Reverse Engineering the Cardiogenic Gene Regulatory Network in the Mammalian Heart

Little is known about the underlying regulatory network responsible for the development
of the mammalian heart. To address this issue, we utilize our recently developed algorithm
to reverse engineer the cardiogenic gene regulatory network using time-series microarray
data obtained from the developing mouse heart. The subnetwork ensembles generated by
the algorithm consist of topologies capable of explaining the experimental data via model
simulation. After pooling these subnetwork topologies together and applying an
appropriate cutoff metric to the gene interaction list, a scale-free, hierarchical network
emerges. The network is validated with known gene interactions and used to identify new
regulatory interactions and network hubs critical to the developing mammalian heart. The
candidate gene interactions identified by the algorithm is prioritized using semantic
similarity and gene profile uniqueness metrics to produce a list of testable interaction
pairs. Among the top 25 gene pairs identified, significant fraction have already been
validated. Furthermore, the network was expanded using the same semantic similarity
and gene profile metrics to include all genes in the mouse genome to form the most likely
cardiogenic gene regulatory network predicted by the algorithm. The method outlined
herein provides an informative approach to network inference and leads to clear testable
hypotheses related to gene regulation. Massively parallel architecture of GPUs have been
tested to study genome-scale problems.