How Computational Microbiology drives disease research & treatment | Prof Karthik Raman | BP2B S2 E8

Karthik Raman 0:00 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]

Speaker 0:07 It's an either-or.

Karthik Raman 0:07 I hate biology, so I go for math.

Speaker 0:09 Yeah.

Karthik Raman 0:09 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.

Speaker 0:34 Hi, this is Amrit. We are at IIT Madras, my alma mater, and India's top university for people who like to build.

Speaker 0:41 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

Speaker 1:13 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.

Karthik Raman 1:23 Hi.

Speaker 1:24 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-

Karthik Raman 1:54 Sure.

Speaker 1:55 -so maybe you can help me understand this. Um, and without this, everything is going to be OHT for me. [chuckles] So-

Karthik Raman 2:00 Sure, sure.

Speaker 2:01 What is systems biology?

Karthik Raman 2:03 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.

Speaker 2:57 Okay.

Karthik Raman 2:57 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-

Speaker 4:29 Professor, Professor, sorry, you're moving too fast for me, [chuckles] so I'm going to break you a bit.

Karthik Raman 4:33 Yeah.

Speaker 4:33 I just want to pick up a word that you used. You used the word "modelling."

Karthik Raman 4:36 Yes.

Speaker 4:36 What does that

Speaker 4:37 mean?

Karthik Raman 4:37 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?

Speaker 4:48 Mm.

Karthik Raman 4:48 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.

Speaker 5:00 Mm.

Karthik Raman 5:00 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?

Speaker 5:13 Yeah.

Karthik Raman 5:13 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?

Speaker 5:37 Right.

Karthik Raman 5:37 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.

Speaker 5:53 Can I just ask you here-

Karthik Raman 5:54 Yeah

Speaker 5:54 ... do you mean how, how did COVID spread in the population, or do you mean-

Karthik Raman 5:57 Yeah, so what is the rate of spread, right?

Speaker 5:59 -how did the virus itself-

Karthik Raman 5:59 So what is... No, no, no. Just what is the rate of spread?

Speaker 6:01 Mm.

Karthik Raman 6:01 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?"

Speaker 6:09 Mm.

Karthik Raman 6:09 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?

Speaker 6:17 Correct.

Karthik Raman 6:17 You know, how much oxygen should I, uh-

Speaker 6:19 And I remember that we all learned the word, uh, the term R. I think, was it R?

Karthik Raman 6:24 I think the BRR, right? So basic reproductive rate or something like that. Like R naught, right?

Speaker 6:29 Yeah, R naught.

Karthik Raman 6:29 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-

Speaker 6:34 Mm.

Karthik Raman 6:34 - 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

Karthik Raman 6:53 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.

Speaker 7:27 Mm.

Karthik Raman 7:28 Right? And this is what you want to figure out, right? So, so, so essentially, these models-

Speaker 7:33 Okay

Karthik Raman 7:33 - try to answer certain important questions that we want to ask of a biological system. [chuckles]

Speaker 7:39 You are giving this, you're giving this COVID as an example of modelling?

Karthik Raman 7:42 As an example, yeah. It's something, in fact, we haven't really worked much on.

Speaker 7:45 Yeah.

Karthik Raman 7:45 But it's like a classic, uh, problem that I teach in my course on systems biology and-

Speaker 7:50 Okay

Karthik Raman 7:50 ... uh, so on. Because this is like, a, a, a nice way to start thinking about how will you model a system.

Speaker 7:56 Mm.

Karthik Raman 7:56 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."

Speaker 8:06 Mm.

Karthik Raman 8:06 "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-

Speaker 8:23 Yeah

Karthik Raman 8:23 ... hot today, right?

Speaker 8:24 Yes.

Karthik Raman 8:24 So you really

Karthik Raman 8:25 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.

Speaker 8:37 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-

Karthik Raman 8:43 [laughing]

Speaker 8:43 ... which is, take a cow, assume it's a sphere.

Karthik Raman 8:45 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."

Speaker 8:55 Yeah.

Karthik Raman 8:56 Right, so-

Speaker 8:57 And so that's the opposite end of digital twin, where-

Karthik Raman 8:59 That, that's the opposite

Karthik Raman 9:00 end, right? So, so, b- but, uh, I think that's a reasonable place to start even, right?

Speaker 9:04 Mm.

Karthik Raman 9:04 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.

Speaker 9:26 Yeah, as dots.

Karthik Raman 9:27 Right?

Speaker 9:28 Yeah.

Karthik Raman 9:28 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-

Speaker 9:43 Mm

Karthik Raman 9:43 ... but some are useful."

Speaker 9:44 Mm.

Karthik Raman 9:44 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-

Speaker 9:57 Mm

Karthik Raman 9:57 ... but they, you'll still be able to answer some very interesting questions about a real-world system.

Speaker 10:01 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?

Karthik Raman 10:09 Exactly.

Speaker 10:10 What, what would be a microbiology example for a modelling problem?

Karthik Raman 10:13 A single cell, right?

Speaker 10:14 Okay.

Karthik Raman 10:15 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?

Speaker 10:32 Mm.

Karthik Raman 10:33 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?

Speaker 11:06 Understood.

Karthik Raman 11:06 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-

Speaker 11:21 Mm

Karthik Raman 11:21 ... 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.

Speaker 11:33 Yeah.

Karthik Raman 11:33 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.

Speaker 11:43 Mm.

Karthik Raman 11:43 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.

Speaker 11:59 Okay.

Karthik Raman 11:59 Because this is a very complex system, and you don't want to do these spherical cow models. You want to make accurate models that- because each time you build a model, you're definitely making approximations, but the whole idea is that you want to have handles that you want to look at.

Speaker 12:13 Mm.

Karthik Raman 12:14 Suppose I want to, you know, manipulate the concentration of a drug and see what happens.... Suppose I wanted to see what are the insulin dose I need to give to a particular patient. If you look at, uh, the, uh, standard, uh, the models of drug action and so on, we have something known as a single-compartment model.

Speaker 12:28 Mm.

Karthik Raman 12:28 The body is a spherical cow, essentially. [laughing] Right? The body is like one compartment where, you know, all the, you know, different, uh-- There is blood with some concentration of X, ma- uh, you know, various, uh, you know, whatever, sugar, and whatnot, and so on. And then you have this insulin coming in, and it's going to cause some... You, you try to predict what is the curve, right? I put insulin here. There is a spi- there is a, you know, uh, uh, there is a slight spike in glucose after that till insulin kicks in, and then there is a drop, and whatever, right? So all these things are built using models, and models have today become very im- so useful and so important that FDA actually- US FDA actually allows you to use models to, you know, to file some of these claims and so on, right? So you, you can back up your... I mean, you should-- Of course, you have, uh, experimental-

Speaker 13:14 Exactly

Karthik Raman 13:14 ... trials and other evidences to back up your, uh, you know, drug applications and so on, but you can use models as a very legitimate component of your applications today.

Speaker 13:22 Understood. So, so from, from what you said, um, uh, a, a, a modeling of a system, of a biological system would help me understand it better, make hypothesis on how to do different things, how to impact the system, and then I would... Maybe that's enough, or maybe then I would do clinical trials or, or experiments.

Karthik Raman 13:42 Absolutely. So, so, so let me just, uh, uh, draw another parallel to engineering, because I think you've been talking about a lot of engineering aspects in your various podcasts.

Speaker 13:49 Yeah. [chuckles]

Karthik Raman 13:50 Typical, what's your DBTL, right? Design, build, test, learn cycle.

Speaker 13:53 Yeah.

Karthik Raman 13:53 The same thing is there in biology as well, right? So in, in modeling, in fact, right? So you start with data or hypothesis. You design a mo-

Speaker 14:00 Right.

Karthik Raman 14:00 You design a model, then you run it, then you test.

Speaker 14:04 Mm.

Karthik Raman 14:04 That's, like, the most exciting or scary part [chuckles] for most, uh, students and so on, right? So when you test it against data, you will find there are deviations.

Speaker 14:11 Mm.

Karthik Raman 14:12 So now go debug, right? Why are these deviations happening? Why is... Okay, COVID, I'm not able to predict the spread simply because there are too many asymptomatic people. SIR model doesn't fit the bill now.

Speaker 14:22 Mm.

Karthik Raman 14:22 I need to have another variable called-

Speaker 14:25 Yeah

Karthik Raman 14:25 ... asymptomatic, SIAR model or something like that, and I need to start factoring for these things-

Speaker 14:31 Mm

Karthik Raman 14:31 ... to make my model more accurate. So design, build, test, and then learn from your mistakes. Learn from the deviations the model shows with respect to the, uh, reality.

Speaker 14:41 Very nice. Thank you so much. Um, second question is, what is computational system biology? And also I've noticed that you've written a book on it, so I guess you're the right person to ask.

Karthik Raman 14:49 Yeah.

Speaker 14:49 Uh, what did the addition of the word computational change here?

Karthik Raman 14:52 Yeah, that's a, that's a good point, right? So systems biology has, uh, essentially two aspects to it, right? So one is experimental systems biology. Imagine, so how do you think one experimentally studies biological systems at scale? Which means you need these excellent methodologies to look into the cell. So I'll now throw a little more, more jargon at you, but hopefully everybody started hearing these, right? I think everybody started hear- hearing genomics. [chuckles] Right? So this

Karthik Raman 15:19 omics idea is basically the, the om', essentially is a sort of, uh, suffix that says that you're looking at the whole thing.

Speaker 15:25 Mm.

Karthik Raman 15:26 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.

Speaker 15:35 Mm.

Karthik Raman 15:36 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?

Speaker 16:38 Okay.

Karthik Raman 16:38 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.

Speaker 16:59 Mm.

Karthik Raman 16:59 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?

Speaker 17:18 Mm.

Karthik Raman 17:18 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.

Speaker 17:30 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?

Karthik Raman 17:39 Yes, yes.

Speaker 17:39 So-

Karthik Raman 17:40 So, so these data form the basis for building the models.

Speaker 17:43 Mm.

Karthik Raman 17:43 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?

Speaker 17:54 Mm.

Karthik Raman 17:54 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?

Speaker 18:17 Mm.

Karthik Raman 18:17 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?

Speaker 18:24 So it becomes a co- correlation, causation, identification kind of-

Karthik Raman 18:27 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.

Speaker 18:36 Okay. So then, because there's an huge availability of data, it then becomes a mathematical computation.

Karthik Raman 18:42 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-

Speaker 18:57 So like a g- the Genome Project would be an experiment.

Karthik Raman 18:59 Exactly, exactly.

Speaker 19:00 Okay.

Karthik Raman 19:00 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?

Speaker 19:18 Mm.

Karthik Raman 19:18 So then, using this data, these data, we build models, and then these models, we use them to ask questions about the cell and then say, "Okay, this makes sense. This does not make sense. My model is predicting that this particular enzyme is not essential for cellular function, but when I remove it, the cell is dying. So what's happening?"

Speaker 19:37 Mm.

Karthik Raman 19:37 Go figure.

Speaker 19:38 Right.

Karthik Raman 19:38 So this is our, uh, design, build, test, learn cycle.

Speaker 19:41 Understood. And as you're explaining it, I get the sense that... I mean, I, I guess you s- said it in one of your lectures also, that this is a field which is very interdisciplinary. Uh, there's a computer science, uh-

Karthik Raman 19:51 Yeah

Speaker 19:52 ... uh, element, there is a biology element, there's a chemistry element. Uh, it's all coming together, right?

Karthik Raman 19:57 Yeah, yeah.

Speaker 19:59 Professor, uh, some words that you have used: biome, metabolite, and I think you used the word pathway also in the beginning. Um, what do these mean?

Karthik Raman 20:07 So this is- just think of this as the... So, so biome again is, um, uh, um, it's literally another word for an ecosystem, right? So all the biological stuff that resides in any environment, right? So typically, we- you will hear, you will see the word microbiome a lot more-

Speaker 20:25 Mm

Karthik Raman 20:25 ... because we are most interested about the microbes that reside in an- in any environment.

Speaker 20:30 Right.

Karthik Raman 20:30 Because, and I think it's more to do with the fact that we haven't, uh, seen them enough, or we haven't considered them enough, and we've suddenly realized that they are just so central to life and, uh, health and well-being and so on. Right?

Speaker 20:45 Yeah.

Karthik Raman 20:45 So you have, like, a ton of microbes on your skin.

Speaker 20:48 Yeah.

Karthik Raman 20:48 Right? So they, uh, uh, have an impact on skin health and, uh, so on and so, so forth. And similarly, you have a lot of microbes that reside within the body. The original estimate was something about, um, ten times the number of human cells, but now the more accurate estimates are coming out as like three or four times, uh, the number of human cells. So I don't know, are we human or microbes? We're microbial, right? So [chuckles] we are outnumbered, uh, the three to one or four to one by microbial cells.

Speaker 21:15 And this is not just the gut, it's skin, gut-

Karthik Raman 21:17 This is... Yeah, this is everything. Yes, yes. So, so there is this beautiful, I think, science or nature cover image which, uh, has a picture of a human, but almost, you know, it's just microbes-

Speaker 21:27 Mm

Karthik Raman 21:27 ... right? So it, it really tells you the story that, um, that microbes are, uh... And, uh, the other really cool thing is that there is another picture that you will see in all these, uh, uh, review articles and so on, which ties microbes in various parts of the body to every single disease.

Speaker 21:44 Mm.

Karthik Raman 21:45 Right? So you have the gut-brain axis, that is like, talk about how the gut microbiome has this significant influence on autism, and so on. So that is this whole set of things that are manipulated or influenced by the microbes in the body. And that is why there is this quest to understand them better.

Speaker 22:01 Mm.

Karthik Raman 22:02 And if you see what we s- talked about systems biology, right? I would more narrowly classify the work I do as systems microbiology.

Speaker 22:09 Mm.

Karthik Raman 22:09 'Cause we s- s- typically don't work enough with human systems. We have done some work, but we typically work within a single microbial cell, trying to understand the single microbial cell and, for instance, how it interacts with other microbes, right? Does it help or hinder the growth of other microbes? And so on.

Speaker 22:25 So your work with microbiomes, uh, spans from, I've read, and maybe you can explain it a bit, uh, spans

Speaker 22:32 from gut microbiome to microbiomes in the space station. Is that right?

Karthik Raman 22:36 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.

Speaker 23:08 Mm.

Karthik Raman 23:08 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-

Speaker 23:34 Oh, yeah

Karthik Raman 23:34 ... 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-

Speaker 23:42 Yeah

Karthik Raman 23:43 ... is converted to energy.

Speaker 23:44 Uh, ADT, uh-

Karthik Raman 23:46 ATP. ATP is produced.

Speaker 23:47 ATP, yeah.

Karthik Raman 23:47 So, so glycolysis-

Speaker 23:48 Wow

Karthik Raman 23:48 ... 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.

Speaker 23:58 Okay.

Karthik Raman 23:58 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-

Speaker 24:27 What, what, what is metabolite?

Karthik Raman 24:29 ... Metabolites are these small molecules.

Speaker 24:31 Okay.

Karthik Raman 24:31 So it's just a metabolite, so you can- I mean, in school we ca- talk about them as compounds.

Speaker 24:35 Okay.

Karthik Raman 24:35 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.

Speaker 24:43 Hmm.

Karthik Raman 24:43 Because, uh, metabolism is basically breakdown and synthesis of various chemicals within the body-

Speaker 24:48 Right

Karthik Raman 24:48 ... within a living cell.

Speaker 24:49 Right. So my system's definition of a microbiome, uh, has to have, uh, the, the metabolites that are being measured. Is that correct?

Karthik Raman 24:57 Yep. Yep.

Speaker 24:58 The metabolites are being, being measured, and what we are trying to-

Karthik Raman 24:59 Specifically, what is measured is only the microbes.

Speaker 25:01 Okay.

Karthik Raman 25:02 Right? Everything else is actually extrapolated. I wish we could measure the metabolites as well, and people are developing some of these things and so on, and people do do that, right? So they do stool metabolome, right? Because, um, you, you try to find out what are all the, uh, metabolites that are present in the gut and so on. But most classic microbiome experiment is, you try to find out how much of each microbe is there.

Speaker 25:24 Okay.

Karthik Raman 25:24 You literally try to create one vector of abundances. I have X microbe at Y abundance, right?

Speaker 25:31 Oh, okay.

Karthik Raman 25:31 So X1 microbe at Y1 abundance, X2 microbe at Y2 abundance. I have this long vector.

Speaker 25:36 Okay.

Karthik Raman 25:36 Right? So this is really the most important data that we work with.

Speaker 25:40 Yeah. Okay.

Karthik Raman 25:40 Right, so, so this vector will change based on deep sea, gut, International Space Station-

Speaker 25:48 Right

Karthik Raman 25:48 ... or anything else in between, right? And how do these microbes interact with each other? So when I find higher, high levels of X1, do I also find high levels of X20, and how do they interact with each other? So these are some of the questions that we like to ask.

Speaker 26:04 Professor, immediately, the question comes to my mind. There are three examples: gut microbiome, uh, extra- e- extremophiles, and, uh, the microbiomes in, in the ISS. It makes obvious sense to me to study the gut mi- microbiome, because you're saying that gut microbiome can influence-

Karthik Raman 26:22 Health and disease

Speaker 26:22 ... uh, health in many ways. Obviously, daily health, but also maybe something like autism and diabetes, maybe, I don't know. Why are we studying, um, extremophiles and microbiomes in ISS? Isn't that... Doesn't it sound like an- it sounds like an exotic problem.

Karthik Raman 26:38 That's true, that's true. So but the thing is, uh, uh, see, uh, i- if you see, there is a lot of push towards green manufacturing today.

Speaker 26:45 Mm.

Karthik Raman 26:45 And green manufacturing in the context of, how do you make, say, drugs or various other... Or even, like, say, butanol or ethanol, which is, you know, a common blend in petrol today and so on. So how do you microbially synthesize these things?

Speaker 26:58 Mm.

Karthik Raman 26:58 And for this, the typical quest is, can I find exotic microbes with pathways that can do this?

Speaker 27:05 Oh, so then my-

Karthik Raman 27:05 And so there is this very classic-

Speaker 27:06 ... metabolite is a useful compound.

Karthik Raman 27:08 Metabolite is typically a useful compound, right? And there's a very classic, uh, uh, fantastic study, very innovative study, uh, where, um, there is this anti-malarial drug called, uh, artemisinin, right? That is only produced in certain plants.

Speaker 27:24 Mm.

Karthik Raman 27:25 But what they did was they copy-pasted this pathway from plants to microbes to mass manufacture it. So this is- this comes under the purview of metabolic engineering. This is, again, something that, uh, we, uh, really like doing in our lab. So we build a lot of models that can help in metabolic engineering, which is essentially, how do I tune these microbial cells to overproduce a particular, uh, compound of commercial interest? Okay, so how do I... And it could not- it could be a single cell, it could be like a combination of cells, like a microbial co-culture. Can you use that? Because there is division of labor, can you have one microbe help the other produce a particular metabolite or a compound, chemical compound, and so on. So, so this is the reason why we try to look at all kinds of... We, we like to go around characterizing organisms everywhere.

Speaker 28:12 Mm.

Karthik Raman 28:12 We can characterize, uh, organisms in the IITM soil.

Speaker 28:15 Mm.

Karthik Raman 28:15 Maybe there's, like, one microbe that is resistant to something, and there's some salt or something, and it's able to produce an exotic metabolite. And in, and so, and so the, the example for this is, in fact, our collaborators have identified, um, this, uh, this, uh, compound called surfactant, uh, in the International Space Station microbes, and that, uh, compound is, um, heavily used in cosmetics, so they have this very nice term of "space to your face," [chuckles] right? So it's the, uh, it's actually a co- a co- an organism that was, uh, identified in the International Space Station, but then that is able to, you know, produce this compound called surfactant, which is a very valuable molecule in, uh, cosmetics and so on.

Speaker 28:57 Very interesting. Um, there, there was a word you used I want- that's actually the last word on my list of terms I need to understand to understand what you're saying, uh, which is the word network. Um, what is the context of network in the work-

Karthik Raman 29:10 Yeah

Speaker 29:10 ... you're talking about?

Karthik Raman 29:11 So, [clears throat] so, so literally, I mean, there are networks at various levels, right? So in general, a network is simply, um... or a graph, as computer scientists call it, uh, or mathematicians, it's just a collection of nodes and edges. I think all of us are familiar with, uh, social networks.

Speaker 29:26 Yeah.

Karthik Raman 29:26 Right? So in Facebook or Twitter, your nodes are people or businesses-

Speaker 29:30 Mm

Karthik Raman 29:30 ... and the edges represent, you know-

Speaker 29:31 Connections or friendships

Karthik Raman 29:32 ... connections and so on.

Speaker 29:33 Yeah.

Karthik Raman 29:34 It is essentially the same thing, right? But here we are looking at the social network of a cell-

Speaker 29:38 Mm

Karthik Raman 29:38 ... or, uh, where the nodes could be microbes, and the intra- and the co- connections could be the interactions. This microbe, uh, influences the other microbe, and so on. Or you can delve within a cell. There are networks at so many different levels, which is what makes it fascinating, and if you see, that is the most recurring theme in our lab. I don't think you will find a... We, we work on a various, uh, s- set of things. We work on metabolic engineering applications in one sense. We look at microbiome. We look at omics data analysis. We even look at systems theory, where we collaborate with chemical engineers, and so on. But across all of these domains, you will see that networks is a completely recurring theme, and this is because we study networks at various levels. So one is microbial

Karthik Raman 30:20 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?

Speaker 30:59 Mm.

Karthik Raman 30:59 So the, the coefficients-

Speaker 31:01 Yeah

Karthik Raman 31:01 ... of these, uh, uh, how many moles, how many compo- molecules are required to make this other molecule, and so on.

Speaker 31:07 Mm.

Karthik Raman 31:08 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-

Speaker 31:24 Right

Karthik Raman 31:24 ... and still start looking at a single chemical as a network.

Speaker 31:27 Mm.

Karthik Raman 31:28 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.

Speaker 31:46 Okay.

Karthik Raman 31:46 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-

Speaker 31:55 Yeah

Karthik Raman 31:55 ... 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-

Speaker 32:11 Yeah

Karthik Raman 32:11 ... from one place to another. That is nothing but pathfinding on a network.

Speaker 32:14 Mm.

Karthik Raman 32:15 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.

Speaker 32:31 Mm.

Karthik Raman 32:31 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.

Speaker 32:41 Mm.

Karthik Raman 32:42 Right? So that is my-

Speaker 32:43 More.

Karthik Raman 32:43 So the cell wants to do something, I want to push it towards something that's commercially useful in an industrial process.

Speaker 32:51 Understood. Once you convert it to a network, all, all, all kinds of algorithms that are used in network-

Karthik Raman 32:56 Exactly

Speaker 32:56 ... thinking can come back.

Karthik Raman 32:57 Exactly.

Speaker 32:59 Is this a word that you use often, molecule of interest? It sounds very, [chuckles] uh, quotable.

Karthik Raman 33:04 Yeah, yeah, yeah. Yeah. Because we always have, like, you know, ethanol might be your molecule of interest, surfactant may be your molecule of interest, artemisinin may be your molecule of interest, right? So using this, uh, these mo- various, uh... I mean, we want to engineer the cell. Metabolic engineering is all about rerouting stuff, rerouting pathways within the cell, towards your molecule that is, uh, commercially viable, commercially important.

Speaker 33:28 Very interesting. So I'm just going to, uh, sort of go through all the terms that I learned from you: systems biology, cus- computational systems biology, microbiome, metabolite, molecule of interest, pathways, modeling, and network in the context, uh, extremophile, which I'm fascinated by the word extremophile. I'm going to just use it everywhere. [laughing]

Karthik Raman 33:47 [laughing]

Speaker 33:47 For people, like, "You can survive this. You are an extremophile."

Karthik Raman 33:49 Yes, yes, yes. [laughing]

Speaker 33:52 [laughing] Uh, this is great. Uh, thank you so much. Uh, I, [chuckles] I'm sorry this comes so late, like we've been talking for twenty minutes, but now I must ask you, uh, um, you head the IBSE,

Speaker 34:05 right? Uh, can you tell us what it is and what is the work that is actually done?

Karthik Raman 34:08 Yeah.

Speaker 34:09 What is your research area?

Karthik Raman 34:10 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?

Speaker 35:31 Mm.

Karthik Raman 35:31 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.

Speaker 37:12 Okay.

Karthik Raman 37:12 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.

Speaker 37:26 Mm.

Karthik Raman 37:27 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.

Speaker 38:38 Mm.

Karthik Raman 38:38 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-

Speaker 38:48 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-

Karthik Raman 39:05 Definitely.

Speaker 39:06 - indication of, uh-

Karthik Raman 39:06 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-

Speaker 39:18 Mm

Karthik Raman 39:18 ... right? To see what are the transmission of microbes across continents and things like that.

Speaker 39:22 Very interesting.

Karthik Raman 39:22 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-

Speaker 39:37 But the gut-

Karthik Raman 39:37 - surveilling microbes.

Speaker 39:38 - gut microbiome, gut microbiome work is pretty mainstream now, right? Like, everybody's talking about it, and, uh, there are products hitting the market-

Karthik Raman 39:45 To use it as a surveillance tool.

Speaker 39:47 Oh, okay, okay.

Karthik Raman 39:47 Right? So-

Speaker 39:48 Ah, definitely.

Karthik Raman 39:49 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-

Speaker 40:00 Mm

Karthik Raman 40:00 ... ahead of time and so on, I don't think we are there yet, but there are, like, tantalizing possibilities.

Speaker 40:06 Uh, Professor, can you, uh, just so- some of the work that your group has done, uh, maybe is of common interest. So I was really fascinated by the work that, uh, is done, uh, with respect to the gut biome. Uh, there's a lot of conversation that happens online about gut being a second brain. I don't know what that means, but I assume that means that-

Karthik Raman 40:24 So, so in fact, I will say that, uh, uh, rather than a second brain, um, it's probably the most important metabolic organ. Maybe you can... I don't know, the, probably the jury is out on to rank it above the liver or below the liver, [chuckles] but it's-

Speaker 40:36 Mm

Karthik Raman 40:36 ... it's literally a very important metabolic organ because it, it produces a lot of useful metabolites for us, various compounds that we are unable to synthesize on our own. So I'm sure you remember, I, uh... You must have unfortunately taken antibiotics at some point of time, and your doctor would have likely prescribed, like, uh, uh, vitamin B alongside the antibiotics. Or in fact, now if you see a lot of doctors actually, uh, prescribe what is, uh, like a concoction of microbes along with your antibiotics, right? Because they know that by having antibiotics, you're gonna like really upset the gut balance of microbes. And can you actually i- instead ingest certain helpful microbes that will go and restore the balance faster-

Speaker 41:17 Mm

Karthik Raman 41:18 ... right? Or worst case, you're not gonna be able to produce some vitamin B in the, in your gut because you've taken these antibiotics. Can you take a supplement of vitamin B so that you don't get blisters or whatever, and so on. Right? So, so this is, like, I think, so the, the gut is actually a very, very important metabolic organ because it does a lot of stuff. There's like a whole million, there are trillions of microbes there, which basically produce a lot, and they break down a lot of compounds that humans aren't able to break down on their own.

Speaker 41:46 Mm.

Karthik Raman 41:46 Right? So whatever you eat gets broken down through the metabolic pathways and enter the gut. In the gut, these microbes and their metabolic pathways break down these molecules into much simpler molecules, and so on. Many of them are very helpful for you. And then, you know, there's also a lot of interest in what happens to these, uh, the drugs that we take, right? So whatever medicines that we take, how do they get broken down in the gut? What kind of do they, you know, uh, what is the infect- what is the impact on the gut microbiome, and so on. So all these are very interesting questions.

Speaker 42:16 Just, just building on that, but I have... I mean, I, uh, maybe I take, like, one antibiotic course a year or maybe two, because, well, that's how my metabolism is. But there are others who are, uh, who react to, um, antibiotics very differently, right? So the, the, the, the impact of antibiotics on gut must be different for everyone.

Karthik Raman 42:36 Definitely, right. So, so potentially, everyone's gut microbiome is unique, right? I mean, it's, it's not like unique, unique, [chuckles] that, uh, probably you can group it into a few categories. So there was this classic paper that appeared maybe like ten years ago, which said there are, like, three major types of guts, and so on. But again, you know, I don't think there was enough Indian representation in the data and, uh, things like that, so we may have to do this study. There is an Indian gut microbiome study that has, uh, been done-... right? I think they've, uh, uh, looked at about three thousand Indian gut samples and so on. We are still, uh, awaiting some of the results from that study, and I believe it will be really fascinating when we get to understand what is the abundance of various microbes in the Indian guts, uh, right? And, we were actually part of a very exciting study, uh, with, uh, our collaborators in Singapore, where they actually studied what happens when you take antibiotics. And they had a nice cartoon to show that it's like a forest fire. So when you take an antibiotic, it's like a wipeout of [chuckles] what happens. It's not a true wipeout, but it, it really has a significant impact on the gut. And then slowly, as the for- as the forest ecosystem recovers after a forest fire, your gut nicely recovers after, uh, these ant- uh, an antibiotic administration, right? But sometimes it's anywhere between six months to two years, and you're saying that, you know, you might take another dose of antibiotics within that, uh, period. So your gut is, you know, uh, runs a serious, uh, threat of being dysbiosed, as they call it.

Speaker 44:02 If, if, if, if it's unique for everyone, then that supplement that the doctor is giving has to be unique for everyone, right?

Karthik Raman 44:09 Well, very good point, right? So, so this is a very interesting question that we've been, uh, grappling with, and a lot of people have been, uh, saying, that while the gut is, um... How do you say it? It's, it's, uh, uh, organisms wise, it might be unique, but function wise, it's quite similar.

Speaker 44:28 Mm.

Karthik Raman 44:28 Right? I might- so in my gut, there may be different organisms that produce the same lactic acid or, you know, uh, the micro- the compounds that are like ribo- the b- vitamin B2 or something that's required for our health, as in your gut. It might just be a different set of microbes that are doing it.

Speaker 44:43 Okay.

Karthik Raman 44:44 But the point is, can you somehow push the gut towards those healthy things? And typically, this would be something like, can I improve the pH of the gut?

Speaker 44:51 Mm.

Karthik Raman 44:52 Right? And so I'll give you these three microbes that will improve the pH of the gut. Something like that, right? So typically, we look at prod... uh, the, uh, production of various acids like, uh, uh, propionate, butyrate, and so on, to just make sure that the, the gut health is improved, and so on. And but today, we really want to systematically design these concoctions, and for which, again, we believe modeling will be very handy.

Speaker 45:18 Sure.

Karthik Raman 45:18 Right? I know that, hey, if I put these microbes, my gut will start improving, right? So this is- these microbes will interact in different ways. So in fact, a lot of our work, uh, involves, of course, you know, starting with a list of reactions, converting them into a network, and then I try to put these networks together and see how these networks interact with each other. Is this organism producing like a particular molecule that this organism can, or the other organism can benefit from and grow faster? Do they collaborate? Do they cooperate, right? Or is one being a parasite on the other? It is just, you know, pushing out something to kill the other organism. So we try to study these things to understand which microbes can coexist, and ultimately, which microbes

Karthik Raman 46:00 can help each other grow-

Speaker 46:01 Mm

Karthik Raman 46:01 ... or, you know, restore gut health and so on.

Speaker 46:04 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.

Karthik Raman 46:23 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-

Speaker 46:35 Compounds. Okay

Karthik Raman 46:35 ... 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?

Speaker 46:52 Yeah. [chuckles]

Karthik Raman 46:53 And it's, it couldn't be farther from the truth, right?

Speaker 46:56 Understood.

Karthik Raman 46:56 [chuckles] Because these, you have far more helpful microbes in the body than, you know, harmful microbes.

Speaker 47:03 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?

Karthik Raman 47:28 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-

Speaker 47:43 Mm

Karthik Raman 47:43 ... 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?

Speaker 48:22 Mm.

Karthik Raman 48:22 Right? Typically, it survives. And then you are like, "Okay, so this protein is not useful for the cell." [chuckles]

Speaker 48:28 Professor, I remember this article. So first you start with a sample of ten thousand-

Karthik Raman 48:32 [chuckles]

Speaker 48:32 ... non-working radios, [chuckles] and then from the first one, you pull out a capacitor and see.

Karthik Raman 48:37 Yes, yes. Right. So you literally have to-- everything has to be done by reverse engineering, right?

Speaker 48:43 Mm.

Karthik Raman 48:43 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?

Speaker 48:48 Interesting.

Karthik Raman 48:48 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?

Speaker 48:55 Correct, correct.

Karthik Raman 48:56 Right? You'll be killing your- killing microbes is ethical, uh, at least as far as we are concerned today.

Karthik Raman 49:00 [chuckles]

Speaker 49:00 At least it's not not ethical. [chuckles]

Karthik Raman 49:02 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.

Speaker 49:18 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?

Karthik Raman 49:40 Yep.

Speaker 49:41 And then from chemical engineering undergraduate, you went to, uh, computer science-

Karthik Raman 49:45 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.

Speaker 50:12 Mm.

Karthik Raman 50:12 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.

Speaker 50:47 Mm.

Karthik Raman 50:48 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-

Speaker 51:03 Mm.

Karthik Raman 51:03 -in school, right? So, so now this-

Speaker 51:05 But that's more a reflection of how biology is taught in school.

Karthik Raman 51:07 Exactly.

Speaker 51:08 Yeah.

Karthik Raman 51:08 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?

Speaker 51:19 Yeah.

Karthik Raman 51:19 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?

Speaker 51:37 Yeah.

Karthik Raman 51:37 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.

Speaker 51:58 Mm.

Karthik Raman 51:59 But we do less of that and more of taxonomy. You know, remember, who was the scientist who discovered [chuckles] -

Speaker 52:05 Yeah

Karthik Raman 52:05 ... this particular disease or this particular plant or this particular-

Speaker 52:07 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-

Karthik Raman 52:18 Perhaps. Perhaps.

Speaker 52:18 And, and that part-

Karthik Raman 52:19 Yes

Speaker 52:19 ... is just not mentioned, right?

Karthik Raman 52:21 Yep, yep.

Speaker 52:22 Uh-

Karthik Raman 52:22 This, I mean, this definitely [chuckles] requires some reform, right? I, I keep, uh... Because it's a, it's a problem for people like me, because I need students who like biology and like mathematics.

Speaker 52:31 Mm.

Karthik Raman 52:31 And I have to typically tell people that, "Hey, don't worry about biology, you will start liking it. You will, when I start teaching it," because I know, [chuckles] -

Speaker 52:39 That's very, very interesting because you mentioned that, and a lot of times students make a choice between biology and mathematics. It's an either/or.

Karthik Raman 52:46 I hate biology, so I go for math.

Speaker 52:47 Yeah.

Karthik Raman 52:47 I, I can't do math, so I'll go into medicine or biology.

Speaker 52:50 Yeah.

Karthik Raman 52:51 So I think this has to change because

Karthik Raman 52:53 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]

Speaker 53:14 Mm.

Karthik Raman 53:14 And he said that the innumeracy of biologists is, is being, uh, you know, a, a problem.

Speaker 53:20 Bottleneck.

Karthik Raman 53:20 Right?

Speaker 53:20 Yeah.

Karthik Raman 53:20 It's, it's being a, a stumbling block for de- development. It was-

Speaker 53:24 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-

Karthik Raman 53:33 Definitely, definitely.

Speaker 53:34 We're measuring it.

Karthik Raman 53:34 And, you know, it might be a very simple way to introduce, like, uh, trivial modeling in schools.

Speaker 53:39 Mm.

Karthik Raman 53:39 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?

Speaker 53:48 Yeah.

Karthik Raman 53:48 I'm actually doing a prediction, and that's probably fascinating for a child, right?

Speaker 53:51 Yeah.

Karthik Raman 53:51 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.

Speaker 54:03 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-

Karthik Raman 54:31 Yeah

Speaker 54:31 ... in the '90s.

Karthik Raman 54:32 Yeah.

Speaker 54:33 Uh, okay, was that something very, like, is it a big step?

Karthik Raman 54:36 It's huge, huge.

Speaker 54:37 And what was the impact of that?

Karthik Raman 54:38 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

Karthik Raman 55:00 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.

Speaker 56:16 Mm.

Karthik Raman 56:18 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?

Speaker 56:38 But also a point in time where the amount of data available suddenly exploded, right?

Karthik Raman 56:43 Yeah. So that is because the, of the improvement in technologies-

Speaker 56:46 Mm.

Karthik Raman 56:46 -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-

Speaker 56:55 Yeah

Karthik Raman 56:55 ... transistors on a, uh, on a, you know-

Speaker 56:57 Yeah

Karthik Raman 56:57 ... in a, in a chip, uh, doubling every eighteen months-

Speaker 57:00 Yeah

Karthik Raman 57:00 ... 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.

Speaker 57:59 Mm.

Karthik Raman 58:01 And what you have is-

Speaker 58:02 Understood

Karthik Raman 58:02 ... typically, when I see these plots, these are exponential on a log scale. [chuckles] Okay.

Speaker 58:08 Understood.

Karthik Raman 58:08 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.

Speaker 58:19 And as experiments, experimental systems biology improves, that, that will just keep growing and growing and growing.

Karthik Raman 58:25 Exactly. Exactly.

Speaker 58:26 Mm-hmm. Fantastic. Um, so pro- uh, Professor, just to go back to that thread of your career, um, [chuckles] after your UG-

Karthik Raman 58:35 Yes

Speaker 58:35 ... you jumped to computer science PhD.

Karthik Raman 58:36 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]

Speaker 59:16 Yeah.

Karthik Raman 59:16 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-

Speaker 59:28 Mm

Karthik Raman 59:28 ... 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-

Speaker 1:00:27 Mm

Karthik Raman 1:00:27 ... 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]

Speaker 1:00:45 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-

Karthik Raman 1:00:55 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-

Speaker 1:01:19 That's the path to-

Karthik Raman 1:01:20 That's the path to-

Speaker 1:01:21 Professorship.

Karthik Raman 1:01:22 Yeah.

Speaker 1:01:22 Is that, is that the right word?

Karthik Raman 1:01:24 Yeah.

Speaker 1:01:24 Professorship.

Karthik Raman 1:01:25 Professorship.

Speaker 1:01:26 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.

Karthik Raman 1:01:48 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-

Speaker 1:02:36 Okay

Karthik Raman 1:02:36 ... for the entire se- semester, right? So I usually give them a, a book of their choice and, uh, so on. So, so this is-- I, I think I've really enjoyed my, uh, j- journey at, uh, IITM, and, um, uh, and it's, it's, uh, very nice to be able to interact with students from various departments.

Speaker 1:02:53 Mm.

Karthik Raman 1:02:54 And, um, I really like-- So, uh, in fact, I've interacted with a couple of aerospace students. They're just so enthusiastic in my course on systems biology, and they come up with very innovative, uh, p- projects, uh, uh, in the course and things like that. So I think that is probably one of the really cool aspects of being at IITM, right?

Speaker 1:03:12 Mm.

Karthik Raman 1:03:12 And I'm sure the students also enjoy that, where they sit in stud- classes with students from so many departments. You do that in the first year, it's not a big deal.

Speaker 1:03:19 Yeah.

Karthik Raman 1:03:19 But when you do it in, like, your later years-

Speaker 1:03:21 Yeah

Karthik Raman 1:03:22 ... you know, looking at cutting-edge topics and so on, I think it's, like, very fascinating because you really get, uh, very complimentary perspectives and things like that, especially when they-

Speaker 1:03:30 And your third-year students by then also have a set of tools that they can bring. They have a perspective.

Karthik Raman 1:03:34 True, true.

Speaker 1:03:35 First-year students, of course, are-

Karthik Raman 1:03:37 Yeah

Speaker 1:03:38 ... new.

Karthik Raman 1:03:38 Yeah. They're just trying to stay afloat and, you know-

Speaker 1:03:40 Yeah

Karthik Raman 1:03:40 ... figure the system and, uh-

Speaker 1:03:41 Yeah

Karthik Raman 1:03:42 ... uh, things like that, right? And, uh, and they still don't know their subject yet.

Speaker 1:03:45 Yeah.

Karthik Raman 1:03:46 They're all school kids who are doing math, uh, sitting in the same class. So no matter if you are there from different departments, it doesn't make a difference.

Speaker 1:03:51 Mm. Professor, this was great. Thank you so much. Uh, it does look like this field requires a lot of patience. I'm curious to do-- know, though, uh, what are, what, what is the department, or what is your research group, uh, doing in the next two to five years? What is, what is it that you're looking forward to?

Karthik Raman 1:04:05 Yeah. So, so we are very excited about these, uh, various microbiomes that we have been studying, right? And what we want to really do is to make sure that we can understand the dynamics of these microbiomes. So we are still at an, at this understanding, uh, uh, phase. Uh, we eventually want to get to some, you know, work closely with industry to build some products or, uh, what we call, uh, these concoctions that we've been talking, right? Of, uh, that will help you restore your gut health and so on. They're called probiotics, you know, at the risk of throwing more jargon. Uh, right, so these, uh, probiotics, how do we design them, uh, more, uh, systematically, right? So can I, can I design a specific probiotic for-

Speaker 1:04:46 Me

Karthik Raman 1:04:46 ... for you?

Speaker 1:04:46 Yeah.

Karthik Raman 1:04:47 Right? So because I know what allergies you have, or what, uh, what is the antibiotic you're taking, or what is the disease that you, uh, what is the infection that you currently have, and can we have the right concoction that will help you get out of this without much trouble? So, and but for this, first, we need to really understand and, um, uh, understand the, the gut microbiome, and it's the interactions that are happening there, and how do you sort of change the state of the microbiome? I have a microbiome in this state, and I want to push it to this state. So how do I push it? And there may be different paths, right? What is the best path, right? All these are, you know, fascinating questions that we like to ask and hopefully answer over the next, uh, uh, few years. And we have a lot of tools that we've built over the years. So one of the things that we've been, kept, we've kept on doing is that we've been building the various algorithms. So we have algorithms that, again, a lot of them, or all of them, use networks, but we have algorithms that tell you how microbes interact with each other, right? How does one microbe promote the growth of the other, and so on. We have algorithms to design small communities of micro-- minimal communities of microbes that can have a specific function. And we have algorithms that can infer from various kinds of datasets, right? So, uh, so even in this, when you construct these metagenomic datasets, where you have datasets from, uh, different organ- you are trying to get the sequence of different organisms, you can get very little or, like, partial information or complete information, what is known as whole genome sequencing, or just like sequencing, like a particular gene, which will give you, it's called 16S. Uh, uh, it'll give you only like a very specific insight into what kind of mi-- it'll only tell you what, um, that, um, uh, Escherichia is there, not that E. coli is there. I, I will not know the whole organism. So we have tools that we've developed to understand, to work with these datasets. So using a combination of all these tools, right, and building further, we want to understand the dynamics of microbial communities. I think that's, uh, very fascinating to us. For instance, uh, are these communities prone to invasion? Like, a new microbe, can it come and disrupt this, or it's going to get kicked out, right? So all these questions we want to try and answer, u- ultimately, you know, towards understanding how to manipulate gut health-

Speaker 1:07:05 Nice

Karthik Raman 1:07:05 ... or manipulate the health of any environment. In fact, you know, the other very fascinating study we've been doing is we've been studying, uh, corals in the Andaman, where we've been working with, um, the National Institute of Ocean Technology. So they've done the diving to get the samples and so on, and we're trying to understand what is the microbial load present in these corals. And you see that in certain places, the corals are getting bleached.... and, you know, uh, dying out, and so on. Whereas in other places, they are quite, they are quite healthy. And we have collaborators, uh, uh, in, uh, Saudi Arabia, in- who've been working on the Red Sea corals, and so on. They have seen that, um, you know, they're able to restore the health of these corals by, you know, giving a concoction of microbes.

Speaker 1:07:50 Like a microbiome, biological-

Karthik Raman 1:07:52 The, the probiotics for the coral.

Speaker 1:07:53 Oh, okay.

Karthik Raman 1:07:53 Coral probiotics.

Speaker 1:07:54 Nice.

Karthik Raman 1:07:55 Right? So could we do the same thing here, right? So it's, uh, like, lots of fascinating questions, and I like the- and typically, I, I think of myself and our lab as the methods guys, right? So we build these methods. We are agnostic of what system we work on, right? It could be a gut microbiome, it could be an environmental microbiome, so on, but we try to build the basic math and the tools to build these models, and hopefully, by using these models and tools, we'll be able to better answer many of these questions.

Speaker 1:08:23 Nice. That's a great point to stop. Um, thank you so much, Professor. I learned a lot, and I actually had a lot of trouble keeping up, uh, but thank you for the patience and, uh, yeah, that's it.

Karthik Raman 1:08:34 Thanks. I, I, too, really enjoyed it. I know some of these can get a little technical, but, um, I think, you know, if you start looking beneath the jargon, uh, biology is very easy to sort of navigate. And, uh, it's, there are so many simple ways to introduce biology to, uh, uh, like school children, and so on. So you can definitely make sure that people can appreciate, you know, all the complexity that is there within a cell. Ultimately, these are networks, right? So, so the networks and the complexity, you look at all of them, and finally start understanding how... And because we are always very interested in understanding a little bit more about ourselves, right? So that's why the ques- the study of biology is the study of life, so it's, it's quite fascinating to understand them at a network level and try to see, uh, how ultimately these insights can empower us to handle disease and health better.

Speaker 1:09:21 Nice. Thank you so much. [mouse clicking] Okay, there you have it. Um, if you have questions about what we spoke about, questions that I can answer or the professor can answer, drop them in the comments, uh, and remember to like, share, subscribe. Thank you.