On one end, there is a gentle tug from the ghosts of Barbara McClintock, Martha Chase, and Alfred Hershey, reminding you of their elegant experiments that became part of the canon of genetics. Farther along, figures like Jim Watson grip the thread more fervently as they advocate for the centrality of their discoveries in the birth of molecular biology. If you put one hand in front of the other and continue to follow where it takes you, you’ll pass through the rise of genomics and end up on the frontier of biology.
Of course, I’m talking about Cold Spring Harbor Laboratory. For over one hundred years, this little research institute in Long Island, New York has punched well above its weight. CSHL played a critical role in multiple paradigm shifts in biology—including genetics, molecular biology, and genomics—as evidenced by the eight Nobel Prizes awarded to researchers from “The Lab” over the years. When normalizing for size, the Nature Index ranked CSHL as the most prolific biomedical research institution in the world.
I’ll never forget my first visit to The Lab. In February of 2020, I flew from Seattle to interview for the CSHL graduate school program. Famously (among researchers on the grad school interview circuit), they would arrange for each recruit to be picked up in a black car from the airport.
The campus itself, which is a direct physical representation of the magical thread that The Lab preserves, is equally memorable. A cluster of pristinely maintained colonial buildings, each painted white, borders the water. Above them is the Upper Campus, consisting of darker, modern renditions of the same pattern. Scientific art installations—like the Waltz of the Polypeptides or a gazebo with a phage structure on the tip—can be found along the walking trails.
Over the course of three days, I hurried around The Lab for a wide range of activities, including eleven interviews with faculty—two to three times the number that most other graduate school programs typically scheduled. It was wonderful and intense.
Ultimately, I was persuaded to go west for graduate school. Thankfully, there are many reasons to continue coming back to CSHL, which has been described as “the crossroads of biology.” Each year, they host dozens of conferences and courses that draw top researchers from around the world.
But one particular conference stands out in importance. Since 1933, CSHL has hosted an annual Symposium on Quantitative Biology. Reginald Harris, who conceived of the conference, wrote that the “primary motive of the conference symposia is to consider a given biological problem from its chemical, physical and mathematical, as well as from its biological aspects.” In retrospect, this was visionary.
Over the next several decades, chemists and physicists would revolutionize the life sciences. In 1944, Erwin Schrödinger, a leading physicist, wrote What is Life?, a book exploring open questions in biology through a new lens. It inspired many researchers and students, including a young James Watson, to pursue biological research. In 1953, at the 20th annual CSHL Symposium, Watson presented the structure of DNA for the first time in public.
For obvious reasons, this gave the CSHL Symposia a sort of “mythic quality” moving forward. This reputation compounded quickly. Over the next 15 years, the pioneers of molecular genetics would travel each year to present their most important discoveries—such as the central dogma and the genetic code—at CSHL.
The tradition continues to this day. Each year, the Symposium is organized around a topic considered to represent the frontier of life sciences research.
Which brings us to the topic of the 90th Cold Spring Harbor Laboratory Symposium on Quantitative Biology: AI in Biology.
Readers of this newsletter are not strangers to the fact that AI is reshaping biology. The tools derived from breakthroughs such as AlphaFold have been adopted by seemingly all biologists at this point. But it was stunning to see these advances celebrated so prominently in this venue. It felt historical.
As Bruce Stillman, CSHL’s current President, pointed out in his opening remarks, this topic connects back to the very origin of the Symposia—as the name suggests. Harris had spotted the emergence of a new quantitative paradigm in biology. Between then and now, molecular genetics did in fact transform biology into an information science.
It’s becoming more clear each day that the next chapter of this story is AI. Sydney Brenner, one of the most central figures of molecular biology, gave one of the most incisive criticisms of the field in his Nobel Prize lecture: “We’re drowning in a sea of data and starving for knowledge.” AI is starting to change that equation.
For five days, top researchers in the field shared updates on their efforts to use machine learning to decipher the mechanisms of DNA, RNA, proteins, cells, tissues, organs (especially the brain), and how information flows between these different biological scales. And there were examples of how AI agents might be able to autonomously carry out some of this research—which was met with a combination of excitement and anxiety from attendees.
It was one of the most compelling conferences I’ve ever attended, so I want to share some of what I saw. Before jumping in, this requires a few quick notes on the format of the event.
First, attending a Symposium feels like drinking from a scientific firehose—by design. CSHL is truly a Temple, or maybe even a monastery. Most attendees stay on campus and don’t leave for the duration of the conference. Talks are back-to-back all day in the main auditorium, followed by communal meals and poster sessions that run throughout the evening. It’s non-stop. My goal isn’t to give an exhaustive blow-by-blow, but to highlight some of the themes and topics I found most exciting.
Second, following in the tradition of Watson, many researchers share more new and unpublished data than is typical at other conferences. To respect this tradition, I’m going to focus on the data shared that has already been published, with more high-level descriptions of new research directions and results.
With all that said, let’s get into it! [...]
Agents, Agents, Agents
Maybe I’m in a bubble in San Francisco, but it’s hard not to constantly hear about AI agents in the year 2026. It’s strange to think, but it’s been three and a half years since ChatGPT was first released. That’s long enough for many humans to feel frustrated by the shortcomings of what was once magic. Now, we want these models to do work for us, and to carry out longer, more complex projects that require reasoning.
There are now many efforts to develop systems for “agentic science,” where AI models are able to autonomously develop new hypotheses, design experiments, and analyze results. This concept was another recurring theme at the symposium.
Pushmeet Kohli hit on this the first evening. The last third of his talk focused on DeepMind’s efforts to build an AI Co-Scientist, which they published a new paper on last month. Given a research goal by a human scientist, this system develops a research plan and then kicks off a “tournament” of agents competing to develop new hypotheses. Agents within this system have different tasks. Some are designed to “reflect” on the ideas being generated. Others are tasked with “evolving” them.
While the goal is hypothesis generation, the AI Co-Scientist itself is no longer just a hypothetical. DeepMind has already given early access to academic researchers working in a wide variety of biomedical domains. Kohli highlighted a high profile example where the Co-Scientist was able to predict a new mechanism of bacterial gene transfer before the result was published in the literature.
by Elliot Hershberg, The Century of Biology | Read more:
Image: uncredited/CSHL
The tradition continues to this day. Each year, the Symposium is organized around a topic considered to represent the frontier of life sciences research.
Which brings us to the topic of the 90th Cold Spring Harbor Laboratory Symposium on Quantitative Biology: AI in Biology.
Readers of this newsletter are not strangers to the fact that AI is reshaping biology. The tools derived from breakthroughs such as AlphaFold have been adopted by seemingly all biologists at this point. But it was stunning to see these advances celebrated so prominently in this venue. It felt historical.
As Bruce Stillman, CSHL’s current President, pointed out in his opening remarks, this topic connects back to the very origin of the Symposia—as the name suggests. Harris had spotted the emergence of a new quantitative paradigm in biology. Between then and now, molecular genetics did in fact transform biology into an information science.
It’s becoming more clear each day that the next chapter of this story is AI. Sydney Brenner, one of the most central figures of molecular biology, gave one of the most incisive criticisms of the field in his Nobel Prize lecture: “We’re drowning in a sea of data and starving for knowledge.” AI is starting to change that equation.
For five days, top researchers in the field shared updates on their efforts to use machine learning to decipher the mechanisms of DNA, RNA, proteins, cells, tissues, organs (especially the brain), and how information flows between these different biological scales. And there were examples of how AI agents might be able to autonomously carry out some of this research—which was met with a combination of excitement and anxiety from attendees.
It was one of the most compelling conferences I’ve ever attended, so I want to share some of what I saw. Before jumping in, this requires a few quick notes on the format of the event.
First, attending a Symposium feels like drinking from a scientific firehose—by design. CSHL is truly a Temple, or maybe even a monastery. Most attendees stay on campus and don’t leave for the duration of the conference. Talks are back-to-back all day in the main auditorium, followed by communal meals and poster sessions that run throughout the evening. It’s non-stop. My goal isn’t to give an exhaustive blow-by-blow, but to highlight some of the themes and topics I found most exciting.
Second, following in the tradition of Watson, many researchers share more new and unpublished data than is typical at other conferences. To respect this tradition, I’m going to focus on the data shared that has already been published, with more high-level descriptions of new research directions and results.
With all that said, let’s get into it! [...]
Agents, Agents, Agents
Maybe I’m in a bubble in San Francisco, but it’s hard not to constantly hear about AI agents in the year 2026. It’s strange to think, but it’s been three and a half years since ChatGPT was first released. That’s long enough for many humans to feel frustrated by the shortcomings of what was once magic. Now, we want these models to do work for us, and to carry out longer, more complex projects that require reasoning.
There are now many efforts to develop systems for “agentic science,” where AI models are able to autonomously develop new hypotheses, design experiments, and analyze results. This concept was another recurring theme at the symposium.
Pushmeet Kohli hit on this the first evening. The last third of his talk focused on DeepMind’s efforts to build an AI Co-Scientist, which they published a new paper on last month. Given a research goal by a human scientist, this system develops a research plan and then kicks off a “tournament” of agents competing to develop new hypotheses. Agents within this system have different tasks. Some are designed to “reflect” on the ideas being generated. Others are tasked with “evolving” them.
While the goal is hypothesis generation, the AI Co-Scientist itself is no longer just a hypothetical. DeepMind has already given early access to academic researchers working in a wide variety of biomedical domains. Kohli highlighted a high profile example where the Co-Scientist was able to predict a new mechanism of bacterial gene transfer before the result was published in the literature.
by Elliot Hershberg, The Century of Biology | Read more:
Image: uncredited/CSHL