Inference of higher order substitution dynamics by Markov chain lumping

TL;DR

This paper uses Markov chain lumping to identify higher order amino acid substitution groups from empirical data, providing a more accurate, assumption-free understanding of genetic code evolution and substitution dynamics.

Contribution

It introduces a novel application of Markov chain lumping to derive amino acid groups directly from empirical substitution matrices without relying on prior assumptions.

Findings

Identifies higher order amino acid substitution groups

Derives aggregation from first principles

Captures multi-level substitution structure more accurately

Abstract

We apply Markov chain lumping techniques to aggregate codons from an empirical substitution matrix. The standard genetic code as well as higher order amino acid substitution groups are identified. Since the aggregates are derived from first principles they do not rely on system dependent assumptions made beforehand, e.g. regarding criteria on what should constitute an amino acid group. We therefore argue that the acquired aggregations more accurately capture the multi-level structure of the substitution dynamics than alternative techniques.