Differentiation of Somatic Cells in Multicellular Organisms

Abstract

Multicellular organisms employ intricate gene regulatory networks (GRNs) to orchestrate cell fate decisions, yet the precise regulatory mechanisms that govern transcription factors (TFs) within these networks remain exceptionally complex. A long-standing question in this field pertains to how these intricate interactions synergistically contribute to decision-making processes. To gain a comprehensive understanding of the role of regulatory logic in cell fate determinations, we developed a logical model of GRNs and examined its behavior under two distinct driving forces—one governed by stochastic noise and the other by deterministic signaling. Under noise-driven conditions, we identified a correlation between fate biasing, regulatory logic, and noise profile dynamics. In the signal-driven mode, we established a connection between regulatory logic and the trade-off between accuracy and progression speed, revealing distinct reprogramming trajectories influenced by specific logical motifs. Through differentiation studies, we characterized a unique priming stage that is dependent on regulatory logic, employing decision landscapes for analysis. Finally, we applied our findings to elucidate three biological cases: hematopoiesis, embryogenesis, and transdifferentiation. Orthogonally to classical expression profile analysis, we leveraged noise pattern recognition to construct GRNs corresponding to fate transitions. Our research presents a generalizable framework for downstream investigations of fate determination and offers a practical approach for the taxonomy of cell fate decisions.