ArticleiScience2022
Approximating solutions of the Chemical Master equation using neural networks.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- 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
- Article
- Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve.iScience · 2025Article
- Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data.Nature methods · 2025Article
- Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations.PLoS computational biology · 2025Article
- Article
- Methods in quantitative biology-from analysis of single-cell microscopy images to inference of predictive models for stochastic gene expression.Physical biology · 2025Review
- Is Ockham's razor losing its edge? New perspectives on the principle of model parsimony.Proceedings of the National Academy of Sciences of the United States of America · 2025Review
- From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation.Computational and structural biotechnology journal · 2025Article
- Abstraction-based segmental simulation of reaction networks using adaptive memoization.BMC bioinformatics · 2024Article
- Spectral neural approximations for models of transcriptional dynamics.Biophysical journal · 2024Article
- Tensor product algorithms for inference of contact network from epidemiological data.BMC bioinformatics · 2024Article
- Holimap: an accurate and efficient method for solving stochastic gene network dynamics.Nature communications · 2024Article
- Stochastic Modeling of Biophysical Responses to Perturbation.bioRxiv : the preprint server for biology · 2024Article
- Distilling dynamical knowledge from stochastic reaction networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Avoiding matrix exponentials for large transition rate matrices.The Journal of chemical physics · 2024Article
- Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction.Patterns (New York, N.Y.) · 2024Article
- New and notable: Revisiting the "two cultures" through extrinsic noise.Biophysical journal · 2024Article
- The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology.Frontiers in systems biology · 2024Review
- Using mixture density networks to emulate a stochastic within-host model of Francisella tularensis infection.PLoS computational biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Chemical Master Equation (CME) provides an accurate description of stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved analytically for most systems of practical interest. Although Monte Carlo methods provide a principled means to probe system dynamics, the large number of simulations typically required can render the estimation of molecule number distributions and other quantities infeasible. In this article, we aim to leverage the representational power of neural networks to approximate the solutions of the CME and propose a framework for the Neural Estimation of Stochastic Simulations for Inference and Exploration (Nessie). Our approach is based on training neural networks to learn the distributions predicted by the CME from relatively few stochastic simulations. We show on biologically relevant examples that simple neural networks with one hidden layer can capture highly complex distributions across parameter space, thereby accelerating computationally intensive tasks such as parameter exploration and inference.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.