Addressing the heterogeneity in liver diseases using biological networks.

Lam S., Doran S., Yuksel H., Altay O., Turkez H., Nielsen J., ...Daha Fazla

Briefings in bioinformatics, cilt.22, ss.1751-1766, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1093/bib/bbaa002
  • Dergi Adı: Briefings in bioinformatics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, CAB Abstracts, EMBASE, Library, Information Science & Technology Abstracts (LISTA), MEDLINE
  • Sayfa Sayıları: ss.1751-1766
  • Anahtar Kelimeler: Systems biology, Computational biology, Liver metabolism, Genome-scale metabolic model, Integrated network, Omics integration, POLYUNSATURATED FATTY-ACIDS, PLASMA MANNOSE LEVELS, VITAMIN-E, AKKERMANSIA-MUCINIPHILA, HEPATIC STEATOSIS, CANCER, DIET, SUPPLEMENTATION, INTERVENTION, ASSOCIATION
  • Atatürk Üniversitesi Adresli: Evet

Özet

The abnormalities in human metabolism have been implicated in the progression of several complex human diseases, including certain cancers. Hence, deciphering the underlying molecular mechanisms associated with metabolic reprogramming in a disease state can greatly assist in elucidating the disease aetiology. An invaluable tool for establishing connections between global metabolic reprogramming and disease development is the genome-scale metabolic model (GEM). Here, we review recent work on the reconstruction of cell/tissue-type and cancer-specific GEMs and their use in identifying metabolic changes occurring in response to liver disease development, stratification of the heterogeneous disease population and discovery of novel drug targets and biomarkers. We also discuss how GEMs can be integrated with other biological networks for generating more comprehensive cell/tissue models. In addition, we review the various biological network analyses that have been employed for the development of efficient treatment strategies. Finally, we present three case studies in which independent studies converged on conclusions underlying liver disease.