Discovery of gene-regulation pathways using local causal search

This paper reports the methods and results of a computer-based algorithm that takes as input the expression levels of a set of genes as given by DNA microarray data, and then searches for causal pathways that represent how the genes regulate each other. The algorithm uses local heuristic search and a Bayesian scoring metric. We applied the algorithm to induce causal networks from a mixture of observational and experimental gene-expression data on genes involved in galactose metabolism in the yeast Saccharomyces cerevisiae. The observational data consisted of gene-expression levels obtained from unmanipulated inverted exclamation mark degrees wild-type inverted exclamation mark +/- cells. The experimental data were produced by deleting ( inverted exclamation mark degrees knocking out inverted exclamation mark +/-) genes and measuring the expression levels of other genes. We used this data to evaluate several variations of the local search method. In each evaluation, causal relationships were predicted for all 36 pairwise combinations of nine key galactose-related genes. These predictions were then compared to the known causal relationships among these genes.