The expression levels of thousands to tens of thousands of genes in a living cell are controlled by internal and external cues which act in a combinatorial manner that can be modeled as a network. High-throughput technologies, such as DNA-microarrays and next generation sequencing, allow for the measurement of gene expression levels on a whole-genome scale. In recent years, a wealth of microarray data probing gene expression under various biological conditions has been accumulated in public repositories, which facilitates uncovering the underlying transcriptional networks (gene networks). Due to the high data dimensionality and inherent complexity of gene interactions, this task inevitably requires automated computational approaches.Various models have been proposed for learning gene networks, with Bayesian networks (BNs) showing promise for the task. However, BN structure learning is an NP-hard problem and both exact and heuristic methods are computationally intensive with limited ability to produce large networks. To address these issues, we developed a set of parallel algorithms. First, we present a communication efficient parallel algorithm for exact BN structure learning, which is work-optimal provided that 2^n \u3e p.log(p), where n is the total number of variables, and p is the number of processors. This algorithm has space complexity within 1.41 of the optimal. Our empirical results demonstrate near perfect scaling on up to 2,048 processors. We further extend this work to the case of bounded node in-degree, where a limit d on the number of parents per variable is imposed. We characterize the algorithm\u27s run-time behavior as a function of d, establishing the range [n/3 - log(mn), ceil(n/2)) of values for d where it affects performance. Consequently, two plateaus regions are identified: for d \u3c n/3 - log(mn), where the run-time complexity remains the same as for d=1, and for d \u3e= ceil(n/2), where the run-time complexity remains the same as for d=n-1. Finally, we present a parallel heuristic approach for large-scale BN learning. This approach aims to combine the precision of exact learning with the scalability of heuristic methods. Our empirical results demonstrate good scaling on various high performance platforms. The quality of the learned networks for both exact and heuristic methods are evaluated using synthetically generated expression data. The biological relevance of the networks learned by the exact algorithm is assessed by applying it to the carotenoid biosynthesis pathway in Arabidopsis thaliana.
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