{"title":"基于瞬时转染哺乳动物细胞的模块组成的遗传电路行为预测","authors":"Junmin Wang, S. Isaacson, C. Belta","doi":"10.1109/LSC.2018.8572174","DOIUrl":null,"url":null,"abstract":"Transient transfection of cells can be highly stochastic, resulting in large variations in plasmid counts across a population. The resulting dynamics of the cells can then also be highly variable, so predicting the behaviors of transfected circuits can be a major challenge. In this work, we provide a precise definition of genetic modules, from which we then develop a method of composition that allows model-based design of circuits in transiently transfected mammalian cells. For validation, we apply our method to cascades consisting of two regulatory switches. Predictions of the mathematical models compare well with the experimental data. Our findings suggest reducing batch effects and selecting a proper model both contribute to improving model predictions.","PeriodicalId":254835,"journal":{"name":"2018 IEEE Life Sciences Conference (LSC)","volume":"76 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Predictions of Genetic Circuit Behaviors Based on Modular Composition in Transiently Transfected Mammalian Cells\",\"authors\":\"Junmin Wang, S. Isaacson, C. Belta\",\"doi\":\"10.1109/LSC.2018.8572174\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Transient transfection of cells can be highly stochastic, resulting in large variations in plasmid counts across a population. The resulting dynamics of the cells can then also be highly variable, so predicting the behaviors of transfected circuits can be a major challenge. In this work, we provide a precise definition of genetic modules, from which we then develop a method of composition that allows model-based design of circuits in transiently transfected mammalian cells. For validation, we apply our method to cascades consisting of two regulatory switches. Predictions of the mathematical models compare well with the experimental data. Our findings suggest reducing batch effects and selecting a proper model both contribute to improving model predictions.\",\"PeriodicalId\":254835,\"journal\":{\"name\":\"2018 IEEE Life Sciences Conference (LSC)\",\"volume\":\"76 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Life Sciences Conference (LSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LSC.2018.8572174\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Life Sciences Conference (LSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LSC.2018.8572174","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predictions of Genetic Circuit Behaviors Based on Modular Composition in Transiently Transfected Mammalian Cells
Transient transfection of cells can be highly stochastic, resulting in large variations in plasmid counts across a population. The resulting dynamics of the cells can then also be highly variable, so predicting the behaviors of transfected circuits can be a major challenge. In this work, we provide a precise definition of genetic modules, from which we then develop a method of composition that allows model-based design of circuits in transiently transfected mammalian cells. For validation, we apply our method to cascades consisting of two regulatory switches. Predictions of the mathematical models compare well with the experimental data. Our findings suggest reducing batch effects and selecting a proper model both contribute to improving model predictions.