Addressing the heterogeneity in liver diseases using biological networks
Review article, 2021

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.

Computational biology

Systems biology

Liver metabolism

Integrated network

Omics integration

Genome-scale metabolic model


Simon Lam

King's College London

Stephen Doran

King's College London

Hatice Hilal Yuksel

Royal Institute of Technology (KTH)

Ozlem Altay

Royal Institute of Technology (KTH)

Hasan Turkez

Atatürk University

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jan Borén

Wallenberg Lab.

Mathias Uhlen

Royal Institute of Technology (KTH)

Adil Mardinoglu

Royal Institute of Technology (KTH)

King's College London

Briefings in Bioinformatics

1467-5463 (ISSN) 1477-4054 (eISSN)

Vol. 22 2 1751-1766

Subject Categories

Pharmaceutical Sciences

Bioinformatics (Computational Biology)

Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)





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6/3/2021 1