Bayesian estimation of Pseudomonas aeruginosa viscoelastic properties based on creep responses of wild type, rugose, and mucoid variant biofilms

Pseudomonas aeruginosa biofilms are relevant for a variety of disease settings, including pulmonary infections in people with cystic fibrosis. Biofilms are initiated by individual bacteria that undergo a phenotypic switch and produce an extracellular polymeric slime (EPS). However, the viscoelastic characteristics of biofilms at different stages of formation and the contributions of different EPS constituents have not been fully explored. For this purpose, we develop and parameterize a mathematical model to study the rheological behavior of three biofilms - P. aeruginosa wild type PAO1, isogenic rugose small colony variant (RSCV), and mucoid variant biofilms against a range of experimental data. Using Bayesian inference to estimate these viscoelastic properties, we quantify the rheological characteristics of the biofilm EPS. We employ a Monte Carlo Markov Chain algorithm to estimate these properties of P. aeruginosa variant biofilms in comparison to those of wild type. This information helps us understand the rheological behavior of biofilms at different stages of their development. The mechanical properties of wild type biofilms change significantly over time and are more sensitive to small changes in their composition than the other two mutants.

Agent-Based Modeling of Microbial Communities

Microbial communities are complex living systems that populate the planet with diverse functions and are increasingly harnessed for practical human needs. To deepen the fundamental understanding of their organization and functioning as well as to facilitate their engineering for applications, mathematical modeling has played an increasingly important role. Agent-based models represent a class of powerful quantitative frameworks for investigating microbial communities because of their individualistic nature in describing cells, mechanistic characterization of molecular and cellular processes, and intrinsic ability to produce emergent system properties. This review presents a comprehensive overview of recent advances in agent-based modeling of microbial communities. It surveys the state-of-the-art algorithms employed to simulate intracellular biomolecular events, single-cell behaviors, intercellular interactions, and interactions between cells and their environments that collectively serve as the driving forces of community behaviors. It also highlights three lines of applications of agent-based modeling, namely, the elucidation of microbial range expansion and colony ecology, the design of synthetic gene circuits and microbial populations for desired behaviors, and the characterization of biofilm formation and dispersal. The review concludes with a discussion of existing challenges, including the computational cost of the modeling, and potential mitigation strategies.

Supplemental nutrients stimulate the amplification of carbapenemase-producing Klebsiella pneumoniae (CPKP) in a sink drain in vitro biofilm reactor model

Liquid wastes (LW) disposed in hospital handwashing sinks may affect colonization of sink P-traps by carbapenemase-producing Klebsiella pneumoniae (CPKP), causing CPKP dispersal into the patient care environment. This study aimed to determine the effect of LW on biofilm formation and CPKP colonization in a P-Trap model (PTM). PTMs containing polymicrobial biofilms grown in autoclaved municipal tap water (ATW) supplemented with 5% dextrose in water (D5W), nutritional shake (Shake), sugar-based soft drink (Soda), or ATW were inoculated with K. pneumoniae ST258 KPC+ (ST258) or K. pneumoniae CAV1016 (CAV1016) and sampled after 7, 14, and 21 d. Biofilm bio-volume, mean thickness, and heterotrophic plate counts were significantly reduced and roughness coefficient significantly increased by Soda compared with D5W, Shake, or ATW. CPKP were significantly reduced by Soda but significantly amplified by D5W (ST258; CAV1016, 7 d) and Shake (ST258) suggesting that reducing LW disposal in sinks may reduce CPKP dispersal into patient care environments.

Agent Based Models of Polymicrobial Biofilms and the Microbiome-A Review

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

Characteristics and Sinking Behavior of Typical Microplastics Including the Potential Effect of Biofouling: Implications for Remediation

Microplastics are ubiquitous pollutants within the marine environment, predominantly (>90%) accumulating in sediments worldwide. Despite the increasing global concern regarding these anthropogenic pollutants, research into the remediation of microplastics is lacking. Here, we examine those characteristics of microplastics that are essential to adequately evaluate potential remediation techniques such as sedimentation and (air) flotation techniques. We analyzed the sinking behavior of typical microplastics originating from real plastic waste samples and identified the best-available drag model to quantitatively describe their sinking behavior. Particle shape is confirmed to be an important parameter strongly affecting the sinking behavior of microplastics. Various common shape descriptors were experimentally evaluated on their ability to appropriately characterize frequently occurring particle shapes of typical microplastics such as spheres, films, and fibers. This study is the first in this field to include film particles in its experimental design, which were found to make up a considerable fraction of marine pollution and are shown to significantly affect the evaluation of shape-dependent drag models. Circularity χ and sphericity Φ are found to be appropriate shape descriptors in this context. We also investigated the effect of biofouling on the polarity of marine plastics and estimated its potential contribution to the settling motion of initially floating microplastics based on density-modification. It is found that biofouling alters the polarity of plastics significantly; this is from (near) hydrophobic (i.e., water contact angles from 70 to 100°) to strong hydrophilic (i.e., water contact angles from 30 to 40°) surfaces, rendering them more difficult to separate from sediment based on polarity as a primary separation factor. Thus, besides providing a better understanding of the fate and behavior of typical marine microplastics, these findings serve as a fundamental stepping-stone to the development of the first large-scale sediment remediation technique for microplastics to address the global microplastic accumulation issue.

Prediction and quantification of bacterial biofilm detachment using Glazier-Graner-Hogeweg method based model simulations

Morphological changes in bacterial biofilm structures arise from the fluid-structure interactions between the biofilm and the surrounding fluid. Depending on the magnitude of the force acting on the structure, the bacteria rearrange to attain an equilibrium shape or get washed away by the moving fluid. Understanding the dynamics behind the evolution of such equilibrium or failed states can aid in development of tools for biofilm removal or eradication. We develop a Glazier-Graner-Hogeweg method-based model to explore the collective evolution of biofilm morphology arising from cell-cell and cell-fluid interactions. We show that low adherence and high motility of the cells leads to sloughing of biofilms. Also, streamers are found to form under laminar flow conditions in tightly packed biofilms. In mixed species biofilms, we found that a species with less cell-cell binding affinity gets eroded faster than its counterpart. Therefore, we hypothesize that in nature these less-adherent species should be present encapsulated within the biofilm structure to maximize their chances of survival.

A Bayesian approach to modelling the impact of hydrodynamic shear stress on biofilm deformation

We investigate the feasibility of using a surrogate-based method to emulate the deformation and detachment behaviour of a biofilm in response to hydrodynamic shear stress. The influence of shear force, growth rate and viscoelastic parameters on the patterns of growth, structure and resulting shape of microbial biofilms was examined. We develop a statistical modelling approach to this problem, using combination of Bayesian Poisson regression and dynamic linear models for the emulation. We observe that the hydrodynamic shear force affects biofilm deformation in line with some literature. Sensitivity results also showed that the expected number of shear events, shear flow, yield coefficient for heterotrophic bacteria and extracellular polymeric substance (EPS) stiffness per unit EPS mass are the four principal mechanisms governing the bacteria detachment in this study. The sensitivity of the model parameters is temporally dynamic, emphasising the significance of conducting the sensitivity analysis across multiple time points. The surrogate models are shown to perform well, and produced ≈ 480 fold increase in computational efficiency. We conclude that a surrogate-based approach is effective, and resulting biofilm structure is determined primarily by a balance between bacteria growth, viscoelastic parameters and applied shear stress.

A mechanistic Individual-based Model of microbial communities

Accurate predictive modelling of the growth of microbial communities requires the credible representation of the interactions of biological, chemical and mechanical processes. However, although biological and chemical processes are represented in a number of Individual-based Models (IbMs) the interaction of growth and mechanics is limited. Conversely, there are mechanically sophisticated IbMs with only elementary biology and chemistry. This study focuses on addressing these limitations by developing a flexible IbM that can robustly combine the biological, chemical and physical processes that dictate the emergent properties of a wide range of bacterial communities. This IbM is developed by creating a microbiological adaptation of the open source Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This innovation should provide the basis for “bottom up” prediction of the emergent behaviour of entire microbial systems. In the model presented here, bacterial growth, division, decay, mechanical contact among bacterial cells, and adhesion between the bacteria and extracellular polymeric substances are incorporated. In addition, fluid-bacteria interaction is implemented to simulate biofilm deformation and erosion. The model predicts that the surface morphology of biofilms becomes smoother with increased nutrient concentration, which agrees well with previous literature. In addition, the results show that increased shear rate results in smoother and more compact biofilms. The model can also predict shear rate dependent biofilm deformation, erosion, streamer formation and breakup.

Continuum and discrete approach in modeling biofilm development and structure: a review

The scientific community has recognized that almost 99% of the microbial life on earth is represented by biofilms. Considering the impacts of their sessile lifestyle on both natural and human activities, extensive experimental activity has been carried out to understand how biofilms grow and interact with the environment. Many mathematical models have also been developed to simulate and elucidate the main processes characterizing the biofilm growth. Two main mathematical approaches for biomass representation can be distinguished: continuum and discrete. This review is aimed at exploring the main characteristics of each approach. Continuum models can simulate the biofilm processes in a quantitative and deterministic way. However, they require a multidimensional formulation to take into account the biofilm spatial heterogeneity, which makes the models quite complicated, requiring significant computational effort. Discrete models are more recent and can represent the typical multidimensional structural heterogeneity of biofilm reflecting the experimental expectations, but they generate computational results including elements of randomness and introduce stochastic effects into the solutions.

One-domain approach for studying multiphase transport phenomena in biofilm growing systems

The one-domain approach (ODA) was used as an alternative to solve fluid-biofilm interfacial behavior in a 2-D model for diffusion-reaction-convection coupled with prediction of irregular growth of biofilms via a cellular automaton strategy. The simulations exhibited errors of <7% compared with the porosity of a previously reported capillary experimental system. Additionally, biofilm surface geometrical aspects were satisfactorily compared with reports of experimental and similar rigorously simulated benchmark systems. The method developed was applied to simulate typical biofilm systems predicting recirculation flow patterns, interface concentration profiles, and clogging of the inlet section of the capillary tube, which are phenomena that affect the efficiency of diverse biotechnological applications, including membrane bioreactors and biofilters. The ODA method applied to the governing equations of momentum and mass transfer combined with a cellular automaton algorithm is a suitable and straightforward approach for modeling solid-state fermentation at different sophistication levels.

A mechanistic Individual-based Model of microbial communities

Accurate predictive modelling of the growth of microbial communities requires the credible representation of the interactions of biological, chemical and mechanical processes. However, although biological and chemical processes are represented in a number of Individual-based Models (IbMs) the interaction of growth and mechanics is limited. Conversely, there are mechanically sophisticated IbMs with only elementary biology and chemistry. This study focuses on addressing these limitations by developing a flexible IbM that can robustly combine the biological, chemical and physical processes that dictate the emergent properties of a wide range of bacterial communities. This IbM is developed by creating a microbiological adaptation of the open source Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This innovation should provide the basis for "bottom up" prediction of the emergent behaviour of entire microbial systems. In the model presented here, bacterial growth, division, decay, mechanical contact among bacterial cells, and adhesion between the bacteria and extracellular polymeric substances are incorporated. In addition, fluid-bacteria interaction is implemented to simulate biofilm deformation and erosion. The model predicts that the surface morphology of biofilms becomes smoother with increased nutrient concentration, which agrees well with previous literature. In addition, the results show that increased shear rate results in smoother and more compact biofilms. The model can also predict shear rate dependent biofilm deformation, erosion, streamer formation and breakup.