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Systems biology in hepatology: approaches and applications

Nature Reviews Gastroenterology & Hepatology volume 15, pages 365–377 (2018)Cite this article

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Abstract

Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein–protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

Key points

  • Detailed characterization of human liver tissue and gut microbiota is enabled by omics technologies.

  • Biological network models are functional tools for the exploration and integration of multiomics data.

  • Systems biology uses a holistic and integrative approach for comprehensive analysis of the biological functions in healthy and diseased states.

  • Systems biology approaches have been successfully employed in gastroenterology and hepatology to identify biomarkers and drug targets.

  • These integrative tools can be used for simulation of liver tissue functions and crosstalk of liver with other tissues.

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Fig. 1: Integration of multiomics data using biological networks.
Fig. 2: Metabolic alterations involved in the development of hepatic steatosis.
Fig. 3: Integration of biological networks.
Fig. 4: Host–gut microbiota crosstalk and effects on whole-body biological functions.

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Acknowledgements

Funding was provided by the Knut and Alice Wallenberg Foundation. The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under European Medical Information Framework grant agreement no. 115372.

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Authors and Affiliations

  1. Science for Life Laboratory, KTH — Royal Institute of Technology, Stockholm, Sweden

    Adil Mardinoglu, Mathias Uhlen & Jens Nielsen

  2. Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Adil Mardinoglu & Jens Nielsen

  3. Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden

    Jan Boren & Ulf Smith

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  1. Adil Mardinoglu
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Correspondence to Adil Mardinoglu.

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A.M., M.U. and J.B. are founders of ScandiBio and ScandiEdge Therapeutics.

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TCSBN: a database of tissue and cancer-specific biological networks: http://inetmodels.com/

Glossary

Proteomics

A field that analyses data to quantify protein abundance.

Transcriptomics

A field that measures mRNA transcript levels.

Metabolomics

A field that analyses data to determine the abundance of small cellular metabolites.

Metagenomics

A field that directly analyses the genomes contained within an environmental sample.

Fluxomics

A field that quantifies the flux of substrates through a reaction step to establish dynamic changes of molecules within a cell over time.

Interactomics

A field that analyses the interactions between major constituents of cells, including proteins and metabolites, to resolve the whole set of molecular interactions in cells.

Systems biology

An interdisciplinary field that studies the complex interactions within cells and/or tissues using a holistic approach.

Biological networks

Models that make up the basis of systems biology and provide a mathematical representation of connections between the constituents of cells or tissues.

Genome-scale metabolic models

Mathematical models for a collection of metabolic biochemical reactions and the associated enzymes and transporters.

Gene regulatory networks

Networks that represent the interactions between transcription factors and their target genes.

Protein–protein interaction networks

Networks that provides detailed information about the protein complexes formed by biochemical events.

Signalling networks

Networks describing sequential molecular reactions that govern how a cell responds to its environment.

Gene co-expression networks

Networks representing the links between the expression of genes that might result in functional connections.

Secretome

A collection of cell components secreted to the outside of the cell or tissue.

Druggable proteome

A collection of the proteins used in the development of drugs.

Network topology

The arrangement of the network components including links, nodes and edges.

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Mardinoglu, A., Boren, J., Smith, U. et al. Systems biology in hepatology: approaches and applications. Nat Rev Gastroenterol Hepatol 15, 365–377 (2018). https://doi.org/10.1038/s41575-018-0007-8

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