Next generation approaches to connect models and quantitative data

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Simple mathematical models have been remarkably successful in helping us understand key processes in biology. Traditionally, the utility of models has been to test biological hypotheses by encoding extremely simple descriptions of the biology in a mathematical framework. Mathematical analysis and computer simulation are then used to test whether qualitative predictions of the model match experimental observations. However, biology has advanced to the stage where experimental researchers can generate stunning images of cells and tissues at a level of resolution previously only dreamt of. Being able to visualise, for example, the dynamics of individual mRNAs and proteins over time, means that we can now generate extremely sophisticated hypotheses for how large gene regulatory networks or cells and tissues function. As a result, the mathematical models we develop to test biological hypotheses are quickly growing in size and complexity. In particular, the so-called agent-based models have become a popular tool in the modern life sciences. These allow the modeller to, for example, follow the fates and interactions of individual cells and, at the same time, include the effects of gene regulation and signalling. For these agent-based models to be truly useful, for them to direct experimental efforts or even, eventually, replace the need for some experiments, we need to calibrate them using quantitative data. This simply stated need, however, poses a formidable set of challenges for the modelling community: (i) the models have many parameters that must be estimated; (ii) the data is complex, of multiple different types and rarely, if ever, are all the relevant cells or proteins measured or tracked, for example; (iii) the data are obscured by noise that is both intrinsic to the measured processes and introduced during the experiments. The proposed research will generate new mathematical and computational tools to overcome these challenges. It will enable scientists in the modern life and biomedical sciences to calibrate models, then select the most appropriate model(s), and hence distinguish between competing biological hypotheses. To make sure they are relevant for biology, these new tools will be developed whilst investigating key biological questions. To ensure that the tools are available for re-use and extension by other researchers in the field, all of our computational codes and resources will be made freely available.