title: New measurement methods of network robustness and response ability via microarray data.
abstract: "Robustness", the network ability to maintain systematic performance in the face of intrinsic perturbations, and "response ability", the network ability to respond to external stimuli or transduce them to downstream regulators, are two important complementary system characteristics that must be considered when discussing biological system performance. However, at present, these features cannot be measured directly for all network components in an experimental procedure. Therefore, we present two novel systematic measurement methods--Network Robustness Measurement (NRM) and Response Ability Measurement (RAM)--to estimate the network robustness and response ability of a gene regulatory network (GRN) or protein-protein interaction network (PPIN) based on the dynamic network model constructed by the corresponding microarray data. We demonstrate the efficiency of NRM and RAM in analyzing GRNs and PPINs, respectively, by considering aging- and cancer-related datasets. When applied to an aging-related GRN, our results indicate that such a network is more robust to intrinsic perturbations in the elderly than in the young, and is therefore less responsive to external stimuli. When applied to a PPIN of fibroblast and HeLa cells, we observe that the network of cancer cells possesses better robustness than that of normal cells. Moreover, the response ability of the PPIN calculated from the cancer cells is lower than that from healthy cells. Accordingly, we propose that generalized NRM and RAM methods represent effective tools for exploring and analyzing different systems-level dynamical properties via microarray data. Making use of such properties can facilitate prediction and application, providing useful information on clinical strategy, drug target selection, and design specifications of synthetic biology from a systems biology perspective.
authors: Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan.
authors: Chien-Ta
authors: CT
authors: Tu
authors: Bor-Sen
authors: BS
authors: Chen
mesh_terms: D000367:Age Factors; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D053263:Gene Regulatory Networks / Q000235:genetics / Q000502:physiology*; D006367:HeLa Cells; D006801:Humans; D046228:Microarray Analysis / Q000379:methods*; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D060066:Protein Interaction Maps / Q000235:genetics / Q000502:physiology*
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