I lead a group of mathematical modellers and computational biologists at the Centre for Regenerative Medicine, University of Edinburgh. I moved to Edinburgh as a Chancellor’s Fellow (tenure track) in 2018. Previously I was a postdoctoral researcher at Imperial College London and the University of Oxford, where I also obtained my DPhil (PhD), based at the Wolfson Centre for Mathematical Biology. For my undergraduate degree I read Natural Sciences (Physics) at the University of Cambridge.

Modelling cell and tissue dynamics in systems medicine

I study the interactions of cells in our tissues. Our bodies are communities of cells that work in concert with remarkable resilience. To understand how this works in health – and how it can go awry in disease – we use mathematical models and computational simulations to study these complex biological systems and discern informative patterns in experimental data.

Aims and areas of interest

The dynamics of a tissue arises from the behaviour of its constituent cells and their interactions. In embryo development, initially homogeneous populations of cells acquire cell fates in specific proportions and spatial arrangements to enable tissue function. How do individual cells coordinate with their neighbours to achieve this? In adult tissues, cell populations self-regulate to enable regeneration after injury without over-proliferating in a malignant manner. How does regeneration only happen when needed, and how does it know when to stop?

We use mathematical models and statistical inference methods to infer from various experimental data the most likely cellular behaviours and regulatory mechanisms underlying changing tissue states. Example methods include birth-death process models of stem cell division and differentiation, extending such models by incorporating regulatory interactions and additional or intermediate cell states, and machine learning tools to learn cell-cell interaction models directly from data in interpretable ways. The applications range from in vitro models of embryo development to adult tissue regeneration that is disrupted in ageing or cancer. We are aspire to work increasingly with data from human tissue samples or in vitro models, such as organoids.

By developing theoretical models, we also bring new perspectives on how to interrogate experimental data. We work closely with experimental collaborators with the aims to formulate principles that apply to multiple biological systems, gain insight into misregulation in disease, and inform improvements to regenerative therapy.

Learning interpretable mathematical models of cell interactions from experimental data, Mathematical models to improve the therapy of Type 1 Diabetes, Clonal dynamics under homeostatic feedback, mutation competition, and ageing

Read an accessible description of Linus Schumacher’s research on the Data-Driven Innovation website: https://ddi.ac.uk/chancellors/linus-schumacher/