title: Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics.
abstract: BACKGROUND
  It has been widely reported that biological networks are robust against perturbations such as mutations. On the contrary, it has also been known that biological networks are often fragile against unexpected mutations. There is a growing interest in these intriguing observations and the underlying design principle that causes such robust but fragile characteristics of biological networks. For relatively small networks, a feedback loop has been considered as an important motif for realizing the robustness. It is still, however, not clear how a number of coupled feedback loops actually affect the robustness of large complex biological networks. In particular, the relationship between fragility and feedback loops has not yet been investigated till now.
  
  
  RESULTS
  Through extensive computational experiments, we found that networks with a larger number of positive feedback loops and a smaller number of negative feedback loops are likely to be more robust against perturbations. Moreover, we found that the nodes of a robust network subject to perturbations are mostly involved with a smaller number of feedback loops compared with the other nodes not usually subject to perturbations. This topological characteristic eventually makes the robust network fragile against unexpected mutations at the nodes not previously exposed to perturbations.
authors.affiliation: 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.
authors.forename: Yung-Keun
authors.initials: YK
authors.lastname: Kwon
authors.forename: Kwang-Hyun
authors.initials: KH
authors.lastname: Cho
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005246:Feedback / Q000502:physiology; D020869:Gene Expression Profiling / Q000379:methods*; D007700:Kinetics; D008954:Models, Biological; D015233:Models, Statistical; D020543:Proteome / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome