For example, researchers at Imperial College were investigating how certain “pirate phages”—these fascinating viruses that hijack other viruses—manage to break into bacteria. Understanding these mechanisms could open up entirely new ways of tackling drug-resistant infections, which is obviously a huge global health challenge.
What Co-scientist brought to this work was the ability to rapidly analyze decades of published research and independently arrive at a hypothesis about bacterial gene transfer mechanisms that matched what the Imperial team had spent years developing and validating experimentally.
What we’re really seeing is that the system can dramatically compress the hypothesis generation phase—synthesizing vast amounts of literature quickly—whilst human researchers still design the experiments and understand what the findings actually mean for patients.
Looking ahead to the next five years, besides proteins and materials, what is the “unsolved problem” that keeps you up at night that these tools can help with?
What genuinely excites me is understanding how cells function as complete systems—and deciphering the genome is fundamental to that.
DNA is the recipe book of life, proteins are the ingredients. If we can truly understand what makes us different genetically and what happens when DNA changes, we unlock extraordinary new possibilities. Not just personalized medicine, but potentially designing new enzymes to tackle climate change and other applications that extend well beyond health care.
That said, simulating an entire cell is one of biology’s major goals, but it’s still some way off. As a first step, we need to understand the cell’s innermost structure, its nucleus: precisely when each part of the genetic code is read, how the signaling molecules are produced that ultimately lead to proteins being assembled. Once we’ve explored the nucleus, we can work our way from the inside out. We’re working toward that, but it will take several more years.
If we could reliably simulate cells, we could transform medicine and biology. We could test drug candidates computationally before synthesis, understand disease mechanisms at a fundamental level, and design personalised treatments. That’s really the bridge between biological simulation and clinical reality you’re asking about—moving from computational predictions to actual therapies that help patients.
This story originally appeared in WIRED Italia and has been translated from Italian.
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