Introduction: The pathogenesis of microbe-mediated diseases involves complex interactions between the host and pathogen. Many microbes are able to sense stress-related changes in the host microenvironment and respond with virulome expression and phenotype shifts. Understanding the coordination, expression and population characteristics of these sense and response mechanisms is a formidable challenge that exceeds the traditional methods of constructing a pathogenesis paradigm. We propose that agent-based modeling (ABM), an object-oriented computational modeling method, is well-suited to dynamically represent knowledge of pathogen and host interactions in an in silico experimental platform.

Methods: An ABM for sense and response mechanisms in Pseudonomas aeruginosa was constructed using NetLogo. Sense and response mechanisms for iron and phosphate depletion, host systemic inflammatory mediators and opioid stress response and changes in the mucous environment were included. Agents representing individual cells interact in a virtual host milieu to model the response to changes in the mucous microenvironment during host stress. Rules governing agent interactions were extrapolated from published experiments and results from our collaborating lab group. Pathogen virulence mechanisms include mucinases, PA-1 lectin (gut permeability), and toxins directed against host epithelial cells and endogenous flora. Changes in the availability and distribution of nutrients and host signals are modifiable to account for variability in host stress response. Representations of microbial constituents within the mucous layer include endogenous flora and opportunistic pathogens.

Results: Host stress was modeled with simulated combinations of ischemia / inflammation and nutrient depletion. Gene sequences for bacterial virulence were represented as both individual modules and as bundled, co-regulatory groups simulating hierarchical packets of virulence expression. Simulation results demonstrated that heterogenous populations of pathogens with variable sense and response capabilities produced the most robust population dynamics. Furthermore, different initial distributions of virulence factors led to different stable and meta-stable outcomes corresponding to varied health and disease states, suggesting that the baseline “virulence potential” of an endogenous bacterial population has significant implications regarding the development and trajectory of microbial-influenced disease.

Conclusion: The challenge of understanding and representing complex interactions in the mucous microenvironment during host stress can be mitigated with dynamic and modifiable instantiation of mechanistic relationships within an ABM. This ABM of the pathogen sense and response mechanisms in response to host stress integrates the knowledge derived from multiple experimental environments into an overall dynamic representation, and allows the performance of in silico “function” knock-outs to parse the contribution of different components of the overall pathogen population response to host stress.