MESSI: metabolic engineering target selection and best strain identification tool.

Area of Science:

• Metabolic engineering and synthetic biology
• Microbial biotechnology
• Systems biology

Background:

• Saccharomyces cerevisiae is a key microorganism for bio-based chemical production.
• Engineering yeast metabolism requires integrative models accounting for pathway and regulatory complexities.
• Current methods lack tools for comprehensive strain and target identification.

Purpose of the Study:

• To develop an integrative web server, MESSI, for predicting efficient yeast strains and regulatory targets for bio-production.
• To facilitate the analysis of metabolomic data for strain selection and metabolic engineering.
• To identify promising genetic targets for enhancing yeast bio-production capabilities.

Main Methods:

• Developed Metabolic Engineering target Selection and best Strain Identification (MESSI) web server.
• Integrated high-throughput metabolomic data analysis with metabolic pathway activity assessment.
• Employed genome-wide association studies to link metabolic activities with natural genetic variation.
• Utilized aggregation algorithms for strain ranking and variant prioritization.

Main Results:

• MESSI provides a ranked list of Saccharomyces cerevisiae strains for specific compound production.
• Identified potential regulatory genes and small variants as metabolic engineering targets.
• The tool analyzes public metabolomic data and allows user-defined parameter settings.
• Offers flexibility in selecting aggregation algorithms and variant types for analysis.

Conclusions:

• MESSI serves as a valuable platform for advancing yeast metabolic engineering and synthetic biology.
• The tool aids in selecting optimal yeast chassis and identifying key genetic targets for bio-production.
• Integrative analysis of metabolomic data and genetic variation enhances the design of microbial cell factories.