Title |
Robustness and Evolvability of the Human Signaling Network
|
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Published in |
PLoS Computational Biology, July 2014
|
DOI | 10.1371/journal.pcbi.1003763 |
Pubmed ID | |
Authors |
Junil Kim, Drieke Vandamme, Jeong-Rae Kim, Amaya Garcia Munoz, Walter Kolch, Kwang-Hyun Cho |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 25% |
Ireland | 2 | 10% |
Australia | 1 | 5% |
India | 1 | 5% |
Spain | 1 | 5% |
United Kingdom | 1 | 5% |
Japan | 1 | 5% |
Unknown | 8 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 11 | 55% |
Scientists | 6 | 30% |
Science communicators (journalists, bloggers, editors) | 2 | 10% |
Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 4% |
United Kingdom | 2 | 2% |
Italy | 1 | 1% |
Brazil | 1 | 1% |
France | 1 | 1% |
Germany | 1 | 1% |
Finland | 1 | 1% |
Japan | 1 | 1% |
Mexico | 1 | 1% |
Other | 0 | 0% |
Unknown | 72 | 86% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 21 | 25% |
Student > Ph. D. Student | 15 | 18% |
Student > Bachelor | 10 | 12% |
Professor | 9 | 11% |
Professor > Associate Professor | 7 | 8% |
Other | 15 | 18% |
Unknown | 7 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 29 | 35% |
Biochemistry, Genetics and Molecular Biology | 16 | 19% |
Computer Science | 9 | 11% |
Physics and Astronomy | 7 | 8% |
Medicine and Dentistry | 6 | 7% |
Other | 7 | 8% |
Unknown | 10 | 12% |