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  • br Conclusion Our goal here is to provide a conceptual

    2018-10-22


    Conclusion Our goal here is to provide a conceptual synthesis of key observations that could ultimately be used to underpin computational simulations of ES cell regulatory circuitry using a novel coarse-grained modeling layer. The TF branching process framework avoids the intricacies of molecular interaction networks, usually framed as dynamical systems. We suggest that ES Pepstatin A entering the decision-making state are fluctuation-dominated systems that self-organize to the edge of chaos to set up decision-making capability. Cell fate computation is hypothesized to occur via an interference pattern where multiple gene expression cascades interfere constructively and destructively. This theory accommodates both an idealised ground state observed in vitro and a similar state that may exist in vivo. However, in vivo pluripotency may include from the onset critical-like fluctuations outside the core circuitry, up until differentiation-affiliated TF branching processes become supercritical. The ideas presented are currently being transformed into a computational model in which TF expression is represented as a branching process that exhibits RSOC dynamics. Unlike some models of ES cell behaviour that simplify the complexity of decision-making using differential equations, we argue that the TF branching process framework will provide valuable insight into how critical-like self-organization underpins ES cell fate computation. Such modeling should be based on TF localization studies. Although genome location analyses detect around 10-fold more target genes than genetic perturbation studies, in eukaryotic organisms there is indeed far more transcription than expected (Struhl, 2007; Willingham and Gingeras, 2006; Carninci et al., 2005; Ebisuya et al., 2008; Berretta and Morillon, 2009). A model based on the TF branching process framework should distinguish between specific regulatory mechanisms crafted by natural selection and those that ‘merely’ reflect background self-organizing circuitry and/or transcriptional noise. Starting with an initial background self-organizing circuitry, then adding known regulatory interactions, we will gain insight into how the core pluripotent circuitry itself wields edge of chaos dynamics within the decision-making circuitry. We will better understand how the core pluripotent circuitry collapses upon exit of ground state, and reveal whether there is anything encoded in TF localization data to indicate a pre-defined and/or graceful collapse. TF localisation data are not yet available for key pluripotency TFs in ground state ES cells. However, such data are available for ES cells cultured in serum and LIF (Chambers and Tomlinson, 2009). These data could be used to model the hypothesised critical-like self-organization within the ES cell fluctuating transcriptome. Interestingly, whereas the Nanog TF branching process must be supercritical in ground state ES cells because Nanog is stably expressed through time, it appears to transit between subcritical and supercritical states under serum and LIF conditions in which Nanog expression fluctuates (Kalmar et al., 2009). How easily would such dynamics emerge in the suggested model based on the TF branching process framework? Histone modification data could easily be included to influence RSOC dynamics and distributed information flow. If the model is approximately accurate, the proportion of times that the Nanog TF branching process becomes subcritical when the model is run repeatedly will be similar to the rate of spontaneous differentiation observed under serum and LIF experimentally. We argue that a model based on the TF branching process framework will be crucial to unveiling the complete process of cell fate computation if this process is indeed underpinned by critical-like self-organization.
    Acknowledgments We thank Paul Sumption, Bruce Beckles, and Stephen Emmott and the Biological Computation Group at Microsoft Research Cambridge UK for discussion about coding concepts, Andrew Elefanty, Edouard Stanley, Elizabeth Ng, Brian Hendrich, José Silva, Paul Bertone, Nicola Reynolds, Elly Tanaka, Graziano Martello, Sabine Dietmann, Isabelle Nett, Sarah Teichmann and Martin Burd for other helpful discussions, and Alexey Gusev for Figure 1 and Kenneth Goldberg of Lawrence Berkeley Pepstatin A National Laboratory for Figure 2. Special thanks go to Jennifer Nichols, Jason Wray, Davide Danovi and Jason Signolet for critical review of the manuscript, and to Business Victoria for a 2009 Victoria Fellowship, which enabled Dr Halley to travel to Canada and the UK where she met Professors Huang and Smith. This study was funded by the Medical Research Council and the European Commission Project EuroSystem.