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Review
. 2011 Jan;12(1):56-68.
doi: 10.1038/nrg2918.

Network medicine: a network-based approach to human disease

Albert-László Barabási  1 , Natali Gulbahce, Joseph Loscalzo
Affiliations
Review

Network medicine: a network-based approach to human disease

Albert-László Barabási et al. Nat Rev Genet. 2011 Jan.

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.

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Figures

Figure 1
Figure 1. Disease and essential genes in the interactome
(a) Of the approximately 25,000 genes, only about 1,700 have been associated with specific diseases. In addition, about 1,600 genes are known to be in utero essential, i.e., their absence is associated with embryonic lethality. (b) Schematic illustration of the differences between essential and non-essential disease genes. Non-essential disease genes (illustrated as blue nodes) are found to segregate at the network periphery whereas in utero essential genes (illustrated as red nodes) tend to be at the functional center (encode hubs, expressed in many tissues) of the interactome.
Figure 2
Figure 2. Disease modules
Schematic illustration of the three modularity concepts discussed in the review. (a) Topological modules correspond to locally dense neighborhoods of the interactome, such that the nodes of the module show a higher tendency to interact with each other than with nodes outside of the module. As such, topological modules represent a pure network property. (b) Functional modules correspond to network neighborhoods in which there is a statistically significant segregation of nodes of related function. A functional module, thus, requires us to define some nodal characteristics (illustrated as gray nodes), and relies on the hypothesis that nodes involved in closely related cellular functions tend to interact with each other and thus are located in the same network neighborhood. (c) A disease module represents a group of nodes whose perturbation (mutations, deletions, copy number variations, or expression changes) can be linked to a particular disease phenotype, shown as red nodes. The tacit assumption in network medicine is that the topological, functional, and disease modules overlap so that functional modules correspond to topological modules and a disease can be viewed as the breakdown of a functional module.
Figure 3
Figure 3. Identifying and validating disease modules
A network-based approach to a particular diseases consist of several steps:

  1. Interactome reconstruction, which merges the most up-to-date information on protein-protein interactions, co-complex memberships, regulatory interactions, and metabolic network maps (Box 1) in the tissue and cell line of interest. These networks are occasionally augmented with phenotypic links, such as coexpression-based relationships, but such phenotypic measures are best utilized later to test the functional homogeneity of the predicted disease module.

  2. Disease gene (seed) identification, collects the known disease-associated genes obtained from linkage analysis, GWAS, or other sources, serving as the seed of the disease module.

  3. Disease module identification. The seed genes are placed on the interactome, aiming to identify a subnetwork that contains most of the disease-associated components, exploiting both the functional and topological modularity of the network. If such statistically significant agglomeration is detected, then one can use a combination of clustering tools– to identify the functionally and topologically compact subgraph that contains most disease components, representing the potential disease module. The closer the phenotypic manifestations are of the two diseases (organ, symptoms, drug response), the more significant is the expected overlap between the modules associated with two diseases.

  4. Pathway identification: Occasionally, the number of components the ascertained disease module contains is so large that it cannot serve as a tractable starting point for further experimental work. In this case it may be necessary to identify the specific molecular pathways whose disruption may be responsible for the disease phenotype. One typically uses the network parsimony principle (Box 3) to select the most likely disease pathways, assuming that causal pathways are the shortest paths connecting the known disease components.

  5. Validation/prediction: The disease modules are tested for their functional and dynamic homogeneity. The nature of the validation depends on the tools and data available to the investigator; gene expression data can validate the dynamical integrity of the disease module, and GWAS can be used to test the potential links between the SNPs of the predicted cellular components and the disease phenotype. Finally, the predicted disease genes and pathways (serving also as potential drug targets) are tested using the available molecular biology tools and animal models.

Figure 4
Figure 4. identifying disease gene candidates
(i) Linkage methods. Genes located within the linkage interval of a disease whose protein products interact with a known disease-associated protein are considered likely candidate disease genes, . (ii) Clustering methods. Clustering or graph partitioning helps us uncover functional and potential disease modules in the interactome. The members of such modules are considered candidate disease genes, . (iii) Diffusion-based methods: Starting from proteins known to be associated with a disease, a random walker (or a propagator) visits each node in the interactome with a certain probability, . The outcome of the algorithm is a disease-association score assigned to each protein, the likelihood that a particular protein is associated with the disease.
Figure 5
Figure 5. Disease networks
(a) Human Disease Network, whose nodes are diseases; two diseases being linked if they share one or several disease-associated genes, as shown in the example involving breast cancer and bone and cartilage cancer. The large panel shows the giant cluster of the obtained disease network. Not shown are small clusters of isolated diseases. Node color reflects the disease class of the corresponding diseases to which they belong, cancers appearing as blue nodes and neurological diseases as red nodes. Node size correlates with the number of genes known to be associated with the corresponding disease (after ref.). The left panel shows the comorbidity between diseases linked in the HDN measured by the logarithm of relative risk, indicating that if the disease-causing mutations affect the same module of the shared disease protein, then the comorbidity is higher. (b) Metabolic Disease Network, linking two diseases if they are both associated with enzymes and if these enzymes catalyze reactions that share a metabolite (after ref.). The comorbidity between metabolically linked diseases is higher than those that are not connected, and diseases whose enzymes catalyze reactions that are coupled with each other at the flux level show even higher comorbidity (bottom left panel).
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