ArticleNature communications2021
Neural network aided approximation and parameter inference of non-Markovian models of gene expression.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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41 citing papers in PubMed.
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- Simulation and inference methods for non-Markovian stochastic reaction networks.PLoS computational biology · 2026Article
- HySimODE: a hybrid stochastic-deterministic simulation framework for multiscale models of biological systems.Bioinformatics (Oxford, England) · 2026Article
- Simulation-based inference captures non-Markovian effects as exemplified in protein production kinetics through cell division.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions.PLoS computational biology · 2026Article
- Article
- Unifying non-Markovian dynamics and agent heterogeneity in scalable stochastic networks.Nature communications · 2026Article
- Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data.Nature methods · 2025Article
- Article
- DelaySSA: stochastic simulation of biochemical systems and gene regulatory networks with or without time delays.PLoS computational biology · 2025Article
- Incorporating spatial diffusion into models of bursty stochastic transcription.Journal of the Royal Society, Interface · 2025Article
- A generative benchmark for evaluating the performance of fluorescent cell image segmentation.Synthetic and systems biotechnology · 2024Article
- Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics.Nature communications · 2024Article
- Advanced methods for gene network identification and noise decomposition from single-cell data.Nature communications · 2024Article
- Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.Bulletin of mathematical biology · 2024Article
- Solving stochastic gene-expression models using queueing theory: A tutorial review.Biophysical journal · 2024Review
- Distilling dynamical knowledge from stochastic reaction networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction.Patterns (New York, N.Y.) · 2024Article
- Transcriptional bursting dynamics in gene expression.Frontiers in genetics · 2024Review
- The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology.Frontiers in systems biology · 2024Review
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8 authors.
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No grant is acknowledged in the PubMed record.
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.
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