{"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-for-biochemical-reaction","title":"Parameter estimation for biochemical reaction networks using Wasserstein distances","arxiv_id":"1907.07986","date":"2019-10-21","proceeding":null,"authors":[],"abstract":"We present a method for estimating parameters in stochastic models of\nbiochemical reaction networks by fitting steady-state distributions using\nWasserstein distances. We simulate a reaction network at different parameter\nsettings and train a Gaussian process to learn the Wasserstein distance between\nobservations and the simulator output for all parameters. We then use Bayesian\noptimization to find parameters minimizing this distance based on the trained\nGaussian process. The effectiveness of our method is demonstrated on the\nthree-stage model of gene expression and a genetic feedback loop for which\nmoment-based methods are known to perform poorly. Our method is applicable to\nany simulator model of stochastic reaction networks, including Brownian\nDynamics.","url_abs":"http://arxiv.org/abs/1907.07986v2","url_pdf":"http://arxiv.org/pdf/1907.07986v2.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-for-biochemical-reaction","repo_url":"https://github.com/kaandocal/wasserstein_inference","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}