MetworkPy: A Python Package for Graph- and Information-theoretic Investigation of Metabolic Networks

Metabolism plays an important role in physiology and disease. In silico Genome-Scale Metabolic Models (GSMMs) are useful for informing analysis of metabolism and contextualizing high-throughput molecular profiling data. Flux balance analysis (FBA) based approaches can use GSMMs to predict metabolic reaction flux states and condition-specific alterations, but most FBA methods are limited by: 1) needing metabolic objective functions that are difficult to define experimentally, and 2) confining comparisons to extrema of reaction flux distributions.

We introduce MetworkPy, a Python package that implements a flexible toolbox for objective-free graph- and information-theoretic analyses of GSMMs and flux distributions. MetworkPy includes methods to construct graph representations of GSMMs, to deconstruct GSMMs into topologically-aware gene sets that inform downstream bioinformatic analyses, and to assess shifts in flux distributions for groups of reactions. 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 . MetworkPy successfully linked reaction flux shifts in ArgR's transcriptome-constrained GSMM to arginine biosynthesis, which cannot be easily ascertained by FBA-based approaches.

MetworkPy is a customization-friendly toolkit for topological- and flux sampling based-analyses of GSMMs. MetworkPy's tools provide multiple complementary metabolic contextualization approaches for microbial or eukaryotic genes of interest, and MetworkPy's architecture facilitates user-designed workflows.