ArticleeLife2024
A logic-incorporated gene regulatory network deciphers principles in cell fate decisions.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Assessing the relative contributions of mosaic and regulatory developmental modes from single-cell trajectories.PLoS computational biology · 2025Article
- A computational approach for perturbation-induced EMT transitions.NPJ systems biology and applications · 2025Article
- Advancing cell therapies with artificial intelligence and synthetic biology.Current opinion in biomedical engineering · 2025Article
- Androgen receptor signalling in non-prostatic malignancies: challenges and opportunities.Nature reviews. Cancer · 2025Review
- Operating principles of interconnected feedback loops driving cell fate transitions.NPJ systems biology and applications · 2025Article
- A mathematical framework for understanding the spontaneous emergence of complexity applicable to growing multicellular systems.PLoS computational biology · 2024Article
- The Blueprint of Logical Decisions in a NF-κB Signaling System.ACS omega · 2024Article
- A neural network-based model framework for cell-fate decisions and development.Communications biology · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.