ORCA: a COBRA toolbox extension for model-driven discovery and analysis.

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Constraint-based modeling is crucial for understanding biological phenotypes and metabolic states at a genome scale.
• Existing modeling software limits the application of constraint-based approaches in biotechnology.
• There is a need for advanced tools to enhance the usability of constraint-based modeling.

Purpose of the Study:

• To introduce ORCA, a novel Matlab package designed to extend Constraint-Based Reconstruction and Analysis (COBRA) metabolic modeling.
• To provide enhanced functionalities for systems biology applications in biotechnology.
• To improve the usability and scope of constraint-based modeling.

Main Methods:

• ORCA integrates multi-objective optimization, robustness analysis, and fractional benefit analysis.
• It includes a framework for metabolic pathway identification with futile loop elimination.
• A dynamic flux balance analysis framework incorporating kinetic constraints is implemented.

Main Results:

• ORCA offers three unique functionalities to augment established COBRA methods.
• The package facilitates deeper insights into metabolic mechanisms and states.
• It supports various use scenarios in systems biology and biotechnology.

Conclusions:

• ORCA significantly enhances the capabilities of constraint-based metabolic modeling.
• The software addresses limitations in existing tools, promoting wider application in biotechnology.
• ORCA provides a powerful platform for analyzing complex biological systems.