The challenge of scale in molecular adaptation: Local searches in astronomical genotype networks

TL;DR

This paper investigates how local searches in vast, high-dimensional genotype spaces enable molecular adaptation, revealing that abundant phenotypes bias evolutionary paths and challenge traditional fitness landscape concepts.

Contribution

It integrates viral evolution insights with genotype-phenotype map theory to explain how local searches efficiently drive adaptation despite immense search spaces.

Findings

Local searches enable phenotypic improvements in vast genotype spaces.

Abundant phenotypes bias evolutionary trajectories.

Adaptation remains efficient despite limited exploration.

Abstract

The exploration of vast genotype spaces poses fundamental challenges for evolving populations. As the number of genotypes encoding viable phenotypes grows exponentially with genome length, populations can only explore a tiny fraction of these immense spaces, a fact consistently supported by empirical and theoretical evidence. Paradoxically, local, mutation-driven searches near abundant sequences allow populations to generate phenotypic improvements and functional innovations despite this immense search space. In this contribution, we integrate insights from viral evolution with theoretical expectations derived from genotype-phenotype maps to re-examine how high-dimensional sequence spaces shape evolutionary dynamics. In resolving the paradox, abundant phenotypes play a crucial role because their combinatorial weight biases evolutionary trajectories. We discuss how this bias, together with limited accessibility of fitness peaks, modifies traditional metaphors -- such as fitness landscapes -- and challenges standard notions of evolutionary optimality. Our results underscore that adaptation is predominantly local yet remarkably efficient, providing a unifying perspective on the coexistence of robustness, innovation, and constrained exploration in molecular evolution.