![]() Under the noise-driven mode, the bias of cell fate decisions largely depends on the spontaneous heterogeneity of gene expressions in the cell population. These abbreviations were used for following Figure 2– 7. Cells (yellow balls) in stem cell fate (denoted as “S”, green well in landscape) differentiate into downstream fates, lineage X (denoted as “LX”, blue well) and lineage Y (denoted as “LY”, purple well). (A-B) Valleys represent stable attractors. Schematic representation of cell fate decisions driven by noise (A) and signal (B) from a view of epigenetic landscape That is, the distortion of the landscape orchestrates fate transitions and is driven by extrinsic signals (termed as “signal-driven”, Fig 1.B).įigure 1. Meanwhile, some researchers argued that the epigenetic landscape is dynamic during fate decisions. On one hand, some perspectives hold that cells reside in a stationary landscape, where decisions are made by switching through discrete valleys, as a result of gene expression noise termed as “noise-driven”, Fig 1.A). ![]() An outstanding question is whether the landscape is static or not, i.e., whether cell fate decisions are driven by noise or signal. While introducing various quantitative models and dissecting diverse fate-decision processes, researchers have further elaborated the Waddington landscape. Over decades, this insightful metaphor has facilitated researchers to distill a myriad of models regarding cell fate decisions. Waddington’s epigenetic landscape is a fundamental and profound conceptualization of cell fate decisions. The modelling approach and analyses provide solid support for the conclusion that distinct driving forces behind fate decisions can be distinguished by their noise profiles and reprogramming trajectories. The study presented in this manuscript makes important contributions to our understanding of cell fate decisions, as well as to the role and effects of noise at various scales in gene regulatory networks involved in such fate decisions. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. Organisms utilize gene regulatory networks (GRNs) to make fate decisions, but the regulatory mechanisms of transcription factors (TFs) in GRNs are exceedingly intricate.
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