A Cellular Automata Model for Biofilm Growth

Abstract

Abstract. Biofilms are aggregates of bacteria attached to surfaces, which are very adaptable to changes in the environment and survive under extreme conditions. These living structures are behind many problems in research and industry. Therefore there is an increasing interest in improving our understanding on biofilms to be able to control them, either designing protocols to destroy them when harmful or promoting their growth when beneficial.A bidimensional cellular automata model for biofilm development is proposed to study the biofilm behaviour as its key growth parameters vary. The model includes several metabolic and spreading mechanisms typical of bacteria: cell division and spreading, detachment mechanisms adapted to the flow and probabilistic rules for EPS matrix generation.Numerical simulations of the model reproduce a number of biofilm patterns observed in real experiments: ripples, streamers, mushroom networks and patches. The influence of the nutrient concentration and the type of flow on the evolution of the bacterial community is monitorized.Biofilm tends to cover the whole surface when enough nutrients are available. Erosion enhances the creation of holes in this cover and promotes a variety of geometric patterns. The survival of the colony and its final shape will depend on the balance between the main growth parameters.Large Reynolds numbers and poor nutrient sources promote the formation of flat, and thin biofilms. Decreasing the Reynolds number or increasing the nutrient and oxygen concentration enhance pattern formation.

Modeling of Bacterial Communication in the Extended Range of Population Dynamics

“…This yields that modeling of bacterial communication needs to take into account the spatial distribution of bacterial cells and, as a result, the heterogeneous distribution of chemical compounds characterizing quorum sensing. Apart from modeling using cellular automata and agent-based approaches (discrete grid-and particle-based models respectively) [19][20][21], ordinary differential equations can be transferred into spatial systems, by combination with diffusion or other mechanisms leading to a spread in space. Mathematically, this may lead to reactiondiffusion equations.…”

Three-Dimensional Cellular Automaton for Modeling of Self-Similar Evolution in Biofilm-Forming Bacterial Populations

“…Individual-based and cellular automaton models are typical representatives of this research direction [38]. In particular, cellular automata have been proposed to simulate the growth of biofilms on a surface [39], evolution, and antibiotic resistance of bacterial populations in heterogeneous environments [40]. In such a class of models, interactions between agents and the environment are defined by a set of rules and parameters.…”

“…The classical cellular automaton was proposed in [39] for modeling the formation of a bacterial film from the point of view of formalizing the rules of "life" of a discrete structure. The authors formalized numerous processes that accompany the formation of bacterial structures (birth, division, erosion, death) to give realistic scenarios for the evolution of the biosystem.…”

In silico studies of bacterial quorum sensing during population dynamics: simulations by using COMSOL Multiphysics

“…An important step in the algorithms associated with the differential quorum sensing models is represented by the formalization of the quantitative characteristics of bacterial populations. Actually, modeling the space-time distribution of bacterial biomass is a subject of separate consideration and this problem requires detailed study [16,17,18,19]. Nevertheless, there have been several attempts to construct hybrid models based on the integration of differential equations and the description of the spatial growth of biomass.…”