ArticleBiophysical journal2015
DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks.
Article in Biophysical journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Switching off: The phenotypic transition to the uninduced state of the lactose uptake pathway.Biophysical journal · 2022Article
- Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs.PLoS computational biology · 2021Article
- An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability.PLoS biology · 2021Article
- Predicting pathological von Willebrand factor unraveling in elongational flow.Biophysical journal · 2021Article
- Wepy: A Flexible Software Framework for Simulating Rare Events with Weighted Ensemble Resampling.ACS omega · 2020Article
- Automatic error control during forward flux sampling of rare events in master equation models.The Journal of chemical physics · 2020Article
- Discrete and continuous models of probability flux of switching dynamics: Uncovering stochastic oscillations in a toggle-switch system.The Journal of chemical physics · 2019Article
- Hydrodynamics of transient cell-cell contact: The role of membrane permeability and active protrusion length.PLoS computational biology · 2019Article
- Analysis of Single-Cell Gene Pair Coexpression Landscapes by Stochastic Kinetic Modeling Reveals Gene-Pair Interactions in Development.Frontiers in genetics · 2019Article
- Discrete flux and velocity fields of probability and their global maps in reaction systems.The Journal of chemical physics · 2018Article
- Rare-event sampling of epigenetic landscapes and phenotype transitions.PLoS computational biology · 2018Article
- Weighted Ensemble Simulation: Review of Methodology, Applications, and Software.Annual review of biophysics · 2017Review
- Path-sampling strategies for simulating rare events in biomolecular systems.Current opinion in structural biology · 2017Review
- Markov State Models of gene regulatory networks.BMC systems biology · 2017Article
- Intrinsic Noise Profoundly Alters the Dynamics and Steady State of Morphogen-Controlled Bistable Genetic Switches.PLoS computational biology · 2016Article
- Quantifying the landscape and kinetic paths for epithelial-mesenchymal transition from a core circuit.Physical chemistry chemical physics : PCCP · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks.
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