The Inferred Cardiogenic Gene Regulatory Network in the Mammalian Heart

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.