The influence of the crowding assumptions in biofilm simulations.

Liliana Angeles-Martinez,Vassily Hatzimanikatis,Kiran Raosaheb Patil

doi:10.1371/journal.pcbi.1009158

Liliana Angeles-Martinez, Vassily Hatzimanikatis + Show 1 more

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https://doi.org/10.1371/journal.pcbi.1009158

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Abstract

Microorganisms are frequently organized into crowded structures that affect the nutrients diffusion. This reduction in metabolite diffusion could modify the microbial dynamics, meaning that computational methods for studying microbial systems need accurate ways to model the crowding conditions. We previously developed a computational framework, termed CROMICS, that incorporates the effect of the (time-dependent) crowding conditions on the spatio-temporal modeling of microbial communities, and we used it to demonstrate the crowding influence on the community dynamics. To further identify scenarios where crowding should be considered in microbial modeling, we herein applied and extended CROMICS to simulate several environmental conditions that could potentially boost or dampen the crowding influence in biofilms. We explore whether the nutrient supply (rich- or low-nutrient media), the cell-packing configuration (square or hexagonal spherical cell arrangement), or the cell growing conditions (planktonic state or biofilm) modify the crowding influence on the growth of Escherichia coli. Our results indicate that the growth rate, the abundance and appearance time of different cell phenotypes as well as the amount of by-products secreted to the medium are sensitive to some extent to the local crowding conditions in all scenarios tested, except in rich-nutrient media. Crowding conditions enhance the formation of nutrient gradient in biofilms, but its effect is only appreciated when cell metabolism is controlled by the nutrient limitation. Thus, as soon as biomass (and/or any other extracellular macromolecule) accumulates in a region, and cells occupy more than 14% of the volume fraction, the crowding effect must not be underestimated, as the microbial dynamics start to deviate from the ideal/expected behaviour that assumes volumeless cells or when a homogeneous (reduced) diffusion is applied in the simulation. The modeling and simulation of the interplay between the species diversity (cell shape and metabolism) and the environmental conditions (nutrient quality, crowding conditions) can help to design effective strategies for the optimization and control of microbial systems.

Highlights

The spatio-temporal modeling of microbial systems can shed light on the dynamics and species interactions [1,2,3,4], the pattern formation [5,6,7] as well as the response of microbial communities to enviromental changes, e.g. the secretion and accumulation of weak acid products [8], the addition of new species to the system [1], the exposure to antibiotics [9] or to a nutrient shift [10]
We found that the traditional use of a reduced diffusion constant fails to capture the heterogeneous nature of a biofilm and could introduce deviations to the dynamics of the system, especially in poor nutrient mediums
The crowding conditions change over time accentuating the heterogeneous nature of the system, where the spatial differences in the local availability of the nutrients affects the dynamics of the whole community

Summary

Introduction

The spatio-temporal modeling of microbial systems can shed light on the dynamics and species interactions [1,2,3,4], the pattern formation [5,6,7] as well as the response of microbial communities to enviromental changes, e.g. the secretion and accumulation of weak acid products [8], the addition of new species to the system [1], the exposure to antibiotics [9] or to a nutrient shift [10]. Several frameworks have already been proposed to integrate the metabolic information of microbial species, estimated using either Monod kinetics [5,6,7,9] or techniques such as Flux Balance Analysis [1,2,3,4,8,10], and the spatial distribution of the nutrients in the system (computed from the diffusion equation) The ability of these models to successfully predict the behavior and interactions within microbial communities makes them a valuable tool for the study of complex ecosystems. This could lead to a miscalculation of the microbial behavior in crowded systems, such as in calculating the effects of antibiotics on treating a biofilm infection

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