ArticleNucleic acids research2019
E. coli gene regulatory networks are inconsistent with gene expression data.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Predicting input signals of transcription factors in Escherichia coli.Molecular systems biology · 2025Article
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- A multidimensional recommendation framework for identifying biological targets to aid the diagnosis and treatment of liver metastasis in patients with colorectal cancer.Molecular cancer · 2024Article
- Studying temporal dynamics of single cells: expression, lineage and regulatory networks.Biophysical reviews · 2024Review
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.Bioinformatics (Oxford, England) · 2023Article
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- Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.PLoS computational biology · 2023Article
- Elucidation of independently modulated genes inmSystems · 2023Article
- Using single cell atlas data to reconstruct regulatory networks.Nucleic acids research · 2023Article
- Improving gene regulatory network inference and assessment: The importance of using network structure.Frontiers in genetics · 2023Article
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- The transcription factor network of E. coli steers global responses to shifts in RNAP concentration.Nucleic acids research · 2022Article
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- System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks.Frontiers in bioengineering and biotechnology · 2022Article
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- iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.Nucleic acids research · 2021Article
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4 authors.
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Abstract
Gene regulatory networks (GRNs) and gene expression data form a core element of systems biology-based phenotyping. Changes in the expression of transcription factors are commonly believed to have a causal effect on the expression of their targets. Here we evaluated in the best researched model organism, Escherichia coli, the consistency between a GRN and a large gene expression compendium. Surprisingly, a modest correlation was observed between the expression of transcription factors and their targets and, most noteworthy, both activating and repressing interactions were associated with positive correlation. When evaluated using a sign consistency model we found the regulatory network was not more consistent with measured expression than random network models. We conclude that, at least in E. coli, one cannot expect a causal relationship between the expression of transcription and factors their targets, and that the current static GRN does not adequately explain transcriptional regulation. The implications of this are profound as they question what we consider established knowledge of the systemic biology of cells and point to methodological limitations with respect to single omics analysis, static networks and temporality.
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