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Title: Parameter Estimation and Uncertainty Quantification for Systems Biology Models

Abstract

Mathematical models can provide quantitative insights into immunoreceptor signaling, and other biological processes, but require parameterization and uncertainty quanti cation before reliable predictions become possible. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of pro le likelihood, bootstrapping, and Bayesian inference for uncertainty quanti cation. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena.

Authors:
 [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
National Institutes of Health (NIH); USDOE
OSTI Identifier:
1574756
Report Number(s):
LA-UR-19-26245
Journal ID: ISSN 2452-3100
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Current Opinion in Systems Biology
Additional Journal Information:
Journal Name: Current Opinion in Systems Biology; Journal ID: ISSN 2452-3100
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; immune cell signaling; biological modeling

Citation Formats

Mitra, Eshan David, and Hlavacek, William Scott. Parameter Estimation and Uncertainty Quantification for Systems Biology Models. United States: N. p., 2019. Web. doi:10.1016/j.coisb.2019.10.006.
Mitra, Eshan David, & Hlavacek, William Scott. Parameter Estimation and Uncertainty Quantification for Systems Biology Models. United States. doi:10.1016/j.coisb.2019.10.006.
Mitra, Eshan David, and Hlavacek, William Scott. Wed . "Parameter Estimation and Uncertainty Quantification for Systems Biology Models". United States. doi:10.1016/j.coisb.2019.10.006.
@article{osti_1574756,
title = {Parameter Estimation and Uncertainty Quantification for Systems Biology Models},
author = {Mitra, Eshan David and Hlavacek, William Scott},
abstractNote = {Mathematical models can provide quantitative insights into immunoreceptor signaling, and other biological processes, but require parameterization and uncertainty quanti cation before reliable predictions become possible. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of pro le likelihood, bootstrapping, and Bayesian inference for uncertainty quanti cation. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena.},
doi = {10.1016/j.coisb.2019.10.006},
journal = {Current Opinion in Systems Biology},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {11}
}

Journal Article:
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This content will become publicly available on November 6, 2020
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