Statistical Methods for Systems Biology

We develop and apply methods from Bayesian inference and information theory to construct mechanistic models for biological systems. Unlike in physics, we cannot derive models from first principles but need to construct models from a combination of experimental data and previous knowledge.

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There is tremendous scope for applying new modelling approaches, including automated model development (3rd generation modelling) and we are exploring approaches that make the development of models in biology more efficient.

Representative Publications

  1. Parametric And Non-parametric Gradient Matching For Network Inference: A Comparison ➟
    Dony, L., He, F., & Stumpf, M.P.H. (2019). BMC Bioinformatics, 20(1), 799.

  2. Systems Biology (Un)Certainties ➟
    Kirk, P.D.W., Babtie, A.C., & Stumpf, M.P.H. (2015). Science, 350(6259), 386–388.

  3. A Framework for Parameter Estimation and Model Selection From Experimental Data in Systems Biology Using Approximate Bayesian Computation ➟
    Liepe, J., Kirk, P.D.W., Filippi, S., Toni, T., Barnes, C.P., & Stumpf, M.P.H. (2014). Nature Protocols, 9(2), 439–456.

  4. Topological Sensitivity Analysis for Systems Biology ➟
    Babtie, A.C., Kirk, P., & Stumpf, M.P.H. (2014). Proceedings of the National Academy of Sciences of the United States of America, 111(52), 18507–18512.

  5. Approximate Bayesian Computation Scheme for Parameter Inference and Model Selection in Dynamical Systems ➟
    Toni, T., Welch, D., Strelkowa, N., Ipsen, A., Stumpf, M.P.H., & Stumpf, M.P.H. (2009). Journal of the Royal Society Interface, 6(31), 187–202.

ScienceSites