title: An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data.
abstract: We present an agent-based model (ABM) to simulate a hepatic inflammatory response (HIR) in a mouse infected by Salmonella that sometimes progressed to problematic proportions, known as "sepsis". Based on over 200 published studies, this ABM describes interactions among 21 cells or cytokines and incorporates 226 experimental data sets and/or data estimates from those reports to simulate a mouse HIR in silico. Our simulated results reproduced dynamic patterns of HIR reported in the literature. As shown in vivo, our model also demonstrated that sepsis was highly related to the initial Salmonella dose and the presence of components of the adaptive immune system. We determined that high mobility group box-1, C-reactive protein, and the interleukin-10: tumor necrosis factor-α ratio, and CD4+ T cell: CD8+ T cell ratio, all recognized as biomarkers during HIR, significantly correlated with outcomes of HIR. During therapy-directed silico simulations, our results demonstrated that anti-agent intervention impacted the survival rates of septic individuals in a time-dependent manner. By specifying the infected species, source of infection, and site of infection, this ABM enabled us to reproduce the kinetics of several essential indicators during a HIR, observe distinct dynamic patterns that are manifested during HIR, and allowed us to test proposed therapy-directed treatments. Although limitation still exists, this ABM is a step forward because it links underlying biological processes to computational simulation and was validated through a series of comparisons between the simulated results and experimental studies.
authors.affiliation: Health Care Operations Resource Center, Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, United States of America.
authors.forename: Zhenzhen
authors.identifier: http://orcid.org/0000-0001-9922-3957
authors.initials: Z
authors.lastname: Shi
authors.affiliation: Division of Biology, Kansas State University, Manhattan, Kansas, United States of America.
authors.forename: Stephen K
authors.initials: SK
authors.lastname: Chapes
authors.affiliation: Health Care Operations Resource Center, Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, United States of America.
authors.forename: David
authors.initials: D
authors.lastname: Ben-Arieh
authors.affiliation: Health Care Operations Resource Center, Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, United States of America.
authors.forename: Chih-Hang
authors.initials: CH
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D058491:Bacterial Load; D015415:Biomarkers; D003198:Computer Simulation; D016207:Cytokines / Q000378:metabolism; D004195:Disease Models, Animal; D006505:Hepatitis / Q000209:etiology* / Q000473:pathology*; D006801:Humans; D018836:Inflammation Mediators / Q000378:metabolism; D008111:Liver Function Tests; D051379:Mice; D008954:Models, Biological; D063868:Patient Outcome Assessment; D012475:Salmonella; D012480:Salmonella Infections / Q000150:complications* / Q000382:microbiology*; D018805:Sepsis / Q000150:complications / Q000382:microbiology
chemical_list: D015415:Biomarkers; D016207:Cytokines; D018836:Inflammation Mediators