Ep 96: The network motifs that run the world (with Uri Alon)
What are network motifs, and how and why do they matter to biological networks?
On this episode, we talk with Uri Alon, systems biologist at the Weizmann Institute of Science, about biological networks. In the early 2000s, Uri discovered some of the fundamental characteristics of these networks and, since then, has worked to understand networks across different levels of biological organization. His work shows that, from genes to whole organisms, networks are filled with repeating patterns of connections known as network motifs, such as feedback and feedforward loops. We talk about how the motifs arise and what they mean for the performance and evolution of the systems in which they’re embedded. Moving farther afield, we also talk about how scientists can productively move into new areas, and how Uri teaches early-stage scientists to leap confidently into the unknown. And a bonus: Uri sings and plays guitar for us!
📚🐛 Big Biology Bookshelf
Cover photo: Keating Shahmehri
-
Cameron Ghalambor 0:00
Do you love science?
Art Woods 0:01
Of course you do.
Cameron Ghalambor 0:02
So if you want to get the freshest possible science directly from the amazing early career researchers who performed the work join Dr. Jonny Coates on the Preprints in Motion podcast every first and third Wednesday.
Art Woods 0:14
Discussions range from COVID-19 to Lego microscopes to squid RNA editing. Each episode also reveals what it's really like to work in academia and the wide range of experiences that early career researchers face.
Cameron Ghalambor 0:27
But it isn't just science, Jonny talks with some of the biggest names in the open science arena to discover the future of scientific communication.
Art Woods 0:34
So if you enjoy learning about fascinating science,
Cameron Ghalambor 0:36
If you want to hear about the newest scientific discoveries before anyone else,
Art Woods 0:40
And if you want discussions on how to improve science
Cameron Ghalambor 0:43
Then subscribe to Preprints in Motion wherever you get your podcasts
Art Woods 0:47
Visit the team at Twitter at motion pod or online at preprints in motion.com.
Art Woods 1:00
Okay, Cam, I did it. I asked ChatGPT a question relevant to today's episode.
Cameron Ghalambor 1:05
Really? What was it? Why does Art say "like" so much on the show?
Art Woods 1:10
Umm like no. I asked about one of the main topics that we discussed with our guests today, I asked ChatGPT what is a network and here's what it said slightly abbreviated: "A network is a group of interconnected devices such as computers, servers, or other electronic devices that are connected to each other and can communicate and exchange data with one another." In essence, networks allow devices to share resources, communicate, and exchange information and cooperate to perform tasks.
Cameron Ghalambor 1:40
Perhaps not surprising that the AI assumed you were talking about computer networks, although really the phrase "that are connected to each other and can communicate and exchange data with one another," I guess that also describes biological networks.
Art Woods 1:55
As does the idea that networks share resources, communicate and exchange information. That's basically a way of describing the functions of gene networks or organ networks. But we still understand pretty little about how biological networks are structured and how they evolve.
Cameron Ghalambor 2:10
Today's guest Uri Alon is a professor of biology at the Weizmann Institute of Science near Tel Aviv. Uri's famous for discovering in the early 2000s. Some of the fundamental characteristics of biological networks,
Art Woods 2:21
We spend a good chunk of the chat talking with Uri about what are called "network motifs." These are smaller patterns of connections that occur over and over again in larger networks, including things like feedback loops, feed forward loops, and bi-fan motifs, in which two source nodes cross-regulate two target nodes.
Cameron Ghalambor 2:40
We talk about networks at three levels of organization. First, at the level of genes, we touch on questions about why there's so few underlying motifs, how evolution produced them, and their consequences for how genetic regulatory systems evolve.
Art Woods 2:54
The second level of organization is about whole animals. Uri has been thinking about whole animal networks a lot more recently in the context of human physiology, diseases and the challenges that we face in our modern exercise, poor but calorie rich lifestyles. We also asked about whether network properties from the gene regulatory and cellular levels scale up readily to explain network properties in whole organisms.
Cameron Ghalambor 3:18
The highest level we discuss is the networks formed by the scientists themselves.
Art Woods 3:23
It's really not an exaggeration to say that Uri himself has played a key role in catalyzing the formation of new kinds of scientific networks, which have since transformed how biology is done.
Cameron Ghalambor 3:34
When he discovered the basic kinds of network motifs, Uri relied on several new public databases of genetic information on E. Coli. That approach set the stage for the development of new models of widespread data sharing and cooperation among scientists, approaches we now take for granted but those had to be invented along the way.
Art Woods 3:52
We also talked about how he trains students to work at the interface between the known and the unknown
Cameron Ghalambor 3:57
In part by approaching science as if it were improv comedy.
Art Woods 4:01
And last thing you may not know that Uri also sings and plays guitar. To get an outsider perspective as it were, I asked ChatGPT what does Uri Alon sing about? Its response? Uri Alon is not known to be a singer.
Cameron Ghalambor 4:16
Haha. Okay, proof that you shouldn't trust answers from ChatGPT.
Art Woods 4:21
All you gotta do is listen to the episode
Cameron Ghalambor 4:23
In which he performs three of his songs for us.
Art Woods 4:26
I'm Art Woods.
Cameron Ghalambor 4:27
And I'm Cameron Ghalambor.
Art Woods 4:28
And this is Big Biology.
Art Woods 4:42
Uri thanks so much for joining us on the show. We're just super excited to talk to you on Big Biology and, you know, thanks for working out the timing of this, this conversation has been a long time coming. We want to cover a lot of ground and talk about both science and sort of some of your recent thinking about the process of being a scientist, and what it takes to be creative and to push the boundaries of fields, and so we thought that might be a really nice place to start. Maybe just talk about your ideas about the mindset of a creative scientist, so sort of like, what does it take intellectually and emotionally to do a sort of new creative science that pushes the boundaries?
Uri Alon 5:23
You know, we, when we learn science, we're taught about science as if there's a question an answer, and science is like a direct path. And when I was just beginning, in science, as a graduate student, I faced, after a few months, a kind of situation where nothing seemed to work, it was very depressing.
Art Woods 5:41
I know that feeling.
Uri Alon 5:43
A lot of life is like that. But for me, it was devastating, I couldn't get out of bed in the morning, I couldn't shave, I couldn't function, because my dream was destroyed, I obviously can't be a scientist, because scientists never get stuck. I can't be like Einstein, or Newton or any other scientist. And so I felt unworthy to cross the threshold of the university. But then I felt like I was able to discover something, you know, some small secret of nature. During my PhD about turbulent mixing, I was in physics.
Art Woods 6:11
Wow
Uri Alon 6:12
And that was a big high. But then again, next second project, I got stuck, totally desperate. And with enough support, I made it through and I started realizing there's a pattern that we just don't talk about, which is the fact that science is done by human beings with our own emotional trajectories that are different from a straight arrow from A to B. And that got me really interested in, luckily, I was also learning improvisational theater at the same time. So like these two lines, and there a lot of concepts about going into the unknown together, because the only thing you learn is to support each other on stage, make each other look good, give each other ideas and leads for creativity. And so I tried to kind of start forming a language for myself inside science, about the process of science, researching and thinking about it a lot, writing about it. And it's become like, my passion now to be a change agent in science, to contribute time to basically the vision is the scientists will have words to talk about our process, like active listening, or being in the cloud, not only hydrogen bonds and, you know, genes and stuff. And then when we have that language, I think we can change our priorities and the way we promote people, mentor people, what we teach, and how we write grants, and etc. And science will be more aligned with value
Art Woods 7:37
Totally. So maybe let's just put some, some specifics on those bones. So, you know, it sounds like you kind of arrived at a process on your own, via these early projects, that allowed you to develop some kind of resilience. So if you were to talk to a young scientist, and advise them on how to do that project, and some more systematic way, what would you say other than just go through it?
Uri Alon 8:01
So first thing I present to students is the concept of the cloud of research, so instead of A arrow B, A is a question B is the answer, research is a straight path. I say A is the question B is the answer. You start going and you get stuck. I present a schema, right? You're stuck, you're stuck, you're stuck, you're stuck, until you reach a place linked with negative emotions, that we're going to call in our group, the cloud. Know you're lost in the cloud, and you can be lost there for a month or three months, or six months, or a whole thesis or whole career, you die, you're reincarnated as a scientist, you're still in the cloud. But if you have enough support, suddenly, you see maybe through the cloud a new answer, let's call it C, and you decide to go there, and things don't work, but you're gonna get there. And then we write a paper, A arrow C, which is a great way to communicate as long as you don't forget our path. And there's no way to get rid of the cloud it's built into, I tell this to the students just because it's not obvious in advance, right? It's part of our craft, because this cloud stands at the boundary between the known and the unknown. And in order to discover something truly new, at least one of your basic assumptions has to change, which is uncomfortable for human beings. And so if you tell me Uri I'm in the cloud. I'll say you probably feel horrible, but I'm kind of happy because maybe we stand a chance to find something new finally we're at the cloud stage. And then as a mentor, I know what to do instead of ignoring you or ordering from Sigma, let's say, a witch to apply some psychological pressure. We know that fear kind of restricts the playful curiosity you need to get out of the cloud. I'm going to find out with you what it means for you to have solidarity and hope, which are the emotional foundations for this creative and playful curiosity. And solidarity and hope mean different things for different people. But it could be maybe it's time for you to go on vacation, do something else right now. Maybe it's time for us to meet more and work on just being together during this. Maybe it's time to talk to people who will ask you so what are your next ideas? And suddenly a voice inside you will find the answer. Maybe it's time to go to experts, maybe it's time to go present at a conference.
Uri Alon 10:05
It's really interesting to me to together to work with each student and with myself to find out what it means for them to go through this cloud. Just having that concept, that word, "the cloud," is detoxifying, because doesn't mean something's wrong with you, it means you finally reached that unavoidable and natural stage of research where things don't work. And I say, in the end, that's how I frame this whole idea of graduate studies. It's finding out how your personality rubs against the unknown, and how to deal with yourself in this situation, in order to be able to go into the land of the unknown. And that's a transferable skill, because most of the problems in our world now that are A arrow B are already done by software. The places where you need a human being to go into the cloud are the interesting and important challenges we have, not only in science, also outside. But science is like a laboratory, safe place to build your personality, and remember that you're not alone in it and find that person that you can talk to, on a daily basis to as an essential ingredient through the cloud. So that's one, just having that framework is transformative for me as a mentor.
Art Woods 11:08
That strikes me is just like, you know, super healthy and productive even to have a framework like that, to be able to talk about these things. Because, you know, I think back to my graduate experiences, and there was no such thing. And you know, I was definitely in the cloud a lot of the time, but there was no, yeah, there was no way to talk about those things you just said, which seem super important.
Cameron Ghalambor 11:25
Yeah, the description of being in the cloud, I like that visualization. But it also reminds me of this paper that I recently discovered by Martin Schwartz called "The importance of stupidity in scientific research." Do you know this paper?
Uri Alon 11:42
Yeah, I posted it on my website.
Cameron Ghalambor 11:44
Oh, okay. Perfect. Yeah. I mean, I think what he describes is, you know, that when you move into the unknown, and there are no answers, it sounds very similar to basically being in that cloud. Cloud sounds a little bit nicer than, you know, feeling stupid. But I think the emotions are very similar.
Uri Alon 12:04
I did a TED talk about that, and it's used by a lot of people also beyond science to just, you know, just these words we have the words are transformative or cultures.
Art Woods 12:15
So how does this translate into how you write papers and sort of present science at conferences? I mean, you know, I think often papers provide what appears to be a very linear sequence of steps, right? Something well thought out. And, you know, you've been walking along this path, and you discovered something, and what doesn't get communicated is the fact of being lost in the cloud. So do you try to also incorporate some of that into the way you structure papers?
Uri Alon 12:43
I don't. I write papers in the A arrow C way, because I think it's very clear, even though it's not a historical record, it's very clear. And it's actually an intellectual effort to try to reduce cognitive load for the reader in every way, including how to structure the story, how to make the graphics, etc, in order to help a certain message come through. And I am interested in how is culture science can add the backstory. So for example, in iGEM, competitions, which are going competitions, were well just build synthetic circuits inside cells. In order to apply for the competition, you have to write down all your experiments that you did, including the failed ones, and put them in a public database. That actually becomes a kind of a figure of merit, because you see, look how much work I've done, and it saves scientists from being doomed to go back on the same path again, and again, because you can find out experiments, they haven't worked, etc. So I think that we have, you know, the technologies available for science to go to the next level and record the chronological path as a second document with each paper, and that's one, I think, intriguing idea for the future.
Cameron Ghalambor 13:53
So I'm curious, you know, this spending time in the cloud, I mean, we can see it in our own research paths when we ask questions and the challenges that arise, but also when we kind of move between disciplines. And so this is something Art and I were talking about, you know, our own kind of arc within science and the challenges we faced as we've moved between different disciplines. And I'm curious how you sort of have approached those kinds of challenges like does it feel very natural to move between, say, physics and biology?
Uri Alon 14:31
It's a key question and, again, underexplored as a community. Yeah I moved from theoretical physics to experimental biology in my postdoc, and spent a lot of time working on bacteria and gene networks and recently I switched again and now I'm working on human physiology and medicine. So I've done it several times and trying to reflect on it. There are kinds of stages. One is when I feel mature enough in the field I'm already working on that I feel that if I'm presented with a new problem, I'll probably be able to do something reasonable. And for me, not for everyone, but for me, that starts the kind of butterflies in my stomach and a tingling feeling that okay, it's time to change. I think for me it is, because I feel like I need to utilize my time on Earth in a way that's kind of unique. It's something that other people can't do. But that's just me. I mean, there's many other kinds of motivations. And then I open up a window for searching for what that field could be. And that's a process that takes years. And when I start entering a new field, there are a lot of fears. I don't have my colleagues and my conferences and my grants and my papers. I'm a newcomer. And so I've learned some things about that. The first is that when people in the field see a newcomer, there's a bias towards seeing them. Who's this new person? What is this for a new person up to? So I reach out to them, and I ask them to teach me. So here's a paper we're thinking of writing. And I have an accent. I'm not let's say a biologist, I'm a physicist I'm speaking with an accent. So I'd like to help me you to help me find out how to improve my accent, and what are my pitfalls, and then you turn a potential enemy into an ally, just by approaching them and learning a lot. So you need to learn a whole set of paradigms and ways of doing things. The papers you read are probably so old now that the whole field is engaged in disproving them, but you don't know that from the literature, so you need to talk to people. And the second principle I found is what's called beneficial ignorance. So I come into a new field, I'm ignorant. And at first, that gives you an advantage because you're not locked into the ways people have been doing. But then you get the sense, okay, now that I've kind of discovered something, I think is new, now let's read and try to go as deep as possible. So when is it that you read is an interesting timing question, and get feedback from experts, etc. And it's really learning a new social context with its own languages and pitfalls that I find just fascinating.
Uri Alon 17:12
I was going to ask, you know, when you shift from one field to another, what do you think you take along with you from the old field to the new? And is it like specific concepts and sort of mental structures you've developed? Or is it like, ways of thinking? Or is it none of that, and you're just starting fresh in the new thing?
Uri Alon 17:31
You bring precious things from your old fields. So from physics, for example, it's not only the tools of mathematical modeling, knowing how to tell the essential from the non-essential to build a minimal model, I think it's the deep belief that physicists have is that nature has an angle where things look simple. That's because the solar system really does have an angle where things look simple, right? Newton's laws. Now that's not guaranteed when you study the thyroid gland, or, you know, bacterial transcription networks. Nobody said that there's going to be any angle of simplicity, because these things evolved to function that for scientists to understand them, but you still bring that belief. And since you bring that belief, you try, you try to find the angle simplicity. If you don't try to find angles of the city, you're guaranteed not to find it. And sometimes you don't, but sometimes you do. That orientation, I think, is just an example of what you bring from one field to another. And I think I'll do my first song here about moving, changing fields.
Cameron Ghalambor 18:28
Okay
Art Woods 18:29
Fantastic. Yes, please.
Uri Alon 18:31
When I moved from physics to biology, I knew nothing. The only thing I knew about biology, basically, was what I read on the back of a cereal box like proteins, carbohydrates, and there was a person in the next lab called Mike Surrette, who was a postdoc. And he took me under his wing and made me feel like no question is stupid. And I just wouldn't be here without Mike Surrette. So this song is about Mike. And if you have someone like that in your career, so I invite you to think about them when I'm singing. And since Mike is from Canada, the music is by another Canadian Leonard Cohen.
Uri Alon 19:11
Mike takes you down to a place by the centrifuges. It's your first day in the lab. And you don't know what a centrifuge is. And he has these precious flasks, and you drop them and they shatter. And you look at him quite meekly, but he says it doesn't matter. They were only the controls. And he gives you of his buffers, and he gives you of his strains. And you wish you had his genome, or at least you had his brain. I came to him one morning with an idea I had been forming my transformants weren't transforming and my swarmers were not swarming. I said, Mike, I'm a failure. I'm going to work in happy burgers. I think I'm quitting science but said, now, don't be hasty. You see, science, like the cafeteria, sometimes nasty, sometimes tasty. And he soothed you so discreetly, and you trusted him completely and your mind, it has been freed. And you know that somewhere, something will succeed. Now Mike is packing his papers in a folder, there's a knapsack on his shoulder, his pipette is in its holder. And as he leaves the floor, the shakers all stop shaking, the columns are all dry, the autoclave stops baking, it'll never be the same. And you know that you must keep him or at least that you must clone him. And you know that you will miss him. And you know that you will phone him all the time. Mike takes you down.
Art Woods 21:19
Awesome. Love it. Centrifuge. That's a hard one to get a rhyme for, huh? So are you still in contact with Mike?
Uri Alon 21:32
Yeah. And he's now, he was a postdoc, now he's a PI in Canada. But the main thing is that those relationships aren't necessarily with your mentor. They could be with other people in the group. And they have no official task to help you, but I think a lot of scientists depend on these interactions. That's why I sing the song. I do it in conferences and meetings, just to highlight that these interactions are what makes science work in a certain sense.
Art Woods 21:58
Yeah, yeah.
Uri Alon 21:59
So when you're moving fields, I think that's another place where they're essential. And then they're the friend, informant ally, that can understand you that wants to help you.
Art Woods 22:10
Yeah, yeah no, that's super great. You just talked briefly about this a few minutes ago about using improv, and your experiences in improv, maybe say a little bit more about that. So how does improv intersect with science?
Uri Alon 22:24
So, as you say, science demands going into the unknown with a group of others. I mean, a lot of science is done alone. But also a lot of science is done together. But and that hand science has no concept we don't, it's like from the neck up, we don't talk about the emotional part of it and how to do it. Improv is the opposite. You go, I mean, like science, you go into the unknown, because you are on stage. You don't know if you'll be play a dad, grandfather, a mafia guy, whatever, you can play a dog a table, you don't know, you have no script. The only thing you can prepare for is actually the emotional group process. And we can learn from that, I think, because this can supply the group process and the words about the process we need in science, right? So for example, the big principle in improv is what's called saying yes, and. So imagine a different conversation where you're blocked right away, your idea is. In improv, blocking sounds like this, you go on stage, and you say, here's a pool of water, and another person says, no, that's just the stage there's no pool of water here. That's blocking. Okay, it's dead. But if you say, here's a pool of water, another one says, "Yeah, let's jump in. Oh, look, there's a whale. Yeah, let's grab it by its tail. It's pulling us to the moon." That's saying yes and. It's agreeing and building. Or if I say, "Dad," and you say, "I'm not your dad," it's over. I say "Dad," you say "Do your homework." I'm saying yes and, right? So now I'm dad. Now imagine that skill in a scientific conversation. You take a person's idea, build on it, build on it, build on build on it, and you get to the moon like that. So that micro interaction could be the difference between a fruitful co-creation, and something that fizzles out, leaving you feeling maybe I wasn't heard. And so that skill is huge. And this whole mindset of going into the unknown together, I would say, is a metaphor for me, to take from the improv world into science. And in science, science could have different metaphors. It could be a battle, right? I have my position, it's entrenched, or it could be whatever. But if it's a going into the unknown together, collective storytelling by community, groping into the unknown, is a very generative metaphor.
Cameron Ghalambor 24:35
Yeah. So this, it sounds like you know, the idea of metaphors can be very important and powerful in helping move things forward. But I'm also curious if there are cases where you find that metaphors can also be inhibiting for progress because they may, for example, give you a misguided, simplistic view of what's going on.
Uri Alon 24:57
So metaphors, actually historically the Royal Society, 16th century tried to build a language without metaphors. That project failed. But nowadays, it's considered the metaphors are major tool for scientists, because when we go into the unknown, one of our only tools to think about a new unknown object is to take something we know and entail some of its properties onto the unknown, right? So the brain is a computer, but of course, it doesn't have silicon, it doesn't have USB ports. And so I think the danger that you mentioned about metaphors is when you're not mindful of the metaphor you're using and its limitations. And if you're mindful of what metaphor you're using, which can take actually some introspection, if you are mindful, it's very powerful. Because they can be misleading, but if we understand them, the powerful tool.
Cameron Ghalambor 25:43
Yeah, I think it's a really important point, because I can't recall having these kinds of explicit conversations either with colleagues or students about the metaphors that we use every day, they're just sort of there. And I think they get incorporated into our worldviews of, you know, how we approach questions and how we interpret our results. But I think we, yeah, don't really explicitly talk about them. And that seems like something really important.
Uri Alon 26:14
I just want to mention two references, maybe one is Lackoff and Johnson's book, Metaphors We Live By. These are two philosophers that just expose the importance of metaphors in our lives. Another one is Evelyn Fox Keller, who's actually a pioneer of biophysics, and also a pioneer of gender studies and philosophy. And she has a book on the use of metaphors in genetics. For example, how metaphors can have a contradiction, like gene action. Gene is like an atom that moves by Mendelian laws. An action is a little homunculus, that does what the gene is supposed to do. And that helps biologists 1900 cross this paradox of a mathematical object moving by Mendel's laws and doing something like in the cell without knowing about, you know, the central dogma. So it's a self contradicting metaphor that was fruitful enough to do research with. And I think it's a beautiful analysis by Evelyn Fox Keller, and it opened my eyes.
Cameron Ghalambor 27:07
I'm a huge fan of her. Her biography of Barbara McClintock is required reading.
Art Woods 27:29
Well, hey, I think this might be a good place to transition to talking about biological circuits, biological networks, and just sort of digging into some of the biological things that Cam and I think about, reasonably often as physiologists and evolutionary biologists. So you are famous in part for a bunch of work you've done over the last, say 20 years, on biological circuits, biological networks, and what we might call network motifs. So maybe let's just start there, what \is a network motif and why do they matter?
Uri Alon 28:00
So when I came to biology around 1999, 2000, there had been decades of amazing work to find out the different proteins in the cell, and which protein interacts with DNA transcription factor to make other proteins be produced. And it's a huge amount of information and was very frustrating that you can't get from that into the functions, the wonderful functions of cells. So that could be described as a network where each, let's say protein, is just a factor for genes that make other proteins and make arrows between them. These networks were called fuzzballs, because thousands of nodes with arrows, you can't even figure. So coming from physics, I guess I had to believe that there has to be some angle of simplicity in this fuzzball. And what I did together with Shai Shen-Orr and Ron Milo, graduate students, my first graduate students, was to first build a database of all the interactions known in E. coli and in simple bacterium, and then to look at it. And we know what we discovered is this complicated network is made of recurring instances of very simple subgraphs or circuits. So we call them network motifs, because like motifs in wallpaper, these little patterns appear again and again and basically make up the entire network.
Uri Alon 29:19
And each pattern had a specific biological function or an information processing function. So there was, for example, a kind of a triangle pattern where x regulates Z, and also regulates a regulator of Z, Y. So it's X that regulates Z and Y that regulates Z. We call that a feed forward loop. And that could be each of those arrows could be activation or repressing, so there's three arrows, two to the power three is eight so there's eight possibilities. But only two of them appear: the coherent feed for loop and the incoherent type one feed forward loop. And one of them is a pulse generator, and the other one is a kind of delay element that responds quickly when the signal gets turned on, but slowly when the signal gets turned off. And we started like exploring, and the same logic appears in many different biological systems inside E coli, with the same wiring. And so we started experimentally tests, what is it doing and how is it important in the function of the cell. So the cell might be making a big outboard motors like a flagella, using a lot of energy. But now it's moving into a place with a lot of food, it doesn't need to swim away, but it doesn't stop all at once making the flagella, because it anticipates that soon food will run out. So you need that delay circuit to turn it on quickly, but turn it off slowly once there's a lot of food, you know, you wait to see if this food is gonna run out. So there's an evolutionary pressure that, we think, again and again to make the same kind of logical or informational processing functions, but each time on a different system inside the cell. And that's why evolution rediscovers, actually, the same patterns. A little bit like engineers reuse, just maybe a hundred, basic circuits, amplifiers and toggle switches to build all devices. So there's network motifs. There's three ones in E. coli, the same ones found in yeast, and then it turns out later work that the same motifs are found throughout life, so they're kind of unifying logical circuits, even though the proteins themselves are completely different and just got wired together in the same ways. This wasn't like an original circuit splitting up and into all its descendants.
Art Woods 31:22
So how many basic motifs are there altogether?
Uri Alon 31:25
So in E Coli, there's three. And then in multicellular organisms, there's another three, so maybe six total. And then the same idea can be applied to any network. So other researchers have discovered network motifs in brain. So we know how neurons are wired together, which patterns occur and which patterns are anti motifs, they occur much less often than you might expect. In ecological networks, and in social networks, and each kind of network has its own network motifs, right? So social networks have a lot of these fully connected triangles, where people that like each other like to be in groups and things. But that doesn't occur so much in transcription networks in biological networks.
Art Woods 32:06
Yeah. So you're saying there's not like a universal set of motifs across all networks, regardless of the medium. Like it can be context specific. Yeah, okay.
Uri Alon 32:15
Yeah, we think they expose what this network evolved to do on this level of computation. So transcriptional networks evolved to process information in a noisy world with noisy components. That might be different from ecological networks, where who knows what they evolve for, but they pass energy from, you know, from lower to higher trophic levels. By the way, there are two networks that are completely different in terms of their scale, but they have the same motifs, and that's when you look at transcription networks inside E Coli, and neural networks, neural connections, in things like worms, C. elegans. And they have the same kind of triangles that I've described, and they lack, they have the same anti-motifs and motifs. And we think the reason is that also the brain of these simple animals evolved to transduce information between noisy inputs and noisy components. And so a lot of the information,
Art Woods 33:05
The same basic problem
Uri Alon 33:06
even though the neurons, you know, are huge cells compared to similar micron-long cells as compared to nanometer sized proteins inside the cell. And so, the scales are different, the makeups are different. But the logic is the same in many ways. So network motifs are very satisfying. The moment I saw them on the paper, I had this very quiet and silent moment where I knew, I just knew that this is a very important and I know what I'm going to do in the next five years. I was a new faculty member was really stressed out to find my direction. I knew to investigate each motif, and do the experiment to find out what it does and the mathematical modeling to know what it does. And each graduate student is going to work on a different motif. And I basically took my entire group, and I persuaded them to change their projects, and that's what we're working on now. It was just amazing. And also the way that biologists responded, I think it was very lucky to be at a moment in time, where biologists were frustrated with the amount of information available, but the lack of concepts to turn it into understanding. And so this came at a very, very good time. And also, I think catalyzed the entry into biology of a lot of people from physics and computer science backgrounds, because they saw this and said, "Oh, I can do something in biology. I had no idea. I can do something, I understand graphs"
Art Woods 34:22
It's like a metaphor doorway. There's an entry into biology, yeah.
Uri Alon 34:27
It also catalyzed me to write a book called Introduction to Systems Biology. Just to, because when you write papers, you're always thinking about reviewers. Here, I wanted like a place to just say it like it is, just to have full autonomy, to paint the whole picture like that. And I think I was very lucky because the timing in the field was so right, the people with me were so right. In the field, it was just the beginning of the internet, people making data available. So it was a database in Mexico, Collado-Vides did a lot of the collected a lot of the information that we could just download. So generous, right? And then we added a lot. And we downloaded. So it was just the beginning of this culture of sharing data. And just the sharing of a huge network helped other people develop ideas. And so that paper became also very impactful, because it was just a source of data. Now, it's obvious that you, you know, you do your large biological experiment, and you put your data online, but then it wasn't like that. So I was a kind of participant in this new culture-forming moment in biology, in multiple ways.
Cameron Ghalambor 35:34
Yeah, that's awesome. So to kind of follow up on the idea of these motifs of all the possible motifs that could be sort of put together, we see just a relatively few numbers. And do you think that reflects a history of sampling different kinds of motifs and then settling on like, a core few numbers that work really well in the context of a given system? Or is there something more general about just, you know, there are only a few ways that you can kind of solve things?
Uri Alon 36:14
So we and many others actually have investigated both questions. So I want to say that when you look at engineering textbooks, and you look at an amplifier circuit, there's a huge number of amplifiers that work on paper, but only very few that work in the real world, because in the real world, your resistors and capacitors are going to have a plus minus, you know, few percent error, and temperature is going to change, and the box will be dropped on the floor. And so the circuit you see in the books is the robust one that doesn't depend on that. In the same way you can say, if you want to make, let's say, a pulse generator, a delay element, there are many, many circuits you can write down on paper. But there are very few that will work, despite the fact that biological cells have unavoidable noise. If you want to have 100 proteins on average of a certain kind, one cell will have 110 another cell will have 90. You can't avoid that. So there's a lot of constraints circuits need to do to work and work in the real world. And that limits tremendously the number of circuits. So we think that there are actually only a few solutions, and that biology has no choice but to work with them.
Uri Alon 37:19
The other question is how you arrive at them. So I think just like you said, there is a search process, because if you're talking about transcription that works. So a transcription factor protein needs to bind the DNA at a certain site that may have DNA base pair letters. There's so many bacteria that you scan all possible changes of the letter quite quickly in a small test tube. And so a bacteria that has tiny fitness advantage, over time, will grow. And so it's not difficult. And we even showed it in the lab to show that when you change environments, the circuit will actually evolve a different design that's more optimal. So bacteria have no problem doing it. How multicellular animals do it, I think is a little bit more difficult to understand, because we have so many cells and so many circuits in each kind of cell and so many fitness questions. So I think that's an avenue of current research. And how optimal things are in multicellular organisms versus historical accidents, or nearly good enough designs is, I think, a fascinating, I find very appealing question right now.
Cameron Ghalambor 38:18
Yeah. So if I could just follow up on that. So what do we know about how much variation exists in networks, like across a genotypes in a population, given that there's this capacity for the network to evolve, we have these conserved network motif elements, but then, you know, those are just part of larger interacting networks. So if I could line up a hundred, E. coli or a hundred individuals of flies in a population and I compared their networks, how similar would they be or how different?
Uri Alon 38:54
Yeah. So I think it's quite striking. If we think about human beings, let's say, our cells, each division, we have one new point mutation in a genome, but even if you don't divide, you have several tens of point mutation per year. So when you reach, you know, age 60, or something, you might have a hundred, a thousand mutations. Now, if those mutations do something very, very bad to the cell, so of course, it's eliminated. But I think that means that each cell has actually, at least one arrow is different in its network, probably multiple ones. So we're actually a mosaic of many interacting instances. And, of course, in our body, if a change like that could lead to a cell going out of control like to cancer, and then making a clone it's very fit inside our body, but it can kill the entire organism. And because we rely on the germ cells to pass on, the germ cell is are very different, but only about a few tens of mutations between father, parent and child. But in our body, we have thousands of mutations accumulating in each stem cell, let's say. There's a paper that just did that in 2022 by sequencing individual stem cells and seeing how they were mutations rising linearly with age reaching 3000, on average at age 80, both in neurons and in gut cells. So and then we're, in old age, we're made of little creamier, like kingdoms of cells that come, they're closely related, but are different from the kingdom next door. So that's been shown in multiple organs. And if you think about a tree, each branch originates, it's actually genetically different from the other branch. So if you look at a tree, it's actually also a phylogenetic tree, sight? So living organisms are made of examples of different variations of the network inside of them.
Cameron Ghalambor 40:34
But so, the accumulation of, of mutations sort of within the lifetime of an organism, many of those are not passed on. So I can see how, you know, for example, that could create variation in lifespan, in fitness. But then sort of the transgenerational sort of what gets passed on through the germline, you know, that I assume is going to be a much smaller subset of all of those mutations? Is that some area that is kind of at the intersection of evolutionary biology and maybe this type of systems biology?
Uri Alon 41:10
Yeah, for sure. The mutation rate and transmission of the germline seems to be quite constant, despite differences in the size of the genome. So that might be a kind of conserved quantity that is balancing the trade off between having enough evolutionary change to adapt and being deleterious and lowering fitness. And then when you try to understand the mutations plus the network motifs and the circuits, so you need to go between the mutation in the DNA, and its phenotypic consequences on the shape of the organism, you have to go through a circuit design. So actually, a lot of our work now is figuring out how circuits can be designed to resist mutations. And I'll just give one example, immune cells are very good to fight viruses and bacteria. So they sense these foreign pieces of protein, and they say, "Oh, that's something new, we need to kill it." But imagine now that we have a mutant immune cell that doesn't really bind an actual pathogen, but its receptor has a mutation thinking it's binding, so it's locked on. So this is now a paranoid T cell, thinking there's enemies when there aren't, it's going to do what a good T cell does start dividing, dividing, dividing, and attacking and killing. So T cells and B cells have a built-in cell autonomous suicide mechanism. When they're activated too much, they kill themselves. So that's built into their design. So that also helps them not attack self, because self is found in such huge amounts, that they kill themselves. But also, if they happen to be a mutant, they kill themselves because they're hyperactive. So we termed this kind of biphasic mutant resistance, where your reaction makes you more active, but then makes you less active, if the input is too large. And that's seen also in neurons where hyperactivation leads to neuron death. And that's seen also in the Beta cells that make insulin. If they sense glucose, they make insulin to remove glucose, if they send too much glucose, they kill themselves. It's called glucose toxicity. So glucose kills the cells that protect us against glucose. So I'm working on how circuits could be resilient to mutants, and that actually opens your eyes and then you understand things that might not look weird, like, why should glucose kill the beta cells to defend it, even though they're built to control glucose. It's also of course, related to diseases like type two diabetes, or so on autoimmune diseases in the case of T cells or neurodegenerative diseases in case of neurons, because that suicide can, it can trigger these degenerative diseases.
Art Woods 43:49
So I have a maybe related question about robustness of these biological circuits, which I think a sort of broadly speaking, what we're talking about here, and this is just occurred to me and tugged on my sort of physiological interests. So I work a lot on small ectotherms insects and marine invertebrates that are ectotherms, so their body temperatures vary a lot. And one of the ways that I think about their biology is to think about the temperature coefficients of the various subprocesses that are making up organisms, you know, so some things inside organisms are very sensitive to temperature, other things are not. And if we sort of apply that thinking to the biological networks that are operating inside these organisms, you can imagine that as their body temperatures go up and down, the different parts of the network are responding differently to temperature. So given that, how do these networks become robust, to be able to, like, do the things that they do, despite all of this massive external variation that's imposing shifts in the relationships among the parts and maybe another more specific way of saying that is like, you know, a Drosophila embryo developing is going to do a good job of making a little Drosophila maggot, you know, regardless of whether it's wt 15 degrees or 20 degrees or 25 degrees. How do networks have robustness in the face of that sort of environmental variation? And then I guess the follow up is like, does that make them more robust also to things like mutations impinging on those networks?
Uri Alon 45:15
Yeah. So what just asked is of great interest to me, and we work a lot on this question. And my point of view is that the need to be robust to things like temperature, that vary naturally, is a prime reason for the way the circuits are the way they are. And so there are several principles of circuit design that appear in biology that help you be robust to things like temperature. I'll just give one example is called paradoxical components or antagonistic pleiotropy, on the level of enzymes. So there are, let's say, enzymes, the two component systems in bacteria, for example, which do a reaction and also it's opposite, in two different sites. So for example, they phosphorylate a protein and also they're the phosphatase for that protein. The two opposite reactions are on the same protein at the two different sites. And that seems confusing, why do that? But the reason is that, if you know, let's say, if the concentration of that enzyme rises, both reactions rise together, and the ratio stays the same. And that means that the phosphorylated product is going to stay the same because it’s production divided by removal. And so you become robust to something that varies between cell to cell, which is the concentration of an enzyme that can vary.
Uri Alon 46:32
And if you want to deal with temperature, I guess just like in mechanic's, where you build a little piece of a watch out of two metals that have opposing temperature expansion coefficients, you can make sure that temperature dependence of opposing parts are designed in a way to compensate for each other. And there's actually a concept called, I think, something like thermal genetic congruence or something like that. That is exactly the idea you said, that one set of noise or perturbations can make you robust to other ones. So it could be that mutations, the robust design against temperature or osmotic pressure, something like that can also protect against mutations that in certain ways mimic the effects of deforming the protein. So those are pretty deep concepts. Engineers also spend a lot of time thinking about robustness, because, again a real world problem, and they have solutions like integral feedback where you have that's when I'm cooling my room. And I'm sensing the temperature, and I control the power to the heater, according to if the temperature isn't right, I start integrating that error, so accumulating it over time and increasing the signal. So if it's not right, for a long time, I really pump it up, and that has no choice but to push the temperature back to normal. That's called integral feedback. And that occurs, we find, in many, many circuits in biology where the integrator or things like concentration of a protein or phosphorylation that kind of accumulates the error signal, you can then mathematically prove that the system is going to return to its setpoint, no matter what the parameters are, no matter what the speed of the reactions are. So engineers and evolution apparently rediscovered like the same math principles, and I think the reason is that there are very few ways to make things absolutely robust.
Art Woods 48:22
One follow up about temperature specifically. So did you say that in some sense, there's going to be coevolution of the temperature coefficients of the parts of these biological circuits, in order to make them robust? Or is there some alternative way of making them robust that doesn't require this sort of parallel evolution of temperature coefficients?
Uri Alon 48:40
The parallel evolution of temperature coefficients is definitely a possibility. And I guess it also, there's studies about that. But there are ways to bypass that. So if I build an interval feedback loop, suppose I want to keep homeostasis of something inside the organism, homeostasis keeping it the same level, let's say for example, let's say glucose or something like that. So what you build is you build a detector for glucose that releases a hormone like insulin, all that is going to be affected by temperature, the release rate, how much insulin affects the receptors, all that's going to be temperature dependent. And your blood volume is going to change, maybe you're pregnant, 50% more blood volume and nutrition is always gonna change. But what do you make sure to do is that the growth rate of the cells that secrete insulin is somehow going to be increasing with the amount of glucose, again, it can be temperature dependent. What's going to happen is if glucose is off, that is too high, the cell is going to start dividing, expanding, expanding, expanding and making more insulin, more insulin. And it's only going to stop when glucose reaches the setpoint. So the amount of cells is like an integrator, it integrates over the error as long as glucose is not at the setpoint. And so it's going to reach a new mass that's exactly right for the temperature you're working on, for the blood volume you have, for the nutrition you have, for your genetics. You just need to make a very kind of qualitative demand on the circuit in order to just make the growth rate go up with glucose. That's all you need to do. And that, mathematically, all the parameters that are temperature dependent, or whatever drop out, and you left with very, very few parameters that are kind of hardwired. And that's like, that's the integral feedback principle that you can find in cells, you can find processes. And that's what engineers use, because I have a thermometer here that controls temperature made by one company, and my heater is made by a completely different company. And they talk to each other perfectly because of this interval feedback, right? And maybe my heater is already old. And, you know, per unit power, it makes less temperature or more temperature, maybeI l open the window now. Maybe I will put another heater in, it's still reaching the same temperature. It's robust to almost everything. And that's the same principle that cells can use, and proteins can use by accumulating cells or accumulating modifications. So I believe there are ways to do it that don't require coevolution of temperature coefficients.
Cameron Ghalambor 51:13
Yeah. So if I could follow up on that,then robustness in this kind of way, you know, whether we think about it as homeostasis or integrating other kinds of information, seems to be a very universal property of these kinds of systems. And yet, despite this robustness, sometimes they fail, like if we get sick, for example. And so what goes wrong? Are there breaking points within networks that kind of compromise the whole system?
Uri Alon 51:44
Exactly. So this is what you just asked me is like the narrative of the book I'm writing right now, which is called systems medicine, circuits, why they're essential and what their fragilities are, and how you can derive the diseases. So if you take a look at those Beta cells, one huge factor, of course, is that evolution works in a certain place in time with a certain amount of nutrition and exercise, and you take it out to a different place in time. So you put the circuits and then environment they weren't designed for plus aging, they were not designed for old age. Old age, it takes again, the circuits outside of their working range. So what can go wrong, for example. In human beings, those beta cells, they stop dividing after age five or something like that, but in order to compensate, they grow their size, it's called hypertrophy, they grow their size, or shrink their size to give you that compensation I was talking about. So you stick with a certain number of beta cells, but cells can grow their size by more than a factor of two, before getting into trouble, unless they're polynucleated. So there's a carrying capacity, there's a maximum they can compensate. And so when that integrator hits a limit, the number or the total mass of beta cells can't grow anymore, because it's reached its limit. That's when compensation stops, and then you expose those fragilities, right? Now, you have insulin resistance, insulin doesn't work anymore. Before that, beta cells mass could rise, rise, rise to compensate and make more insulin, but once they max out and reach your carrying capacity. Now more insulin resistance is going to raise glucose, and I'm going to have pre diabetes, a little bit more diabetes, a little more glucose gets so high, those vessels kill themselves to avoid the mutant problem. Now you have end stage diabetes. So I solved one problem mutant resistance, but now, taking the circuit out of its natural resources, that suicide mechanism turns into a loss of beta cell function, because glucose really is that high, it's not a mutation. So the circuits have these essential functions, and they have fragilities. And you see those residuals, when you take the circuit out of its operating context. The carrying capacity is a huge one, I would say, all biological processes saturate, so eventually, you can't compensate anymore.
Uri Alon 53:43
That also can tell you a lot about what happens in aging when you have damaged cells that the body can deal with when you're young with because we have a certain amount of garbage trucks to take care of the garbage. But we're not designed to increase that number of trucks, the immune system doesn't increase with age. But the fact that cells are imprisoned in the body for decades and accumulate epigenetic changes and mutations that rises linearly with age, so the amount of damaged cells produced rises linearly with age, the trucks that get saturated, and boom, you have a problem and certain age, garbage starts piling up. That medical model point of view explains a lot of patterns you see in aging. And so all biological processes saturate, and cells mutate, and all cells come from cells, those kind of basic principles you can build together, it's kind of far reaching conclusions about what happens in physiology and when it breaks down.
Art Woods 54:40
So is the book gonna make recommendations about how to keep your circuits happy and healthy as long as possible as you age?
Uri Alon 54:46
It's going to interpret, in a coherent framework, a lot of interventions that in model organisms kind of seem to slow down aging and help a lot of different age-related diseases at once. So like, why does caloric restriction do that and why does lowering metabolic rate using insulin kind of mutations and why? It basically places a lot of these interventions in one kind of coherent scheme. So some of them help the trucks, other ones reduce the production of garbage. And then you can make predictions based on that. So, of course, for human beings, it's still unclear what works and what doesn't. But at least for model organisms, there appears to be some kind of universality in aging. The same intervention seems to work across organisms and that way of thinking can help you form the concepts to at least comprehended it all within one kind of unifying framework.
Cameron Ghalambor 55:41
Yeah. So just one follow up question on that, I find that kind of perspective, very satisfying. But I also see that there have been arguments for how, for example, stress, and the breakdown of these types of like, mechanisms for taking out the garbage, so to speak, can increase variation that then natural selection can act upon and be sort of a catalyst for evolutionary change. And I'm thinking about sort of like these evolutionary capacitance models.
Uri Alon 56:18
Lindquist's work?
Cameron Ghalambor 56:19
Yeah, yeah, exactly. So you know, the ability to kind of fix those misshapen proteins, if that system gets, you know, stressed too much, then you all of a sudden release all this hidden variation. So can fit into this paradigm as well?
Uri Alon 56:36
So first, I didn't think about that particular direction, but if I were to improvise, I would say that it is garbage truck picture, which, if you think inside the cell, and you have your damage repair mechanisms, a lot of them do get upregulated with more damage, they're not the ones dangerous in aging. But if there are ones that have a limit, and damage accumulates more, then when they're saturated, you will expose, I think, just like is a new and unexpected shapes of proteins. And that would be very detrimental inside an organism. But just like Susan Lindquist showed that can help you get through a rough patch by exposing hidden genetic variation. So maybe the body also uses that because of the unexpectedness, which we talked about in improv, right? You go into the unknown together, all of our cells do that all the time they go into the unknown together, and they do it quite well, so.
Cameron Ghalambor 57:26
I mean, but I think the idea is that most of the time, it doesn't turn out well. But by chance, you might get a good combination that comes out.
Art Woods 57:42
Uri, we're starting to get on in time. And we had some questions about organisms, and the sort of roles of organisms in biology. And I guess this sort of overall idea comes out of Cam and I and some other colleagues, including Marty our other co host, thinking over the last few years and arguing with each other a lot about what organisms are, and whether they're special in some way. And you know, how to think about organisms in the levels of biological organization in the world. And I want to maybe ask you this by posing it in terms of biological circuits and networks, and then thinking about the sort of level that we've been discussing these things at. So physiological networks, regulatory networks, or gene-gene networks, and asking, as you scale up to organisms, do you need new concepts in order to understand what organisms are and how they operate? Or is it just a question of scaling up the ideas to sort of a different level of organization?
Uri Alon 58:41
I resonate with that question, because I switched fields from thinking about E. coli, to thinking about human physiology. And I had some kind of shocks that I had to get my mind across, one of them has to do with we were discussing the need to resist mutation. So E coli, if there is a mutation, that's helpful, it will multiply the mutation, if it's deleterious it will be eliminated. It's not built to withstand mutations, I would say. But because we are a collective of cells, we're a society of cells that need to go into the unknown together, we need to worry a lot about mutations and, or cheater detection, you might say, and that creates a whole set of needs for the multicellular organism. We need both cell autonomous kind of suicide mechanisms and also police forces, like we think that the adaptive immune system plays a role we call autoimmune surveillance where they play a role in also eliminating cells that are mutant, and not only cancer cells, but cells that are like doing too much of what they should do. So they have no new proteins, but they're just making more proteins and dividing. And then we think those T cells are the basis for autoimmune diseases too. So you can't keep the thyroid without eliminating mutant thyroid cells that thinks you should make more thyroid hormone than divided make these toxic nodules, you have to kill them using T cells we think. But the same T cells in 4% of population, mostly women, turn into autoimmune disease that kills your entire thyroid. So there's these extra multicellular constraints that you need when you think now of an organism as a collection of cells. But it gets very tricky, like when thinking of the tree with where each branch is a different genome. And what is an organism? I think organism is a category, and it's limited because of the human beings, we want to have one organism but in fact, it's a continuum of, I guess, tying back to what we said before of a continuum of networks or genetic entities living together in a multicellular organism. And it's kind of an illusion to think that there is will be a clean category.
Cameron Ghalambor 1:00:46
Yeah, so it sounds like a lot of what you've been describing kind of falls under what evolutionary biologists would kind of refer to as a levels of selection problem. And in particular, I'm thinking about this tension that plays out at all levels of biological organization, between conflict and cooperation. And so, you know, we can see, for example, at the level of genome selfish genetic elements, like transposable elements, or a lot of the mechanisms that you've been describing, and then the counter system that keeps those things in check. So you have selfishness, but then, you know, that compromises the system, whether it's a whole organism or any other kind of network. And, and in order for the network to work, there has to be this cooperation for it to function properly. And so, to me, it seems that this is something that's really kind of characterizes, certainly organisms, but maybe just biological systems in general. And maybe is something that makes organisms and biological systems fundamentally different from the kinds of things that are engineered and not sort of living in that way. Is that going too far? Or is that a fair assessment? I'm not sure.
Uri Alon 1:02:08
I think that certainly when you think of an engineering until very recently, that can design away this problem, like we were looking at my thermostat, it's sitting there, it doesn't start mutating and dividing. So engineers don't have that problem. But when you think about you know, computer viruses and mal, you know, software, fake news, then we're in a different field, where we need to have an immune system, and we need to have a way to tell self from non-self. And we need, so I think engineering now is gonna have to learn from, of course, it is already from biological metaphors. If we talk about metaphors,
Cameron Ghalambor 1:02:46
I've seen some of these kinds of computer-based evolution models where you can kind of compete different systems against one another. Is that something that that you've thought about with regards to-
Uri Alon 1:03:01
Yeah we learned a lot from this. And I'll just tell you one, small story. biological organisms are usually very modular, we have a lung, we have a liver. Inside the liver, we have hepatocytes, they do one thing and cholangiocytes do another thing. When you try to evolve a program or network to do something, you get non-modular, everything gets connected to everything in the best possible way. And you can't understand how it works. It's non-modular, because there's so many ways to efficiently distribute the calculation between the network, so we were thinking how does modularity evolve? And the way we got that work through simulations is to notice that the problem space that organisms face is not just random, it's the same problems, but in different combinations. So you might want to eat this sugar or eat this sugar or this sugar, but you always going to need to break it up into some carbon pieces that are universal or so that we call them modularly varying goals. The world has goals, but the goals are made of sub goals that combine in different ways. When we ask the computer network now to solve problems like that, like logic gates, like A and B or C and D, and change that or to an and and to and or and like that every several hundred generations, we got modular solutions. They developed an And gate, they developed an Or gate and just rewired it with one kind of mutation. So I think modularity in biology, one answer is it learns the different subproblems we have.
Uri Alon 1:04:30
Okay, so we have a breathing subproblem, feeding subproblem, reproduction subproblem, and that induces a structure on-the reason where modular is because problem space we're dealing with is modular, basically. And otherwise we would lose modularity and the liver and the lung might fuse into a superoptimal organ, but then when we change condition, that won't work anymore. And then you can see that in the ribosomes, it's non-modular, it's a super optimal thing because it needs to do the same thing for billions of years. So some things like that I think in biology are non-modular. So we had a lot of fun with computer simulation and I think they can show you examples of evolution, which are not biological and therefore different from biology and then you can think oh, what do we need to add in order to understand biological evolution? They're superb metaphors.
Art Woods 1:05:18
Yeah, yeah. Super cool. Cam, do you have any follow up questions or should we start to start to wrap?
Cameron Ghalambor 1:05:23
Well, I'd love to hear another song.
Uri Alon 1:05:27
Yeah, let's end with a song
Cameron Ghalambor 1:05:28
But the question is what is the subject of the song?
Uri Alon 1:05:33
Let me do one I want the listeners to stay with. It's actually it's a pair of songs. The first one is called decision with capital letters.
Uri Alon 1:05:46
Dear author, we have now have heard three referees. The comments are attached below. As you will see, they have raised concerns about your interpretation of the facts, your choice of model systems, your references, and the style of your prose. Referee number one says the topic is interesting, results are incorrect. Referee number two says the results are okay, the topic isn't interesting. Referee number 3 suggests 14 additional experiments although tangential to your main point will be nice to have. Oh yeah. As a result, we regret that we cannot offer publication at this time. Please know that we value your work and we'd like to see more of it in the future. At the same time, we remain committed to the high standards of the Archival Journal of Upper Nasal Cavity Research.
Art Woods 1:06:44
Awesome
Uri Alon 1:06:46
You know, it's about how we teach ourselves to review in science so we get a cycle of aggression. So the second song is a reply to that.
Uri Alon 1:06:55
When I get a paper to review, I look for what is fresh and new and how to realize its full potential. Of course I note what to improve but I always let the authors choose after all they know their own work better than I do. Hallelujah, I'll review ya. Hallelujah, I'll review ya. Now, you know I've been reviewed before, and I lay crushed upon the floor. But I won't pass on the aggression to ya. Science is a social affair, we help each other to be there to find the truth and help it to shine through yeah. Hallelujah, I'll review ya. Hallelujah, I'll review. Hallelujah, I'll review ya. Hallelujah, I'll review ya.
Cameron Ghalambor 1:08:25
Oh boy, I'm never going to be able to listen to Leonard Cohen the same way again.
Art Woods 1:08:33
Well, fantastic. Uri thanks so much for joining us for this conversation. It's been really a lot of fun.
Uri Alon 1:08:39
With great pleasure. Thanks.
Cameron Ghalambor 1:08:41
Yeah yeah. Thanks so much
Marty Martin 1:08:50
Thanks for listening to this episode. If you like what you hear, let us know via Twitter, Facebook, Instagram, or leave a review wherever you get your podcasts. And if you don't, we'd love to know that too. All feedback is good feedback
Art Woods 1:09:00
Thanks to Steve Lane who manages the website and Ruth Demree for producing the episode.
Marty Martin 1:09:04
Thanks also to interns Dayna De La Cruz and Kyle Smith for helping produce the episode. And Keating Shahmehri produces our fantastic cover art
Art Woods 1:09:11
Thanks to the College of Public Health at the University of South Florida. The College of Humanities and Sciences at the University of Montana and the National Science Foundation for support.
Marty Martin 1:09:19
Music on the episode is from Podington Bear and Tieren Costello.