ArticleNature communications2019
Probabilistic controllability approach to metabolic fluxes in normal and cancer tissues.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Controllability Analysis of Intercellular Protein-Protein Interaction Networks.Computational and structural biotechnology journal · 2026Article
- 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
- 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
- Optimizing Genomic Control in Hit Network-Target Set Model Associations with Lung Adenocarcinoma.Journal of Cancer · 2023Article
- Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.Biology · 2022Article
- Domination based classification algorithms for the controllability analysis of biological interaction networks.Scientific reports · 2022Article
- Contribution of genome-scale metabolic modelling to niche theory.Ecology letters · 2022Article
- Probabilistic Critical Controllability Analysis of Protein Interaction Networks Integrating Normal Brain Ageing Gene Expression Profiles.International journal of molecular sciences · 2021Article
- Uncovering and classifying the role of driven nodes in control of complex networks.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent research has shown that many types of cancers take control of specific metabolic processes. We compiled metabolic networks corresponding to four healthy and cancer tissues, and analysed the healthy-cancer transition from the metabolic flux change perspective. We used a Probabilistic Minimum Dominating Set (PMDS) model, which identifies a minimum set of nodes that act as driver nodes and control the entire network. The combination of control theory with flux correlation analysis shows that flux correlations substantially increase in cancer states of breast, kidney and urothelial tissues, but not in lung. No change in the network topology between healthy and cancer networks was observed, but PMDS analysis shows that cancer states require fewer controllers than their corresponding healthy states. These results indicate that cancer metabolism is characterised by more streamlined flux distributions, which may be focused towards a reduced set of objectives and controlled by fewer regulatory elements.
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