dingo: a Python package for metabolic flux sampling.

Area of Science:

• Computational Biology
• Systems Biology
• Metabolic Engineering

Background:

• Metabolic models are crucial for understanding cellular metabolism.
• Efficiently sampling the flux space of these models is computationally challenging.
• Existing tools struggle with the scale of modern metabolic models.

Purpose of the Study:

• To introduce dingo, a novel Python package for metabolic flux space sampling.
• To provide a tool that overcomes computational limitations of existing software.
• To enable advanced analyses of large-scale metabolic models.

Main Methods:

• Utilizes state-of-the-art random walks and rounding methods for flux sampling.
• Implements uniform sampling strategies for enhanced efficiency.
• Supports common metabolic modeling analyses like flux balance and variability analysis.

Main Results:

• Dingo achieves significant speed-ups in flux sampling compared to existing software.
• It can sample the largest metabolic model (Recon3D) on a personal computer in under a day.
• Enables flux sampling in high-dimensional metabolic models (thousands of variables).

Conclusions:

• Dingo provides a powerful and efficient solution for metabolic flux sampling.
• It democratizes the analysis of large-scale metabolic models.
• The package enhances the capabilities of computational systems biology research.