A model for the emergence of the genetic code as a transition in a noisy information channel

TL;DR

This paper models the emergence of the genetic code as a phase transition in a noisy information channel, driven by error minimization and constrained by the topology of codon confusion, offering a new evolutionary perspective.

Contribution

It introduces a phase transition framework for genetic code evolution based on error minimization in a noisy channel, linking topology to amino acid diversity.

Findings

Genetic code emergence occurs at a supercritical transition in a noisy channel.

The topology of codon confusion constrains the maximum number of amino acids.

Emergent code modes are governed by smooth Laplacian modes related to error-graph topology.

Abstract

The genetic code maps the sixty-four nucleotide triplets (codons) to twenty amino-acids. Some argue that the specific form of the code with its twenty amino-acids might be a 'frozen accident' because of the overwhelming effects of any further change. Others see it as a consequence of primordial biochemical pathways and their evolution. Here we examine a scenario in which evolution drives the emergence of a genetic code by selecting for an amino-acid map that minimizes the impact of errors. We treat the stochastic mapping of codons to amino-acids as a noisy information channel with a natural fitness measure. Organisms compete by the fitness of their codes and, as a result, a genetic code emerges at a supercritical transition in the noisy channel, when the mapping of codons to amino-acids becomes nonrandom. At the phase transition, a small expansion is valid and the emergent code is governed by smooth modes of the Laplacian of errors. These modes are in turn governed by the topology of the error-graph, in which codons are connected if they are likely to be confused. This topology sets an upper bound - which is related to the classical map-coloring problem - on the number of possible amino-acids. The suggested scenario is generic and may describe a mechanism for the formation of other error-prone biological codes, such as the recognition of DNA sites by proteins in the transcription regulatory network.