How to Reconstruct a Large Genetic Network from
Abstractn Gene Perturbations in Fewer than n2 Easy Steps [gzipped postscript] [postscript] [pdf] Andreas Wagner I present an algorithm to reconstruct direct regulatory interactions in gene networks from the effects of genetic perturbations on gene activity. Genomic technology has made feasible large-scale experiments that perturb the activity of many genes and then assess the effect of each individual perturbation on all other genes in an organism. Current experimental techniques can not distinguish between direct and indirect effects of a genetic perturbation. An example of an indirect effect is a gene X encoding a protein kinase, which phosphorylates and activates a transcription factor Y, which then activates transcription of gene Z. X influences the activity of gene Y directly, whereas it influences Z indirectly. To reconstruct a genetic network means to identify, for each gene and within the limits of experimental resolution, the direct effects of a perturbed gene on other genes. One can think of this as identifying the causal structure of the network. I introduce an algorithm that performs this task for networks of arbitrary size and complexity. It is based on a graph representation of a genetic network. Algorithmic complexity in both storage and time is low, less than O(n2). In practice, the algorithm can reconstruct networks of several thousand genes in mere CPU seconds on a desktop workstation.
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Bibliographic InfoPaper provided by Santa Fe Institute in its series Working Papers with number 01-09-047.
Date of creation: Sep 2001
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Genomics; reverse engineering; microarray;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-10-16 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Andreas Wagner, 2001. "The Yeast Protein Interaction Network Evolves Rapidly and Contains Few Redundant Duplicate Genes," Working Papers 01-04-022, Santa Fe Institute.
- Andreas Wagner & David Fell, 2000. "The Small World Inside Large Metabolic Networks," Working Papers 00-07-041, Santa Fe Institute.
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