ArticleProceedings of the National Academy of Sciences of the United States of America2023
Minimal frustration underlies the usefulness of incomplete regulatory network models in biology.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- PROFET predicts continuous gene expression dynamics from scRNA-seq data to elucidate heterogeneity of cancer treatment responses.Cell systems · 2026Article
- DANSE: a pipeline for dynamic modelling of time-series multi-omics data.BMC bioinformatics · 2025Article
- Identifying key regulators in neuronal transdifferentiation by gene regulatory network analysis.PNAS nexus · 2025Article
- A computational approach for perturbation-induced EMT transitions.NPJ systems biology and applications · 2025Article
- Chemical compensation to mechanical loss in cell mechanosensation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Transition paths across the epithelial-mesenchymal transition landscape are dictated by network logic.Development (Cambridge, England) · 2025Article
- Evolving concepts of the protein universe.iScience · 2025Review
- Low dimensionality of phenotypic space as an emergent property of coordinated teams in biological regulatory networks.iScience · 2025Article
- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- Energy Landscape Reveals the Underlying Mechanism of Cancer-Adipose Conversion in Gene Network Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Irreversibility in bacterial regulatory networks.Science advances · 2024Article
- Biologically meaningful regulatory logic enhances the convergence rate in Boolean networks and bushiness of their state transition graph.Briefings in bioinformatics · 2024Article
- Cell reprogramming design by transfer learning of functional transcriptional networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Regulatory networks as large and complex as those implicated in cell-fate choice are expected to exhibit intricate, very high-dimensional dynamics. Cell-fate choice, however, is a macroscopically simple process. Additionally, regulatory network models are almost always incomplete and/or inexact, and do not incorporate all the regulators and interactions that may be involved in cell-fate regulation. In spite of these issues, regulatory network models have proven to be incredibly effective tools for understanding cell-fate choice across contexts and for making useful predictions. Here, we show that minimal frustration-a feature of biological networks across contexts but not of random networks-can compel simple, low-dimensional steady-state behavior even in large and complex networks. Moreover, the steady-state behavior of minimally frustrated networks can be recapitulated by simpler networks such as those lacking many of the nodes and edges and those that treat multiple regulators as one. The present study provides a theoretical explanation for the success of network models in biology and for the challenges in network inference.
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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 Socratic graph.