A scientific workflow framework for (13)C metabolic flux analysis.

Area of Science:

• Systems biology
• Metabolic engineering
• Computational biology

Background:

• Metabolic flux analysis (MFA) quantifies intracellular reaction rates using (13)C labeling data.
• The computational workflow for (13)C MFA is complex and requires significant scientific expertise.
• Current methods lack automation, hindering efficiency and reproducibility.

Purpose of the Study:

• To develop a scientific workflow framework (SWF) for creating, executing, and controlling (13)C MFA workflows.
• To address the challenges of interactivity and context-dependency in (13)C MFA computational workflows.
• To enhance the automation and flexibility of (13)C MFA processes.

Main Methods:

• Integration of (13)C MFA tools and libraries (e.g., 13CFLUX2) as web services within a service-oriented architecture.
• Development of a framework for workflow steering, provenance collection, and ad hoc scripting.
• Support for cloud computing to handle compute-intensive tasks.

Main Results:

• The SWF successfully integrates specialized (13)C MFA tools into a cohesive computational workflow.
• The framework provides transparent provenance tracking and flexible scripting capabilities.
• Proof-of-concept use cases demonstrate the framework's effectiveness in solving (13)C MFA challenges.

Conclusions:

• The proposed SWF effectively addresses the complexities of (13)C MFA computational workflows.
• The framework enhances automation, flexibility, and reproducibility in metabolic flux analysis.
• This approach facilitates more efficient and reliable quantification of intracellular reaction rates.