Best Place To BuildHow Computational Microbiology drives disease research & treatment | Prof Karthik Raman | BP2B S2 E8
EVERY SPOKEN WORD
70 min read · 14,271 words- 0:00 – 0:41
Intro
- KRKarthik Raman
old physicist, uh, joke, right? So they say, "Hey, let's, uh, assume a spherical cow." I used to hate biology like everybody else in school. [chuckles]
- SPSpeaker
It's an either-or.
- KRKarthik Raman
I hate biology, so I go for math.
- SPSpeaker
Yeah.
- KRKarthik Raman
I, I can't do math, so I'll go into medicine or biology. Usually start sharing the first class in my course on systems biology, right? This is called, "Can a biologist fix a radio?" Compound called, uh, surfactant in the International Space Station microbes. Heavily used in cosmetics, deep sea, gut, International Space Station. How do these microbes interact with each other? You literally have to- everything has to be done by reverse engineering.
- SPSpeaker
Hi, this is Amrit. We are at IIT Madras, my alma mater, and India's top university for people who like to build.
- 0:41 – 1:13
Welcome to the best place to build
- SPSpeaker
We are here to meet some builders, ask them: what are you building? What does it take to build? And what makes IIT Madras the best place to build? [upbeat music] Today on the Best Place to Build podcast, we have with us Professor Karthik Raman, a professor in
- 1:13 – 4:37
Introducing Prof. Karthik Raman
- SPSpeaker
the Department of Data Science and AI in the Wadhwani School of Data Science and AI at IIT Madras. Previously, he was a professor in the Biotech Department. Uh, Professor, welcome to the podcast.
- KRKarthik Raman
Hi.
- SPSpeaker
I also personally know you from our Ask IITM demo days. You've been answering questions. When students come in, they have a lot of questions about how IIT works, and, uh, I want to just tell you that his answering speed is insane. He'll answer at one question per second. Professor, I- we had, we had a conversation earlier this week about the work you do, and I've come to realize that [chuckles] there is a lot that I don't understand at all. So if I may ask, can you give me a basic primer, and I've noted down some words-
- KRKarthik Raman
Sure.
- SPSpeaker
-so maybe you can help me understand this. Um, and without this, everything is going to be OHT for me. [chuckles] So-
- KRKarthik Raman
Sure, sure.
- SPSpeaker
What is systems biology?
- KRKarthik Raman
Yeah. So, so I think, you know, it's, it's, uh, it's because, um, uh, in, in a sense, biology has become quite, uh, you know, um, diverse from engineering in, in a lot of our education, right? So I think that's, that's one of the reasons, and I hope, you know, we'll, uh, we'll talk about that and emphasize that at some point of time. Um, uh, but I think it's very important today to be able to straddle both the biology and math, and so on. And if you look at systems biology, it's essentially like, um, it's, um... There are various flavors of computational biology I like to think of, right? So that is, uh, you'll see this term quantitative or computational biology in a lot of, uh, scenarios. Or you must have, uh, heard of the term bioinformatics, right? That's, uh, you know, there are even degrees on bioinformatics, and so on. So I like to think of bioinformatics as IT intersection biology, computational biology as computer science intersection biology, and systems biology actually as engineering or chemical or computational engineering intersection biology.
- SPSpeaker
Okay.
- KRKarthik Raman
Right? So, so that is what I like to think of. Um, but essentially, the, the whole idea is that, um, we today have the technology, the wherewithal, to build complex models of various kinds of systems, right? So the idea is that we can build these complex models to understand more about these systems, right? Because you cannot just, um... If you look at a lot of traditional biology, there would be- uh, you look at all the Nobel Prizes that were awarded for the last, uh, several, uh, decades, you will see that they would, uh, focus on identifying, like, one protein, one pathway, and so on. That is absolutely critical, but the thing is that that does not give you, like, a holistic picture of what happens within a cell. And suppose the whole idea is to- so there's, uh, Tom Knight, who once said, "Can we tame biology as an engineering discipline?" Right? Because in an engineering discipline, you, you always know... You, you have a mobile phone, you know how it works, right? You know what are the components, how it works, how it functions. You pull this out, it doesn't work. You put this- you replace this, this starts working, and so on. But in biology, you really cannot do that, right? So you do not have, like, a full picture of what happens within a cell. Can we try and get close to that in some fashion by building models of these complex systems? So that is the idea of systems biology, but the idea is I want to manip- I want to be able to ultimately manipulate these systems, so I need to know what happens when I touch different parts of the system. So if you see, one of the most popular methods of studying biological systems is by perturbation, right? So you take something-
- SPSpeaker
Professor, Professor, sorry, you're moving too fast for me, [chuckles] so I'm going to break you a bit.
- KRKarthik Raman
Yeah.
- SPSpeaker
I just want to pick up a word that you used. You used the word "modelling."
- KRKarthik Raman
Yes.
- SPSpeaker
What does that
- 4:37 – 6:53
What exactly is modelling?
- SPSpeaker
mean?
- KRKarthik Raman
So modelling is just, you know, um, um, mathematical modelling. So, so, uh, I mean, we've all been exposed to models right from school, right? So you've definitely seen a ball-and-stick model in your chemistry class, right?
- SPSpeaker
Mm.
- KRKarthik Raman
It, it shows that, you know, there are these kinds of angles in the methane molecule or something like that. So what you have is that it, uh, every model essentially tries to mimic certain interesting characteristics of a real-world system.
- SPSpeaker
Mm.
- KRKarthik Raman
A ball-and-stick model, for instance, uh, scales reasonably, right? It, it, it, uh, gives you an idea of what is the size of the molecule with respect to the bond. But of course, it's, like, much bigger than a real molecule actually is, right?
- SPSpeaker
Yeah.
- KRKarthik Raman
But then you understand what is the angle between the different carbons and hydrogens, and so on when you look at the ball-and-stick model. So every model that way is interested in mimicking a part of reality so that you can understand it better. And what we do is, we build models of biological systems. For instance, you know, a classic model that people were really splitting hairs or even trading blows was, uh, how does COVID spread?
- SPSpeaker
Right.
- KRKarthik Raman
Right? So this is, like, a very in- interesting model for people to figure out, and COVID was just notorious there. It was so difficult simply because there's so many more layers. People have been building, uh, epidemiological models for maybe centuries, and they all got stumped when confronted with COVID.
- SPSpeaker
Can I just ask you here-
- KRKarthik Raman
Yeah
- SPSpeaker
... do you mean how, how did COVID spread in the population, or do you mean-
- KRKarthik Raman
Yeah, so what is the rate of spread, right?
- SPSpeaker
-how did the virus itself-
- KRKarthik Raman
So what is... No, no, no. Just what is the rate of spread?
- SPSpeaker
Mm.
- KRKarthik Raman
Because typically, some of these are, like, higher level models. So you can build these models at different level of ab- abstractions. I can also say, "Hey, what is the next variant going to emerge?"
- SPSpeaker
Mm.
- KRKarthik Raman
It's going to be far harder, and I don't think anybody has solved that problem. Um-... but more likely you are interested in how much, how many beds do I need in the hospital?
- SPSpeaker
Correct.
- KRKarthik Raman
You know, how much oxygen should I, uh-
- SPSpeaker
And I remember that we all learned the word, uh, the term R. I think, was it R?
- KRKarthik Raman
I think the BRR, right? So basic reproductive rate or something like that. Like R naught, right?
- SPSpeaker
Yeah, R naught.
- KRKarthik Raman
So what, R naught, right? So what is the rate at which... So if, uh, so that is when you announce something as a pandemic-
- SPSpeaker
Mm.
- KRKarthik Raman
- essentially, right? So when R naught is high, you say it's a pandemic. You know, the, the, the, the rate at which the, um, epidemic is growing is much higher than the rate at which you're treating or, uh, you know, um, uh, people are getting less infected, and so on. And these are like, there is a very famous and simple model that everybody can look up. It's called the SIR model. It's a very simple
- 6:53 – 8:25
The math behind a pandemic
- KRKarthik Raman
model. It just- s- super simple, you can- I can explain it in a minute. Just says, "Split the population into susceptible, infectious, and recovered." So all people are potentially susceptible. Some people have gotten infected, some have recovered. They might again become susceptible or whatever, and so on. So just this, right? So w- how does the rate of... And this is like classic differential equations that you'll write in, like, say, you know, uh, chemical engineering course or a mechanical engineering course. So ds by dt, di by dt, dr by dt, set up the equations, and you try to predict what is the rate at which the infectious people are increasing.
- SPSpeaker
Mm.
- KRKarthik Raman
Right? And this is what you want to figure out, right? So, so, so essentially, these models-
- SPSpeaker
Okay
- KRKarthik Raman
- try to answer certain important questions that we want to ask of a biological system. [chuckles]
- SPSpeaker
You are giving this, you're giving this COVID as an example of modelling?
- KRKarthik Raman
As an example, yeah. It's something, in fact, we haven't really worked much on.
- SPSpeaker
Yeah.
- KRKarthik Raman
But it's like a classic, uh, problem that I teach in my course on systems biology and-
- SPSpeaker
Okay
- KRKarthik Raman
... uh, so on. Because this is like, a, a, a nice way to start thinking about how will you model a system.
- SPSpeaker
Mm.
- KRKarthik Raman
Because every time you're confronted with a modelling chal- challenge, you, you're gonna pick, "Hey, I'm gonna assume that these things don't exist. I'm not gonna, um, assume resistance. I'm not gonna assume vaccination."
- SPSpeaker
Mm.
- KRKarthik Raman
"I'm, I'm gonna keep my model simple, and then start incrementally building the model," right? So the model is basically a very simplified construct of any real-world system, trying to answer certain questions. So you may now, you know, at the complete other end, you have these things called digital twins that are becoming really-
- SPSpeaker
Yeah
- KRKarthik Raman
... hot today, right?
- SPSpeaker
Yes.
- KRKarthik Raman
So you really
- 8:25 – 9:00
What’s a digital twin?
- KRKarthik Raman
want to talk about digital twins, that's essentially like, a, what, a, a computer program or a model or something that you have access to on a computer, where you go and tap stuff there. It has to behave exactly like your real-world system.
- SPSpeaker
Yeah, and, and [chuckles] yeah, I want to just say, because one of your, uh, lectures I was listening to, you mentioned the spherical cow model-
- KRKarthik Raman
[laughing]
- SPSpeaker
... which is, take a cow, assume it's a sphere.
- KRKarthik Raman
Yeah. So this is a joke, right? So this is, like, the, uh, an, uh, old physicist, uh, joke, right? So they say, "Hey, let's, uh, assume a spherical cow in a vacuum, ignoring the effec- effects of gravity."
- SPSpeaker
Yeah.
- KRKarthik Raman
Right, so-
- SPSpeaker
And so that's the opposite end of digital twin, where-
- KRKarthik Raman
That, that's the opposite
- 9:00 – 15:19
You wanna start with a spherical cow
- KRKarthik Raman
end, right? So, so, b- but, uh, I think that's a reasonable place to start even, right?
- SPSpeaker
Mm.
- KRKarthik Raman
So as a modeler, you want to start with spherical cows even, I mean, even though we mock them and so on. Because it's like, you, you start fixing certain ideas. You, you at least start capturing the simplest concepts. And, uh, one of my colleagues always gives this example of, um, you know, looking at, um, when you compute escape, when you do your Chandrayaan or Mangalyaan and so on, you ultimately, you know, some of the models assume Earth, Mars, Moon, as point objects.
- SPSpeaker
Yeah, as dots.
- KRKarthik Raman
Right?
- SPSpeaker
Yeah.
- KRKarthik Raman
Right, w- with, with, like, a mass, which is very important, right? So that mass helps you compute the escape velocity, and so on. We also have studied that in school. But s- all these models are actually useful, and there is a very famous, uh, quote by a statistician called Box, who just says, "All models are wrong-
- SPSpeaker
Mm
- KRKarthik Raman
... but some are useful."
- SPSpeaker
Mm.
- KRKarthik Raman
Right? [chuckles] So, so that's the whole point, right? So you'll still have wrong models, but which can still give you very interesting or useful insights about real-world systems. Because we will make potentially atrocious approximations-
- SPSpeaker
Mm
- KRKarthik Raman
... but they, you'll still be able to answer some very interesting questions about a real-world system.
- SPSpeaker
Understood, but the example we took is of a macro system. Um, I, I see that your work is more to do with microbiology, right?
- KRKarthik Raman
Exactly.
- SPSpeaker
What, what would be a microbiology example for a modelling problem?
- KRKarthik Raman
A single cell, right?
- SPSpeaker
Okay.
- KRKarthik Raman
So how do I, how do I manipulate a single cell, right? So, for instance, my PhD work, uh, was on how do we kill tuberculosis? How do we kill the organism that causes tuberculosis? There's this organism called Mycobacterium tuberculosis. It has four thousand genes. Which of those four thousand genes do you go and target with a drug?
- SPSpeaker
Mm.
- KRKarthik Raman
Okay, so these are some very interesting questions that one tries to answer. So I need to study the, how these, what these genes are making. So these, every gene typically makes something known as a protein, and these proteins, essentially, they'll contribute to various things. For example, our hair or our, uh, body structure. There are, uh, structural proteins that hold these things in place. But inside a microbial cell, there are lots of proteins that catalyze various chemical reactions. So how, which of these chemical reactions is so essential for the cell, that if you go and target it with a drug, the cell ceases to function?
- SPSpeaker
Understood.
- KRKarthik Raman
So these are the questions that we try to answer, right? So how do you pick the right proteins to target? The problem is, certain proteins, if you target, there'll be other side effects, right? You may not be able to uniquely target that protein. By targeting that protein, you might end up hitting some human proteins-
- SPSpeaker
Mm
- KRKarthik Raman
... or you might end up hitting some, uh, other proteins of other beneficial bacteria that live in the body, right? We will talk about the gut microbiome later on, which is, you know, one of my, uh, favorite topics and a lot of what our lab works on.
- SPSpeaker
Yeah.
- KRKarthik Raman
But you don't want to... You want to be very selective in how you go and, uh, hit these systems, right? And for which you need a systems perspective.
- SPSpeaker
Mm.
- KRKarthik Raman
You cannot, you know, look at one part of the cell and assume that if you hit it, nothing will happen. What will happen is that there'll be a cascading effect that messes up a lot of other things. So, so the whole idea of systems biology, coming back, is to make sure that you get some sort of handle on this complexity.
- SPSpeaker
Okay.
- 15:19 – 22:32
Introduction to the omic studies
- KRKarthik Raman
omics idea is basically the, the om', essentially is a sort of, uh, suffix that says that you're looking at the whole thing.
- SPSpeaker
Mm.
- KRKarthik Raman
Right. So versus looking at, like, one gene, if you look at all the genes in the human body, all the genes in a bacterium, you're talking about bacterial or human genomics.
- SPSpeaker
Mm.
- KRKarthik Raman
Or then, so then, um, I ke- I keep telling my students, "The most important aspect of biology you need to understand is just the flow of information in biology," right? So there is DNA. DNA makes RNA. RNA is then translated to proteins, and proteins do all the hard work in the cell, right? So if you look at all the DNA within the cell, that is called genomics. If you look at all the RNA within the cell, these are called RNA transcripts, okay? And these are called... This is called transcriptomics. And then when, when you look at all the proteins in the cell, this is proteomics. Okay? There are excellent experimental methods that have been devised where you can study these things at scale, or you can also look at what is known as metabolomics, which is like all compounds, all small molecules within the cell. So you can completely, you know, characterize the cell at various levels, right? At the genomic level, at the transcriptomic level, at the proteomic level, at the metabolomic level, to understand what is happening within the cell, right?
- SPSpeaker
Okay.
- KRKarthik Raman
So of course, you know, the- I've not talked about the other thing, which is, like, microscopy, right? But microscopy is basically trying to look at the cell, look at what's happening within the cell. But again, there you'll end up focusing on, like, certain aspects of the cell, certain reactions, or certain exciting areas within a cell, and so on. But this is like a whole cell parts catalog in, in a sense.
- SPSpeaker
Mm.
- KRKarthik Raman
And systems biology or computational systems biology is about putting these parts together. We all have, have these, uh, slides and images, which just shows, like, you know, a big box of Legos or, like, a big list of parts from an engine or something like that. Can you really look at that and divine what the original engine is or what the original system is?
- SPSpeaker
Mm.
- KRKarthik Raman
Truly not, right? So you need to look at the system, you need to look at the parts, and then you build, need to build the models that will try to answer these questions, that connect the parts to the system.
- SPSpeaker
The word computation here seems to imply that there's a lot of, uh, data computation that's happening. These data are coming from these experiments that you're talking about?
- KRKarthik Raman
Yes, yes.
- SPSpeaker
So-
- KRKarthik Raman
So, so these data form the basis for building the models.
- SPSpeaker
Mm.
- KRKarthik Raman
I now know what are all the metabolites that are present in the cell, what are all the compounds, chemical, small, uh, chemical molecules that are present within a cell. Can I now figure out what are all the reactions that are present? What are the chemical reactions happening?
- SPSpeaker
Mm.
- KRKarthik Raman
Can I figure out what proteins are catalyzing these chemical reactions, and can I find out the rate of these reactions from the level of RNA within the cell? Because this RNA concentration will be proportional to protein concentration, and so on. So essentially, there is like a whole network within the cell, because all, all these things interacting with each other. Can you start figuring out what controls what?
- SPSpeaker
Mm.
- KRKarthik Raman
What impinges on what, right? So when I change this-... so many other things are gonna change downstream. So how do I figure these things very carefully?
- SPSpeaker
So it becomes a co- correlation, causation, identification kind of-
- KRKarthik Raman
True, true, true. Causation is like a real challenge here, but definitely we see lots of correlations, and we use that to ultimately build these models.
- SPSpeaker
Okay. So then, because there's an huge availability of data, it then becomes a mathematical computation.
- KRKarthik Raman
Exactly. Exactly, right? So, so to, to really, you know, go back to your question, so systems biology, experimental systems biology is trying to do all these expensive experiments to characterize every molecule within the cell, every DNA molecule, every protein molecule, every small molecule-
- SPSpeaker
So like a g- the Genome Project would be an experiment.
- KRKarthik Raman
Exactly, exactly.
- SPSpeaker
Okay.
- KRKarthik Raman
Right, and we have the Genome India project, in fact, which, we are a, a part of, uh, and so on. So we'll, we'll get to it when we get to it. Uh, but, uh, there are so many exciting things that are happening within the cell. And you need, first, experimental data, and then you need models on the other hand, and then we go through our DBTL cycle, right?
- SPSpeaker
Mm.
- 22:32 – 30:20
Scope of Prof. Karthik Raman’s work
- SPSpeaker
from gut microbiome to microbiomes in the space station. Is that right?
- KRKarthik Raman
Yes, yes. And in fact, even further down, right? We've started with the deep sea. We've looked at some deep three hydro- deep sea hydrothermal vents, uh, data. We have a fantastic collaborator, uh, in Wisconsin, Madison, and, uh, from him, we have these, uh, data sets from the Guaymas Basin off, uh, uh, the, uh, California. And there you have these, um, microbes that live under the sea, and there are these deep sea hydrothermal vents are essentially like deep sea volcanoes, and so they are very hot, and the sea, of course, is very cold. And what you have in the midst is a gradient of temperatures where various kinds of microbes survive.
- SPSpeaker
Mm.
- KRKarthik Raman
And typically, these are extreme conditions, so the microbes that survive there, we call them extremophiles, right? They like these extreme conditions, or they tolerate these extreme conditions. And because of this, they have very exotic metabolisms, okay? They cannot have, like, your E. coli or your usually famous bugs cannot survive in those temperatures. So they need to have different, you know, pathways. And so these pathways are essentially, uh, you know, long chains of reactions. You know, some-
- SPSpeaker
Oh, yeah
- KRKarthik Raman
... A becomes B, B, B becomes C, C becomes D, and so on. So for instance, there is a big pathway through which the glucose that we ingest-
- SPSpeaker
Yeah
- KRKarthik Raman
... is converted to energy.
- SPSpeaker
Uh, ADT, uh-
- KRKarthik Raman
ATP. ATP is produced.
- SPSpeaker
ATP, yeah.
- KRKarthik Raman
So, so glycolysis-
- SPSpeaker
Wow
- KRKarthik Raman
... is one of the, uh, you know, like, uh, classic, uh, uh, pathways. And then you have what is known as the tricarboxylic acid cycle, and so many other pathways are there.
- SPSpeaker
Okay.
- KRKarthik Raman
So very classic. So this is what a biochemist does. A biochemist tries to characterize all of these pathways. And, uh, I, I show these pictures in my talks, and you will see this on the walls of many of biochemists, and be like this huge poster, which shows all the pathways that are happening within the human body. And it's, it's very intimidating, and I still... If you still look at it closely, it's only like a very simplified view of what happens within the cell, because you only typically show only the metabolites in that picture. Where are the proteins? Where are the RNA? You can't ever-
- SPSpeaker
What, what, what is metabolite?
- KRKarthik Raman
... Metabolites are these small molecules.
- SPSpeaker
Okay.
- KRKarthik Raman
So it's just a metabolite, so you can- I mean, in school we ca- talk about them as compounds.
- SPSpeaker
Okay.
- KRKarthik Raman
Right? So, uh, uh, metabolite is just a more technical name because they f- are part of these meta- these are actually called metabolic pathways.
- SPSpeaker
Hmm.
- KRKarthik Raman
Because, uh, metabolism is basically breakdown and synthesis of various chemicals within the body-
- SPSpeaker
Right
- KRKarthik Raman
... within a living cell.
- SPSpeaker
Right. So my system's definition of a microbiome, uh, has to have, uh, the, the metabolites that are being measured. Is that correct?
- KRKarthik Raman
Yep. Yep.
- SPSpeaker
The metabolites are being, being measured, and what we are trying to-
- KRKarthik Raman
Specifically, what is measured is only the microbes.
- 30:20 – 34:05
Levels of microbiology
- KRKarthik Raman
networks, then get inside the microbe, right? There, you have these networks of various molecules, pathways. Every pathway is essentially a network, right? So there is glucose that is connected to another, uh, molecule it makes, that is connected to another molecule it can break down into maybe two, three molecules it can break down into. Depending upon the catalyst, you might have different outcomes of different reactions. So you essentially take a large-... catalog of chemical reactions. This is, in fact, a very key aspect of all our, most of our work. What I want is a large list of chemical reactions, A plus B giving C plus D. In fact, let's say two A plus three B giving C plus D. I like to know the stoichiometry, right?
- SPSpeaker
Mm.
- KRKarthik Raman
So the, the coefficients-
- SPSpeaker
Yeah
- KRKarthik Raman
... of these, uh, uh, how many moles, how many compo- molecules are required to make this other molecule, and so on.
- SPSpeaker
Mm.
- KRKarthik Raman
And using that, we have this long list of reactions. We then translate it to networks of different kinds. So one network, I would have metabolites as nodes, like all these chemical molecules as nodes, and what it can convert to as edges, and so on. But then you can go further within this-
- SPSpeaker
Right
- KRKarthik Raman
... and still start looking at a single chemical as a network.
- SPSpeaker
Mm.
- KRKarthik Raman
Right? So you now have carbon, nitrogen, oxygen as nodes, and all your double bonds, single bonds, chiral bonds, as edges, and so on. So in fact, this is actually, we've used this to predict how new molecules can be made, and now very recently, we even used this to predict the synthesis of drug-like molecules and things like that.
- SPSpeaker
Okay.
- KRKarthik Raman
So, so I would just like to sort of, uh, again, say that, uh, networks are just all-pervading within the cell, and it's a very important aspect of computational thinking-
- SPSpeaker
Yeah
- KRKarthik Raman
... to, to really, you know, cast any problem into a network, uh, problem. And the, the standard... In fact, the other analogy I use is this, um, essentially like Google Maps, right? So all of us have used Google Maps to get from one place to another, and you try to find the fastest way to get-
- SPSpeaker
Yeah
- KRKarthik Raman
... from one place to another. That is nothing but pathfinding on a network.
- SPSpeaker
Mm.
- KRKarthik Raman
And essentially, that is what we do in metabolic engineering, loosely speaking, right? I want to find the best way to get from molecule A to a commercially important molecule. And the problem is that is, uh, I need to reroute the traffic within the cell so that I can get more of my molecule of interest.
- SPSpeaker
Mm.
- KRKarthik Raman
So the cell is doing, cell is trying to grow. Cell is not trying to make ethanol for you. But as a metabolic engineering, I want, engineer, I want to push the cell to make more ethanol.
- SPSpeaker
Mm.
- KRKarthik Raman
Right? So that is my-
- SPSpeaker
More.
- KRKarthik Raman
So the cell wants to do something, I want to push it towards something that's commercially useful in an industrial process.
- SPSpeaker
Understood. Once you convert it to a network, all, all, all kinds of algorithms that are used in network-
- KRKarthik Raman
Exactly
- SPSpeaker
... thinking can come back.
- KRKarthik Raman
Exactly.
- SPSpeaker
Is this a word that you use often, molecule of interest? It sounds very, [chuckles] uh, quotable.
- 34:05 – 46:00
IBSE & Genome India
- SPSpeaker
right? Uh, can you tell us what it is and what is the work that is actually done?
- KRKarthik Raman
Yeah.
- SPSpeaker
What is your research area?
- KRKarthik Raman
So, so, uh, so IBSE is, uh, we started out as the Initiative for Biological Systems Engineering back in 2015, with, under the mentorship of, uh, Professor Ashok Venkitaraman, who was then at Cambridge. So he wa- he was like, um, uh, "We have lots of really bright young minds at IIT Madras, who are very quantitatively inclined. Let them, let us get them to work on biological problems. Let's not worry about generating biological data, which is like a, you know, an expensive proposition, but there is so much data that's available online, which we can really leverage to do big things with it." And well, now we've gone as far as generating our own data, right? We are part of this very large national project called Genome India, where for the first time, uh, we've been involved in sequencing, and in fact, IIT Madras has been involved in analyzing these sequenced genomes. So we have close to ten thousand Indian genomes that have been sequenced, and I think that's a great achievement, and it, again, gives us lots of insights into, you know, the, uh, the kind of mutations that are present in the Indian population, what it means for our health, disease, and so on. Right? So going back, IBSE was set up in, uh, uh, uh, 2015 as the Initiative of Biological Systems Engineering, and then, you know, given how we've evolved, we've sta- started calling ourselves the Center for Integrative Biology and Systems Medicine, because we want to focus on medicine, right? So, so IBSE, now the E stands for the last E in medicine, [chuckles] right?
- SPSpeaker
Mm.
- KRKarthik Raman
So it's like a contrived acronym. The, the thing is that we've, uh... So I head this, uh, center along with Professor Himanshu Sinha from the Department of Biotech, and Himanshu and I have been sort of leading the center from twenty, uh, uh, sixteen. And, uh, it's, it's been a fascinating journey, where we have faculty from different departments. We have faculty from chemical engineering, from computer science, uh, now, of course, a few of them have moved to data science, uh, including myself, and, uh, we have faculty, and of course, biotech. So we have faculty from all these departments that come together to work on very exciting problems in- at the intersection of, say, you know, biology, modeling, engineering, and so on. Right? So this is what we do, and we are involved in large, uh, uh, you know, in- international and national projects. Uh, one of our, uh, biggest success stories is the project that Professor Himanshu led, which is called the Garbhini project, where, uh, this was a study on preterm birth in India, and it turns out that all the models that we have for gestational age are based on, like, American populations and so on. So they are really not ideally portable to the Indian context. So Himanshu and, uh, his students have developed, uh, new gestational age models, uh, for India and so on, which are much more accurate and, uh, things like that. So this has been, like, a major contribution from IBSE, and we are part of various other, uh, uh, international initiatives, one of them being MetaSub.... which is, uh, trying to, uh, look at, um, uh, I'm gonna throw one more, some more jargon at you- [laughing] - which is, uh, metagenomics, right? So we've talked about genomics, which is the study of all the genes within a single cell. Metagenomics is the study of all the genes in a particular place. It could come from different organisms.
- SPSpeaker
Okay.
- KRKarthik Raman
Right? So typically, if you're trying to study the gut microbiome, you're going to do gut metagenomics, because I need to take all the, like, a stool sample or something like that, and then sequence it, and I will find a list of all microbes. And using that, I generate the vector I was talking to you about.
- SPSpeaker
Mm.
- KRKarthik Raman
How much of, uh, X one is present, how much of X two is present, and, uh, so on. So we are part of this MetaSub consortium. So MetaSub initially started off with, uh, I think probably around two thousand and fifteen, where they, uh, sampled the New York Subway. They just swabbed New York Subway, right? Various surfaces, uh, you know, the handrail, the ticket kiosks, and so on, to see what is the microbial signature of New York City. And now we've done that for Chennai, so it's like a really exciting study that's currently under review. And what we find is Chennai has its own unique, uh, microbial signature. There are a few organisms that are present only in, uh, Chennai, and typically these come from the skin and, uh, so on. And, uh, interestingly, and happily, we didn't find enough, uh, resistant organisms and things like that. So it's, it's a very interesting signature. And, uh, we've also been studying other, uh, um, uh, environments. For instance, we've been looking at some drinking water datasets, and, uh, we hope to also look at, you know, sewage water datasets, and so on, because these can all track the flow of, uh, you know, resistant microbes and, uh, things like that, right? So, uh, uh, you know, as we build towards, um, smart cities and so on, genomic surveillance is also going to be important.
- SPSpeaker
Mm.
- KRKarthik Raman
Right? So you can see, you know, the, the change in these microbial loads, and, you know, maybe they have a story to tell in terms of, you know, like a disease or like, you know, so on. So-
- SPSpeaker
I do vaguely remember reading about, uh, uh, in the newspapers about, um, uh, uh, the, the microbial load, I'm using the term, [chuckles] of, uh, COVID, uh, in, uh, sewage water across cities and how it's changing, and that could be like a, uh-
- KRKarthik Raman
Definitely.
- SPSpeaker
- indication of, uh-
- KRKarthik Raman
Definitely. Yeah, yeah. In fact, there was this, uh, uh, you know, really... See, some of these are a little gross sounding because there's a lot of, you know, work with stool samples and so on, but they actually analyze airline waste-
- SPSpeaker
Mm
- KRKarthik Raman
... right? To see what are the transmission of microbes across continents and things like that.
- SPSpeaker
Very interesting.
- KRKarthik Raman
Right? So all these- so we potentially have a chance to surveil these at some point of time, right? So, uh, this has still not become mainstream, but I would re- really imagine that in, like, um, a decade or two, this will definitely become mainstream, where we start-
- SPSpeaker
But the gut-
- KRKarthik Raman
- surveilling microbes.
- SPSpeaker
- gut microbiome, gut microbiome work is pretty mainstream now, right? Like, everybody's talking about it, and, uh, there are products hitting the market-
- KRKarthik Raman
To use it as a surveillance tool.
- SPSpeaker
Oh, okay, okay.
- KRKarthik Raman
Right? So-
- SPSpeaker
Ah, definitely.
- KRKarthik Raman
So we have the... Exactly, so we do have the, uh, the, the basic tools now, which have been, you know, really improved and fine-tuned, but to actually regularly use it for surveillance, to make sure that we catch, like, an outbreak-
- SPSpeaker
Mm
- KRKarthik Raman
... ahead of time and so on, I don't think we are there yet, but there are, like, tantalizing possibilities.
- 46:00 – 49:00
Debunking gut microbe supplement marketing
- KRKarthik Raman
can help each other grow-
- SPSpeaker
Mm
- KRKarthik Raman
... or, you know, restore gut health and so on.
- SPSpeaker
Mm, interesting. Although, although I must say that, um, I mean, I, I know that you're talking from an engineering point of view, but from the marketing point of view, it feels like there's some ten thousand new companies which are selling some gut-related product, and I have no idea whether they have gone through this rigorous modeling testing. I don't know what they're doing.
- KRKarthik Raman
Yeah. So, so a lot of them, they, they probably... I, I'm not sure, uh, I mean, some of them do, uh, some basic modeling and so on, but in general, they just look for, you know, uh, key helpful microbes-
- SPSpeaker
Compounds. Okay
- KRKarthik Raman
... that will, uh, that will potentially produce some beneficial compounds, right? So most of these are harmless microbes, so I think it's, it's not a problem if you ingest them, because microbes are just so much all around us. And, um, I don't know, I think perhaps in school, we are taught that microbes are bad and, you know, we actually use them as a synonym for germs, no?
- SPSpeaker
Yeah. [chuckles]
- KRKarthik Raman
And it's, it couldn't be farther from the truth, right?
- SPSpeaker
Understood.
- KRKarthik Raman
[chuckles] Because these, you have far more helpful microbes in the body than, you know, harmful microbes.
- SPSpeaker
Understood. Damn, cool. Um, I do, as you are speaking, uh, realize that, um, maybe from a mechanical engineering, electrical engineering perspective, there's a lot of difference in the way you are addressing everything. Uh, it feels like in mechanical engineering, electrical engineering, we're building a system from scratch, from components we understand. Here, you are trying to sort of break down and figure out how a much more complex system works. Is that a good understanding?
- KRKarthik Raman
Yeah, that's true. That's true, right? So there's this famous, uh, article that I usually sta- share in the first, uh, uh, cl- class in my course on systems biology. This is called, "Can a Biologist Fix a Radio?" [chuckles] Right? So it really looks at, uh, how a radio is normally fixed-
- SPSpeaker
Mm
- KRKarthik Raman
... and then if a biologist were to fix it, how would he or she fix the radio? Because a biologist typically does not have an understanding of all the components within a radio, right? Or within a cell, for that matter. The idea is an electrical engineer really understands... Some electrical en- engineer really understands every component in the radio, and therefore, he or she put it in. Whereas if you look at a, a, a biological system, you have no understanding of how it is. So if you open it up, again, a mess of wires, or you can't even see it, or if you see it in the microscope, you see a bunch of molecules, uh, running around, right? So what do you do? I go and pull out one capacitor or one protein from the cell, and what happens to the cell?
- SPSpeaker
Mm.
- KRKarthik Raman
Right? Typically, it survives. And then you are like, "Okay, so this protein is not useful for the cell." [chuckles]
- SPSpeaker
Professor, I remember this article. So first you start with a sample of ten thousand-
- KRKarthik Raman
[chuckles]
- SPSpeaker
... non-working radios, [chuckles] and then from the first one, you pull out a capacitor and see.
- KRKarthik Raman
Yes, yes. Right. So you literally have to-- everything has to be done by reverse engineering, right?
- SPSpeaker
Mm.
- KRKarthik Raman
Or like, you're, like, sort of blindfolded with one hand tied behind your back, and you have to figure out this, uh, system, right?
- SPSpeaker
Interesting.
- KRKarthik Raman
Because... And, uh, and this is where modeling is actually very helpful, because it is not very ethical to do some of these experiments on biological systems, right?
- SPSpeaker
Correct, correct.
- KRKarthik Raman
Right? You'll be killing your- killing microbes is ethical, uh, at least as far as we are concerned today.
- 49:00 – 52:53
Prof Karthik Raman’s journey from Chemical Engineering to Computational Engineering
- KRKarthik Raman
[chuckles]
- SPSpeaker
At least it's not not ethical. [chuckles]
- KRKarthik Raman
Exactly, right? Uh, whereas, you know, if you're doing it, any experiment on any higher organism, there are so many ethical considerations, right? And these models can potentially help us answer these questions and create more meaningful hypotheses for us to evaluate in a lab and so on.
- SPSpeaker
Nice.... as you're saying it, I'm thinking there should be an Indian Institute of Reverse Engineering. [laughing] Uh, Professor, uh, this is fascinating. Um, I'm very curious. You, you have such a grasp of the subject, and you are speaking at it with such speed, and I really have to catch up. Uh, but your, um, y- your journey till here started with a chemical engineering undergraduate, right?
- KRKarthik Raman
Yep.
- SPSpeaker
And then from chemical engineering undergraduate, you went to, uh, computer science-
- KRKarthik Raman
Computational science. Computational science. So, so in fact, my undergrad was in, um, um, in, uh, Institute of Chemical Technology, where there is a very fascinating course. It's a BTech in chemical technology, but you can specialize in various fields. You could specialize in, uh, textiles, you could specialize in polymers, you could specialize in food engineering, and so on. And my specialization was actually in food engineering, and you can literally think of this as, like, the precursor to all the BTech, biotech courses that we have.
- SPSpeaker
Mm.
- KRKarthik Raman
So what we studied was we studied chemical reaction engineering, process control. In fact, process control is really what caught my imagination, and process control is literally no different from systems biology, [chuckles] right? You basically start building models of reactors and, uh, and how to control them. And here we build models of cells and try to figure out how to control them. So I would really say, the thing that my fascination for systems biology probably started in my, uh, in my undergrad. But then what I did, what helped me there was, we had a course on, or at least like, uh, we used to have these very something like what we could call today a two-credit course, like half courses.
- SPSpeaker
Mm.
- KRKarthik Raman
So I had a half course on microbiology and a half course on biochemistry, and those things really gave me a perspective on all these molecules and, uh, you know, it, it gave me the ability to work with these things without being afraid of them, because I used to hate biology like everybody else-
- SPSpeaker
Mm.
- KRKarthik Raman
-in school, right? So, so now this-
- SPSpeaker
But that's more a reflection of how biology is taught in school.
- KRKarthik Raman
Exactly.
- SPSpeaker
Yeah.
- KRKarthik Raman
Or, you know, or even what biology is taught, right? So both of these things are there, right? So what biology... Even my, my son today tells me, "Hey, why should I draw all these diagrams?" And so on, right?
- SPSpeaker
Yeah.
- KRKarthik Raman
So we still focus- uh, so biology becomes like a, essentially a drawing exercise or something like that, right? So you look at, you typically still look at parts of a flower and things like that, which is a very important aspect of biology, but that's more traditional biology. Today, biology has become so quantitative and, and molecular, right?
- SPSpeaker
Yeah.
- KRKarthik Raman
You need to understand the molecules. In fact, in school, uh, uh, in our, uh, twelfth-standard chemistry, there was this very fascinating, uh, uh, chapter on chemistry in action, and that talks about DNA and, uh, the genetic code and how the genetic code is conserved across organisms, and from the genetic code, you can find out what is the protein being made and so on. It was really fascinating.
- SPSpeaker
Mm.
- KRKarthik Raman
But we do less of that and more of taxonomy. You know, remember, who was the scientist who discovered [chuckles] -
- SPSpeaker
Yeah
- KRKarthik Raman
... this particular disease or this particular plant or this particular-
- SPSpeaker
Even there, some of the scientists who have discovered these drugs and plants have gone through a fairly computational process. They've done a lot of experiments. They've figured out so many things-
- KRKarthik Raman
Perhaps. Perhaps.
- SPSpeaker
And, and that part-
- KRKarthik Raman
Yes
- SPSpeaker
... is just not mentioned, right?
- 52:53 – 55:00
The intersection of maths and biology
- KRKarthik Raman
we've had, uh, any number of discussions about how to integrate, uh, say, mathematics, mathematics before, and today we are talking about AI into, say, medical curricula. It, it is important, right? And, uh, there's a very famous and controversial quote, I think, from, from the head of NIH or NSF, who said that "Biology is too important to be left to the biologists." [chuckles]
- SPSpeaker
Mm.
- KRKarthik Raman
And he said that the innumeracy of biologists is, is being, uh, you know, a, a problem.
- SPSpeaker
Bottleneck.
- KRKarthik Raman
Right?
- SPSpeaker
Yeah.
- KRKarthik Raman
It's, it's being a, a stumbling block for de- development. It was-
- SPSpeaker
As you're saying that, I'm thinking that we spoke of the COVID example, right? Like, uh, uh, how COVID was spreading and all. That would be a fascinating chapter to study in school, uh, on how we-
- KRKarthik Raman
Definitely, definitely.
- SPSpeaker
We're measuring it.
- KRKarthik Raman
And, you know, it might be a very simple way to introduce, like, uh, trivial modeling in schools.
- SPSpeaker
Mm.
- KRKarthik Raman
Because all, all I want to do is, is if I'm able to predict s- from knowing X of T, I'm predicting X of T plus one, right?
- SPSpeaker
Yeah.
- KRKarthik Raman
I'm actually doing a prediction, and that's probably fascinating for a child, right?
- SPSpeaker
Yeah.
- KRKarthik Raman
It's, because as modelers, we get fascinated by simulations, right? Because we see, "Oh, I never expected this. Why is this happening?" And you get to learn a lot by asking these what-if questions of various systems.
- SPSpeaker
Nice. Thank you so much for that. Um, I forgot how we got onto this. Um, there's, there's a note here that I've written, um, which talks about, um, genome, genomic studies being like a very, um, like a, like a point in time on- in the, in the evolution of biological engineering per se, and the Genome Project, which was... Uh, not the Indian Genome Project, but the Genome Project-
- KRKarthik Raman
Yeah
- SPSpeaker
... in the '90s.
- KRKarthik Raman
Yeah.
- SPSpeaker
Uh, okay, was that something very, like, is it a big step?
- KRKarthik Raman
It's huge, huge.
- SPSpeaker
And what was the impact of that?
- KRKarthik Raman
I mean, it's, it's, it's, it's both ways, right? So, so I think when scientists first started off with it, uh, just like AI, right? So I don't know if you've seen the old, the first, uh, AI. So when the first, uh, perceptron was, uh, invented, people, uh, people said that, uh, they talked about AI advances that are not even conceivable today, as mainstream by, you know, late '70s. Would that they would be mainstream
- 55:00 – 58:00
AI in microbiology
- KRKarthik Raman
by the late '70s, right? Uh, so in, in similar fashion, when the Genome Project was conceived and when the Genome Project was nearing completion, people felt that, "Hey, we will solve disease." And it turns out, and that's where, again, you know, I will make a pitch for networks, is that there are very few single gene diseases.... every disease is actually a dysfunction of the complex network that is within the body. Which part of the network is failing? Which part of the network is malfunctioning? That is what we need to find out today. And you need a compos- uh, you need a, a combination of methodologies from your omic technologies, like genomics, proteomics, or whatever, to models and so on, to really figure out, "Hey, okay, this is what is not working, and how do we repair this? How do we fix this? What's the ideal way to fix it?" Or even so in your gut, right? Your gut is dysbiosis. You have taken some medicines, or there's some problem there is, or there's this, uh, inflammatory bowel disease or something like that. All these are, you know, highly correlated with the gut health. How do I give you the right set of microbes that will restore gut health? Right. So these are all very interesting, potentially engineering problems even, right? But the point is that you needed this kind of data to started- uh, to start looking inwards.
- SPSpeaker
Mm.
- KRKarthik Raman
And what the Genome Project showed us was that, okay, there are only so many genes that are avail- there are so many genes, but not all of them are associated with the disease, right? It is not so straightforward to, you know, map genes to diseases and so on. So this becomes very in- interesting, right? So how do you now start mapping genes to diseases?
- SPSpeaker
But also a point in time where the amount of data available suddenly exploded, right?
- KRKarthik Raman
Yeah. So that is because the, of the improvement in technologies-
- SPSpeaker
Mm.
- KRKarthik Raman
-right? So if you see, uh, there is this, um, uh, famous, uh, thing of, you know, Sanger beating Moore, right? We all know about the Moore's law, which talks about the-
- SPSpeaker
Yeah
- KRKarthik Raman
... transistors on a, uh, on a, you know-
- SPSpeaker
Yeah
- KRKarthik Raman
... in a, in a chip, uh, doubling every eighteen months-
- SPSpeaker
Yeah
- KRKarthik Raman
... and so on, and it sort of kept pace for a long time, might, might taper off. But Sanger, so Sanger is the one who first, he got two Nobel Prizes, and, uh, Sanger is the one who started this whole idea of sequencing. So till today, the, the, uh, you do something called a Sanger sequencing to verify if your sequencing is correct and so on. So Sanger sequencing is a very basic sequencing methodology, but then you have various other things that have evolved today. So you hear a lot about what is known as next-generation sequencing. Doesn't s- mean much, but NGS is the keyword that a lot of biologists were throwing around for the last decade or so on. Wherein you have a very interesting method of sequencing by synthesis. You have a gene, you synthesize a copy of that gene, and as this copy is being synthesized, you emit light. Saying, "This is an A, this is a T, this is a C, this is a G." Different components of the genome, the ATCG bases that make up your genome, and there is a sensor that cap- captures that and says, "Hey, your, your genome is ATCGGG," whatever, right? So this became cheaper, cheaper, cheaper, cheaper.
- SPSpeaker
Mm.
- 58:00 – 1:09:39
Closing thoughts & reflections
- KRKarthik Raman
And what you have is-
- SPSpeaker
Understood
- KRKarthik Raman
... typically, when I see these plots, these are exponential on a log scale. [chuckles] Okay.
- SPSpeaker
Understood.
- KRKarthik Raman
So you see, like, a rapid increase, especially if you see the last five years. You probably generated as much ge- data or more data than you generated in the previous fifty years.
- SPSpeaker
And as experiments, experimental systems biology improves, that, that will just keep growing and growing and growing.
- KRKarthik Raman
Exactly. Exactly.
- SPSpeaker
Mm-hmm. Fantastic. Um, so pro- uh, Professor, just to go back to that thread of your career, um, [chuckles] after your UG-
- KRKarthik Raman
Yes
- SPSpeaker
... you jumped to computer science PhD.
- KRKarthik Raman
So, yeah, so I was always fascinated with computing and, uh, and, uh, of course, towards the middle of my undergrad, really started getting drawn towards modeling and so on. So at that point of time, I wanted to really explore, um, you know, how much more we can do in computing. And IIT, IISc had this perfect course for me, which was an M.Tech in Computational Science, and in fact, today, we will call that an M.Tech in Data Science. [chuckles] Right? And, uh, that course seemed like ideal because it had courses on high-performance computing, on computational methods, and so on, with lots of electives. Strangely, my undergrad, as much as I enjoyed it, we didn't have the concept of an elective. And contrast that with IIT today, right? [chuckles]
- SPSpeaker
Yeah.
- KRKarthik Raman
Where we have forty percent of our courses as electives, and I have my students taking all kinds... uh, students in so many departments taking my course. So when I went to IISc, I had electives, and I took an elective on computational approaches to drug discovery-
- SPSpeaker
Mm
- KRKarthik Raman
... and a course on elements of structural biology, right? [chuckles] And, uh, all of these gave me very interesting insights into how we can sort of use computation across various domains. And I'm always someone who, who thought that you need... I mean, pure computer science is great, but I think you really have to apply it to a domain to really enjoy it, right? You, you can still do the classic, you know, just theoretical computer science, and so on. But if you want to really apply computation, you should find an exciting domain to apply it. It could be transport, it could be mechanical engineering, it could be chemical engineering. There's just so many possibilities, or bio. And I felt that I had a big head start when it came to bio because of my knowledge of biochemistry, microbiology, and I was definitely interested in those, uh, quantitative aspects or, like, molecular biology, and so on. So I thought, "This is what I wanted to do." And then I had, like, a very, very short conversation with my PhD advisor, when we decided that we would start working on, uh, systems biology. Uh, right, so I had, in fact, you know-
- SPSpeaker
Mm
- KRKarthik Raman
... uh, studied a lot about protein engineering, computational design of proteins, and so on, and I went her- went to her with a large write-up on that, and she said, like: "Hey, but why don't we consider systems biology?" And I was like, "Sure," right? Because I was always fascinated about modeling biological systems and sort of never looked back from that, I guess. [chuckles]
- SPSpeaker
And how, uh, d- did you take a decision to become an academic, uh, or like a professor? Uh, because from there you could have gone into industry, right? This is, this is a field that a lot of industry researchers-
- KRKarthik Raman
Yeah, I mean, I was always fascinated by the academic world. [chuckles] So I think for the longest time, I remember I wanted to be an academic, uh, and, um, um, I think it was sort of an, uh, easy decision for me, right? I remember when I was upgrading from my PhD, we had a lot of discussions, me and some of my friends, and at that time I was, like, very clear, right? I just-- I need to do a PhD because, you know, I need to... You know, that's the kind of career I'm-
- SPSpeaker
That's the path to-
- KRKarthik Raman
That's the path to-
- SPSpeaker
Professorship.
- KRKarthik Raman
Yeah.
- SPSpeaker
Is that, is that the right word?
- KRKarthik Raman
Yeah.
- SPSpeaker
Professorship.
- KRKarthik Raman
Professorship.
- SPSpeaker
Um-... Professor, you-- okay, so now, now, this is-- thank you for sharing the body of work that you do. Um, you're also a very popular professor on campus. Your, your courses are taken by lots of students. How has the relationship with, uh, students changed over the last, uh, twenty years since when you were a student to now? The faculty-student relationship.
- KRKarthik Raman
Yeah. So I, I don't know, right? So I've been a faculty in different inst-- I've been a student in different institutes. I've been a faculty only at this institute, and I think this institute has always been, like, a very student-friendly environment and so on. And even when I was an undergrad, I think we had s- faculty who are, like, really student-friendly and so on. And I think I tried to sort of imbibe the best aspects from these various places as I, uh, you know, as a faculty here, right? So I like to be a sort of combination of, uh, you know, strict, yet friendly, right? So I insist on my students to come on time, but, you know, I give them attendance prizes and so on. This is something I've done from my first semester here, right? [chuckles] So there are... Uh, I've had a class once where about, like, of fifty students, about eighteen or nineteen of them had one hundred percent on-time attendance-
- SPSpeaker
Okay
Episode duration: 1:09:44
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