Presentation Information
[2ASBA-03]Writing The Future - From DNA to AI
○Julian Jude1 (1. Twist Bioscience (USA))
Keywords:
Synthetic DNA,AI,Screening,antibody engineering,Enzyme optimisation
Artificial intelligence is reshaping how we design, understand, and engineer biological systems — but the true impact of AI depends on how seamlessly digital predictions can be transformed into real biological molecules. In this talk, Julian Jude (Twist Bioscience) explores how Twist’s silicon-based synthetic DNA platform bridges the digital and physical worlds, bringing AI-generated biological designs to life at unprecedented scale and quality.
At the core of this transformation are Twist’s high-fidelity, multiplexed gene fragments — pooled and up to 500 base pairs in length — alongside Twist’s Gene Pools, which extend this capability to fragments up to 1.8 kb. Together, these solutions enable researchers to rapidly and cost-effectively construct complex libraries of proteins, mRNA UTRs, promoters, and regulatory elements directly from in silico models. This scalable pooled DNA approach empowers genome-scale experiments that were previously out of reach in terms of both throughput and quality.
The talk will explore how scientists are leveraging Twist’s synthetic DNA to translate machine learning predictions into functional biological components, driving discovery across antibody engineering, enzyme optimisation, and novel nuclease development. Case studies will demonstrate how Twist’s platform closes the loop between AI-driven molecular design and wet-lab biology — accelerating timelines, improving precision, and unlocking new frontiers in biopharma, industrial biotechnology, and beyond.
At the core of this transformation are Twist’s high-fidelity, multiplexed gene fragments — pooled and up to 500 base pairs in length — alongside Twist’s Gene Pools, which extend this capability to fragments up to 1.8 kb. Together, these solutions enable researchers to rapidly and cost-effectively construct complex libraries of proteins, mRNA UTRs, promoters, and regulatory elements directly from in silico models. This scalable pooled DNA approach empowers genome-scale experiments that were previously out of reach in terms of both throughput and quality.
The talk will explore how scientists are leveraging Twist’s synthetic DNA to translate machine learning predictions into functional biological components, driving discovery across antibody engineering, enzyme optimisation, and novel nuclease development. Case studies will demonstrate how Twist’s platform closes the loop between AI-driven molecular design and wet-lab biology — accelerating timelines, improving precision, and unlocking new frontiers in biopharma, industrial biotechnology, and beyond.
Comment
To browse or post comments, you must log in.Log in