Comprehensive review explores metabolomics integration with synthetic biology for improving yields in plant tissue culture, highlighting solutions for current challenges.
The growing bioeconomy demands sustainable platforms for the production of high-value pharmaceuticals, nutraceuticals, and industrial compounds. Plant tissue culture (PTC) is an attractive alternative as it provides a means of controlled production of secondary metabolites without seasonal limitations or geographical constraints. Nevertheless, conventional methods do not maximize the multifaceted metabolic networks that control the production of compounds in cultured plant cells. This review reframes PTC as a predictive systems-level engineering field by combining metabolomics with synthetic biology and multiomics frameworks, rather than a trial-and-error practice. This type of integration allows metabolomics to serve as a profiling instrument and a core decision-making layer that connects metabolic phenotypes with rational pathway design, genetic intervention, and process regulation. This review is a synthesis of the recent progress in metabolomics-inspired metabolite enhancement in the context of synthetic biology, including media optimization, stress and elicitor strategies, CRISPR-based pathway editing, synthetic promoters, modular genetic circuits, heterologous expression systems, multiomics integration, and bioreactor scale-up. Together, these strategies form a single design-build-test-learn paradigm of PTC, transforming methodological processes into diagnostic, programmable, and scalable metabolite production. Despite these advances, challenges remain in analytical coverage, culture stability, and metabolic predictability. Accordingly, this Review identifies emerging solutions, including single-cell and spatial metabolomics, digital twin models, real-time biosensors, and modular enzyme assembly, which are further poised to transform metabolomics-driven PTC into a robust biomanufacturing platform.
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Manam et al. (2026) studied this question.
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