Robustness and Evolvability of the Human Signaling Network

Junil Kim, Drieke Vandamme, Jeong Rae Kim, Amaya Garcia Munoz, Walter Kolch, Kwang Hyun Cho

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores.

Original languageEnglish
Article numbere1003763
JournalPLoS Computational Biology
Volume10
Issue number7
DOIs
StatePublished - Jul 2014

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