Casting Polymer Nets to Optimize Noisy Molecular Codes

TL;DR

This paper models molecular codes as polymer networks to optimize their robustness against recognition noise, revealing a phase transition in the number of meanings encoded, with implications for understanding molecular evolution.

Contribution

It introduces a polymer network framework to approximate the optimization of noisy molecular codes, linking physical models to biological coding efficiency.

Findings

Polymer network statistics can model molecular code noise resilience.

A first-order transition increases the number of meanings encoded.

Population dynamics influence molecular code evolution.

Abstract

Life relies on the efficient performance of molecular codes, which relate symbols and meanings via error-prone molecular recognition. We describe how optimizing a code to withstand the impact of molecular recognition noise may be approximated by the statistics of a two-dimensional network made of polymers. The noisy code is defined by partitioning the space of symbols into regions according to their meanings. The "polymers" are the boundaries between these regions and their statistics defines the cost and the quality of the noisy code. When the parameters that control the cost-quality balance are varied, the polymer network undergoes a first-order transition, where the number of encoded meanings rises discontinuously. Effects of population dynamics on the evolution of molecular codes are discussed.