Feedback, Lineages and Self-Organizing Morphogenesis. PLoS Computational Biology, 12(3), 1–34. https://doi.org/10.1371/journal.pcbi.1004814
5. MacLean, Kirk, Stumpf (2015). Cellular population dynamics control the robustness of the stem cell niche. Biology Open, 4(11), 1420–1426. https://doi.org/10.1242/bio.013714



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