A Computational Design of a Programmable Biological Processor

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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.

Citation

The proposed design is described and analysed in the following paper:

Moškon M, Pušnik Ž, Stanovnik L, Zimic N, Mraz M. A Computational Design of a Programmable Biological Processor, BioSystems, 2022, 104778, DOI: 10.1016/j.biosystems.2022.104778.

Examples

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.

Models

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/bioproc/proc_models_to_SBML.py: export models to SBML,
• 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,
• models/SBML: models in SBML format.

Compiler

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

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.

Prerequisites

The source code is written in Python 3 using the following modules and libraries:

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