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Towards AI-designed genomes using a variational autoencoder.

Natasha K Dudek^1,2, Doina Precup^1,2

¹School of Computer Science, McGill University, Montreal, QC H3A 0G4, Canada.

Proceedings. Biological Sciences

|December 10, 2024

Summary

This summary is machine-generated.

This study introduces DeepGenomeVector, a machine learning model that reconstructs bacterial genome composition from incomplete data. This AI framework shows promise for advancing synthetic biology and potentially designing novel genomes.

Keywords:

VAE bioinformatics generative AI machine learning microbial genomics variational autoencoder

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Area of Science:

Computational Biology
Genomics
Synthetic Biology

Background:

Biological systems and genomes are highly complex, limiting human comprehension.
Modeling gene interactions is crucial for understanding and engineering life.

Purpose of the Study:

To develop a machine learning framework for modeling bacterial genome composition.
To demonstrate the capability of AI in understanding and potentially designing genomes.

Main Methods:

Representing genomes as binary "genome vectors" indicating gene presence.
Training a denoising variational autoencoder (DeepGenomeVector) to reconstruct masked genome vectors.
Evaluating model performance using Area Under the Receiver Operating Curve (AUROC) and F1 scores.

Main Results:

DeepGenomeVector effectively captures complex dependencies within genomic networks.
The model achieved high performance with AUROC of 0.98 and F1 score of 0.83 on the test set.
Generated genome vectors encoded interconnected, complete, and ecologically cohesive pathways.

Conclusions:

Machine learning offers powerful tools for synthetic biology applications.
AI agents may eventually be capable of designing functional genomes for artificial cells.