{"title":"The Genomic Code: the genome instantiates a generative model of the organism.","authors":"Kevin J Mitchell, Nick Cheney","doi":"10.1016/j.tig.2025.01.008","DOIUrl":null,"url":null,"abstract":"<p><p>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 (VAEs), 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 formalization.</p>","PeriodicalId":54413,"journal":{"name":"Trends in Genetics","volume":" ","pages":""},"PeriodicalIF":13.6000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.tig.2025.01.008","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
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 (VAEs), 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 formalization.
期刊介绍：
Launched in 1985, Trends in Genetics swiftly established itself as a "must-read" for geneticists, offering concise, accessible articles covering a spectrum of topics from developmental biology to evolution. This reputation endures, making TiG a cherished resource in the genetic research community. While evolving with the field, the journal now embraces new areas like genomics, epigenetics, and computational genetics, alongside its continued coverage of traditional subjects such as transcriptional regulation, population genetics, and chromosome biology.
Despite expanding its scope, the core objective of TiG remains steadfast: to furnish researchers and students with high-quality, innovative reviews, commentaries, and discussions, fostering an appreciation for advances in genetic research. Each issue of TiG presents lively and up-to-date Reviews and Opinions, alongside shorter articles like Science & Society and Spotlight pieces. Invited from leading researchers, Reviews objectively chronicle recent developments, Opinions provide a forum for debate and hypothesis, and shorter articles explore the intersection of genetics with science and policy, as well as emerging ideas in the field. All articles undergo rigorous peer-review.
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