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Title: A stochastic and dynamical view of pluripotency in mouse embryonic stem cells

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 othermore » hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [4]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division and Center for Nonlinear Studies; Univ. of Manchester (United Kingdom). School of Physics and Astronomy
  2. Univ. of Manchester (United Kingdom). School of Physics and Astronomy
  3. Rice Univ., Houston, TX (United States). Dept. of Bioengineering
  4. Iowa State Univ., Ames, IA (United States). Dept. of Chemistry
Publication Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
OSTI Identifier:
1627254
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 14; Journal Issue: 2; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Biochemistry & Molecular Biology; Mathematical & Computational Biology

Citation Formats

Lin, Yen Ting, Hufton, Peter G., Lee, Esther J., and Potoyan, Davit A. A stochastic and dynamical view of pluripotency in mouse embryonic stem cells. United States: N. p., 2018. Web. doi:10.1371/journal.pcbi.1006000.
Lin, Yen Ting, Hufton, Peter G., Lee, Esther J., & Potoyan, Davit A. A stochastic and dynamical view of pluripotency in mouse embryonic stem cells. United States. https://doi.org/10.1371/journal.pcbi.1006000
Lin, Yen Ting, Hufton, Peter G., Lee, Esther J., and Potoyan, Davit A. Fri . "A stochastic and dynamical view of pluripotency in mouse embryonic stem cells". United States. https://doi.org/10.1371/journal.pcbi.1006000. https://www.osti.gov/servlets/purl/1627254.
@article{osti_1627254,
title = {A stochastic and dynamical view of pluripotency in mouse embryonic stem cells},
author = {Lin, Yen Ting and Hufton, Peter G. and Lee, Esther J. and Potoyan, Davit A.},
abstractNote = {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.},
doi = {10.1371/journal.pcbi.1006000},
journal = {PLoS Computational Biology (Online)},
number = 2,
volume = 14,
place = {United States},
year = {Fri Feb 16 00:00:00 EST 2018},
month = {Fri Feb 16 00:00:00 EST 2018}
}

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journal, April 2016

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journal, January 2012

  • Chickarmane, Vijay; Olariu, Victor; Peterson, Carsten
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Computational modelling of embryonic stem-cell fate control
journal, June 2015


Regulated Fluctuations in Nanog Expression Mediate Cell Fate Decisions in Embryonic Stem Cells
journal, July 2009


Functional Heterogeneity of Embryonic Stem Cells Revealed through Translational Amplification of an Early Endodermal Transcript
journal, May 2010


Transcriptional Dynamics of the Embryonic Stem Cell Switch
journal, January 2005


Construction and Validation of a Regulatory Network for Pluripotency and Self-Renewal of Mouse Embryonic Stem Cells
journal, August 2014


A Computational Model for Understanding Stem Cell, Trophectoderm and Endoderm Lineage Determination
journal, October 2008


Generation and Characterization of a Novel Mouse Embryonic Stem Cell Line with a Dynamic Reporter of Nanog Expression
journal, March 2013


Markovian Dynamics on Complex Reaction Networks
text, January 2012


Entropy, Ergodicity and Stem Cell Multipotency
preprint, January 2015


Dichotomous noise models of gene switches
text, January 2015


Stochastic gene expression as a many body problem
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Absolute Rate Theories of Epigenetic Stability
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Works referencing / citing this record:

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