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Protein interaction networks evolve complexity through neutral drift, revealing hidden order and long-term evolutionary memory in sequence space that constrains network development.

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Area of Science:

  • Systems Biology
  • Evolutionary Biology
  • Computational Biology

Background:

  • Understanding the evolution of complex biological networks is crucial for deciphering cellular function.
  • Protein-interaction networks (PINs) are fundamental to cellular processes and their evolution is not fully understood.

Purpose of the Study:

  • To develop a minimal model for the evolution of functional protein-interaction networks.
  • To investigate the impact of neutral drift on network complexity and dynamics.
  • To explore the underlying principles governing the evolution of protein-interaction networks.

Main Methods:

  • A sequence-based mutational algorithm was employed to model network evolution.
  • The model was applied to study neutral drift in networks exhibiting oscillatory dynamics.
  • Analysis focused on the relationship between sequence space topology and network evolution.

Main Results:

  • Random evolutionary drift, even without selection, increases the complexity of protein-interaction networks.
  • A previously unrecognized order within sequence space was discovered.
  • This hidden order leads to long-term evolutionary memory in network development.

Conclusions:

  • Network evolution is significantly constrained by the topology of accessible sequence space.
  • Neutral drift plays a substantial role in shaping the complexity of protein-interaction networks.
  • The findings suggest inherent predictability in the evolutionary trajectories of biological networks.