The Synergistic Potential of AI and Synthetic Biology: Interview with S&P Global’s Miriam Fernández
The rapid convergence of artificial intelligence (AI) and synthetic biology (synbio) is set to revolutionize a multitude of industries, ranging from healthcare and agriculture to renewable energy and environmental remediation. The integration of AI's data-processing capabilities with synbio's genetic manipulation technologies heralds a new era of biological innovation, presenting both unprecedented opportunities and significant challenges.
At the core of synbio is the ability to read, edit, and write DNA, enabling the creation of new organisms and the reengineering of existing ones. This process is inherently data-intensive, as even the simplest organisms contain vast amounts of genetic information. AI's proficiency in handling large datasets and uncovering hidden patterns makes it an indispensable tool in this context. By leveraging machine learning algorithms, researchers can accelerate the identification of promising genetic modifications, streamline experimental processes, and reduce costs.
RELATED: From Gene Editing to Pathway Design: How AI is Transforming Synthetic Biology
The concept of genetic manipulation is not new, tracing back to ancient agricultural practices and the foundational work of Gregor Mendel in the 19th century. However, synbio represents a paradigm shift by enabling direct DNA alteration through advanced technologies like CRISPR. This shift has been accelerated by technological advancements at the intersection of genomics and digitization. The cost of DNA sequencing, for instance, has plummeted from $100 million in 2001 to around $600 today, thanks to the progress in sequencing equipment, as well as innovations in AI and data science.
Despite its potential, the integration of AI and synbio also presents substantial risks. These include ecological disruptions from synthetic organisms, biosecurity threats, and ethical dilemmas related to human genome editing. The scientific community and policymakers must navigate these challenges carefully, balancing innovation with safety and ethical considerations.
A recent report by Miriam Fernández, CFA at S&P Global, titled "Artificial Intelligence Powering Synthetic Biology: The Fundamentals," explores the progress dynamics of AI in synbio and sheds light on the opportunities and challenges in this emerging field.
We reached out to Miriam to provide additional perspective and follow-up thoughts on the report findings and broader implications for regulation, public perception, and global challenges.
Andrii: The integration of AI with synthetic biology involves a complex interplay of biology, engineering, and data science. How do you envision interdisciplinary collaboration evolving in this field, and what steps can be taken to foster effective communication and cooperation among diverse scientific communities?
Miriam: The interdisciplinary collaboration among biology, engineering, and tech isn’t new, but the tech stack available, including generative AI, opens up new possibilities and methods for facilitating imagination, prototyping, and collaboration. This would presumably improve scientists’ ability to design, edit, test, and industrialize the production of organisms, supporting smoother asynchronous collaboration to share insights and expertise quickly across the digitally enabled part of the world. We’ve published material on this in both articles and podcasts, the links of which are available below.
- Article: AI In Healthcare: A Path To Long-Term Immunity?
- Podcast: Artificial Intelligence Insights, Episode 1: AI in Healthcare with Norma Padron and Ignacio Medrano
Andrii: Given the rapid advancements in synthetic biology and AI, what role do you believe policymakers should play in regulating these technologies? Can you suggest any specific regulatory measures that could help balance innovation with safety and ethical considerations?
Miriam: We’re prohibited from providing advice, so please consider this in the context of these remarks. We think that some regulators, including the EU, are leading the way in providing a human-centric and risk-based approach that should help control and mitigate the risks born from AI, but also enable innovation when those risks are low. Regulations in the U.S., for example, tend to follow a sector-based approach which is intended to increase safety, decrease risks, etc., with specific structures in place designed to promote safe, risk-managed innovation. Separately, we think the scientific community and policymakers are relatively well placed (compared with other industries) to develop safe and trustworthy policies around AI-synthetic biology because they have been working closely for decades in designing and applying protocols in areas such as biosecurity or gene editing.
Andrii: Your report addresses the ethical implications of synthetic biology and AI. How do you think public perception and societal acceptance will influence the development and implementation of these technologies? What strategies can be employed to educate and engage the public in these discussions?
Miriam: On one side, the massive diffusion of generative AI to society -- that we have been observing since late 2022 -- should gradually influence society’s perception of what is possible and acceptable. We can think of IVF in the 80s and how society evolved their thinking towards that paradigm. Something similar can happen with AI & synbio when it comes to human genetic changes – we have some specific risks enumerated in the Synthetic Biology piece, around issues like consent, unintended consequences of genome editing, etc.
Andrii: The report highlights the potential of synthetic biology and AI to address global challenges such as climate change and food security. How do you see these technologies contributing to the United Nations' Sustainable Development Goals (SDGs), and what are some key projects or initiatives that exemplify this potential?
Miriam: On the “zero hunger” goal, there are several projects that involve providing nutritious meals for people at risk and also some that relate more generally to AI projects. Applying AI & synbio towards zero hunger seems to be at early stages of implementation, but we recognize that the potential is vast. Companies like MNDL Bio working on improving nutrition levels of plant-based food is an example. Equally, in agriculture, AI & Synbio could support the creation of sustainable, effective fertilizers to boost production.
On the “climate action” front, there are several angles where AI & synbio can contribute: for example, waste-to-energy transformation, transforming CO2 into other materials, and using algae for decarbonization and desalinization (also supporting the “clean water and sanitation” SDG). More examples can be found on the resource website from SGD Synthetic Biology | Sustainable Development Goals - Resource Centre (relx.com).
Andrii: As both of us are based in Spain, I'm particularly interested in understanding how you see this country fitting into the global landscape of synthetic biology and AI. What strengths do you believe Spain brings to this field, and what opportunities exist for Spain to become a leader in synbio and AI innovations? Are there specific Spanish initiatives, research centers, or companies that you find particularly promising in this space?
Miriam: The global synthetic biology market seems rather fragmented, yet the US seems to be dominant (with most Synbio companies in the top-10 being US-based, ordered by revenue). This doesn’t come as a surprise and is consistent with the higher R&D spending (both for AI and in general) by the US compared with the EU, for instance. Unfortunately, I don’t have more granular information than this – related, my colleague wrote an interesting article about labor and investment as key factors to unlock AI’s potential: Investment And Talent Are The Keys To Unlocking AI's Potential.
Andrii: Looking ahead, what do you think will be the most transformative innovations emerging from the intersection of AI and synthetic biology in the next decade? Conversely, what are some of the biggest challenges that researchers and industry leaders will need to overcome to realize these innovations?
Miriam: We think the transformative potential will come with the scalability of synbio powered by AI. By having improved “scientific big data” and combining that with other real world data extracted from IoT sensors/devices, the results of AI models should also improve. Additionally, scaling up the creation of organisms will imply that the production process from design-to-end can run more effectively, cheaper, and faster (powered by digital twins for testing, for example). As a consequence, projects that are now just in testing mode should be able to run at scale. Ultimately, this could be a tremendous opportunity to facilitate the roadmap to many of the UN’s SDGs.
Conversely, some of the key challenges to overcome will be to anticipate and mitigate the potential threats to ecosystem imbalances from the creation of new organisms, and to continue improving digital operational resilience around AI & synbio processes, to prevent access from malevolent actors that could create biosecurity risks.
Topics: Industry Trends
