| Literature DB >> 34048021 |
Abstract
Despite nearly 50 years of research there currently remains no mediator-directed therapy approved for the treatment of sepsis. The failure to effectively translate the copious mechanistic knowledge regarding systemic inflammation to effective therapies is a dramatic example of the translational dilemma. Dynamic computational modeling has been proposed as a vital means of integrating community-wide knowledge into an investigatory framework that allows the application of engineering-like principles to the problem of sepsis. Agent-based modeling is a computational modeling method that has been used to address some of the fundamental issues facing the sepsis research community. This chapter will introduce the rationale to augment traditional research practices with agent-based modeling, describe the basic steps in the construction and use of agent-based models, and provide examples of how the use of agent-based modeling can provide an investigatory pathway to solving the challenge of sepsis.Entities:
Keywords: Acute inflammation; Agent based modeling; Artificial intelligence; In silico trials; Knowledge representation; Machine learning; Mathematical models; Multiple organ failure; Multiscale models; Sepsis; Systemic inflammation; Translational research; Translational systems biology
Year: 2021 PMID: 34048021 DOI: 10.1007/978-1-0716-1488-4_20
Source DB: PubMed Journal: Methods Mol Biol ISSN: 1064-3745