Title: “Quantitative Modelling in biology and beyond: what’s the point?”
Many of us do ‘quantitative modelling’ in one form or another. But why are we actually doing it, apart from the satisfaction achieved from finding a nice and fitting model? What is the scientific purpose of mathematical modelling and what do we get out of it, from a utilitarian point of view? Most of you will be convinced of its benefits, but can you also convince your experimentalist collaborators?
In this talk, I wish to discuss the applications of modelling in very broad strokes, its purposes, and under which circumstances it does provide a lot of ‘added value’ to scientific research (in conjunction with experiments) and also, under which circumstances it doesn’t. I will exemplify this on some works using mathematical modelling in stem cell biology, although the basic principles stay valid beyond that. In particular, I wish to address common pitfalls, in which completely wrong models fit data perfectly (not just because of overfitting), which may confound scientific conclusions substantially. The latter may occur due to “universality”, which can lead of erroneous model fitting, but at the same time is the reason that (mechanistic) quantitative modelling can work at all.