Validated In Silico Model for Biofilm Formation in Escherichia coli

Abstract

Using Escherichia coli as the representative biofilm former, we report here the development of an in silico model built by simulating events that transform a free-living bacterial entity into self-encased multicellular biofilms. Published literature on ∼300 genes associated with pathways involved in biofilm formation was curated, static maps were created, and suitably interconnected with their respective metabolites using ordinary differential equations. Precise interplay of genetic networks that regulate the transitory switching of bacterial growth pattern in response to environmental changes and the resultant multicomponent synthesis of the extracellular matrix were appropriately represented. Subsequently, the in silico model was analyzed by simulating time-dependent changes in the concentration of components by using the R and python environment. The model was validated by simulating and verifying the impact of key gene knockouts (KOs) and systematic knockdowns on biofilm formation, thus ensuring the outcomes were comparable with the reported literature. Similarly, specific gene KOs in laboratory and pathogenic E. coli were constructed and assessed. MiaA, YdeO, and YgiV were found to be crucial in biofilm development. Furthermore, qRT-PCR confirmed the elevation of expression in biofilm-forming clinical isolates. Findings reported in this study offer opportunities for identifying biofilm inhibitors with applications in multiple industries. The application of this model can be extended to the health care sector specifically to develop novel adjunct therapies that prevent biofilms in medical implants and reduce emergence of biofilm-associated resistant polymicrobial-chronic infections. The in silico framework reported here is open source and accessible for further enhancements.

Interplay of cell motility and self-secreted extracellular polymeric substance induced depletion effects on spatial patterning in a growing microbial colony

“…Bacteria are a well-known example of living active matter that exhibit a variety of complex morphodynamics in their lifestyles. They can show a broad spectrum of nonequilibrium collective phenomena, such as patterned colonies, [1][2][3][4][5][6][7] biofilm formation, [8][9][10][11][12][13][14][15][16] swarming, and turbulent motions, [17][18][19][20][21] depending on the species and environmental conditions. The interplay of growth, division, motions, and local interactions of individual components plays a crucial role in controlling such phenomena.…”

Agent-Based Modeling of Microbial Communities

“…A wide variety of modeling approaches have been applied to study microbial communities. They include primary, secondary and tertiary models, , empirical equations, mechanistic models, flux balance analysis, reactive transport models, Bayesian network models, neural networks, cellular automata, and agent-based models (ABMs) …”

“…An important distinguishing characteristic of the various model classes is the spatial and temporal scales at which they operate. While metabolic models , are primarily concerned with the effect of intracellular processes on cell physiology, population-level models seek to elucidate the effect of variables such as temperature, pH, and nutrient concentration on the growth of entire colonies that typically contain over 10 10 cells . Moreover, models that include diffusion and reaction of substrates need to resolve time scales as small as 10 –3 s, whereas biomass growth and decay typically occur over several hours or days. , Recently, there has been much interest in developing hybrid models that integrate several submodels operating at different length and time scales. − ABMs, which are the focus of the present review, constitute one such class of hybrid models.…”

“…A wide variety of modeling approaches have been applied to study microbial communities. They include primary, secondary and tertiary models, 11,12 empirical equations, 13 mechanistic 14 models, flux balance analysis, 15 reactive transport models, 16 Bayesian network models, 17 neural networks, 18 cellular automata, 19 and agentbased models (ABMs). 20 An important distinguishing characteristic of the various model classes is the spatial and temporal scales at which they operate.…”