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Dynamic gene network analysis of heart development
Hokyi Lai, Kurt Zhang
ND INBRE Bioinformatics Core, University of North Dakota, Grand Forks, ND 58201
Abnormal heart development can induce congenital heart diseases, such as atrial septal defects and
ventricular septal defect. Although many gene mutations have been associated with congenital heart
diseases, a systems-level understanding of gene-gene interactions is still limited. In this study, we
performed gene network analysis using time course microarray data from mouse embryo hearts. We
developed a metric called conditional Maximum Information Coefficient (cMIC) to account for the
temporal change of gene expression. By combining the Maximum Information Coefficient that is used
for accounting for the gene-gene interactions, we were able to construct gene network models for
atrium and ventricle developments. Numerous simulation studies have been conducted to validate the
application of cMIC and to select the optimal thresholds for network construction. This method has
been applied to 3 time course microarray studies, mouse atrium development, mouse ventricle
development, and P19CL6 cell differentiation into cardiomyocytes. For each study, at least 6 stages of
microarray data were collected. A large number of network links were shared between atrium and
ventricle developments, which indicated a unified gene regulatory mechanism underlying cardiac
differentiation and development. The gene network identified for P19CL6 differentiation did not show a
high similarity to the networks of mouse data. Therefore, P19CL6 is not an ideal model for studying
genetic mechanisms of heart development.