ArticleScientific reports2014
A stochastic model dissects cell states in biological transition processes.
Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Predicting anti-cancer drug combination responses with a temporal cell state network model.PLoS computational biology · 2023Article
- Decoding brain memory formation by single-cell RNA sequencing.Briefings in bioinformatics · 2022Article
- Network inference from perturbation time course data.NPJ systems biology and applications · 2022Article
- Epigenetic instability may alter cell state transitions and anticancer drug resistance.PLoS computational biology · 2021Article
- Terminal Schwann cell and vacant site mediated synapse elimination at developing neuromuscular junctions.Scientific reports · 2019Article
- Inter-genus gene expression analysis in livestock fibroblasts using reference gene validation based upon a multi-species primer set.PloS one · 2019Article
- Chemically Induced Reprogramming of Somatic Cells to Pluripotent Stem Cells and Neural Cells.International journal of molecular sciences · 2016Review
- Information Thermodynamics of Cytosine DNA Methylation.PloS one · 2016Article
- Single cell transcriptomics: methods and applications.Frontiers in oncology · 2015Review
- Article
- Metabostemness: a new cancer hallmark.Frontiers in oncology · 2014Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations.
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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.