Michael Stumpf
Statistical, mathematical, evolutionary and functional analysis of molecular interaction networks and dynamical processes in biological systems
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 haematopoietic) 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 haematopoietic cancers, including leukaemia and multiple myeloma
- Making better mechanistic 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