Marc Tessier-Lavigne

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407 appearances 1 recordings 1 series first heard Mar 2026 last heard 12 Mar

Marc Tessier-Lavigne’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Mar 2026 with 1.

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Vanguards of Healthcare by Bloomberg Intelligence · Xaira’s War on Trial-and-Error Drug Development · 12 Mar 2026 podcast

We have about 20,000 genes, give or take.

Each gene codes for at least one protein, in some cases, a few versions of a protein.

So 20,000 genes, about 20,000 proteins.

And there's an intermediate.

When a gene is switched on to make the protein in the cell, it goes through an intermediate called mRNA.

Your listeners would not have been familiar with mRNA 20 years ago, but after COVID and the mRNA vaccines, everybody has heard of mRNA.

So mRNA is the messenger between a gene, which

is, you know, unchanged in every cell, but when it gets switched on, it will be read initially as mRNA, then the mRNA gets converted to proteins.

It gets read out to make proteins.

What you can do with cells is a very powerful technology for identifying all the mRNAs in the cell, which tells you all the genes that are switched on.

And by inference, all the proteins that are being made, which are more difficult to quantify and measure,

So you can take cells, measure all the mRNAs, and you can ask of the 20,000 genes in the cell, how many are switched on?

People have done that experiment in many different cell types and cell types that are healthy, that are diseased, put them in the public domain.

And what you end up with is very descriptive data of the state of cells at particular times.

Using AI, it's possible to make models of cells that are descriptive.

In fact, our head of biomedical AI, Bo Wang, created the first such model right out on the heels of Chad GPT.

He created single cell GPT, SCGPT.

which is an AI model that relies on these public domain descriptive data to create a model that's very good at descriptive tasks for cell type annotation, identifying batch effects, or other things of a descriptive nature.

But if you ask the causal question that I started with a few minutes ago, if the cell is at a disease state, how do you bring it to a healthy state?

What changes do you have to make in the cell to do that?

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