Abstract
Biological network alignment aims to find regions of topological and functional (dis)similarities between molecular networks of different species. Then, network alignment can guide the transfer of biological knowledge from well-studied model species to less well-studied species between conserved (aligned) network regions, thus complementing valuable insights that have already been provided by genomic sequence alignment. Here, we review computational challenges behind the network alignment problem, existing approaches for solving the problem, ways of evaluating their alignment quality, and the approaches’ biomedical applications. We discuss recent innovative efforts of improving the existing view of network alignment. We conclude with open research questions in comparative biological network research that could further our understanding of principles of life, evolution, disease, and therapeutics.
Highlights
Bioinformatics research has revolutionized our understanding of cellular functioning
In addition to across-species transfer of functional knowledge discussed above, just as sequence alignment, network alignment can be used to infer phylogenetic relationships of different species based on similarities between their biological networks [38,39,40]
Many of the existing network alignment algorithms use within their node cost function biological information external to network topology, such as protein sequence similarities
Summary
Bioinformatics research has revolutionized our understanding of cellular functioning. Analogous to genomic sequence alignment, biological network alignment aims to find good node mapping between networks of different species that identifies topologically and functionally similar (i.e., conserved) network regions. In addition to across-species transfer of functional knowledge discussed above, just as sequence alignment, network alignment can be used to infer phylogenetic relationships of different species based on similarities between their biological networks [38,39,40].
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