Mathematical modeling and intuition in microbiology: a perspective

TL;DR

This paper discusses how mathematical modeling enhances microbiology by enabling predictions, extracting data insights, and fostering intuition, with practical guidance and a case study on microbial ecosystems.

Contribution

It provides an overview of modeling frameworks, criteria for selecting models, and illustrates their use through a microbial ecosystem case study.

Findings

Models enforce logical consistency in microbiology.

Quantitative predictions are enabled by modeling.

Mechanistic models can generate generalizable intuition.

Abstract

Mathematical models are increasingly a part of microbiological research. Here, we share our perspective on how modeling 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 modeling 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 modeling of microbial ecosystems from our own work to illustrate how mechanistic modeling can yield generalizable intuition. This perspective aims to be an introductory roadmap for integrating mathematical modeling into experimental microbiology.