metworkpy 0.2.1

Tools for creating and working with network models of metabolism.

Welcome to Metworkpy

Metworkpy is a Python library containing tools for working with and analyzing metabolic networks. This functionality includes:

• Generating network representations of Genome Scale Metabolic Networks (GSMMs)
• Integrating gene expression data with GSMMs
• Evaluating where the metabolism is most perturbed using divergence metrics

Issues and Pull Requests

If you experience any problems while using Metworkpy (including the documentation), please create a GitHub issue in this repository. When creating an issue, a minimal reproducible example of the issue will make getting you help much easier. You can also create issues for any enhancements you would like to see in Metworkpy. Contributions are welcome! Feel free to open a pull request. Currently, the contribution guidelines are still being worked out, but for enhanced functionality, please include an explanation of the functionality, any needed citations, and test cases (tests are run using pytest during continuous integration).

Licensing

This project makes use of the following external libraries:

• COBRApy licensed under the LGPL-2.1
• NetworkX licensed under the BSD-3-Clause
• NumPy licensed under the BSD-3-Clause
• optlang licensed under Apace-2.0
• Pandas licensed under the BSD-3-Clause
• SciPy licensed under the BSD-3-Clause
• SymPy licensed under the BSD-3-Clause

The mutual information implementation where partially inspired by those found in the feature_selection module of scikit-learn, and the tests for those methods were adapted from those in scikit-learn, which is licensed under the BSD-3-Clause. Additionally the implementation of the iMAT functionality was inspired by gembox (which uses a GPL-3.0-only license), and dexom-python (which uses the GPL-3.0-only license).

References:

IMAT References:

1. Shlomi T, et al. Network-based prediction of human tissue-specific metabolism, Nat. Biotechnol., 2008, vol. 26 (pg. 1003-1010)

Kulback-Leibler Divergence:

1. Q. Wang, S. R. Kulkarni and S. Verdu, "Divergence Estimation for Multidimensional Densities Via k-Nearest-Neighbor Distances," in IEEE Transactions on Information Theory, vol. 55, no. 5, pp. 2392-2405, May 2009, doi: 10.1109/TIT.2009.2016060.

Mutual Information:

1. Kraskov, A., Stögbauer, H., & Grassberger, P. (2004). Estimating mutual information. Physical Review E, 69(6), 066138.
2. Ross, B. C. (2014). Mutual Information between Discrete and Continuous Data Sets. PLoS ONE, 9(2), e87357