Skip to content

Latest commit

 

History

History
79 lines (48 loc) · 3.88 KB

README.md

File metadata and controls

79 lines (48 loc) · 3.88 KB

License: GPL v3 PyPI version Documentation Status Build Status

MEWpy

MEWpy is an integrated Metabolic Engineering Workbench, under development, for strain design optimization. Developed in Python, MEWpy offers methods to explore different classes of constraint-based models (CBM).

  • Simulation: allows to simulate different metabolic models considering different phenotype.

  • Optimization: performs Evolutionary Computation based strain design optimization by knocking out (KO) or over/under expressing (OU) reactions, genes or enzymes.

    Metabolic Constraints

    Method Strategy
    Reactions KO / OU
    Genes (GPRs) KO / OU

    Enzymatic Constraints

    Method Strategy
    GECKO KO / OU
    sMOMENT KO / OU

    Regulatory Constraints

    Method Strategy
    OptRAM KO / OU
    OptORF KO

  • MEWPy currently supports REFRAMED and COBRApy simulation environments.

  • The optimization engine relies on either inspyred or jMetalPy packages, which are used for creating biologically-inspired computational intelligence algorithms in Python.

  • MEWpy may also be used to run simulation and optimization strategies on Expression and Thermodynamics Flux (ETFL).

  • Next releases will be compatible with Metabolism and Macromolecular Expression (ME) models.

Usage Examples

Examples are provided as in the as jupiter notebooks and as python scripts.

Documentation

The package documentation is avaliable at mewpy.readthedocs.io.

Instalation

Installing from Pypi package repository:

pip install mewpy

Installing from github:

  1. clone the repository

git clone https://github.com/BioSystemsUM/mewpy.git

  1. run python setup.py install

MEWPy requires a compatible solver for linear programming problems, with installed Python dependencies installed, from the following list:

Cite

Vítor Pereira, Fernando Cruz, Miguel Rocha, MEWpy: a computational strain optimization workbench in Python, Bioinformatics, 2021;, btab013, https://doi.org/10.1093/bioinformatics/btab013

Credits and License

Developed at Centre of Biological Engineering, University of Minho (2019-2020)

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 814408.

Released under an Apache License.