Modeling Signaling-dependent Pluripotency with Boolean Logic to Predict Cell Fate Transitions


Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The mechanisms that maintain pluripotency, or that cause its destabilization to initiate development, are complex and incompletely understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to input signals. Our strategy used random asynchronous Boolean simulations (R-ABS) to simulate single-cell fate transitions and strongly connected components (SCCs) strategy to represent population heterogeneity. This framework was applied to a reverse-engineered and curated core GRN for mouse embryonic stem cells (mESCs) and used to simulate cellular responses to combinations of five signaling pathways. Our simulations predicted experimentally verified cell population compositions and input signal combinations controlling specific cell fate transitions. Extending the model to PSC differentiation, we predicted a combination of signaling activators and inhibitors that efficiently and robustly generated a Cdx2+Oct4- cells from naïve mESCs. Overall, this platform provides new strategies to simulate cell fate transitions and the heterogeneity that typically occurs during development and differentiation. Synopsis $<$img class="highwire-embed” alt="Embedded Image” src="”/$>$ This study reports a computational framework that simulates gene regulatory network (GRN) specified cell fate transitions, and cell compositions, from uniform input signals to successfully predict cellular decision processes from signal-perturbed mouse pluripotent stem cells. A novel Boolean simulation framework is developed for predicting signal-controlled GRN logic and stem cell fate decisions.Novel quantitative metrics facilitate the comparison of GRN properties in the context of different cell states.Micro-environmental signals and feedback are incorporated into a GRN simulation framework.Applying this novel framework to an inferred mouse pluripotent stem cell GRN model accurately predicts experimentally observed cell fate outcomes upon exposure to complex exogenous signals.

Molecular Systems Biology