Individual-based modeling unravels spatial and social interactions in bacterial communities

Abstract

Bacterial interactions are fundamental in shaping community structure and function, driving processes that range from plastic degradation in marine ecosystems to dynamics within the human gut microbiome. Yet, studying these interactions is challenging due to difficulties in resolving spatiotemporal scales, quantifying interaction strengths, and integrating intrinsic cellular behaviors with extrinsic environmental conditions. Individual-based modeling addresses these challenges through single-cell-level simulations that explicitly model growth, division, motility, and environmental responses. By capturing both the spatial organization and social interactions, individual-based modeling reveals how microbial interactions and environmental gradients collectively shape community architecture, species coexistence, and adaptive responses. In particular, individual-based modeling provides mechanistic insights into how social behaviors-such as competition, metabolic cooperation, and quorum sensing-are regulated by spatial structure, uncovering the interplay between localized interactions and emergent community properties. In this review, we synthesize recent applications of individual-based modeling in studying bacterial spatial and social interactions, highlighting how their interplay governs community stability, diversity, and resilience. By linking individual-scale interactions with the ecosystem-level organization, individual-based modeling offers a predictive framework for understanding microbial ecology and informing strategies for controlling and engineering bacterial consortia in both natural and applied settings.

Keywords: adaptive strategies; individual variability; individual-based modeling (IbM); metabolic cooperation; niche differentiation; resource gradient; social interactions; spatial heterogeneity; spatial interactions.

Figure 1

Bacterial organizations emerge from spatial and social interactions. (A) Scales of bacterial interactions. Bacterial interactions occur across molecular (nm), single-species (μm), and community (mm) scales. At the community scale, multispecies interactions and environmental factors drive the emergence of complex processes that shape ecosystem functions. The design of this panel was inspired by Gralka et al. [10]. (B) Bacterial organizations. Colonies and biofilms represent two major bacterial organizations. Colonies can grow on surfaces or be submerged in a medium, developing different growth zones based on nutrient and oxygen availability. Zone 1 has sufficient nutrients and oxygen; zone 2 has sufficient nutrients but depleted oxygen; zone 3 has sufficient oxygen but depleted nutrients; zone 4: both nutrients and oxygen are depleted (figure taken from Dens et al. [11]). Biofilm forms in four stages: (i) attachment, where cells attach to surfaces; (ii) early formation, where clusters grow through division, cooperation, and competition; (iii) maturation, where biofilms develop into EPS-stabilized structures; (iv) detachment, where cells disperse to colonize new surfaces. (C) Spatial and social interactions. Spatial interactions arise as cells adapt to physical constraints from growth, division, and environmental pressures. Social interactions occur when cells release and sense chemical molecules in their environment.

Figure 2

Modeling bacterial interactions across scales. (A–F) Models used to describe microbial interactions are depicted with conceptual diagrams that represent the key processes and interactions across different spatial scales. Mechanistic non-spatial models, ranging from simpler, aggregate descriptions, such as (A) Lotka–Volterra and (B) consumer-resource model, to (C) genome-scale metabolic models (GEM) that reconstruct the metabolic network of a microbe. Finer spatial details can be further incorporated by (D) reaction–diffusion model and (E) individual-based model (IbM). In contrast, black-box models, such as (F) regression models and (G) deep learning models, rely on statistical or computational methods to infer patterns and predict microbial behaviors.

Figure 3

Schematic illustration of IbM in studying bacterial interactions. (A) Individual bacterial cells are represented as entities on a spatial grid, with nutrients and chemical signals diffusing around them. Simulated cells locally uptake available nutrients to grow (cellular metabolism) and engage in interactions with their immediate neighbors (interactions). (B) The underlying principle of bacterial dynamics is that biomass accumulation results from the combined effects of biotransformation (i.e. growth) and transportation (i.e. diffusion). In IbM, discrete and continuous models are integrated numerically to describe bacterial growth and substrate (and/or toxin, signals) diffusion using the “hybrid Eulerian-Lagrangian approach.” Another characteristic of IbM is its layered architecture. The interplay between nutrient (and other chemical) diffusion and bacterial spatial interactions is formulated in IbM by discretizing both spatial and temporal domains.

Figure 4

Spatial patterns emerge from IbM simulations. On the left, process schedules are conceptualized from CellModeller [116] and MICRODIMS [23, 29]. Starting with the initialization of cell types and environmental conditions, the simulation progresses through a time-stepped loop that includes nutrient uptake, metabolism, cell growth, division decisions, mechanical interactions, environmental updates, and gene expression regulation. Decision points within the loop determine whether cells divide or the simulation continues to the next time step. (A–D) Spatial patterns emerged from localized individual interactions for different scenarios: (A) metabolic differentiation drives the emergence of the starvation zone in the center of growing colonies (pictures adopted from Tack et al. [29] with permission under CC-BY license); (B) droplet evaporation determines the spatial distributions of bacterial cells and subsequently controls the spread of an antibiotic resistance-encoding plasmid during surface-associated growth (pictures adopted from Ruan et al. [63] under CC-BY license); (C) chamber competitions between T6SS attacker species and susceptible species lead to distinct growth patterns of dual-species communities (pictures adopted from Smith et al. [124] under CC0 license); (D) spatial patterns are formed under different initial anoxic and nitrite conditions by two strains. One strain can reduce nitrate but not nitrite (producer), whereas the other can reduce nitrite but not nitrate (consumer) (pictures adopted from Ciccarese et al. [127] under CC-BY license).