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

Author

Listed:
  • Qingchao Jiang

    (East China University of Science and Technology)

  • Xiaoming Fu

    (East China University of Science and Technology
    The University of Edinburgh)

  • Shifu Yan

    (East China University of Science and Technology)

  • Runlai Li

    (National University of Singapore)

  • Wenli Du

    (East China University of Science and Technology)

  • Zhixing Cao

    (East China University of Science and Technology
    East China University of Science and Technology)

  • Feng Qian

    (East China University of Science and Technology)

  • Ramon Grima

    (The University of Edinburgh)

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.

Suggested Citation

  • Qingchao Jiang & Xiaoming Fu & Shifu Yan & Runlai Li & Wenli Du & Zhixing Cao & Feng Qian & Ramon Grima, 2021. "Neural network aided approximation and parameter inference of non-Markovian models of gene expression," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22919-1
    DOI: 10.1038/s41467-021-22919-1
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    Cited by:

    1. Zhou Fang & Ankit Gupta & Sant Kumar & Mustafa Khammash, 2024. "Advanced methods for gene network identification and noise decomposition from single-cell data," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Hyukpyo Hong & Eunjin Eom & Hyojung Lee & Sunhwa Choi & Boseung Choi & Jae Kyoung Kim, 2024. "Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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