{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Mester R"],"funding":["U.S. Air Force","Advanced Research Projects Agency - Energy","NOK Foundation","United States Agency for International Development","NHGRI NIH HHS","U.S. Public Health Service","National Human Genome Research Institute","National Science Foundation"],"pagination":["e1009598"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9232177"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(6)"],"pubmed_abstract":["Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the "],"journal":["PLoS computational biology"],"pubmed_title":["Differential methods for assessing sensitivity in biological models."],"pmcid":["PMC9232177"],"funding_grant_id":["S002283-USAID","T32 HG002536","PHY-2021825","T32HG002536","308817","FA8750-19-2-1000","OAC-1835443","HG006139","OAC-2103804","R01 HG006139","DE-AR0001222","DE-AR0001211","SII-2029670","GMS141798","ECCS-2029670"],"pubmed_authors":["Rackauckas C","Mester R","Lange K","Landeros A"],"additional_accession":[]},"is_claimable":false,"name":"Differential methods for assessing sensitivity in biological models.","description":"Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jun","modification":"2025-04-04T19:06:33.025Z","creation":"2025-04-04T19:06:33.025Z"},"accession":"S-EPMC9232177","cross_references":{"pubmed":["35696417"],"doi":["10.1371/journal.pcbi.1009598"]}}