Interplay of mutation and disassortativity.

Area of Science:

• Network science
• Evolutionary biology
• Systems biology

Background:

• Disassortativity is common in biological networks, but its evolutionary origins are not well understood.
• Mutations can alter interaction behaviors over evolutionary timescales.

Purpose of the Study:

• To investigate the evolutionary origin of disassortativity in biological networks.
• To determine how system stability and mutation influence network structure.

Main Methods:

• Utilized a genetic algorithm to simulate network evolution under stability maximization.
• Analyzed the impact of mutation probability on the disassortativity coefficient.
• Performed analytical verification for star networks and compared scale-free and random networks.

Main Results:

• Maximizing system stability with a genetic algorithm naturally leads to disassortative network structures.
• Mutation probability was found to control the saturation of the disassortativity coefficient.
• Scale-free networks demonstrated greater stability than random networks under specific conditions.

Conclusions:

• System stability is a key driver for the evolution of disassortative biological networks.
• Mutation dynamics explain the diverse range of disassortativity observed in nature.
• Network topology, such as scale-free versus random, impacts system stability.