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Peer-reviewedCC BY-NC-NDThe journal of physical chemistry. B

A Minimal Stochastic Model of Microbial Ecological Dynamics in a Single-Species-Single-Resource Setting.

  • Cheuk Fung Alvin Leung1,2
  • Anatoly B Kolomeisky1,2,3,4
4 affiliations
  1. 1Department of Chemistry, Rice University, Houston, Texas77005, United States.
  2. 2Center for Theoretical Biological Physics, Rice University, Houston, Texas77005, United States.
  3. 3Department of Physics and Astronomy, Rice University, Houston, Texas77005, United States.
  4. 4Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas77005, United States.
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Abstract

Microbes exhibit complex dynamic behavior as the result of a large number of biochemical processes, spatial and temporal interactions, environmental variations, and evolutionary pressure. Although significant progress has been achieved in understanding microbial ecological dynamics, multiple open questions remain, including the microscopic mechanisms of growth and the roles of nutrients and stochasticity. In this work, we present a minimal theoretical approach to clarify the link between consumption of resources by microbes and their growth. A stochastic model that accounts for a single microbial species consuming a single type of resource while growing via cell division is studied analytically and via Monte Carlo computer simulations. We identify three distinct dynamical regimes of microbial growth determined by the relative magnitudes of resource uptake and division rates and initial conditions. We also show that stochasticity influences the dynamic behavior when the amounts of microbes or resources are low. The model recovers Monod growth kinetics and provides a mechanistic interpretation of the Monod constant and maximal growth rate. The theoretical framework presented captures a wide spectrum of dynamic behaviors in microbial systems, providing a clearer microscopic picture to explain their underlying complex mechanisms.

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Cite

BibTeXRIS
BibTeX
@article{leung2026minimal,
  title = {{A Minimal Stochastic Model of Microbial Ecological Dynamics in a Single-Species-Single-Resource Setting.}},
  author = {Cheuk Fung Alvin Leung and Anatoly B Kolomeisky},
  journal = {The journal of physical chemistry. B},
  year = {2026},
  month = sep,
  doi = {10.1021/acs.jpcb.6c04333},
  url = {https://europepmc.org/article/MED/42917544},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - A Minimal Stochastic Model of Microbial Ecological Dynamics in a Single-Species-Single-Resource Setting.
AU  - Cheuk Fung Alvin Leung
AU  - Anatoly B Kolomeisky
PY  - 2026
DA  - 2026/09/01
JO  - The journal of physical chemistry. B
DO  - 10.1021/acs.jpcb.6c04333
UR  - https://europepmc.org/article/MED/42917544
AB  - Microbes exhibit complex dynamic behavior as the result of a large number of biochemical processes, spatial and temporal interactions, environmental variations, and evolutionary pressure. Although significant progress has been achieved in understanding microbial ecological dynamics, multiple open questions remain, including the microscopic mechanisms of growth and the roles of nutrients and stochasticity. In this work, we present a minimal theoretical approach to clarify the link between consumption of resources by microbes and their growth. A stochastic model that accounts for a single microbial species consuming a single type of resource while growing via cell division is studied analytically and via Monte Carlo computer simulations. We identify three distinct dynamical regimes of microbial growth determined by the relative magnitudes of resource uptake and division rates and initial conditions. We also show that stochasticity influences the dynamic behavior when the amounts of microbes or resources are low. The model recovers Monod growth kinetics and provides a mechanistic interpretation of the Monod constant and maximal growth rate. The theoretical framework presented captures a wide spectrum of dynamic behaviors in microbial systems, providing a clearer microscopic picture to explain their underlying complex mechanisms.
SN  - 1520-6106
SN  - 1520-5207
ER  -