{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T23:49:14Z","timestamp":1783640954885,"version":"3.55.0"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T00:00:00Z","timestamp":1626220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation program","award":["686282"],"award-info":[{"award-number":["686282"]}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["EXC 2047 & EXC 2151"],"award-info":[{"award-number":["EXC 2047 & EXC 2151"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. In addition, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). pyPESTO is available at https:\/\/github.com\/ICB-DCM\/pyPESTO. All application examples and code to reproduce this study are available at https:\/\/doi.org\/10.5281\/zenodo.4507613.<\/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\/btab512","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T15:21:22Z","timestamp":1625757682000},"page":"4493-4500","source":"Crossref","is-referenced-by-count":16,"title":["Efficient gradient-based parameter estimation for dynamic models using qualitative data"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7946-3232","authenticated-orcid":false,"given":"Leonard","family":"Schmiester","sequence":"first","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen \u2013 German Research Center for Environmental Health , Neuherberg 85764, Germany"},{"name":"Center for Mathematics, Technische Universit\u00e4t M\u00fcnchen , Garching 85748, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9963-6057","authenticated-orcid":false,"given":"Daniel","family":"Weindl","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen \u2013 German Research Center for Environmental Health , Neuherberg 85764, 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 85764, Germany"},{"name":"Center for Mathematics, Technische Universit\u00e4t M\u00fcnchen , Garching 85748, Germany"},{"name":"Faculty of Mathematics and Natural Sciences, University of Bonn , Bonn 53113, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"2023061310550499100_btab512-B1","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1038\/msb.2011.50","article-title":"Division of labor by dual feedback regulators controls JAK2\/STAT5 signaling over broad ligand range","volume":"7","author":"Bachmann","year":"2011","journal-title":"Mol. 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