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

Abstract

Sepsis, a manifestation of the body’s inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process in the clinical setting. We propose that this is in great part due to the considerable heterogeneity of the clinical trajectories that constitute clinical “sepsis,” and that determining how this system can be controlled back into a state of health requires the application of concepts drawn from the field of dynamical systems. In this work, we consider the human immune system to be a random dynamical system, and investigate its potential controllability using an agent-based model of the innate immune response (the Innate Immune Response ABM or IIRABM) as a surrogate, proxy system. Simulation experiments with the IIRABM provide an explanation as to why single/limited cytokine perturbations at a single, or small number of, time points is unlikely to significantly improve the mortality rate of sepsis. We then use genetic algorithms (GA) to explore and characterize multi-targeted control strategies for the random dynamical immune system that guide it frommore » a persistent, non-recovering inflammatory state (functionally equivalent to the clinical states of systemic inflammatory response syndrome (SIRS) or sepsis) to a state of health. We train the GA on a single parameter set with multiple stochastic replicates, and show that while the calculated results show good generalizability, more advanced strategies are needed to achieve the goal of adaptive personalized medicine. This work evaluating the extent of interventions needed to control a simplified surrogate model of sepsis provides insight into the scope of the clinical challenge, and can serve as a guide on the path towards true “precision control” of sepsis.« less

Authors:
ORCiD logo; ORCiD logo;
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1421309
Alternate Identifier(s):
OSTI ID: 1544028
Grant/Contract Number:  
B616283; AC02-05CH11231
Resource Type:
Published Article
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online) Journal Volume: 14 Journal Issue: 2; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science (PLoS)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Biochemistry & Molecular Biology; Mathematical & Computational Biology

Citation Formats

Cockrell, Robert Chase, An, Gary, and Hunt, ed., C. Anthony. Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation. United States: N. p., 2018. Web. doi:10.1371/journal.pcbi.1005876.
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Cockrell, Robert Chase, An, Gary, & Hunt, ed., C. Anthony. Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation. United States. https://doi.org/10.1371/journal.pcbi.1005876
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Cockrell, Robert Chase, An, Gary, and Hunt, ed., C. Anthony. Thu . "Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation". United States. https://doi.org/10.1371/journal.pcbi.1005876.
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@article{osti_1421309,
title = {Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation},
author = {Cockrell, Robert Chase and An, Gary and Hunt, ed., C. Anthony},
abstractNote = {Sepsis, a manifestation of the body’s inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process in the clinical setting. We propose that this is in great part due to the considerable heterogeneity of the clinical trajectories that constitute clinical “sepsis,” and that determining how this system can be controlled back into a state of health requires the application of concepts drawn from the field of dynamical systems. In this work, we consider the human immune system to be a random dynamical system, and investigate its potential controllability using an agent-based model of the innate immune response (the Innate Immune Response ABM or IIRABM) as a surrogate, proxy system. Simulation experiments with the IIRABM provide an explanation as to why single/limited cytokine perturbations at a single, or small number of, time points is unlikely to significantly improve the mortality rate of sepsis. We then use genetic algorithms (GA) to explore and characterize multi-targeted control strategies for the random dynamical immune system that guide it from a persistent, non-recovering inflammatory state (functionally equivalent to the clinical states of systemic inflammatory response syndrome (SIRS) or sepsis) to a state of health. We train the GA on a single parameter set with multiple stochastic replicates, and show that while the calculated results show good generalizability, more advanced strategies are needed to achieve the goal of adaptive personalized medicine. This work evaluating the extent of interventions needed to control a simplified surrogate model of sepsis provides insight into the scope of the clinical challenge, and can serve as a guide on the path towards true “precision control” of sepsis.},
doi = {10.1371/journal.pcbi.1005876},
journal = {PLoS Computational Biology (Online)},
number = 2,
volume = 14,
place = {United States},
year = {Thu Feb 15 00:00:00 EST 2018},
month = {Thu Feb 15 00:00:00 EST 2018}
}
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Journal Article:
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https://doi.org/10.1371/journal.pcbi.1005876
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