{"title":"信号分子浓度的最大似然估计的化学反应网络","authors":"C. Chou","doi":"10.1145/3109453.3109476","DOIUrl":null,"url":null,"abstract":"The use of maximum likelihood (ML) to estimate molecule concentration has been investigated theoretically. The key contribution of this paper is to show how the theoretical ML estimator can be implemented as a chemical reaction network (CRN). The proposed CRN can be implemented by DNA strand displacement reactions and therefore leads to a biochemical implementation of the theoretical ML estimator.","PeriodicalId":400141,"journal":{"name":"Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Chemical reaction networks for maximum likelihood estimation of the concentration of signalling molecules\",\"authors\":\"C. Chou\",\"doi\":\"10.1145/3109453.3109476\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of maximum likelihood (ML) to estimate molecule concentration has been investigated theoretically. The key contribution of this paper is to show how the theoretical ML estimator can be implemented as a chemical reaction network (CRN). The proposed CRN can be implemented by DNA strand displacement reactions and therefore leads to a biochemical implementation of the theoretical ML estimator.\",\"PeriodicalId\":400141,\"journal\":{\"name\":\"Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3109453.3109476\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3109453.3109476","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Chemical reaction networks for maximum likelihood estimation of the concentration of signalling molecules
The use of maximum likelihood (ML) to estimate molecule concentration has been investigated theoretically. The key contribution of this paper is to show how the theoretical ML estimator can be implemented as a chemical reaction network (CRN). The proposed CRN can be implemented by DNA strand displacement reactions and therefore leads to a biochemical implementation of the theoretical ML estimator.