Key Points
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Bioinformatics approaches for integrating molecular networks across various types of interaction data, omics profiles, conditions or species have demonstrated considerable power for the detection and interpretation of biological modules.
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Module-discovery approaches are broadly classified into four categories: identification of 'active modules' through the integration of networks and molecular profiles, identification of 'conserved modules' across multiple species, identification of 'differential modules' across different conditions and identification of 'composite modules' through the integration of different interaction types.
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Active modules mark regions of a network that are most active during a given cellular or disease response and can identify important biomarkers, disease mechanisms and therapeutic targets.
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Conserved modules are revealed through the alignment or comparison of networks across multiple species. Such modules reflect biologically important pathways that have been conserved over long evolutionary periods.
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Differential modules are identified through differential analyses of experimentally mapped interactions across multiple conditions.
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Composite modules are detected through the simultaneous integration of diverse types of molecular interactions.
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Such integrative approaches reviewed here substantially increase the scope, scale and depth of bioinformatics analysis, by permitting joint interpretation of ensembles of distinct biological information.
Abstract
A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function — that is, 'modules'. Here, we classify such integrative approaches into four broad categories, describe their bioinformatic principles and review their applications.
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Acknowledgements
We gratefully acknowledge US National Institutes of Health (NIH) grants P41 GM103504 and P50 GM085764 in support of this work.
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Supplementary information
Supplementary information S1 (table)
Sources of molecular interaction and 'omics' profiling data (PDF 296 kb)
Glossary
- Epistasis
-
The phenomenon whereby the function of one gene affects the phenotype (for example, growth) of another gene in a non-additive manner.
- Synthetic lethality
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An extreme case of negative genetic epistasis in which the mutation of two genes in combination, but not individually, causes a lethal phenotype.
- Degree
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The number of interactions (edges) that a molecule (node) has in a network.
- Betweenness centrality
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A statistical intuition of how 'central' the status of a given molecule (node) or interaction (edge) is within a network. This is inferred by the fraction of shortest paths between all pairs of nodes that pass through a particular node or edge.
- Network topology
-
The overall arrangement of nodes and edges in a given network.
- Metabolic flux
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The flow of chemicals through any metabolic reaction (for example, an enzymatic reaction).
- Hubs
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Molecules with the highest number of interactions (degree) in a network.
- Orthologous
-
Refers to the evolutionary relationship between two genes in two species that have descended from a common ancestor. Such genes are denoted as orthologues.
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Mitra, K., Carvunis, AR., Ramesh, S. et al. Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 14, 719–732 (2013). https://doi.org/10.1038/nrg3552
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DOI: https://doi.org/10.1038/nrg3552
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