iDynoMiCS: next‐generation individual‐based modelling of biofilms

Abstract

Individual-based modelling of biofilms accounts for the fact that individual organisms of the same species may well be in a different physiological state as a result of environmental gradients, lag times in responding to change, or noise in gene expression, which we have become increasingly aware of with the advent of single-cell microbiology. But progress in developing and using individual-based modelling has been hampered by different groups writing their own code and the lack of an available standard model. We therefore set out to merge most features of previous models and incorporate various improvements in order to provide a common basis for further developments. Four improvements stand out: the biofilm pressure field allows for shrinking or consolidating biofilms; the continuous-in-time extracellular polymeric substances excretion leads to more realistic fluid behaviour of the extracellular matrix, avoiding artefacts; the stochastic chemostat mode allows comparison of spatially uniform and heterogeneous systems; and the separation of growth kinetics from the individual cell allows condition-dependent switching of metabolism. As an illustration of the model's use, we used the latter feature to study how environmentally fluctuating oxygen availability affects the diversity and composition of a community of denitrifying bacteria that induce the denitrification pathway under anoxic or low oxygen conditions. We tested the hypothesis that the existence of these diverse strategies of denitrification can be explained solely by assuming that faster response incurs higher costs. We found that if the ability to switch metabolic pathways quickly incurs no costs the fastest responder is always the best. However, if there is a trade-off where faster switching incurs higher costs, then there is a strategy with optimal response time for any frequency of environmental fluctuations, suggesting that different types of denitrifying strategies win in different environments. In a single environment, biodiversity of denitrifiers is higher in biofilms than chemostats, higher with than without costs and higher at intermediate frequency of change. The highly modular nature of the new computational model made this case study straightforward to implement, and reflects the sort of novel studies that can easily be executed with the new model.

Architectural transitions in Vibrio cholerae biofilms at single-cell resolution

“…However, mechanical effects alone are not sufficient to determine the entire biofilm architecture, as a mutant that does not produce the biofilm matrix protein RbmA exhibits a dramatically different biofilm architecture than the wild type and other mutant strains. Additional analyses to quantify the forces involved in V. cholerae surface adhesion (16,70), cell-cell adhesion, and cell-matrix interaction strength are needed to develop accurate simulations of biofilm growth (71)(72)(73)(74). Such simulations will be required to distinguish the effects of forces resulting from cell growth and division from those due to matrix production and adhesion in developing biofilms.…”

Vibrio cholerae biofilm growth program and architecture revealed by single-cell live imaging

“…To represent the biofilm growth process in silico and uncover the forces underlying the cell reorientation events, we developed an agent-based simulation of biofilm formation that incorporates rod-shaped bacteria with and without cell-to-surface bonds (27,28) (Fig. 2 C and D and Movie S5).…”

Cutting through the complexity of cell collectives

“…Similarly, powerful theory has been developed for understanding the evolution of social interaction [26][27][28][29][30], but this theory is often difficult to apply directly to cell groups in realistic contexts (but see [31][32][33]). Computational individual-based modelling offers an alternative approach, implementing cells in two-or threedimensional space that behave independently in response to their local microenvironments [34,35]. Such models allow subtle details of biology and physics to be considered and are excellent for studying cell group heterogeneity, but they are typically complex and sacrifice generality for realism.…”