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 quantification 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 profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena.
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (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; TRN: US2001141
- Grant/Contract Number:
- 89233218CNA000001
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Current Opinion in Systems Biology
- Additional Journal Information:
- Journal Volume: 18; Journal ID: ISSN 2452-3100
- Publisher:
- Elsevier
- 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. https://doi.org/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. https://doi.org/10.1016/j.coisb.2019.10.006. https://www.osti.gov/servlets/purl/1574756.
@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 quantification 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 profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. 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 = 18,
place = {United States},
year = {Wed Nov 06 00:00:00 EST 2019},
month = {Wed Nov 06 00:00:00 EST 2019}
}
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