Linking Spatial Drug Heterogeneity to Microbial Growth Dynamics in Theory and Experiment
Zhijian Hu, Yuzhen Wu, Tomas Freire, Erida Gjini, Kevin Wood
Abstract
Drugs play a central role in limiting bacterial population spread, yet laboratory studies typically assume well-mixed environments when assessing microbial drug responses. In contrast, bacteria in the human body often occupy spatially structured habitats where drug concentrations vary. Understanding how this heterogeneity shapes growth and decline is therefore essential for controlling infections and mitigating resistance evolution.
Introduction
Antibiotics are widely used to control bacterial infections. However, the environments in which bacterial populations live, especially within the human body, are often spatially structured and heterogeneous. Previous studies have primarily examined how spatial drug heterogeneity shapes long-timescale resistance evolution. Under most conditions, monotonic spatial gradients in drug concentration dramatically accelerate resistance evolution.
Materials and Methods:
Here we use the classic one-dimensional Fisher–KPP model, linearized at low population densities with two Dirichlet (absorbing) boundaries, as our theoretical framework.
For heterogeneous drug environments, there is no closed-form analytical expression for . Instead, we introduce i. a constrained optimization method to identify the “arrangement-dependent” condition under which survival depends on the specific spatial drug arrangement, and ii.
Discussion
In this paper, we developed a minimal experimental system automated by a pipetting robot, to investigate the effects of spatial drug heterogeneity on a confined 1D inhabitat. We first recapitulated the classic “critical-patch-size” model result, and then found out that, different spatial arrangements of drugs, even with the same spatially averaged growth rates, can lead to divergent bacterial population outcomes, resulting in a “arrangement-dependent” phase of mixed responses, or “mixed” phase.
Citation: Hu Z, Wu Y, Freire T, Gjini E, Wood K (2026) Linking spatial drug heterogeneity to microbial growth dynamics in theory and experiment. PLoS Comput Biol 22(1): e1013896. https://doi.org/10.1371/journal.pcbi.1013896
Editor: Pedro Mendes, University of Connecticut School of Medicine, UNITED STATES OF AMERICA
Received: February 6, 2025; Accepted: January 7, 2026; Published: January 20, 2026.
Copyright: © 2026 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data is available in the GitHub repository https://github.com/Zzzzzhijian/Linking_SpatialHetero.
Funding: This study was supported by NIH (R35GM124875 to KBW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
