Molecular basis for evolving modularity in the yeast protein interaction network

14Citations
Citations of this article
64Readers
Mendeley users who have this article in their library.

Abstract

Scale-free networks are generically defined by a power-law distribution of node connectivities. Vastly different graph topologies fit this law, ranging from the assortative, with frequent similar-degree node connections, to a modular structure. Using a metric to determine the extent of modularity, we examined the yeast protein network and found it to be significantly self-dissimilar. By orthologous node categorization, we established the evolutionary trend in the network, from an "emerging" assortative network to a present-day modular topology. The evolving topology fits a generic connectivity distribution but with a progressive enrichment in intramodule hubs that avoid each other. Primeval tolerance to random node failure is shown to evolve toward resilience to hub failure, thus removing the fragility often ascribed to scale-free networks. This trend is algorithmically reproduced by adopting a connectivity accretion law that disfavors like-degree connections for large-degree nodes. The selective advantage of this trend relates to the need to prevent a failed hub from inducing failure in an adjacent hub. The molecular basis for the evolutionary trend is likely rooted in the high-entropy penalty entailed in the association of two intramodular hubs. © 2007 Ariel Fernández.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Fernández, A. (2007). Molecular basis for evolving modularity in the yeast protein interaction network. PLoS Computational Biology, 3(11), 2247–2254. https://doi.org/10.1371/journal.pcbi.0030226

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 27

45%

Professor / Associate Prof. 17

28%

Researcher 14

23%

Lecturer / Post doc 2

3%

Readers' Discipline

Tooltip

Agricultural and Biological Sciences 33

60%

Computer Science 10

18%

Physics and Astronomy 6

11%

Biochemistry, Genetics and Molecular Bi... 6

11%

Save time finding and organizing research with Mendeley

Sign up for free