# Hidden long evolutionary memory in a model biochemical network

## TL;DR

This paper presents a minimal model demonstrating that biochemical networks can develop long-term evolutionary memory through neutral drift, revealing hidden constraints in sequence space that influence network evolution.

## Contribution

It introduces a sequence-based mutational model for protein-interaction networks, uncovering hidden long-term evolutionary memory and constraints in network evolution.

## Key findings

- Neutral drift increases network complexity without selective pressure
- Hidden order in sequence space leads to long-term evolutionary memory
- Topology of accessible sequence space constrains network evolution

## Abstract

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.