title: Using large-scale perturbations in gene network reconstruction.
abstract: BACKGROUND
  Recent analysis of the yeast gene network shows that most genes have few inputs, indicating that enumerative gene reconstruction methods are both useful and computationally feasible. A simple enumerative reconstruction method based on a discrete dynamical system model is used to study how microarray experiments involving modulated global perturbations can be designed to obtain reasonably accurate reconstructions. The method is tested on artificial gene networks with biologically realistic in/out degree characteristics.
  
  
  RESULTS
  It was found that a relatively small number of perturbations significantly improve inference accuracy, particularly for low-order inputs of one or two genes. The perturbations themselves should alter the expression level of approximately 50-60% of the genes in the network.
  
  
  CONCLUSIONS
  Time-series obtained from perturbations are a common form of expression data. This study illustrates how gene networks can be significantly reconstructed from such time-series while requiring only a relatively small number of calibrated perturbations, even for large networks, thus reducing experimental costs.
authors.affiliation: COMPLEX, University College London, 4 Stephenson Way, London NW1 2HE, UK. t.maccarthy@ucl.ac.uk
authors.forename: Thomas
authors.initials: T
authors.lastname: MacCarthy
authors.forename: Andrew
authors.initials: A
authors.lastname: Pomiankowski
authors.forename: Robert
authors.initials: R
authors.lastname: Seymour
mesh_terms: D000465:Algorithms; D002138:Calibration; D002468:Cell Physiological Phenomena; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005656:Fungal Proteins / Q000378:metabolism; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D005796:Genes; D005800:Genes, Fungal; D005823:Genetics; D023281:Genomics; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D025941:Protein Interaction Mapping; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D049490:Systems Biology; D013997:Time Factors
chemical_list: D005656:Fungal Proteins