MITSGRN: A Novel Computational Framework for Reconstructing Sleep Rhythm Gene Regulatory Networks Based on Mutual Information and Time-Series Big Data

Abstract

Disruptions in sleep rhythms have emerged as a global health concern, posing serious risks to the physical and mental well-being of modern populations. Elucidating the molecular regulatory mechanisms underlying the periodic nature of sleep rhythms remains a critical scientific challenge. In this study, we propose an innovative computational framework for gene regulatory network (GRN) reconstruction based on mutual information and large-scale time-series data. The proposed framework leverages the temporal characteristics of gene expression profiles associated with sleep rhythms, and integrates k-means clustering, mutual information, and Pearson lag correlation analysis in a synergistic manner to support GRN reconstruction. We systematically evaluate the performance of our method using BEELINE open-source datasets of varying scales, with precision, recall, and cross-validation accuracy as evaluation metrics. Experimental results demonstrate that our approach significantly outperforms existing methods such as dynGENIE3 and transfer entropy in terms of both accuracy and generalization capability. Furthermore, we successfully applied the proposed framework to reconstruct the GRN governing sleep rhythms in rats. The resulting network exhibits topological features and identifies key regulatory components that are highly consistent with previously published findings. Our results highlight the advantages of mutual information-based GRN reconstruction in deciphering complex biological rhythm regulatory systems. This method not only provides a novel perspective for investigating the gene regulatory mechanisms underlying sleep rhythms, but also establishes a solid methodological foundation for exploring the pathogenesis of sleep-related disorders and advancing the development of targeted therapies.