Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

Area of Science:

• Plant Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Growing demand for plant genome-scale metabolic reconstructions to understand crop yield.
• Need for accurate interpretation of plant transcriptome data across various conditions.
• Existing gene-reaction mappings in metabolic models often contain errors, leading to unreliable predictions.

Purpose of the Study:

• Introduce an improved, evidence-based genome-scale metabolic reconstruction for maize.
• Present a novel method for predicting active metabolic genes using transcriptome data.
• Develop organ- and tissue-specific maize models for detailed metabolic activity analysis.

Main Methods:

• Created a new maize metabolic reconstruction with enhanced gene-reaction associations.
• Developed a method to predict active metabolic genes by prioritizing high-expression genes.
• Constructed organ-specific (maize leaf) and tissue-specific (maize embryo, endosperm) models.

Main Results:

• Significantly improved accuracy of gene-reaction associations in the maize metabolic model.
• Successfully applied the new method to generate specific maize organ and tissue models.
• Validated models using fluxomics data, demonstrating improved fit and predictive capacity.

Conclusions:

• The new maize metabolic model and prediction method offer higher accuracy for metabolic studies.
• Organ- and tissue-specific models provide deeper insights into plant metabolic variations.
• The generally applicable method facilitates in silico analysis of diverse plant genomes.