Speaker
Description
Rice (Oryza sativa) is highly sensitive to cold stress, imposing major constraints on its productivity and geographical distribution. Elucidating cold-induced metabolic reprogramming is essential for understanding the underlying mechanisms of rice adaptive responses.
Constraint-based metabolic modeling is a powerful framework for capturing metabolic interactions. A key challenge, however, is selecting an appropriate objective function, a decision that is critical for model predictions. In this study, we integrated transcriptomics data with a genome-scale rice metabolic model using the E-Flux algorithm to construct a temporal model of cold stress response. We analyzed the resulting model via two approaches: (i) random sampling of the transcriptome-informed solution space, and (ii) Pareto frontier analysis quantifying trade-offs between biomass production and proline accumulation, a well-studied stress marker.
Pareto analysis revealed carbon flux redistribution to support increased substrate availability for proline biosynthesis. We identified branch points where proline accumulation competes with growth, which can serve as potential engineering targets for improving cold resilience without yield penalties. Machine learning-assisted pathway enrichment analysis of the sampling data highlighted temporal metabolic transitions across time points. These findings offer a pathway-level view of cold-induced metabolic reprogramming in rice and targets for crop improvement.
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copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
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