POPI4SB: Population-based Parameter Identification for Dynamical Models of Biological Networks

This repository provides a Python framework for population-based parameter identification of dynamical models in systems biology. The code is built on top of PySCeS (http://pysces.sourceforge.net/) and could be used for any dynamical model included in JWS Online database (https://jjj.bio.vu.nl/) or defined by a user. The model must be in the .psc format.

Dependencies

Before you download the code, please be sure to install the following packages:

• PySCeS (http://pysces.sourceforge.net/)
• Numpy (https://numpy.org/)
• SciPy (https://www.scipy.org/)
• Scikit-Learn (https://scikit-learn.org/)

Use

1. Install all necessary packages.
2. Clone this repository.
3. Prepare/download your model (.psc).
4. Prepare a file (.json) with a specification of the model. If you use real data, please prepare the specification accordingly (i.e., the number of points, the beginning and the end of the experiment).
5. Prepare a file (.json) with a specification of an optimizer.
6. Update PySCeS solver information if necessary, or use the default setting.
7. Run popi4sb.py and follow the instructions.

Features

• An integration with PySCeS.
• Easy-to-use to run simulators (i.e., dynamical models) in systems biology.
• Parameter identification of dynamical models using either one of the provided optimizers, or own optimizer.
• A possibility to add new optimizers (see algorithms/population_optimization_algorithms.py).
• An intuitive code structure.

Reference

If you use this code in your research, please cite our paper:

@article{weglarz2021population,
  title={Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae},
  author={Weglarz-Tomczak, Ewelina and Tomczak, Jakub M and Eiben, Agoston E and Brul, Stanley},
  journal={Processes},
  volume={9},
  number={1},
  pages={98},
  year={2021},
  publisher={Multidisciplinary Digital Publishing Institute}
}