Identifying gene regulatory networks from experimental data
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
A Mathematical Theory of Communication
A Mathematical Theory of Communication
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After the sequencing of whole genomes and the identification of the genes contained in them, one of the main challenges remaining is to understand the mechanisms that regulate the expression of genes within the genome in order to gain knowledge about structural, biochemical, physiological and behavioral characteristics of organisms. Some of these mechanisms are controlled by so-called Genetic Regulatory Networks (GRNs). Boolean networks can help model biological GRNs. In this paper, a genetic algorithm is used to make inferences in Boolean networks, in combination with the Quine-McCluskey algorithm, when not all the output states of the genes have been determined. This lack of information could be treated as "don't care" states. Genetic algorithms are useful in multi-objective optimization problems, such as minimization of Gene Regulatory Functions, where it is important not only to have the smallest quantity of disjunctions, but also the smallest quantity of genes involved in the regulation.