A rule-based approach to the modelling of bacterial ecosystems

In Search of Proximate Triggers of Anthrax Outbreaks in Wildlife: A Hypothetical Individual-Based Model of Plasmid Transfer within Bacillus Communities

Bacillus anthracis, the causative agent of anthrax in humans, livestock, and wildlife, exists in a community with hundreds of other species of bacteria in the environment. Work on the genetics of these communities has shown that B. anthracis shares a high percentage of chromosomal genes with both B. thuringiensis and B. cereus, and that phenotypic differences among these bacteria can result from extra-chromosomal DNA in the form of plasmids. We developed a simple hypothetical individual-based model to simulate the likelihood of detecting plasmids with genes encoding anthrax toxins within bacterial communities composed of B. anthracis, B. thuringiensis, and B. cereus, and the surrounding matrix of extra-cellular polymeric substances. Simulation results suggest the horizontal transfer of plasmids with genes encoding anthrax toxins among Bacillus species persisting outside the host could function as a proximate factor triggering anthrax outbreaks.

Biomimicry of quorum sensing using bacterial lifecycle model

BACKGROUND

Recent microbiologic studies have shown that quorum sensing mechanisms, which serve as one of the fundamental requirements for bacterial survival, exist widely in bacterial intra- and inter-species cell-cell communication. Many simulation models, inspired by the social behavior of natural organisms, are presented to provide new approaches for solving realistic optimization problems. Most of these simulation models follow population-based modelling approaches, where all the individuals are updated according to the same rules. Therefore, it is difficult to maintain the diversity of the population.

RESULTS

In this paper, we present a computational model termed LCM-QS, which simulates the bacterial quorum-sensing (QS) mechanism using an individual-based modelling approach under the framework of Agent-Environment-Rule (AER) scheme, i.e. bacterial lifecycle model (LCM). LCM-QS model can be classified into three main sub-models: chemotaxis with QS sub-model, reproduction and elimination sub-model and migration sub-model. The proposed model is used to not only imitate the bacterial evolution process at the single-cell level, but also concentrate on the study of bacterial macroscopic behaviour. Comparative experiments under four different scenarios have been conducted in an artificial 3-D environment with nutrients and noxious distribution. Detailed study on bacterial chemotatic processes with quorum sensing and without quorum sensing are compared. By using quorum sensing mechanisms, artificial bacteria working together can find the nutrient concentration (or global optimum) quickly in the artificial environment.

CONCLUSIONS

Biomimicry of quorum sensing mechanisms using the lifecycle model allows the artificial bacteria endowed with the communication abilities, which are essential to obtain more valuable information to guide their search cooperatively towards the preferred nutrient concentrations. It can also provide an inspiration for designing new swarm intelligence optimization algorithms, which can be used for solving the real-world problems.

Modeling microbial dynamics in heterogeneous environments: growth on soil carbon sources

We have developed a new kinetic model to study how microbial dynamics are affected by the heterogeneity in the physical structure of the environment and by different strategies for hydrolysis of polymeric carbon. The hybrid model represented the dynamics of substrates and enzymes using a continuum representation and the dynamics of the cells were modeled individually. Individual-based biological model allowed us to explicitly simulate microbial diversity, and to model cell physiology as regulated via optimal allocation of cellular resources to enzyme synthesis, control of growth rate by protein synthesis capacity, and shifts to dormancy. This model was developed to study how microbial community functioning is influenced by local environmental conditions in heterogeneous media such as soil and by the functional attributes of individual microbes. Microbial community dynamics were simulated at two spatial scales: micro-pores that resemble 6-20-μm size portions of the soil physical structure and in 111-μm size soil aggregates with a random pore structure. Different strategies for acquisition of carbon from polymeric cellulose were investigated. Bacteria that express membrane-associated hydrolase had different growth and survival dynamics in soil pores than bacteria that release extracellular hydrolases. The kinetic differences suggested different functional niches for these two microbe types in cellulose utilization. Our model predicted an emergent behavior in which co-existence of membrane-associated hydrolase and extracellular hydrolases releasing organisms led to higher cellulose utilization efficiency and reduced stochasticity. Our analysis indicated that their co-existence mutually benefits these organisms, where basal cellulose degradation activity by membrane-associated hydrolase-expressing cells shortened the soluble hydrolase buildup time and, when enzyme buildup allowed for cellulose degradation to be fast enough to sustain exponential growth, all the organisms in the community shared the soluble carbon product and grew together. Although pore geometry affected the kinetics of cellulose degradation, the patterns observed for the bacterial community dynamics in the 6-20 μm-sized micro-pores were relevant to the dynamics in the more complex 111-μm-sized porous soil aggregates, implying that micro-scale studies can be useful approximations to aggregate scale studies when local effects on microbial dynamics are studied. As shown with examples in this study, various functional niches of the bacterial communities can be investigated using complex predictive mathematical models where the role of key environmental aspects such as the heterogeneous three-dimensional structure, functional niches of the community members, and environmental biochemical processes are directly connected to microbial metabolism and maintenance in an integrated model.

QSAR MODEL OF THE QUORUM-QUENCHING N-ACYL-HOMOSERINE LACTONE LACTONASE ACTIVITY

A quantitative structure-activity relationship (QSAR) study was performed to model the lactonolysis activity of N-acyl-homoserine lactone lactonase. A data set comprising of 20 homoserine lactones and related compounds was taken from the work of Wang et al. Quantum chemical descriptors were calculated using the semiempirical AM1 method. Partial least squares regression was utilized to construct a predictive model. This computational approach reliably reproduced the lactonolysis activity with high accuracy as illustrated by the correlation coefficient in excess of 0.9. It is demonstrated that the combined use of quantum chemical descriptors with partial least squares regression are suitable for modeling the AHL lactonolysis activity.

Spatial aspects in biological system simulations

Mathematical models of the dynamical properties of biological systems aim to improve our understanding of the studied system with the ultimate goal of being able to predict system responses in the absence of experimentation. Despite the enormous advances that have been made in biological modeling and simulation, the inherently multiscale character of biological systems and the stochasticity of biological processes continue to present significant computational and conceptual challenges. Biological systems often consist of well-organized structural hierarchies, which inevitably lead to multiscale problems. This chapter introduces and discusses the advantages and shortcomings of several simulation methods that are being used by the scientific community to investigate the spatiotemporal properties of model biological systems. We first describe the foundations of the methods and then describe their relevance and possible application areas with illustrative examples from our own research. Possible ways to address the encountered computational difficulties are also discussed.

Dynamics of a laterally evolving community of ribozyme-like agents as studied with a rule-based computing system

The very early forms of life probably comprised ribozyme-like agents that were able to catalyze reactions and serve as templates for their own replication. The early evolution has also been suggested to occur mainly horizontally between proto-cells or inorganic compartments rather than vertically from parent cell to their dividing siblings. In order to study the evolutionary dynamics of such a community a rule-based computing system entitled as PrimordialEvo was developed. The system simulates a three dimensional matrix of compartments in which replicators, resource collectors and various other actors thrive. Horizontal movement between compartments may be due to genetically induced vesicle formation or random drift. Analysis of the simulation experiments suggests that active sharing of innovations between compartments is important for the overall reproductive success of life. The capability of natural selection to favor genes in the system was also tested, and, for example, the frequency of anti-parasites was observed to increase when parasites were allowed to emerge.

Rule-based computing system for microbial interactions and communications: evolution in virtual bacterial populations

We have developed a novel rule-based computing system of microbial interactions and communications, referred to as COSMIC-Rules, for simulating evolutionary processes within populations of virtual bacteria. The model incorporates three levels: the bacterial genome, the bacterial cell and an environment inhabited by such cells. The virtual environment in COSMIC-Rules can contain multiple substances, whose relative toxicity or nutrient status is specified by the genome of the bacterium. Each substance may be distributed uniformly or in a user-defined manner. The organisms in COSMIC-Rules possess individually-defined physical locations, size, cell division status and genomes. Genes and/or gene systems are represented by abstractions that may summate sometimes complex phenotypes. Central to COSMIC-Rules is a simplified representation of bacterial species, each containing a functional genome including, where desired, extrachromosomal elements such as plasmids and/or bacteriophages. A widely applicable computer representation of biological recognition systems based on bit string matching is essential to the model. This representation permits, for example, the modelling of protein-protein interactions, receptor-ligand interactions and DNA-DNA transactions. COSMIC-Rules is intended to inform studies on bacterial adaptation and evolution, and to predict behaviour of populations of pathogenic bacteria and their viruses. The framework is constructed for parallel execution across a large number of machines and efficiently utilises a 64 processor development cluster. It will run on any Grid system and has successfully tested simulations with millions of bacteria, of multiple species and utilising multiple substrates. The model may be used for large-scale simulations where a genealogical record for individual organisms is required.

The Paton individual-based model legacy

Ray Paton oversaw the creation of a long lineage of Individual-based Models (IbMs) and this paper discusses the five most successful. All of these concern the development of adaptation, covering both evolutionary time and organism lifetime (somatic time). Of the five models discussed here, the first is based on a plant-herbivore model, the other four are based on a substrate-bacteria model, with the option of antibiotics.