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Related Concept Videos

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Model-assisted analysis for tuning anthocyanin composition in grape berries.

Yongjian Wang1,2, Boxing Shang1,2,3, Michel Génard4

  • 1State Key Laboratory of Plant Diversity and Specialty Crops and Beijing Key Laboratory of Grape Science and Enology, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, China.

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|October 18, 2023
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A new mechanistic model accurately simulates anthocyanin composition in grapes during ripening. This tool aids in understanding and bioengineering targeted anthocyanin profiles for improved wine quality.

Keywords:
Anthocyanin profilebiochemical decorationmechanistic modelmetabolic pathways

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Area of Science:

  • Plant Science
  • Biotechnology
  • Computational Biology

Background:

  • Anthocyanins are key pigments in grapes and wine, influencing color and quality.
  • Anthocyanin biosynthesis is complex, regulated by genetic, developmental, and environmental factors, posing challenges for targeted manipulation.
  • Understanding and controlling anthocyanin accumulation is crucial for grape and wine production.

Purpose of the Study:

  • To develop and validate a mechanistic model simulating anthocyanin composition dynamics during grape ripening in *Vitis vinifera*.
  • To assess the model's accuracy and robustness across different genotypes and environmental conditions.
  • To explore the potential of the model for guiding bioengineering strategies to achieve specific anthocyanin profiles.

Main Methods:

  • Construction of a mechanistic model based on a consensus anthocyanin biosynthesis pathway.
  • Calibration and validation using six datasets from eight cultivars under 37 growth conditions.
  • Analysis of model parameters, robustness, and prediction accuracy using statistical metrics (R², RRMSE) and cross-validation.

Main Results:

  • The model accurately simulated individual anthocyanin accumulation, with R² values ranging from 0.92 to 0.99 and RRMSEs between 16.8% and 42.1%.
  • Model parameters demonstrated robustness across environments for each genotype, with high prediction quality confirmed by cross-validation.
  • Genotype-specific virtual experiments indicated that targeted anthocyanin profiles can be achieved by manipulating at least three parameters.

Conclusions:

  • The developed mechanistic model provides a robust and accurate methodology for characterizing temporal anthocyanin composition changes in grapes.
  • The model offers a valuable framework for understanding genotype-environment interactions affecting anthocyanin profiles.
  • This approach lays a foundation for bioengineering efforts aimed at precise control of grape anthocyanin composition for enhanced quality.