Parameter estimation and uncertainty quantification for systems biology models. Issue 18 (December 2019)

• Record Type: Journal Article
• Authors: Mitra, Eshan D.; Hlavacek, William S.
• Is Part Of: Current opinion in systems biology. Issue 18(2019)
• Page Start: 9
• Page End: 18
• Publication Date: 2019-12
• Subjects: Statistical inference -- Curve fitting -- Bayesian parameter estimation -- Optimization -- Markov chain Monte Carlo -- Profile likelihood -- Bootstrapping -- Software -- Mathematical models; Systems biology -- Periodicals; 570
• DOI: 10.1016/j.coisb.2019.10.006
• Languages: English
• ISSNs: 2452-3100

Abstract

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. Highlights: Mathematical models include parameters that must be estimated from data. New tools including PyBioNetFit and AMICI/PESTO support automated parameter estimation. Optimization algorithms can be used to obtain point estimates of parameter values. Parameter estimation can incorporate both quantitative and qualitative data. Uncertainty quantification assesses confidence in parameter values and model predictions.