An ensemble of models of the acute inflammatory response to bacterial lipopolysaccharide in rats: Results from parameter space reduction

Daun, Silvia; Rubin, Jonathan E.; Vodovotz, Yoram; Roy, Anirban; Parker, Robert S.; Clermont, Gilles

doi:10.1016/j.jtbi.2008.04.033

Cited by 53 publications

(58 citation statements)

References 29 publications

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“…The model also described the time course of the unobserved activation of phagocytic cells by endotoxin, as well as unaccessible variables, like tissue damage (D) caused by the activated phagocytic cells and the slow-acting anti-inflammatory mediator (C A ). The data and the resulting model response display significant nonlinearity across the three challenge levels [27]. Hence, the accurate prediction of the intermediate dose without the need for additional parameters or parameter value changes supports the validity of the model structure, as well as its interpolative, and possibly extrapolative, utility.…”

Section: Discussionmentioning

confidence: 64%

“…This work presents and analyzes an 8-state differential equation model of the acute inflammatory response system [27], explicitly representing the dynamics of a variety of specific cytokines that were treated as more abstract factors in previously developed 3-state model of the acute inflammatory response to pathogen or endotoxin. The present focus is analyzing the dynamics of this more detailed model to identify model structures that are consistent, in a least-squares sense, with longitudinal rat experimental data for 3 of the 8 state variables, which were measured following endotoxin dose challenges at 3, 6, and 12 mg/kg.…”

Section: Discussionmentioning

confidence: 99%

“…In high doses, endotoxin may lead to organ dysfunction and death. Administration of endotoxin in laboratory animals serves as a laboratory model of sepsis and acute inflammation [27,28].…”

Section: Endotoxemia: a Model Of Sepsismentioning

confidence: 99%

“…The model developed above is a simplification of our earlier model [27,52] that eliminates nonlinear up-and down-regulatory functions that did not display significant dynamics over the endotoxin challenge range employed here. The parameter values used here are those published in Table 1 of [27].…”

Section: Mathematical Model Of Acute Inflammationmentioning

confidence: 99%

“…The parameter values used here are those published in Table 1 of [27]. Alternative modeling options for the various interactions are discussed in Appendix A.…”

Section: Mathematical Model Of Acute Inflammationmentioning

confidence: 99%

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Modeling and Hemofiltration Treatment of Acute Inflammation

et al. 2016

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Abstract:The body responds to endotoxins by triggering the acute inflammatory response system to eliminate the threat posed by gram-negative bacteria (endotoxin) and restore health. However, an uncontrolled inflammatory response can lead to tissue damage, organ failure, and ultimately death; this is clinically known as sepsis. Mathematical models of acute inflammatory disease have the potential to guide treatment decisions in critically ill patients. In this work, an 8-state (8-D) differential equation model of the acute inflammatory response system to endotoxin challenge was developed. Endotoxin challenges at 3 and 12 mg/kg were administered to rats, and dynamic cytokine data for interleukin (IL)-6, tumor necrosis factor (TNF), and IL-10 were obtained and used to calibrate the model. Evaluation of competing model structures was performed by analyzing model predictions at 3, 6, and 12 mg/kg endotoxin challenges with respect to experimental data from rats. Subsequently, a model predictive control (MPC) algorithm was synthesized to control a hemoadsorption (HA) device, a blood purification treatment for acute inflammation. A particle filter (PF) algorithm was implemented to estimate the full state vector of the endotoxemic rat based on time series cytokine measurements. Treatment simulations show that: (i) the apparent primary mechanism of HA efficacy is white blood cell (WBC) capture, with cytokine capture a secondary benefit; and (ii) differential

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Section: Discussionmentioning

confidence: 64%

Section: Discussionmentioning

confidence: 99%

“…In high doses, endotoxin may lead to organ dysfunction and death. Administration of endotoxin in laboratory animals serves as a laboratory model of sepsis and acute inflammation [27,28].…”

Section: Endotoxemia: a Model Of Sepsismentioning

confidence: 99%

Section: Mathematical Model Of Acute Inflammationmentioning

confidence: 99%

“…The parameter values used here are those published in Table 1 of [27]. Alternative modeling options for the various interactions are discussed in Appendix A.…”

Section: Mathematical Model Of Acute Inflammationmentioning

confidence: 99%

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Modeling and Hemofiltration Treatment of Acute Inflammation

et al. 2016

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Mathematical modelling of chemical processes—obtaining the best model predictions and parameter estimates using identifiability and estimability procedures

2011

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Chemical engineers who develop fundamental models often have difficulties estimating all model parameters due to problems with parameter identifiability and estimability. These two concepts are reviewed, as are techniques for assessing identifiability and estimability. When some parameters are not estimable from the data, modellers must decide whether to conduct new experiments, change the model structure, or to estimate only a subset of the parameters and leave the others at fixed values. Estimating a reduced number of parameters can lead to better model predictions with lower mean squared error (MSE). MSE‐based techniques for parameter subset selection are discussed and compared. © 2011 Canadian Society for Chemical Engineering

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In silico augmentation of the drug development pipeline: examples from the study of acute inflammation

Bartels²,

Vodovotz

2010

Drug Development Research

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The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and –content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.

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An ensemble of models of the acute inflammatory response to bacterial lipopolysaccharide in rats: Results from parameter space reduction

Cited by 53 publications

References 29 publications

Modeling and Hemofiltration Treatment of Acute Inflammation

Modeling and Hemofiltration Treatment of Acute Inflammation

Mathematical modelling of chemical processes—obtaining the best model predictions and parameter estimates using identifiability and estimability procedures

In silico augmentation of the drug development pipeline: examples from the study of acute inflammation

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