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• W2525366608 abstract "We present computational methods for aligning, analyzing, and modeling biological networks that give insights into biological function, disease, and evolution. Comparing networks of different species is an important problem in evolutionary and systems biology. Existing methods incorporate some a priori information about nodes such as sequence similarities of proteins in protein-protein interaction (PPI) networks. We introduce novel network alignment algorithms that are based solely on network topology. As such, our methods can align any type of networks, not just biological ones. We demonstrate that both biological function of proteins and species phylogeny can be extracted from our topological alignments of PPI and metabolic networks. Similar to network comparisons across species that can help in functional annotation of proteins and in identification of conserved functional modules or protein complexes, biological function of uncharacterized proteins in a PPI network of a single species can be determined from the function of other, well described proteins from the same network. Since proteins interact to perform a function rather than act in isolation, analyzing patterns of their interactions in PPI networks is expected to provide insights into their biological function. Indeed, we identify a close relationship between network topology and various biological properties. We find that PPI network topology successfully captures proteins’ homology information, and thus, it could potentially be used as a complementary method to sequence-based approaches for homology detection. We demonstrate that topological characteristics of proteins in a human PPI network imply their involvement in cancer. We identify from network topology novel pigment regulators that are components of melanogenesis-related pathways and our predictions are phenotypically validated. Finally, we acquire the most comprehensive assessment to date of the PPI network of the yeast protasome responsible for protein degradation. Finding an adequate model for biological networks is an important step towards understanding them. We investigate topological properties of protein structure networks to find a well-fitting network model for them; our results are of biological importance since understanding these networks might provide insights into protein folding, stability, and function. Additionally, we present an approach that applies probabilistic classifiers to network topology to predict the best-fitting network model for RIGs and PPI networks. The hope is that a good network model could provide insights into understanding of biological function, disease, and evolution. The significance of our results is that we extract biologically significant meaning from a new source of information, pure network topology, independently of sequence or any other commonly used biological information. We believe that we have just barely scratched the surface of the information that can be extracted from network topology." @default.
• W2525366608 created "2016-10-07" @default.
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• W2525366608 date "2010-01-01" @default.
• W2525366608 modified "2023-09-25" @default.
• W2525366608 title "From topological network analyses and alignments to biological function, disease, and evolution" @default.
• W2525366608 hasPublicationYear "2010" @default.
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