gMCSpy: efficient and accurate computation of genetic minimal cut sets in Python.

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Genome-scale metabolic models (GEMs) are crucial for identifying genetic interventions to modulate metabolic processes.
• Minimal Cut Sets (MCSs) and genetic Minimal Cut Sets (gMCSs) are key concepts for this task.
• Existing computational tools for gMCS calculation have limitations, often relying on commercial software.

Purpose of the Study:

• To introduce gMCSpy, an efficient Python package for calculating gMCSs in GEMs.
• To demonstrate the superior performance of gMCSpy compared to previous tools and existing algorithms.
• To provide a versatile and accessible tool for systems biology research.

Main Methods:

• Development of gMCSpy, a Python package utilizing commercial and non-commercial optimization solvers.
• Benchmarking gMCSpy against a previous tool (GMCS) and recently published algorithms.
• Evaluation of accuracy and computation time for gMCS calculation.

Main Results:

• gMCSpy demonstrates substantial performance improvements over the previous GMCS tool.
• gMCSpy shows significant enhancements in accuracy and computation time compared to competing algorithms.
• The package supports both commercial and non-commercial optimization solvers.

Conclusions:

• gMCSpy offers an efficient and accurate solution for calculating gMCSs in GEMs.
• The package is a valuable asset for researchers in systems biology, impacting health and biotechnology.
• gMCSpy is publicly available, promoting accessibility and further research.