Understanding how replication processes can maintain systems away from equilibrium using Algorithmic Information...

Area of Science:

• Computational Biology
• Thermodynamics
• Information Theory

Background:

• Replication is a fundamental process for generating and maintaining order in systems far from thermodynamic equilibrium.
• Self-regulation in replicating systems counters degradation by accessing high-quality energy and expelling disorder.

Purpose of the Study:

• To quantify the entropy requirements for maintaining far-from-equilibrium conditions using Landauer's principle and Algorithmic Information Theory.
• To explore how replication processes can self-regulate and adapt to environmental changes.

Main Methods:

• Application of Landauer's principle to quantify information changes (ΔH bits) due to destabilizing processes under the second law of thermodynamics.
• Analysis of how replication accesses order, ejects disorder, and counters entropic changes without external intervention.
• Investigating the role of structural diversity and system coupling in enhancing self-regulation and adaptation.

Main Results:

• Replication processes can counteract degradation and maintain system order by accessing energy and expelling disorder.
• Diversity and coupling of replicated structures enhance self-regulation and adaptation by selecting for efficient resource utilization.
• Selection processes minimize information loss and irreversibility at the structural level, though overall system dissipation increases with proliferation.

Conclusions:

• Replication is a computational process that maintains order far-from-equilibrium, leveraging Landauer's principle and information theory.
• Self-regulation and adaptation in replicating systems are driven by energy access, disorder ejection, and structural diversity.
• This framework offers insights into incremental system changes and broad system behavior, despite challenges in direct application to complex real-world systems.