A genome‐scale computational study of the interplay between transcriptional regulation and metabolism
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This paper presents a new method, steady-state regulatory flux balance analysis (SR-FBA), for predicting gene expression and metabolic fluxes in a large-scale integrated metabolic-regulatory model. Using SR-FBA to study the metabolism of Escherichia coli, we quantify the extent to which the different levels of metabolic and transcriptional regulatory constraints determine metabolic behavior: metabolic constraints determine the flux activity state of 45-51% of metabolic genes, depending on the growth media, whereas transcription regulation determines the flux activity state of 13-20% of the genes. A considerable number of 36 genes are redundantly expressed, that is, they are expressed even though the fluxes of their associated reactions are zero, indicating that they are not optimally tuned for cellular flux demands. The undetermined state of the remaining approximately 30% of the genes suggests that they may represent metabolic variability within a given growth medium. Overall, SR-FBA enables one to address a host of new questions concerning the interplay between regulation and metabolism.
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The dynamics of metabolite levels shaped by enzyme kinetics and enzymatic regulation can only be adequately captured by a kinetic model, because existing constraint-based models with Boolean regulatory architecture ignore enzymatic regulation.
“These dynamics can only be adequately captured by a kinetic model, as, for example, existing constraint-based models with Boolean regulatory architecture such as developed and used by Covert et al (2004) and Shlomi et al (2007) ignore enzymatic regulation.”
From Bacterial adaptation through distributed sensing of metabolic fluxes (2010) · cites this paper as Shlomi et al (2007)
The here proposed distributed sensing mechanism emerges from the system dynamics and could not have been deduced from a recently proposed constraint-based model integrating metabolic and transcriptional regulation.
“The here proposed distributed sensing mechanism emerges from the system dynamics and could not have been deduced from a recently proposed constraint-based model integrating metabolic and transcriptional regulation (Covert et al, 2004; Shlomi et al, 2007).”
From Bacterial adaptation through distributed sensing of metabolic fluxes (2010) · cites this paper as Shlomi et al, 2007
The extended constraint-based model replaces the unknown sensing mechanism through Boolean rules of the form 'If carbon source X is present, TF Y is active, otherwise not,' and is as such not suited to understand the detailed molecular processes leading to the activation of TF Y.
“This model replaces the unknown sensing mechanism through Boolean rules of the form ‘If carbon source X is present, TF Y is active, otherwise not,’ and is as such not suited to understand the detailed molecular processes leading to the activation of TF Y.”
From Bacterial adaptation through distributed sensing of metabolic fluxes (2010) · cites this paper as Shlomi et al, 2007
The underlying view of the extended constraint-based model is that top-level regulatory proteins recognize environmental conditions and adjust downstream metabolic operation.
“The underlying view of the extended constraint-based model is that top-level regulatory proteins recognize environmental conditions and adjust downstream metabolic operation.”
From Bacterial adaptation through distributed sensing of metabolic fluxes (2010) · cites this paper as Shlomi et al, 2007
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An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models
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Metabolomics and modelling approaches for systems metabolic engineering
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Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms
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- Authors
- Shlomi, T., Eisenberg, Y., Sharan, R., & Ruppin, E.
- Affiliations
- Tel Aviv University (IL)
- Corresponding author
- Shlomi, Tomer
- Ruppin, Eytan
- Venue
- Molecular Systems Biology
- Published
- 2007-04-17
- Volume
- 3
- Issue
- 1
- Pages
- 101-101
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC1865583
- OpenAlex
- W2112933558
- Cited by
- 254
- References
- 22
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 7 · 2023: 9 · 2022: 8 · 2021: 5 · 2020: 12 · 2019: 12 · 2018: 7 · 2017: 13 · 2016: 15 · 2015: 17 · 2014: 14 · 2013: 17 · 2012: 21
- FWCI
- 8.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Molecular Systems Biology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Israel Science Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- transcriptional regulation, metabolic flux, gene expression prediction, Escherichia coli metabolism, metabolic constraints, metabolic variability
- MeSH terms
- Escherichia coli, Gene Expression Regulation, Transcription, Genetic, Genome, Bacterial
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