Benchmarking optimization methods for parameter estimation in large kinetic models

9 pages, 2 figures, 2 tables.-- This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly citedMotivation: 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 latter may be combined with deterministic local searches, leading to hybrid methods. A fair comparison is ensured through a collaborative evaluation and a consideration of multiple performance metrics. We discuss possible evaluation criteria to assess the trade-off between computational efficiency and robustness. Our results show that, thanks to recent advances in the calculation of parametric sensitivities, a multi-start of gradient-based local methods is often a successful strategy, but a better performance can be obtained with a hybrid metaheuristic. The best performer combines a global scatter search metaheuristic with an interior point local method, provided with gradients estimated with adjoint-based sensitivities. We provide an implementation of this method to render it available to the scientific community. Availability and implementation: The code to reproduce the results is provided as Supplementary Material and is available at Zenodo https://doi.org/10.5281/zenodo.1304034.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 686282 (‘CANPATHPRO’), from the Spanish MINECO/FEDER projects SYNBIOFACTORY (DPI2014-55276-C5-2-R) and SYNBIOCONTROL (DPI2017-82896-C2-2-R), and from the German Research Foundation (DFG) through the Graduate School of Quantitative Biosciences Munich (QBM; F.F.)Peer reviewe