Abstract
Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.
Highlights
Over the last half century, our understanding of life at the molecular level has advanced tremendously
Often they are represented as graphs, this is not straightforward for many molecular interactions
We briefly review graph theory and discuss graph representations of molecular interaction networks
Summary
Over the last half century, our understanding of life at the molecular level has advanced tremendously. Motifs may not be simple biological circuits [21], but they established the idea that local structure is important; one way in which this was later exploited was to compute node signatures for use in function prediction in molecular networks [56] and alignment of molecular networks [69]. The approaches above focus on high-level similarities between networks without attempting to match individual nodes in the networks By performing such alignments, clustering and significant feature detection applied across species can lead to more insight. Network biology allows additional focus on node relations, making it possible to diagnose molecular diseases that cannot be well characterized by the traditional techniques [152] This so-called differential analysis, finding changes in network structure [31], is currently complicated by the fact that construction of high-quality molecular networks requires considerable time and resources. These techniques promise to allow associating local information with driver/observation nodes and to predict global properties from limited available data
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