Computational biology of cell populations

Tissue regeneration is an emergent phenomenon at the scale of cell populations – an individual cell only proliferates, remains quiescent, or dies, but does not regenerate. This poses a constraint on the ways in which cells can get together 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. These opposing demands raise a basic question: How does regeneration only happen when needed, and how does it know when to stop?

Despite a rich history of insights from developmental biology, quantitative understanding of regeneration and repair remains elusive. Recent advances in stem cell biology, imaging tools, single cell sequencing, and computational biology are poised to change this. Our interest lies in using computational modelling to predict outcomes of hypothesised regulatory mechanisms in development and regeneration. By developing theoretical models we also bring new perspectives on how to interrogate experimental data. We work closely with experimental collaborators with the aim to formulate principles that apply to multiple biological systems, gain insight into misregulation in disease, and inform improvements to regenerative therapy.

Read an accessible description of Linus Schumacher’s research on the Data-Driven Innovation website

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