Gene regulatory network reconstruction with prior knowledge over mRNA data for COPD patients and controls

Chronic obstructive pulmonary disease (COPD) is a type of lung disease characterized by persistent bronchitis and emphysema. Current therapy is restricted to alleviate lung tissue inflammation, but is not able to stabilize or improve lung function of patients making necessary to understand the underlying molecular mechanisms of COPD. Genome-wide gene expression of lung tissue provides a powerful tool to elucidate molecular mechanism of COPD patients. In particular, Bayesian Networks (BNs) have been applied to infer genetic regulatory interactions from microarray gene expression data. In this study we aim obtain a clearer understanding of the genes interaction in COPD patients by learning a BN over microarray expression data. A subset of genes was selected for the study fulfilling that i) the genes were significantly expressed in COPD stage 4 and ii) there is reported gene-gene experimental association. The reported associations are introduced as prior biological knowledge in the reconstruction.