Literature DB >> 34048021

Agent-Based Modeling of Systemic Inflammation: A Pathway Toward Controlling Sepsis.

Gary An1, R Chase Cockrell2.   

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


  38 in total

1.  The search for effective therapy for sepsis: back to the drawing board?

Authors:  Derek C Angus
Journal:  JAMA       Date:  2011-12-21       Impact factor: 56.272

2.  Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care.

Authors:  D C Angus; W T Linde-Zwirble; J Lidicker; G Clermont; J Carcillo; M R Pinsky
Journal:  Crit Care Med       Date:  2001-07       Impact factor: 7.598

3.  In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling.

Authors:  Gary An
Journal:  Crit Care Med       Date:  2004-10       Impact factor: 7.598

Review 4.  Translational systems biology: introduction of an engineering approach to the pathophysiology of the burn patient.

Authors:  Gary An; James Faeder; Yoram Vodovotz
Journal:  J Burn Care Res       Date:  2008 Mar-Apr       Impact factor: 1.845

5.  Precision Medicine for Critical Illness and Injury.

Authors:  Timothy G Buchman; Timothy R Billiar; Eric Elster; Allan D Kirk; Ramzy H Rimawi; Yoram Vodovotz; Barbara A Zehnbauer
Journal:  Crit Care Med       Date:  2016-09       Impact factor: 7.598

Review 6.  Immunotherapy of sepsis: flawed concept or faulty implementation?

Authors:  A S Cross; S M Opal; A K Bhattacharjee; S T Donta; P N Peduzzi; E Fürer; J U Que; S J Cryz
Journal:  Vaccine       Date:  1999-10-01       Impact factor: 3.641

7.  In silico design of clinical trials: a method coming of age.

Authors:  Gilles Clermont; John Bartels; Rukmini Kumar; Greg Constantine; Yoram Vodovotz; Carson Chow
Journal:  Crit Care Med       Date:  2004-10       Impact factor: 7.598

8.  Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine.

Authors:  Brenden K Petersen; Jiachen Yang; Will S Grathwohl; Chase Cockrell; Claudio Santiago; Gary An; Daniel M Faissol
Journal:  J Comput Biol       Date:  2019-01-25       Impact factor: 1.479

Review 9.  A new paradigm for the treatment of sepsis: is it time to consider combination therapy?

Authors:  Alan S Cross; Steven M Opal
Journal:  Ann Intern Med       Date:  2003-03-18       Impact factor: 25.391

10.  Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation.

Authors:  Robert Chase Cockrell; Gary An
Journal:  PLoS Comput Biol       Date:  2018-02-15       Impact factor: 4.475

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