Growth dependence of conjugation explains limited plasmid invasion in biofilms: an individual-based modelling study.

Plasmid invasion in biofilms is often surprisingly limited in spite of the close contact of cells in a biofilm. We hypothesized that this poor plasmid spread into deeper biofilm layers is caused by a dependence of conjugation on the growth rate (relative to the maximum growth rate) of the donor. By extending an individual-based model of microbial growth and interactions to include the dynamics of plasmid carriage and transfer by individual cells, we were able to conduct in silico tests of this and other hypotheses on the dynamics of conjugal plasmid transfer in biofilms. For a generic model plasmid, we find that invasion of a resident biofilm is indeed limited when plasmid transfer depends on growth, but not so in the absence of growth dependence. Using sensitivity analysis we also find that parameters related to timing (i.e. a lag before the transconjugant can transfer, transfer proficiency and scan speed) and spatial reach (EPS yield, conjugal pilus length) are more important for successful plasmid invasion than the recipients' growth rate or the probability of segregational loss. While this study identifies one factor that can limit plasmid invasion in biofilms, the new individual-based framework introduced in this work is a powerful tool that enables one to test additional hypotheses on the spread and role of plasmids in microbial biofilms.

iDynoMiCS: next-generation individual-based modelling of biofilms.

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.