Nobel Prize in Chemistry 2024From Knowable Magazine, August 18:
AI and machine learning are kicking synthetic biology up to new levels of innovation. Researchers see huge potential for novel drugs and other chemicals; some also see risks.
James Field believes his automated laboratory is closing in on a powerful new drug to kill cancer cells with unprecedented precision. Yet the drug wasn’t created by any of the biologists working in his lab — it was generated by artificial intelligence.
Field is a protein engineer working in synthetic biology, a discipline that uses the latest technology to engineer novel cells or biological components. In his world, researchers often talk about a cycle known as DBTL — design, build, test, learn — an iterative process where the end product, whether it is a molecule or a new strain of bacteria, is repeatedly optimized until it has the traits scientists seek. Field’s company, LabGenius, is one of a group of startups that is at the forefront of a revolution in which AI increasingly runs the DBTL cycle from beginning to end.
The work takes place at the company’s sophisticated automated laboratory in a former biscuit factory in south London. Such laboratories, known as biofoundries, first emerged in the 2010s as gene editing technology, combined with “high-throughput” machines — which can conduct hundreds of experiments at once — accelerated the DBTL cycle by many orders of magnitude.
Experiments using biofoundries often involve adapting a piece of DNA that is then added to a host organism — frequently the bacterium Escherichia coli or yeast — to get it to produce a desired molecule, which can be anything from an antibody to an ingredient for sustainable plastic.
Algorithms have been integral to biofoundries from the start in managing the operation of high-throughput machines. But the industry is now approaching a point where AI models are becoming sophisticated enough to program DNA to produce new medicines and industrial chemicals, an emerging field known as SynBioxAI.
Rather than helping only to run the machinery, AI can now work iteratively, learning from results and suggesting new avenues of experimentation, reducing the development time for new drugs or industrial chemicals from years to months.
According to Paul Freemont, who heads synthetic biology at Imperial College London, we are approaching a powerful point of “convergence of automation, data and machine learning and AI.” He was the founding chair of the Global Biofoundries Alliance, an international group of publicly funded biofoundries that launched in 2019 in Kobe, Japan, with 16 members. The group has now grown to over 40 members across the world, spread from China to Mexico.
Since that launch, countries have been rapidly investing in biofoundry capacity. The United States, for example, is spending $75 million on the development of five new biofoundries, while China has named biomanufacturing and synthetic biology as core strategic priorities for its 2026-2030 five-year plan.
The objective is to engineer biology, programming cells to produce new drugs and industrial products, a technology that Freemont says is going to “become essential for mankind.”
However, he believes the field hasn’t fully reached this point of convergence. “I think there’s a massive amount of technical work that needs to be done to achieve the vision of what people are trying to do,” he says....
....MUCH MORE
*Some previous posts:
July 2021 - NVIDIA Opens UK's Fastest Supercomputer To Outside Researchers, Academic and Commercial
NVIDIA Claims Install of UK’s Top Supercomputer, for Research in AI and Healthcare
Announced last October, NVIDIA today launched Cambridge-1, calling it the United Kingdom’s most powerful supercomputer. Enabling scientists and healthcare experts to use the combination of AI and simulation to accelerate the digital biology revolution, Cambridge-1 represents a $100 million investment by NVIDIA.....
March 2023 - "The Biorevolution: Its Implications for U.S. National Security, Economic Competitiveness, and National Power"
The author of this piece, Dr. Tara O'Toole is Senior Vice-President of the CIA's venture capital arm, In-Q-Tel.
January 2024 - Boston Consulting Group: "Synthetic Biology Is Getting Closer to Industrial Scale"
January 2024 - We Are Going To Hear More And More About Digital Biology (NVDA)
February 2024 - Taking Nvidia's Jensen Huang Seriously: Paris-Based "Bioptimus primed with $35m to unravel disease biology using AI"
Two pitches that Nvidia's CEO will be making at next month's NVDA lovefest (Nvidia GTC Conference, March 17 - 21, 300,000 attendees in-person and online) are 1) sovereign AI, every country, even every city needs its own supercomputer powered by Nvidia chips and its own Large Language Model trained on those supercomputers and 2) digital biology - everything from rapid drug discovery to individually tailored gene therapy.*
February 2024 - "17 key takeaways from the 2024 J.P. Morgan Healthcare Conference"
April 2024 - "RAND: 'Artificial Intelligence and Biotechnology: Risks and Opportunities"
October 2024 - Paris-based baCta Is Using Engineered Bacteria to Grow Natural Rubber and Slash CO2 Emissions
AI-powered synthetic biology is so hot right now.
April 2026 - "Genomics Has Revealed An Age Undreamed Of"
This piece was published at Palladium magazine in 2023, so a few years after Nvidia's Jensen Huang started talking about AI and medicine and artificial biology*, but before the possibilities really started to become apparent. Now genomics looks to be very investable.**
In many ways the vibe feels similar to the zeitgeist around chips and training AI a dozen years ago.
I don't know if it is going to work out as well as 2013's "Why Is Machine Learning (CS 229) The Most Popular Course At Stanford?"—which was followed by 2014's Deep Learning is VC Worthy—which was followed by 2015-to-date: "Saaaay, this Nvidia may be on to something."
But we shall see.
