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- Smith, Keith M., et al.
(författare)
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A computational exploration of resilience and evolvability of protein-protein interaction networks
- 2021
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Ingår i: Communications Biology. - : Nature Publishing Group UK. - 2399-3642 .- 2399-3642. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms-random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.Brennan Klein et al. propose an adapted network resilience measure, the prospective resilience (PR), to explore the resilience of protein-protein interaction networks following addition of new nodes. They apply PR to public datasets and find that resilience of a network is maximized when attachment of new nodes, or proteins, is based on the gene expression levels of the existing proteins in the network.
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