ArticlePLoS computational biology2018
A stochastic and dynamical view of pluripotency in mouse embryonic stem cells.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 37 citations in OpenAlex.
- Transcriptional Bursting in Pluripotent Stem Cells.Biology · 2026Review
- AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis.PLoS computational biology · 2024Article
- Inferring structural and dynamical properties of gene networks from data with deep learning.NAR genomics and bioinformatics · 2022Article
- Intrinsically Disordered Proteins: Critical Components of the Wetware.Chemical reviews · 2022Review
- Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma.Biophysics reviews · 2022Review
- Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics.BMC bioinformatics · 2022Article
- Protein conformational dynamics and phenotypic switching.Biophysical reviews · 2021Review
- Review
- Analytic solutions for stochastic hybrid models of gene regulatory networks.Journal of mathematical biology · 2021Article
- Liquid-liquid phase separation driven compartmentalization of reactive nucleoplasm.Physical biology · 2021Article
- Exact Probability Landscapes of Stochastic Phenotype Switching in Feed-Forward Loops: Phase Diagrams of Multimodality.Frontiers in genetics · 2021Article
- Mesoscale Liquid Model of Chromatin Recapitulates Nuclear Order of Eukaryotes.Biophysical journal · 2020Article
- A system-level mechanistic explanation for asymmetric stem cell fates: Arabidopsis thaliana root niche as a study system.Scientific reports · 2020Article
- Landscape inferred from gene expression data governs pluripotency in embryonic stem cells.Computational and structural biotechnology journal · 2020Article
- Transcriptional Profiling of Stem Cells: Moving from Descriptive to Predictive Paradigms.Stem cell reports · 2019Review
- Generalizing Gillespie's Direct Method to Enable Network-Free Simulations.Bulletin of mathematical biology · 2019Article
- Exact and efficient hybrid Monte Carlo algorithm for accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts.The Journal of chemical physics · 2019Article
- WASABI: a dynamic iterative framework for gene regulatory network inference.BMC bioinformatics · 2019Article
- A multiscale model of epigenetic heterogeneity-driven cell fate decision-making.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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals.
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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 Socratic graph.