Michael Stumpf

I hold the Chair of Theoretical Systems Biology, and in my research I am trying to understand how cells make decisions (e.g. whether to divide/proliferate, differentiate or commit to controlled cell death). Such decisions are the result of computations done by complex molecular networks. The structure and dynamics of these networks are generally not known and much of my work deals with the development of new methods that allow us to get better and deeper understanding and mechanistic insights into these networks.

We use mathematical models, coupled to state-of-the-art statistical methods – generally developed in the group – to analyse biological systems, that range from stem cells to bacterial organisms. Often these systems show rich dynamical behaviour, which is caused by the interplay of non-linearities and stochastic dynamics; the differentiation of stem cells and the development of tissues offer good examples for this. The analysis of such data, and the development of useful, that is quantitative, predictive and explanatory, models offers a wealth of statistical and intellectual challenges, in addition to the intrinsic biological and biomedical interest in such systems. We work in close collaboration with experimental and clinical groups wherever possible.

Because we combine a broad range of mathematical, statistical and computational tools, including bioinformatics, evolutionary analysis and text-mining, we have been able to make progress in the analysis of molecular machines (such as the proteasome), signal transduction and gene regulation networks, as well developmental processes. More recently we have started to consider different aspects of the population and evolutionary dynamics of stem cells and their progeny in both health and disease, especially cancer.

In silico models of biological systems have phenomenal scope in guiding future experiments, replacing and reducing the number of experiments carried out in animals. In addition to modelling and statistical approaches we also use comparative genomics to identify and develop suitable alternative experimental model systems.

My original background is in theoretical physics, which I studied in Tübingen, Göttingen and Oxford. But straight after my DPhil in Statistical Physics in Oxford I moved into biological research in the Department of Zoology in Oxford, a move that I have never regretted. Some of our work has, nevertheless, implications for physics and is published in the physics literature.

Research Interests

The research in the Theoretical Systems Biology Group uses mathematical, statistical and computational methods to explore functional, evolutionary and statistical problems in systems biology. The methods we use can be applied widely in systems biology, developmental and stem cell biology.

Our research covers biological problems related to:

  • Information processing and cell-fate decision machineries in eukaryotic and prokaryotic cells
  • Regulatory and signalling networks in (embryonic and haematopietic) stem cell differentiation
  • Innate immune dynamics analysed via in vivo imaging and molecular data
  • Stem cell population dynamics in haematopoiesis
  • Regulatory and genomic determinants of haemtatopietic cancers, including leukaemia and multiple myeloma
  • Making better mechanstic models for cellular machines such as the proteasome

We address these problems using sophisticated new modelling and analysis tools, which we develop in the group. Our methodological work is centred around Bayesian reverse engineering of biological systems using statistical, comparative and text-mining approaches. In particular we are interested in

  • Development of inferential procedures for model selection and parameter estimation in complex dynamical systems
  • Network inference using graphical models and information theoretical functionals.
  • Approximate Bayesian computation, especially for challenging scientific modelling problems involving multi-scale problems, agent-based approaches and non-linear stochastic systems
  • Stochastic processes and statistical inference in the functional and evolutionary analysis of biological networks

Brief CV

  • 2007 - Professor for Theoretical Systems Biology, Imperial College London
  • 2003-2006 Reader in Bioinformatics and Wellcome Trust Research Career Development Fellow, Imperial College London
  • 2002-2003 Wellcome Trust Research Career Development Fellow, University College London
  • 1999-2002 Wellcome Trust Mathematical Biology Research Training Fellowship, University of Oxford and EPA Cephalosporin Junion Research Fellow Linacre College Oxford
  • 1995-1999 DPhil Student, University of Oxford (Kekule Scholarship and Balliol College Jowett Exhibition)
  • 1990-1995 Physics studies at the Universities of Tübingen and Göttingen finishing with Diplom (1.0) in Göttingen

Honours and Awards

  • 2013 Mieyungah Distinguished Fellowship, University of Melbourne
  • 2012 Election to Fellowship of the Society of Biology
  • 2010 Royal Society Wolfson Merit Award
  • 2005 EMBO Young Investigator

Selected Publications