January 17, 2025, by Brigitte Nerlich

Synthetic biology in the era of AI: From dominating nature to collaborating with it

Today’s post is a guest post by Christian Gude. He has a PhD in synthetic biology from the University of Nottingham (where we met when I was still doing synbio and RRI at the SBRC) and is now working at Phenotypeca Ltd as IP Analyst in a multidisciplinary role between science and intellectual property. In this blog post, he reflects on his experiences in the field of synbio in the new era of AI.

***

The synthetic biology industry has undergone a dramatic transformation over the past decade, with its initial gold rush mentality giving way to widespread disillusionment. As someone who has worked in synthetic biology since 2016, this shift resonates with my own journey in the field.

After studying biochemistry, my first project involved engineering bacteria to produce nylon precursors from syngas. Even then, I understood the risks of overengineering strains to the point where their metabolic burden would make them “sick.” I also recognised that traditional strain optimisation often traps us in local maxima on the yield landscape, unable to uncover more diverse solutions. Later, I joined a company exploring strain engineering in yeast by combining rational genetic engineering with breeding processes to access a broader landscape of genetic diversity. This experience was a lesson: biology resists simplistic, reductionist engineering paradigms. However, it seems that this lesson is difficult to learn, until markets fail, and trust is lost.

This naturally raises the obvious question: How did it come to pass that an entire industry failed so spectacularly that the market’s trust in its underlying technologies was shaken to its core? And why are reductive metaphors that have shaped synthetic biology’s successes and failures part of that story?

It would be too easy to point fingers at struggling companies like Ginkgo and Zymergen, claiming the problem isn’t systemic but rather the result of a few prominent black sheep who overpromised and underdelivered. Ginkgo marketed itself as a “biotech foundry,” leveraging automation and high-throughput genetic design to program cells for applications ranging from biofuels to flavours and fragrances. Similarly, Zymergen emphasised machine learning and automation in bio-manufacturing, targeting innovative products like high-performance materials. Both companies sold investors on the idea that engineering cells could be as predictable and scalable as coding software.

The crisis of trust isn’t limited to a few companies and their flagship technologies; it has spread like wildfire across the field. We must therefore confront the promises of a young industry that claimed to bend the living world to the rules of the mechanical through engineering prowess. A hubris that, when faced with the complexity of living systems, proved fatal—and precisely because of this, found fertile ground in one place: Silicon Valley. The same Silicon Valley that hailed AI technology and its seemingly boundless artificial creativity, which challenges human inventiveness. Their hope: That once released from the bottle, the AI genie was to revolutionise biotechnology and decipher the “language of the genome.”

It turned out that of all the foolish metaphors sold to Californian tech bros with deep pockets during rehearsed investment pitches, this one was by far the most dangerous: “DNA is the programming language of life, and we’ve cracked the code.”

Years ago, I came across a concept in Erich Fromm’s The Anatomy of Human Destructiveness that reshaped my thinking: biophilia, the love of the living, the complex, and the dynamically changing. The phrase about DNA as executable code is its exact opposite. It ignores the complexity and interconnections of regulatory processes and interactions across the levels of DNA, chromatin, RNA, proteins, their folding, conformations, and modification states, the compartmentalisation of cellular processes, cell-to-cell communication, and metabolism.

Simple monocausal models, which were based on well-researched polymorphisms or on single gene knockouts that produce a clear phenotype, were collected as evidence that life could be controlled. That we brought order to the chaos. That we tamed nature. A story as old as mankind and rooted in the fear of the unknown, the uncontrollable. Understandably, scientists have attempted to exert control to soothe that primordial fear – and in this case it just led to a terrible business case. And what about the unexplored parts of the “operating system”? Dismissed as useless viral bio-ballast accumulated from millions of years of evolution, unworthy of concern, or soon to be integrated into LLMs, which surely would make sense of the nucleotide gibberish in due time.

With this mindset, engineers began reprogramming complex systems—and failed in the face of reality. They worked against nature instead of with it. So, does the industry even have a future? Yes perhaps, but certainly not through rebranding it as ‘Engineering Biology‘ in an attempt to band-aid over past failures and repeating the cycle. It is time to accept that we must stop believing that we can fully understand (and then engineer) complex systems like the eukaryotic cell. The successful biotech CSO of the future won’t be the computer nerd. They will be an intuitive, biophilic type who respects the complexity of their field. They will understand that engineering life is not about taming nature but working with it, embracing its complexity and respecting its unpredictability.

When it comes to engineering life forms, AI must be seen not as a replacement for human intuition but as an artisan’s tool. Strain engineering is less a science than an art, requiring not only data-driven insights but also a respect for the complexity of living systems. The best effects in networked biological systems often emerge from subtle changes working synergistically, a process that AI can assist with, but not fully replace.

Despite its setbacks, synthetic biology is not without hope. If we move beyond the reductive metaphors that have held it back and embrace the complexity of life, the field can find new ways to thrive. By embracing the idea of biophilia and treating AI as a tool, rather than a universal solution or, even worse, a false idol, synthetic biology can find a path forward—one that collaborates with the living world rather than attempting to dominate it.

Image: Pixabay Forest, Jungle, Tropical image

• Share on Facebook
• Share on Twitter
• Share on Google+
• Share on LinkedIn