{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T13:11:54Z","timestamp":1773839514598,"version":"3.50.1"},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2003,3,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters.<\/jats:p>\n               <jats:p>Results: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.<\/jats:p>\n               <jats:p>Contact: kikuchi@sfc.keio.ac.jp<\/jats:p>\n               <jats:p>* To whom correspondence should be addressed.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btg027","type":"journal-article","created":{"date-parts":[[2003,3,21]],"date-time":"2003-03-21T19:31:39Z","timestamp":1048275099000},"page":"643-650","source":"Crossref","is-referenced-by-count":323,"title":["Dynamic modeling of genetic networks using genetic algorithm\nand S-system"],"prefix":"10.1093","volume":"19","author":[{"given":"Shinichi","family":"Kikuchi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daisuke","family":"Tominaga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masanori","family":"Arita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katsutoshi","family":"Takahashi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masaru","family":"Tomita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2003,3,22]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/19\/5\/643\/48904138\/bioinformatics_19_5_643.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/19\/5\/643\/48904138\/bioinformatics_19_5_643.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T16:45:19Z","timestamp":1674665119000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/19\/5\/643\/239103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,3,22]]},"references-count":0,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2003,3,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btg027","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2003,3,22]]},"published":{"date-parts":[[2003,3,22]]}}}