Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41467-021-22919-1
Title: Neural network aided approximation and parameter inference of non-Markovian models of gene expression
Authors: Jiang, Qingchao
Fu, Xiaoming
Yan, Shifu
Li, Runlai 
Du, Wenli
Cao, Zhixing
Qian, Feng
Grima, Ramon
Issue Date: 11-May-2021
Publisher: Nature Research
Citation: Jiang, Qingchao, Fu, Xiaoming, Yan, Shifu, Li, Runlai, Du, Wenli, Cao, Zhixing, Qian, Feng, Grima, Ramon (2021-05-11). Neural network aided approximation and parameter inference of non-Markovian models of gene expression. Nature Communications 12 (1) : 2618. ScholarBank@NUS Repository. https://doi.org/10.1038/s41467-021-22919-1
Rights: Attribution 4.0 International
Abstract: Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markovian models by the solutions of much simpler time-inhomogeneous Markovian models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markovian model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markovian models learnt by the neural network accurately reflect the stochastic dynamics across parameter space. © 2021, The Author(s).
Source Title: Nature Communications
URI: https://scholarbank.nus.edu.sg/handle/10635/231947
ISSN: 2041-1723
DOI: 10.1038/s41467-021-22919-1
Rights: Attribution 4.0 International
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