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Ensemble models of neutrophil trafficking in severe sepsis

Song, SOK and Hogg, J and Peng, ZY and Parker, R and Kellum, JA and Clermont, G (2012) Ensemble models of neutrophil trafficking in severe sepsis. PLoS Computational Biology, 8 (3). ISSN 1553-734X

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Abstract

A hallmark of severe sepsis is systemic inflammation which activates leukocytes and can result in their misdirection. This leads to both impaired migration to the locus of infection and increased infiltration into healthy tissues. In order to better understand the pathophysiologic mechanisms involved, we developed a coarse-grained phenomenological model of the acute inflammatory response in CLP (cecal ligation and puncture)-induced sepsis in rats. This model incorporates distinct neutrophil kinetic responses to the inflammatory stimulus and the dynamic interactions between components of a compartmentalized inflammatory response. Ensembles of model parameter sets consistent with experimental observations were statistically generated using a Markov-Chain Monte Carlo sampling. Prediction uncertainty in the model states was quantified over the resulting ensemble parameter sets. Forward simulation of the parameter ensembles successfully captured experimental features and predicted that systemically activated circulating neutrophils display impaired migration to the tissue and neutrophil sequestration in the lung, consequently contributing to tissue damage and mortality. Principal component and multiple regression analyses of the parameter ensembles estimated from survivor and non-survivor cohorts provide insight into pathologic mechanisms dictating outcome in sepsis. Furthermore, the model was extended to incorporate hypothetical mechanisms by which immune modulation using extracorporeal blood purification results in improved outcome in septic rats. Simulations identified a sub-population (about 18% of the treated population) that benefited from blood purification. Survivors displayed enhanced neutrophil migration to tissue and reduced sequestration of lung neutrophils, contributing to improved outcome. The model ensemble presented herein provides a platform for generating and testing hypotheses in silico, as well as motivating further experimental studies to advance understanding of the complex biological response to severe infection, a problem of growing magnitude in humans. © 2012 Song et al.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Song, SOK
Hogg, J
Peng, ZY
Parker, Rrparker@pitt.eduRPARKER0000-0002-9913-4847
Kellum, JAkellum@pitt.eduKELLUM0000-0003-1995-2653
Clermont, Gcler@pitt.eduCLER
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
EditorPapin, Jason A.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date: 1 March 2012
Date Type: Publication
Journal or Publication Title: PLoS Computational Biology
Volume: 8
Number: 3
DOI or Unique Handle: 10.1371/journal.pcbi.1002422
Schools and Programs: School of Medicine > Computational and Systems Biology
Swanson School of Engineering > Chemical Engineering
Swanson School of Engineering > Petroleum Engineering
Refereed: Yes
ISSN: 1553-734X
MeSH Headings: Animals; Computer Simulation; Immunologic Factors--immunology; Models, Immunological; Neutrophil Activation--immunology; Neutrophils--immunology; Rats; Sepsis--immunology; Systemic Inflammatory Response Syndrome--immunology
Other ID: NLM PMC3297568
PubMed Central ID: PMC3297568
PubMed ID: 22412365
Date Deposited: 13 Sep 2012 18:23
Last Modified: 26 Jan 2019 10:55
URI: http://d-scholarship.pitt.edu/id/eprint/14150

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