{"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}
引用次数: 0
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.