{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/parameter-estimation-in-computational-biology","title":"Parameter Estimation in Computational Biology by Approximate Bayesian Computation coupled with Sensitivity Analysis","arxiv_id":"1704.09021","date":"2017-04-28","proceeding":null,"authors":["Xin Liu","Mahesan Niranjan"],"abstract":"We address the problem of parameter estimation in models of systems biology\nfrom noisy observations. The models we consider are characterized by\nsimultaneous deterministic nonlinear differential equations whose parameters\nare either taken from in vitro experiments, or are hand-tuned during the model\ndevelopment process to reproduces observations from the system. We consider the\nfamily of algorithms coming under the Bayesian formulation of Approximate\nBayesian Computation (ABC), and show that sensitivity analysis could be\ndeployed to quantify the relative roles of different parameters in the system.\nParameters to which a system is relatively less sensitive (known as sloppy\nparameters) need not be estimated to high precision, while the values of\nparameters that are more critical (stiff parameters) need to be determined with\ncare. A tradeoff between computational complexity and the accuracy with which\nthe posterior distribution may be probed is an important characteristic of this\nclass of algorithms.","url_abs":"http://arxiv.org/abs/1704.09021v1","url_pdf":"http://arxiv.org/pdf/1704.09021v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"parameter-estimation-in-computational-biology","repo_url":"https://github.com/brianliu2/ABCSMC-SA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"sensitivity","task_name":"Sensitivity"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}