Scalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform
Inference of Gene Regulatory Networks (GRNs) remains an important open challenge in computational biology. The goal of bio-model inference is to, based on time-series of gene expression data, obtain the sparse topological structure and the parameters that quantitatively understand and reproduce the dynamics of biological system. Nevertheless, the inference of a GRN is a complex optimization problem that involve processing S-System models, which include large amount of gene expression data from hundreds (even thousands) of genes in multiple time-series (essays). This complexity, along with the amount of data managed, make the inference of GRNs to be a computationally expensive task. Therefore, the generation of parallel algorithmic proposals that operate efficiently on distributed processing platforms is a must in current reconstruction of GRNs. In this paper, a parallel multi-objective approach is proposed for the optimal inference of GRNs, since minimizing the Mean Squared Error using S-System model and Topology Regularization value. A flexible and robust multi-objective cellular evolutionary algorithm is adapted to deploy parallel tasks, in form of Spark jobs. The proposed approach has been developed using the framework jMetal, so in order to perform parallel computation, we use Spark on a cluster of distributed nodes to evaluate candidate solutions modeling the interactions of genes in biological networks.
KeywordsGene Regulatory Networks Multi-objective Metaheuristics Distributed Computing jMetal Spark
This work was partially funded by Grants TIN2017-86049-R, TIN2014-58304 (Spanish Ministry of Education and Science) and P12-TIC-1519 (Plan Andaluz de Investigación, Desarrollo e Innovación). C. Barba-González was supported by Grant BES-2015-072209 (Spanish Ministry of Economy and Competitiveness). J. García-Nieto is the recipient of a Post-Doctoral fellowship of “Captación de Talento para la Investigación” at Universidad de Málaga.
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