Abstract
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.
Highlights
Reverse engineering of a gene regulatory network (GRN) is an inverse problem that remains a significant challenge [1,2,3,4,5]
Despite high-throughput gene expression data obtained from methods such as some modified real-time PCR assays [6], high-density DNA microarrays [7,8] and RNA Seq [9], complex interactions embedded in GRNs often overwhelm current methods of network inference [10,11]
A new approach that relies on a combination of linear and nonlinear relationships to account for the dynamic nature of biology was developed [31]
Summary
Reverse engineering of a gene regulatory network (GRN) is an inverse problem that remains a significant challenge [1,2,3,4,5]. Several additional methods have been suggested to infer GRNs from expression data using prior knowledge of the GRN, perturbation responses, and other techniques (for details, see [4,10,21,22,23,24]) Most of these methods rely on linear relationships to reconstruct the network without considering any combinatorial effects, noise or time delays; these approaches fail to capture any nonlinear interactions and interdependencies within the network [25]. General measures of dependency based on mutual information have been used to capture these interactions in gene expression patterns [26,27,28,29,30]; mutual information does not give interaction directions and requires a significant amount of initial data To circumvent these issues, a new approach that relies on a combination of linear and nonlinear relationships to account for the dynamic nature of biology was developed [31]. Though the approach was validated with in silico data, the present study represents the first large-scale application to a dataset derived from a biological process such as cardiogenesis
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