Agent-based model of fecal microbial transplant effect on bile acid metabolism on suppressing Clostridium difficile infection: an example of agent-based modeling of intestinal bacterial infection

Abstract

Agent-based modeling is a computational modeling method that represents system-level behavior as arising from multiple interactions between the multiple components that make up a system. Biological systems are thus readily described using agent-based models (ABMs), as multi-cellular organisms can be viewed as populations of interacting cells, and microbial systems manifest as colonies of individual microbes. Intersections between these two domains underlie an increasing number of pathophysiological processes, and the intestinal tract represents one of the most significant locations for these inter-domain interactions, so much so that it can be considered an internal ecology of varying robustness and function. Intestinal infections represent significant disturbances of this internal ecology, and one of the most clinically relevant intestinal infections is Clostridium difficile infection (CDI). CDI is precipitated by the use of broad-spectrum antibiotics, involves the depletion of commensal microbiota, and alterations in bile acid composition in the intestinal lumen. We present an example ABM of CDI (the Clostridium difficile Infection ABM, or CDIABM) to examine fundamental dynamics of the pathogenesis of CDI and its response to treatment with anti-CDI antibiotics and a newer treatment therapy, Fecal Microbial Transplant (FMT). The CDIABM focuses on one specific mechanism of potential CDI suppression: commensal modulation of bile acid composition. Even given its abstraction, the CDIABM reproduces essential dynamics of CDI and its response to therapy, and identifies a paradoxical zone of behavior that provides insight into the role of intestinal nutritional status and the efficacy of anti-CDI therapies. It is hoped that this use case example of the CDIABM can demonstrate the usefulness of both agent-based modeling and the application of abstract functional representation as the biomedical community seeks to address the challenges of increasingly complex diseases with the goal of personalized medicine.

A systematic review and checklist presenting the main challenges for health economic modeling in personalized medicine: towards implementing patient-level models

“…From these publications the full text was screened, resulting in five more exclusions from the sample for different reasons (i.e. papers presenting a biomedical model [32], a prediction model [33], or that did not include diagnostics or personalized risk estimations [34][35][36]). Analyzing the full text of the included articles resulted in an enrichment of the sample with 13 additional publications by cross-referencing.…”

Trends in mathematical modeling of host–pathogen interactions

“…Both ABM and CA are suitable methods to simulate the behavior of a system in a spatio-temporal manner. However, CA are gridbased and do not allow a free movement in space as the (usually) lattice-free ABM does [14,21,96,118]. Thus, ABM can be considered as an extension or generalization of CA [26,96].…”

“…However, CA are gridbased and do not allow a free movement in space as the (usually) lattice-free ABM does [14,21,96,118]. Thus, ABM can be considered as an extension or generalization of CA [26,96]. In the unconventional ABM approach, individual independent agents are defined (such as fungal, bacterial, or human cells) with interaction rules [7,13,21,44,96,118].…”

The Cellular Immunity Agent Based Model (CIABM): Replicating the cellular immune response to viral respiratory infection

“…Agent-based modeling has been used in multiple domains, such as ecology [12], social/political science [13], microeconomics [14] and epidemiology [15]. Agent-based modeling has also been increasingly applied to biomedical research, primarily in terms of characterizing multi-cellular interactions, such as in the study of sepsis [16][17][18][19] cancer [6,[20][21][22][23] cellular trafficking [24][25][26][27][28], host-microbe interactions [29,30], gastrointestinal biology [31][32][33] and wound healing [10,34,35].…”