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Genotypic-phenotypic landscape computation based on first principle and deep learning.

Yuexing Liu1, Yao Luo2, Xin Lu1

  • 1Guangzhou Laboratory, Guangzhou, Guangdong Province 510005, China.

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|May 3, 2024
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Summary
This summary is machine-generated.

We developed a new Phenotypic-Embedding theorem to map genotypes to fitness, enabling accurate prediction of viral evolution and immune escape mutations. This computational biology approach creates genotype-phenotype landscapes.

Keywords:
SARS-CoV-2deep learninggenotype-fitness landscapeimmune escapeinterpretabilitythe relative basic reproduction number (R0)

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Area of Science:

  • Theoretical and Computational Biology
  • Evolutionary Biology
  • Genetics

Background:

  • Mapping genotype to phenotype and fitness is crucial for understanding evolution but remains a significant challenge.
  • Existing methods lack quantitative precision in bridging the genotype-fitness gap.

Purpose of the Study:

  • To introduce the Phenotypic-Embedding (P-E) theorem as a novel framework for quantitatively linking genotype to phenotype.
  • To establish a computable basis for a general first principle correlating genotype and phenotype.
  • To develop a computational model for simulating viral evolution and predicting immune escape mutations.

Main Methods:

  • Developed the Phenotypic-Embedding (P-E) theorem using an encoder-decoder deep learning framework.
  • Proposed a general first principle for genotype-phenotype correlation.
  • Created a Co-attention based Transformer model for genotype-fitness mapping.
  • Applied the P-E theorem to calculate the basic reproduction number (R0) of SARS-CoV-2.

Main Results:

  • Accurately simulated neutral evolution of viruses and predicted immune escape mutations.
  • Quantitatively linked viral immune escape to viral fitness.
  • Successfully derived the basic reproduction number (R0) of SARS-CoV-2 from first principles.
  • Constructed a genotype-fitness landscape.

Conclusions:

  • The P-E theorem provides a general, interpretable method for constructing genotype-phenotype landscapes.
  • This work establishes a new paradigm for theoretical and computational biology.
  • The developed model accurately predicts viral evolution and immune escape, with implications for public health and vaccine development.