Programmable evolution of computing circuits in cellular populations

---

This repository is complementing the paper Programmable evolution of computing circuits in cellular populations.

Main Files

• cell.py: a Python class implementing a synthetic cell.
• population.py: functionalities to generate a synthetic population, distribute the functionalities among the cells in the population, and run the simulations.
• models.py: Hill functions and protein degradation models composing the models of logic functions.
• convergence2_simple.py: analysis of convergence for 2-input logic functions using YES and NOT operons.
• convergence2_minterms.py: analysis of convergence for 2-input logic functions using operons encoding minterms.
• convergence3_minterms.py: analysis of convergence for 3-input logic functions using operons encoding minterms.
• plot_convergence.ipynb: plot the graphs visualising the convergence of solutions.
• parameter_values.py: paramemeter values used in simulations.
• simulate_2_input.py: an example of a simulation of the evolution process using YES and NOT operons.
• simulate_2_input_minterms.py: an example of a simulation of the evolution process using operons encoding minterms.
• simulate_2_input_fixed_and.py: an example of a simulation in which AND function is preset as well as optimised and thus sustained throughout the simulation.
• simulate_2_input_fixed_or.py: an example of a simulation in which OR function is preset as well as optimised and thus sustained throughout the simulation.
• simulate_2_input_fixed_xor.py: an example of a simulation in which XOR function is preset as well as optimised and thus sustained throughout the simulation.

Dependencies

The code can be used in a combination with Python 3 programming environment with an additional installation of the following libraries:

• numpy
• matplotlib
• pandas
• sympy

How to cite this work

Please cite this work as:

Moškon, M., Mraz, M. Programmable evolution of computing circuits in cellular populations. Neural Comput & Applic (2022). https://doi.org/10.1007/s00521-022-07532-7

The paper is available at https://doi.org/10.1007/s00521-022-07532-7.

Contact

Please direct your questions and comments to [email protected]