ArticleScientific reports2017
Stochastic fluctuations can reveal the feedback signs of gene regulatory networks at the single-molecule level.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives.PLoS computational biology · 2025Article
- Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives.bioRxiv : the preprint server for biology · 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
- Inference on autoregulation in gene expression with variance-to-mean ratio.Journal of mathematical biology · 2023Article
- Stochastic Modeling of Autoregulatory Genetic Feedback Loops: A Review and Comparative Study.Biophysical journal · 2020Review
- Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks.Journal of mathematical biology · 2020Article
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4 authors.
Funding
Abstract
Understanding the relationship between spontaneous stochastic fluctuations and the topology of the underlying gene regulatory network is of fundamental importance for the study of single-cell stochastic gene expression. Here by solving the analytical steady-state distribution of the protein copy number in a general kinetic model of stochastic gene expression with nonlinear feedback regulation, we reveal the relationship between stochastic fluctuations and feedback topology at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations. Based on such relationship, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data. The theory is demonstrated by numerical simulations and, more importantly, validated quantitatively by single-cell data analysis of a synthetic gene circuit integrated in human kidney cells.
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