
Gene regulatory networks and logic motifs play a crucial role in shaping cell fate decisions under different driving forces.


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Organisms utilize gene regulatory networks (GRNs) to make fate decisions, but the regulatory mechanisms of transcription factors (TFs) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision- making procedures. 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). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top- down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

### Competing Interest Statement

The authors have declared no competing interest.

A Logic-incorporated Gene Regulatory Network Deciphers Principles in Cell Fate Decisions

deciphering-cell-fate-decisions-with-logic-incorporated-gene-regulatory-networks


Deciphering Cell Fate Decisions with Logic-Incorporated Gene Regulatory Networks
