φ-evo: A program to evolve phenotypic models of biological networks.

Area of Science:

• Computational Biology
• Systems Biology
• Evolutionary Biology

Background:

• Molecular networks govern cellular processes but are complex to understand.
• Reverse engineering network architecture from function aids in prediction and experimental design.

Purpose of the Study:

• To introduce φ-evo, an open-source program for in silico evolution of phenotypic networks.
• To provide a user-friendly platform for studying network evolution and predicting biological functions.

Main Methods:

• Utilizing C, Python 3, and a Jupyter interface for program development and analysis.
• Implementing evolutionary algorithms for biochemical adaptation, immune recognition, and embryonic patterning.
• Incorporating Pareto evolution for multi-objective optimization.

Main Results:

• φ-evo successfully recovered the lac operon's asymmetrical regulation from symmetrical constraints.
• Demonstrated emergence of effective fitness from multi-objective evolution using Hox-like patterning.
• Showcased the program's capability in phenotypic prediction and numerical study of evolution.

Conclusions:

• φ-evo offers an efficient and accessible approach for predicting molecular network phenotypes.
• The program facilitates numerical studies of evolutionary processes and network dynamics.
• Provides a powerful tool for generating new biological predictions and guiding experimental research.