Review Article | Published:

Network medicine: a network-based approach to human disease

Nature Reviews Genetics volume 12, pages 5668 (2011) | Download Citation

Abstract

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Key points

  • A disease phenotype is rarely a consequence of an abnormality in a single effector gene product, but reflects various pathobiological processes that interact in a complex network.

  • Here we present an overview of the organizing principles that govern cellular networks and the implications of these principles for understanding disease. Network-based approaches have potential biological and clinical applications, from the identification of disease genes to better drug targets.

  • Whereas essential genes tend to be associated with hubs, or highly connected proteins, disease genes tend to segregate at the network's functional periphery, avoiding hubs.

  • Disease genes have a high propensity to interact with each other, forming disease modules. The identification of these disease modules can help us to identify disease pathways and predict other disease genes.

  • The highly interconnected nature of the interactome means that, at the molecular level, it is difficult to consider diseases as being independent of one another. The mapping of network-based dependencies between pathophenotypes has culminated in the concept of the diseasome, which represents disease maps whose nodes are diseases and whose links represent various molecular relationships between the disease-associated cellular components.

  • Diseases linked at the molecular level tend to show detectable comorbidity.

  • Network medicine has important applications to drug design, leading to the emergence of network pharmacology, and also in disease classification.

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Acknowledgements

We thank Z. Oltvai, A. Sharma, D.-S. Lee and J. Park for useful discussions and suggestions. A.L.B. and N.G. were supported by the US National Institutes of Health (NIH) through the Center of Excellence in Genomic Sciences (CEGS), and J.L. was supported by NIH grants HL061795 (Merit Award), HL81587, HL70819 and HL48743.

Author information

Affiliations

  1. Center for Complex Networks Research and Department of Physics, Northeastern University, 110 Forsyth Street, 111 Dana Research Center, Boston, Massachusetts 02115, USA.

    • Albert-László Barabási
    •  & Natali Gulbahce
  2. Center for Cancer Systems Biology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, Massachusetts 02115, USA.

    • Albert-László Barabási
    •  & Natali Gulbahce
  3. Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, Massachusetts 02115, USA.

    • Albert-László Barabási
    •  & Joseph Loscalzo
  4. Department of Cellular and Molecular Pharmacology, University of California, 1700 4th Street, Byers Hall 309, Box 2530, San Francisco, California 94158, USA.

    • Natali Gulbahce

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Albert-László Barabási.

Glossary

Node (or vertex)

A system component that, by interacting with other components, forms a network. In biological networks, nodes can denote proteins, genes, metabolites, RNA molecules or even diseases and phenotypes.

Link (or edge)

A link represents the interactions between the nodes of a network. In biological systems, interactions can correspond to protein–protein binding interactions or metabolic coupling, or they may represent connections between diseases based on a common genetic origin or shared phenotypic characteristics.

Degree

The degree of a node is the number of links that connect to it. The degree of a protein could represent the number of proteins with which it interacts with, whereas the degree of a disease may represent the number of other diseases that are associated with the same gene or that have a common phenotype.

Module (or community)

A dense subgraph on the network that often represents a set of nodes that have a joint role. In biology, a module could correspond to a group of molecules that interact with each other to achieve some common function.

Comorbidity

Comorbidity implies the presence of one or more disorders (or diseases) in addition to a primary disease or disorder that the patient has. Comorbidity may hide causal effects, when one disease enhances the emergence of some other disease, such as the much-studied comorbidity between diabetes and obesity.

Edgetic

Edgetic perturbations denote mutations that do not result in the complete loss of a gene product, but affect one or several interactions (and thus functions) of a protein. From a network perspective, an edgetic perturbation removes one or several links, but leaves the other links and the node unaffected.

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