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

Area of Science:

• Plant Physiology
• Computational Systems Biology
• Genomics
• Epigenomics

Background:

• High-resolution multi-omics technologies are revolutionizing plant physiology.
• Computational systems biology enables predictive, mechanistic, and field-relevant insights.
• Understanding crop performance, adaptation, and resilience is crucial.

Purpose of the Study:

• To synthesize advancements in multi-omics and computational biology for plant science.
• To outline integrative analytical frameworks for understanding plant function.
• To propose a roadmap for translating omics discoveries into agricultural applications.

Main Methods:

• Integrative synthesis of genomics, transcriptomics, proteomics, metabolomics, epigenomics, phenomics, single-cell, and spatial omics.
• Application of computational frameworks including gene regulatory network inference, machine learning, and explainable AI (XAI).
• Analysis of case studies from model organisms to crop improvement.

Main Results:

• Computational unification of omics data enables cell-type-resolved and tissue-specific understanding of plant function.
• Integrative frameworks accelerate discovery of key regulators and improve genotype-environment interaction modeling.
• Multi-omics integration bridges lab discoveries with field validation for crop improvement.

Conclusions:

• Systems-level plant biology, integrating molecular mechanisms with ecosystem performance, is key to predictive, design-driven, and climate-resilient agriculture.
• Addressing bottlenecks like data heterogeneity and spatiotemporal resolution is vital.
• FAIR data infrastructures, advanced omics, hybrid AI, and digital twins are proposed solutions.