mfapy is a Python toolbox for 13C-metabolic flux analysis developed by Matsuda and Shimizu lab group in Osaka university, Japan.
mfapy supports:
- The elementary metabolite unit (EMU) framework developed by Antoniewicz et al 2007.
- Isotopically nonstationary 13C metabolic flux analysis (INST-MFA)
- Parallel labeling experiment without limitation of number of experiments.
- Metabolic flux, net metabolic flux of reversible reaction, and metabolite concentration could be considered as fitting parameters.
- G-value by introducing the "pseudo" reaction
- Goodness-of-fit analysis
- Parallel performing non-linear optimization jobs using joblib package.
- Automated model construction from model description file
Please check 'mfapy-document' repository. https://fumiomatsuda.github.io/mfapy-document/
This software is released under the MIT License, see LICENSE.txt.
Python 3.10 or later
mfapy requires the packages:
NumPy and SciPy (van der Walt, Colbert & Varoquaux, 2011)
nlopt https://nlopt.readthedocs.io/en/latest/
joblib
mkl-service (for Anaconda)
mfapy was developed and tested in 64 bit version of Miniconda and official python + pip distribution in Windows 11.
- Clean up Anaconda files by anaconda-clean module
>conda install anaconda-clean
>anaconda-clean
- Install 64 bit version of Miniconda
- Please use "Anaconda prompt" as a terminal
- Create virtual environment such as named "mfapy" and install required packages
> conda create -n mfapy
> conda activate mfapy
> conda install -c conda-forge numpy scipy joblib nlopt mkl-service
- Install 64 bit version of Python
- Please start terminal
- Please move to your project folder.
- Create virtual environment such as named "mfapy" and install required packages
> python -m venv mfapy
> mfapy\Scripts\activate
> python -m pip install numpy scipy matplotlib nlopt joblib
- Download mfapy package from GitHub.
- Unzip the downloaded file and set as current folder
- Install mfapy
> python -m pip install .
> python -m unittest
./sample/Tutorial 1_13C-MFAEcoli.py explains how to use mfapy for 13C-MFA.
./sample/Tutorial 2_INST-MFAtoymodel.py explains how to use mfapy for INST-MFA.
./sample/Tutorial 3_mfapy_functions.py is a advanced exlanation on many mfapy function.
Detailed explanations are available from docstrings
In the ./sample folder, example python codes are also available.
Learn more https://fumiomatsuda.github.io/mfapy-document/
19/06/28 class MetabolicModel: search_ci Grid search procedure is updated.
19/06/28 class MdvData: add_gaussian_noise "normalize" option is newly added.
19/07/01 optimize: def fit_r_mdv_deep add global optimization by "GN_CRS2_LM" before iteration
19/12/27 Support Python 3.8
19/12/27 Example_1_toymodel_INST.py is updated to explain a method to construct time course mdv data from mdv files.
20/7/12 initializing_Rm_fitting, fit_r_mdv_scipy, fit_r_mdv_nlopt in optimizaton: Expection is newly raised to avoid error in paralell processing.
20/7/13 joblib instead of pp is employed for paralell proceccing.
20/7/30 Format of model definition file was updated to support external with a backward compatibility.
20/7/30 load_metabolic_model_reactions in mfapyio: Support external id.
20/7/30 load_metabolic_model_metabolites in mfapyio: Support external id.
20/7/30 load_metabolic_model_reversibles in mfapyio: Support external id.
20/7/30 load_metabolic_model_fragments in mfapyio: Support external id.
20/7/30 External id data was added to Example_0_toymodel_model.txt,
20/7/30 External id data was added to Example_1_toymodel_model.txt.
20/7/30 External id data was added to Example_2_MCF7_taxol_model.txt.
20/7/30 show_results in metablicmodel: Output format was modified for more beautiful alignment.
20/7/30 show_results in metablicmodel: "checkrss" option was added to check RSS levels of each fragment and "fitting" reactions and metabolites.
20/8/19 Files for Example 6 (the Metropolis-Hastings algorithm reported in Scientific Reports volume 10, Article number: 286 (2020)) were newly included in sample folder.
20/8/19 mfapy.carbonsource.set_carbonsources method is newly developed for batch loading of labelling pattern information of carbon sources.
20/8/31 Files for Explanation 1 were newly included in sample folder to demonstrate the functions of mfapy.carbonsource.set_carbonsources
20/8/31 mfapy.metabolicmodel.show_results_in_map is newly developed to project flux data on the metabolic map (.GML) available in Vanted. Files of Example 2 were updated to generate "Example_2_cancer_map_mapped.gml" from a blank map "Example_2_cancer_map_new.gml".
20/9/3 Files for Example 7 (the Metropolis-Hastings algorithm reported in Scientific Reports volume 10, Article number: 286 (2020)) were updated to reproduce MCF-7 example.
20/9/3 A bug in mfapy.metabolicmodel.calc_rss was fixed.
20/9/6 test_metabolicmodel.py was updated to support mfapy.metabolicmodel.generarate_flux_distribution => mfapy.metabolicmodel.generarate_state
20/12/24 Model check functions were added to MetabolicModel.
21/1/9 Example files were totally reorganized.
21/1/9 Explanation 1 files were newly added to explain mfapy functions.
21/2/26 Support Google docstring format.
21/2/26 'mfapy-document' repository was created. https://fumiomatsuda.github.io/mfapy-document/
22/2/14 Limitation in the maximum EMU size was removed.
22/11/7 Bugs in MetabolicModel.show_results was fixed.
24/3/4 DocString contents were updated.
24/3/4 "Tutorial 1_13C-MFAEcoli.py" was created to explain how to use mfapy for 13C-MFA.
24/3/4 "Tutorial 2_INST-MFAtoymodel.py" was created to explain how to use mfapy for INST-MFA.
24/3/4 "Tutorial_3_mfapy_functions.py" was created to explain many mfapy functions.
24/9/6 Target environments are changed from Anaconda to Miniconda+conda-forge and Official python+pip.
If you want to learn more about setup.py files, check out `this repository https://github.com/fumiomatsuda/mfapy