Linus Schumacher

Biography

Background

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.

Open to PhD supervision enquiries?

Yes

Areas of interest for supervision

Applicants requiring funding should also consider the following doctoral programmes: EASTBIO DTP (http://www.eastscotbiodtp.ac.uk), UKRI Artificial Intelligence Centre for Doctoral Training in Biomedical Innovation (https://www.ai4biomed.io), Precision Medicine (https://www.ed.ac.uk/usher/precision-medicine)

Current PhD students supervised

  • Thanakorn Jaemthaworn. Integrating network state representations for modelling cell state dynamics
  • Alex Richardson. Learning the rules of biological pattern formation (co-supervised with Richard Blythe and Tibor Antal)
  • Cameron Kerr.  Computational modelling of clonal haematopoeisis and myelodysplastic syndrome (co-supervised with Martin Taylor)

Past PhD students supervised

  • Rodrigo Garcia. Development of mathematical tools for identifying hallmarks of regulatory mechanisms in stem cell lineages (co-supervised with Ramon Grima)
  • Viktoria Freingruber. Collective chemotaxis: how cells work together to migrate more efficiently (co-supervised with Kevin Painter & Mariya Ptashnyk)
  • Jorge Lemos. Computational modelling of human stem cell fitness in ageing blood (co-supervised with Tamir Chandra and Kristina Kirschner)

Research summary

Computational biology of cell populations

Tissue development and regeneration can be seen as group behaviours of cell populations. To understand development and regeneration, we need to consider the interactions between stem cells and the rest of the cells that make up a tissue. We use mathematical models and computational simulations to predict tissue behaviour from the behaviour of cells. This allows us to develop and test hypotheses in complex biological systems and discern informative patterns in experimental data.

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

Aims and areas of interest

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.

Current research interests

Bayesian inference of cell state transitions, Data-driven modelling of immune cell interactions in tissue regeneration and repair, Quantitative analysis and modelling of immune cell migration in wound response, Clonal dynamics under homeostatic feedback, mutation competition, and ageing

Past research interests

Neural crest cell migration, Collective behaviour of C. elegans nematodes, Noise-induced phenomena in stochastic pattern formation

Affiliated research centres

Current project grants

University of Edinburgh Chancellor's Fellowship
Academy of Medical Sciences Springboard Award
Wellcome Leap Delta Tissue grant (as co-I)
Leverhulme research grant (as co-I)

Past project grants

EPSRC Doctoral Prize
Wellcome Trust Institutional Strategic Support Fund

View all 20 publications on Research Explorer

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