title: Inferring robust gene networks from expression data by a sensitivity-based incremental evolution method.
abstract: BACKGROUND
  Reconstructing gene regulatory networks (GRNs) from expression data is one of the most important challenges in systems biology research. Many computational models and methods have been proposed to automate the process of network reconstruction. Inferring robust networks with desired behaviours remains challenging, however. This problem is related to network dynamics but has yet to be investigated using network modeling.
  
  
  RESULTS
  We propose an incremental evolution approach for inferring GRNs that takes network robustness into consideration and can deal with a large number of network parameters. Our approach includes a sensitivity analysis procedure to iteratively select the most influential network parameters, and it uses a swarm intelligence procedure to perform parameter optimization. We have conducted a series of experiments to evaluate the external behaviors and internal robustness of the networks inferred by the proposed approach. The results and analyses have verified the effectiveness of our approach.
  
  
  CONCLUSIONS
  Sensitivity analysis is crucial to identifying the most sensitive parameters that govern the network dynamics. It can further be used to derive constraints for network parameters in the network reconstruction process. The experimental results show that the proposed approach can successfully infer robust GRNs with desired system behaviors.
authors.affiliation: Department of Information Management, National Sun Yat-sen University, 70, Lienhai Road, Kaohsiung, Taiwan.
authors.forename: Yu-Ting
authors.initials: YT
authors.lastname: Hsiao
authors.forename: Wei-Po
authors.initials: WP
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics* / Q000378:metabolism; D053263:Gene Regulatory Networks; D013014:SOS Response, Genetics