  Cardiac development is complex, multiscale process encompassing cell fate
adoption, differentiation and morphogenesis. To elucidate pathways underlying
this process, a recently developed algorithm to reverse engineer gene
regulatory networks was applied to time-course microarray data obtained from
the developing mouse heart. The algorithm generates many different putative
network topologies that are capable of explaining the experimental data via
model simulation. To cull specious network interactions, thousands of
topologies are merged and filtered to generate a scale-free, hierarchical
network. The network is validated with known gene interactions and used to
identify regulatory pathways critical to the developing mammalian heart. The
predicted gene interactions are prioritized using semantic similarity and gene
profile uniqueness metrics. Using these metrics, the network is expanded to
include all known mouse genes to form the most likely cardiogenic gene
regulatory network. The method outlined herein provides an informative approach
to network inference and leads to clear testable hypotheses related to gene
regulation.
