Welcome to incawrapper’s documentation!¶
incawrapper is a Python package which wraps around the matlab application INCA. INCA is a tool for 13C metabolic flux analysis [1,2]. The incawrapper package allows to import data, setup the model and run INCA all from within Python. The results can be exported back to Python for further analysis and simply saved as .csv files. Furthermore, it is possible to export results from INCA runs entirely done through the GUI to Python.
The INCAWrapper code is freely available under an MIT License. However, to run INCA, you need a MATLAB and INCA licenses. Additionally, methods using COBRA tools need a GUROBI license. An INCA license is free for non-commercial use at mfa.vueinnovations.com and GUROBI offers free academic licenses at gurobi.com. For more installation, please check our pre-requisites and installation guide.
What can the incawrapper do for me?¶
Provide a Python interface to use INCA 100% independent of the INCA GUI
Provide a data structure that can be imported to INCA
Provide methods for exporting results from INCA to Python
Provide methods for plotting results from INCA in Python
Provide methods for creating INCA models with data, which can then be used in the INCA GUI
Run both Isotopically Non-Stationary (INST) and Isotopically Stationary (IS) 13C-MFA
Estimate fluxes and confidence intervals through the following INCA algorithms: estimate, parameter continuation, and Monte Carlo sampling
What can the incawrapper NOT do for me?¶
Integration of NMR data
Simulation of experiments
Optimization of experimental design
Overview of the documentation¶
- 1. Pre-requisites and installation
- 2. Quick start
- 3. Input data structure
- 4. Handling multiple experiments
- 5. Confidence intervals through Monte Carlo sampling
- 6. Using the incawrapper and the INCA GUI
- 7. Controlling INCA option/settings
- 8. Low level API
- 9. Example analysis notebooks
- 10. Contributor Guide
References¶
[1] Young, Jamey D. “INCA: A Computational Platform for Isotopically Non-Stationary Metabolic Flux Analysis.” Bioinformatics 30, no. 9 (May 1, 2014): 1333–35. https://doi.org/10.1093/bioinformatics/btu015.
[2] Rahim, Mohsin, Mukundan Ragavan, Stanislaw Deja, Matthew E. Merritt, Shawn C. Burgess, and Jamey D. Young. “INCA 2.0: A Tool for Integrated, Dynamic Modeling of NMR- and MS-Based Isotopomer Measurements and Rigorous Metabolic Flux Analysis.” Metabolic Engineering 69 (January 2022): 275–85. https://doi.org/10.1016/j.ymben.2021.12.009.