Tools

Addressing this problem has been made possible by recent advances in genetic sequencing and development of a whole new generation of sophisticated biological tools. The most promising technique to date is based on the view of gene systems as a logical network of nodes that influence each other's expression levels. Consequently, one may obtain some information on gene interactions in the network by measurement of gene expression. The level of gene expression, i.e., the production rate of a given protein changes during execution of a genetic process involving it and can be monitored by several biological experiments. A variety of experimental tools have been developed recently with the ability to observe the expression of many genes simultaneously. At the forefront of these technologies lies the DNA microarray, commonly used to monitor gene expression at the level of mRNA abundance. Similarly, the rapid identification of proteins and their abundances is becoming possible through methods such as 2D polyacrylamide gel electrophoresis, 2-hybrid systems, protein chips, etc. The main contribution of all of these technologies is that numerous genes can be monitored in the same experiment, making it possible to perform a global expression analysis of the cell. Additional information about a genetic network may be gleaned experimentally by applying a directed perturbation to the network, and observing expression levels of every gene in the network in the presence of the perturbation. Perturbations may be genetic, in which the expression levels of one or more genes are fixed by knockout (removal of the gene) or overexpression (higher than usual level of gene expression), or biological, in which one or more non-genetic factors are altered, such as a change in environment, nutrition, or a temperature increase. Such biological experiments are very costly and very few such perturbations may be performed at one time. Thus, reducing the number and cost of experiments is crucial. Methods presented above supply biological data in terms of expression levels of many genes at different time points and in different conditions. The functional analysis of the data can be defined as a computational problem, aiming to infer some plausible model of the network from the observations with minimal number or cost of biological experiments. The model should describe how the expression level of each gene in the network depends on external stimuli and expression levels of other genes. Additional goals include construction of a knowledge-base of gene regulatory networks, verification of pathways or gene networks hypotheses.