The Genomic Code: The genome instantiates a generative model of the organism

TL;DR

This paper proposes that the genome functions as a generative model of the organism, similar to machine learning models, explaining complex genetic architecture and developmental robustness.

Contribution

It introduces a novel analogy between the genome and generative models like variational autoencoders, providing a formal framework for understanding genetic encoding.

Findings

Genome encodes a generative model of the organism

Explains distributed genetic architecture and robustness

Links evolution, development, and formal modelling

Abstract

How does the genome encode the form of the organism? What is the nature of this genomic code? Inspired by recent work in machine learning and neuroscience, we propose that the genome encodes a generative model of the organism. In this scheme, by analogy with variational autoencoders, the genome comprises a connectionist network, embodying a compressed space of latent variables, with weights that get encoded by the learning algorithm of evolution and decoded through the processes of development. The generative model analogy accounts for the complex, distributed genetic architecture of most traits and the emergent robustness and evolvability of developmental processes, while also offering a conception that lends itself to formalisation.