{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:02:04Z","timestamp":1775264524786,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008495","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T00:00:00Z","timestamp":1609804800000}}],"reference-count":22,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T00:00:00Z","timestamp":1608508800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Practical identifiability of Systems Biology models has received a lot of attention in recent scientific research. It addresses the crucial question for models\u2019 predictability: how accurately can the models\u2019 parameters be recovered from available experimental data. The methods based on profile likelihood are among the most reliable methods of practical identification. However, these methods are often computationally demanding or lead to inaccurate estimations of parameters\u2019 confidence intervals. Development of methods, which can accurately produce parameters\u2019 confidence intervals in reasonable computational time, is of utmost importance for Systems Biology and QSP modeling.<\/jats:p>\n<jats:p>We propose an algorithm Confidence Intervals by Constraint Optimization (CICO) based on profile likelihood, designed to speed-up confidence intervals estimation and reduce computational cost. The numerical implementation of the algorithm includes settings to control the accuracy of confidence intervals estimates. The algorithm was tested on a number of Systems Biology models, including Taxol treatment model and STAT5 Dimerization model, discussed in the current article.<\/jats:p>\n<jats:p>The CICO algorithm is implemented in a software package freely available in Julia (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/insysbio\/LikelihoodProfiler.jl\" xlink:type=\"simple\">https:\/\/github.com\/insysbio\/LikelihoodProfiler.jl<\/jats:ext-link>) and Python (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/insysbio\/LikelihoodProfiler.py\" xlink:type=\"simple\">https:\/\/github.com\/insysbio\/LikelihoodProfiler.py<\/jats:ext-link>).<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008495","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T19:47:17Z","timestamp":1608580037000},"page":"e1008495","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":15,"title":["Confidence intervals by constrained optimization\u2014An algorithm and software package for practical identifiability analysis in systems 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