For decades, DNA synthesis has been the limiting reagent in synthetic biology — reliable for short sequences, but increasingly error-prone and costly as designs scale. That ceiling is now cracking. New enzymatic synthesis platforms, error-correction chemistries, and assembly pipelines are extending what’s possible, opening the door to rapid construction of full pathways, microbial genomes, and even mammalian chromosomes. This session will explore how innovators are breaking past barriers, what technical and economic breakthroughs are needed next, and how longer, cheaper, and faster synthesis could fundamentally change how we design biology at scale.

AI-enabled, self-driving labs are still emerging, but their foundations are already transforming how teams design, run, and interpret experiments. This session offers a practical guide for scientists and R&D leaders who want to understand what can be done today — from tightening design–test–learn loops to reducing manual error and capturing early benefits of autonomous experimentation. Rather than presenting an unrealized future, speakers will focus on practical, real-world steps that give organizations a competitive edge as SDL capabilities evolve and mature. Speakers will explore what’s working, what’s not, and how autonomous lab systems are reshaping protein engineering, pathway optimization, and therapeutic design.

Biology is entering an era where AI agents don’t just analyze data — they co-design, plan, and execute experiments. Multi-agent systems like CRISPR-GPT demonstrate how AI can act as a true lab co-pilot: decomposing complex genome editing projects into stepwise workflows, selecting tools, troubleshooting, and even drafting protocols that allow junior researchers to perform sophisticated edits on their first attempt . Beyond CRISPR, new systems like BioMARS integrate reasoning agents with robotics, while biotech companies are testing “AI lab assistants” that monitor and adjust experiments in real time. This session explores how multi-agent copilots are making biology more reproducible, democratizing complex workflows, and pushing the boundaries of lab autonomy. The central question: when AI can plan, troubleshoot, and validate experiments end-to-end, how should scientists and institutions govern this new power?

For decades, meaningful progress in biotechnology depended on access to million to billion-dollar facilities, specialized infrastructure, and industrial-scale equipment. Today, that paradigm is rapidly shifting. A new generation of tools, from smart shake flasks and modular bioreactors to microfluidic platforms, desktop DNA printers, and compact sequencing devices; is compressing the scale of biological experimentation while expanding who can participate. These technologies are transforming the economics of innovation, enabling startups, academic labs, and distributed research teams to design, build, and test biological systems without massive capital investment. As instrumentation becomes smaller, smarter, and increasingly automated, biology is moving from centralized mega-facilities toward a more distributed model of experimentation. This session explores how advances in lab automation, miniaturized bioreactors, and accessible bioinstrumentation are lowering the barriers to experimentation — and what this shift means for the speed, diversity, and geography of the next wave of bioinnovation.

Reading, writing, and editing DNA were just the prelude. The next frontier is composition, designing complex genetic systems and large DNA architectures from first principles using AI-driven models and scalable synthesis technologies. As datasets grow and design tools mature, biology is shifting from incremental editing toward intentional genome-scale engineering. This new paradigm treats DNA not simply as a sequence to modify but as a programmable substrate where genes, regulatory elements, and entire genomic regions can be composed, tested, and iterated like engineered systems. Advances in generative design, large-scale DNA assembly, and precision integration technologies are enabling researchers to construct increasingly complex genetic structures with higher predictability and functional intent. From next-generation recombinases and genome restructuring platforms to AI-guided design workflows that bridge computation and physical DNA construction, the emerging toolkit is redefining how biological complexity is created. The session explores how compositional genome engineering could unlock new capabilities across therapeutics, industrial biology, and synthetic life design.