Dr. Linus Schumacher
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- Email: Linus.Schumacher@ed.ac.uk
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- Web: https://www.ed.ac.uk/regenerative-medicine/research/linus-schumacher
The dynamics of a tissue in development and regeneration arises from the behaviour of its constituent cells and their interactions. In embryo development, initially homogeneous populations of cells have to 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 have to self-regulate so as 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 dynamics, 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.
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.
Read an accessible description of Linus Schumacher’s research on the Data-Driven Innovation website: https://ddi.ac.uk/chancellors/linus-schumacher/
I moved to the Edinburgh as a Chancellor’s Fellow in 2018. Previously I was a postdoctoral researcher at Imperial College London and the University of Oxford, where I also obtained my DPhil, based at the Wolfson Centre for Mathematical Biology. For my undergraduate degree I read Natural Sciences at the University of Cambridge.