title: Topology of gene expression networks as revealed by data mining and modeling.
abstract: BACKGROUND
  Interpretation of high-throughput gene expression profiling requires a knowledge of the design principles underlying the networks that sustain cellular machinery. Recently a novel approach based on the study of network topologies has been proposed. This methodology has proven to be useful for the analysis of a variety of biological systems, including metabolic networks, networks of protein-protein interactions, and gene networks that can be derived from gene expression data. In the present paper, we focus on several important issues related to the topology of gene expression networks that have not yet been fully studied.
  
  
  RESULTS
  The networks derived from gene expression profiles for both time series experiments in yeast and perturbation experiments in cell lines are studied. We demonstrate that independent from the experimental organism (yeast versus cell lines) and the type of experiment (time courses versus perturbations) the extracted networks have similar topological characteristics suggesting together with the results of other common principles of the structural organization of biological networks. A novel computational model of network growth that reproduces the basic design principles of the observed networks is presented. Advantage of the model is that it provides a general mechanism to generate networks with different types of topology by a variation of a few parameters. We investigate the robustness of the network structure to random damages and to deliberate removal of the most important parts of the system and show a surprising tolerance of gene expression networks to both kinds of disturbance.
authors.affiliation: Biogen Inc., 14 Cambridge Center, Cambridge, MA 02142, USA. alex_lukashin@biogen.com
authors.forename: Alexander V
authors.initials: AV
authors.lastname: Lukashin
authors.forename: Matvey E
authors.initials: ME
authors.lastname: Lukashev
authors.forename: Rainer
authors.initials: R
authors.lastname: Fuchs
mesh_terms: D000465:Algorithms; D045744:Cell Line, Tumor; D002468:Cell Physiological Phenomena; D016000:Cluster Analysis; D003198:Computer Simulation; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D025941:Protein Interaction Mapping / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology; D015003:Yeasts / Q000235:genetics / Q000378:metabolism