ArticleNature communications2018
Linear mapping approximation of gene regulatory networks with stochastic dynamics.
Article in Nature communications, 2018. 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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Who cites it
41 citing papers in PubMed.
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions.PLoS computational biology · 2026Article
- Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data.Nature methods · 2025Article
- DelaySSA: stochastic simulation of biochemical systems and gene regulatory networks with or without time delays.PLoS computational biology · 2025Article
- Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression.Nature communications · 2025Article
- A generative benchmark for evaluating the performance of fluorescent cell image segmentation.Synthetic and systems biotechnology · 2024Article
- Holimap: an accurate and efficient method for solving stochastic gene network dynamics.Nature communications · 2024Article
- A stochastic vs deterministic perspective on the timing of cellular events.Nature communications · 2024Article
- What can we learn when fitting a simple telegraph model to a complex gene expression model?PLoS computational biology · 2024Article
- Effects of microRNA-mediated negative feedback on gene expression noise.Biophysical journal · 2023Article
- Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks.Journal of the Royal Society, Interface · 2023Article
- Review
- Inference on autoregulation in gene expression with variance-to-mean ratio.Journal of mathematical biology · 2023Article
- Shadow enhancers mediate trade-offs between transcriptional noise and fidelity.PLoS computational biology · 2023Article
- Article
- Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics.Nucleic acids research · 2023Article
- An approach of gene regulatory network construction using mixed entropy optimizing context-related likelihood mutual information.Bioinformatics (Oxford, England) · 2023Article
- Frequency spectra and the color of cellular noise.Nature communications · 2022Article
- Inference and uncertainty quantification of stochastic gene expression via synthetic models.Journal of the Royal Society, Interface · 2022Article
- Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes.ACS omega · 2022Article
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2 authors.
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Abstract
The presence of protein-DNA binding reactions often leads to analytically intractable models of stochastic gene expression. Here we present the linear-mapping approximation that maps systems with protein-promoter interactions onto approximately equivalent systems with no binding reactions. This is achieved by the marriage of conditional mean-field approximation and the Magnus expansion, leading to analytic or semi-analytic expressions for the approximate time-dependent and steady-state protein number distributions. Stochastic simulations verify the method's accuracy in capturing the changes in the protein number distributions with time for a wide variety of networks displaying auto- and mutual-regulation of gene expression and independently of the ratios of the timescales governing the dynamics. The method is also used to study the first-passage time distribution of promoter switching, the sensitivity of the size of protein number fluctuations to parameter perturbation and the stochastic bifurcation diagram characterizing the onset of multimodality in protein number distributions.
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