ArticleScientific reports2013
Gene network requirements for regulation of metabolic gene expression to a desired state.
Article in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- The galactokinase enzyme of yeast senses metabolic flux to stabilize galactose pathway regulation.Nature metabolism · 2025Article
- Making sense of gene expression control by flux sensing.Nature metabolism · 2025Article
- Escherichia coli robustly expresses ATP synthase at growth rate-maximizing concentrations.The FEBS journal · 2022Article
- WINNER: A network biology tool for biomolecular characterization and prioritization.Frontiers in big data · 2022Article
- Searching for principles of microbial physiology.FEMS microbiology reviews · 2020Review
- Maintaining maximal metabolic flux by gene expression control.PLoS computational biology · 2018Article
- Transcriptional reprogramming of metabolic pathways in critically ill patients.Intensive care medicine experimental · 2016Article
- Adaptive Benefits of Storage Strategy and Dual AMPK/TOR Signaling in Metabolic Stress Response.PloS one · 2016Article
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Authors and funding
3 authors.
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Abstract
Gene circuits that control metabolism should restore metabolic functions upon environmental changes. Whether gene networks are capable of steering metabolism to optimal states is an open question. Here we present a method to identify such optimal gene networks. We show that metabolic network optimisation over a range of environments results in an input-output relationship for the gene network that guarantees optimal metabolic states. Optimal control is possible if the gene network can achieve this input-output relationship. We illustrate our approach with the best-studied regulatory network in yeast, the galactose network. We find that over the entire range of external galactose concentrations, the regulatory network is able to optimally steer galactose metabolism. Only a few gene network parameters affect this optimal regulation. The other parameters can be tuned independently for optimisation of other functions, such as fast and low-noise gene expression. This study highlights gene network plasticity, evolvability, and modular functionality.
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Registered trials
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