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 »
- Authors:
-
- 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
- Univ. of Manchester (United Kingdom). School of Physics and Astronomy
- Rice Univ., Houston, TX (United States). Dept. of Bioengineering
- 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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Functional Heterogeneity of Embryonic Stem Cells Revealed through Translational Amplification of an Early Endodermal Transcript
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- PLoS Computational Biology, Vol. 10, Issue 8
A Computational Model for Understanding Stem Cell, Trophectoderm and Endoderm Lineage Determination
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- Chickarmane, Vijay; Peterson, Carsten
- PLoS ONE, Vol. 3, Issue 10
Generation and Characterization of a Novel Mouse Embryonic Stem Cell Line with a Dynamic Reporter of Nanog Expression
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Markovian Dynamics on Complex Reaction Networks
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- Goutsias, John; Jenkinson, Garrett
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Entropy, Ergodicity and Stem Cell Multipotency
preprint, January 2015
- Ridden, Sonya J.; Chang, Hannah H.; Zygalakis, Konstantinos C.
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Dichotomous noise models of gene switches
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- Potoyan, Davit A.; Wolynes, Peter G.
- arXiv
Stochastic gene expression as a many body problem
text, January 2003
- Sasai, Masaki; Wolynes, Peter G.
- arXiv
Absolute Rate Theories of Epigenetic Stability
text, January 2005
- Walczak, Aleksandra M.; Onuchic, José N.; Wolynes, Peter G.
- arXiv
Works referencing / citing this record:
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A multiscale model of epigenetic heterogeneity-driven cell fate decision-making
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