Perspectives for self-driving labs in synthetic biology (2210.09085v2)

Abstract: Self-driving labs (SDLs) combine fully automated experiments with AI that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

• The paper presents a comprehensive examination of how self-driving labs, with 24/7 autonomy and AI integration, enable high-throughput and reproducible synthetic biology experiments.
• The study details the implementation of closed design-build-test-learn cycles and highlights pioneering examples like robotic gene function determination.
• The paper discusses opportunities and challenges in deploying fully autonomous SDLs, emphasizing integration of AI, CRISPR, and advanced computational methods for sustainable bioproduct development.

Perspectives on the Role of Self-Driving Laboratories in Synthetic Biology

The paper "Perspectives for self-driving labs in synthetic biology" presents a comprehensive examination of how self-driving laboratories (SDLs) can redefine research paradigms, particularly within synthetic biology. SDLs, characterized by full automation and AI-driven experiment cycles, extend the capabilities for high-throughput, reproducible, and scalable scientific investigation. Though partial autonomous systems exist in chemistry and materials sciences, the paper highlights synthetic biology as a unique frontier due to its focus on genetic manipulation as a singular target for a variety of biological outcomes.

Conceptual Framework of SDLs

The SDL framework demands complete autonomy to harness its full potential. This includes 24/7/365 operational cycles, high experiment reproducibility, and increased throughput. Full autonomy in SDLs also allows for large-scale data generation, enriching AI models that depend on extensive datasets. Current SDLs reach level 3 autonomy, involving closed Design-Build-Test-Learn loops, with further advancements in levels expected to decrease human intervention to mere managerial roles.

Existing SDL Examples and Applications

The maturity of automation and ML methods in chemistry and materials science has led to the development of SDLs capable of autonomously optimizing chemical reactions and material properties. Noteworthy implementations include platforms for organic synthesis and nanomaterial manufacturing. In synthetic biology, pioneering efforts like the development of Adam and Eve, the first robotic scientists, demonstrated SDL capabilities for gene function determination and drug repurposing.

Opportunities and Challenges in Synthetic Biology

Synthetic biology benefits from unique advantages such as the centralized genomic information that can be easily manipulated. CRISPR-enabled gene-editing technologies elevate these capabilities by facilitating intricate genetic modifications. This accessibility presents unprecedented potential for SDL applications in synthetic biology, promising broad societal impacts from sustainable manufacturing to personalized medicine.

However, transitioning fully autonomous technologies from concept to practice involves tackling nascent automation capabilities in biology and the integration of computational and experimental skill sets, areas that current educational curricula may not adequately address.

Implications and Speculation on Future Developments

The paper posits numerous transformative implications for SDLs, advancing scientific understanding through systematic experimentation and bioproduct development. It identifies critical challenges requiring SDL intervention, such as optimizing microbial strains for bio-production, mapping regulatory networks, and studying extraterrestrial biological behavior. Theoretical implications involve SDLs contributing to a more mechanistic and predictable understanding of biological systems, ultimately enabling the inverse design of biological entities.

Anticipated future expansions involve the development of AI that can seamlessly integrate prior knowledge, contextualize discoveries, and produce scientifically interpretable outputs. This requires foundational models capable of extending the understanding gained from SDLs across related experiments and conditions.

Conclusion

In conclusion, the paper underscores the value of SDLs in propelling the boundaries of scientific inquiry and synthetic biology applications. While the full realization of SDLs poses technical, sociological, and ethical challenges, their development promises substantial advancements in scientific research methodologies. The envisioned future for SDLs is not without risks, including potential misuse and alignment with societal values, which requires careful management and governance. As akin to the Human Genome Project, SDLs have the potential to fundamentally transform biological research and its contributions to technology and society.