Computational interference of gene regulatory networks on the growth and development of millets

Abstract

Millets are among the cereal crops cultivated for nutrient-rich food. They are generally considered as resilient crops in terms of growth requirements, as they can withstand harsh climatic factors such as unpredictable climate change and nutrient-depleted soils. The present review highlighted that gene regulatory networks are rewired to control the adaptable traits and to understand the transcriptional regulatory system against environmental stress. By combining machine learning, predictive modeling, and multi-omics data to unravel intricate regulatory relationships, computational methods have entirely changed the study of GRNs (gene regulatory networks), making and identifying important transcription factors, co-regulators, and signaling networks. Recent developments in artificial intelligence, systems biology, and bioinformatics have made reconstructing and analyzing millet GRNs, providing new information on blooming mechanisms, nutrient absorption,and drought resistance. Data scarcity, species-specific heterogeneity, and the requirement for high-throughput functional validation. Computational models incorporating transcriptomics, proteomics, and metabolomics help to improve crop improvement by enabling targeted genetic alterations and increasing predictive accuracy. This study discusses critical approaches, accessible datasets, and new developments in computational GRN investigations in millets. Deep learning, CRISPR-based gene editing, and synthetic biology in millet research are among the opportunities to develop new genotypes. By using computational methods to gain a thorough understanding of millet GRNs, it will be possible to create millet varieties that are more nutritious and climate-robust, promoting sustainable agriculture.