Connecting models and data

Simple mathematical models have already provided remarkable insights into cell invasion; take, for example, the ubiquitous use of Fisher’s equation. However, modern developmental biology studies require more sophisticated models that incorporate driving processes on a range of spatial and temporal scales. These detailed, multi-scale models can provide vital insights where data alone cannot, but they require accurate data-driven calibration to do so. We are working to develop a range of tools that enable models to be calibrated using quantitative data.

  • D. J. Warne, R. E. Baker and M. J. Simpson (2017). Multi-level rejection sampling for approximate Bayesian computation. arXiv
  • J. U. Harrison and R. E. Baker (2017). An automatic adaptive method to combine summary statistics in approximate Bayesian computation. arXiv
  • J. U. Harrison and R. E. Baker (2017). The impact of temporal sampling resolution on parameter inference for biological transport models. arXiv
  • D. J. Warne, R. E. Baker and M. J. Simpson (2017). Optimal quantification of contact inhibition in cell populations. Biophys. J. 113(9):1920-1924. DOI arXiv
  • R. J. H. Ross, R. E. Baker, A. Parker, M. J. Ford, R. L. Mort and C. A. Yates (2017). Using approximate Bayesian computation to quantify cell–cell adhesion parameters in a cell migratory process. npj Sys. Biol. Appl. 3:9. DOI bioRxiv
  • R. Ross, C. A. Yates and R. E. Baker (2015). Inference of cell-cell interactions from population density characteristics and cell trajectories on static and growing domains. Math. Biosci. 264:108-118. DOI

Bacterial motility

We have developed approaches that combine mechanistic models with hidden Markov model approaches to better understand bacterial motility. Part of our work also considers how to design “optimal experiments” for the measurement of different model parameters.

  • G. Rosser, R. E. Baker, J. P. Armitage and A. G. Fletcher (2014). Modelling and analysis of bacterial tracks suggest an active reorientation mechanism in Rhodobacter spheroides. J. Roy. Soc. Interface 11(97):20140320.  DOI bioRxiv
  • G. Rosser, A. G. Fletcher, D. A. Wilkinson, J. A. de Beyer, C. A. Yates, J. P. Armitage, P. K. Maini and R. E. Baker (2013). Novel Methods for Analysing Bacterial Tracks Reveal Persistence in Rhodobacter sphaeroides. PLoS Comput. Biol. 9(10):e1003276. DOI
  • G. Rosser, A. G. Fletcher, P. K. Maini and R. E. Baker (2013). The effect of sampling rate on observed statistics in a correlated random walk. J. Roy. Soc. Interface 10(85):20130273. DOI
  • J. U. Harrison and R. E. Baker (2017). The impact of temporal sampling resolution on parameter inference for biological transport models. arXiv

Adventures in the world of mathematical biology