The influence of the crowding assumptions in biofilm simulations

Author summary In nature, many organisms grow in crowded biofilms that protect against stressful conditions, making their control/eradication a challenge. Modeling these microbial systems is a valuable tool for studying the interactions among cells and exploring strategies for manipulating the system. Even though the composition of biofilms changes over time due to the accumulation of biomolecules in the medium as well as the growth of cells-both in size and number, many current modeling methods do not explicitly take into account for these changes. This study analyzes how sensitive the biofilm simulation is to these crowding conditions to determine whether they can be safely ignored or need to be included for accurate results. We compared different simplifications of the crowding effect on spatio-temporal microbial simulations under several scenarios. 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 (biomass, phenotypes, metabolic production), especially in poor nutrient mediums. The crowding conditions modeling in microbial systems can provide a guidence for selecting effective treatments to disrupt and control biofilms associated to chronic diseases. 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.

Automated summary

This study analyzes the sensitivity of biofilm simulation to crowding conditions and finds that traditional methods may not accurately capture the diverse nature of biofilms. The modeling of crowding conditions in microbial systems can guide the selection of effective treatments for disrupting and controlling biofilms associated with chronic diseases.