{
  "id": 1819188,
  "title": "Artificial intelligence boosts automated biolabs",
  "url": "https://urgent.news/2026/08/18/artificial-intelligence-boosts-automated-biolabs",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-18T00:00:18.000Z",
  "source": {
    "name": "Knowable Magazine",
    "slug": "knowable-magazine",
    "url": "https://knowablemagazine.org/content/article/technology/2026/ai-boosts-biofoundries-synthetic-biology"
  },
  "original_language": "en",
  "account": "James Field's automated laboratory in a former London brewery is on the verge of developing a revolutionary drug capable of eliminating cancer cells with unparalleled precision. The drug was not conceived by the biologists in his lab; it was generated by artificial intelligence (AI). Field is a protein engineer in the field of synthetic biology, which employs advanced technology to design new cells or biological components. The iterative process known as DBTL—design, build, test, and learn—is central to this work, where the final product is continuously optimized until it possesses the desired traits. Field's company, LabGenius, is among several startups at the forefront of a revolution in which AI progressively manages the entire DBTL cycle.\n\nBiofoundries, sophisticated automated laboratories, emerged in the 2010s alongside gene editing technology and high-throughput machines, enabling the acceleration of the DBTL cycle through multiple orders of magnitude. These laboratories typically involve adapting DNA to be added to host organisms—often Escherichia coli or yeast—to produce desired molecules, ranging from antibodies to sustainable plastic ingredients. Algorithms have long been instrumental in biofoundries for managing high-throughput machines. However, AI is now capable of iteratively learning from results and suggesting new experimental pathways, significantly reducing the time required to develop new drugs or industrial chemicals from years to months.\n\nPaul Freemont, who oversees synthetic biology at Imperial College London, emphasizes the convergence of automation, data, machine learning, and AI. He is the founding chair of the Global Biofoundries Alliance, an international organization that has since grown to include over 40 members worldwide. Countries like the United States and China are heavily investing in biofoundry capacity, aiming to engineer biology to produce new drugs and industrial products, a technology Freemont believes will become \"essential for mankind.\" However, he acknowledges that significant technical work remains to be done, particularly in the areas of standardization and complexity management. Standardization efforts are underway, with a proposed lexicon and standardized workflow published by Freemont and colleagues in 2025. Another challenge lies in the complexity of biological data, which often lacks standardization and is scattered across various databases. Recent advancements, such as the reconstitution of a 20-step drug-making process in a tobacco plant, highlight the potential for creating next-generation drugs from plant genomes. However, the vast majority of these genomes are still unsequenced and unanalyzed, presenting a significant hurdle for the field.",
  "summary": "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.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "Simon Willison",
        "title": "Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index",
        "url": "https://urgent.news/2026/08/17/qwen-3-8-27b-scores-52-on-the-artificial-analysis-intelligence-index",
        "published": "2026-08-17T23:58:14.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}