Inference of active transcriptional networks by integration of gene expression kinetics modeling and multisource data

Inference of gene expression networks has become one of the primary challenges in computational biology. Analysis of microarray experiments using appropriate mathematical models can reveal interactions among protein regulators and target genes. This paper presents a combined approach to the inference of gene expression networks from time series measurements, ChIP-on-chip experiments, analyses of promoter sequences, and protein–protein interaction data. A recursive model of gene expression allowing for identification of active gene expression control networks with up to two regulators of one target gene is presented. The model was used to inspect all possible regulator–target gene combinations and predict those that are active during the underlying biological process. The procedure was applied to the inference of part of a regulatory network of the S. cerevisiae cell cycle, formed by 37 target genes and 128 transcription factors. A set of the most probable networks was suggested and analyzed.