Google DeepMind发布AlphaGenome 2可解析单细胞基因组

Welcome to Google DeepMind. The podcast sequencing human DNA took 13 years, and the result is a string of text that is many hundreds of times longer than the complete works of Shakespeare, and deciphering what those strings of letters actually do has taken much longer. As Eric Lander, a geneticist involved in the Human Genome project, put it. We bought the book, but it's quite hard to read. But now with AI, that book is gradually becoming more legible.

Now, way back in season two of this podcast, we heard how Google DeepMind had developed a transformer model that could predict the effect of DNA sequences across the body. We're trying to build AI that essentially can understand the genome. The fact is, we now have the kind of level of data where deep learning can really take hold, and we've been able to build together the kind of deep learning models that are able to read a DNA sequence, a bit like you might read a piece of text.

Since then, progress has been rapid. In 2025, they unveiled alpha genome, which predicts the function of non-coding DNA across whole tissues. And now alpha genome two zooms down to individual cell types to shed light on how single mutations drive disease. Adam, thank you so much. Welcome to the podcast. Thanks for having us. In a nutshell, what is alpha genome actually designed to do. Give it to me simply so alpha genome is designed to read a stretch of DNA or the genetic material or the instruction manual in the cell and tell us what's happening inside the cell at that location in the genome.

And also tell us what happens if we make a small change to the DNA sequence. If we change the letter A to a letter T, what would change at that location in the genome. So it's a tool for us to study sequences and variants at the molecular level. Let me ask you both what was your personal motivation to work on this. I mean, you started off as a physicist, didn't you. Yes during my studies, I realized that physics is not my true calling.

And I stumbled upon a machine learning course. And then I learned that machine learning has a lot of components that I loved in terms of problem solving maths. And it was also very applied. And it turned out this course was taught by two researchers in genomics. So after the course, I went to them and asked if they had any interesting problems in machine learning in genomics. And they had of course, we do have. And my first project was in trying to pinpoint variants in a rare disease patients.

And I feel like that problem of not knowing what mutations do and how should we think about them really stuck with me over the years. And I feel like the motivation I feel like for our work is both to help patients with rare diseases, and also patients potentially suffering from other diseases, as well as the feeling of being able to advance the frontier of science is something I find very exciting. How about you. Well, actually, my background is in computer science and operations research.

I think for me, I'm actually coming into this field from the lens of a family illness. And I think what's refreshing about doing things to improve human health is that there's a lot of different ways you can do it. And for me, I literally just googled like machine learning biology like 12 years ago. And then took this route that pretty much ended here. What, because you had the personal experience of knowing living with rare diseases, cancer in this case, but OK.

Yeah so basically googled it and came across this really interesting field called single cell genomics. And then one step after another, I sort of entered into computational biology and more serious machine learning models. And I think I'm really happy with where we're at in terms of applying these models. And I think in the future, really making an impact on human health, on things like cancer and rare diseases, as you were describing.

OK well, for the non-biologists among us, i.e. me, maybe let's start from the beginning here because you've got a genotype and phenotype. And I mean, tell me what those are first of all. And how big of a challenge it is connecting them both up. Yeah so genotype is really the set of letters that we have in our DNA. Acgt I think a lot of people are familiar with that. And humans have three billion. The genome is $3 billion in length.

And the phenotype this is really an overused word in biology. This can mean like hair color. But we really think about phenotypes at the cellular level. So how genes change their expression in different cell types. And the connection between phenotype and genotype and phenotype is how does variation in the genome affect these phenotypes at different molecular scales. Because you're right that one example that people often use, or maybe I often use, is that the MC1 gene is the thing that gives you red hair.

You've got kind of genotype and phenotype. But we're talking down at a much smaller level here. Yes I mean, ultimately you'd want to really understand the relationship between high level phenotypes, such as whether somebody has a certain disease like Alzheimer's or type 2 diabetes. But this distance between genotype and phenotype is very long and very complex. It's a very complex relationship. A lot of things have to happen in order for the phenotype to happen.

So we try to simplify the problem by focusing on things that are closer to the molecular biology, to really the underpinning of how the genome works at the molecular level, because we feel like it's a simpler process and we have a lot more data in order to make accurate predictions, because I think for a while there was sort of a suggestion that if you had a particular mutation that would then causally result in a particular change in the body.

I'm thinking about the BRCA1 gene, for example, here where we know that it's associated with increased risk of certain types of cancers, breast cancer in particular. But it's more complicated than that. Yeah so I think we can break down, these sort of diseases in two classes. There's Mendelian diseases where you have a variant that affects a gene, and this gene directly causes a particular disease. And then there's complex diseases.

And essentially as humans, we have a risk profile that's determined by our genetic background. And these are often also caused by environmental factors. So it's not always just one variant one disease. But it can be quite complicated across the genome. And many genes can be involved in a disease because I guess there's a lot going on in the DNA that is switching things on and off. I mean, people used to call this junk DNA, didn't they.

Yes, exactly. Yeah so I think the main role of the genome is to produce proteins, which are these building blocks in the cell. And I like to use an analogy of LEGO building blocks. You can think of proteins as these LEGO building blocks, and we all started off as a single cell that contained one instruction manual from our mother and one from our father. And then the cell divided and specialized into different cell types in our body.

So all the cells carry the same genetic material, yet they get specialized. And by specialization this means that they need to produce different building blocks to build up different machines, so that they can carry out specific functions, such as cells in our I need to be able to detect light. Muscles need to be able to perform physical movement, so they need different machines inside them. So that's why the 2% of the genome encodes for these.

What are those building blocks. You think of this as the shape and the color of the LEGO LEGOs. But then the rest of the genome, the 98% tells the cells how many of these building blocks should they produce, so that they will assemble into the right machines to carry out the function. And this code to generate the code of when and when. Where to produce these proteins can be quite complex because we have so many different cell types in our body.

And they all have