MetworkPy: A Python Package for Graph- and Information-theoretic Investigation of Metabolic Networks
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Summary
We present MetworkPy, a python package for investigating in silico genome-scale models of metabolism (GSMM). By using novel graph- and information-theoretic methods to explore the feasible reaction flux space, MetworkPy quantifies network context and simulates metabolic relationships between sets of enzyme-encoding genes without imposing assumptions of optimal growth. To demonstrate utility, we used MetworkPy to identify metabolic features perturbed by the transcription factor ArgR, a known regulator of arginine biosynthesis in Mycobacterium tuberculosis , based on published transcriptome data generated from an argR mutant strain. MetworkPy successfully linked reaction flux shifts in ArgR’s transcriptome-constrained GSMM to arginine biosynthesis, which cannot be easily ascertained by conventional constraint-based optimization modeling approaches. MetworkPy offers a flexible toolbox for metabolic contextualization of genes-of-interest in microbial, eukaryotic, and multi-organism systems with potential applications for medicine and bioengineering.
Availability and implementation
The MetworkPy package can be retrieved from PyPi ( https://pypi.org/project/metworkpy/ ) and GitHub ( https://github.com/Ma-Lab-Seattle-Childrens-CGIDR/metworkpy ). Code for analyses performed in this paper can be retrieved from GitHub ( https://github.com/Ma-Lab-Seattle-Childrens-CGIDR/metworkpy_application_note )
Supplementary Information
Supplementary data are available online at bioRxiv.
