ArticleBMC bioinformatics2005
Using large-scale perturbations in gene network reconstruction.
Article in BMC bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- How to understand the cell by breaking it: network analysis of gene perturbation screens.PLoS computational biology · 2010Article
- Transcriptional profiling of growth perturbations of the human malaria parasite Plasmodium falciparum.Nature biotechnology · 2010Article
- Model selection in the reconstruction of regulatory networks from time-series data.BMC research notes · 2009Article
- Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis.Biotechnology and bioengineering · 2008Article
- Genome-scale model for Clostridium acetobutylicum: Part II. Development of specific proton flux states and numerically determined sub-systems.Biotechnology and bioengineering · 2008Article
- The limits of subfunctionalization.BMC evolutionary biology · 2007Article
- Systems interface biology.Journal of the Royal Society, Interface · 2006Review
- Topology-based cancer classification and related pathway mining using microarray data.Nucleic acids research · 2006Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
backgroundRecent 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.
resultsIt 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.
conclusionsTime-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.
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Registered trials
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