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Systems medicine

Systems medicine is an interdisciplinary subject focusing on systems of biological components and using computational models and experimental technologies such as genomics, transcriptomics, proteomics, metabolomics, and metagenomics. It includes application and development of systems biological methods with an emphasis on integration, analysis, and modeling of big data using biological networks. In the HPA, these concepts were used to study various clinically relevant diseases, including liver metabolism diseases and human cancers. In addition, several drug candidates for various clinical indications have been developed, and several clinical trials have been initiated in humans.

Key publication

  • Lee S et al., Integrated Network Analysis Reveals an Association between Plasma Mannose Levels and Insulin Resistance. Cell Metab. (2016)
    PubMed: 27345421 DOI: 10.1016/j.cmet.2016.05.026

Other selected publications

  • Mardinoglu A et al., Plasma Mannose Levels Are Associated with Incident Type 2 Diabetes and Cardiovascular Disease. Cell Metab. (2017)
    PubMed: 28768165 DOI: 10.1016/j.cmet.2017.07.006

  • Mardinoglu A et al., Personal model-assisted identification of NAD(+) and glutathione metabolism as intervention target in NAFLD. Mol Syst Biol. (2017)
    PubMed: 28254760 

  • Mardinoglu A et al., An Integrated Understanding of the Rapid Metabolic Benefits of a Carbohydrate-Restricted Diet on Hepatic Steatosis in Humans. Cell Metab. (2018)
    PubMed: 29456073 DOI: 10.1016/j.cmet.2018.01.005

  • Bidkhori G et al., Metabolic Network-Based Identification and Prioritization of Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma. Front Physiol. (2018)
    PubMed: 30065658 DOI: 10.3389/fphys.2018.00916

  • Zhang C et al., The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alcoholic fatty liver disease. Mol Syst Biol. (2020)
    PubMed: 32337855 DOI: 10.15252/msb.209495

Figure legend: Integrated networks, which are the combination of genome-scale metabolic networks, protein–protein interaction networks, signaling networks, and transcriptional regulatory networks, can be used for integration of 'omics data and for discovery of biomarkers and drug targets.

Key facts

  • Integration of clinical data using a holistic approach
  • Discovery of potential biomarkers for stratification of patients and early detection of disease
  • Identification of novel drug targets for development of efficient treatment strategies
  • Systems biology-based drug repositioning for development of therapies