Cardiac development is a 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. Approximately 200 genes of interest were input into the algorithm to generate putative network topologies that are capable of explaining the experimental data via model simulation. To cull specious network interactions, thousands of putative networks are merged and filtered to generate scale-free, hierarchical networks that are statistically significant and biologically relevant. The networks are validated with known gene interactions and used to predict regulatory pathways important for the developing mammalian heart. Area under the precision-recall curve and receiver operator characteristic curve are 9% and 58%, respectively. Of the top 10 ranked predicted interactions, 4 have already been validated. The algorithm is further tested using a network enriched with known interactions and another depleted of them. The inferred networks contained more interactions for the enriched network versus the depleted network. In all test cases, maximum performance of the algorithm was achieved when the purely data-driven method of network inference was combined with a data-independent, functional-based association method. Lastly, the network generated from the list of approximately 200 genes of interest was expanded using gene-profile uniqueness metrics to include approximately 900 additional known mouse genes and to form the most likely cardiogenic gene regulatory network. The resultant network supports known regulatory interactions and contains several novel cardiogenic regulatory interactions. The method outlined herein provides an informative approach to network inference and leads to clear testable hypotheses related to gene regulation.
Pubmed ID: 24971943 RIS Download
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View all literature mentionsA web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.
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