Bacterial Metabolic Heterogeneity: from Stochastic to Deterministic Models

TL;DR

This paper introduces a stochastic model for bacterial metabolic heterogeneity during diauxic growth, bridging the gap between individual cell variability and large population behavior, and analyzing how parameters influence growth dynamics.

Contribution

It presents a novel stochastic modeling approach for microbial heterogeneity, linking it to deterministic models and exploring parameter effects on growth phases.

Findings

Stochastic model captures metabolic heterogeneity in microbial populations.

Large population approximation links stochastic and deterministic behaviors.

Model parameters significantly influence lag-phase and growth dynamics.

Abstract

We revisit the modeling of the diauxic growth of a pure microorganism on two distinct sugars which was first described by Monod. Most available models are deterministic and make the assumption that all cells of the microbial ecosystem behave homogeneously with respect to both sugars, all consuming the first one and then switching to the second when the first is exhausted. We propose here a stochastic model which describes what is called "metabolic heterogeneity". It allows to consider small populations as in microfluidics as well as large populations where billions of individuals coexist in the medium in a batch or chemostat. We highlight the link between the stochastic model and the deterministic behavior in real large cultures using a large population approximation. Then the influence of model parameter values on model dynamics is studied, notably with respect to the lag-phase observed in real systems depending on the sugars on which the microorganism grows. It is shown that both metabolic parameters as well as initial conditions play a crucial role on system dynamics.