Addressing the heterogeneity in liver diseases using biological networks. Issue 2 (23rd March 2020)

• Record Type: Journal Article
• Authors: Lam, Simon; Doran, Stephen; Yuksel, Hatice Hilal; Altay, Ozlem; Turkez, Hasan; Nielsen, Jens; Boren, Jan; Uhlen, Mathias; Mardinoglu, Adil
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 2(2021)
• Page Start: 1751
• Page End: 1766
• Publication Date: 2020-03-23
• Subjects: Systems biology -- Computational biology -- Liver metabolism -- Genome-scale metabolic model -- Integrated network -- Omics integration; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa002
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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.