Mathematical Biology Seminar Niall M Mangan, Northwestern University Wednesday, October 21, 2020 3:05pm on Zoom Data-driven methods for identifying mechanisms in complex biological systems Abstract: Inferring the structure and dynamical interactions of complex biological systems is critical to understanding and controlling their behavior. I am interested in discovering models, assuming I have time-series data of important state variables and knowledge of the possible types of interactions between state variables. The problem is then selecting which interactions, or model terms, are most likely responsible for the observed dynamics. Several challenges make model selection difficult including nonlinearities, varying parameters or equations, and unmeasured state variables. I will discuss methods for reframing these problems so that sparse model selection is possible including implicit formulation and data clustering. I will also discuss preliminary results on parameter estimation and experimental design to characterize a spatially organized metabolism pathway in bacteria. Parameter estimation is challenging in this case because only some of the metabolite pools can be measured and the other variables are hidden or latent. We use a combination of scaling based on mass conservation and sensitivity analysis in a systematic fashion to identify the most informative experiments.