Modelling Cells

Most models in cell biology are either based on simple networks or stoichiometric metabolic models, or deal only with small pathways. Ultimately we will require models of whole cells. We are interested in combining approaches from text-mining, bioinformatics, comparative genomics, statistical inference, machine learning, and mathematical modelling in order to arrive at models for whole cells. In the first instance, we are focusing on bacteria.

Ultimately such models are (i) the most stringent test of our understanding; and (ii) an essential prerequisite for applying rational design and engineering approaches to biological systems. Maintaining the balance of model complexity to explanatory power is a conceptually hugely exciting problem; and tackling parameter inference for thousands of parameters is technically equally exciting.

Representative Publications

  1. How to Deal With Parameters for Whole-Cell Modelling →
    Babtie, A.C., & Stumpf, M.P.H. (2017). Journal of the Royal Society, Interface / the Royal Society, 14(133), 20170237.

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

  3. Quantitative Time-Resolved Analysis Reveals Intricate, Differential Regulation of Standard- and Immuno-Proteasomes →
    Liepe, J., Holzhütter, H.-G., Bellavista, E., Kloetzel, P.M., Stumpf, M.P.H., & Mishto, M. (2015). eLife, 4, e07545.

ScienceSites