Modular network evolution under selection for robustness to noise

Area of Science:

• Network science
• Evolutionary biology
• Systems biology

Background:

• Real-world networks often display modularity, a structure where networks decompose into subnetworks.
• The evolutionary origins of modular networks are debated, with a leading hypothesis involving multiple, decomposable goals and selection for sparse links.
• This study explores an alternative evolutionary constraint: enhanced robustness to noise.

Purpose of the Study:

• To investigate if evolutionary pressure for noise robustness can lead to modular network structures.
• To examine the role of noise in the evolution of network modularity.
• To present novel network models demonstrating this evolutionary mechanism.

Main Methods:

• Development of noise-interfused network models, including an analytically solvable linear system and biologically inspired nonlinear systems.
• Theoretical analysis of linear systems to understand the mathematical underpinnings of noise robustness and modularity.
• Computer simulations to validate findings across various nonlinear network functions.

Main Results:

• The models demonstrate that modular networks can evolve under conditions of modularly changing goals and increased robustness to noise.
• Evolutionary constraints favoring noise robustness lead to balanced sensitivities across multiple noise sources.
• This process results in modular networks that mirror a modular structure in their underlying goals.
• Simulations confirm the robustness of these mechanisms to variations in network nonlinearity.

Conclusions:

• Noise can play a positive and constructive role in the evolution of modular networks.
• Increased robustness to noise is a viable evolutionary pathway to network modularity, complementing existing hypotheses.
• The findings offer a new perspective on how complex, modular structures emerge in biological and other real-world systems.