Informative gene selection and design of regulatory networks using integer optimization

A central problem in bioinformatics and systems biology is the selection of appropriate models in a rational and systematic way. This fundamentally combinatorial problem can be readily formulated and addressed within an integer optimization framework. In this paper we examine two such applications related to the identification of informative genes and the quantification of regulatory networks. We demonstrate how multiple alternatives can be systematically derived and assess the information content of the proposed solutions.