Modeling signaling-dependent pluripotency with Boolean logic to predict cell fate transitions

Mol Syst Biol. 2018 Jan 29;14(1):e7952. doi: 10.15252/msb.20177952.

Abstract

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.

Keywords: asynchronous Boolean simulation; embryonic stem cell; gene regulatory network; heterogeneity; pluripotency.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cell Differentiation
  • Cell Line
  • Gene Expression Profiling
  • Gene Regulatory Networks*
  • Mice
  • Mouse Embryonic Stem Cells / cytology*
  • Mouse Embryonic Stem Cells / metabolism
  • Pluripotent Stem Cells / cytology*
  • Pluripotent Stem Cells / metabolism
  • Reverse Genetics
  • Sequence Analysis, RNA
  • Signal Transduction
  • Single-Cell Analysis / methods*
  • Systems Biology / methods

Grant support