A 3D individual‐based model to investigate the spatially heterogeneous response of bacterial biofilms to antimicrobial agents

Abstract

The response of bacterial biofilms to treatment with antimicrobial agents is often characterized by the emergence of recalcitrant cellular microcolonies. We present an individual-based model to investigate the biophysical mechanisms of the selective resistance that arises within the biofilm and leads to a spatially heterogeneous response upon treatment with antibiotics. The response occurs in 3 distinct phases. In the first phase, the subpopulation of metabolically active cells diminishes due to antibiotic-induced cell death. Subsequently, in the second phase, increased nutrient availability allows dormant cells in the lower layers of the biofilm to transform into metabolically active cells. In the third phase, survival of the biofilm is governed by the interplay between 2 contrasting factors: (1) rate of antibiotic-induced cell death and (2) rate of transformation of dormant cells into active ones. Metabolically active cells at the distal edge of the biofilm sacrifice themselves to protect the dormant cells in the interior by (1) reducing local antibiotic concentrations and (2) increasing nutrient availability. In the presence of quorum sensing, biofilms exhibit increased tolerance compared with the quorum sensing-negative strains. Extracellular polymeric substance (EPS) forms a protective layer at the top of the biofilm, thereby limiting antibiotic penetration. The surviving cells, in turn, produce EPS resulting in a feedback-like mechanism of resistance. Whereas resistance in QS − biofilms occurs because of transformation of dormant cells into metabolically active cells, this transformation is less pronounced in QS + biofilms, and resistance is a consequence of the sequestration of the antibiotic by EPS.

Quorum sensing-induced phenotypic switching as a regulatory nutritional stress response in a competitive two-species biofilm: An individual-based cellular automata model

“…We have previously described the 3D rectangular domain used in this model (Machineni et al 2017(Machineni et al , 2018. The bottom plane is a square with side 120 µm and represents the stationary substratum upon which the biofilm develops.…”

“…Modeling of cellular behavior including growth, division, death, and detachment has been described in detail elsewhere (Machineni et al 2017(Machineni et al , 2018. Here, we present a brief description of these processes along with the governing equations.…”

“…Biofilms are spatially heterogeneous with cell behavior being tightly regulated by the local fluid environment, reaction-diffusion barriers, and interactions with neighboring cells. Concentration gradients due to diffusion-limitation establish heterogeneous niches within the biofilm that can produce spatial variation in the biomass density in monoculture (Stewart 1998;Machineni et al 2017Machineni et al , 2018 and multi-species biofilms (Woods et al 2012;Mazumdar et al 2013). Several computational studies have modeled the growth dynamics of monoculture biofilms to investigate the influence of spatial heterogeneity (Machineni et al 2017), cell death (Fozard et al 2012), dispersal (Emerenini et al 2015), and QS (Langebrake et al 2014).…”

Quorum sensing-induced phenotypic switching as a regulatory nutritional stress response in a competitive two-species biofilm: An individual-based cellular automata model

“…Previously, we have used our model to investigate the roles of nutrient availability and QS on the growth dynamics of a monomicrobial biofilm (Machineni et al 2017(Machineni et al , 2018. In the present work, we extend the model to a two-species biofilm comprising of P. aeruginosa and S. aureus, competing for nutrients.…”