The repository includes the Python code for the analysis of a biological processor proposed in the paper A Computational Design of a Programmable Biological Processor.

Implementation of computational models and their analysis is available in models folder. The main files are as follows:

• models/bioproc/proc_models.py: implementation of different processor topologies,
• models/bioproc/proc_opt.py: analysis of viable spaces for a selected topology,
• models/robustness_analysis.py: analysis of robustness of the obtained solutions,
• models/analyse_proc.ipynb: interactive Python notebook with an example of analysis of different topologies.

Implementation of the compiler and examples of different programs and their analysis is available in compiler folder. The main files are as follows:

• compiler/generate_model.py: implementation of the biological compiler,
• compiler/simulate_program.py: simulator that uses the compiler to generate an ODE-based model and simulates its dynamics with the given parameter set,
• compiler/simulate_processor.ipynb: interactive Python notebook with the description of the biological compiler, the processor language syntax and with the examples of different programs and their analysis.

Data and results are available in the following folders:

• models/results_opt, models/results_opt_rep1, models/results_opt_rep2: results of three optimization replications,
• models/results_robustness: results of the robustness analyses,
• compiler/programs: examples of different models in txt format together with their translation into Python models.
• compiler/figs/programs: simulation results performed on different biological programs.

Examples are available as interactive Python notebooks:

• compiler/simulate_processor.ipynb: interactive Python notebook with the description of the biological compiler, the processor language syntax and with the examples of different programs and their analysis.
• models/analyse_proc.ipynb: interactive Python notebook with an example of analysis of different topologies.

The source code is written in Python 3.7 using the following modules and libraries:

• numpy
• scipy
• matplotlib
• pandas
• pickle
• seaborn
• deap
• sklearn
• mpl_toolkits
• peakutils
• multiprocessing

Shield:

This work is licensed under a Creative Commons Attribution 4.0 International License.