{"title":"Evolutionary design of oscillatory genetic networks in silico","authors":"Yuki Naruse, Hiroyuki Hamada, T. Hanai, H. Iba","doi":"10.1109/CEC.2015.7257078","DOIUrl":null,"url":null,"abstract":"The design of genetic networks has been studied for implementing desired biological systems, and in particular, some researchers have proposed automatic design methods using optimization techniques. However, it is difficult to implement genetic networks designed by previous methods due to overly simplified model descriptions whose parameters are infeasible in the real world. Additionally, the methods do not ensure robustness against parameter perturbation. In this paper, we propose a two-stage design method and a fitness function evaluating robustness to create genetic networks which can be implemented experimentally. Further, we suggest the knowledge about robust network structures from results of optimization.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Congress on Evolutionary Computation (CEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2015.7257078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The design of genetic networks has been studied for implementing desired biological systems, and in particular, some researchers have proposed automatic design methods using optimization techniques. However, it is difficult to implement genetic networks designed by previous methods due to overly simplified model descriptions whose parameters are infeasible in the real world. Additionally, the methods do not ensure robustness against parameter perturbation. In this paper, we propose a two-stage design method and a fitness function evaluating robustness to create genetic networks which can be implemented experimentally. Further, we suggest the knowledge about robust network structures from results of optimization.