This repository contains the code for the paper: Shuhua Gao, Changkai Sun, Cheng Xiang, Kairong Qin, and Tong Heng Lee. Finite-Horizon Optimal Control of Boolean Control Networks: A Unified Graph-Theoretical Approach published on IEEE transactions on neural networks and learning systems.
Organization
The core algorithm implementation is in the folder src/algorithm. We use three examples to show how to use this algorithm in the src folder, which correspond to three examples in the paper. The benchmark experiment with the Ara operon network is given in src/benchmark_ara_operon.py. The network transition matrix L of this network is presented in src/ara_operon.txt.
Requirement
Python 3.6 or higher.
How to run
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Download or clone this repository to your local computer.
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In the command line (such as cmd, PowerShell on Windows or terminal on Ubuntu), go into the src folder
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To run an example, just type
python ./example_3.py. (Of course, you can also use certain IDEs like PyCharm.)
How to cite this work
Gao, S., Sun, C., Xiang, C., Qin, K., & Lee, T. H. (2020). Finite-Horizon Optimal Control of Boolean Control Networks: A Unified Graph-Theoretical Approach. IEEE transactions on neural networks and learning systems, PP, 10.1109/TNNLS.2020.3027599. Advance online publication. https://doi.org/10.1109/TNNLS.2020.3027599