{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T22:40:09Z","timestamp":1773096009265,"version":"3.50.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T00:00:00Z","timestamp":1564099200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","award":["HA7376\/1-1"],"award-info":[{"award-number":["HA7376\/1-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"German Federal Ministry of Education and Research","award":["01ZX1310B"],"award-info":[{"award-number":["01ZX1310B"]}]},{"name":"European Union\u2019s Horizon 2020","award":["686282"],"award-info":[{"award-number":["686282"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (&amp;gt;1000 state variables, &amp;gt;4000 parameters) using relative protein, phosphoprotein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Supplementary code and data are available online at http:\/\/doi.org\/10.5281\/zenodo.3254429 and http:\/\/doi.org\/10.5281\/zenodo.3254441.<\/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\/btz581","type":"journal-article","created":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T07:27:00Z","timestamp":1563953220000},"page":"594-602","source":"Crossref","is-referenced-by-count":38,"title":["Efficient parameterization of large-scale dynamic models based on relative measurements"],"prefix":"10.1093","volume":"36","author":[{"given":"Leonard","family":"Schmiester","sequence":"first","affiliation":[{"name":"Institute of Computational Biology , Helmholtz Zentrum M\u00fcnchen \u2013 German Research Center for Environmental Health, 85764 Neuherberg, Germany"},{"name":"Center for Mathematics, Technische Universit\u00e4t M\u00fcnchen , 85748 Garching, Germany"}]},{"given":"Yannik","family":"Sch\u00e4lte","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology , Helmholtz Zentrum M\u00fcnchen \u2013 German Research Center for Environmental Health, 85764 Neuherberg, Germany"},{"name":"Center for Mathematics, Technische Universit\u00e4t M\u00fcnchen , 85748 Garching, Germany"}]},{"given":"Fabian","family":"Fr\u00f6hlich","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology , Helmholtz Zentrum M\u00fcnchen \u2013 German Research Center for Environmental Health, 85764 Neuherberg, Germany"},{"name":"Center for Mathematics, Technische Universit\u00e4t M\u00fcnchen , 85748 Garching, Germany"}]},{"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, 85764 Neuherberg, Germany"},{"name":"Center for Mathematics, Technische Universit\u00e4t M\u00fcnchen , 85748 Garching, Germany"},{"name":"Mathematics and Natural Sciences, University of Bonn Faculty of , 53113 Bonn, Germany"}]},{"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, 85764 Neuherberg, Germany"}]}],"member":"286","published-online":{"date-parts":[[2019,7,26]]},"reference":[{"key":"2023013112064158000_btz581-B1","doi-asserted-by":"crossref","DOI":"10.1098\/rsbl.2017.0660","article-title":"Mechanistic models versus machine learning, a fight worth fighting for the biological community?","volume":"14","author":"Baker","year":"2018","journal-title":"Biol. 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