Benchmarking optimization methods for parameter estimation in large kinetic models.

ABSTRACT:

Motivation

Kinetic models contain unknown parameters that are estimated by optimizing the fit to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested to surmount these issues, it is difficult to choose the best one for a given problem a priori. A systematic comparison of parameter estimation methods for problems with tens to hundreds of optimization variables is currently missing, and smaller studies provided contradictory findings.

Results

We use a collection of benchmarks to evaluate the performance of two families of optimization methods: (i) multi-starts of deterministic local searches and (ii) stochastic global optimization metaheuristics; the

SUBMITTER: Villaverde AF 

PROVIDER: S-EPMC6394396 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature