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Chapter 1: What is the main topic discussed in this episode?
You're listening to TED Talks Daily, where we bring you new ideas to spark your curiosity every day. I'm your host, Elise Hu. I'll be honest, before this conversation, the term virtual cell wasn't something that existed in my vocabulary. Turns out it's a real thing and could have major implications for some of the most complex diseases we know.
Alzheimer's, for example, has stumped the medical field for decades because each patient's biology is uniquely tangled. But bioengineer and neuroscientist Silvana Connerman, who is a 2025 Audacious Project grant recipient, thinks that artificial intelligence holds the key to finally help us untangle it.
We've just seen over, I would say, really the last two years that it's getting real. I think that within four years, five years, we will be able to have these models that are accurate enough to be useful. And then it's a totally different way of doing biology.
Silvana works at the ARC Institute where she and her team are using single cell sequencing or CRISPR as well as AI to run a billion physical cellular experiments.
Chapter 2: How does Silvana Konermann define a virtual cell?
In other words, they're training a model that can speak the language of cells similar to the way large language models learn to speak ours. The goal, a universal virtual cell that tells researchers exactly which interventions could turn a real diseased cell back into a healthy one. It would transform a century of guess and check medicine into something more like a cheat code.
In this conversation with TED chairman Chris Anderson, she shares how close they actually are, what the model can already do, and why she's making it available to researchers everywhere rather than keeping it behind closed doors. That conversation is coming up right after a short break.
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Chapter 3: What challenges do complex diseases like Alzheimer's present?
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And now our conversation of the day.
Great to see you.
Welcome to TED.
Now, Sivana, you've been passionate about science for quite a long time. Tell me about this picture.
Yeah, so this picture is when I was 15. So I was born in a small town in Switzerland. My parents weren't into science, but somehow I got really fascinated just with nature around me and also just how we worked as humans in our biology. So I really wanted to find a way to be able to get into a lab to do some science. It was actually pretty tricky for me, but
Eventually, I talked one of my science teachers into convincing one of his colleagues to let me go into the lab. And so this is me with that first science project where I went on to win the national competition and then also the European Union competition. And I think that's really where, you know, I got, I think, the confidence to continue with science since then.
But there's a drawback to being a scientific prodigy, which is that you end up feeling like you might have a responsibility to do something with that. And I think you've had that your whole life, and you've thought about what are the biggest problems you could work on.
Yeah, I have been, you know, I guess doing science now for more than 20 years, but I did want to say this is actually my first real public appearance. So I am very, very much usually behind the scenes.
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Chapter 4: How can AI help decode the language of human cells?
That's different from an infection, where you have one cause.
And several of these diseases are similarly fundamentally complex.
That's right. So heart disease, many cancers, obviously not accidents, but stroke and Alzheimer's disease, these are all complex diseases.
And so that's why it's been so resistant to dramatic advancements in medical science in recent years.
Basically, yeah. Basically, all of these have a combination of genetic changes, environmental factors, and each patient is unique. They have a unique combination of risk factors. And so we've been really struggling, I guess, as a scientific community, understanding what do all these different patients have in common that we could target and then fix the disease.
But you're seeing now an opportunity to have a different kind of assault on these diseases. What has changed?
I think there are now three things really that have come together really just in the last one or two years that make it possible to understand such a complex problem like Alzheimer's disease and other diseases like it. And that's at the high level three areas. If you kind of summarize it really quickly, it's measuring, changing and understanding.
And so measuring, what that means for us is really single-cell sequencing. So this is a technology that allows us to look at one cell at a time and take a snapshot of key dynamic processes in the cell, which is the RNA expression of the cell. So RNA is like the language of the cell, and this takes a snapshot, one cell at a time, of what's going on inside it.
And then the second step, which is changing, we need to have the ability to change something very precise. So changing one gene at a time, stopping it from making the RNA or changing it to upregulate the RNA. This is the area that I've been working on now for 15 years, CRISPR technology. And as a field, we've made a
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