{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:14:42Z","timestamp":1787008482644,"version":"3.56.0"},"reference-count":66,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2018,8,23]],"date-time":"2018-08-23T00:00:00Z","timestamp":1534982400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["686282"],"award-info":[{"award-number":["686282"]}]},{"DOI":"10.13039\/501100003329","name":"MINECO","doi-asserted-by":"publisher","award":["DPI2014-55276-C5-2-R"],"award-info":[{"award-number":["DPI2014-55276-C5-2-R"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SYNBIOCONTROL","award":["DPI2017-82896-C2-2-R"],"award-info":[{"award-number":["DPI2017-82896-C2-2-R"]}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Graduate School of Quantitative Biosciences Munich"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The code to reproduce the results is provided as Supplementary Material and is available at Zenodo https:\/\/doi.org\/10.5281\/zenodo.1304034.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty736","type":"journal-article","created":{"date-parts":[[2018,8,22]],"date-time":"2018-08-22T15:19:20Z","timestamp":1534951160000},"page":"830-838","source":"Crossref","is-referenced-by-count":140,"title":["Benchmarking optimization methods for parameter estimation in large kinetic models"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7401-7380","authenticated-orcid":false,"given":"Alejandro F","family":"Villaverde","sequence":"first","affiliation":[{"name":"Bioprocess Engineering Group, IIM-CSIC, Vigo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabian","family":"Fr\u00f6hlich","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen \u2013 German Research Center for Environmental Health, Neuherberg, Germany"},{"name":"Chair of Mathematical Modeling of Biological Systems, Center for Mathematics, Technische Universit\u00e4t M\u00fcnchen, Garching, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Weindl","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen \u2013 German Research Center for Environmental Health, Neuherberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4935-3312","authenticated-orcid":false,"given":"Jan","family":"Hasenauer","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen \u2013 German Research Center for Environmental Health, Neuherberg, Germany"},{"name":"Chair of Mathematical Modeling of Biological Systems, Center for Mathematics, Technische Universit\u00e4t M\u00fcnchen, Garching, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4245-0320","authenticated-orcid":false,"given":"Julio R","family":"Banga","sequence":"additional","affiliation":[{"name":"Bioprocess Engineering Group, IIM-CSIC, Vigo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,8,23]]},"reference":[{"key":"2023013107251152500_bty736-B1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ymben.2014.03.007","article-title":"Kinetic models in industrial biotechnology \u2013 improving cell factory performance","volume":"24","author":"Almquist","year":"2014","journal-title":"Metab. 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