Mathematical Modelling and Intuition in Microbiology: A Perspective

Abstract

Mathematical models are increasingly a part of microbiological research. Here, we share our perspective on how modelling advances the discipline by: (i) enforcing logical consistency, (ii) enabling quantitative prediction, (iii) extracting hidden parameters from data, and (iv) generating intuitive understanding. We map a spectrum of modelling frameworks, from whole-cell simulations to minimal logistic growth equations, and provide interactive examples for some common frameworks. Building on this overview, we outline pragmatic criteria for choosing an appropriate level of description to capture phenomena of interest. Finally, we present a case study in modelling of microbial ecosystems from our own work to illustrate how mechanistic modelling can yield generalizable intuition. This perspective aims to be an introductory roadmap for integrating mathematical modelling into experimental microbiology.