Maximum Power Efficiency and Criticality in Random Boolean Networks

TL;DR

This paper investigates how random Boolean networks, models of biological systems, optimize power efficiency and operate near criticality, using thermodynamic principles and Landauer's erasure principle to analyze entropy costs.

Contribution

It demonstrates that critical Boolean networks maximize power efficiency, linking system criticality to thermodynamic optimality in biological models.

Findings

Critical Boolean networks maximize power efficiency.

Power efficiency is optimized near the critical point.

Results suggest relevance to biological systems and ecosystems.

Abstract

Random Boolean networks are models of disordered causal systems that can occur in cells and the biosphere. These are open thermodynamic systems exhibiting a flow of energy that is dissipated at a finite rate. Life does work to acquire more energy, then uses the available energy it has gained to perform more work. It is plausible that natural selection has optimized many biological systems for power efficiency: useful power generated per unit fuel. In this letter we begin to investigate these questions for random Boolean networks using Landauer's erasure principle, which defines a minimum entropy cost for bit erasure. We show that critical Boolean networks maximize available power efficiency, which requires that the system have a finite displacement from equilibrium. Our initial results may extend to more realistic models for cells and ecosystems.