Treat DNA Like Code: Transformer Models for De Novo DNA Sequence Optimization

This workshop aims to provide participants with an understanding of how transformer models in artificial intelligence can be used to optimize de novo DNA sequences for synthetic biology applications. Focusing on how AI can treat DNA sequences like code, participants will learn how these models predict sequence behavior, enhance gene synthesis, and streamline the design of optimized, functional genetic constructs. The program bridges machine learning, genomics, and synthetic biology to advance genetic engineering.

As DNA sequencing technology advances, the need for accurate, efficient DNA sequence optimization becomes paramount in synthetic biology. Designing functional genetic constructs often involves trial-and-error methods, which are resource-intensive and time-consuming. Transformer models, which have shown remarkable success in natural language processing tasks, are now being applied to DNA sequence optimization by treating DNA sequences like coding language. By using large-scale data and pattern recognition, transformer models can predict the function and behavior of gene sequences, leading to more efficient designs for synthetic biology.

This workshop introduces participants to transformer-based approaches to optimize DNA sequences for a variety of applications, including gene synthesis, CRISPR guide RNA design, metabolic engineering, and more. Participants will gain practical insights into how AI models like BERT, GPT, and T5 can be adapted for DNA sequence design, enabling faster and more accurate optimization. Dry-lab hands-on exercises will allow participants to work with AI tools to optimize de novo sequences for improved expression and functionality in living systems.

• Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
• Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
• University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
• Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
• Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.

Participants will be able to:

• Understand how transformer models can be applied to DNA sequence design.
• Learn how to use AI for optimizing gene synthesis and CRISPR guide design.
• Predict the functionality of DNA sequences and optimize genetic constructs using machine learning.
• Gain experience using AI tools for real-world DNA sequence optimization.
• Explore the role of transformers in synthetic biology and other applications like metabolic engineering.