back to indexManolis Kellis: Human Genome and Evolutionary Dynamics | Lex Fridman Podcast #113
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The following is a conversation with Manolis Kellis.
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He's a professor at MIT and head
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of the MIT Computational Biology Group.
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He's interested in understanding the human genome
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from a computational, evolutionary, biological,
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and other cross disciplinary perspectives.
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He has more big, impactful papers and awards
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than I can list, but most importantly,
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he's a kind, curious, brilliant human being,
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and just someone I really enjoy talking to.
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His passion for science and life in general is contagious.
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The hours honestly flew by,
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and I'm sure we'll talk again on this podcast soon.
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And now, here's my conversation with Manolis Kellis.
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What to you is the most beautiful aspect
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of the human genome?
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Don't get me started.
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So. We've got time.
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The first answer is that the beauty of genomes
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transcends humanity.
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So it's not just about the human genome.
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Genomes in general are amazingly beautiful.
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And again, I'm obviously biased.
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So in my view, the way that I like to introduce
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the human genome and the way that I like to introduce
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genomics to my class is by telling them,
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you know, we're not the inventors
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of the first digital computer.
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We are the descendants of the first digital computer.
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Basically, life is digital.
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And that's absolutely beautiful about life.
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The fact that at every replication step,
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you don't lose any information
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because that information is digital.
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If it was analog, if it was just sprouting concentrations,
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you'd lose it after a few generations.
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It would just dissolve away.
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And that's what the ancients
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didn't understand about inheritance.
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The first person to understand digital inheritance
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was Mendel, of course.
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And his theory, in fact, stayed in a bookshelf
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for like 50 years while Darwin was getting famous
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about natural selection.
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But the missing component was this digital inheritance,
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the mechanism of evolution that Mendel had discovered.
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So that aspect in my view is the most beautiful aspect
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but it transcends all of life.
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And can you elaborate maybe the inheritance part?
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What was the key thing that the ancients didn't understand?
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So the very theory of inheritance as discrete units,
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throughout the life of Mendel and well after he's writing,
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people thought that his P experiments
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were just a little fluke,
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that they were just a little exception
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that would normally not even apply to humans,
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that basically what they saw
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is this continuum of eye color,
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this continuum of skin color,
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this continuum of hair color,
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this continuum of height.
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And all of these continuums did not fit
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with a discrete type of inheritance
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that Mendel was describing.
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But what's unique about genomics
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and what's unique about the genome
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is really that there are two copies
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and that you get a combination of these.
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But for every trait,
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there are dozens of contributing variables.
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And it was only Ronald Fisher in the 20th century
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that basically recognized that even five Mendelian traits
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would add up to a continuum like inheritance pattern.
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And he wrote a series of papers
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that still are very relevant today
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about sort of this Mendelian inheritance
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of continuum like traits.
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And I think that that was the missing step in inheritance.
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So well before the discovery of the structure of DNA,
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which is again, another amazingly beautiful aspect,
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what I like to call the most noble molecule of our time,
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holds within it the secret of that discrete inheritance,
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but the conceptualization of discrete elements
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is something that precedes that.
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So even though it's discrete,
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when it materializes itself into actual traits that we see,
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it can be continuous.
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Basically arbitrarily rich and complex.
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So if you have five genes that contribute to human height,
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and there aren't five, there's a thousand.
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If there's only five genes
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and you inherit some combination of them,
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and every one makes you two inches taller
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or two inches shorter,
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it'll look like a continuous trait.
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But instead of five, there are thousands.
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And every one of them contributes to less than one millimeter.
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We change in height more during the day
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than each of these genetic variants contributes.
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So by the evening, you're shorter than you walk up with.
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Isn't that weird then
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that we're not more different than we are?
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Why are we all so similar
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if there's so much possibility to be different?
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Yeah, so there are selective advantages to being medium.
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If you're extremely tall or extremely short,
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you run into selective disadvantages.
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So you have trouble breathing, you have trouble running,
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you have trouble sitting if you're too tall.
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If you're too short, you might, I don't know,
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have other selective pressures are acting against that.
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If you look at natural history of human population,
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there's actually selection for height in Northern Europe
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and selection against height in Southern Europe.
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So there might actually be advantages
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to actually being not super tall.
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And if you look across the entire human population,
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for many, many traits,
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there's a lot of push towards the middle.
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Balancing selection is the usual term
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for selection that sort of seeks to not be extreme
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and to sort of have a combination of alleles
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that sort of keep recombining.
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And if you look at mate selection,
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super, super tall people
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will not tend to sort of marry super, super tall people.
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Very often you see these couples
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that are kind of compensating for each other.
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And the best predictor of the kid's age
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is very often just take the average of the two parents
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and then adjust for sex and boom, you get it.
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It's extremely heritable.
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Let me ask, you kind of took a step back to the genome
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outside of just humans,
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but is there something that you find beautiful
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about the human genome specifically?
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So I think the genome,
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if more people understood the beauty of the human genome,
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there would be so many fewer wars,
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so much less anger in the world.
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I mean, what's really beautiful about the human genome
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is really the variation
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that teaches us both about individuality
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and about similarity.
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So any two people on the planet are 99.9% identical.
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How can you fight with someone who's 99.9% identical to you?
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It's just counterintuitive.
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And yet any two siblings of the same parents
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differ in millions of locations.
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So every one of them is basically two to the million unique
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from any pair of parents,
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let alone any two random parents on the planet.
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So that's, I think, something that teaches us
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about sort of the nature of humanity in many ways,
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that every one of us is as unique as any star
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and way more unique in actually many ways.
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And yet we're all brothers and sisters.
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Yeah, just like stars, most of it is just fusion reactions.
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Yeah, you only have a few parameters to describe stars.
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Mass, size, initial size, and stage of life.
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Whereas for humans, it's thousands of parameters
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scattered across our genome.
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So the other thing that makes humans unique,
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the other things that makes inheritance unique in humans
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is that most species inherit things vertically.
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Basically instinct is a huge part of their behavior.
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The way that, I mean, with my kids,
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we've been watching this nest of birds
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with two little eggs outside our window
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for the last few months,
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for the last few weeks as they've been growing.
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And there's so much behavior that's hard coded.
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Birds don't just learn as they grow.
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There's no culture.
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Like a bird that's born in Boston
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will be the same as a bird that's born in California.
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So there's not as much inheritance of ideas, of customs.
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A lot of it is hard coding in their genome.
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What's really beautiful about the human genome
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is that if you take a person from today
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and you place them back in ancient Egypt,
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or if you take a person from ancient Egypt
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and you place them here today,
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they will grow up to be completely normal.
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That is not genetics.
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This is the other type of inheritance in humans.
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So on one hand, we have the genetic inheritance,
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which is vertical from your parents down.
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On the other hand, we have horizontal inheritance,
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which is the ideas that are built up at every generation
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are horizontally transmitted.
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And the huge amount of time
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that we spend in educating ourselves,
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a concept known as neoteny,
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neo for newborn and then teny for holding.
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So if you look at humans,
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I mean, the little birds that were eggs two weeks ago,
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and now one of them has already flown off.
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The other one's ready to fly off.
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In two weeks, they're ready to just fend for themselves.
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Humans, 16 years, 18 years, 24, getting out of college.
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I'm still learning.
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So that's so fascinating,
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this picture of a vertical and the horizontal.
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When you talk about the horizontal,
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is it in the realm of ideas?
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Okay, so it's the actual social interactions.
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That's exactly right.
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That's exactly right.
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So basically the concept of neoteny
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is that you spend acquiring characteristics
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from your environment
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in an extremely malleable state of your brain
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and the wiring of your brain for a long period of your life.
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Compared to primates, we are useless.
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You take any primate at seven weeks
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and any human at seven weeks, we lose the battle.
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But at 18 years, you know, all bets are off.
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Like we basically, our brain continues to develop
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in an extremely malleable form till very late.
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And this is what allows education.
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This is what allows the person from Egypt
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to do extremely well now.
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And the reason for that is that the wiring of our brain
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and the development of that wiring is actually delayed.
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So, you know, the longer you delay that,
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the more opportunity you have to pass on knowledge,
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to pass on concepts, ideals, ideas
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from the parents to the child.
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And what's really absolutely beautiful about humans today
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is that that lateral transfer of ideas and culture
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is not just from uncles and aunts and teachers at school,
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but it's from Wikipedia and review articles on the web
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and thousands of journals
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that are sort of putting out information for free
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and podcasts and videocasts and all of that stuff
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where you can basically learn about any topic,
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pretty much everything that would be in any
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super advanced textbook in a matter of days,
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instead of having to go to the library of Alexandria
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and sail there to read three books
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and then sail for another few days to get to Athens
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and et cetera, et cetera, et cetera.
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So the democratization of knowledge
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and the spread, the speed of spread of knowledge
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is what defines, I think, the human inheritance pattern.
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So you sound excited about it, are you also a little bit
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afraid or are you more excited by the power
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of this kind of distributed spread of information?
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So you put it very kindly that most people
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are kind of using the internet and looking Wikipedia,
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reading articles, reading papers and so on,
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but if we're honest, most people online,
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especially when they're younger,
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probably looking at five second clips on TikTok
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or whatever the new social network is,
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are you, given this power of horizontal inheritance,
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are you optimistic or a little bit pessimistic
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about this new effect of the internet
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and democratization of knowledge on our,
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what would you call this, this genome,
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would you use the term genome, by the way, for this?
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Yeah, I think we use the genome to talk about DNA,
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but very often we say, I'm Greek,
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so people ask me, hey, what's in the Greek genome?
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And I'm like, well, yeah, what's in the Greek genome
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is both our genes and also our ideas
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and our ideals and our culture.
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So the poetic meaning of the word.
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Exactly, exactly, yeah.
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So I think that there's a beauty
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to the democratization of knowledge,
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the fact that you can reach as many people
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as any other person on the planet
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and it's not who you are,
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it's really your ideas that matter,
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is a beautiful aspect of the internet.
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I think there's, of course, a danger of my ignorance
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is as important as your expertise.
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The fact that with this democratization
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comes the abolishment of respecting expertise.
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Just because you've spent 10,000 hours of your life
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studying, I don't know, human brain circuitry,
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why should I trust you?
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I'm just gonna make up my own theories
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and they'll be just as good as yours,
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is an attitude that sort of counteracts
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the beauty of the democratization.
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And I think that within our educational system
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and within the upbringing of our children,
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we have to not only teach them knowledge,
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but we have to teach them the means to get to knowledge.
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And that, it's very similar to sort of you fish,
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you catch a fish for a man for one day,
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you fed them for one day, you teach them how to fish,
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you fed them for the rest of their life.
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So instead of just gathering the knowledge
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they need for any one task,
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we can just tell them, all right,
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here's how you Google it,
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here's how you figure out what's real and what's not,
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here's how you check the sources,
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here's how you form a basic opinion for yourself.
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And I think that inquisitive nature
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is paramount to being able to sort through
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this huge wealth of knowledge.
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So you need a basic educational foundation
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based on which you can then add on
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the sort of domain specific knowledge,
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but that basic educational foundation
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should just not just be knowledge,
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but it should also be epistemology,
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the way to acquire knowledge.
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I'm not sure any of us know how to do that
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in this modern day, we're actually learning.
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One of the big surprising thing to me
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about the coronavirus, for example,
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is that Twitter has been
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one of the best sources of information.
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Basically like building your own network of experts,
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as opposed to the traditional centralized expertise
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of the WHO and the CDC,
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or maybe any one particular respectable person
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at the top of a department in some kind of institution,
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you instead look at 10, 20, hundreds of people,
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some of whom are young kids that are incredibly good
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at aggregating data and plotting and visualizing that data.
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That's been really surprising to me.
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I don't know what to make of it.
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I don't know how that matures into something stable.
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I don't know if you have ideas.
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If you were to just try to explain to your kids
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of where should you go to learn about coronavirus,
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what would you say?
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It's such a beautiful example.
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And I think the current pandemic
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and the speed at which the scientific community has moved
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in the current pandemic,
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I think exemplifies this horizontal transfer
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and the speed of horizontal transfer of information.
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The fact that the genome was first sequenced
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the first sample was obtained December 29, 2019,
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a week after the publication of the first genome sequence,
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Moderna had already finalized its vaccine design
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and was moving to production.
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I mean, this is phenomenal.
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The fact that we go from not knowing
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what the heck is killing people in Wuhan
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to wow, it's SARS CoV2 and here's the set of genes,
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here's the genome, here's the sequence,
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here are the polymorphisms, et cetera,
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in the matter of weeks is phenomenal.
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In that incredible pace of transfer of knowledge,
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there have been many mistakes.
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So, some of those mistakes
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may have been politically motivated
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or other mistakes may have just been innocuous errors.
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Others may have been misleading the public
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for the greater good, such as don't wear masks
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because we don't want the mask to run out.
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I mean, that was very silly in my view
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and a very big mistake.
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But the spread of knowledge
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from the scientific community was phenomenal.
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And some people will point out to bogus articles
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that snuck in and made the front page.
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But within 24 hours, they were debunked
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and went out of the front page.
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And I think that's the beauty of science today.
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The fact that it's not, oh, knowledge is fixed.
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It's the ability to embrace that nothing is permanent
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when it comes to knowledge,
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that everything is the current best hypothesis
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and the current best model that best fits the current data
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and the willingness to be wrong.
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The expectation that we're gonna be wrong
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and the celebration of success based on
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how long was I not proven wrong for,
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rather than, wow, I was exactly right.
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Because no one is gonna be exactly right
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with partial knowledge.
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But the arc towards perfection,
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I think is so much more important
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than how far you are in your first step.
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And I think that's what sort of
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the current pandemic has taught us.
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The fact that, yeah, no, of course,
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we're gonna make mistakes,
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but at least we're gonna learn from those mistakes
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and become better and learn better
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and spread information better.
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So if I were to answer the question of,
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where would you go to learn about coronavirus?
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First textbook, it all starts with a textbook.
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Just open up a chapter on virology
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and how coronaviruses work.
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Then some basic epidemiology
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and sort of how pandemics have worked in the past.
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What are the basic principles surrounding
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these first wave, second wave?
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Why do they even exist?
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Then understanding about growth,
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understanding about the R0 and RT
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at various time points.
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And then understanding the means of spread,
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how it spreads from person to person.
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Then how does it get into your cells?
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From when it gets into the cells,
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what are the paths that it takes?
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What are the cell types that express
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the particular ACE2 receptor?
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How is your immune system interacting with the virus?
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And once your immune system launches a defense,
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how is that helping or actually hurting your health?
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What about the cytokine storm?
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What are most people dying from?
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Why are the comorbidities
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and these risk factors even applying?
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What makes obese people respond more
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or elderly people respond more to the virus
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while kids are completely,
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very often not even aware that they're spreading it?
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So I think there's some basic questions
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that you would start from.
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And then I'm sorry to say,
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but Wikipedia is pretty awesome.
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Yeah, it is. Google is pretty awesome.
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It used to be a time,
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it used to be a time maybe five years ago.
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but people kind of made fun of Wikipedia
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for being an unreliable source.
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I never quite understood it.
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I thought from the early days, it was pretty reliable
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or better than a lot of the alternatives.
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But at this point,
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it's kind of like a solid accessible survey paper
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on every subject ever.
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There's an ascertainment bias and a writing bias.
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So I think this is related to sort of people saying,
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oh, so many nature papers are wrong.
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And they're like, why would you publish in nature?
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So many nature papers are wrong.
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And my answer is no, no, no.
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So many nature papers are scrutinized.
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And just because more of them are being proven wrong
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than in other articles is actually evidence
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that they're actually better papers overall
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because they're being scrutinized at a rate
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much higher than any other journal.
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So if you basically judge Wikipedia
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by not the initial content,
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but by the number of revisions,
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then of course it's gonna be the best source
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of knowledge eventually.
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It's still very superficial.
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You then have to go into the review papers,
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et cetera, et cetera, et cetera.
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But I mean, for most scientific topics,
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it's extremely superficial,
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but it is quite authoritative
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because it is the place that everybody likes to criticize
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You say that it's superficial.
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And a lot of topics that I've studied a lot of,
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I find it, I don't know if superficial is the right word.
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Because superficial kind of implies that it's not correct.
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I don't mean any implication of it not being correct.
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It's just superficial.
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It's basically only scratching the surface.
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For depth, you don't go to Wikipedia.
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You go to the review articles.
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But it can be profound in the way that articles rarely,
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one of the frustrating things to me
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about certain computer science,
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like in the machine learning world,
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articles, they don't as often take the bigger picture view.
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There's a kind of data set and you show that it works
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and you kind of show that here's an architecture thing
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that creates an improvement and so on and so forth.
link |
But you don't say, well, what does this mean
link |
for the nature of intelligence for future data sets
link |
we haven't even thought about?
link |
Or if you were trying to implement this,
link |
like if we took this data set of 100,000 examples
link |
and scale it to 100 billion examples with this method,
link |
like look at the bigger picture,
link |
which is what a Wikipedia article would actually try to do,
link |
which is like, what does this mean in the context
link |
of the broad field of computer vision or something like that?
link |
Yeah, no, I agree with you completely, but it depends
link |
I mean, for some topics, there's been a huge amount of work.
link |
For other topics, it's just a stub.
link |
Well, yeah, actually the, which we'll talk on,
link |
genomics was not great.
link |
Yeah, it's very shallow, yeah, yeah.
link |
It's not wrong, it's just shallow.
link |
Yeah, every time I criticize something,
link |
I should feel partly responsible.
link |
Basically, if more people from my community went there
link |
and edited, it would not be shallow.
link |
It's just that there's different modes of communication
link |
in different fields.
link |
And in some fields, the experts have embraced Wikipedia.
link |
In other fields, it's relegated.
link |
And perhaps the reason is that if it was any better
link |
to start with, people would invest more time.
link |
But if it's not great to start with,
link |
then you need a few initial pioneers who will basically
link |
go in and say, ah, enough, we're just gonna fix that.
link |
And then I think it'll catch on much more.
link |
So if it's okay, before we go on to genomics,
link |
can we linger a little bit longer on the beauty
link |
of the human genome?
link |
You've given me a few notes.
link |
What else do you find beautiful about the human genome?
link |
So the last aspect of what makes the human genome unique,
link |
in addition to the, you know, similarity and the differences
link |
and the individuality is that, so very early on,
link |
people would basically say, oh, you don't do that
link |
experiment in human, you have to learn about that in fly,
link |
or you have to learn about that in yeast first,
link |
or in mouse first, or in a primate first.
link |
And the human genome was in fact relegated to sort of,
link |
oh, the last place that you're gonna go
link |
to learn something new.
link |
That has dramatically changed.
link |
And the reason that changed is human genetics.
link |
We are the species in the planet
link |
that's the most studied right now.
link |
It's embarrassing to say that,
link |
but this was not the case a few years ago.
link |
It used to be, you know, first viruses, then bacteria,
link |
then yeast, then the fruit fly and the worm,
link |
then the mouse, and eventually human was very far last.
link |
So it's embarrassing that it took us this long
link |
to focus on it, or the...
link |
It's embarrassing that the model organisms
link |
have been taken over because of the power of human genetics.
link |
That right now, it's actually simpler to figure out
link |
the phenotype of something by mining
link |
this massive amount of human data
link |
than by going back to any of the other species.
link |
And the reason for that is that if you look
link |
at the natural variation that happens
link |
in a population of seven billion,
link |
you basically have a mutation in almost every nucleotide.
link |
So every nucleotide you wanna perturb,
link |
you can go find a living, breathing human being
link |
and go test the function of that nucleotide
link |
by sort of searching the database and finding that person.
link |
Wait, why is that embarrassing?
link |
It's a beautiful data set.
link |
It's a beautiful data set.
link |
It's embarrassing for the model organism.
link |
I mean, do you feel on a small tangent,
link |
is there something of value in the genome of a fly
link |
and other of these model organisms that you miss
link |
that we wish we would be looking at deeper?
link |
So directed perturbation, of course.
link |
So I think the place where humans are still lagging
link |
is the fact that in an animal model,
link |
you can go and say,
link |
well, let me knock out this gene completely
link |
and let me knock out these three genes completely.
link |
And the moment you get into combinatorics,
link |
it's something you can't do in the human
link |
because there just simply aren't enough humans
link |
And again, let me be honest,
link |
we haven't sequenced all seven billion people.
link |
It's not like we have every mutation,
link |
but we know that there's a carrier out there.
link |
So if you look at the trend and the speed
link |
with which human genetics has progressed,
link |
we can now find thousands of genes involved
link |
in human cognition, in human psychology,
link |
in the emotions and the feelings
link |
that we used to think are uniquely learned.
link |
It turns out there's a genetic basis to a lot of that.
link |
So the human genome has continued to elucidate
link |
through these studies of genetic variation,
link |
so many different processes that we previously thought
link |
were something like free will.
link |
Free will is this beautiful concept
link |
that humans have had for a long time.
link |
In the end, it's just a bunch of chemical reactions
link |
happening in your brain.
link |
And the particular abundance of receptors
link |
that you have this day based on what you ate yesterday
link |
or that you have been wired with based on your parents
link |
and your upbringing, et cetera,
link |
determines a lot of that quote unquote free will component
link |
to sort of narrow and narrow sort of slices.
link |
So how much on that point, how much freedom
link |
do you think we have to escape the constraints
link |
You're making it sound like more and more
link |
we're discovering that our genome is actually has the,
link |
a lot of the story already encoded into it.
link |
How much freedom do we have?
link |
I, so let me describe what that freedom would look like.
link |
That freedom would be my saying,
link |
ooh, I'm gonna resist the urge to eat that apple
link |
because I choose not to.
link |
But there are chemical receptors that made me
link |
not resist the urge to prove my individuality
link |
and my free will by resisting the apple.
link |
So then the next question is,
link |
well, maybe now I'll resist the urge to resist the apple
link |
and I'll go for the chocolate instead
link |
to prove my individuality.
link |
But then what about those other receptors that, you know?
link |
That might be all encoded in there.
link |
So it's kicking the bucket down the road
link |
and basically saying, well, your choice
link |
will may have actually been driven by other things
link |
that you actually are not choosing.
link |
So that's why it's very hard to answer that question.
link |
It's hard to know what to do with that.
link |
I mean, if the genome has,
link |
if there's not much freedom, it's a...
link |
It's the butterfly effect.
link |
It's basically that in the short term,
link |
you can predict something extremely well
link |
by knowing the current state of the system.
link |
But a few steps down, it's very hard to predict
link |
based on the current knowledge.
link |
Is that because the system is truly free?
link |
When I look at weather patterns,
link |
I can predict the next 10 days.
link |
Is it because the weather has a lot of freedom
link |
and after 10 days it chooses to do something else?
link |
Or is it because in fact the system is fully deterministic
link |
and there's just a slightly different magnetic field
link |
of the earth, slightly more energy arriving from the sun,
link |
a slightly different spin of the gravitational pull
link |
of Jupiter that is now causing all kinds of tides
link |
and slight deviation of the moon, et cetera.
link |
Maybe all of that can be fully modeled.
link |
Maybe the fact that China is emitting
link |
a little more carbon today is actually gonna affect
link |
the weather in Egypt in three weeks.
link |
And all of that could be fully modeled.
link |
In the same way, if you take a complete view
link |
of a human being now, I model everything about you.
link |
The question is, can I predict your next step?
link |
Probably, but at how far?
link |
And if it's a little further, is that because of stochasticity
link |
and sort of chaos properties of unpredictability
link |
of beyond a certain level?
link |
Or was that actually true free will?
link |
Yeah, so the number of variables might be so,
link |
you might need to build an entire universe to be able to model.
link |
To simulate a human, and then maybe that human
link |
will be fully simulatable.
link |
But maybe aspects of free will will exist.
link |
And where's that free will coming from?
link |
It's still coming from the same neurons
link |
or maybe from a spirit inhabiting these neurons.
link |
But again, it's very difficult empirically
link |
to sort of evaluate where does free will begin
link |
and sort of chemical reactions and electric signals.
link |
So on that topic, let me ask the most absurd question
link |
that most MIT faculty rolled their eyes on.
link |
But what do you think about the simulation hypothesis
link |
and the idea that we live in a simulation?
link |
I think it's complete BS.
link |
There's no empirical evidence.
link |
No, it's not. Absolutely not.
link |
Not in terms of empirical evidence or not,
link |
but in terms of a thought experiment,
link |
does it help you think about the universe?
link |
I mean, so if you look at the genome,
link |
it's encoding a lot of the information
link |
that is required to create some of the beautiful
link |
human complexity that we see around us.
link |
It's an interesting thought experiment.
link |
How much parameters do we need to have
link |
in order to model this full human experience?
link |
Like if we were to build a video game,
link |
how hard it would be to build a video game
link |
that's like convincing enough and fun enough
link |
and it has consistent laws of physics, all that stuff.
link |
It's not interesting to use a thought experiment.
link |
I mean, it's cute, but it's Occam's razor.
link |
I mean, what's more realistic,
link |
the fact that you're actually a machine
link |
or that you're a person?
link |
What's the fact that all of my experiences exist
link |
inside the chemical molecules that I have
link |
or that somebody is actually simulating all that?
link |
Well, you did refer to humans
link |
as a digital computer earlier.
link |
Of course, of course.
link |
But that does not.
link |
It's a kind of a machine, right?
link |
But I think the probability of all that is nil
link |
and let the machines wake me up
link |
and just terminate me now if it's not.
link |
I challenge you machines.
link |
They're gonna wait a little bit
link |
to see what you're gonna do next.
link |
It's fun to watch, especially the clever humans.
link |
What's the difference to you
link |
between the way a computer stores information
link |
and the human genome stores information?
link |
So you also have roots and your work.
link |
Would you say when you introduce yourself at a bar.
link |
It depends who I'm talking to.
link |
Would you say it's computational biology?
link |
Do you reveal your expertise in computers?
link |
It depends who I'm talking to, truly.
link |
I mean, basically, if I meet someone who's in computers,
link |
I'll say, oh, I'm a professor in computer science.
link |
If I meet someone who's in engineering,
link |
I say computer science and electrical engineering.
link |
If I meet someone in biology,
link |
I'll say, hey, I work in genomics.
link |
If I meet someone in medicine,
link |
I'm like, hey, I work on genetics.
link |
So you're a fun person to meet at a bar.
link |
I got you, but so.
link |
No, no, but what I'm trying to say is that I don't,
link |
I mean, there's no single attribute
link |
that I will define myself as.
link |
There's a few things I know.
link |
There's a few things I study.
link |
There's a few things I have degrees on
link |
and there's a few things that I grant degrees in.
link |
And I publish papers across the whole gamut,
link |
the whole spectrum of computation to biology, et cetera.
link |
I mean, the complete answer is that I use computer science
link |
to understand biology.
link |
So I develop methods in AI and machine learning,
link |
statistics and algorithms, et cetera.
link |
But the ultimate goal of my career
link |
is to really understand biology.
link |
If these things don't advance our understanding
link |
of biology, I'm not as fascinated by them.
link |
Although there are some beautiful computational problems
link |
by themselves, I've sort of made it my mission
link |
to apply the power of computer science
link |
to truly understand the human genome, health, disease,
link |
and the whole gamut of how our brain works,
link |
how our body works and all of that,
link |
which is so fascinating.
link |
And so the dream, there's not an equivalent
link |
sort of complimentary dream of understanding
link |
human biology in order to create an artificial life
link |
or an artificial brain or artificial intelligence
link |
that supersedes the intelligence
link |
and the capabilities of us humans.
link |
It's an interesting question.
link |
It's a fascinating question.
link |
So understanding the human brain is undoubtedly coupled
link |
to how do we make better AI?
link |
Because so much of AI has in fact been inspired
link |
It may have taken 50 years
link |
since the early days of neural networks
link |
till we have all of these amazing progress
link |
that we've seen with deep belief networks
link |
and all of these advances in Go, in Chess,
link |
in image synthesis, in deep fakes, in you name it.
link |
But the underlying architecture is very much inspired
link |
by the human brain,
link |
which actually posits a very, very interesting question.
link |
Why are neural networks performing so well?
link |
And they perform amazingly well.
link |
Is it because they can simulate any possible function?
link |
And the answer is no, no.
link |
They simulate a very small number of functions.
link |
Is it because they can simulate every function
link |
And that's where it gets interesting.
link |
The answer is actually, yeah, a little closer to that.
link |
And here's where it gets really fun.
link |
If you look at human brain and human cognition,
link |
it didn't evolve in a vacuum.
link |
It evolved in a world with physical constraints,
link |
like the world that inhabits us.
link |
It is the world that we inhabit.
link |
And if you look at our senses, what do they perceive?
link |
They perceive different parts of the electromagnetic spectrum.
link |
The hearing is just different movements in air,
link |
the touch, et cetera.
link |
I mean, all of these things,
link |
we've built intuitions for the physical world
link |
And our brains and the brains of all animals evolved
link |
And the AI systems that we have built
link |
happen to work well with images
link |
of the type that we encounter
link |
in the physical world that we inhabit.
link |
Whereas if you just take noise and you add random signal
link |
that doesn't match anything in our world,
link |
neural networks will not do as well.
link |
And that actually basically has this whole loop around this,
link |
which is this was designed by studying our own brain,
link |
which was evolved for our own world.
link |
And they happen to do well in our own world.
link |
And they happen to make the same types of mistakes
link |
that humans make many times.
link |
And of course you can engineer images
link |
by adding just the right amount of sort of pixel deviations
link |
to make a zebra look like a bamboo and stuff like that,
link |
But ultimately the undoctored images at least
link |
are very often mistaken, I don't know,
link |
between muffins and dogs, for example,
link |
in the same way that humans make those mistakes.
link |
So there's no doubt in my view
link |
that the more we understand about the tricks
link |
that our human brain has evolved
link |
to understand the physical world around us,
link |
the more we will be able to bring
link |
new computational primitives in our AI systems
link |
to again better understand not just the world around us,
link |
but maybe even the world inside us,
link |
and maybe even the computational problems that arise
link |
from new types of data that we haven't been exposed to,
link |
but are yet inhabiting the same universe that we live in
link |
with a very tiny little subset of functions
link |
from all possible mathematical functions.
link |
Yeah, and that small subset of functions,
link |
all that matters to us humans really, that's what makes.
link |
It's all that has mattered so far.
link |
And even within our scientific realm,
link |
it's all that seems to continue to matter.
link |
But I mean, I always like to think about our senses
link |
and how much of the physical world around us we perceive.
link |
And if you look at the LIGO experiment
link |
over the last year and a half has been all over the news.
link |
It created a new sense for human beings,
link |
a sense that has never been sensed
link |
in the history of our planet.
link |
Gravitational waves have been traversing the earth
link |
since its creation a few billion years ago.
link |
Life has evolved senses to sense things
link |
that were never before sensed.
link |
Light was not perceived by early life.
link |
And eventually photoreceptors evolved
link |
and the ability to sense colors
link |
by sort of catching different parts
link |
of that electromagnetic spectrum.
link |
And hearing evolved and touch evolved, et cetera.
link |
But no organism evolved a way to sense neutrinos
link |
floating through earth or gravitational waves
link |
flowing through earth, et cetera.
link |
And I find it so beautiful in the history
link |
of not just humanity, but life on the planet
link |
that we are now able to capture additional signals
link |
from the physical world than we ever knew before.
link |
And axions, for example, have been all over the news
link |
in the last few weeks.
link |
And the concept that we can capture and perceive
link |
more of that physical world is as exciting
link |
as the fact that we were blind to it
link |
is traumatizing before.
link |
Because that also tells us, you know, we're in 2020.
link |
Picture yourself in 3020 or in 20, you know.
link |
What new senses might we discover?
link |
Is it, you know, could it be that we're missing
link |
nine tenths of physics?
link |
That like, there's a lot of physics out there
link |
that we're just blind to, completely oblivious to it.
link |
And yet they're permeating us all the time.
link |
Yeah, so it might be right in front of us.
link |
So when you're thinking about premonitions,
link |
yeah, a lot of that is ascertainment bias.
link |
Like, yeah, you know, every now and then you're like,
link |
oh, I remember my friend.
link |
And then my friend doesn't appear
link |
and I'll forget that I remembered my friend.
link |
But every now and then my friend will actually appear.
link |
I'm like, oh my God, I thought about you a minute ago.
link |
You just called me, that's amazing.
link |
So, you know, some of that is this,
link |
but some of that might be that there are,
link |
within our brain, sensors for waves
link |
that we emit that we're not even aware of.
link |
And this whole concept of when I hug my children,
link |
there's such an emotional transfer there
link |
that we don't comprehend.
link |
I mean, sure, yeah, of course we're all like hard wire
link |
for all kinds of touchy feely things
link |
between parents and kids, it's beautiful,
link |
between partners, it's beautiful, et cetera.
link |
But then there are intangible aspects
link |
of human communication
link |
that I don't think it's unfathomable
link |
that our brain has actually evolved waves and sensors
link |
for it that we just don't capture.
link |
We don't understand the function
link |
of the vast majority of our neurons.
link |
And maybe our brain is already sensing it,
link |
but even worse, maybe our brain is not sensing it at all.
link |
And we're oblivious to this until we build a machine
link |
that suddenly is able to sort of capture
link |
so much more of what's happening in the natural world.
link |
So what you're saying is physics
link |
is going to discover a sensor for love.
link |
And maybe dogs are off scale for that.
link |
And we've been oblivious to it the whole time
link |
because we didn't have the right sensor.
link |
And now you're gonna have a little wrist that says,
link |
oh my God, I feel all this love in the house.
link |
I sense a disturbance in the forest.
link |
It's all around us.
link |
And dogs and cats will have zero.
link |
But let's take a step back to our unfortunate place.
link |
To one of the 400 topics that we had actually planned for.
link |
But to our sad time in 2020
link |
when we only have just a few sensors
link |
and very primitive early computers.
link |
So you have a foot in computer science
link |
and a foot in biology.
link |
In your sense, how do computers represent information
link |
differently than like the genome or biological systems?
link |
So first of all, let me correct
link |
that no, we're in an amazing time in 2020.
link |
Computer science is totally awesome.
link |
And physics is totally awesome.
link |
And we have understood so much of the natural world
link |
So I am extremely grateful and feeling extremely lucky
link |
to be living in the time that we are.
link |
Cause you know, first of all,
link |
who knows when the asteroid will hit.
link |
And second, you know, of all times in humanity,
link |
this is probably the best time to be a human being.
link |
And this might actually be the best place
link |
to be a human being.
link |
So anyway, you know, for anyone who loves science,
link |
This is a great time.
link |
At the same time, just a quick comment.
link |
All I meant is that if we look several hundred years
link |
from now and we end up somehow not destroying ourselves,
link |
people will probably look back at this time
link |
in computer science and at your work of Manos at MIT.
link |
As infantile and silly and how ignorant it all was.
link |
I like to joke very often with my students
link |
that, you know, we've written so many papers.
link |
We've published so much.
link |
We've been citing so much.
link |
And every single time I tell my students, you know,
link |
the best is ahead of us.
link |
What we're working on now
link |
is the most exciting thing I've ever worked on.
link |
So in a way, I do have this sense of, yeah,
link |
even the papers I wrote 10 years ago,
link |
they were awesome at the time,
link |
but I'm so much more excited about where we're heading now.
link |
And I don't mean to minimize any of the stuff
link |
we've done in the past,
link |
but you know, there's just this sense of excitement
link |
about what you're working on now
link |
that as soon as a paper is submitted,
link |
it's like, ugh, it's old.
link |
You know, I can't talk about that anymore.
link |
I'm not gonna talk about it.
link |
At the same time, you're not,
link |
you probably are not going to be able to predict
link |
what are the most impactful papers and ideas
link |
when people look back 200 years from now at your work,
link |
what would be the most exciting papers.
link |
And it may very well be not the thing that you expected.
link |
Or the things you got awards for or, you know.
link |
This might be true in some fields.
link |
I feel slightly differently about it in our field.
link |
I feel that I kind of know what are the important ones.
link |
And there's a very big difference
link |
between what the press picks up on
link |
and what's actually fundamentally important for the field.
link |
And I think for the fundamentally important ones,
link |
we kind of have a pretty good idea what they are.
link |
And it's hard to sometimes get the press excited
link |
about the fundamental advances,
link |
but you know, we take what we get
link |
and celebrate what we get.
link |
And sometimes, you know, one of our papers,
link |
which was in a minor journal,
link |
made the front page of Reddit
link |
and suddenly had like hundreds of thousands of views.
link |
Even though it was in a minor journal
link |
because, you know, somebody pitched it the right way
link |
that it suddenly caught everybody's attention.
link |
Whereas other papers that are sort of truly fundamental,
link |
you know, we have a hard time
link |
getting the editors even excited about them
link |
when so many hundreds of people
link |
are already using the results and building upon them.
link |
So I do appreciate that there's a discrepancy
link |
between the perception and the perceived success
link |
and the awards that you get for various papers.
link |
But I think that fundamentally, I know that, you know,
link |
some paper, I'm so, so when you write.
link |
So is there a paper that you're most proud of?
link |
See, now you just, you trapped yourself.
link |
No, no, no, no, I mean.
link |
Is there a line of work that you have a sense
link |
is really powerful that you've done to date?
link |
You've done so much work in so many directions,
link |
which is interesting.
link |
Is there something where you think is quite special?
link |
I mean, it's like asking me to say
link |
which of my three children I love best.
link |
So, I mean, and it's such a gimme question
link |
that is so, so difficult not to brag
link |
about the awesome work that my team
link |
and my students have done.
link |
And I'll just mention a few off the top of my head.
link |
I mean, basically there's a few landmark papers
link |
that I think have shaped my scientific path.
link |
And, you know, I like to somehow describe it
link |
as a linear continuation of one thing led to another
link |
and led to another led to another.
link |
And, you know, it kind of all started with,
link |
skip, skip, skip, skip, skip.
link |
Let me try to start somewhere in the middle.
link |
So my first PhD paper was the first comparative analysis
link |
of multiple species.
link |
So multiple complete genomes.
link |
So for the first time we basically developed the concept
link |
of genome wide evolutionary signatures.
link |
The fact that you could look across the entire genome
link |
and understand how things evolve.
link |
And from these signatures of evolution
link |
you could go back and study any one region
link |
and say, that's a protein coding gene.
link |
That's an RNA gene.
link |
That's a regulatory motif.
link |
That's a, you know, binding site and so on and so forth.
link |
I'm sorry, so comparing different.
link |
Different species.
link |
Species of the same.
link |
So take human, mouse, rat and dog.
link |
You know, they're all animals, they're all mammals.
link |
They're all performing similar functions with their heart,
link |
with their brain, with their lungs, et cetera, et cetera.
link |
So there's many functional elements
link |
that make us uniquely mammalian.
link |
And those mammalian elements are actually conserved.
link |
99% of our genome does not code for protein.
link |
1% codes for protein.
link |
The other 99%, we frankly didn't know what it does
link |
until we started doing this comparative genomic studies.
link |
So basically these series of papers in my career
link |
have basically first developed that concept
link |
of evolutionary signatures and then apply them to yeast,
link |
apply them to flies, apply them to four mammals,
link |
apply them to 17 fungi,
link |
apply them to 12 Drosophila species,
link |
apply them to then 29 mammals and now 200 mammals.
link |
So sorry, so can we.
link |
So the evolutionary signatures seems like
link |
it's such a fascinating idea.
link |
And we're probably gonna linger on your early PhD work
link |
But what is, how can you reveal something interesting
link |
about the genome by looking at the multiple,
link |
multiple species and looking at the evolutionary signatures?
link |
Yeah, so you basically align
link |
the matching regions.
link |
So everything evolved from a common ancestor way, way back.
link |
And mammals evolved from a common ancestor
link |
about 60 million years back.
link |
So after the meteor that killed off the dinosaurs landed
link |
near Machu Picchu, we know the crater.
link |
It didn't allegedly land.
link |
That was the aliens, okay.
link |
No, just slightly north of Machu Picchu
link |
in the Gulf of Mexico, there's a giant hole
link |
that that meteor impact.
link |
Sorry, is that definitive to people?
link |
Have people conclusively figured out
link |
what killed the dinosaurs?
link |
So it was a meteor?
link |
Well, volcanic activity, all kinds of other stuff
link |
is coinciding, but the meteor is pretty unique
link |
and we now have. That's also terrifying.
link |
I wouldn't, we still have a lot of 2020 left,
link |
No, no, but think about it this way.
link |
So the dinosaurs ruled the earth for 175 million years.
link |
We humans have been around for what?
link |
Less than 1 million years.
link |
If you're super generous about what you call humans
link |
and you include chimps basically.
link |
So we are just getting warmed up
link |
and we've ruled the planet much more ruthlessly
link |
than Tyrannosaurus Rex.
link |
T Rex had much less of an environmental impact
link |
And if you give us another 174 million years,
link |
humans will look very different if we make it that far.
link |
So I think dinosaurs basically are much more
link |
of life history on earth than we are in all respects.
link |
But look at the bright side, when they were killed off,
link |
another life form emerged, mammals.
link |
And that's that whole evolutionary branching
link |
So you kind of have,
link |
when you have these evolutionary signatures,
link |
is there basically a map of how the genome changed?
link |
Yeah, exactly, exactly.
link |
So now you can go back to this early mammal
link |
that was hiding in caves and you can basically ask
link |
what happened after the dinosaurs were wiped out.
link |
A ton of evolutionary niches opened up
link |
and the mammals started populating all of these niches.
link |
And in that diversification,
link |
there was room for expansion of new types of functions.
link |
So some of them populated the air with bats flying,
link |
a new evolution of flight.
link |
Some populated the oceans with dolphins and whales
link |
going off to swim, et cetera.
link |
But we all are fundamentally mammals.
link |
So you can take the genomes of all these species
link |
and align them on top of each other
link |
and basically create nucleotide resolution correspondences.
link |
What my PhD work showed is that when you do that,
link |
when you line up species on top of each other,
link |
you can see that within protein coding genes,
link |
there's a particular pattern of evolution
link |
that is dictated by the level at which
link |
evolutionary selection acts.
link |
If I'm coding for a protein and I change
link |
the third codon position of a triplet
link |
that codes for that amino acid,
link |
the same amino acid will be encoded.
link |
So that basically means that any kind of mutation
link |
that preserves that translation that is invariant
link |
to that ultimate functional assessment
link |
that evolution will give is tolerated.
link |
So for any function that you're trying to achieve,
link |
there's a set of sequences that encode it.
link |
You can now look at the mapping,
link |
the graph isomorphism, if you wish,
link |
between all of the possible DNA encodings
link |
of a particular function and that function.
link |
And instead of having just that exact sequence
link |
at the protein level, you can think of the set
link |
of protein sequences that all fulfill the same function.
link |
What's evolution doing?
link |
Evolution has two components.
link |
One component is random, blind, and stupid mutation.
link |
The other component is super smart, ruthless selection.
link |
That's my mom calling from Greece.
link |
Yes, I might be a fully grown man, but I am a Greek.
link |
Did you just cancel the call?
link |
Wow, you're in trouble.
link |
I know, I'm in trouble.
link |
No, she's gonna be calling the cops.
link |
Honey, are you okay?
link |
I'm gonna edit this clip out and send it to her.
link |
So there's a lot of encoding
link |
for the same kind of function.
link |
Yeah, so you now have this mapping
link |
between all of the set of functions
link |
that could all encode the same,
link |
all of the set of sequences
link |
that can all encode the same function.
link |
What evolutionary signatures does
link |
is that it basically looks at the shape
link |
of that distribution of sequences
link |
that all encode the same thing.
link |
And based on that shape, you can basically say,
link |
ooh, proteins have a very different shape
link |
than RNA structures, than regulatory motifs, et cetera.
link |
So just by scanning a sequence, ignoring the sequence
link |
and just looking at the patterns of change,
link |
I'm like, wow, this thing is evolving like a protein
link |
and that thing is evolving like a motif
link |
and that thing is evolving.
link |
So that's exactly what we just did for COVID.
link |
So our paper that we posted in bioRxiv about coronavirus
link |
basically took this concept of evolutionary signatures
link |
and applied it on the SARS CoV2 genome
link |
that is responsible for the COVID 19 pandemic.
link |
And comparing it to?
link |
To 44 serbicovirus species.
link |
So this is the beta.
link |
What word did you just use, serbicovirus?
link |
Serbicovirus, so SARS related beta coronavirus.
link |
It's a portmanteau of a bunch.
link |
So that whole family of viruses.
link |
How big is that family by the way?
link |
We have 44 species that, or I mean.
link |
There's 44 species in the family?
link |
Yeah. Virus is a clever bunch.
link |
No, no, but there's just 44.
link |
And again, we don't call them species in viruses.
link |
We call them strains.
link |
But anyway, there's 44 strains.
link |
And that's a tiny little subset of maybe another 50 strains
link |
that are just far too distantly related.
link |
Most of those only infect bats as the host
link |
and a subset of only four or five have ever infected humans.
link |
And we basically took all of those
link |
and we aligned them in the same exact way
link |
that we've aligned mammals.
link |
And then we looked at what proteins are,
link |
which of the currently hypothesized genes
link |
for the coronavirus genome
link |
are in fact evolving like proteins and which ones are not.
link |
And what we found is that ORF10,
link |
the last little open reading frame,
link |
the last little gene in the genome is bogus.
link |
That's not a protein at all.
link |
It's an RNA structure.
link |
That doesn't have a.
link |
It doesn't get translated into amino acids.
link |
And that, so it's important to narrow down
link |
to basically discover what's useful and what's not.
link |
Basically, what is even the set of genes?
link |
The other thing that these evolutionary signatures showed
link |
is that within ORF3A lies a tiny little additional gene
link |
encoded within the other gene.
link |
So you can translate a DNA sequence
link |
in three different reading frames.
link |
If you start in the first one, it's ATG, et cetera.
link |
If you start on the second one, it's TGC, et cetera.
link |
And there's a gene within a gene.
link |
So there's a whole other protein
link |
that we didn't know about that might be super important.
link |
So we don't even know the building blocks of SARS COVID 2.
link |
So if we want to understand coronavirus biology
link |
and eventually find it successfully,
link |
we need to even have the set of genes
link |
and these evolutionary signatures
link |
that I developed in my PhD work.
link |
Are you really useful here?
link |
We just recently used.
link |
You know what, let's run with that tangent
link |
for a little bit, if it's okay.
link |
Can we talk about the COVID 19 a little bit more?
link |
What's your sense about the genome, the proteins,
link |
the functions that we understand about COVID 19?
link |
Where do we stand in your sense?
link |
What are the big open problems?
link |
And also, you kind of said it's important to understand
link |
what are the important proteins
link |
and why is that important?
link |
So what else does the comparison of these species tell us?
link |
What it tells us is how fast are things evolving?
link |
It tells us about at what level is the acceleration
link |
or deceleration pedal set for every one of these proteins.
link |
So the genome has 30 some genes.
link |
Some genes evolve super, super fast.
link |
Others evolve super, super slow.
link |
If you look at the polymerase gene
link |
that basically replicates the genome,
link |
that's a super slow evolving one.
link |
If you look at the nucleocapsid protein,
link |
that's also super slow evolving.
link |
If you look at the spike one protein,
link |
this is the part of the spike protein
link |
that actually touches the ACE2 receptor
link |
and then enables the virus to attach to your cells.
link |
That's the thing that gives it that visual...
link |
Yeah, the corona look basically.
link |
The corona look, yeah.
link |
So basically the spike protein sticks out of the virus
link |
and there's a first part of the protein S1
link |
which basically attaches to the ACE2 receptor.
link |
And then S2 is the latch that sort of pushes and channels
link |
the fusion of the membranes
link |
and then the incorporation of the viral RNA inside our cells
link |
which then gets translated into all of these 30 proteins.
link |
So the S1 protein is evolving ridiculously fast.
link |
So if you look at the stop versus gas pedal,
link |
the gas pedal is all the way down.
link |
ORF8 is also evolving super fast
link |
and ORF6 is evolving super fast.
link |
We have no idea what they do.
link |
We have some idea but nowhere near what S1 is.
link |
Isn't that terrifying that S1 is evolving?
link |
That means that's a really useful function
link |
and if it's evolving fast,
link |
doesn't that mean new strains could be created
link |
or it does something?
link |
That means that it's searching for how to match,
link |
how to best match the host.
link |
So basically anything in general in evolution,
link |
if you look at genomes,
link |
anything that's contacting the environment
link |
is evolving much faster than anything that's internal.
link |
And the reason is that the environment changes.
link |
So if you look at the evolution of the cervical viruses,
link |
the S1 protein has evolved very rapidly
link |
because it's attaching to different hosts each time.
link |
We think of them as bats,
link |
but there's thousands of species of bats
link |
and to go from one species of bat to another species of bat,
link |
you have to adjust S1 to the new ACE2 receptor
link |
that you're gonna be facing in that new species.
link |
Sorry, quick tangent.
link |
Is it fascinating to you that viruses are doing this?
link |
I mean, it feels like they're this intelligent organism.
link |
I mean, does it give you pause how incredible it is
link |
that the evolutionary dynamics that you're describing
link |
is actually happening and they're freaking out,
link |
figuring out how to jump from bats to humans
link |
all in this distributed fashion?
link |
And then most of us don't even say
link |
they're alive or intelligent or whatever.
link |
So intelligence is in the eye of the beholder.
link |
Stupid is as stupid does, as Forrest Gump would say,
link |
and intelligent is as intelligent does.
link |
So basically if the virus is finding solutions
link |
that we think of as intelligent,
link |
yeah, it's probably intelligent,
link |
but that's again in the eye of the beholder.
link |
Do you think viruses are intelligent?
link |
Oh, of course not.
link |
It's so incredible.
link |
So remember when I was talking about the two components
link |
of evolution, one is the stupid mutation,
link |
which is completely blind,
link |
and the other one is the super smart selection,
link |
which is ruthless.
link |
So it's not viruses who are smart.
link |
It's this component of evolution that's smart.
link |
So it's evolution that sort of appears smart.
link |
And how is that happening?
link |
By huge parallel search across thousands of parallel
link |
of parallel infections throughout the world right now.
link |
Yes, but so to push back on that,
link |
so yes, so then the intelligence is in the mechanism,
link |
but then by that argument,
link |
viruses would be more intelligent
link |
because there's just more of them.
link |
So the search, they're basically the brute force search
link |
that's happening with viruses
link |
because there's so many more of them than humans,
link |
then they're taken as a whole are more intelligent.
link |
I mean, so you don't think it's possible that,
link |
I mean, who runs, would we even be here if viruses weren't,
link |
I mean, who runs this thing?
link |
So humans or viruses?
link |
So let me answer, yeah, let me answer your question.
link |
So we would not be here if it wasn't for viruses.
link |
And part of the reason is that
link |
if you look at mammalian evolution early on
link |
in this mammalian radiation
link |
that basically happened after the death of the dinosaurs
link |
is that some of the viruses that we had in our genome
link |
spread throughout our genome
link |
and created binding sites
link |
for new classes of regulatory proteins.
link |
And these binding sites that landed all over our genome
link |
are now control elements that basically control our genes
link |
and sort of help the complexity of the circuitry
link |
of mammalian genomes.
link |
So, you know, everything's coevolution.
link |
That's fascinating, we're working together.
link |
And yet you say they're dumb.
link |
We've coopted them.
link |
No, I never said they're dumb.
link |
They just don't care.
link |
Another thing, oh, is the virus trying to kill us?
link |
The virus is not trying to kill you.
link |
It's actually actively trying to not kill you.
link |
So when you get infected, if you die,
link |
bomber, I killed him,
link |
is what the reaction of the virus will be.
link |
Why? Because that virus won't spread.
link |
Many people have a misconception of,
link |
oh, viruses are smart or oh, viruses are mean.
link |
It's like, you have to clean yourself
link |
of any kind of anthropomorphism out there.
link |
So there's a sense when taken as a whole that there's...
link |
It's in the eye of the beholder.
link |
Stupid is as stupid does.
link |
Intelligent is as intelligent does.
link |
So if you want to call them intelligent, that's fine.
link |
Because the end result is that
link |
they're finding amazing solutions.
link |
I mean, I am in awe.
link |
They're so dumb about it.
link |
They're just doing dumb.
link |
They're not dumb and they're just don't care.
link |
The care word is really interesting.
link |
I mean, there could be an argument that they're conscious.
link |
They're just dividing.
link |
They're just dividing.
link |
They're just a little entity
link |
which happens to be dividing and spreading.
link |
It just doesn't want to kill us.
link |
In fact, it prefers not to kill us.
link |
It just wants to spread.
link |
And when I say wants, again, I'm anthropomorphizing,
link |
but it's just that if you have two versions of a virus,
link |
one acquires a mutation that spreads more,
link |
that's going to spread more.
link |
One acquires a mutation that spreads less,
link |
that's going to be lost.
link |
One acquires a mutation that enters faster,
link |
that's going to be kept.
link |
One acquires a mutation that kills you right away,
link |
it's going to be lost.
link |
So over evolutionary time,
link |
the viruses that spread super well
link |
but don't kill the host
link |
are the ones that are going to survive.
link |
Yeah, but so you brilliantly described
link |
the basic mechanisms of how it all happens,
link |
but when you zoom out and you see the entirety of viruses,
link |
maybe across different strains of viruses,
link |
it seems like a living organism.
link |
I am in awe of biology.
link |
I find biology amazingly beautiful.
link |
I find the design of the current coronavirus,
link |
however lethal it is, amazingly beautiful.
link |
The way that it is encoded,
link |
the way that it tricks your cells
link |
into making 30 proteins from a single RNA.
link |
Human cells don't do that.
link |
Human cells make one protein from each RNA molecule.
link |
They don't make two, they make one.
link |
We are hardwired to make only one protein
link |
from every RNA molecule.
link |
And yet this virus goes in,
link |
throws in a single messenger RNA.
link |
Just like any messenger RNA,
link |
we have tens of thousands of messenger RNAs
link |
in our cells in any one time.
link |
In every one of our cells.
link |
It throws in one RNA and that RNA is so,
link |
I'm gonna use your word here, not my word, intelligent.
link |
That it hijacks the entire machinery of your human cell.
link |
It basically has at the beginning,
link |
a giant open reading frame.
link |
That's a giant protein that gets translated.
link |
Two thirds of that RNA make a single giant protein.
link |
That single protein is basically
link |
what a human cell would make.
link |
It's like, oh, here's a start code.
link |
I'm gonna start translating here.
link |
Human cells are kind of dumb.
link |
Again, this is not the words I would normally use.
link |
But the human cell basically says,
link |
oh, this is an RNA, must be mine.
link |
And it starts translating it.
link |
And then you're in trouble.
link |
Because that one protein as it's growing,
link |
gets cleaved into about 20 different peptides.
link |
The first peptide and the second peptide start interacting
link |
and the third one and the fourth one.
link |
And they shut off the ribosome of the whole cell
link |
to not translate human RNAs anymore.
link |
So the virus basically hijacks your cells
link |
and it cuts, it cleaves every one of your human RNAs
link |
to basically say to the ribosome,
link |
don't translate this one, junk.
link |
Don't look at this one, junk.
link |
And it only spares its own RNAs
link |
because they have a particular mark that it spares.
link |
Then all of the ribosomes that normally make protein
link |
in your human cells are now only able
link |
to translate viral RNAs.
link |
And then more and more and more and more of them.
link |
That's the first 20 proteins.
link |
In fact, halfway down about protein 11,
link |
between 11 and 12,
link |
you basically have a translational slippage
link |
where the ribosome skips reading frame.
link |
And it translates from one reading frame
link |
to another reading frame.
link |
That means that about half of them
link |
are gonna be translated from one to 11.
link |
And the other half are gonna be translated
link |
And then you're done.
link |
Then that mRNA will never translate the last 10 proteins
link |
but spike is the one right after that one.
link |
So how does spike even get translated?
link |
This positive strand RNA virus has a reverse transcriptase
link |
which is an RNA based reverse transcriptase.
link |
So from the RNA on the positive strand,
link |
it makes an RNA on the negative strand.
link |
And in between every single one of these genes,
link |
these open reading frames,
link |
there's a little signal AACGCA or something like that,
link |
that basically loops over to the beginning of the RNA.
link |
And basically instead of sort of having
link |
a single full negative strand RNA,
link |
it basically has a partial negative strand RNA
link |
that ends right before the beginning of that gene.
link |
And another one that ends right before
link |
the beginning of that gene.
link |
These negative strand RNAs now make positive strand RNAs
link |
that then look to the human whole cell
link |
just like any other human mRNA.
link |
It's like, ooh, great, I'm gonna translate that one
link |
because it doesn't have the cleaving
link |
that the virus has now put on all your human genes.
link |
And then you've lost the battle.
link |
That cell is now only making proteins for the virus
link |
that will then create the spike protein,
link |
the envelope protein, the membrane protein,
link |
the nucleocapsid protein that will package up the RNA
link |
and then sort of create new viral envelopes.
link |
And these will then be secreted out of that cell
link |
in new little packages
link |
that will then infect the rest of the cells.
link |
Repeat the whole process again.
link |
It's beautiful, right?
link |
It's mind boggling.
link |
It's hard not to anthropomorphize it.
link |
I know, but it's so gorgeous.
link |
So there is a beauty to it.
link |
Is it terrifying to you?
link |
So this is something that has happened throughout history.
link |
Humans have been nearly wiped out
link |
over and over and over again,
link |
and yet never fully wiped out.
link |
So yeah, I'm not concerned about the human race.
link |
I'm not even concerned about the impact
link |
on sort of our survival as a species.
link |
This is absolutely something,
link |
I mean, human life is so invaluable
link |
and every one of us is so invaluable,
link |
but if you think of it as sort of,
link |
is this the end of our species?
link |
By no means, basically.
link |
So let me explain.
link |
The Black Death killed what, 30% of Europe?
link |
That has left a tremendous imprint,
link |
a huge hole, a horrendous hole
link |
in the genetic makeup of humans.
link |
There's been series of wiping out of huge fractions
link |
of entire species or just entire species altogether.
link |
And that has a consequence on the human immune repertoire.
link |
If you look at how Europe was shaped
link |
and how Africa was shaped by malaria, for example,
link |
all the individuals that carry a mutation
link |
that protects you from malaria
link |
were able to survive much more.
link |
And if you look at the frequency of sickle cell disease
link |
and the frequency of malaria,
link |
the maps are actually showing the same pattern,
link |
the same imprint on Africa.
link |
And that basically led people to hypothesize
link |
that the reason why sickle cell disease
link |
is so much more frequent is because
link |
sickle cell disease is so much more frequent
link |
in Americans of African descent
link |
is because there was selection in Africa against malaria
link |
leading to sickle cell, because when the cells sickle,
link |
malaria is not able to replicate inside your cells as well.
link |
And therefore you protect against that.
link |
So if you look at human disease,
link |
all of the genetic associations that we do
link |
with human disease,
link |
you basically see the imprint
link |
of these waves of selection killing off
link |
gazillions of humans.
link |
And there's so many immune processes that are coming up
link |
as associated with so many different diseases.
link |
The reason for that is similar
link |
to what I was describing earlier,
link |
where the outward facing proteins evolve much more rapidly
link |
because the environment is always changing.
link |
But what's really interesting in the human genome
link |
is that we have coopted many of these immune genes
link |
to carry out nonimmune functions.
link |
For example, in our brain,
link |
we use immune cells to cleave off neuronal connections
link |
that don't get used.
link |
This whole use it or lose it, we know the mechanism.
link |
It's microglia that cleave off neuronal synaptic connections
link |
that are just not utilized.
link |
When you utilize them, you mark them in a particular way
link |
that basically when the microglia come,
link |
tell it, don't kill this one, it's used now.
link |
And the microglia will go off
link |
and kill the ones you don't use.
link |
This is an immune function,
link |
which is coopted to do nonimmune things.
link |
If you look at our adipocytes,
link |
M1 versus M2 macrophages inside our fat
link |
will basically determine whether you're obese or not.
link |
And these are again, immune cells that are resident
link |
and living within these tissues.
link |
So many disease associations.
link |
That's it, that we coopt these kinds of things
link |
for incredibly complicated functions.
link |
Exactly, evolution works in so many different ways,
link |
which are all beautiful and mysterious.
link |
But not intelligent.
link |
Not intelligent, it's in the eye of the beholder.
link |
But the point that I'm trying to make is that
link |
if you look at the imprint that COVID will have,
link |
hopefully it will not be big.
link |
Hopefully the US will get attacked together
link |
and stop the virus from spreading further.
link |
But if it doesn't, it's having an imprint
link |
on individuals who have particular genetic repertoires.
link |
So if you look at now the genetic associations
link |
of blood type and immune function cells, et cetera,
link |
there's actually association, genetic variation
link |
that basically says how much more likely am I or you to die
link |
if we contact the virus.
link |
And it's through these rounds of shaping the human genome
link |
that humans have basically made it so far.
link |
And selection is ruthless and it's brutal
link |
and it only comes with a lot of killing.
link |
But this is the way that viruses and environments
link |
have shaped the human genome.
link |
Basically, when you go through periods of famine,
link |
you select for particular genes.
link |
And what's left is not necessarily better,
link |
it's just whatever survived.
link |
And it might have been the surviving one back then,
link |
not because it was better,
link |
maybe the ones that ran slower survived.
link |
I mean, again, not necessarily better,
link |
but the surviving ones are basically the ones
link |
that then are shaped for any kind
link |
of subsequent evolutionary condition
link |
and environmental condition.
link |
But if you look at, for example, obesity,
link |
obesity was selected for basically the genes
link |
that now predisposes to obesity
link |
were at 2% frequency in Africa.
link |
They rose to 44% frequency in Europe.
link |
Wow, that's fascinating.
link |
Because you basically went through the ice ages
link |
and there was a scarcity of food.
link |
So there was a selection to being able to store
link |
every single calorie you consume.
link |
Eventually, environment changes.
link |
So the better allele, which was the fat storing allele,
link |
became the worst allele
link |
because it's the fat storing allele.
link |
It still has the same function.
link |
So if you look at my genome, speaking of mom calling,
link |
mom gave me a bad copy of that gene, this FTO locus.
link |
Basically, makes me.
link |
The one that has to do with.
link |
Yeah, I basically now have a bad copy from mom
link |
that makes me more likely to be obese.
link |
And I also have a bad copy from dad
link |
that makes me more likely to be obese.
link |
And that's the allele, it's still the minor allele,
link |
but it's at 44% frequency in Southeast Asia,
link |
42% frequency in Europe, even though it started at 2%.
link |
It was an awesome allele to have 100 years ago.
link |
Right now, it's pretty terrible allele.
link |
So the other concept is that diversity matters.
link |
If we had 100 million nuclear physicists
link |
living the earth right now, we'd be in trouble.
link |
You need diversity, you need artists
link |
and you need musicians and you need mathematicians
link |
and you need politicians, yes, even those.
link |
And you need like.
link |
Well, let's not get crazy.
link |
But because then if a virus comes along or whatever.
link |
So, no, there's two reasons.
link |
Number one, you want diversity in the immune repertoire
link |
and we have built in diversity.
link |
So basically, they are the most diverse.
link |
Basically, if you look at our immune system,
link |
there's layers and layers of diversity.
link |
Like the way that you create your cells generates diversity
link |
because of the selection for the VDJ recombination
link |
that basically eventually leads
link |
to a huge number of repertoires.
link |
But that's only one small component of diversity.
link |
The blood type is another one.
link |
The major histocompatibility complex, the HLA alleles
link |
are another source of diversity.
link |
So the immune system of humans is by nature,
link |
incredibly diverse and that basically leads to resilience.
link |
So basically what I'm saying that I don't worry
link |
for the human species because we are so diverse immunologically,
link |
we are likely to be very resilient
link |
against so many different attacks like this current virus.
link |
So you're saying natural pandemics may not be something
link |
that you're really afraid of because of the diversity
link |
in our genetic makeup.
link |
What about engineered pandemics?
link |
Do you have fears of us messing with the makeup of viruses
link |
or well, yeah, let's say with the makeup of viruses
link |
to create something that we can't control
link |
and would be much more destructive
link |
than it would come about naturally?
link |
Remember how we were talking about how smart evolution is?
link |
Humans are much dumber.
link |
You mean like human scientists, engineers?
link |
Yeah, humans, humans just like.
link |
Yeah, humans overall.
link |
But I mean, even the sort of synthetic biologists
link |
you know, basically if you were to create,
link |
you know, virus like SARS that will kill a lot of people,
link |
you would probably start with SARS.
link |
So whoever, you know, would like to design such a thing
link |
would basically start with a SARS tree
link |
or at least some relative of SARS.
link |
The source genome for the current virus
link |
was something completely different.
link |
It was something that has never infected anyone
link |
and never infected humans.
link |
No one in their right mind would have started there.
link |
But when you say sources like the nearest.
link |
The nearest relative.
link |
Is in a whole other branch.
link |
No species of which has ever infected humans
link |
So, you know, let's put this to rest.
link |
This was not designed by someone to kill off the human race.
link |
So you don't believe it was engineered?
link |
Yeah, the path to engineering a deadly virus
link |
did not come from this strain that was used.
link |
Moreover, there's been various claims of,
link |
ha ha, this was mixed and matched in lab
link |
because the S1 protein has three different components,
link |
each of which has a different evolutionary tree.
link |
So, you know, a lot of popular press basically said,
link |
aha, this came from pangolin
link |
and this came from, you know, all kinds of other species.
link |
This is what has been happening
link |
throughout the coronavirus tree.
link |
So basically the S1 protein has been recombining
link |
across species all the time.
link |
Remember when I was talking about the positive strand,
link |
the negative strand, sub genomic RNAs,
link |
these can actually recombine.
link |
And if you have two different viruses
link |
infecting the same cell,
link |
they can actually mix and match
link |
between the positive strand and the negative strand
link |
and basically create a new hybrid virus with recombination
link |
that now has the S1 from one
link |
and the rest of the genome from another.
link |
And this is something that happens a lot in S1,
link |
in Orfet, et cetera.
link |
And that's something that's true of the whole tree.
link |
For the whole family of viruses.
link |
So it's not like someone has been messing with this
link |
for millions of years and, you know, changing.
link |
This happens naturally.
link |
That's, again, beautiful that that somehow happens,
link |
that they recombine.
link |
So two different strands can infect the body
link |
and then recombine.
link |
So all of this actually magic happens inside hosts.
link |
Yeah, that's why classification wise,
link |
virus is not thought to be alive
link |
because it doesn't self replicate.
link |
It's not autonomous.
link |
It's something that enters a living cell
link |
and then co ops it to basically make it its own.
link |
But by itself, people ask me,
link |
how do we kill this bastard?
link |
I'm like, you stop it from replicating.
link |
It's not like a bacterium that will just live
link |
in a, you know, puddle or something.
link |
Viruses don't live without their host.
link |
And they only live with their host for very little time.
link |
So if you stop it from replicating,
link |
it'll stop from spreading.
link |
I mean, it's not like HIV, which can stay dormant
link |
Basically, coronaviruses just don't do that.
link |
They're not integrating genomes.
link |
They're RNA genomes.
link |
So if it's not expressed, it degrades.
link |
It doesn't just stick around.
link |
Well, let me ask also about the immune system you mentioned.
link |
A lot of people kind of ask, you know,
link |
how can we strengthen the immune system
link |
to respond to this particular virus,
link |
but the viruses in general.
link |
Do you have from a biological perspective,
link |
thoughts on what we can do as humans
link |
to strengthen our immune system?
link |
If you look at the death rates across different countries,
link |
people with less vaccination have been dying more.
link |
If you look at North Italy,
link |
the vaccination rates are abysmal there.
link |
And a lot of people have been dying.
link |
If you look at Greece, very good vaccination rates.
link |
Almost no one has been dying.
link |
So yes, there's a policy component.
link |
So Italy reacted very slowly.
link |
Greece reacted very fast.
link |
So yeah, many fewer people died in Greece,
link |
but there might actually be a component
link |
of genetic immune repertoire.
link |
Basically, how did people die off, you know,
link |
in the history of the Greek population
link |
versus the Italian population.
link |
That's interesting to think about.
link |
And then there's a component
link |
of what vaccinations did you have as a kid
link |
and what are the off target effects of those vaccinations?
link |
So basically a vaccination can have two components.
link |
One is training your immune system
link |
against that specific insult.
link |
The second one is boosting up your immune system
link |
for all kinds of other things.
link |
If you look at allergies,
link |
Northern Europe, super clean environments,
link |
tons of allergies.
link |
Southern Europe, my kids grew up eating dirt.
link |
So growing up, I never had even heard of what allergies are.
link |
Like, was it really allergies?
link |
And the reason is that I was playing in the garden.
link |
I was putting all kinds of stuff in my mouth from,
link |
you know, all kinds of dirt and stuff,
link |
tons of viruses there, tons of bacteria there.
link |
You know, my immune system was built up.
link |
So the more you protect your immune system from exposure,
link |
the less opportunity it has to learn
link |
about non self repertoire in a way that prepares it
link |
for the next insult.
link |
So that's the horizontal thing too,
link |
like the, so it's throughout your lifetime
link |
and the lifetime of the people that, your ancestors,
link |
that kind of thing.
link |
So again, it returns against free will.
link |
On the free will side of things,
link |
is there something we could do
link |
to strengthen our immune system in 2020?
link |
Is there like, you know, exercise, diet,
link |
all that kind of stuff?
link |
So it's kind of funny.
link |
There's a cartoon that basically shows two windows
link |
with a teller in each window.
link |
One has a humongous line and the other one has no one.
link |
The one that has no one above says health.
link |
No, it says exercise and diet.
link |
And the other one says pill.
link |
And there's a huge line for pill.
link |
So we're looking basically for magic bullets
link |
for sort of ways that we can, you know,
link |
beat cancer and beat coronavirus and beat this
link |
And it turns out that the window with like,
link |
just diet and exercise is the best way
link |
to boost every aspect of your health.
link |
If you look at Alzheimer's, exercise and nutrition.
link |
I mean, you're like, really?
link |
For my brain, neurodegeneration?
link |
If you look at cancer, exercise and nutrition.
link |
If you look at coronavirus, exercise and nutrition,
link |
every single aspect of human health gets improved.
link |
And one of the studies we're doing now
link |
is basically looking at what are the effects
link |
of diet and exercise?
link |
How similar are they to each other?
link |
We basically take in diet intervention
link |
and exercise intervention in human and in mice.
link |
And we're basically doing single cell profiling
link |
of a bunch of different tissues
link |
to basically understand how are the cells,
link |
both the stromal cells and the immune cells
link |
of each of these tissues responding
link |
to the effect of exercise.
link |
What are the communication networks
link |
between different cells?
link |
Where the muscle that exercises sends signals
link |
through the bloodstream, through the lymphatic system,
link |
through all kinds of other systems
link |
that give signals to other cells that I have exercised
link |
and you should change in this particular way,
link |
which basically reconfigure those receptor cells
link |
with the effect of exercise.
link |
How well understood is those reconfigurations?
link |
We're just starting now, basically.
link |
Is the hope there to understand the effect on,
link |
so like the effect on the immune system?
link |
On the immune system, the effect on brain,
link |
the effect on your liver, on your digestive system,
link |
on your adipocytes?
link |
Adipose, the most misunderstood organ.
link |
Everybody thinks, oh, fat, terrible.
link |
No, fat is awesome.
link |
Your fat cells is what's keeping you alive
link |
because if you didn't have your fat cells,
link |
all those lipids and all those calories
link |
would be floating around in your blood
link |
and you'd be dead by now.
link |
Your adipocytes are your best friend.
link |
They're basically storing all these excess calories
link |
so that they don't hurt all of the rest of the body.
link |
And they're also fat burning in many ways.
link |
So, again, when you don't have
link |
the homozygous version that I have,
link |
your cells are able to burn calories much more easily
link |
by sort of flipping a master metabolic switch
link |
that involves this FTO locus that I mentioned earlier
link |
and its target genes, RX3 and RX5,
link |
that basically switch your adipocytes
link |
during their three first days of differentiation
link |
as they're becoming mature adipocytes
link |
to basically become either fat burning
link |
or fat storing fat cells.
link |
And the fat burning fat cells are your best friend.
link |
They're much closer to muscle
link |
than they are to white adipocytes.
link |
Is there a lot of difference between people
link |
that you could give, science could eventually give advice
link |
that is very generalizable
link |
or is our differences in our genetic makeup,
link |
like you mentioned, is that going to be basically
link |
something we have to be very specialized individuals,
link |
any advice we give in terms of diet,
link |
like what we were just talking about?
link |
Believe it or not, the most personalized advice
link |
that you give for nutrition
link |
don't have to do with your genome.
link |
They have to do with your gut microbiome,
link |
with the bacteria that live inside you.
link |
So most of your digestion is actually happening
link |
by species that are not human inside you.
link |
You have more nonhuman cells than you have human cells.
link |
You're basically a giant bag of bacteria
link |
with a few human cells along.
link |
And those do not necessarily have to do
link |
with your genetic makeup.
link |
They interact with your genetic makeup.
link |
They interact with your epigenome.
link |
They interact with your nutrition.
link |
They interact with your environment.
link |
They're basically an additional source of variation.
link |
So when you're thinking about sort of
link |
personalized nutritional advice,
link |
part of that is actually how do you match your microbiome?
link |
And part of that is how do we match your genetics?
link |
But again, this is a very diverse set of contributors.
link |
And the effect sizes are not enormous.
link |
So I think the science for that is not fully developed yet.
link |
Speaking of diets,
link |
because I've wrestled in combat sports,
link |
but sports my whole life were weight matters.
link |
So you have to cut and all that stuff.
link |
One thing I've learned a lot about my body,
link |
and it seems to be, I think,
link |
true about other people's bodies,
link |
is that you can adjust to a lot of things.
link |
That's the miraculous thing about this biological system,
link |
is like I fast often.
link |
I used to eat like five, six times a day
link |
and thought that was absolutely necessary.
link |
How could you not eat that often?
link |
And then when I started fasting,
link |
your body adjusted to that.
link |
And you learn how to not eat.
link |
And it was, if you just give it a chance
link |
for a few weeks, actually,
link |
over a period of a few weeks,
link |
your body can adjust to anything.
link |
And that's a miraculous, that's such a beautiful thing.
link |
So I'm a computer scientist,
link |
and I've basically gone through periods of 24 hours
link |
without eating or stopping.
link |
And then I'm like, oh, must eat.
link |
I used to order two pizzas just with my brother.
link |
So I've gone through these extremes as well,
link |
and I've gone the whole intermittent fasting thing.
link |
So I can sympathize with you both on the seven meals a day
link |
to the zero meals a day.
link |
So I think when I say everything with moderation,
link |
I actually think your body responds interestingly
link |
to these different changes in diet.
link |
I think part of the reason why we lose weight
link |
with pretty much every kind of change in behavior
link |
is because our epigenome and the set of proteins
link |
and enzymes that are expressed and our microbiome
link |
are not well suited to that nutritional source.
link |
And therefore, they will not be able
link |
to sort of catch everything that you give them.
link |
And then a lot of that will go undigested.
link |
And that basically means that your body can then
link |
lose weight in the short term,
link |
but very quickly will adjust to that new normal.
link |
And then we'll be able to sort of perhaps gain
link |
a lot of weight from the diet.
link |
So anyway, I mean, there's also studies in factories
link |
where basically people dim the lights
link |
and then suddenly everybody started working better.
link |
It was like, wow, that's amazing.
link |
Three weeks later, they made the lights a little brighter.
link |
Everybody started working better.
link |
So any kind of intervention has a placebo effect of,
link |
wow, now I'm healthier and I'm gonna be running
link |
more often, et cetera.
link |
So it's very hard to uncouple the placebo effect
link |
of, wow, I'm doing something to intervene on my diet
link |
from the, wow, this is actually the right thing for me.
link |
Yeah, from the perspective from a nutrition science,
link |
psychology, both things I'm interested in,
link |
especially psychology, it seems that it's extremely difficult
link |
to do good science because there's so many variables
link |
involved, it's so difficult to control the variables,
link |
so difficult to do sufficiently large scale experiments,
link |
both sort of in terms of the number of subjects
link |
and temporal, like how long you do the study for,
link |
that it just seems like it's not even a real science
link |
for now, like nutrition science.
link |
I wanna jump into the whole placebo effect
link |
for a little bit here.
link |
And basically talk about the implications of that.
link |
If I give you a sugar pill and I tell you it's a sugar pill,
link |
you won't get any better.
link |
But if I tell you a sugar pill and I tell you,
link |
wow, this is an amazing drug,
link |
it actually will stop your cancer,
link |
your cancer will actually stop with much higher probability.
link |
What does that mean?
link |
That's so amazing.
link |
That means that if I can trick your brain
link |
into thinking that I'm healing you,
link |
your brain will basically figure out a way to heal itself,
link |
And that tells us that there's so much
link |
that we don't understand in the interplay
link |
between our cognition and our biology,
link |
that if we were able to better harvest
link |
the power of our brain to sort of impact the body
link |
through the placebo effect,
link |
we would be so much better in so many different things.
link |
Just by tricking yourself into thinking
link |
that you're doing better, you're actually doing better.
link |
So there's something to be said
link |
about sort of positive thinking, about optimism,
link |
about sort of just getting your brain
link |
and your mind into the right mindset
link |
that helps your body and helps your entire biology.
link |
Yeah, from a science perspective, that's just fascinating.
link |
Obviously most things about the brain
link |
is a total mystery for now,
link |
but that's a fascinating interplay
link |
that the brain can help cure cancer.
link |
I don't even know what to do with that.
link |
I mean, the way to think about that is the following.
link |
The converse of the equation is something
link |
that we are much more comfortable with.
link |
Like, oh, if you're stressed,
link |
then your heart rate might rise
link |
and all kinds of sort of toxins might be released
link |
and that can have a detrimental effect in your body,
link |
et cetera, et cetera, et cetera.
link |
So maybe it's easier to understand your body
link |
healing from your mind
link |
by your mind is not killing your body,
link |
or at least it's killing it less.
link |
So I think that aspect of the stress equation
link |
is a little easier for most of us to conceptualize,
link |
but then the healing part is perhaps the same pathways,
link |
perhaps different pathways,
link |
but again, something that is totally untapped scientifically.
link |
I think we try to bring this question up a couple of times,
link |
but let's return to it again,
link |
is what do you think is the difference
link |
between the way a computer represents information,
link |
the human genome represents and stores information?
link |
And maybe broadly, what is the difference
link |
between how you think about computers
link |
and how you think about biological systems?
link |
So I made a very provocative claim earlier
link |
that we are a digital computer.
link |
Like I said, at the core lies a digital code
link |
and that's true in many ways,
link |
but surrounding that digital core,
link |
there's a huge amount of analog.
link |
If you look at our brain, it's not really digital.
link |
If you look at our sort of RNA
link |
and all of that stuff inside our cell,
link |
it's not really digital.
link |
It's really analog in many ways,
link |
but let's start with the code
link |
and then we'll expand to the rest.
link |
So the code itself is digital.
link |
You can think of the genes as, I don't know,
link |
the procedures, the functions inside your language.
link |
And then somehow you have to turn these functions on.
link |
How do you call a gene?
link |
How do you call that function?
link |
The way that you would do it in old programming languages
link |
is go to address whatever in your memory
link |
and then you'd start running from there.
link |
And modern programming languages
link |
have encapsulated this into functions
link |
and objects and all of that.
link |
And it's nice and cute, but in the end, deep down,
link |
there's still an assembly code
link |
that says go to that instruction
link |
and it runs that instruction.
link |
If you look at the human genome
link |
and the genome of pretty much most species out there,
link |
there's no go to function.
link |
You just don't start transcribing in position 13,000,
link |
13,527 in chromosome 12.
link |
You instead have content based indexing.
link |
So at every location in the genome,
link |
in front of the genes that need to be turned on,
link |
I don't know, when you drink coffee,
link |
there's a little coffee marker in front of all of them.
link |
And whenever your cells that metabolize coffee
link |
need to metabolize coffee,
link |
they basically see coffee and they're like,
link |
ooh, let's go turn on all the coffee marked genes.
link |
So there's basically these small motifs,
link |
these small sequences that we call regulatory motifs.
link |
They're like patterns of DNA.
link |
They're only eight characters long or so,
link |
like GAT, GCA, et cetera.
link |
And these motifs work in combinations
link |
and every one of them has some recruitment affinity
link |
for a different protein that will then come and bind it
link |
and together collections of these motifs
link |
create regions that we call regulatory regions
link |
that can be either promoters near the beginning of the gene
link |
and that basically tells you
link |
where the function actually starts, where you call it,
link |
and then enhancers that are looping around of the DNA
link |
that basically bring the machinery
link |
that binds those enhancers
link |
and then bring it onto the promoter,
link |
which then recruits the right sort of the ribosome
link |
and the polymerase and all of that thing,
link |
which will first transcribe and then export
link |
and then eventually translate in the cytoplasm,
link |
you know, whatever RNA molecule.
link |
So the beauty of the way
link |
that the digital computer that's the genome works
link |
is that it's extremely fault tolerant.
link |
If I took your hard drive
link |
and I messed with 20% of the letters in it,
link |
of the zeros and ones and I flipped them,
link |
you'd be in trouble.
link |
If I take the genome and I flipped 20% of the letters,
link |
you probably won't even notice.
link |
And that resilience.
link |
That's fascinating, yeah.
link |
Is a key design principle.
link |
And again, I'm anthropomorphizing here,
link |
but it's a key driving principle
link |
of how biological systems work.
link |
They're first resilient and then anything else.
link |
And when you look at this incredible beauty of life
link |
from the most, I don't know, beautiful,
link |
I don't know, human genome maybe of humanity
link |
and all of the ideals that should come with it
link |
to the most terrifying genome,
link |
like, I don't know, COVID 19, SARS COVID 2
link |
and the current pandemic,
link |
you basically see this elegance
link |
as the epitome of clean design,
link |
It's, you know, the way to get there is hugely messy.
link |
And that's something that we as computer scientists
link |
We like to have clean code.
link |
You know, like in engineering,
link |
they teach you about compartmentalization,
link |
about sort of separating functions,
link |
about modularity, about hierarchical design.
link |
None of that applies in biology.
link |
Yeah, biology does plenty of that.
link |
But I mean, through evolutionary exploration.
link |
But if you look at biological systems,
link |
first they are robust
link |
and then they specialize to become anything else.
link |
And if you look at viruses,
link |
the reason why they're so elegant
link |
when you look at the design of this, you know, genome,
link |
it seems so elegant.
link |
And the reason for that is that it's been stripped down
link |
from something much larger
link |
because of the pressure to keep it compact.
link |
So many compact genomes out there
link |
have ancestors that were much larger.
link |
You don't start small and become big.
link |
You go through a loop of add a bunch of stuff,
link |
increase complexity, and then, you know, slim it down.
link |
And one of my early papers was in fact on genome duplication.
link |
One of the things we found is that baker's yeast,
link |
which is the, you know, yeast that you use to make bread,
link |
but also the yeast that you use to make wine,
link |
which is basically the dominant species
link |
when you go in the fields of Tuscany
link |
and you say, you know, what's out there,
link |
it's basically saccharomyces cerevisiae,
link |
or the way my Italian friends say,
link |
saccharomyces cerevisiae.
link |
Oh, which means what?
link |
Oh, saccharomyces, okay, I'm sorry, I'm Greek.
link |
So yeah, zacharo, mikis, zacharo is sugar,
link |
Yes, cerevisiae, cerveza, beer.
link |
So it means the sugar fungus of beer.
link |
You know, less, less sounding to the ear.
link |
Still poetic, yeah.
link |
So anyway, saccharomyces cerevisiae,
link |
basically the major baker's yeast out there
link |
is the descendant of a whole genome duplication.
link |
Why would a whole gene duplication even happen?
link |
When it happened is coinciding
link |
with about a hundred million years ago
link |
and the emergence of fruit bearing plants.
link |
Why fruit bearing plants?
link |
Because animals would eat the fruit
link |
and would walk around and poop huge amounts of nutrients
link |
along with a seed for the plants to spread.
link |
Before that, plants were not spreading through animals,
link |
they were spreading through wind
link |
and all kinds of other ways.
link |
But basically the moment you have fruit bearing plants,
link |
these plants are basically creating this abundance
link |
of sugar in the environment.
link |
So there's an evolutionary niche that gets created.
link |
And in that evolutionary niche,
link |
you basically have enough sugar
link |
that a whole genome duplication,
link |
which initially is a very messy event,
link |
allows you to then, you know,
link |
relieve some of that complexity.
link |
So I had to pause, what does genome duplication mean?
link |
That basically means that instead of having eight chromosomes,
link |
you can now have 16 chromosomes.
link |
So, but the duplication at first,
link |
when you go to 16, you're not using that.
link |
Yeah, so basically from one day to the next,
link |
you went from having eight chromosomes
link |
to having 16 chromosomes.
link |
Probably a non disjunction event during a duplication,
link |
during a division.
link |
So you basically divide the cell
link |
instead of half the genome going this way
link |
and half the genome going the other way
link |
after duplication of the genome,
link |
you basically have all of it going to one cell
link |
and then there's sufficient messiness there
link |
that you end up with slight differences
link |
that make most of these chromosomes
link |
be actually preserved.
link |
It's a long story short to me.
link |
But that's a big upgrade, right?
link |
because what happens immediately thereafter
link |
is that you start massively losing
link |
tons of those duplicated genes.
link |
So 90% of those genes were actually lost
link |
very rapidly after whole gene duplication.
link |
And the reason for that is that biology is not intelligent,
link |
it's just ruthless selection, random mutation.
link |
So the ruthless selection basically means
link |
that as soon as one of the random mutations hit one gene,
link |
ruthless selection just kills off that gene.
link |
if you have a pressure to maintain a small compact genome,
link |
you will very rapidly lose the second copy
link |
of most of your genes and a small number 10%
link |
were kept in two copies.
link |
And those had to do a lot with environment adaptation,
link |
with the speed of replication,
link |
with the speed of translation and with sugar processing.
link |
So I'm making a long story short
link |
to basically say that evolution is messy.
link |
Like, so the example that I was giving
link |
of messing with 20% of your bits in your computer,
link |
Duplicating all your functions
link |
and just throwing them out there in the same function,
link |
just totally bogus.
link |
Like this would never work in an engineer system.
link |
But biological systems,
link |
because of this content based indexing
link |
and because of this modularity that comes
link |
from the fact that the gene is controlled
link |
by a series of tags.
link |
And now if you need this gene in another setting,
link |
you just add some more tags
link |
that will basically turn it on also in those settings.
link |
So this gene is now pressured to do two different functions
link |
and it builds up complexity.
link |
I see a whole gene duplication
link |
and gene duplication in general
link |
as a way to relieve that complexity.
link |
So you have this gradual buildup of complexity
link |
as functions get sort of added onto the existing genes.
link |
And then boom, you duplicate your workforce.
link |
And you now have two copies of this gene.
link |
One will probably specialize to do one
link |
and the other one will specialize to do the other
link |
or one will maintain the ancestral function.
link |
The other one will sort of be free to evolve
link |
and specialize while losing the ancestral function
link |
and so on and so forth.
link |
So that's how genomes evolve.
link |
They're just messy things,
link |
but they're extremely fault tolerant
link |
and they're extremely able to deal with mutations
link |
because that's the very way that you generate new functions.
link |
So new functionalization comes
link |
from the very thing that breaks it.
link |
So even in the current pandemic,
link |
many people are asking me which mutations matter the most.
link |
And what I tell them is,
link |
well, we can study the evolutionary dynamics
link |
of the current genome to then understand
link |
which mutations have previously happened or not.
link |
And which mutations happen in genes
link |
that evolve rapidly or not.
link |
And one of the things we found, for example,
link |
is that the genes that evolved rapidly in the past
link |
are still evolving rapidly now in the current pandemic.
link |
The genes that evolved slowly in the past
link |
are still evolving slowly.
link |
Which means that they're useful?
link |
Which means that they're under
link |
the same evolutionary pressures.
link |
But then the question is what happens in specific mutations?
link |
So if you look at the D614 gene mutations,
link |
that's been all over the news.
link |
So in position 614, in the amino acids 614 of the S protein,
link |
there's a D2 gene mutation
link |
that sort of has creeped over the population.
link |
That mutation, we found out through my work,
link |
disrupts a perfectly conserved nucleotide position
link |
that has never been changed in the history
link |
of millions of years of equivalent
link |
per million evolution of these viruses.
link |
That basically means that it's a completely new adaptation
link |
And that mutation has now gone from 1% frequency
link |
to 90% frequency in almost all outbreaks.
link |
So this mutation, I like how you say the 416,
link |
what was it, okay.
link |
D614, so literally, so what you're saying
link |
is this is like a chess move.
link |
So it just mutated one letter to another.
link |
And that hasn't happened before.
link |
And this somehow, this mutation is really useful.
link |
It's really useful in the current environment of the genome,
link |
which is moving from human to human.
link |
When it was moving from bat to bat,
link |
it couldn't care less for that mutation,
link |
but it's environment specific.
link |
So now that it's moving from human to human,
link |
it's moving way better, like by orders of magnitude.
link |
What do you, okay, so you're like tracking
link |
this evolutionary dynamics, which is fascinating,
link |
but what do you do with that?
link |
So what does that mean?
link |
What does this mean, what do you make,
link |
what do you make of this mutation
link |
in trying to anticipate, I guess,
link |
is one of the things you're trying to do
link |
is anticipate where, how this unrolls into the future,
link |
this evolutionary dynamics.
link |
Such a great question.
link |
So there's two things.
link |
Remember when I was saying earlier,
link |
mutation is the path to new things,
link |
but also the path to break old things.
link |
So what we know is that this position
link |
was extremely preserved through gazillions of mutations.
link |
That mutation was never tolerated
link |
when it was moving from bats to bats.
link |
So that basically means that that position
link |
is extremely important in the function of that protein.
link |
That's the first thing it tells.
link |
The second one is that that position
link |
was very well suited to bat transmission,
link |
but now is not well suited to human transmission,
link |
so it got rid of it.
link |
And it now has a new version of that amino acid
link |
that basically makes it much easier
link |
to transmit from human to human.
link |
So in terms of the evolutionary history
link |
teaching us about the future,
link |
it basically tells us here's the regions
link |
that are currently mutating.
link |
Here's the regions that are most likely
link |
to mutate going forward.
link |
As you're building a vaccine,
link |
here's what you should be focusing on
link |
in terms of the most stable regions
link |
that are the least likely to mutate.
link |
Or here's the newly evolved functions
link |
that are the most likely to be important
link |
because they've overcome this local maximum
link |
that it had reached in the bat transmission.
link |
So anyway, it's a tangent to basically say
link |
that evolution works in messy ways.
link |
And the thing that you would break
link |
is the thing that actually allows you
link |
to first go through a lull
link |
and then reaching new local maximum.
link |
And I often like to say that if engineers
link |
had basically designed evolution,
link |
we would still be perfectly replicating bacteria
link |
because it's my making the bacterium worse
link |
that you allow evolution to reach a new optimum.
link |
That's, just to pause on that,
link |
that's so profound.
link |
That's so profound for the entirety
link |
of this scientific and engineering disciplines.
link |
We as engineers need to embrace breaking things.
link |
We as engineers need to embrace robustness
link |
as the first principle beyond perfection
link |
because nothing's gonna ever be perfect.
link |
And when you're sending a satellite to Mars,
link |
when something goes wrong, it'll break down.
link |
As opposed to building systems that tolerate failure
link |
and are resilient to that.
link |
And in fact, get better through that.
link |
So the SpaceX approach versus NASA for the...
link |
Is there something we can learn about the incredible,
link |
take lessons from the incredible biological systems
link |
in their resilience, in the mushiness, the messiness
link |
to our computing systems, to our computers?
link |
It would basically be starting from scratch in many ways.
link |
It would basically be building new paradigms
link |
that don't try to get the right answer all the time,
link |
but try to get the right answer most of the time
link |
or a lot of the time.
link |
Do you see deep learning systems in the whole world
link |
of machine learning as kind of taking a step
link |
in that direction?
link |
Absolutely, absolutely.
link |
Basically by allowing this much more natural evolution
link |
of these parameters, you basically...
link |
And if you look at sort of deep learning systems again,
link |
they're not inspired by the genome aspect of biology,
link |
they're inspired by the brain aspect of biology.
link |
And again, I want you to pause for a second
link |
and realize the complexity of the entire human brain
link |
with trillions of connections within our neurons,
link |
with millions of cells talking to each other,
link |
is still encoded within that same genome.
link |
That same genome encodes every single freaking cell type
link |
of the entire body.
link |
Every single cell is encoded by the same code.
link |
And yet specialization allows you to have
link |
the single viral like genome that self replicates,
link |
the single module, modular automaton,
link |
work with other copies of itself, it's mind boggling.
link |
Create complex organs through which blood flows.
link |
And what is that blood?
link |
The same freaking genome.
link |
Create organs that communicate with each other.
link |
And what are these organs?
link |
The exact same genome.
link |
Create a brain that is innervated by massive amounts
link |
of blood pumping energy to it,
link |
20% of our energetic needs to the brain from the same genome.
link |
And all of the neuronal connections,
link |
all of the auxiliary cells, all of the immune cells,
link |
the astrocytes, the ligodendrocytes, the neurons,
link |
the excitatory, the inhibitory neurons,
link |
all of the different classes of parasites,
link |
the blood brain barrier, all of that, same genome.
link |
One way to see that in a sad, this one is beautiful.
link |
The sad thing is thinking about the trillions
link |
of organisms that died to create that.
link |
You mean on the evolutionary path to humans?
link |
On the evolutionary path to humans.
link |
It's crazy, there's two descendant of apes
link |
just talking on a podcast.
link |
Okay, it's just so mind boggling.
link |
Just to boggle our minds a little bit more.
link |
Us talking to each other,
link |
we are basically generating a series of vocal utterances
link |
through our pulsating of vocal cords received through this.
link |
The people who listen to this
link |
are taking a completely different path
link |
to that information transfer, yet through language.
link |
But imagine if we could connect these brains
link |
directly to each other.
link |
The amount of information that I'm condensing
link |
into a small number of words is a huge funnel,
link |
which then you receive and you expand
link |
into a huge number of thoughts from that small funnel.
link |
In many ways, engineers would love
link |
to have the whole information transfer,
link |
just take the whole set of neurons and throw them away.
link |
I mean, throw them to the other person.
link |
This might actually not be better
link |
because in your misinterpretation
link |
of every word that I'm saying,
link |
you are creating new interpretation
link |
that might actually be way better
link |
than what I meant in the first place.
link |
The ambiguity of language perhaps
link |
might be the secret to creativity.
link |
Every single time you work on a project by yourself,
link |
you only bounce ideas with one person
link |
and your neurons are basically fully cognizant
link |
of what these ideas are.
link |
But the moment you interact with another person,
link |
the misinterpretations that happen
link |
might be the most creative part of the process.
link |
With my students, every time we have a research meeting,
link |
I very often pause and say,
link |
let me repeat what you just said in a different way.
link |
And I sort of go on and brainstorm
link |
with what they were saying,
link |
but by the third time,
link |
it's not what they were saying at all.
link |
And when they pick up what I'm saying,
link |
they're like, oh, well, dah, dah, dah.
link |
Now they've sort of learned something very different
link |
from what I was saying.
link |
And that is the same kind of messiness
link |
that I'm describing in the genome itself.
link |
It's sort of embracing the messiness.
link |
And that's a feature, not a book.
link |
And in the same way, when you're thinking
link |
about sort of these deep learning systems
link |
that will allow us to sort of be more creative perhaps
link |
or learn better approximations of these complex functions,
link |
again, tuned to the universe that we inhabit,
link |
you have to embrace the breaking.
link |
You have to embrace the,
link |
how do we get out of these local optima?
link |
And a lot of the design paradigms
link |
that have made deep learning so successful
link |
are ways to get away from that,
link |
ways to get better training
link |
by sort of sending long range messages,
link |
these LSTM models and the sort of feed forward loops
link |
that sort of jump through layers
link |
of a convolutional neural network.
link |
All of these things are basically ways to push you out
link |
of these local maxima.
link |
And that's sort of what evolution does.
link |
That's what language does.
link |
That's what conversation and brainstorming does.
link |
That's what our brain does.
link |
So this design paradigm is something that's pervasive
link |
and yet not taught in schools,
link |
not taught in engineering schools
link |
where everything's minutely modularized
link |
to make sure that we never deviate
link |
from whatever signal we're trying to emit
link |
as opposed to let all hell breaks loose
link |
because that's the path to paradise.
link |
The path to paradise.
link |
Yeah, I mean, it's difficult to know how to teach that
link |
and what to do with it.
link |
I mean, it's difficult to know how to build up
link |
the scientific method around messiness.
link |
I mean, it's not all messiness.
link |
We need some cleanness.
link |
And going back to the example with Mars,
link |
that's probably the place where I want
link |
to sort of moderate error as much as possible
link |
and sort of control the environment as much as possible.
link |
But if you're trying to repopulate Mars,
link |
well, maybe messiness is a good thing then.
link |
On that, you quickly mentioned this
link |
in terms of us using our vocal cords
link |
to speak on a podcast.
link |
So Elon Musk and Neuralink are working
link |
on trying to plug, as per our discussion
link |
with computers and biological systems,
link |
to connect the two.
link |
He's trying to connect our brain to a computer
link |
to create a brain computer interface
link |
where they can communicate back and forth.
link |
On this line of thinking, do you think this is possible
link |
to bridge the gap between our engineered computing systems
link |
and the messy biological systems?
link |
My answer would be absolutely.
link |
You know, there's no doubt that we can understand
link |
more and more about what goes on in the brain
link |
and we can sort of train the brain.
link |
I don't know if you remember the Palm Pilot.
link |
Yeah, Palm Pilot, yeah.
link |
Remember this whole sort of alphabet that they had created?
link |
Am I thinking of the same thing?
link |
It's basically, you had a little pen
link |
and for every character, you had a little scribble
link |
that was unique that the machine could understand.
link |
And that instead of trying the machine
link |
and trying to teach the machine
link |
to recognize human characters,
link |
you had basically, they figured out
link |
that it's better and easier to train humans
link |
to create human like characters
link |
that the machine is better at recognizing.
link |
So in the same way, I think what will happen
link |
is that humans will be trained
link |
to be able to create the mind pattern
link |
that the machine will respond to
link |
before the machine truly comprehends our thoughts.
link |
So the first human brain interfaces
link |
will be tricking humans to speak the machine language
link |
where with the right set of electrodes,
link |
I can sort of trick my brain into doing this.
link |
And this is the same way that many people teach,
link |
like learn to control artificial limbs.
link |
You basically try a bunch of stuff
link |
and eventually you figure out how your limbs work.
link |
That might not be very different
link |
from how humans learn to use their natural limbs
link |
when they first grow up.
link |
Basically, you have these, you know,
link |
neoteny period of, you know,
link |
this puddle of soup inside your brain,
link |
trying to figure out how to even make neural connections
link |
before you're born and then learning sounds
link |
in utero of, you know, all kinds of echoes
link |
and, you know, eventually getting out in the real world.
link |
And I don't know if you've seen newborns,
link |
but they just stare around a lot.
link |
You know, one way to think about this
link |
as a machine learning person is,
link |
oh, they're just training their edge detectors.
link |
And eventually they figure out
link |
how to train their edge detectors.
link |
They work through the second layer of the visual cortex
link |
and the third layer and so on and so forth.
link |
And you basically have this learning
link |
how to control your limbs
link |
that probably comes at the same time.
link |
You're sort of, you know, throwing random things there
link |
and you realize that, oh, wow,
link |
when I do this thing, my limb moves.
link |
Let's do the following experiment.
link |
What muscles did you flex?
link |
Now take another breath and think what muscles do I flex?
link |
The first thing that you're thinking
link |
when you're taking a breath
link |
is the impact that it has on your lungs.
link |
You're like, oh, I'm now gonna increase my lungs
link |
or I'm not gonna bring air in.
link |
But what you're actually doing
link |
is just changing your diaphragm.
link |
That's not conscious, of course.
link |
You never think of the diaphragm as a thing.
link |
That's probably the same reason
link |
why I think of moving my finger
link |
when I actually move my finger.
link |
I think of the effect instead of actually thinking
link |
of whatever muscle is twitching
link |
that actually causes my finger to move.
link |
So we basically in our first years of life
link |
build up this massive lookup table
link |
between whatever neuronal firing we do
link |
and whatever action happens in our body that we control.
link |
If you have a kid grow up with a third limb,
link |
I'm sure they'll figure out how to control them
link |
probably at the same rate as their natural limbs.
link |
And a lot of the work would be done by the...
link |
If a third limb is a computer,
link |
you kind of have a, not a faith, but a thought
link |
that the brain might be able to figure out...
link |
The plasticity would come from the brain.
link |
The brain would be cleverer than the machine at first.
link |
When I talk about a third limb,
link |
that's exactly what I'm saying, an artificial limb
link |
that basically just controls your mouse while you're typing.
link |
Perfectly natural thing.
link |
I mean, again, in a few hundred years.
link |
Maybe sooner than that.
link |
But basically, as long as the machine is consistent
link |
in the way that it will respond to your brain impulses,
link |
you'll figure out how to control that
link |
and you could play tennis with your third limb.
link |
And let me go back to consistency.
link |
People who have dramatic accidents
link |
that basically take out a whole chunk of their brain
link |
can be taught to coopt other parts of the brain
link |
to then control that part.
link |
You can basically build up that tissue again
link |
and eventually train your body how to walk again
link |
and how to read again and how to play again
link |
and how to think again, how to speak a language again,
link |
So there's a massive amount of malleability
link |
that happens naturally in our way of controlling our body,
link |
our brain, our thoughts, our vocal cords, our limbs,
link |
And human machine interfaces are inevitable
link |
if we sort of figure out how to read these electric impulses,
link |
but the resolution at which we can understand human thought
link |
right now is nil, is ridiculous.
link |
So how are human thoughts encoded?
link |
It's basically combinations of neurons that cofire
link |
and these create these things called engrams
link |
that eventually form memories and so on and so forth.
link |
We know nothing of all that stuff.
link |
So before we can actually read into your brain
link |
that you wanna build a program
link |
that does this and this and this and that,
link |
we need a lot of neuroscience.
link |
Well, so to push back on that,
link |
do you think it's possible that without understanding
link |
the functionally about the brain or from the neuroscience
link |
or the cognitive science or psychology,
link |
whichever level of the brain we'll look at,
link |
do you think if we just connect them,
link |
just like per your previous point,
link |
if we just have a high enough resolution
link |
between connection between a Wikipedia and your brain,
link |
the brain will just figure it out with us understanding
link |
because that's one of the innovations of Neuralink
link |
is they're increasing the number of connections
link |
to the brain to like several thousand,
link |
which before was in the dozens or whatever.
link |
You're still off by a few orders of magnitude
link |
on the order of seven.
link |
Right, but the thing is, the hope is if you increase
link |
that number more and more and more,
link |
maybe you don't need to understand anything
link |
about the actual how human thought
link |
is represented in the brain.
link |
You can just let it figure it out by itself.
link |
Keanu Reeves waking up and saying, I know cook food.
link |
You don't have faith in the plasticity of the brain
link |
It's not about brain plasticity.
link |
It's about the input aspect.
link |
Basically, I think on the output aspect,
link |
being able to control a machine is something
link |
that you can probably train your neural impulses
link |
that you're sending out to sort of match
link |
whatever response you see in the environment.
link |
If this thing moved every single time I thought
link |
a particular thought, then I could figure out,
link |
I could hack my way into moving this thing
link |
with just a series of thoughts.
link |
I could think guitar, piano, tennis ball,
link |
and then this thing would be moving.
link |
And then I would just have the series of thoughts
link |
that would sort of result in the impulses
link |
that will move this thing the way that I want it.
link |
And then eventually it'll become natural
link |
because I won't even think about it.
link |
I mean, in the same way that we control our limbs
link |
in a very natural way, but babies don't do that.
link |
Babies have to figure it out.
link |
And some of that is hard coded,
link |
but some of that is actually learned
link |
based on whatever soup of neurons you ended up with,
link |
whatever connections you pruned them to,
link |
and eventually you were born with.
link |
A lot of that is coded in the genome,
link |
but a huge chunk of that is stochastic.
link |
And sort of the way that you sort of create
link |
all these neurons, they migrate, they form connections,
link |
they sort of spread out,
link |
they have particular branching patterns,
link |
but then the connectivity itself,
link |
unique in every single new person.
link |
All this to say that on the output side,
link |
absolutely, I'm very, very, you know,
link |
hopeful that we can have machines
link |
that read thousands of these neuronal connections
link |
on the output side, but on the input side, oh boy.
link |
I don't expect any time in the near future
link |
we'll be able to sort of send a series of impulses
link |
that will tell me, oh, earth to sun distance,
link |
7.5 million, et cetera, et cetera.
link |
I mean, I think language will still be the input way
link |
rather than sort of any kind of more complex.
link |
It's a really interesting notion
link |
that the ambiguity of language is a feature.
link |
And we evolved for millions of years
link |
to take advantage of that ambiguity.
link |
And yet no one teaches us the subtle differences
link |
between words that are near cognates,
link |
and yet evoke so much more than, you know,
link |
one from the other.
link |
And yet, you know, when you're choosing words
link |
from a list of 20 synonyms,
link |
you know exactly the connotation
link |
of every single one of them.
link |
And that's something that, you know, is there.
link |
So yes, there's ambiguity,
link |
but there's all kinds of connotations.
link |
And in the way that we select our words,
link |
we have so much baggage that we're sending along,
link |
the way that we're emoting,
link |
the way that we're moving our hands
link |
every single time we speak,
link |
the, you know, the pauses, the eye contact, et cetera.
link |
So much higher baud rate than just a vocal,
link |
you know, string of characters.
link |
Well, let me just take a small tangent on that.
link |
We haven't done that yet.
link |
Let's do a tangent.
link |
We'll return to the origin of life after.
link |
So, I mean, you're Greek,
link |
but I'm going on this personal journey.
link |
I'm going to Paris for the explicit purpose
link |
of talking to one of the most famous,
link |
a couple who's a famous translators of Russian literature,
link |
Dostoevsky, Tolstoy, and they go,
link |
that's their art is the translation.
link |
And everything I've learned about the translation art,
link |
it's so profound in a way that's so much more profound
link |
than the natural language processing papers
link |
I read in the machine learning community,
link |
that there's such depth to language
link |
that I don't know what to do with.
link |
I don't know if you've experienced that in your own life
link |
with knowing multiple languages.
link |
I don't know what to,
link |
I don't know how to make sense of it,
link |
but there's so much loss in translation
link |
between Russian and English,
link |
and getting a sense of that.
link |
Like, for example,
link |
there's like just taking a single sentence
link |
from Dostoevsky, and like, there's a lot of them.
link |
You could talk for hours
link |
about how to translate that sentence properly.
link |
That captures the meaning, the period,
link |
the culture, the humor, the wit,
link |
the suffering that was in the context of the time,
link |
all of that could be a single sentence.
link |
You could talk forever about what it takes
link |
to translate that correctly.
link |
I don't know what to do with that.
link |
So being Greek, it's very hard for me
link |
to think of a sentence or even a word
link |
without going into the full etymology of that word,
link |
breaking up every single atom of that sentence
link |
and every single atom of these words
link |
and rebuilding it back up.
link |
I have three kids.
link |
And the way that I teach them Greek
link |
is the same way that, you know,
link |
the documentary I was mentioning earlier
link |
about sort of understanding the deep roots
link |
of all of these, you know, words.
link |
And it's very interesting
link |
that every single time I hear a new word
link |
that I've never heard before,
link |
I go and figure out the etymology of that word
link |
because I will never appreciate that word
link |
without understanding how it was initially formed.
link |
Interesting, but how does that help?
link |
Because that's not the full picture.
link |
No, no, of course, of course.
link |
But what I'm trying to say is that knowing the components
link |
teaches you about the context of the formation of that word
link |
and sort of the original usage of that word.
link |
And then of course the word takes new meaning
link |
as you create it, you know, from its parts.
link |
And that meaning then gets augmented.
link |
And two synonyms that sort of have different roots
link |
will actually have implications
link |
that carry a lot of that baggage
link |
of the historical provenance of these words.
link |
So before working on genome evolution,
link |
my passion was evolution of language
link |
and sort of tracing cognates across different languages
link |
through their etymologies.
link |
That's fascinating that there's parallels between,
link |
I mean, the idea that there's evolutionary dynamics
link |
Yeah, every single word that you utter, parallels, parallels.
link |
What does parallels mean?
link |
Para means side by side.
link |
Alleles from alleles, which means identical twins.
link |
I mean, name any word and there's so much baggage,
link |
so much beauty in how that word came to be
link |
and how this word took a new meaning
link |
than the sum of its parts.
link |
Yeah, and there's just, there's so many different words
link |
that are just words.
link |
They don't have any physical grounding.
link |
And now you take these words
link |
and you weave them into a sentence.
link |
The emotional invocations of that weaving are fathomless.
link |
And all of those emotions all live in the brains of humans.
link |
In the eye of the beholder.
link |
No, seriously, you have to embrace this concept
link |
of the eye of the beholder.
link |
It's the conceptualization that nothing takes meaning
link |
with one person creating it.
link |
Everything takes meaning in the receiving end
link |
and the emergent properties of these communication networks
link |
where every single, you know,
link |
if you look at the network of our cells
link |
and how they're communicating with each other,
link |
every cell has its own code.
link |
This code is modulated by the epigenome.
link |
This creates a bunch of different cell types.
link |
Each cell type now has its own identity.
link |
Yet they all have the common root of the stem cells
link |
that sort of led to them.
link |
Each of these identities is now communicating
link |
They take meaning in their interaction.
link |
There's an emergent property that comes
link |
from a bunch of cells being together
link |
that is not in any one of the parts.
link |
If you look at neurons communicating,
link |
again, these engrams don't exist in any one neuron.
link |
They exist in the connection and the combination of neurons.
link |
And the meaning of the words that I'm telling you
link |
is empty until it reaches you
link |
and it affects you in a very different way
link |
than it affects whoever's listening
link |
to this conversation now.
link |
Because of the emotional baggage that I've grown up with,
link |
that you've grown up with, and that they've grown up with.
link |
And that's, I think, the magic of translation.
link |
If you start thinking of translation
link |
as just simply capturing that emotional set of reactions
link |
that you evoke, you need a different set of words
link |
to evoke that same set of reactions to a French person
link |
than to a Russian person,
link |
because of the baggage of the culture that we grew up in.
link |
Yeah, I mean, there's...
link |
So basically, you shouldn't find the best word.
link |
Sometimes it's a completely different sentence structure
link |
that you will need,
link |
matched to the cultural context
link |
of the target audience that you have.
link |
Yeah, there's a lot of different words
link |
in the target audience that you have.
link |
Yeah, it's, I mean, you're just...
link |
I usually don't think about this,
link |
but right now, there's this feeling,
link |
as a reminder, that it's just you and I talking,
link |
but there's several hundred thousand people
link |
will listen to this.
link |
There's some guy in Russia right now running,
link |
like in Moscow, listening to us.
link |
There's somebody in India, I guarantee you.
link |
There's somebody in China and South America.
link |
There's somebody in Texas,
link |
they all have different...
link |
Emotional baggage.
link |
They probably got angry earlier on
link |
about the whole discussion about coronavirus
link |
and about some aspect of it.
link |
Yeah, and there's that network effect that's...
link |
It's a beautiful thing.
link |
And this lateral transfer of information,
link |
that's what makes the collective, quote unquote,
link |
genome of humanity so unique from any other species.
link |
So you somehow miraculously wrapped it back
link |
to the very beginning of when we were talking
link |
about the beauty of the human genome.
link |
So I think this is the right time,
link |
unless we wanna go for a six to eight hour conversation.
link |
We're gonna have to talk again,
link |
but I think for now, to wrap it up,
link |
this is the right time to talk about
link |
the biggest, most ridiculous question of all,
link |
Off mic, you mentioned to me
link |
that you had your 42nd birthday.
link |
42nd being a very special, absurdly special number.
link |
And you had a kind of get together with friends
link |
to discuss the meaning of life.
link |
So let me ask you,
link |
in your, as a biologist, as a computer scientist,
link |
and as a human, what is the meaning of life?
link |
I've been asking this question for a long time,
link |
ever since my 42nd birthday,
link |
but well before that,
link |
in even planning the meaning of life symposium.
link |
And symposium, sim means together,
link |
posy actually means to drink together.
link |
So symposium is actually a drinking party.
link |
Can you actually elaborate about this meaning of life
link |
symposium that you put together?
link |
It's like the most genius idea I've ever heard.
link |
So 42 is obviously the answer to life,
link |
the universe and everything,
link |
from the Hitchhiker's Guide to the Galaxy.
link |
And as I was turning 42,
link |
I've had the theme for every one of my birthdays.
link |
When I was turning 32, it's one, zero, zero, zero, zero, zero
link |
So I celebrated my 100,000th binary birthday,
link |
and I had a theme of going back 100,000 years,
link |
let's dress something in the last 100,000 years.
link |
Anyway, it was, I've always had these.
link |
It's such an interesting human being.
link |
Okay, that's awesome.
link |
I've always had these sort of numerology
link |
related announcements for my birthday parties.
link |
So what came out of that meaning of life symposium
link |
is that I basically asked 42 of my colleagues,
link |
42 of my friends, 42 of my collaborators,
link |
to basically give seven minutes species
link |
on the meaning of life, each from their perspective.
link |
And I really encourage you to go there
link |
because it's mind boggling
link |
that every single person said a different answer.
link |
Every single person started with,
link |
I don't know what the meaning of life is, but,
link |
and then give this beautifully eloquently answer,
link |
And they were all different,
link |
but they all were consistent with each other
link |
and mutually synergistic and together forming
link |
a beautiful view of what it means to be human in many ways.
link |
Some people talked about the loss of their loved one,
link |
their life partner for many, many years
link |
and how their life changed through that.
link |
Some people talked about the origin of life.
link |
Some people talked about the difference
link |
between purpose and meaning.
link |
I'll maybe quote one of the answers,
link |
which is this linguistics professor,
link |
friend of mine at Harvard, who basically said,
link |
that she was gonna, she's Greek as well.
link |
And she said, I will give a very Pythian answer.
link |
So Pythia was the Oracle of Delphi,
link |
who would basically give these very cryptic answers,
link |
very short, but interpretable in many different ways.
link |
There was this whole set of priests
link |
who were tasked with interpreting what Pythia had said.
link |
And very often you would not get a clean interpretation,
link |
but she said, I will be like Pythia
link |
and give you a very short and multiply interpretable answer.
link |
But unlike her, I will actually also give you
link |
three interpretations.
link |
And she said, the answer to the meaning of life
link |
And the first interpretation is like a child,
link |
become one year old with the excitement
link |
of discovering everything about the world.
link |
Second interpretation, in whatever you take on,
link |
become one, the first, the best, excel,
link |
drive yourself to perfection for every one of your tasks
link |
and become one when people are separate,
link |
become one, come together, learn to understand each other.
link |
Damn, that's an answer.
link |
And one way to summarize
link |
this whole meaning of life symposium
link |
is that the very symposium was illustrating
link |
the quest for meaning,
link |
which might itself be the meaning of life.
link |
This constant quest for something sublime,
link |
something human, something intangible,
link |
some aspect of what defines us as a species
link |
and as an individual.
link |
Both the quest of me as a person through my own life,
link |
but the meaning of life could also be
link |
the meaning of all of life.
link |
What is the whole point of life?
link |
Because we've been talking about the history
link |
and evolution of life,
link |
but we haven't talked about why life in the first place?
link |
Is life inevitable?
link |
Is life part of physics?
link |
Does life transcend physics
link |
by fighting against entropy,
link |
by compartmentalizing and increasing concentrations
link |
rather than diluting away?
link |
Is life a distinct entity in the universe
link |
beyond the traditional very simple physical rules
link |
that govern gravity and electromagnetism
link |
and all of these forces?
link |
Is life another force?
link |
Is there a life force?
link |
Is there a unique kind of set of principles that emerge,
link |
of course, built on top of the hardware of physics,
link |
but is it sort of a new layer of software
link |
or a new layer of a computer system?
link |
And so that's at the level of big questions.
link |
There's another aspect of gratitude
link |
of basically what I like to say is,
link |
during this pandemic,
link |
I've basically worked from 6 a.m. until 7 p.m.
link |
every single day, nonstop, including Saturday and Sunday.
link |
I've basically broken all boundaries
link |
of where life, personal life begins
link |
and work life ends.
link |
And that has been exhilarating for me,
link |
just the intellectual pleasure that I get
link |
from a day of exhaustion,
link |
where at the end of the day, my brain is hurting.
link |
I'm telling my wife, wow, I was useful today.
link |
And there's a certain pleasure
link |
that comes from feeling useful.
link |
And there's a certain pleasure
link |
that comes from feeling grateful.
link |
So I've written this little sort of prayer for my kids
link |
to say at bedtime every night,
link |
where they basically say,
link |
thank you, God, for all you have given me
link |
and give me the strength to give onto others
link |
with the same love that you have given onto me.
link |
We as a species are so special,
link |
the only ones who worry about the meaning of life.
link |
And maybe that's what makes us human.
link |
And what I like to say to my wife and to my students
link |
during this pandemic work extravaganza
link |
is every now and then they ask me, but how do you do this?
link |
And I'm like, I'm a workaholic.
link |
This is me in the most unfiltered way.
link |
The ability to do something useful,
link |
to feel that my brain is being used,
link |
to interact with the smartest people on the planet
link |
day in, day out, and to help them discover aspects
link |
of the human genome, of the human brain,
link |
of human disease and the human condition
link |
that no one has seen before
link |
with data that we're capturing that has never been observed.
link |
And there's another aspect, which is on the personal life.
link |
Many people say, oh, I'm not gonna have kids, why bother?
link |
I can tell you as a father,
link |
they're missing half the picture, if not the whole picture.
link |
Teaching my kids about my view of the world
link |
and watching through their eyes
link |
the naivete with which they start
link |
and the sophistication with which they end up,
link |
the understanding that they have
link |
of not just the natural world around them, but of me too.
link |
The unfiltered criticism that you get from your own children
link |
that knows no bounds of honesty.
link |
And I've grown components of my heart
link |
that I didn't know I had
link |
until you sense that fragility,
link |
that vulnerability of the children,
link |
that immense love and passion,
link |
the unfiltered egoism,
link |
that we as adults learn how to hide so much better.
link |
It's just this back of emotions
link |
that tell me about the raw materials that make a human being
link |
and how these raw materials can be arranged
link |
with more sophistication that we learn through life
link |
to become truly human adults.
link |
But there's something so beautiful
link |
about seeing that progression between them
link |
and seeing that progress and that progress
link |
and that progression between them,
link |
the complexity of the language growing
link |
as more neural connections are formed
link |
to realize that the hardware is getting rearranged
link |
as their software is getting implemented on that hardware,
link |
that their frontal cortex continues to grow
link |
for another 10 years.
link |
There's neuronal connections that are continuing to form,
link |
new neurons that actually get replicated and formed.
link |
And it's just incredible that we have these,
link |
not just you grow the hardware for 30 years
link |
and then you feed it all of the knowledge.
link |
No, no, the knowledge is fed throughout
link |
and is shaping these neural connections as they're forming.
link |
So seeing that transformation from either your own blood
link |
or from an adopted child
link |
is the most beautiful thing you can do as a human being.
link |
And it completes you, it completes that path, that journey.
link |
The create life, oh sure, that's at conception, that's easy.
link |
But create human life to add the human part,
link |
that takes decades of compassion, of sharing,
link |
of love and of anger and of impatience and patience.
link |
I think I've become a very different kind of teacher
link |
because again, I'm a professor.
link |
My first role is to bring adult human beings
link |
into a more mature level of adulthood
link |
where they learn not just to do science,
link |
but they learn the process of discovery
link |
and the process of collaboration, the process of sharing,
link |
the process of conveying the knowledge
link |
of encapsulating something incredibly complex
link |
and sort of giving it up in sort of bite sized chunks
link |
that the rest of humanity can appreciate.
link |
I tell my students all the time, if you, you know,
link |
like when an apple fall,
link |
when a tree falls in the forest
link |
and no one's there to listen, has it really fallen?
link |
The same way you do this awesome research,
link |
if you write an impenetrable paper that no one will understand,
link |
it's as if you never did the awesome research.
link |
So conveying of knowledge, conveying this lateral transfer
link |
that I was talking about at the very beginning
link |
of sort of humanity and sort of the sharing of information,
link |
all of that has gotten so much more rich
link |
by seeing human beings grow in my own home
link |
because that makes me a better parent
link |
and that makes me a better teacher and a better mentor
link |
to the nurturing of my adult children,
link |
which are my research group.
link |
First of all, beautifully put, connects beautifully
link |
to the vertical and the horizontal inheritance of ideas
link |
that we talked about at the very beginning.
link |
I don't think there's a better way to end it
link |
on this poetic and powerful note.
link |
Manolis, thank you so much for talking to me.
link |
It was a huge honor.
link |
We'll have to talk again about the origin of life,
link |
about epigenetics, epigenomics,
link |
and some of the incredible research you're doing.
link |
Truly an honor. Thanks so much for talking to me.
link |
Thank you. Such a pleasure. It's such a pleasure.
link |
I mean, your questions are outstanding.
link |
I've had such a blast here and I can't wait to be back.
link |
Thanks for listening to this conversation
link |
with Manolis Kellis, and thank you to our sponsors,
link |
Blinkist, 8sleep, and Masterclass.
link |
Please consider supporting this podcast
link |
by going to blinkist.com slash lex,
link |
8sleep.com slash lex, and masterclass.com slash lex.
link |
Click the links, buy the stuff, get the discount.
link |
It's the best way to support this podcast.
link |
If you enjoy this thing, subscribe on YouTube,
link |
review it with five stars on Apple Podcast,
link |
support it on Patreon,
link |
or connect with me on Twitter at lexfreedman.
link |
And now let me leave you with some words
link |
from Charles Darwin that I think Manolis
link |
represents quite beautifully.
link |
If I had my life to live over again,
link |
I would have made a rule to read some poetry
link |
and listen to some music at least once every week.
link |
Thank you for listening, and hope to see you next time.