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Gene Networks of Fully Connected Triads with Complete Auto-Activation Enable Multistability and Stepwise Stochastic Transitions

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2014

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Public Library of Science
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Faucon, Philippe C., Keith Pardee, Roshan M. Kumar, Hu Li, Yuin-Han Loh, and Xiao Wang. 2014. “Gene Networks of Fully Connected Triads with Complete Auto-Activation Enable Multistability and Stepwise Stochastic Transitions.” PLoS ONE 9 (7): e102873. doi:10.1371/journal.pone.0102873. http://dx.doi.org/10.1371/journal.pone.0102873.

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.

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Biology and Life Sciences, Biochemistry, Biochemical Simulations, Biotechnology, Bioengineering, Biomedical Engineering, Computational Biology, Systems Biology, Computer and Information Sciences, Network Analysis, Regulatory Networks, Systems Science, Nonlinear Dynamics, Engineering and Technology, Physical Sciences, Mathematics, Probability Theory, Stochastic Processes

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