Addressing the heterogeneity in liver diseases using biological networks

• Published in: Briefings in bioinformatics Vol. 22; no. 2; pp. 1751 - 1766
• Main Authors: Lam, Simon, Doran, Stephen, Yuksel, Hatice Hilal, Altay, Ozlem, Turkez, Hasan, Nielsen, Jens, Boren, Jan, Uhlen, Mathias, Mardinoglu, Adil
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 22.03.2021; Oxford Publishing Limited (England)
• Subjects: Abnormalities; akkermansia-muciniphila; association; Biochemistry & Molecular Biology; Biochemistry and Molecular Biology; Bioinformatics (Computational Biology); Bioinformatik (beräkningsbiologi); Biokemi och molekylärbiologi; Biomarkers; Cancer; Cell culture; Computational biology; diet; Genome-scale; Genome-scale metabolic model; Genomes; hepatic steatosis; Heterogeneity; Integrated network; intervention; Liver; Liver diseases; Liver metabolism; Mathematical & Computational Biology; metabolic model; Metabolism; Molecular modelling; Omics integration; plasma mannose levels; polyunsaturated fatty-acids; Review; supplementation; Systems biology; Therapeutic targets; vitamin-e; Integrated network; Omics integration; Genome-scale metabolic model; Systems biology; Liver metabolism; Computational biology
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbaa002

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.