Analysis of feedback loops and robustness in network evolution based on Boolean models.
BACKGROUND
Many biological networks such as protein-protein interaction networks, signaling networks, and metabolic networks have topological characteristics of a scale-free degree distribution. Preferential attachment has been considered as the most plausible evolutionary growth model to explain this topological property. Although various studies have been undertaken to investigate the structural characteristics of a network obtained using this growth model, its dynamical characteristics have received relatively less attention.


RESULTS
In this paper, we focus on the robustness of a network that is acquired during its evolutionary process. Through simulations using Boolean network models, we found that preferential attachment increases the number of coupled feedback loops in the course of network evolution. Whereas, if networks evolve to have more coupled feedback loops rather than following preferential attachment, the resulting networks are more robust than those obtained through preferential attachment, although both of them have similar degree distributions.


CONCLUSIONS
The presented analysis demonstrates that coupled feedback loops may play an important role in network evolution to acquire robustness. The result also provides a hint as to why various biological networks have evolved to contain a number of coupled feedback loops.
Kwon
Yung-Keun
YK
Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Republic of Korea. kwon@soar.snu.ac.kr
Cho
Kwang-Hyun
KH
D000818:Animals; D002468:Cell Physiological Phenomena; D019143:Evolution, Molecular; D025461:Feedback, Physiological / Q000502:physiology*; D005786:Gene Expression Regulation / Q000502:physiology; D006801:Humans; D016015:Logistic Models; D053858:Metabolic Networks and Pathways / Q000235:genetics; D008954:Models, Biological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D025941:Protein Interaction Mapping; D012641:Selection, Genetic; D015398:Signal Transduction / Q000235:genetics; D013269:Stochastic Processes; D013598:Systems Theory