ArticleNature communications2024
Holimap: an accurate and efficient method for solving stochastic gene network dynamics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- Using the simple telegraph model to decipher transcriptional burst regulation across genome-wide data.iScience · 2026Article
- Design principles for robust multistability in coupled feedforward-feedback regulatory circuits.NPJ systems biology and applications · 2026Article
- Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions.PLoS computational biology · 2026Article
- Efficient approximations of transcriptional bursting effects on the dynamics of a gene regulatory network.Journal of the Royal Society, Interface · 2025Article
- Holimap: an accurate and efficient method for solving stochastic gene network dynamics.Nature communications · 2024Article
- What can we learn when fitting a simple telegraph model to a complex gene expression model?PLoS computational biology · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
Gene-gene interactions are crucial to the control of sub-cellular processes but our understanding of their stochastic dynamics is hindered by the lack of simulation methods that can accurately and efficiently predict how the distributions of gene product numbers vary across parameter space. To overcome these difficulties, here we present Holimap (high-order linear-mapping approximation), an approach that approximates the protein or mRNA number distributions of a complex gene regulatory network by the distributions of a much simpler reaction system. We demonstrate Holimap's computational advantages over conventional methods by applying it to predict the stochastic time-dependent dynamics of various gene networks, including transcriptional networks ranging from simple autoregulatory loops to complex randomly connected networks, post-transcriptional networks, and post-translational networks. Holimap is ideally suited to study how the intricate network of gene-gene interactions results in precise coordination and control of gene expression.
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Registered trials
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