ArticleiScience2025
Low dimensionality of phenotypic space as an emergent property of coordinated teams in biological regulatory networks.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Multiomic State-Transitions Reveal Post-Treatment Transcriptome Desynchronization in Acute Myeloid Leukemia.bioRxiv : the preprint server for biology · 2026Article
- Hallmarks of epithelial-mesenchymal plasticity in cancer.Molecular cancer · 2026Review
- Unifying theories in high-dimensional biophysics: approaches, challenges and opportunities.NPJ systems biology and applications · 2026Article
- Genotype-fitness mapping of adaptive mutants reveals shifting low-dimensional structure across divergent environments.PLoS biology · 2026Article
- A computational approach for perturbation-induced EMT transitions.NPJ systems biology and applications · 2025Article
- GRiNS: a python library for simulating gene regulatory network dynamics.BMC bioinformatics · 2025Article
- Generative prediction of causal gene sets responsible for complex traits.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Tumor microenvironment governs the prognostic landscape of immunotherapy for head and neck squamous cell carcinoma: A computational model-guided analysis.PLoS computational biology · 2025Article
- Mutually exclusive teams-like patterns of gene regulation characterize phenotypic heterogeneity along the noradrenergic-mesenchymal axis in neuroblastoma.Cancer biology & therapy · 2024Article
- Data- and theory-driven approaches for understanding paths of epithelial-mesenchymal transition.Genesis (New York, N.Y. : 2000) · 2024Review
- State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia.Leukemia · 2024Article
- Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma.iScience · 2024Article
- State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell-fate decisions involve coordinated genome-wide expression changes, typically leading to a limited number of phenotypes. Although often modeled as simple toggle switches, these rather simplistic representations often disregard the complexity of regulatory networks governing these changes. Here, we unravel design principles underlying complex cell decision-making networks in multiple contexts. We show that the emergent dynamics of these networks and corresponding transcriptomic data are consistently low-dimensional, as quantified by the variance explained by principal component 1 (PC1). This low dimensionality in phenotypic space arises from extensive feedback loops in these networks arranged to effectively enable the formation of two teams of mutually inhibiting nodes. We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. Using artificial networks of varied topologies, we also establish the conditions for generating canalized cell-fate landscapes, offering insights into diverse binary cellular decision-making networks.
Indexed as
Identifiers
What Socratic holds
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.