COMPLEXITY: Physics of Life

C. Brandon Ogbunu on Epistasis & The Primacy of Context in Complex Systems

Episode Notes

Context is king: whether in language, ecology, culture, history, economics, or chemistry. One of the core teachings of complexity science is that nothing exists in isolation — especially when it comes to systems in which learning, memory, or emergent behaviors play a part. Even though this (paradoxically) limits the universality of scientific claims, it also lets us draw analogies between the context-dependency of one phenomenon and others: how protein folding shapes HIV evolution is meaningfully like the way that growing up in a specific neighborhood shapes educational and economic opportunity; the paths through a space of all possible four-letter words are constrained in ways very similar to how interactions between microbes impact gut health; how we make sense both depends on how we’ve learned and places bounds on what we’re capable of seeing.

Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.

This week on Complexity, we talk to Yale evolutionary biologist C. Brandon Ogbunu (Twitter, Google Scholar, GitHub) about the importance of environment to the activity and outcomes of complex systems — the value of surprise, the constraints of history, the virtue and challenge of great communication, and much more. Our conversation touches on everything from using word games to teach core concepts in evolutionary theory, to the ways that protein quality control co-determines the ability of pathogens to evade eradication, to the relationship between human artists, algorithms, and regulation in the 21st Century. Brandon works not just in multiple scientific domains but as the author of a number of high-profile blogs exploring the intersection of science and culture — and his boundaryless fluency shines through in a discussion that will not be contained, about some of the biggest questions and discoveries of our time.

If you value our research and communication efforts, please subscribe to Complexity Podcast wherever you prefer to listen, rate and review us at Apple Podcasts, and/or consider making a donation at You'll find plenty of other ways to engage with us at

Thank you for listening!

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Discussed in this episode:

“I do my science biographically…I find a personal connection to the essence of the question.”

– C. Brandon Ogbunugafor on RadioLab

"Environment x everything interactions: From evolution to epidemics and beyond"
Brandon’s February 2022 SFI Seminar (YouTube Video + Live Twitter Coverage)

A Reflection on 50 Years of John Maynard Smith’s ‘Protein Space’
C. Brandon Ogbunugafor in GENETICS

Collective Computing: Learning from Nature
David Krakauer presenting at the Foresight Institute in 2021 (with reference to Rubik’s Cube research)

Optimal Policies Tend to Seek Power
Alexander Matt Turner, Logan Smith, Rohin Shah, Andrew Critch, Prasad Tadepalli in arXiv

A New Take on John Maynard Smith's Concept of Protein Space for Understanding Molecular Evolution
C. Brandon Ogbunugafor, Daniel Hartl in PLOS Computational Biology

The 300 Most Common Words
by Bruce Sterling

The Host Cell’s Endoplasmic Reticulum Proteostasis Network Profoundly Shapes the Protein Sequence Space Accessible to HIV Envelope
Jimin Yoon, Emmanuel E. Nekongo, Jessica E. Patrick, Angela M. Phillips, Anna I. Ponomarenko, Samuel J. Hendel, Vincent L. Butty, C. Brandon Ogbunugafor, Yu-Shan Lin, Matthew D. Shoulders in bioRxiv

Competition along trajectories governs adaptation rates towards antimicrobial resistance
C. Brandon Ogbunugafor, Margaret J. Eppstein in Nature Ecology & Evolution

Scientists Need to Admit What They Got Wrong About COVID
C. Brandon Ogbunugafor in WIRED

Deconstructing higher-order interactions in the microbiota: A theoretical examination
Yitbarek Senay, Guittar John, Sarah A. Knutie, C. Brandon Ogbunugafor in bioRxiv

What Makes an Artist in the Age of Algorithms?
C. Brandon Ogbunugafor in WIRED

Not mentioned in this episode but still worth exploring:

“Part of what I was getting after with Blackness had to do with authoring ideas that are edgy or potentially threatening. That as a scientist, you can generate ideas in the name of research, in the name of breaking new ground, that may stigmatize you. That may kick you out of the club, so to speak, because you’re not necessarily following the herd.”
– Physicist Stephon Alexander in an interview with Brandon at Andscape

How Afrofuturism Can Help The World Mend
C. Brandon Ogbunugafor in WIRED

The COVID-19 pandemic amplified long-standing racial disparities in the United States criminal justice system
Brennan Klein, C. Brandon Ogbunugafor, Benjamin J. Schafer, Zarana Bhadricha, Preeti Kori, Jim Sheldon, Nitish Kaza, Emily A. Wang, Tina Eliassi-Rad, Samuel V. Scarpino, Elizabeth Hinton in medRxiv

Also mentioned:

Simon Conway Morris, Geoffrey West, Samuel Scarpino, Rick & Morty, Stuart Kauffman, Frank Salisbury, Stephen Jay Gould, Frances Arnold, John Vervaeke, Andreas Wagner, Jennifer Dunne, James Evans, Carl Bergstrom, Jevin West, Henry Gee, Eugene Shakhnovich, Rafael Guerrero, Gregory Bateson, Simon DeDeo, James Clerk Maxwell, Melanie Moses, Kathy Powers, Sara Walker, Michael Lachmann, and many others...

Episode Transcription

C. Brandon Ogbunu (0s): The thing about genetic information is that it's digital. It comes in these digits. We know how it's copied. We know how it's encoded and that's what makes it the study of DNA and genetics such a cool and powerful thing. The thing is this, just because we can digitize something doesn't mean it's the most important part of the information in that complex system. It's just the piece of information in that complex system. So for example, now the environment on the other hand, because we can't digitize it and diskatize it and sometimes they have a hard time naming it. Sometimes we have a hard time characterizing it. We think, well, it just can't be real or it's not relevant, or it's not the thing we're studying.

It's like, no, no, no, no, no, no. Just because you can't study it don't mean it's not the thing that's driving the system. You just ain't thought that way. Then science is we study the thing that we can identify digitally, which I get, I sequence stuff too. But if I want to understand any complex phenotype, like how do you get antibiotic resistance? Or how do you get smart people? Or how do you get a criminal justice system? Or how do you get a championship baseball team? The digital part of that information is a key part, but it ain't the only part. And oftentimes, it isn't the most meaningful part. And so this is the framing that I've used to all my questions is there's different types of information that define how a system does the contextual stuff. That's muddy sometimes that's hard to put a finger on sometimes really isn't if you sit down and carefully, think about it in a certain way and try to measure it. And it is this dimension. I feel like that is under-appreciated across the board and studying complex systems.

Michael Garfield (1m 52s): Context is king, whether in language, ecology, culture, history, economics, or chemistry, one of the core teachings of complexity science is that nothing exists in isolation, especially when it comes to systems in which learning memory or emergent behaviors play a part. Even though this paradoxically limits the universality of scientific claims, it also lets us draw analogies between the context dependency of one phenomenon and others, how protein folding shapes HIV evolution is meaningfully like the way that growing up in a specific neighborhood shapes, educational and economic opportunity, the paths through a space of all possible four letter words are constrained in ways very similar to how interactions between microbes impact gut health, how we make sense, both depends on how we've learned and places bounds on what we're capable of seeing. Welcome to Complexity, the official podcast of the Santa Fe Institute. I'm your host, Michael Garfield, and every other week, we'll bring you with us for far ranging conversations with our worldwide network of rigorous researchers, developing new frameworks to explain the deepest mysteries of the universe. This week on complexity, we talked to Yale evolutionary biologist, C. Brandon Ogbunu about the importance of environment to the activity and outcomes of complex systems, the value of surprise, the constraints of history, the virtue and challenge of great communication and much more. Our conversation touches on everything from using word games to teach core concepts and evolutionary theory to the ways that protein quality control code determines the ability of pathogens to evade eradication, to the relationship between a human artists, algorithms and regulation in the 21st century. Brandon works not just in multiple scientific domains, but as the author of a number of high profile blogs, exploring the intersection of science and culture and his boundaryless fluid and sees shines through in a discussion that will not be contained about some of the biggest questions and discoveries of our time.

If you value our research and communication efforts, please subscribe to complexity podcasts, wherever you prefer to listen, rate and review at Apple Podcasts or Spotify and/or consider making a donation at You'll find plenty of other ways to engage with us at Be sure to check out those job listings and thank you for listening.

 Brandon. It's awesome to have you on the show, man. Thanks for joining.

C. Brandon Ogbunu (4m 34s): Oh, thank you for having me. This is quite an honor and a pleasure.

Michael Garfield (4m 38s): So a little while ago you gave a talk at SFI and it was on the environment, the idea of the environment to the degree that I considered this program context, rather than content provision. That's where I want to start. That's where we always start, which as you said on a radio lab, I do my science biographically. So let's start with your biography where you come from and what brought you into science and into the kind of questions that you pursue in your work and how you ended up collaborating with folks like Samuel Scarpino and Tina Eliassi-Rad at SFI.


C. Brandon Ogbunu (5m 13s): So I do my son's biographically. I think a lot of people have landed on that one as one that they appreciate. And I think what that means is I think we're all products of the experiences that we've had and they trickle into the things that we're interested in ways even that were maybe are not so clear or are unconscious to us. I think with me, it's a lot more explicit. I mean, I think the quickest way to describe my career, I say this all the time, but I'm basically just a copy of my mother just would a lot more opportunities. It really is the shortest cut way to get us to exactly how I ended up there.

I mean, she taught social studies and math and special education. So she was a school teacher and that's about as far as people went in her demographic in that generation born in the forties, so I was raised sick. She was curious in, is curious, loved science, loved Star Trek , was a trekkie in the sixties. And the whole night, like I had all of that really was in the Russian literature. We always just had an expensive mind. So I think being flooded with ideas was a part of what my upbringing was and loving math and science was a part of who she was.

And then I think being advocate for social justice and talking about things like class, race and gender in the household was a part of who she was. She kind of was of age during the civil rights movement. And so thinking about the African-American experience and how central that is to kind of with this country is, was a part of my household as well. So when you intersect those three things, you get who I am in terms of my proclivities. Now my specific proclivities and the things that interest that's where things that are just generational, I'm more of a child of the HIV AIDS pandemic.

Not in the sense that I was personally. I mean, I was indirectly personally affected by it, but not that anybody in my family was affected by it. That was the defining problem of my youth. I was young when Magic Johnson announced that he was HIV positive and I was a huge, huge kind of jock and sports junkie as a kid. So these were the things that crafted my imagination around what was possible. And so thinking about health and disease was always the scaffold set of problems that I thought about working on really from high school. And I think I was an underachiever in high school.

One thing about me is, and this is still true, I'm not especially competitive with other people. I don't get joy out of beating people. I just like making and doing cool things. I'm one of those people. And I think fields like, we'll get to that, but it feels like complex systems have been good for people who think that way. So I was underachieving, went to college and got my act together and I sorted between multiple career paths. That's another thing about me. I can't keep my on one job. I spend time in medicine, I spent time in basic science and in math and chemistry and all these different things, and it kind of led to this panoramic career where now I'm a basic scientist.

I study evolution and inflammation and disease. And I think those arenas allow me to think about problems as diverse as the COVID pandemic to antibiotic resistance, to kind of diseases like type two diabetes to forces like racism and incarceration.

Michael Garfield (8m 14s): Well, in order to get into your work on evolution, I kind of want to back into it because you wrote a couple of pieces reflecting on John Maynard Smith, legendary evolutionary biologist and his idea of protein space. And that gives us a really great sort of hook into talking about context through the way that you relate that to this concept of epistasis. So I'd love for you to introduce epistasis and to talk about Maynard Smith and this piece and why you found it so valuable.

And then also to talk a little bit about the research that you did with Daniel Hurdle on using that as an analogy with epistasis because that's very, very rich. And I want to spend some time on that with you.

C. Brandon Ogbunu (9m 2s): So just to extend this biographical thing about who I am, again, my mother taught me to care deeply about history. Every time I enter a field, I'm curious how the field arrived, where it arrived. I'm always been interested in that. And this is one of the things that I try to tell my trainees, the people that I'm responsible for as you know, I walked into evolution, mostly blind. I came out of my second year of medical school. Didn't love that because again, that's just all information in the people following protocol. That's what, maybe I could do the job, but I knew I wasn't going to love it.

So I walk into evolution and I'm like, wow. And I had had a little bit exposure from that. It turns when I spend time in Kenya studying mosquitoes, but I read everything voraciously. And one of the things about evolution, this is one of these great fields where history is a part of the craft, like the historical figures, reading the Origin of Species, learning about who the big figures were learning about the modern synthesis. And the great thing about reading history of your field is you meet people that are kind of like you or people that you wish you were like or people who saw the world. And so when it came to people who couldn't keep their mind on one idea, John Maynard Smith just shouted at me, right?

And so John Maynard Smith, I had been exposed to gang theory, John Maynard Smith, I've been exposed to evolution, the sex, John Maynard Smith. And then I read his 1970 paper on protein space where he basically, it was a rebuttal to a, kind of a quasi-creationist paper that had come out. But he basically talked about how you can think about protein evolution as we're into it, analogize it to this game called word ladder where words like evolving mutation by mutation through space. And he kind of encoded evolution as occurring through the sequence space of information that other people had kind of thought about analogous ideas.

But I think he did it in this extremely clear and basic way. That was so important for my understanding of evolution at the time. And I think another thing that was cool about it is he was one of the great mathematical biologists ever, but that paper was very, very, very simple. And so it's kinda one of those things where you always make things as easy and simple as they need to be. And so he had a very, very profound impact on how I think about things. And so it stayed with me. I kind of owed a lot to him in that paper for kind of introducing me to basic concepts about how evolution works. Now, flash forward I think during my post-doc, I did my PhD doing virus evolution and I didn't actually interact with that paper or protein space or information or computation very much at all during graduate school. And when I was a post-doc, I've read a bunch of things and had a bunch of revelations. And I was like, huh, I wonder if we can expand on this analogy to really do some real evolutionary work, to potentially teach how evolution is done and I've even created a game based on that real, real kind of digital tool and I've used that analogy, the word evolution analogy to teach fundamental concepts and evolution like epistasis.

Now, what is epistasis? Epistasis is this cutting-edge concept in modern evolutionary genetics and what epistasis is when do you have a mutation. That does one thing. Say for example, you've mutation A is responsible for a phenotype mutation. B gives you a different phenotype, but when you combine them, you get a phenotype that you could not have predicted from individuals. So it's this surprising interaction between things. And I think in complex systems as a whole not linear interactions is one of the fundamental kind of pieces in characteristics and descriptions of a complex system.

For example, emergence as a phenomenon comes because you stick things together and something summarizing emergence. So I think epistasis is this manifestation of kind of features of a complex system that manifests in genetic systems. And I've been able to use this basic word game. Word ladder, where you basically moving from one word to another changing one letter at a time, I've been able to use this analogy to think about measure, teach, and even develop new tools for how we think about episode states. So that's been really, really cool.

Michael Garfield (12m 55s): So there are so many different twigs off of this, or like, you know, mycorrhizal branches off of this, into other work that we've discussed on this show. And you know, one of the ones that comes to mind is SFI president David Krakauer is really interested in fundamental theory of intelligence and in particular how people become experts in games like the Rubik's cube, which you know, is kind of literalization of the hypercube that you're talking about across which you find paths, or like the rugged landscape across which systems evolve as they adapt.

So I remember seeing him give a talk last year in which he was saying that basically what defines smart or expert agents from stupid agents. And he's kind of notorious for talking about how there needs to be a fundamental theory of stupidity, not just a fundamental theory of intelligence, is the ease with which a system will find that strategy, but like a big piece of what you're talking about here and I think a piece that is crucial in thinking about this stuff is again, the environment and the context and the way that we don't exist, sort of in isolation. We ourselves are not stupid.

You made this point in your talk when you were talking about how similar you are from your mother, how much more opportunity you have. And we talked for folks that have been listening to the show for awhile, with Matthew Jackson, whose work on social networks and the dynamics of power and social networks have everything to do with this term between centrality where you find yourself on the network and how basically if you want to improve someone's position or their status in society, you just move them to a different neighborhood. You don't just like give them a bunch of money. It doesn't do them any good. You've got to move them somewhere else.

And so, I just want to throw one more log on this fire and then toss it back to you to riff on which is there was a famous paper in artificial intelligence that came out a few years ago, Alexander Matt Turner that optimal policies tend to seek power. And it was talking about how basically that this has to do with work that you've done. And maybe we're kind of like jumping ahead here, but work that you've done on the evolution of antimicrobial resistance and on the way that viruses like HIV evolve in response to stress where they were basically saying that in a sense, again, power seeking or the ability to flatten or smooth out the rugged fitness landscape available to an agent, or, an organism is what kind of determines its intelligence, or it's like a proxy for intelligence.

And so AI is going to be this thing in their estimation that is basically a jail breaking algorithm that's constantly trying to escape its confinement. And again, you talked about this, you talked about this on radio lab, on the liberation of RNA. So I would love to hear you go on this kind of thing and the role of one sort of placement in systems in the way that we think about the intelligence of that agent and how that shows up in your other research.

C. Brandon Ogbunu (15m 59s): I love it. I love it. Like for 15 reasons. I mean, my goodness, you expose yourself to so many ideas, you consume so many things. It's just so impressive how you've been able to triangulate this. I got a lot to say. You talked about my relationship with my mother and how I had a very good example of how context and environment in history influenced opportunity. So I did not need any teaching to tell me that success or intelligence or any of these traits that we associate with our ability to kind of hang out in these fancy places that they are kind of profoundly influenced by our context in who we've been around and the opportunities.

I had an example for that at home, flat out, I saw somebody who was computationally gifted, get no opportunity flat out. I saw it every day. I saw somebody get disrespected in my house. So I had a heads up and so, yeah, I've carried that one forward. So the question is, what is it that I've done with this interest in how the environment and how have I actually tried to fold that into my research program and, or unfold it and wrap it around how I think about complex systems? And this is how I think about it. If you think about genetic systems, which I love, and I studied, I was trained in population genetics.

The thing about genetic information is that it's digital. It comes in these digits. We know how it's copied. We know how it's encoded and that's what makes it beautiful. That's what makes it kind of the study of DNA and genetics such a cool and powerful thing. We can identify a string of information. We can associate it with certain outcomes and that makes it so powerful and beautiful. The thing is this, just because we can digitize something doesn't mean it's the most important part of the information in that conflict system. It's just the piece of information in that complex system.

So for example, now the environment on the other hand, because we can't digitize it and democratize it. Sometimes they have a hard time naming it. Sometimes we have a hard time characterizing it. We think, well, this can't be real or it's not relevant, or it's not the thing we're studying. It's like, no, no, no, no, no, no. Just cause you can't study don't mean it's not the thing that's driving the system. You just ain't thought that way. Then science is we studied the thing that we can identify digitally, which I get. I sequence stuff too. When I took the sequences on my computer, I look at those and I go, ha that's why that, but if I want to understand any complex phenotype, like how do you get antibiotic resistance?

Or how do you get smart people? Or how do you get a criminal justice system? Or how do you get a championship baseball team? The digital part of that information is a key part, but it ain't the only part. And oftentimes it isn't the most meaningful part. And so this is the framing that I've used to. All my questions is there's different types of information that define how a system does the contextual stuff that's muddy sometimes, that's hard to put a finger on sometimes, really isn't if you sit down and carefully, think about it in a certain way and try to measure it.

And it is this dimension. I feel like that is under-appreciated across the board and studying complex systems. You think about something like genetics, the way geneticists historically talked about genetic. You talk about Mendel's experiments. Talk about fundamental grid experiments. They almost talk about the gene as if it operates independent of a context. Like this is just a universal truth for what a gene is supposed to do. I got a gene for hemoglobin. No, no, no, no, no. There is never been a piece of DNA that's ever functioned without a context. It's never happened. Every single one has operated in the context of other genes and other environments.

And so to fully appreciate whatever the complex system is, social, biological, physical, you have to kind of understand, appreciate this dimension.

Michael Garfield (19m 24s): So again, there's a ton there and it's always a challenge to prune it down and find the next path through this space of possible conversations. But that's exactly what I want to talk to you about because this particular work really gets to this thread throughout complex systems thinking that I love to hammer on with people which one of the first workshops that I ever attended at SFI was on developmental bias in evolution. And this is a big piece of what you're talking about here.

If you think about somebody like Geoffrey West and his work on biophysical scaling, one of the big takeaways from that work is that we have this idea, and this is reflected in the work of evolutionary biologist Simon Conway Morris is also popular talking about the convergence in evolution towards sort of particular basins of possibility and why it is that when we look the metabolisms of animals in Geoffrey West case, everything falls on this, like kind of around this one line out of this huge space of possibility.

And why is that? And in what ways are the physical constraints of organisms, as important as the quote unquote random variability of these things in the space of all possible phenotypes? One of the things that you talk about in this piece is in reference to John Maynard Smith, talking about how selection has to operate in such a way that the path that you're talking about, whether it's the protein space path of the evolution of letters, sequences, or bit strings, they can't pass through non-functional intermediate.

And so you functional molecules that you say are not dispersed randomly through spaces of possible sequences, they're clustered in networks so that natural selection serves as an effective search algorithm for locating biophysically viable sequences. So when people are talking about, you have a number of awesome columns, one of which is at wired, you have a very adroit way of communicating things in terms of pop cultural references. So I don't feel totally out of place here bringing up stuff like Rick and Morty, where they have like an alternate universe in which everyone is like a piece of toast or a share or some nonsense.

And it's like, okay, that doesn't happen. Why is that unlikely? And your work gets into this. And so there's that piece of it. And then another piece that I feel like is worth commenting on here is, you know, there's this popular cause we're talking about fitness landscapes, there's this popular idea that was espoused by Stuart Kauffman about the adjacent possible and how as time goes on and evolution, the adjacent possible expands because you've got more and more things interacting with each other.

And so it's just sort of an explosive recombinant space. So the space of actual and possible meaning itself expands in this way. And so when you're talking about Maynard Smith responding to the guy's name was Frank Salisbury, the guy that eventually came out of the closet as a creationist, you know, the way that evolution is taught, it doesn't really emphasize this. It doesn't understand specifically, you know what, Stephen Jay Gould called acceptation or what colloquially be understood as remixing comes into the way that order emerges from the interaction of all of these things and why it looks like evolution falls uphill.

And so that's just a ton for you to play with, but I'd love to hear you talk about that. And then from there we can get into, I think, a little bit more about the way that you studied this stuff, the work you did with Google engrams and the implications. This has communicating complex ideas.

C. Brandon Ogbunu (23m 1s): No, all amazing stuff. Wow. So I think when it comes to like Stuart, Kauffman's adjacent possible, you know, I think the fitness landscape is one of these profoundly important ideas from the modern synthesis that reframed how we thought about the process of adaptive evolution. It's kind of like climbing peaks and falling into valleys or basins like you described. And I think like a lot of analogies in science it's obviously profoundly useful, but I think it might be time for us to kind of think about other analogies or expansions of it because they also limited analyses all is have their limits as well.

I think some people have little realized the fitness landscape and kind of made everything a little too rigid in my view. So I think the adjacent possible is an addendum. It's something that expands on that idea, the idea that, all right, it's not just kind of singular solutions, but that, that space of possibility expands is that there's more potential solutions to a given problem. I think that's really cool. I think this is more recent idea of the fitness seascape. So the fitness seascape, this notion people use it differently, but I interpreted as this, I notion that it's actually something that's constantly moving, that’s shifting balance, which is related to the original idea as well.

But the idea that environments are constantly shifting that the problems that evolution has to solve are not static ones, but complicated ones or the adjacent possible. All of these things are applications of how the environment influences how we think about this problem. So what I'm saying is I think the next iteration of these models in formulations will invoke things like the environment and epistasis much more rigorously because that's actually how Lee's problems work. They're not simple static problems. They're much more complicated dynamic.

That doesn't mean we can't study them. It just means we have to kind of think about them a little bit differently. So that's how I feel with regards to that. I think protein space is a good example. You get the same type of information in protein space as you can get in a fitness landscape. You can actually take protein space, which is a word going from word toward a gore to gone to gene you're moving through and moving letter by letter. That's a lot like a path quote unquote uphill in an adaptive landscape. I think what I identified in the original protein space analogy was I said, okay, John Maynard Smith came up with this beautiful analogy to explain evolution.

And as I explained in the article, I was really, really excited to hear that Frances Arnold who won the Nobel Prize in 2018, talked about this paper very centrally in her quest to understand how to evolve proteins using selection. So my point is it's been a profoundly important analogy for a lot of people, but what I said was, okay, can we add to it? Can we add to them? I'm always trying to take the analysis that we use to take the tools that we use an add a level to them so that we can now understand something even better. One of the limitations of the Maynard Smith analogy was there was no quantitative information in the words. It was just a word made sense.

So it didn't, it was binary where it made sense to it didn't or it made sense didn't. And so what I said to myself, I had read this paper on Google engram. And for those who don't understand that space of this project, where once of science is scanned, all the books released in a given year, like 1800, 1850, 1842, what have you? 1928 all the way from basically from 1800 to 2000 and had calculated the number of times that every word was used that year. And so when I saw that, I said, okay, well I have numbers now I can associate with the words in protein space, in the war to gene thing. And so now I can actually transform us into a real fitness landscape where the words now have a height associated with a quote unquote fitness. And that fitness would be how much the words was used in a given year. And so given this, that now I've taken John Maynard Smith original analogy, which was very simple, but used to communicate a profound point. And now I've imbued it with this other level of sophistication that allows us to study.

So I've done real population genetic theory on these word landscapes that I've created. I have a paper where I introduced a way to create, not just word the gene. You can create all kinds of four little words flipping into other four-letter words. I told you, I designed a game around this. I've taught this game in class. I now have this instrument that I can use to not only perform population genetics, but explain complicated and non-intuitive concepts like the fitness landscape and epistasis to broader audiences. And so far I've been successful. I've taught, it's been taught to Harvard undergraduates.

It's been taught now multiple semesters, Yale undergraduates taught this semester. I taught it the other day and a guest lecture. I know in a college class, I could teach antibiotic resistance with it, the evolution with it. I've legitimately use this analogy to build research questions in my laboratory. And so that's the dream to have something that has scientific utility for me as I'm able to use it to do real quote unquote, basic science. And I can also build it for, I don't want to say social good, but to be able to teach people things, hopefully introduce new people to the ideas and empower people in some way.

Michael Garfield (27m 58s): So I love that idea that you can use engram data to use the popularity of a word as a proxy for its fitness. I don't know if you ever saw Bruce Sterling wrote this sort of a Dadaist short story using the 300 most popular words in the English language only. And it's sort of like an abstract painting. It's fascinating. I'll try and dig up the link for you, but you make a point in this paper with Hartl that certain paths through, again, to really drill this certain paths through are kind of inaccessible because word to war, for instance is a drop in fitness.

And so you only way you're going to get there is I love the idea of the fitness seascape, is if the water is stormy enough, mutation rates are high enough or there's enough drift going on that you're gonna wobble the landscape and get over there. But the thing is that statistically, those events are relatively unlikely. And so even that intermediate, if it's given a chance, it's never going to appear in high abundance. So it has me wondering how we can think about this in terms of leveraging this kind of thinking as a predictive model for genotype and phenotype frequencies, or you pull out and you're thinking about this in terms of using these concepts as tools for improving the communication of complex ideas to the general public, which is something that you're obviously really talented at.

And of course we have this issue now that people like John Vervaeke are calling the meaning crisis. And it's a crisis of social epistemology of like we live in such a complex world and you've addressed this, you address in a piece that you wrote at Wired on the problems that scientists and policymakers are facing with communicating the science to the public during a crisis like COVID. And so there's this question of like, to what extent can we think of this as the seascape getting more turbulent? In what way is that an opportunity in the way that people like SFI External Professor Andreas Wagner, who you quote in some of your research are thinking about systems navigating this kind of thing.

I guess there's just one more piece to stack on this, way back when we had Jennifer Dunne on the show and episodes five and six, and she's studying food webs and they asked her back then to what degree she thought that looking at food webs could be a useful way of thinking about opportunities in technological evolution, specifically opportunities for research and investment in innovation, and like understanding where to look. We talked about that with James Evans on the show where he's using computational techniques to explore possible areas of fruitful research in between disciplines, where, like you said earlier, you know, like these are not well specified.

We don't know how to think about them quite yet. And so we're not even necessarily looking there. And so when we had Carl Bergstrom and Jevin West on the show, and they were talking about calling bullshit and how it's, how important it is to have a non-expert or a fool in the room, they're kind of like acting like noise that you're feeding into the overfit machine learning algorithm of the experts in the room where it's like, you need somebody to stand out and look in and inject a naïveté or a foolishness into this.

And I dunno, there's just probably way too much to pick out there. But I'd love to hear you talk about how we can kind of think in these ways to improve the way that we do science and, and improve the way that we talk about science to the public.

C. Brandon Ogbunu (31m 40s): I'll start with my mother, I moved through Bergstrom. I moved to Maynard Smith and I think I hit all those things. So let's see how we do here. You know, because I think there's a threat. And I think one of the things about my mother that you asked me, where did I, you know, I had it, I arrive and what was my influence. And I have a lot of amazing scientific mentors, but one of the things that was incredibly important to me as a young person was watching my mother's ability to move in different audiences and communicate well with everybody or sternly with everybody. You know what I'm saying?

I've seen my mother in a room full of suited men, take them down, cut them down. Like, I mean, like with incisive quick, witty or charm. And I was a little boy watching this and being like, wow, this woman has it. I would see her talk to an individual who is addicted to a drug and talk with the type of compassion, but also be stern. My mother famously broke up a fight in a movie theater to the point where I got street cred in my neighborhood for years, literally for years. And I'm saying that to say, being able to communicate and put your best foot forward across audiences I saw the power of that from young. And so I took that and I've taken that through my career. So I take that and I'm reading John Maynard Smith, 1970. I already know John Maynard Smith is one of the great mathematical biases to ever live, but he is invoking the child's word game to cut down the creations. He could have wielded all kinds of fancy mathematics. Of course he could have, but he didn't do that. He picked the tool that he needed to pick, to have the conversation needed to have, the same thing my mother did. You see what I'm saying? So what I would say is this challenge of how we can get ideas into the most people's heads across contexts, I would argue is the biggest scientific challenge of our time.

That's the one we need a Manhattan Project for. That's the one we need an Apollo mission for it. It is how can we get more people to understand this stuff? Because like you said, it's too much and only, right. I'm not saying we shouldn't stop trying to unify the laws of physics. Of course we should. But when we do, if I can't explain that to anybody but 14 people in front of a Hadron Collider, what's the point?

Michael Garfield (33m 47s): Just get the scientists off the Moon now.

C. Brandon Ogbunu (33m 49s): Exactly. So I believe this is a front seat now I'm also a grumpy old scientist. And so there's a scientific communication movement. And I look at that cynically too, when I'm like, I don't know if that's it, I'm not solid, but everybody needs to be rapping on Tik ToK about their work. That's not what I'm doing dances. That's not what I mean. And that don't we wrong? That's fine. That's great. Like, I'm glad that people are doing that work. What I'm saying is scientific communication is a technical frontier. We need to be having intelligent conversations about how it is exactly we're going to get these ideas into the most minds possible. You know, Bergstrom is a hero of mine. I mean, he knows that. And I think a lot of the stuff that he's done has influenced how I think about a lot of the world. So that's how I would think about it. I think this ability to be able to communicate in different spaces with different audiences to assert itself, to make things clear is incredibly powerful and important. I think it was important for me growing up. It was such a part of my identity growing up and then seeing that in the sciences that I admire has also been important for me.

Michael Garfield (34m 50s): So a week or two ago, I was lucky enough to get into conversation off of the show with Henry Gee who's one of the senior editors of Nature and just wrote this really cool book, a very short history of life on earth, where one of the threads through that book is the way that mass extinctions have acted on earth. And of course, I think about this all the time. I probably bring up episode 29 of complexity podcast, where I talked about mass extinctions, favoring generalists, and kind of punishing specialists as they perturb ecological networks talked about that with David Krakauer and Gee and I were talking about the way that this is kind of like resistance training for the biosphere.

It's a stress test that challenges our creativity as organisms. And this is where I feel like we can plug into your work that you've done on microbial resistance. And on specifically this piece, the host cells, endoplasmic reticulum, proteostasis network, profoundly shapes the protein sequence space available to the HIV envelope. Again, you're not thinking like atomistically about these single interventions, but about a complex stress impact and adaptive response and how these stress responses as you and your co-authors put it this is human unit at tuned, the levels, both chaperone and quality control mechanisms simultaneously. And again, I speak to you in your role as a representative of an underrepresented group of people. You know, it's like a cliche or a truism that so many creative innovations emerges out of the black community. Or emerges in a kind of a different way of thinking about this emerges out of island communities, like the British invasion or, popular music coming out of Iceland or Australia, that there's something about these contexts that generate creativity.

And I'm curious, given what you've written about all of this and what you've written about something, we talk about a lot on this show actually, which is the way that COVID revealed that certain kinds of science anyway, have to operate as crisis disciplines. You have to throw stuff at the wall and test things and act before you have any kind of certainty. And it's actually improving the way that we both practice and understand science as this fluid provisional exploratory thing.

So I'd love to hear how you anchor this specifically in your work on microbial evolution. And then from there kind of branch out to address all the analogical stuff I just brought up

C. Brandon Ogbunu (37m 22s): Beautiful, beautiful, beautiful. You know how to throw these nice fast balls. It's like they’re so juicy. They just so rich. Thank you for that. Thank you. So we have a lot of people at SFI, for example, to think about the origin of life. And I think I heard this lecture when I was in medical school, the lecture was born. One point was made in it. It completely changed my career. I guess these moments that we know from my time met me. It was mostly worthless, but there was a few moments from my time there that were really useful. And they were talking about the origin of life and somebody who studied the cell biologist said the DNA is not the molecule of life.

The most important kind of structure of life is the cell membrane. And I was like, ha, now of course, this person studied the cell membrane so they can see what I'm saying. It was subservient for the lecture they was given. But the point is you needed to be able to separate the information from the outside world. And that's the first time I had ever really thought about how important it is that you have this environment inside of a cell and you have to keep the order of things inside the cell. A cell is a dynamic thing.

It's not just like a membrane and a piece of DNA sitting in the middle and a couple of machinery to copy. No, no. It is a complex set of fluid dynamics and instruments that are crashing into each other and exploding and dying and living and being repackaged and energy. It's this kind of extremely complicated machine. And so I got into thinking about when we think about DNA to RNA to protein as the essential dogma as the flow of information. Well, again, to my environmental context thing, once you get that protein is the job done. No, you have to have a sophisticated set of machinery inside of a cell to make sure the proteins are folded, to make sure they get into where they need to go to make sure that bad and mis-folded ones are gotten rid of.

And so what I would say is that I hope to have this debate with Chris Kempes and Michael Lachmann and these people who study origin of life questions, let me not debate, but a fun debate about in my view, it wasn't life until you can regulate these things, you need it, all these other geniuses at SFI who study origin of life. You know what I'm saying? That was my thing. My thing is you have to be able to think carefully about how the information is processed, packaged, and discarded. When you get bad information inside of a cell, you have to have a mechanism to do that. And so that's where I got thinking about protein quality control, which is the system in place to make sure that proteins are getting out.

If a you mis-folded, you've got to go, but you might be naive. You need some help folding and the cell has these ways of sensing subtly. It's like life. Sometimes you need a little bit of help. Sometimes you need to get your behind out. And the cell has this way of being able to tell which one of those things to do. My research program is all into this now as like, how did it evolve? What is it to do to evolution? If you tweak aspects of it, how does that change protein space? So if you're not folding your proteins properly, does that change the shape of protein space?

So now I have all these types of questions I'm writing some, you know, I've written some papers about this and writing more about this, working with some people, Matthew Shoulders at MIT, Eugene Shankhovich at Harvard, a bunch of other people, Sam Scarpino, Rafael Guerrero at NC State. Brilliant. I've worked with him a lot of really amazing people. Maggie Epstein at University of Vermont, a lot of amazing people I've worked with on some of these problems. So that's how I got into the ________ thing. And that's how I've again, the biography of context. It absolutely manifests there. It's the protein quality control stuff. Now what's fascinating about the second part of what you said with regards to the kind of this cultural stuff is the thing about folding protein.

We think like protein chaperones, these proteins that help us fold proteins. One of the original keyshot protein for example, was one of the original ones. We've thought about the connection between that and innovation from the beginning, because the idea is if you have help folding your proteins, right, if me and you have a lot of help in life, okay, what does that do to our evolvability or our ability to innovate that is maybe I can carry around more baggage if I know I have help to get me right. But if you don't have chaperones around, if I got nobody to help, I got to only carry the things that are fit in a certain context.

But if I got friends around to help me out, I look at my life, look at all my flaws, I'm riddled with flaws. And the reason why I breaded with flaws is because I had a great mother. She masked me from those environment, from the deleterious effects of my flaws. Same thing with my friends in college, who protected me and took care of me, same things with a lot of my friends and colleagues and mentors. Now, same thing with Sam Scarpino. He covers for my flaws. And so now how has that associated now with innovation? And I look at that with the African-American experience, the black experience in America, that black experience around the world, for example, just as one, like you said, I love how you articulated it is it's true.

It's island communities. That's totally right. So the experience I'm gonna use is just an iteration of that. When you're in a difficult situation, you have to rely on your community. That's the way it works. You have to rely on your community. And in fact, some of the great cultural innovations in the black experience in America comes specifically at difficult times. Most recently, it's not hip hop came during the Reagan war on drugs. And so that's a time when communities needed to band together, they needed to kind of talk about the pain. They needed an artful way to do that.

And boom, you had that. And I think jazz has a kind of an analogous, you know, history. Rock has an analogous history. Bluegrass has an analogous history. The blues is called the blues for a reason. So a lot of innovation has that type of story where it's communities banding together, serving as each of the chaperones and now allowing to transform what could be a deal with serious mutation actually isn't deleterious. So now if I have proximity to crack cocaine or two drug dealers, or there's gangs in my neighborhood, that would be deleterious.

But now because of this cultural pride and pride of my community, I'm gonna have an artful and insightful way to describe this. And that's the birth of hip hop. And I think a lot of innovation shares those characteristics.

Michael Garfield (43m 16s): So this seems like a great place to plug into the paper on deconstructing higher order interactions in the microbiome, a theoretical examination. This is really cool. This is a piece where you're talking about gut flora. And typically the way that this research has been done in the past is you have these lab animals growing up in sterile environments that are like the boy in the bubble kind of thing. And then you introduce one taxon at a time and you say, okay, with H. pylori, this thing doesn't do so well.

But with acidophilus, maybe it's got better resistance, but you're sitting here and you've come up with this in silico animal model where you're able to look at the nth body, nth order interactions. And so I'd love to hear you talk about this in relationship to everything you just said specifically about one of the weird things that came up for me in this paper was I wasn't familiar until reading this, that insects have relatively fewer gut microbe taxa than vertebrates do. And I'm curious about that.

What is it about insects that's different from like mice or humans. And then the other thing was about how you find that there are sweet spots specifically at like ninth order interactions. You have the spike where it seems like the effect of these interactions is much more pronounced. And this is all connected to me with something you were just talking about, which is about innovation and the way that we think about this in terms of information theory, as surprise like Gregory Bateson talks about information is the difference that makes a difference. Yeah. Like that's, I think three different questions. I'm sorry, but like there's something about epistasis and surprise. That's one piece of it. And the other, the other piece is why different systems have sort of engendered or preference levels in which they're cultivating more or less surprise. And that of course has to do with everything you were just talking about in terms of adversity and diversity and something that's really common in SFI research, which is the benefits of diversity to solving complex problems.

C. Brandon Ogbunu (45m 31s): Oh my goodness. You read and understand so much. You incredibly impressive. Thank you for this. Like I said, you understood this work. I'm just like, that's amazing. Like you read this stuff and you're like, wow. You know, maybe we didn't write it that badly after all

Michael Garfield (45m 44s): It's heady stuff, man. There's no lie. I was, I was like sitting there like chomping nootropics, trying to get through this stuff. 

C. Brandon Ogbunu (45m 50s): No. Okay. Fair enough. Now we gotta do better than that, but nonetheless, you seem to have really gotten to the spirit of all this. And I think with regards to that work, which I'm delighted that you read this a collaboration with a couple of good colleagues of mine who studied, who were really like insect ecology, disease ecology people who formally studied the microbiota in their laboratory, Sarah Knutie at Yukon, Yitbarek Senay at University in North Carolina, Chapel Hill. And like you said, when I read those papers, I was talking to them.

It's just shouted at me the analogous problem in genetics with regards to mutations. Because again, we love to find the smoking gun thing in oh, gene for diabetes, where's it at gene for resistance. Where's it at in this case, the taxa that's associated that I need to drink in my kombucha so that I can get a good night's rest so that I can fix my hangover or whatever we want to have that taxa. But what we're learning is not unlike genetic systems where it's not a gene, it's a suite of genes interacting in this non-linear fashion that create this surprising phenotype, be it your height, be it, your diabetes risk, be it, whatever other phenotype we're interested in, mixed with an environment, the same exact thing from an informational perspective, the problem is exactly the same in microbiomes. So the idea there is, it's not that you have a taxa that's responsible for a phenotype it's that you have these non-linear interactions between taxa that are conferring the phenotype. So I think the things that we use is like risk of disease or something like that. Probably this insect can be invaded by a parasite of something like that. And certain kinds of combinations of microbes in the microbiota can prevent this or make you more susceptible.

And so we made this point completely abstractly, but just making a point that this type of system is not different than a genetic system in the sense, if you really got to understand the interaction between things, if you want to understand how the microbiota works, you're wrong. I'm not, poo-pooing it. I'm not saying it's bad, no pun intended. I'm just saying that you have to begin to embrace the complexity of this system and people that are doing it. I think there are people like Alvaro Sanchez. My colleague at Yale who does this in the microbiome? Brilliant physicist by training does work on kind of nonlinear interactions between microbiota.

So this is catching on and we weren't necessarily the first ones, but I am proud of that work. That's not published yet. I'm proud of that work for that reason. So that's where we landed there, that there's interactions. But again, it doesn't stop with genetic systems in microbiota. It goes to, I think businesses, it goes to sports teams. My view is that higher end interactions is a deep and important feature of all complex systems. Now you ask the question about what is it about certain organisms that have certain types of kind of nth order or higher order interactions between what it may be the microbes in their microbiota?

Yeah, that would, I don't know. And I'm not sure. I mean, obviously I'm going to ask somebody who studies the microbiome. I think the quick answer would be that it would be about the complexity of the diet because you know, microbes are in the diet. So I think the more things you're sampling from is probably going to contribute, but this is an empirical question. That would be a cool question. In fact, there are people who have asked that in humans and done stuff like humans who are vegetarian versus humans who are eats, or they've looked at different diet. Eric Ahm, Tammi Lieberman, people have looked at mice, Seth Rakoff. Other people have looked at mice diets and looked at how that changes the complexity of the microbiota.

And so the people asking those questions that trumps, we are seeing the signature of how diets influence and from what I understand overall, the complexity of the diet is related to the complexity, the Michael Bayada. But I think that's still a little hand-wavy. Like I can't tell you for sure. For example, if one of those diets has more higher order interactions between microbes. I think that's a level of nuance that I don't think we quite have the technical acumen in the task at the level, which is why we worked the theoretical paper because we like yo, I mean, we didn't say yo, you know what I mean? In the playbook, we're like, yo, you need to look at these higher order and registry Michaels, if you really want to get after how this highly complicated in critically important piece of life is functioning and contributing to, as we know weight loss and psychology it's as all these influences on all these aspects of who we are in order to get there and to be able to manipulate it one day, we're going to have to know a little bit more or less about what each individual is doing, but more about how they're interacting.

Michael Garfield (50m 5s): So this, for me, calls back again to Bergstrom and West, but also to conversation I had as episode 72 with Simon DeDeo where he was talking about people having different explanatory, aesthetics basically, or heuristics by which one person or another or one school of thought or another determines or decides on what constitutes a satisfying explanation and how he said basically that in a way, there's this deep congruence in thinking styles between a breakthrough physicist who offers some grand unifying like electromagnetism, he said is basically like a conspiracy theory, joining what we're seeing in electricity and what we're seeing in magnetism.

And that the difference between somebody like James Clerk Maxwell and your sort of tinfoil hat lunatic, is that they are surrounding themselves with people who think differently. There's a diversity of cognitive styles by which scientists are holding one another accountable. And that this is not true in communities that are eager to sort of reinforce the biases of a given approach. And so, when you were talking about this with West, one of the things that came up was again about the media diet.

And so this is where I want to tie into your piece that you were for Wired about how scientists need to admit what they got wrong about COVID where you say that. First of all, you say direct quote “scientific community is reluctance to come clean about uncertainties and missteps not only understandable, but even appropriate, there is a time and place to have abstract debates about the true meaning of efficacy at a time to act on the information that we have in service of the public good.” But of course, the problem with all of that, and this is something we talked about again in 42 with Bergstrom was the Harvard research on how misinformation travels faster through a network than the debunking of that information.

And so there's a sense in which if you want to think about it in terms of fake news is like a smaller particle that's able to like get through the membrane more easily. And in order to think critically about something it's making the seascape, if you will, a little bit steeper that path, the learning curve in that way, in that sense is steeper. I feel like everything that you're talking about here points to a real deep and pervasive problem, which is just that intellectual honesty and rigor.

These things are hard and they require friction. It's like telling somebody a lie to be kind of uncharitable about these things is easy Explaining why something has been rigorously checked and fought over. And people come to a consensus about something through blood, sweat, and tears. This is hard. And so I'm just curious how this reflects for you on, again, this issue of social sense-making and of how we can do better as a society with addressing the fact that cheating is always going to be a little bit, it has a kind of a competitive advantage in this respect.

C. Brandon Ogbunu (53m 25s): Are great, great questions. You know, I think we're venturing into the world that's at the event horizon between my technical views on ideas and you know, my spiritual views on ideas. I think we're at that, ironically, but they're related. And I think they all are empirical. I think while I don't really believe in moral arcs to the universe, I'd certainly don't believe in or, or a more pink areas, pink area in view of like things were just getting better. I don't believe in either of those things. I definitely don't believe in either of those things.

What I do is when I think about the misinformation problem that we're having now and how difficult it is to convince somebody that a vaccine works. I think about conversations that were had that invoke history in the United States. I mean, it was easier to believe that the enslaved were lower on the evolutionary scale and that that's just how the world was shaking out. Normally that that was natural. That was easier for people to believe because the alternatives to that required them to change.

They were view it's like, wait a minute. That's, what's amazing about reading Thomas Jefferson. You read Thomas Jefferson. Actually Jefferson kind of understood. He actually knew that it was wrong. And he said, if this is a wrong thing, we're in trouble. He was smart enough that he was able to actually begin to grasp that. I'm saying it's to say, despite that, right, you look at the reflection that the country was able to do back then. And abolition was a movement for a reason. We went to war for a reason. And even though the reasons we went were complicated and certainly Abraham Lincoln himself was complicated.

We have overcome worse before. There was a time when we didn't believe that. We don't have to go back to the 1850s and 1860s. We can go back to the 1950s and 1960s. I had this amazing conversation with a very famous scientist, a white scientist who grew up in Alabama and was talking about what segregation actually looked like. I actually never had a white person telling me what segregation was like from their perspective is one of the most profound things I've ever heard in my life. And there was a time when him doing nothing, which he did and he kind of regrets it was what people did. Meaning there was a time when believing that these people couldn't sit next to you. People would rather stand up than sit next to a person with a different race, like things that were completely like absurd. That sound today. There's a time now where that's ridiculous. Despite the backlash against critical race theory, I'm saying this to say there's been a lot of dark convenient ideas that people have believed because it's served what they were doing in a way they were living. And they've been conquered before, and it's just not going to be easy.

This one, we can conquer the war against science. We can conquer this one as well. And what it was, I think this was true for 1860. This is true for 1960. It took courageous, bold leaders and people to believe in what was possible for a better society. And so I think the thing we need to fight against is that nihilism that because we can't convince our uncle at turkey day dinner or whatever, or because we're arguing with people on Facebook and I get Bergstrom, his work talks about how much harder it is and people are double down.

I get that, but it can't be harder now than it was during abolition. You have a hard time convincing me that. It can't be harder now. It just means that we got to be more creative. We got to get better. Or for example, to make a point I made earlier about this communication has to be a frontier of science. I think what President Krakauer did with regards to SFI Press, I saw that I was like, see, that's what I'm talking about. You got to make that aside civic and technical frontier of an Institute like SFI that has always been on the cutting edge.

It's not, again, I don't have a quick answer for that. I'm delving into my spiritual views on that. We can just do better. But I do think, there are signs that we can create ideas that are more digestible, that can compete with the bullshit and convey which the bullshit, because we've done it before with ideas, at least as destructive, dark in the past.

Michael Garfield (57m 22s): So maybe it's actually that the moral arc and bends towards justice. We've got to make the road a little smoother. And that's again, like to call on what we were just talking about a few moments ago. That's maybe happening because certain things that seemed hard in the past, the shape of things is changing in order to make those paths a little less costly. It's making it easier for us to adopt these practices in the face of everything else. I talked about this with Tyler Marghetis on the show, you brought up jazz and these things as a response to trauma.

And that's one of the things that we discussed was the way that sometimes again, to the Andreas Wagner piece is like, sometimes you just have to improvise your way out of these situations. You can't play by the book. So this is where I want to get the last kind of piece I want to explore with you as a little Sci-fi.i I loved that in your column at wired, you interviewed BT, the techno artist who just unbundled himself into 24 hours of generative musicals samples. And so you come to this thing, it lives on the blockchain and you just go into it.

And it's always serving you a different kind of music depending on the time of day, et cetera. And I've been thinking about this kind of stuff for years, and it was really cool to read this conversation between the two of you, which like everything else we've mentioned is episode I'll link to in the show notes, he was talking about how cool it's going to be for artists when you can transfer their style with machine learning to an algorithm that's generating he gave the example of prince, if it's generating new Prince. Like not that like, I mean, we could probably never hear the end of Prince’s unreleased music anyway, but like, why not make a machine that can customize Prince-like music to your biometric data and give you, you know, something that suits your mood at some given time.

And you know, his vision is that this is all going to be, I guess, on the blockchain in some kind of way. So that it's automatic royalty payments are managed ethically and so on, but it gets to this deep thing, which you kind of talked about earlier and we talked about this on the show a lot, which is how fundamental research like SFI is research into the unknown. And so we don't know how to describe it in a way that offers the kind of traditional investor, a clear return on their investment.

It's not like, okay, we're asking for particular pharmaceutical results. You know, we don't know what the impact of this work might be for another 50 to a hundred years. The people that support this work are people that tend to be standing on the top of a mountain, seeing very, very far and yet, when you think about what it means to quantify a person, then you're getting into these issues like you get into where some of the crowning achievements of our artificial intelligence are these programs like AlphaGo that managed to surprise a go master.

And again, and it's this question of how do you get something that looks like a creative or an innovative response out of something that's been trained on a data set that you've got like variables that are known variables. And then again, to give a kind of like justice angle to this, then, there's so many good people. People like, you know, Melanie Moses and Kathy Powers and others at the algorithmic justice project at SFI are really concerned about the way that this is refracted through the lens of how algorithmic decision-making impacts the lives of real people in the world, how it impacts criminal justice and housing programs and whether somebody can get a loan.

And so if we're making decisions for people on the basis of the biases involved in quantifying them so that they can be analyzed with our machine cognitive adjuncts, then people are falling through the gaps in those systems. And also there's something that Prince can do that a Prince bot can't do, which is surprise you with the next album. And so that piece was what makes an artist in the age of algorithms.

And I think that this is a tip of a much bigger kind of question, which just again, to throw a historical race example on this, you know, you've got people like Henrietta Lacks whose nine consensual medical contributions have yielded immense value. And of course it just seems naive to me that BT can assume that artists would be fairly compensated in this kind of regime. So that's an invitation to get kind of real big and speculative with you. But I mean, I know you got it, so I'd love to hear you riff on that.

C. Brandon Ogbunu (1h 2m 0s): Nah, nah, I love this. And I love this pivot because I think it invokes certain things, but I think it kind of taps into a different, you know, I think like a lot of people in the SFI community and I think the sciences were supposed to be built in the future. You have to be thinking about the future in all realms. And I think what was fascinating about BT, who I met through various other social media engagements, what I respected about us, number one, he was extremely well-regarded as an artist. Like he's very, very, very well regarded musician. Making music is something he's very, very gifted that he's had a lot of success doing and he will always have success doing, but he's thought carefully, I guess, for an outsider.

Or I'm like, oh, I'm a musician. You just play music. You know, but the way that musicians are treated and rewarded and acknowledged and compensated is something that I just had taken for granted. You know what I'm saying? And so on one end, what he's talking about is you can have an algorithm that trains on Prince that gives us prince music. When we in a bad mood and we want to hear a Red Corvette song or whatever, or the Purple Rain joint, when we're in a soulful mood, it can deliver a song that's like that, that sounds artificial and cheesy.

But I think what he would say is, number one, the industry already copies. We already use algorithms. Every record executives already be like, give me one of those or, you know what you need to watch you perform. And give me, give me one, oh, that new hit song. Give me one of those. It already does that. 

Michael Garfield (1h 3m 27s): The Weeknd is just like the Michael Jackson bot. 

C. Brandon Ogbunu (1h 3m 30s): Exactly. Unless you already does that. Number one, but when it does it, that's much more pernicious than an algorithm that does it. And it's making sure that the Prince's training set is whoever did the training set gets the money. And I think that's, what's interesting. It's like if Prince’s training set it doesn't matter all these iterations or variations on them again, I just thought that was a fascinating way to think about art is his whole thing is, I mean, he wouldn't say he didn't say it this way, but like art just, isn't some abstract thing that people appreciate.

There are people in bodies and laws behind it who have to raise their families, who want to maintain the legacy who want to make sure that whether I agree with uses of the blockchain or not, or I he has been thoughtful about the manner that artists get appreciated and connected to their art. I think the NFT thing is very, very close to that. And that's another conversation, but I appreciate that step, which is why I find it intriguing. That's much whether I agree or disagree, but I find it intriguing.

And I appreciate people who are thinking more deeply about what an artist is. It's not somebody that just stands in front of a stage and plays a guitar. It's somebody who's creating something that could conceivably itself be creating something. I just think that's a super dope idea. The notion that you're bigger than the things that you've created, that someday, that they're algorithms that can get good enough and that can kind of pivot around, can evolve around the things that you're doing. I find that to be extremely dope.

So that's what I've found fascinating. And I think that he might be or ideas like that might be on to something.

Michael Garfield (1h 5m 9s): Well, yeah, agreed. And you know, that kind of, again, to tie that into SFI, we just had a really, really wonderful community lecture, the first two years with Sara Walker at ASU. And I can't hear, I can't believe we had her brought her up in the conversation yet because there’s stuff on traversing the hypercube of protein space or letter strings is so adjacent to the work that she's doing with Lee Cronin and others on assembly theory and thinking about life in this much more basic physics way, like as not dependent on a particular chemistries, but as something that we can identify through mass spectrometry, just based on the observed complexity of the data that we're getting from the spectrum emitted by other worlds.

And so, you know that this idea, you know, the string of the assembly space, she actually quoted Michael Lachmann in the talk as saying, when people ask him how old he is, he says he's three and a half billion years old because that's the informational continuity that he is currently embodying. And so in that way, like you just said in that way, and David Krakauer says this all the time. He said he chose not to have kids because he sees his cultural contributions as being sort of more significant and that he's not hung up on the idea of having biological children per se.

He has idea children. And so there's that sense in which you could say an algorithm trained on Prince is like the idea child of Prince and in the broadest sense, the kind of complex systems way of thinking about life as more informational than chemical, then you're there. You're like living on it. Anyway,I want to get before he would go. I just want to give you the opportunity to give a parting shot to people. And whether that's talk about the work that you're doing now, what you're excited about, what questions you're prosecuting with your collaborators or, and or whether you have any advice to passionate, hungry minds about how to get over the feelings of like the imposter syndrome stuff, the inadequacy, the sense of exclusion from being able to party with these kinds of ideas and questions.

You know, those are two things that I'd love to hear you talk about before we go.

C. Brandon Ogbunu (1h 7m 18s): Yeah. I think just regards to things that I think about, I just got back from this really cool workshop called in re-imagining the central dogma. It's about a lot of the things that we talked about, challenging, basic conventions and hyper simplistic thinking in biology. And that doesn't mean we completely disabuse ourselves of the whole thing. It's not the baby bath water dichotomy, but begin to kind of embrace and lean into. And this is something complex systems did first as a field is lean into the uncertainty.

It feels like chaos embraced that kind of unpredictability and create a formalized science of it. And I just think I want to do that with regards to higher order interactions. I want to do that with regards to environment and in thinking about that across a bunch of different problems. So I think my research program continues to work at the molecular level and at the social level. And we're going to continue to push on these questions for the foreseeable future. And I'm excited about that. I think when it comes to the second question about how do we get over these feelings that we have, be they imposter syndrome, be they would have you, and number one, I haven't processed right now in this conversation.

I'm thinking, oh my gosh, you mentioned Sara Walker, Sara, welcome is going to hear me stop. And I'm a gosh Sara Walker is going to think I'm an idiot, you know, because Sara Walker is so damn brilliant. She probably talks to everybody and thinks that way, you know, but it's also very nice. So I don't think, you know, well, I'm, I'm saying this to say, I always think that when I'm in these types of things, and I think there's a little bit of that, frankly, that's healthy because you know, there's a little bit of, Ooh, you know, let me make sure that the stuff that I'm saying is well organized and is fact-based. I think that the way that one emerges from imposter syndrome and the thing that's helped me is learning and seeing so many people who I admire or who did admirable things be shockingly and embarrassing.

Ain't a single person I've ever admired who was right about everything. It's never, ever, ever, ever happened. Ever. I think the difference with society is that some people get punished differently for being wrong than others. I was just at a workshop with Eric Wieschaus, Nobel Laureate. And he asked the most questions ever. I think, simple, complicated. It never ever. I mean, of course at that level, you don't really feel the need to impress anybody, but the odds are, he was like that along the way, which is how he made big discoveries.

There's no question too simple for him ever, because if he doesn't understand the basics of the question, that's your fault, not his fault. And that has become my attitude. I don't give a damn what the room is. I'll give her some of them I studied for 30 years. I know how to read and write pretty well. I'm gonna tell you that much right now. I'm no Michael Lachmann, but I know how to read and write pretty well. And so what I'm saying is if I don't understand something, that's your damn fault. And that's my attitude now. And I'll ask you and you have to explain that.

And if you don't explain that the fault is yours and not mine, and that's true for everybody regardless of where you are and what your discipline is and what stage of your training it is, or what lot in life. It's like explaining things is the job of the person. Now, of course it takes effort on your part. You got to do the reading and what have you. So I would say that number one, with regards to imposter syndrome. I think the other thing about fitting in is so many of us, and this is one thing that I learned having been in these fancy schools since after college.

So many people are, and I learned this in medical school early. I think I had a lot of really amazing medical school classmates, but so many of them were driven entirely by other people's opinions. Like that was the only reason they woke up. And it was kind of fascinating for me to see. It was like, wow, like you really put the career so that people at a cocktail party would be impressed. Like that's, that's enough for you. And so when I learned how people are driven by other people's opinions, I was both frightened and inspired.

I was frightened because it's like, wow, I can't believe I'm around these people. But number two, it was inspiring for me because I make my own goals. People ask me, why do I write for Wired? Why do I write about this? Because I want to how's that going to be looked upon by your colleagues? I don't know. You ask them. That's why, that's my answer. What's that going to be like for your tenure file? I don't know. I'm not on the committee. Ask someone on the committee. I got one life to live. I got one bite at the apple here. I'm going to do the things that I think are important. I'm going to do the things that I think are original and help us understand life and each other.

And if I can do that at the end of my career, regardless of where it gives me, I feel satisfied. So I can't tell anybody to have my perspective, but what I can say is that living for other people is a bridge to nowhere. And once you disabuse yourself for that, there's so much fun to be had, so many discoveries to be made. So many things to learn so many cool people and ideas to embrace.

Michael Garfield (1h 12m 22s): That's a fabulous place to end it, man. Brandon, this is so great. I'm so glad that you came to SFI, that I had a chance to talk with you. I really hope you're back soon. I really hope it happens. 

C. Brandon Ogbunu (1h 12m 35s): Look, my trip to SFI changed my life, flat out. I don't know how old you are. It doesn't matter. I remember there was an old, alright, so did you like grunge back in the day? There was a old video called Blind Melon, but a bee girl. It was a bee girl and it was a video and the bee girl she's hopping around and she's looking like doing the bee dance and everybody's looking at it like she's crazy. And at the end of the video, she finds this bee island and everybody has a beat. That's how I felt like when I was at SFI, like I'm like, man, everybody here is just as crazy as I am.

C. Brandon Ogbunu (1h 13m 5s): You know, but of course, head hurting. Brilliant. You know? And so it was incredibly empowering for me to be around that community. And I think it told me that I got a lot of hard work to do to be as good as anybody there. But I think my way of thinking about the world is welcome somewhere. So thank you for that. Thank you for everything you're doing at SFI. This podcast is the best of its kind. You're doing a credible work, reading, everything you are so SFI and the way you do your job. It's just cool to see.

Michael Garfield (1h 13m 34s): Thanks. I mean, I think we were just talking about the soul show. It's like, you know, I'm just some random fool. If I'm wondering in the woods, I'm lucky to be surrounded by wonderful people. And that includes you, man. So again, thanks. And I look forward to getting this out there. Take care.

C. Brandon Ogbunu (1h 13m 48s): All right.

Michael Garfield (1h 13m 51s): Thank you for listening. Complexity is produced by the Santa Fe Institute, a nonprofit hub for complex systems science located in the high desert of New Mexico. For more information, including transcripts research links and educational resources, or to support our science and communication efforts. Visit