# Origin of life in a digital microcosm

## TL;DR

This study uses digital evolution to explore the origin of self-replicating entities, analyzing their network structure, evolvability, and competitive success to shed light on early life emergence.

## Contribution

It provides a comprehensive analysis of minimal-genome self-replicators in a digital system, revealing how genomic architecture influences evolvability and competitive advantage.

## Key findings

- Evolvability depends on genomic architecture.
- Progenitor genotypes cluster in a small region of replicator space.
- Probability of out-competing others varies among replicators.

## Abstract

While all organisms on Earth descend from a common ancestor, there is no consensus on whether the origin of this ancestral self-replicator was a one-off event or whether it was only the final survivor of multiple origins. Here we use the digital evolution system Avida to study the origin of self-replicating computer programs. By using a computational system, we avoid many of the uncertainties inherent in any biochemical system of self-replicators (while running the risk of ignoring a fundamental aspect of biochemistry). We generated the exhaustive set of minimal-genome self-replicators and analyzed the network structure of this fitness landscape. We further examined the evolvability of these self-replicators and found that the evolvability of a self-replicator is dependent on its genomic architecture. We studied the differential ability of replicators to take over the population when competed against each other (akin to a primordial-soup model of biogenesis) and found that the probability of a self-replicator out-competing the others is not uniform. Instead, progenitor (most-recent common ancestor) genotypes are clustered in a small region of the replicator space. Our results demonstrate how computational systems can be used as test systems for hypotheses concerning the origin of life.