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Decoding Natural Behavior from Neuroethological Embedding
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Decoding the architecture of living systems.

Manlio De Domenico1,2,3,4

  • 1Department of Physics and Astronomy 'Galileo Galilei', University of Padua, Via F. Marzolo 8, 315126 Padova, Italy.

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|December 15, 2025
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Summary
This summary is machine-generated.

Biological systems evolve through complex networks, or circuitries, that actively drive change and enhance adaptability. These non-trivial networks, favored for efficiency, are key to understanding biological innovation and complexity.

Keywords:
biological networkscomplex networkscomplex systemsevolutionnonequilibrium processesnonlinear dynamical systemsstatistical physics

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Area of Science:

  • Evolutionary biology
  • Systems biology
  • Statistical physics

Background:

  • Living systems' logic may be constrained by evolutionary forces, thermodynamics, computation, and ecology.
  • Biological implementation relies on complex regulatory, metabolic, and signaling networks (circuitries).

Purpose of the Study:

  • To review and discuss how biological circuitries actively drive the evolution of evolvability.
  • To analyze the role of non-trivial network topologies in evolutionary transitions.
  • To propose a unifying framework for modeling biological complexity.

Main Methods:

  • Analysis of non-trivial topologies in evolutionary transitions using statistical physics and nonlinear dynamics.
  • Examination of network properties (interconnectivity, plasticity, interdependency) through dynamical systems theory and non-equilibrium thermodynamics.
  • Modeling evolutionary dynamics using the replicator-mutator equation within a constrained variational non-equilibrium process.

Main Results:

  • Biological innovations are linked to circuitry deviating from trivial structures and thermodynamic equilibria.
  • Sparse, hierarchical, and modular networks are favored due to trade-offs in energetic costs, redundancy, and error correction.
  • Slow evolutionary dynamics emerge from constrained non-equilibrium processes.

Conclusions:

  • Circuitries are active agents that enhance evolvability through hierarchical and modular organization.
  • Dynamical systems theory and non-equilibrium thermodynamics offer powerful tools for studying biological complexity.
  • Understanding network topology is crucial for comprehending biological innovation and evolutionary trajectories.