High-throughput data pipelines for metabolic flux analysis in plants

Area of Science:

• Biotechnology
• Systems Biology
• Computational Biology

Background:

• Metabolic Flux Analysis (MFA) is essential for understanding cellular metabolism.
• High-throughput methods are needed to manage complex biological data.
• Integrating experimental data with computational analysis presents challenges.

Purpose of the Study:

• To present a methodology for high-throughput metabolic flux analysis.
• To develop an integrated data pipeline for MFA.
• To create adaptable software tools for automating MFA workflows.

Main Methods:

• Developing an end-to-end data pipeline integrating wet lab experiments and analytics.
• Utilizing hardware and software automation for standardized data representation.
• Creating iMS2Flux as a platform-independent tool for data integration and management.
• Automating MFA software using high-performance computing for parallel processing.

Main Results:

• Successful integration of wet lab experiments and computational analysis for MFA.
• Standardized data handling and compatibility with third-party tools.
• Development of iMS2Flux for flexible data format adaptation, verification, and correction.
• Automation of MFA enabling high-throughput analysis.

Conclusions:

• The developed pipeline and tools facilitate high-throughput metabolic flux analysis.
• This methodology enables MFA to be a core component of the omics data family.
• The approach enhances the efficiency and scalability of metabolic pathway investigations.