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

• Published in: PLoS computational biology 2018-02, Vol.14 (2), p.e1006000-e1006000
• Main Authors: Lin, Yen Ting, Hufton, Peter G, Lee, Esther J, Potoyan, Davit A
• Format: Article
• Language: English
• Subjects: Algorithms; Animal models; Animals; BASIC BIOLOGICAL SCIENCES; Biochemistry & Molecular Biology; Bioengineering; Biology; Biology and Life Sciences; Boolean algebra; Cell differentiation; Cell Differentiation - genetics; Cell Lineage; Cell lines; Cell self-renewal; Computational Biology - methods; Computer and Information Sciences; Dynamics; Embryo cells; Embryonic stem cells; Embryos; Epigenesis, Genetic; Epigenetics; Gene expression; Gene Expression Regulation; Gene Regulatory Networks; Gene sequencing; Information theory; Laboratories; Learning algorithms; Machine Learning; Mathematical & Computational Biology; Methods; Mice; Models, Theoretical; Mouse Embryonic Stem Cells - cytology; Network topologies; Noise; Ordinary differential equations; Physiological aspects; Pluripotency; Pluripotent Stem Cells - cytology; Principal Component Analysis; Principal components analysis; Promoter Regions, Genetic; Protein Binding; Signal Transduction; Signaling; Simulation; Stem cell research; Stem cell transplantation; Stem cells; Stochastic Processes; Stochasticity; Switching theory; Topology; Transcription factors; Transcription Factors - metabolism
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006000

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.