COBRAxy: constraint-based metabolic modeling in Galaxy

This is the authors' abstract. We don't add key points for this paper.

MOTIVATION: Metabolic network modeling is essential for understanding metabolic shifts occurring in complex physio-pathological processes. Currently, constraint-based modeling frameworks for metabolic networks primarily rely on Python or MATLAB libraries, requiring some coding skills. In contrast, more user-friendly tools lack essential features such as flux sampling or transcriptomic data integration. RESULTS: We introduce COBRAxy, a Python-based tool suite integrated into the Galaxy Project. COBRAxy enables constraint-based modeling and sampling techniques, allowing users to compute metabolic flux distributions for multiple biological samples. The tool also enables the integration of medium composition information to refine flux predictions. Additionally, COBRAxy provides a user-friendly interface for visualizing significant flux differences between populations on an enriched metabolic map. This extension provides a comprehensive and accessible framework for advanced metabolic analysis, enabling researchers without extensive programming expertise to explore complex metabolic processes. AVAILABILITY AND IMPLEMENTATION: COBRAxy is available in the Galaxy ToolShed https://toolshed.g2.bx.psu.edu/view/bimib/cobraxy/9f78303dbd88.