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Neural network aided approximation and parameter inference of non-Markovian models of gene expression

Jiang, Qingchao
Fu, Xiaoming
Yan, Shifu
Li, Runlai
Du, Wenli
Cao, Zhixing
Qian, Feng
Grima, Ramon
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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).
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Source Title
Nature Communications
Publisher
Nature Research
Series/Report No.
Organizational Units
Organizational Unit
CHEMISTRY
dept
Rights
Attribution 4.0 International
Date
2021-05-11
DOI
10.1038/s41467-021-22919-1
Type
Article
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