ArticleGenome research2016
Control of fluxes in metabolic networks.
Article in Genome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Controllability Analysis of Intercellular Protein-Protein Interaction Networks.Computational and structural biotechnology journal · 2026Article
- A constraint-based framework for exploring the impact of multireaction dependencies on metabolic functions.NPJ systems biology and applications · 2025Article
- Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.BioData mining · 2025Review
- Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes.Molecular systems biology · 2024Article
- A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks.NPJ systems biology and applications · 2024Article
- Target control of linear directed networks based on the path cover problem.Scientific reports · 2024Article
- Enzymatic Metabolic Switches of Astrocyte Response to Lipotoxicity as Potential Therapeutic Targets for Nervous System Diseases.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Measuring criticality in control of complex biological networks.NPJ systems biology and applications · 2024Article
- Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets.International journal of molecular sciences · 2023Review
- Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony.PLoS computational biology · 2023Article
- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- The hidden simplicity of metabolic networks is revealed by multireaction dependencies.Science advances · 2022Article
- Probabilistic Critical Controllability Analysis of Protein Interaction Networks Integrating Normal Brain Ageing Gene Expression Profiles.International journal of molecular sciences · 2021Article
- Characterization of effects of genetic variants via genome-scale metabolic modelling.Cellular and molecular life sciences : CMLS · 2021Review
- Uncovering and classifying the role of driven nodes in control of complex networks.Scientific reports · 2021Article
- The Large Scale Structure of Human Metabolism Reveals Resilience via Extensive Signaling Crosstalk.Frontiers in physiology · 2020Article
- Probabilistic controllability approach to metabolic fluxes in normal and cancer tissues.Nature communications · 2019Article
- Cycles of external dependency drive evolution of avian carotenoid networks.Nature communications · 2019Article
- Evolutionary design principles in metabolism.Proceedings. Biological sciences · 2019Review
- Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.
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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 OpenQuestion graph.