Using AI Agents and Bio-Foundation Models to Automate Vector Design for Cell Line Development
In this GEN webinar, Asimov’s VP of Business Development and Commercial Strategy Jamie Freeman, PhD, will show how Asimov combines bio-foundation models, AI agents, and synthetic biology to tailor expression vector design for each molecule. The post Using AI Agents and Bio-Foundation Models to Automate Vector Design for Cell Line Development appeared first on GEN - Genetic Engineering and…
In a GEN webinar, Asimov's VP of Business Development and Commercial Strategy Jamie Freeman, PhD, unveiled how the company integrates bio-foundation models, AI agents, and synthetic biology to automate vector design for cell line development, particularly for complex molecules. Freeman elaborated on how Kernel, Asimov's AI-driven genetic design software, aids scientists in automating the creation of optimized expression vectors.
The presentation included case studies demonstrating how AI-designed vectors surpassed Asimov's earlier designs, how real-time stable pool data influenced the advancement of a complex lead, and how a predictive model assisted in achieving correct pairing ratios without compromising titer.
Key insights from the webinar highlighted the potential benefits of exploring genetic design space to improve titer and product quality for both standard and intricate formats. Freeman also discussed how AI models trained on intracellular measurements can learn expression mechanisms and employ them in vector design. Additionally, the integration of model-guided workflows and stable pools facilitated more informed vector design and lead selection decisions.
The webinar concluded with a live Q&A session, allowing attendees to engage with the expert panelist.
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