Parameter Estimation and Uncertainty Quantification for Systems Biology Models

Mitra, Eshan David; Hlavacek, William Scott

doi:10.1016/j.coisb.2019.10.006

Title: Parameter Estimation and Uncertainty Quantification for Systems Biology Models

Journal Article · 05 November 2019 · Current Opinion in Systems Biology

DOI: https://doi.org/10.1016/j.coisb.2019.10.006 · OSTI ID:1574756

Mitra, Eshan David ^[1];

^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: National Institutes of Health; USDOE

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1574756

Report Number(s):: LA-UR--19-26245

Journal Information:: Current Opinion in Systems Biology, Journal Name: Current Opinion in Systems Biology Vol. 18; ISSN 2452-3100

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
biological modeling
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