Stem cell decision making and critical-like exploratory networks

A sound theoretical or conceptual model of gene regulatory processes that control stem cell fate is still lacking, compromising our ability to manipulate stem cells for therapeutic benefit. The complexity of the regulatory and signaling pathways limits development of useful, predictive models that employ solely reductionist methods using molecular components. However, there is clear evidence from other complex systems that coarse-grained or mesoscale models can yield useful insights and provide workable models for the prediction of some emergent properties such as cell phenotype. We present such a coarse-grained model of stem cell decision making, utilizing the concept of self-organized criticality, which is an order that propagates in some nonequilibrium systems. The model proposes that stochastic gene expression within a stem cell gene regulatory network self-organizes to a critical-like state, characterized by cascades of gene expression that prime various transcriptional programs associated with different cell fates. This diversity of cell fate options is reduced during the decision-making process, which involves a supercritical connectivity in the gene regulatory network as a stem cell leaves its niche microenvironment and an overall increase in transcription occurs. As modules of genes that correspond to specific cell fates approach their critical points, competitive interactions occur between them that are influenced by prevailing microenvironmental conditions. The conceptual model incorporates both intrinsic and extrinsic factors governing stem cell fate and provides a logical pathway to the development of a computational model. We further suggest that rapid self-organized criticality, rather than self-organized criticality, best describes the mesoscale organization of gene regulatory networks.