SCIENTIFIC RESEARCH

Machine learning designs gene regulators to slow ageing

04.02.2025

Machine learning is used to design synthetic gene regulators that precisely target specific cell types, outperforming natural sequences. This could enable highly targeted therapies for ageing-related diseases, improving gene therapy precision and reducing off-target effects, potentially enhancing longevity by addressing cellular dysfunction more effectively.

October 2024 – Nature

 

Key takeaways

 

  • Synthetic gene regulators outperform natural ones: Machine-designed regulatory elements drive gene expression with higher precision than natural sequences. These advancements could lead to more effective genetic interventions for age-related diseases by ensuring targeted delivery, reducing unwanted side effects, and optimising therapeutic outcomes in longevity-focused treatments
  • Cell-type precision improves gene therapy: Highly specific gene activation in targeted tissues enhances the safety and effectiveness of treatments. By reducing off-target effects, these synthetic regulators provide a more reliable approach for addressing cellular ageing, tissue degeneration, and other age-related conditions
  • AI accelerates personalised genetic therapies: Machine learning enables rapid design of regulatory sequences tailored to individual needs. With this technology, gene therapies can become more accessible, personalised, and effective in addressing the genetic and epigenetic factors contributing to ageing
  • New tools for anti-ageing interventions: Precision-engineered regulatory elements offer new possibilities for gene therapy, CRISPR applications, and regenerative medicine. These innovations could help slow ageing, repair damaged tissues, and enhance cellular function to improve healthspan and longevity

 

Read the article at: Gosai, Sager J., et al. “Machine-Guided Design of Cell-Type-Targeting Cis-Regulatory Elements.” Nature, vol. 634, 31 Oct. 2024, pp. 1211–1219, https://doi.org/10.1038/s41586-024-08070-z.

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