{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T15:58:53Z","timestamp":1768924733388,"version":"3.49.0"},"reference-count":26,"publisher":"Wiley","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61303028"],"award-info":[{"award-number":["61303028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672388"],"award-info":[{"award-number":["61672388"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61303028"],"award-info":[{"award-number":["61303028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672388"],"award-info":[{"award-number":["61672388"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Inference of the biochemical systems (BSs) via experimental data is important for understanding how biochemical components in vivo interact with each other. However, it is not a trivial task because BSs usually function with complex and nonlinear dynamics. As a popular ordinary equation (ODE) model, the S-System describes the dynamical properties of BSs by incorporating the power rule of biochemical reactions but behaves as a challenge because it has a lot of parameters to be confirmed. This work is dedicated to proposing a general method for inference of S-Systems by experimental data, using a biobjective optimization (BOO) model and a specially mixed-variable multiobjective evolutionary algorithm (mv-MOEA). Regarding that BSs are sparse in common sense, we introduce binary variables indicating network connections to eliminate the difficulty of threshold presetting and take data fitting error and the <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:msub><mml:mrow><mml:mi>L<\/mml:mi><\/mml:mrow><mml:mrow><mml:mn mathvariant=\"normal\">0<\/mml:mn><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>-norm as two objectives to be minimized in the BOO model. Then, a selection procedure that automatically runs tradeoff between two objectives is employed to choose final inference results from the obtained nondominated solutions of the mv-MOEA. Inference results of the investigated networks demonstrate that our method can identify their dynamical properties well, although the automatic selection procedure sometimes ignores some weak connections in BSs.<\/jats:p>","DOI":"10.1155\/2017\/3020326","type":"journal-article","created":{"date-parts":[[2017,5,21]],"date-time":"2017-05-21T17:00:34Z","timestamp":1495386034000},"page":"1-9","source":"Crossref","is-referenced-by-count":3,"title":["Inference of Biochemical S-Systems via Mixed-Variable Multiobjective Evolutionary Optimization"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8118-7262","authenticated-orcid":true,"given":"Yu","family":"Chen","sequence":"first","affiliation":[{"name":"School of Science, Wuhan University of Technology, Wuhan, Hubei 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Science, Wuhan University of Technology, Wuhan, Hubei 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5294-0764","authenticated-orcid":true,"given":"Xiufen","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0001672"},{"key":"2","volume":"11, article 46","year":"2013","journal-title":"Cell Communication and Signaling"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1089\/106652700750050961"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.2174\/1574893609666140421210333"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2013.10.003"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2014.03.040"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2014.02.011"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1038\/srep09283"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0119294"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bth140"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2012.2218610"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2011.11.020"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/362738"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti099"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2011.126"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btn075"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2008.917202"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.07.030"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008202821328"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2013.2287153"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-11-S1-S56"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti071"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg027"},{"issue":"2","key":"31","first-page":"205","volume":"16","year":"2005","journal-title":"Genome Informatics"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1002\/bit.10676"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl122"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2017\/3020326.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2017\/3020326.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2017\/3020326.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,5,21]],"date-time":"2017-05-21T17:00:45Z","timestamp":1495386045000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2017\/3020326\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":26,"alternative-id":["3020326","3020326"],"URL":"https:\/\/doi.org\/10.1155\/2017\/3020326","relation":{},"ISSN":["1748-670X","1748-6718"],"issn-type":[{"value":"1748-670X","type":"print"},{"value":"1748-6718","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}