{"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/inferring-phenomenological-models-of-first","title":"Inferring phenomenological models of first passage processes","arxiv_id":"2008.05007","date":"2020-08-11","proceeding":null,"authors":[],"abstract":"Biochemical processes in cells are governed by complex networks of many\nchemical species interacting stochastically in diverse ways and on different\ntime scales. Constructing microscopically accurate models of such networks is\noften infeasible. Instead, here we propose a systematic framework for building\nphenomenological models of such networks from experimental data, focusing on\naccurately approximating the time it takes to complete the process, the First\nPassage (FP) time. Our phenomenological models are mixtures of Gamma\ndistributions, which have a natural biophysical interpretation. The complexity\nof the models is adapted automatically to account for the amount of available\ndata and its temporal resolution. The framework can be used for predicting the\nbehavior of various FP systems under varying external conditions. To\ndemonstrate the utility of the approach, we build models for the distribution\nof inter-spike intervals of a morphologically complex neuron, a Purkinje cell,\nfrom experimental and simulated data. We demonstrate that the developed models\ncan not only fit the data but also make nontrivial predictions. We demonstrate\nthat our coarse-grained models provide constraints on more mechanistically\naccurate models of the involved phenomena.","url_abs":"http://arxiv.org/abs/2008.05007v1","url_pdf":"http://arxiv.org/pdf/2008.05007v1.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":"inferring-phenomenological-models-of-first","repo_url":"https://github.com/criver9/Inferring-FPP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}