Integrated transcriptomics-metabolomics framework for predictive and translational biostimulant design: Insights into smart systems for sustainable agriculture

Despite robust commercial adoption of biostimulants, with a global market projected to reach USD 7.84 billion by 20230, the molecular mechanism by which these products modulate plant growth, stress tolerance, and crop performance remains poorly understood, particularly for the most widely adopted classes such as seaweed extracts (SWEs) and plant growth-promoting rhizobacteria (PGPR). This review addresses this knowledge gap by synthesising current evidence for integrated transcriptomics-metabolomics approaches as a systems biology framework for decoding the biostimulant mode of action, primarily drawing on plant-specific case studies involving SWEs and PGPR. We provide a comprehensive end-to-end pipeline linking chemical characterisation, paired omics profiling, computational integration, and predictive modelling, which require experimental validation to establish causation, while addressing commercialisation pathways through omics-informed quality control, biomarker-based efficacy screening, intellectual property strategies, and regulatory compliance frameworks. Critical limitations remain that constrain progress, including substantial gaps in metabolite annotation with approximately 30–50% of the metabolome remaining uncharacterised, disconnects between transcript abundance and protein activity and downstream metabolite biosynthesis, the absence of standardised experimental and analytical protocols, limited transferability of greenhouse-derived biomarkers to field conditions, and high costs that restrict adoption of integrated omics approaches in resource-limited settings. Transitioning from empirical biostimulant screening toward predictive, mechanism-guided formulation design remains a future goal that will require streamlined integration strategies, multi-environment validation trials, standardised data reporting, and development of field-deployable omics platforms.