Evidence map›Paper›PMID 25057990›Full record

ArticlePloS one2014

Gene networks of fully connected triads with complete auto-activation enable multistability and stepwise stochastic transitions.

Philippe C Faucon, Keith Pardee, Roshan M Kumar, Hu Li, Yuin-Han Loh, Xiao Wang

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
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  8. Review
  9. Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network.Proceedings of the ... American Control Conference. American Control Conference · 2019
    Article
  10. Article
  11. Article
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  15. Article
  16. Build to understand: synthetic approaches to biology.Integrative biology : quantitative biosciences from nano to macro · 2016
    Review
  17. Article
  18. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Philippe C FauconSchool of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America.
Keith PardeeWyss Institute for Biological Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America; Center for BioDynamics and Center for Advanced Biotechnology, Boston University, Boston, Massachusetts, United States of America.
Roshan M KumarWyss Institute for Biological Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America; Center for BioDynamics and Center for Advanced Biotechnology, Boston University, Boston, Massachusetts, United States of America.
Hu LiDepartment of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America.
Yuin-Han LohEpigenetics and Cell Fates Laboratory, A*STAR Institute of Molecular and Cell Biology, Department of Biological Sciences, National University of Singapore, Singapore, Singapore.
Xiao WangSchool of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona, United States of America.

Funding

Canadian Institutes of Health Research
6 · The paper itself

Abstract

Fully-connected triads (FCTs), such as the Oct4-Sox2-Nanog triad, have been implicated as recurring transcriptional motifs embedded within the regulatory networks that specify and maintain cellular states. To explore the possible connections between FCT topologies and cell fate determinations, we employed computational network screening to search all possible FCT topologies for multistability, a dynamic property that allows the rise of alternate regulatory states from the same transcriptional network. The search yielded a hierarchy of FCTs with various potentials for multistability, including several topologies capable of reaching eight distinct stable states. Our analyses suggested that complete auto-activation is an effective indicator for multistability, and, when gene expression noise was incorporated into the model, the networks were able to transit multiple states spontaneously. Different levels of stochasticity were found to either induce or disrupt random state transitioning with some transitions requiring layovers at one or more intermediate states. Using this framework we simulated a simplified model of induced pluripotency by including constitutive overexpression terms. The corresponding FCT showed random state transitioning from a terminal state to the pluripotent state, with the temporal distribution of this transition matching published experimental data. This work establishes a potential theoretical framework for understanding cell fate determinations by connecting conserved regulatory modules with network dynamics. Our results could also be employed experimentally, using established developmental transcription factors as seeds, to locate cell lineage specification networks by using auto-activation as a cipher.

Indexed as

Gene Regulatory NetworksModels, StatisticalCell DifferentiationCell LineageGene Expression RegulationHomeodomain ProteinsHumansInduced Pluripotent Stem CellsModels, BiologicalNanog Homeobox ProteinOctamer Transcription Factor-3Signal TransductionSOXB1 Transcription FactorsStochastic ProcessesTranscription, GeneticHomeodomain ProteinsNanog Homeobox ProteinNANOG protein, humanOctamer Transcription Factor-3POU5F1 protein, humanSOX2 protein, humanSOXB1 Transcription Factors

Identifiers

PMID25057990
PMCPMC4109943

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Registered trials

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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.