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• W3048757019 startingPage "e1008740" @default.
• W3048757019 abstract "Biochemical processes in cells are governed by complex networks of many chemical species interacting stochastically in diverse ways and on different time scales. Constructing microscopically accurate models of such networks is often infeasible. Instead, here we propose a systematic framework for building phenomenological models of such networks from experimental data, focusing on accurately approximating the time it takes to complete the process, the First Passage (FP) time. Our phenomenological models are mixtures of Gamma distributions, which have a natural biophysical interpretation. The complexity of the models is adapted automatically to account for the amount of available data and its temporal resolution. The framework can be used for predicting the behavior of various FP systems under varying external conditions. To demonstrate the utility of the approach, we build models for the distribution of inter-spike intervals of a morphologically complex neuron, a Purkinje cell, from experimental and simulated data. We demonstrate that the developed models can not only fit the data but also make nontrivial predictions. We demonstrate that our coarse-grained models provide constraints on more mechanistically accurate models of the involved phenomena." @default.
• W3048757019 created "2020-08-18" @default.
• W3048757019 creator A5022198472 @default.
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• W3048757019 date "2021-03-05" @default.
• W3048757019 modified "2023-10-16" @default.
• W3048757019 title "Inferring phenomenological models of first passage processes" @default.
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• W3048757019 doi "https://doi.org/10.1371/journal.pcbi.1008740" @default.
• W3048757019 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7968746" @default.
• W3048757019 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33667218" @default.
• W3048757019 hasPublicationYear "2021" @default.
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