ArticleCell systems2017
Stem Cell Differentiation as a Non-Markov Stochastic Process.
Article in Cell systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
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Who cites it
87 citing papers in PubMed.
- Gillespie-based simulation and inference for non-Markovian stochastic reaction networks.Briefings in bioinformatics · 2026Article
- Article
- Unifying non-Markovian dynamics and agent heterogeneity in scalable stochastic networks.Nature communications · 2026Article
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Building resilience by cultivating difference: A role for noise in development.Current opinion in plant biology · 2025Review
- Engineering Inducible Cell Fate Transitions by Harnessing Epigenetic Silencing.bioRxiv : the preprint server for biology · 2025Article
- Towards a mathematical framework for modelling cell fate dynamics.Journal of mathematical biology · 2025Article
- CLADES: a hybrid NeuralODE-Gillespie approach for unveiling clonal cell fate and differentiation dynamics.Nature communications · 2025Article
- Metformin alters mitochondria-related metabolism and enhances human oligodendrocyte function.Nature communications · 2025Article
- Noise-driven morphogenesis independent of transcriptional regulatory programs.bioRxiv : the preprint server for biology · 2025Article
- Uncovering candidate Nanog-Helper genes in early mouse embryo differentiation using differential entropy and network inference.Scientific reports · 2025Article
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- Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity.NPJ systems biology and applications · 2025Article
- Exploring cell-to-cell variability and functional insights through differentially variable gene analysis.NPJ systems biology and applications · 2025Article
- Empirical methods that provide physical descriptions of dynamic cellular processes.Biophysical journal · 2025Review
- A multi-step completion process model of cell plasticity.Briefings in bioinformatics · 2025Article
- Informeasure: an R/bioconductor package for quantifying nonlinear dependence between variables in biological networks from an information theory perspective.BMC bioinformatics · 2024Article
- A statistical approach for systematic identification of transition cells from scRNA-seq data.Cell reports methods · 2024Article
- Multiscale chromatin dynamics and high entropy in plant iPSC ancestors.Journal of cell science · 2024Article
- Improving iPSC Differentiation Using a Nanodot Platform.ACS applied materials & interfaces · 2024Article
27 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
Pluripotent stem cells can self-renew in culture and differentiate along all somatic lineages in vivo. While much is known about the molecular basis of pluripotency, the mechanisms of differentiation remain unclear. Here, we profile individual mouse embryonic stem cells as they progress along the neuronal lineage. We observe that cells pass from the pluripotent state to the neuronal state via an intermediate epiblast-like state. However, analysis of the rate at which cells enter and exit these observed cell states using a hidden Markov model indicates the presence of a chain of unobserved molecular states that each cell transits through stochastically in sequence. This chain of hidden states allows individual cells to record their position on the differentiation trajectory, thereby encoding a simple form of cellular memory. We suggest a statistical mechanics interpretation of these results that distinguishes between functionally distinct cellular "macrostates" and functionally similar molecular "microstates" and propose a model of stem cell differentiation as a non-Markov stochastic process.
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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 OpenQuestion graph.