Linus Schumacher

Centre for Regenerative Medicine

Street: Centre for Regenerative Medicine,
Institute for Regeneration and Repair,
The University of Edinburgh,
Edinburgh BioQuarter,
5 Little France Drive,
City: Edinburgh
Post code: EH16 4UU

Biography

Background

I moved to the CRM 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.

Teaching & PhD supervision

Open to PhD supervision enquiries?

Yes

Areas of interest for supervision

Applicants requiring funding should also consider the following doctoral programmes: MAC-MIGS (https://www.mac-migs.ac.uk), Biomedical AI (http://web.inf.ed.ac.uk/cdt/biomedical-ai), Precision Medicine (https://www.ed.ac.uk/usher/precision-medicine)

Current PhD students supervised

Miguel Robles Garcia. Forward engineering of pattern formation: Models and experiments towards predictive multicellular self-organisation (co-supervised with Guilaume Blin)
Anna Popravko. Defining the transcriptome of the first functional haematopoietic stem cells in the mouse embryo (co-supervised with Elaine Dzierzak)
Viktoria Freingruber. Collective chemotaxis: how cells work together to migrate more efficiently (co-supervised with Kevin Painter & Mariya Ptashnyk)

Past PhD students supervised

PhD rotation projects

Leslie Nitsche. A computational pipeline for profiling of metabolic states in neutrophils (co-supervised with Sarah Walmsley)
Ivan Croydon Veleslavov. Quantifying complexity of collective behaviour & Bayesian inference in agent-based models of collective behaviour (co-supervised with Robert Endres)

Research

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

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.

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