{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:39:02Z","timestamp":1757313542009,"version":"3.37.3"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"24","license":[{"start":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T00:00:00Z","timestamp":1560470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"National Science and Engineering Research Council of Canada"},{"name":"Materials, Manufacturing and Mimetics"},{"name":"Industrial Biocatalysis Network"},{"name":"Ontario Research Fund and Genome Canada"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>In kinetic models of metabolism, the parameter values determine the dynamic behaviour predicted by these models. Estimating parameters from in vivo experimental data require the parameters to be structurally identifiable, and the data to be informative enough to estimate these parameters. Existing methods to determine the structural identifiability of parameters in kinetic models of metabolism can only be applied to models of small metabolic networks due to their computational complexity. Additionally, a priori experimental design, a necessity to obtain informative data for parameter estimation, also does not account for using steady-state data to estimate parameters in kinetic models.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we present a scalable methodology to structurally identify parameters for each flux in a kinetic model of metabolism based on the availability of steady-state data. In doing so, we also address the issue of determining the number and nature of experiments for generating steady-state data to estimate these parameters. By using a small metabolic network as an example, we show that most parameters in fluxes expressed by mechanistic enzyme kinetic rate laws can be identified using steady-state data, and the steady-state data required for their estimation can be obtained from selective experiments involving both substrate and enzyme level perturbations. The methodology can be used in combination with other identifiability and experimental design algorithms that use dynamic data to determine the most informative experiments requiring the least resources to perform.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/LMSE\/ident.<\/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\/btz445","type":"journal-article","created":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T19:18:49Z","timestamp":1559762329000},"page":"5216-5225","source":"Crossref","is-referenced-by-count":7,"title":["A scalable method for parameter identification in kinetic models of metabolism using steady-state data"],"prefix":"10.1093","volume":"35","author":[{"given":"Shyam","family":"Srinivasan","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering and Applied Chemistry, 200 College Street, University of Toronto , Toronto, ON, M5S3E5, Canada"}]},{"given":"William R","family":"Cluett","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering and Applied Chemistry, 200 College Street, University of Toronto , Toronto, ON, M5S3E5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1270-9063","authenticated-orcid":false,"given":"Radhakrishnan","family":"Mahadevan","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering and Applied Chemistry, 200 College Street, University of Toronto , Toronto, ON, M5S3E5, Canada"},{"name":"Institute of Biomaterials and Biomedical Engineering, 164 College Street, University of Toronto , Toronto, ON, M5S 3G9, Canada"}]}],"member":"286","published-online":{"date-parts":[[2019,6,14]]},"reference":[{"key":"2023013108374652400_btz445-B1","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.ymben.2016.01.009","article-title":"Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models","volume":"35","author":"Andreozzi","year":"2016","journal-title":"Metab. 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