Medusa is a tool for constraint-based reconstruction and analysis (COBRA) of ensembles. It builds on the COBRApy package (https://github.com/opencobra/cobrapy) by extending most single-model functionality to efficient ensemble-scale analysis. Additionally, Medusa provides novel functions for the analysis of ensembles.
Medusa is developed openly and we welcome contributions in the form of feedback, requests, ideas, and code. Please contact us by opening an issue if you are interested in a feature we have not yet implemented.
Use pip to install medusa from PyPI (Note: we support Python 3 only; use Python 2 at your own peril):
pip install medusa-cobra
Medusa can be loaded in python with:
import medusa
Check out the tutorials, examples, and API reference in the readthedocs documentation: https://medusa.readthedocs.io/en/latest/
When using medusa, please cite the COBRApy software paper, the medusa software paper, and the paper describing the method you used that was implemented within COBRApy or medusa. The COBRApy/Medusa citations are provided below. Please note either the release (if installed via pip) and/or the commit (if working from the development branch between versions) in your publication.
- Ebrahim A, Lerman JA, Palsson BO, Hyduke DR. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Systems Biology, 2013. https://doi.org/10.1186/1752-0509-7-74
- Medlock GL, Papin JA (2019) Medusa: software to build and analyze ensembles of genome-scale metabolic network reconstructions. bioRxiv, 2019. https://doi.org/10.1101/547174
We recommend familiarizing yourself with standard COBRA methods in the COBRApy package (https://github.com/opencobra/cobrapy). Some examples of ensemble analyses with genome-scale metabolic models can be found in our recent preprint and the accompanying code repository.
For reading on ensembles in metabolic modeling and systems biology, see our recent papers:
- Biggs MB, Papin JA (2017) Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA. PLoS Comput Biol 13(3): e1005413. https://doi.org/10.1371/journal.pcbi.1005413
- Medlock GL, Papin JA (2018) Guiding the refinement of biochemical knowledgebases with ensembles of metabolic networks and semi-supervised learning. bioRxiv, 2018. https://doi.org/10.1101/460071
We greatly appreciate feedback from current and prospective users from all backgrounds. Feel free to post issues via github or contact us directly via email with any questions or thoughts.
Authors: Greg Medlock (glm5uh [at] virginia [dot] edu) Jason Papin (papin [at] virginia [dot] edu)