A preliminary study of sepsis progression in an animal model using agent-based modeling

Abstract

Patients who have sepsis are most likely to suffer from organ dysfunction. Organ dysfunction caused by sepsis results in high mortality rate and health care financial loss. The complexity of sepsis results in various dynamic patterns of sepsis for individual patients. Agent-based models provide a means to capture complex information in sepsis development by synthesizing concepts of sepsis progression to a model. Compared to a traditional mathematical modeling approach, agent-based models map interactions among agents to interactions among cells by embedding spatial effect, stochastic nature, and dynamics transitions, thereby demonstrating suitability for the development of complex biological processes. This article presents an integrated-mathematical-multi-agent-based model (IMMAB) to model and simulate acute inflammatory response (AIR), an initial stage of sepsis progression. To our knowledge, this IMMAB is the first version of an agent-based model that measures quantitative levels of indicators in sepsis progression by incorporating experimental data. Despite abstraction and generality, our simulated results reproduced dynamic patterns of AIR progression reported in existing experimental studies. Furthermore, our IMMAB captured diverged outcomes of individual patients when initial loads of pathogen are from 300 to 1000 numerical count. In the end, we inferred that Monocyte and IL-10 are possibly potential therapeutic targets in pre-clinic experiments.

Keywords:

• IMMAB
• sepsis
• complex biological processes

Acknowledgments

ZZS was supported by China Scholarship Council scholarship. The authors would like to thank Dr Stephen Chapes from Division of Biology at Kansas State University and Dr Steven Simpson from University of Kansas Medical Center for helping develop IMMAB.