GAlibrate is a python toolkit that provides an easy to use interface for model calibration/parameter estimation using an implementation of continuous genetic algorithm-based optimization. Its functionality and API were designed to be familiar to users of the PyDREAM, simplePSO, and Gleipnir packages.
Although GAlibrate provides a general framework for running continuous genetic algorithm-based optimizations, it was created with systems biology models in mind. It therefore supplies additional tools for working with biological models in the PySB format.
|GAlibrate is still under heavy development and may rapidly change.|
GAlibrate installs as the
galibrate package. It is compatible (i.e., tested) with Python 3.6 and 3.7.
galibrate has the following core dependencies:
You can install the latest release of the
galibrate package using
pip sourced from the GitHub repo:
pip install -e git+https://github.com/blakeaw/GAlibrate@v0.4.0#egg=galibrate
However, this will not automatically install the core dependencies. You will have to do that separately:
pip install numpy scipy
You can install the
galibrate package from the
conda install -c blakeaw galibrate
NumPy and SciPy dependencies will be automatically installed with this version.
Recommended additional software
The following software is not required for the basic operation of GAlibrate, but provides extra capabilities and features when installed.
GAlibrate includes an implementation of the core genetic algorithm that is written in Cython, which takes advantage of Cython-based optimizations and compilation to accelerate the algorithm. This version of genetic algorithm is used if Cython is installed.
GAlibrate also includes an implementation of the core genetic algorithm that takes advantage of Numba-based JIT compilation and optimization to accelerate the algorithm. This version of genetic algorithm is used if Numba is installed.
PySB is needed to run PySB models, and it is therfore needed if you want to use tools from the galibrate.pysb_utils package.
This project is licensed under the MIT License - see the LICENSE file for details
Documentation and Usage
Principally, GAlibrate defines the GAO (continuous Genetic Algorithm-based Optimizer ) class,
from galibrate import GAO
which defines an object that can be used setup and run a continuous genetic algorithm-based optimization (i.e., a maximization) of a user-defined fitness function over the search space of a given set of (model) parameters.
Additionally, GAlibrate has a
pysb_utils sub-package that provides the
galibrate_it module, which defines the GaoIt and GAlibrateIt classes (importable from the pysb_utils package level),
from galibrate.pysb_utils import GaoIt, GAlibrateIt
which create objects that abstract away some of the effort to setup and generate GAO instances for PySB models; examples/pysb_dimerization_model provides some
examples for using GaoIt and GAlibrateIt objects. The
galibrate_it module can also be called from the command line to generate a template run script for a PySB model,
python -m galibrate.pysb_utils.galibrate_it pysb_model.py output_path
which users can then modify to fit their needs.
Additional example scripts that show how to setup and launch Genetic Algorithm runs using GAlibrate can be found under examples.
If you use the GAlibrate software in your research, please cite it. You can export the GAlibrate citation in your preferred format from its Zenodo DOI entry.
Also, please cite the following references as appropriate for software used with/via GAlibrate:
Packages from the SciPy ecosystem
These include NumPy and SciPy for which references can be obtained from: https://www.scipy.org/citing.html
- Lopez, C. F., Muhlich, J. L., Bachman, J. A. & Sorger, P. K. Programming biological models in Python using PySB. Mol Syst Biol 9, (2013). doi:10.1038/msb.2013.1