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What does evolution make? Learning in living lineages and machines.

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Genomic information creates self-constructing organisms through developmental genetics and machine learning. Evolution and learning share symmetries, with the genome acting as a generative model for problem-solving.

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Area of Science:

  • Developmental Genetics
  • Machine Learning
  • Evolutionary Biology
  • Neuroscience

Background:

  • Understanding how genomic information leads to complex, problem-solving organisms is a fundamental biological question.
  • The relationship between genes and traits is intricate, involving physiological computations and plasticity.
  • Traditional approaches often view the genotype-phenotype map as complex and indirect.

Purpose of the Study:

  • To review recent progress unifying developmental genetics and machine learning (ML) for gene-trait mapping.
  • To highlight the parallels between evolution and learning, framing the genome as a generative model.
  • To explore how ML and neuroscience offer quantitative formalisms for understanding evolutionary learning and developmental processes.

Main Methods:

  • Review of current research integrating developmental genetics and ML.
  • Emphasis on the concept of the genome as a generative model.
  • Application of ML and neuroscience principles to understand biological information processing.

Main Results:

  • Identified deep symmetries between evolutionary processes and machine learning.
  • Characterized physiological computations as a source of plasticity and robustness, not just complexity.
  • Demonstrated the utility of ML and neuroscience formalisms for interpreting evolutionary learning and morphogenesis.

Conclusions:

  • The genome can be understood as a generative model, shaped by evolutionary learning.
  • Physiological computations are key to organismal robustness and problem-solving.
  • This interdisciplinary approach offers new insights into genetics, evolutionary developmental biology, regenerative medicine, and synthetic morphoengineering.