Sustainable agriculture and bio-based production require a quantitative understanding of how living systems allocate carbon, energy, and nutrients. Metabolic Flux Analysis (MFA) addresses this need by combining stable isotope tracing with computational modeling to quantify intracellular reaction rates and reveal how metabolic networks function in vivo. Over the past two decades, MFA has identified key metabolic nodes that regulate carbon partitioning and biomass formation, providing actionable insights for improving biological efficiency. Flux studies further demonstrate that metabolism operates as an integrated network spanning organelles, tissues, and cell types, rather than as isolated linear pathways, and frequently involves metabolic cycling and dynamic carbon redistribution that support resilience and efficient resource use. These findings challenge simplified pathway models and highlight the importance of flux level measurements for understanding metabolic regulation. Expanding MFA from isolated tissues to whole organisms and ecological interfaces, such as plant rhizosphere systems, remains a key challenge, but advances in multi-isotope labeling, spatial metabolomics, and data-driven modeling are rapidly increasing its resolution and predictive power. By revealing metabolic inefficiencies and guiding targeted engineering strategies, MFA is emerging as a powerful framework for improving crop productivity, resource use efficiency, and the sustainability of agricultural and biotechnological systems.
Kambhampati et al. (Wed,) studied this question.
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