How does the genome encode the form of the organism? What is the nature of this genomic code? Common metaphors, such as a blueprint or program, fail to capture the complex, indirect, and evolutionarily dynamic relationship between the genome and organismal form, or the constructive, interactive processes that produce it. Such metaphors are also not readily formalized, either to treat empirical data or to simulate genomic encoding of form in silico. Here, we propose a new analogy, inspired by recent work in machine learning and neuroscience: that the genome encodes a generative model of the organism. In this scheme, by analogy with variational autoencoders, the genome does not encode either organismal form or developmental processes directly, but comprises a compressed space of latent variables. These latent variables are the DNA sequences that specify the biochemical properties of encoded proteins and the relative affinities between trans-acting regulatory factors and their target sequence elements. Collectively, these comprise a connectionist network, with weights that get encoded by the learning algorithm of evolution and decoded through the processes of development. The latent variables collectively shape an energy landscape that constrains the self-organising processes of development so as to reliably produce a new individual of a certain type, providing a direct analogy to Waddingtons famous epigenetic landscape. The generative model analogy accounts for the complex, distributed genetic architecture of most traits and the emergent robustness and evolvability of developmental processes. It also provides a new way to explain the independent selectability of specific traits, drawing on the idea of multiplexed disentangled representations observed in artificial and neural systems and lends itself to formalisation.
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