Decoding plant physiology through systems biology: Integrative multi-omics and computational perspectives for...

The convergence of high-resolution multi-omics technologies with computational systems biology is transforming plant physiology by enabling predictive, mechanistic, and field-relevant insights into crop performance, adaptation, and resilience. This review presents an integrative and forward-looking synthesis spanning genomics, transcriptomics, proteomics, metabolomics, epigenomics, phenomics, and the rapidly emerging fields of single-cell and spatial omics, highlighting how these complementary layers can be computationally unified to achieve cell-type-resolved and tissue-specific insights into plant function. We discuss integrative analytical frameworks that combine gene regulatory network inference, machine learning, and explainable artificial intelligence (XAI), illustrating how these approaches accelerate the identification of key regulators, improve genotype-environment interaction modeling, and advance multiscale phenotypic prediction. Representative case studies demonstrate how multi-omics integration-ranging from single-cell transcriptomic atlases in Arabidopsis to nitrogen-use-efficiency modeling and omics-guided genome editing in cereals-bridges laboratory-scale discovery with field-level validation. We further propose a translational roadmap that links persistent bottlenecks, including data heterogeneity, limited spatiotemporal resolution, and the underrepresentation of non-model species, with actionable solutions such as FAIR-compliant data infrastructures, high-resolution spatiotemporal omics, hybrid mechanistic artificial intelligence (AI) modeling, and digital twin frameworks. By connecting molecular mechanisms to ecosystem-level performance, this review articulates a coherent vision for predictive, design-driven, and climate-resilient agriculture grounded in systems-level plant biology.