{"title":"用于生物模型全局敏感性分析的分布式软件包","authors":"Q. Thai, S. Yu, Yongseong Cho","doi":"10.1109/BIBE.2012.6399724","DOIUrl":null,"url":null,"abstract":"Mathematical modeling is a vital tool for studying biological systems. Due to the system complexity and technical challenges in molecular level measurement, it is commonly the case that a large number of model parameters have uncertain values. Analyzing model dynamics from a single estimated parameter set is insufficient and liable for misleading results. Global sensitivity analysis (GSA) has been recommended as a must-have step in the process of developing reliable models. However, the technique comes at high computation costs as it is based on Monte Carlo simulation which requires a large number of model evaluations and manipulating on massive data for sensitivity estimation. In this work, we develop a software package for global sensitivity estimation of biological models. The software is deployed on KISTI high performance computing (HPC) cluster environment to provide web-service to system biology modelers.","PeriodicalId":330164,"journal":{"name":"2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A distributed software package for global sensitivity analysis of biological models\",\"authors\":\"Q. Thai, S. Yu, Yongseong Cho\",\"doi\":\"10.1109/BIBE.2012.6399724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mathematical modeling is a vital tool for studying biological systems. Due to the system complexity and technical challenges in molecular level measurement, it is commonly the case that a large number of model parameters have uncertain values. Analyzing model dynamics from a single estimated parameter set is insufficient and liable for misleading results. Global sensitivity analysis (GSA) has been recommended as a must-have step in the process of developing reliable models. However, the technique comes at high computation costs as it is based on Monte Carlo simulation which requires a large number of model evaluations and manipulating on massive data for sensitivity estimation. In this work, we develop a software package for global sensitivity estimation of biological models. The software is deployed on KISTI high performance computing (HPC) cluster environment to provide web-service to system biology modelers.\",\"PeriodicalId\":330164,\"journal\":{\"name\":\"2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2012.6399724\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2012.6399724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A distributed software package for global sensitivity analysis of biological models
Mathematical modeling is a vital tool for studying biological systems. Due to the system complexity and technical challenges in molecular level measurement, it is commonly the case that a large number of model parameters have uncertain values. Analyzing model dynamics from a single estimated parameter set is insufficient and liable for misleading results. Global sensitivity analysis (GSA) has been recommended as a must-have step in the process of developing reliable models. However, the technique comes at high computation costs as it is based on Monte Carlo simulation which requires a large number of model evaluations and manipulating on massive data for sensitivity estimation. In this work, we develop a software package for global sensitivity estimation of biological models. The software is deployed on KISTI high performance computing (HPC) cluster environment to provide web-service to system biology modelers.