The following is a conversation with Lee Cronin, his third time in this podcast. He is a chemist from University of Glasgow, who is one of the most fascinating, brilliant, and fun to talk to scientists have ever had the pleasure of getting to know.
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So your big assembly theory paper was published in Nature. Congratulations. Thanks. It created, I think it's fair to say, a lot of controversy, but also a lot of interesting discussion. So maybe I can try to summarize assembly theory and you tell me if I'm wrong.
Go for it. So assembly theory says that if we look at any object in the universe, any object, that we can quantify how complex it is by trying to find the number of steps it took to create it. And also, we can determine if it was built by a process akin to evolution by looking at how many copies of the object there are. Yeah, that's spot on spot on spot. I was not expecting that. Okay. So let's go through definitions.
So there's a central equation I'd love to talk about, but definition wise, what is an object?
So if I'm going to try to be as meticulous as possible, objects need to be finite and they need to be decomposable into subunits. All human-made artifacts are objects. Is a planet an object? Probably yes, if you scale out. So an object is finite and countable and decomposable.
I suppose mathematically, but yeah I still wake up some days ago to think to myself what is an object because it's a non-trivial question. Persists over time, I'm quoting from the paper here. An object that's finite is distinguishable, so that's a weird adjective, distinguishable.
We've had so many people help offering to rewrite the paper after it came out. You wouldn't believe it so funny persists over time and is breakable such that the set of constraints to construct it from elementary building blocks is quantifiable. Such that the set of constraints to construct it from elementary building blocks is quantifiable.
The history is in the objects. It's kind of cool, right? What defines the object is its history or memory, whichever is the sexier word. I'm happy with both, depending on the day. The set of steps that took to create the object, so there's a sense in which every object in the universe has a history.
And that is part of the thing that is used to describe its complexity, how complicated it is. Okay. What is an assembly index?
So the assembly index, if you take the object apart and be super lazy about it or minimal, say, well, because you've got a really short term memory. So what you do is you lay all the parts on the path and you find the minimum number of steps you take on the path to add the parts together to reproduce the object. And that minimum number is the assembly index. It's a minimum bound.
And it was always my intuition, the minimum bound in assembly theory was really important. And that only worked out why a few weeks ago, which is kind of funny. Because I was just like, no, this is sacrosanct. I don't know why. It will come to me one day. And then when I was pushed by a bunch of mathematicians, we came up with the correct physical explanation, which I can get to. But it's the minimum. And it's really important. It's the minimum. And the reason I knew the minimum was right is because we could measure it. So almost before this paper came out,
with published papers, explain how you can measure the assembly index of molecules. Okay, so that's not so trivial to figure out. So when you look at an object, we can say molecule, we can say object more generally to figure out the minimum number of steps to take to create that object.
That doesn't seem like a trivial thing to do. So with molecules, it's not trivial, but it is possible because what you can do, and because I'm a chemist, so I'm kind of like, I see the lens of the world for just chemistry, I break the molecule apart and break bonds.
And if you break up, if you take a molecule and you break it all apart, you have a bunch of atoms. And then you say, OK, I'm going to then form, take the atoms and form bonds and go up the chain of events to make the molecule. And that's what made me realize, take a toy example, literally toy example, take a Lego object, which is broken up of Lego blocks. So you could do exactly the same thing. In this case, the Lego blocks are naturally the smallest. They're the atoms in the actual composite.
Lego architecture, but then if you maybe take a couple of blocks and put them together in a certain way, maybe they're offset in some way, that offset is on the memory. You can use that offset again with only a penalty of one, and you can then make a square triangle and keep going, and you remember those motifs on the chain, so you can then leap from the
the start with all the Lego blocks or atoms just laid out in front of you and say, right, I'll take you, you, you connect and do the least amount of work. So it's really like the smallest steps you can take on the graph to make the object. And so for molecules, it came relatively intuitively. And then we started to apply it to language. We've even started to apply it to mathematical theorems, but I'm somewhat out of my depth. But it looks like you can take minimum set of axioms and then start to build up
kind of mathematical architectures in the same way. And then the shortest path to get there is something interesting that I don't yet understand. So what's the computational complexity of figuring out the shortest path? And with molecules, with language, with mathematical theorems, it seems that once you have the fully constructed Lego castle,
or whatever your favorite Lego world is, figuring out how to get there from the basic building blocks. Is that an empty heart problem? It's a heart problem. It's a heart problem, but actually, if you look at it, so the best way to look at it, let's take a molecule.
13 bonds. First of all, take 13 copies of the molecule and just cut all the bonds, so take 12 bonds, and then you just put them in order, and then that's how it works. So you keep looking for symmetry or copies, so you can then shorten it as you go down, and that becomes commentarly quite hard. For some natural product molecules,
It comes very hard it's not impossible but we're looking at the bounds on that the moment but as the object gets bigger it becomes really hard and but that's the bad news but the good news is there are shortcuts. And we might even be able to physically measure the complexity without computationally calculating it which is kind of insane. How would you do that?
Well, in the case of molecule, so if you shine light on a molecule, let's take it infrared, the molecule has each of the bonds absorbs the infrared differently in what we call the fingerprint region. And so it's a bit like, and because it's quantized as well, you have all these discrete kind of absorbances.
And my intuition after we realized we could cut molecules up in mass spec, that was the first go at this. We did it with using infrared and the infrared gave us an even better correlation assembly index and we used another technique as well. In addition to infrared called NMR, nuclear magnetic resonance, which tells you about the number of different magnetic environments in a molecule. And that also worked out. So we have three techniques, which each of them independently gives us
the same or tending towards the same assembly index from molecule that we can calculate mathematically. Okay, so these are all methods of mass spectrometry, mass spec. You scan a molecule, it gives you data in the form of a mass spectrum, and you're saying that the data correlates to the assembly index. Yeah.
How generalizable is that shortcut? First of all, two chemistry. Second of all, beyond that. Because that seems like a nice hack. And you're extremely knowledgeable about various aspects of chemistry. So you can say, okay, it kind of correlates.
but the whole idea behind assembly theory paper, and perhaps why it's so controversial, is that it reaches bigger. It reaches for the bigger general theory of objects in the universe.
Yeah, I'd say so, I'd agree. So I've started assembly theory of emoticons with my lab, believe it or not, so take emojis, pixelate them, and work out the assembly index of emoji, and then work out how many emojis you can make on the path of emoji. So there's the uber emoji from which all other emojis emerge, and then you can then take a photograph, and by looking at the shortest path,
on by reproducing the pixels to make the image you want, you can measure that. So then you start to be able to take spatial data. Now there's some problems there. What is in the definition of the object? How many pixels? How do you break it down? And so we're just learning all this right now.
So how do you compute the, how would you begin to compute the assembly index of a graphical like a set of pixels on a 2D plane that form a thing? So you would first of all, determine the resolution. So then what is your X, Y and what the number on the X and Y plane and then look at the surface area. And then you take all your emojis and make sure they're all looked at the same resolution. Yes. And then we were basically then,
do the exact same thing we would do for cutting the bonds, you'd cut bits out of the emoji and look at them, you'd have a bag of pixels, and you would then add those pixels together to make the overall emoji. First of all, not every pixels.
I mean, this is at the core sort of machine learning computer vision. Not every pixel is that important and there's like macro features, there's micro features and all that kind of stuff. Exactly. Like, you know, the eyes appear in a lot of them. The smile appears in a lot of them.
So in the same way in chemistry, we assume the bond is fundamental. What we do, and they're here, is we assume the resolution at the scale at which we do it is fundamental. And we're just working that out. And you're right, that will change, right? Because as you take your lens out a bit, it will change dramatically. But it's just a new way of looking at not just compression, what we do right now in computer science and data, one big kind of
kind of misunderstanding as assembly theory is telling you about how compressed the object is. That's not right. It's a how much information is required on a chain of events, because the nice thing is if when you do compression in computer science, we're wandering a bit here, but it's kind of worth wondering, I think, and you assume you have instantaneous access to all the information in the memory.
In assembly theory, you say, no, you don't get access to that memory until you've done the work. And then you don't access that memory. You can have access, but not to the next one. And this is how in assembly theory, we talk about the four universes, the assembly universe, the assembly possible, and the assembly contingent, and then the assembly observed. And they're all scales in this combinatorial universe. Yeah. Can you explain each one of them?
Yeah, so the assembly universe is like anything goes, just this just combinatorial kind of explosion in everything. So that's the biggest one. That's the biggest one's massive. Assembly universe, assembly possible, assembly contingent, assembly observed. And on the y-axis is assembly steps in time. Yeah. And the x-axis as the thing expands through time, more and more unique objects appear.
So, yeah, so, assembly universe, everything goes. Yeah. Assembly possible laws of physics come in, in this case in chemistry bonds, in assembly, so that means... Those are actually constraints, I guess.
Yes, and they're the only constraints, they're the constraints of the base, so the way to look at it, you've got all your atoms, they're quantized, you can just bung them together. So then you can become a kind of, so in the way in computer science speak, I suppose the assembly universe is just like no laws of physics. Things can fly through mountains beyond the speed of light. In the assembly possible, you have to apply the laws of physics, but
You can get access to all the motifs instantaneously with no effort. That means you could make anything. Then the assembly contingent says, no, you can't have access to the highly assembled object in the future until you've done the work in the past on the causal chain.
And that's really the really interesting shift where you go from assembly possible to assembly contingent. That is really the key thing in assembly theory that says you cannot just have instantaneous access to all those memories. You have to have done the work somehow. The universe has to have somehow built a system that allows you to select that path rather than other paths. And then the final thing
the assembly observed is basically us saying, oh, these are the things we actually see. We can go backwards now and understand that they have been created by this causal process.
Wait a minute, so when you say the universe has to construct the system that does the work, is that like the environment that allows for like selection? Yeah, yeah, yeah. That's the thing that does the selection. You could think about in terms of a von Neumann constructor versus a selection, a ribosome, a Tesla, a plant, assembling Teslas, you know, the difference between the assembly universe in Tesla land and the the test of factory is
Everyone says, no, Teslas are just easy. They just spring out. You know how to make them all in a Tesla factory. You have to put things in sequence and out comes a Tesla. Do you talk about the factory?
Yes, this is really nice. Super important point is that when I talk about the universe having a memory or there's some magic, it's not that. It's that tells you that there must be a process encoded somewhere in physical reality, be it a cell, a Tesla factory or something else that is making that object. I'm not saying there's some kind of
woo woo memory in the universe, you know, morphic resonance or something. I'm saying that there is an actual causal process that is being directed constrained in some way. And so it's not kind of just making everything. Yeah, but Lee, what's the factory? They made the factory.
So, first of all, you assume the laws of physics has just sprung to the existence at the beginning. Those are constraints. But what makes the factory the environment that does the selection? Well, it's the first interesting question that I want to answer out of four.
I think the factory emerges in the environment, the interplay between the environment and the objects that are being built. I'll have a go at explaining to the shortest path. So why is the shortest path important? Imagine you've got, I'm going to have to go chemistry for a moment and abstract it. So imagine you've got
An environment, a given environment that you have a budget of atoms, you're just flinging together. And the objective of those atoms that being flung together and say molecule A, they have a decompose, so molecules decompose over time. So the molecules,
in this environment, in this magic environment, have to not die, but they do die. They have a half-life. So the only way the molecules can get through that environment out the other side, let's pretend the environment is a box and go in and out without dying, and there's just an infinite supply of atoms coming, or a large supply.
The molecule gets built, but the molecule that is able to template itself being built and survives in the environment will basically reign supreme. Now, let's say that that molecule takes 10 steps and it's using a finite set of atoms. Now, let's say another molecule, smart-ass molecule will call it comes in and can survive in that environment
and can copy itself, but it only needs five steps.
the molecule that only needs five steps, because both molecules are being destroyed, but they're creating themselves faster they can be destroyed. You can see that the shortest path reigns supreme. So the shortest path tells us something super interesting about the minimal amount of information required to propagate that motif in time and space. And it seems to be like some kind of conservation law. So one of the intuitions you have
is the propagation of motifs in time will be done by the things that can construct themselves in the shortest path. So like, you can assume that most of the objects in the universe are built in the shortest and most efficient way.
That the, so. Big loop I just had there. Yeah, yes and no, because there are other things. So in the limit, yes, because you want to tell the difference between things that have required a factory to build them and just random processes. But you can find instances where the shortest path isn't taken for an individual object, an individual function. And people go, ah,
That means the shortest path isn't right. And then I say, well, I don't know. I think it's right still. So of course, because there are other driving forces, it's not just one molecule. Now, when you start to consider two objects, you have a joint assembly space. And now it's a compromise between not just making A and B in the shortest path. You want to make A and B in the shortest path, which might mean that A is slightly longer. You have a compromise.
So when you see slightly more nesting in the construction, when you take a given object, that can look longer, but that's because the overall function is the object is still trying to be efficient. And this is still very hand wavy, and maybe have no legs to stand on. But we think we're getting somewhere with that. And there's probably some parallelization. Yeah. Right? So this is all, this is not sequential. The building is
I guess when you're talking about complex objects, you don't have to work sequentially, you can work in parallel, you can get your friends together. And the thing we're working on right now is how to understand these parallel processes. Now there's a new thing we've introduced called assembly depth. And assembly depth can be lower than the assembly index for a molecule.
when they're cooperating together, because exactly this parallel processing is going on. And my team have been working this out in the last few weeks, because we're looking at what compromises does nature need to make when it's making molecules in a cell? And I wonder if, you know, I may be like, well, I'm always leaping out of my competition. But in economics, I'm just wondering if you could apply this in an economic process. It seems like capitalism is very good at finding shortest path.
You know every time and there are ludicrous things that happen because actually the cost function has been minimized and so I keep seeing parallels everywhere where there are complex nested systems where if you give it enough time and You introduce a bit of heterogeneity the system readjusts and finds a new shorts path But the shortest path isn't fixed on just one molecule now. It's in the actual
existence of the object over time, and that object could be a city, it could be a cell, it could be a factory, but I think we're going way beyond molecules, and my competence probably should go back to molecules. Before we get too far, let's talk about the assembly equation. How should we do this? Let me just even read that part of the paper.
We define assembly as the total amount of selection necessary to produce an ensemble of observed objects, quantified using equation one. The equation basically has a on one side, which is the assembly of the ensemble. And then a sum.
from 1 to n, where n is the total number of unique objects. And then there is a few variables in there that include the assembly index, the copy number, which we'll talk about. That's an interesting, I don't remember you talking about that. That's an interesting addition, and I think a powerful one.
has to do with what that you can create pretty complex objects randomly. And in order to know that they're not random, that there's a factory involved, you need to see a bunch of them. That's that's the intuition there. It's an interesting intuition. And then some normalization. What else is it? And in minus one, just to make sure that I'm more than one object, one object could be a one off and random. Yep. And then you have more than one identical object. That's interesting.
When there's one, there's two of a thing. Two of the thing is super important, especially if the index assembly index is high. So we could say several questions here. One, let's talk about selection. What is this term selection? What is this term evolution that we're referring to? Which aspect of Darwinian evolution are we referring to? That's interesting here.
So, yeah, so this is probably what, you know, the paper, we should talk about the paper second, the paper did what it did is it kind of annoyed. Um, we didn't know it. I mean, it got intention and obviously angry people, the angry people were annoyed. There's angry people in the world. That's good.
So what happened is evolutionary biologists got angry. We were not expecting that because we thought evolutionary biologists would be cool. I knew that some, not many, computational complexity people will get angry because I've kind of been poking them and maybe I deserved it. But I was trying to poke them in a productive way. And then the physicists kind of got grumpy because the initial conditions tell everything.
The pre-biotic chemist got slightly grumpy because there's no enough chemistry and then finally when the creationist said it wasn't creationist enough I was like, no, I've done my job. You say in the physics they say, because you're basically saying that physics is not enough to tell the story of how biology emerges. I think so. And then they said a few physics is the beginning and the end of the story.
Yeah. So what happened is the reason why people put the phone down on the call of the paper, I mean, if you, if you're reading the paper like a phone call, they got to the abstract. Yep. And in the abstract, it's... For sentences, pretty... The first two sentences caused everybody... Scientists have grappled with reconciling biological evolution with the immutable laws of the universe defined by physics. True, right? There's nothing wrong with that statement. Totally true.
Yeah. These laws underpin life's origin, evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Wow. First, I would say the title of the paper. This is paper was accepted and published in Nature. The title is assembly theory explains and quantifies selection and evolution. Very humble title. And the entirety of the paper, I think, presents interesting ideas, but reaches high.
I am not. I would do it all again. This people is actually on the preprint server for over a year. You regret nothing. Yeah, I think, yeah, I don't regret anything. You and Frank Sinatra did it your way. What I love about being a scientist is kind of sometimes, because I'm a bit dim, I'm like, and I don't understand what people tell me. I want to get to the point. This paper says, Hey, laws of physics are really cool. The universe is great. But.
they don't really, it's not intuitive that you just run the standard model and get life out. I think most physicists might go, yeah, it's not just, we can't just go back and say that's what happened, because physics can't explain the origin of life yet. That doesn't mean it won't or can't, okay? Just to be clear, sorry intelligent designers, we are gonna get there. Second point, we say that evolution works, but we don't know how evolution
got going, so biological evolution and biological selection. So for me, this seems like a simple continuum. So when I mentioned selection and evolution in the title, I think, and in the abstract, we should have maybe prefaced that and said, non-biological selection and non-biological evolution. And then that might have made it even more crystal clear, but I didn't think that evolutionary biology should be so bold to claim ownership of selection and evolution.
And secondly, a lot of evolutionary biologists seem to dismiss the origin of life questions to say is obvious. And that causes a real problem scientifically, because when two different, when the physicists are like, we own the universe, universe is good, we explain all of it, look at us, and even biologists say we can explain biology, and the poor chemist in the middle ground, but hang on.
And this paper kind of says, hey, there is an interesting disconnect between physics and biology. And that's at the point in which memories get made in chemistry through bonds. And hey, let's look at this close and see if we can quantify it. So yeah, I mean, I never expected the paper to kind of get that much interest. And still, I mean, it's only been published just over a month ago now.
It's just the link on the selection. What is the broader sense of what selection means? Yeah, that's really good. For selection, I think for selection, this is where, for me, the concept of an object is something that can persist in time and not die, but basically can be broken up.
So if I was going to bolster the definition of an object, so if something can form and persist for a long period of time, under an existing environment that could destroy other, and I'm going to use anthropomorphic terms, I apologize, that weaker objects or less robust,
Then the environment could have selected that. So good chemistry examples, if you took some carbon and you made a chain of carbon atoms, whereas if you took some, I don't know, some carbon nitrogen and oxygen and made chains from those, you'd start to get different reactions and rearrangements. So a chain of carbon atoms might be more resistant to falling apart under acidic or basic conditions.
versus another set of molecules. So it survives in that environment. So the acid pond, the molecule, the resistant molecule can get through. And then that molecule goes into another environment. So that environment now maybe being an acid pond is a basic pond, or maybe it's an oxidizing pond. And so if you've got carbon and it goes an oxidizing pond, maybe the carbon starts to oxidize and break apart. So you go through all these kinds of
Obstacle courses if you like given by reality. So selection is the ability happens when object survives in your environment for some time But and this is the thing that's super
subtle, the object has to be continually being destroyed and made by process. So it's not just about the process, the object now is about the process and time that makes it because a rock could just stand on the mountainside for four billion years and nothing happened to it. And that's not necessarily really advanced selection. So for selection to get really interesting, you need to have a turnover in time. You need to be continually creating objects, producing them,
what we call discovery time. So there's a discovery time for an object. When that object is discovered, if it's a molecule that can then act on itself or the chain of events that caused itself to bolster its formation, then you go from discovery time to production time and suddenly you have more of it in the universe. So it could be a self-replicating molecule and the interaction of the molecule in the environment in the warm little pond or in the sea or wherever in the bubble
could then start to build a protofactory, the environment. So really to answer your question, what the factory is, the factory is the environment, but it's not very autonomous. It's not very redundant. There's lots of things that could go wrong. So once you get high enough up the hierarchy of networks of interactions, something needs to happen that needs to be compressed into a smaller volume and made resistant robust. Because in biology,
selection and evolution is robust, that you have error correction built in, you have really, you know, there's good ways of basically making sure propagation goes on. So really the difference between inorganic, abiotic selection and evolution and evolution and stuff in biology is robustness, the ability to kind of propagate over the ability to survive in lots of different environments, whereas
are poor little inorganic, so molecule whatever just dies in lots of different environments. So there's something super special that happens from the inorganic environment, molecule in the environment kills it to where you've got evolution and cells can survive everywhere. How special is that? How do you know those kinds of evolution factors on everywhere in the universe?
I don't, and I'm excited because I think selection isn't special at all. I think what is special is the history of the environments on Earth that gave rise to the first cell that now has taken all those environments and is now more autonomous. And I would like to think that this paper
could be very wrong. But I don't think it's very wrong. It meets certainly wrong, but it's less wrong than some other ideas, right? And if this allows inspires us to go and look for selection in the universe, because we now have an equation where we can say, we can look for selection going on and say, Oh, that's interesting. We seem to have a process that's giving us
giving us high copy number objects that also are highly complex, but that doesn't look like life as we know it. And we use that and say, oh, there's a hydrothermal vent. Oh, there's a process going on. There's molecular networks because the assembly equation is not only meant to identify at the higher end advanced
selection, what you get, I record in biology, you super advanced selection. And even, I mean, you could use the assembly equation to look for technology and go for bid, we could talk about consciousness and abstraction. But let's keep it primitive molecules and biology. So I think the real power of the assembly equation is to say how much selection is going on in this space.
And there's a really simple thought experiment I could do is you, you know, have a little petri dish and on that petri dish you put some simple food. So the assembly index of all the sugars and everything is quite low. So then, and you put a single e-coli cell. And then you say, I'm going to, I'm going to measure the assembly in this amount of assembly in the box. So it's quite low, but.
The rate of change of assembly, DADT, will go from sigmoidal as it eats all the food and the number of coli cells will replicate because they take all the food, they can copy themselves, the assembly index of all the molecules goes up, up, up and up until the food is exhausted in the box. So now the, now the coli's stop.
I mean, dying is probably a strong way. They stop respiring because all the food is gone. But suddenly, the amount of assembly in the box has gone up, giganticly, because of that one E. coli factory has just eaten through, milled lots of other E. coli factories, run out of food and stopped. And so in the initial box, although the amount of assembly was really small,
It was able to replicate and use all the food and go up and that's what we're trying to do in the lab actually is kind of make those kind of experiments and see if we can spot the emergence of molecular networks are producing complexity as we feed in raw materials and we feed a challenge and environment. We try and kill the molecules and really that's the main kind of idea for the entire paper.
Yeah, and see if you can measure the changes in the assembly index throughout the whole system. Yeah. Well, okay, what about if I show up to a new planet, we'll go to Mars or some other planet from a different solar system. And how do we use assembly index there to discover alien life?
in very simply actually, let's say we'll go to Mars with a mass spectrometer with a sufficiently high resolution. So what you have to be able to do, so a good thing about mass spec is that you can select the molecule from the mass and then if it's high enough resolution, you can be more and more sure that you're just seeing identical copies, you can count them and then you fragment them and you count the number of fragments and look at the molecular weight and the higher the molecular weight,
and the higher the number of fragments or higher the assembly index. So if you go to Mars and you take a mass spec or high enough resolution and you can find molecules, and I'll guide on Earth, if you could find molecules say greater than 350 molecular weight with more than 15 fragments, you have found artifacts that can only be produced, at least on Earth, by life.
And now you would say, oh, well, maybe the geological process. I would argue very virimally that that is not the case. But we can say, look, if you don't like the cut off on Earth, go up higher, 30, 100, right? Because there's going to be a point where you can find a molecule with so many different parts. The chances of you getting a molecule that has 100 different parts.
and finding a million identical copies. That's just impossible. That could never happen in an infinite set of universes. Can you just link around this copy number thing? A million different copies. What do you mean by copies and why is the number of copies important? Yeah, that was so interesting.
always understood the copy number is really important, but I never explained it properly for ages. And I kept having this, it goes back to this, if I give you a, I don't know, a really complicated molecule, and I say it's complicated, you could say, hey, that's really complicated, but is it just really random? And so I realized that ultimate randomness and ultimate complexity are indistinguishable.
until you can see a structure in the randomness. So you can see copies. So copies implies structure. Yeah. The factory. I mean, there's a deep profound thing in there. Because if you just have a random, random process, you're going to get a lot of complex, beautiful, sophisticated things, what makes them
Complex in the way we think life is complex or yeah Something like a factory that's operating under a selection process is there should be copies is there like some looseness about copies like What does it mean for two objects to be equal?
It's all to do with the telescope or the microscope you're using. And so at the maximum resolution, so in the nice thing about chemists, as they have this concept of the molecule and they're all familiar with the molecule and molecules, you can hold, you know, on your hand, lots of them, identical copies. And molecules are actually a super important thing in chemistry to say, look, you can have a mole of a molecule, an Avogadro's number of molecules.
And they're identical. What does that mean? That means that the molecular composition, the bonding and so on, the configuration is all, is indistinguishable. You can hold them together. You can overlay them.
So the way of do it is if I say, here's a bag of 10 identical molecules. That's pretty identical. You pick one out of the bag and you basically observe it using some technique, and then you take it away and then you take another one out. If you observe it using technique, you can see no differences. They're identical. It's really interesting to get right because if you take, say, two molecules, molecules can be in different vibrational and rotational states. They're moving all the time. So with this respect, identical molecules have identical bonding.
In this case, we don't even talk about chirality because we don't have a chirality detector. Two, I met technical molecules in one conception assembly theory, basically considers both hands as being the same. But of course, they're not, they're different. As soon as you have a chiral to distinguish her, detect the left and the right hand, they become different. It's to do with the detection system that you have and the resolution.
So I wonder if there's an art and science to the which detection system is used when you show up to a new planet. Yeah. Yeah. So like you're talking about chemistry a lot today. We have kind of standardized detection systems of how to compare molecules. So when you start to talk about emojis and language and mathematical theorems and
I don't know, more sophisticated things. At a different scale, at a smaller scale, the molecules, at a larger scale, the molecules. Like, what detection... If we look at the difference between you and me, flexibly, are we the same? Are we different?
Sure, I mean, of course, we're different close up, but if you zoom out a little bit, we'll morphologically look the same, you know, high characteristics, hair length, stuff like that. We'll also like the species and yeah, yeah, yeah. And also there's a sense why we're both from Earth.
Yeah, I agree. I mean, this is the power of assembly theory in that regard. So if everything, so the way to look at it, if you have a box of objects, if they're all indistinguishable, then using your technique, what you then do is you then look at the assembly index. Now, if the assembly index of them is really low, and they're all indistinguishable, then it's telling you that you have to go to another resolution.
So that would be, you know, it's kind of a sliding scale. It's kind of nice. So you guys. So those two kind of are attention with each other. Yeah. The number of copies and the assembly index. Yeah. That's really, really interesting. So, okay. So you show up to a new planet. You'll be doing what? I would do mass spec. I would bring on a sample of what like, first of all, like how big of a scoop do you take? Did you just take a scoop? Like what? Like, uh, so we're looking for primitive life.
I would, I would look, yeah, so if you're just going to Mars or Titan or Enceladus or somewhere. So a number of ways of doing it. So you could take a large scoop or you go for the atmosphere and detect stuff. So, and you could make a life, a life meter, right? So,
One of Sarah's colleagues at ASU, Paul Davies keeps calling it a life meter, a life meter, which is quite a nice idea because you think about it. If you've got a living system that's producing these highly complex molecules and they drift away and they're in a highly kind of,
Demanding environment they could be burnt right so they could just be falling apart So you want to sniff a little bit of complexity and say warmer warmer warmer warmer Oh, we found life. We found the alien we found we found the alien Elon Musk smoking a joint in the bottom of the cave on Mars or Elon himself Whatever right you say okay found it so what you can do is a mass spectrometer
You could just look for things in the gas phase or you go on the surface drill down because you want to find molecules that are. You've either got to find the source living system because the problem with just looking for complexity is it gets burned away.
So in a harsh environment on, say, on the surface of Mars, there's a very low probability that you're going to find really complex molecules because of all the radiation and so on. If you drill down a little bit, you could drill down a bit into soil that's billions of years old.
Then I would put in some solvent, water, alcohol or something, or take a scoop, make it volatile, put it into the mass spectrometer and just try and detect high complexity, high abundant molecules. And if you get them, hey, presto, you can have evidence of life.
Wouldn't that then be great if you could say, okay, we've found evidence of life. Now we want to keep the life meter, keep searching for more and more complexity until you actually find living cells. You can get those new living cells and then you can bring them back to earth or you could try and sequence them. You could see that they have different DNA and proteins.
How would you build a life meter? Let's say we're together starting a new company and launching a life meter. Mass spectrometer would be the first way of doing it. No, no, no, but that's one of the major components of it. But I'm talking about like, if it's a device and branding logo, we're going to talk about that later. But what's the input? How do you get to a metered output?
So I would take my life meter, our life meter. Thank you. Yeah, you're welcome. I would have both infrared and mass spec. So it would have two ports so it could shine a light. And so what it would do is you would have a vacuum chamber and you would have an electrostatic analyzer and you'd have a monochromator to producing infrared.
You'd add the sump, so you'd take a scoop of the sample, put it in the life meter, it would then add a solvent or heat up the sample, so some volatiles come off. The volatiles would then be put into the mass spectrometer, into electrostatic trap, and you'd weigh the molecules and fragment them.
Alternatively, you'd shine infrared light on them, you count the number of bands, but you'd have to, in that case, do some separation because you want to separate in. And so in my spec, it's really nice and convenient because you can separate electrostatically, but you need to have that. Can you do it in real time? Yeah, pretty much it. Pretty much. Yeah. So let's go all the way back. So this, OK, we're really going to get this circle. The Lexus life meat, Lexus leaves. Actually, it's a good, a good ring to it.
All right, so you have a vacuum chamber, you have a little nose. The nose would have a packing material. So you would take your sample, add it onto the nose, add a solvent or a gas. It would then be sucked up the nose. And that would be separated using what we call chromatography. And then as each band comes off the nose, we'll then do mass spec and infrared.
And in the account in the case the infrared count the number of bands in the case of mass spec count number fragments and weigh it, and then the further up in molecular weight range for the mass spec and number of bands you go up and up and up from the, you know, dead. Interesting interesting over the threshold. Oh my gosh. Earth life.
And then right up to the batshit crazy, this is definitely, you know, alien intelligence that's made this life, right? You could almost go all the way there. Same with the infrared. And pretty simple. The thing that is really problematical is that for many years, decades,
What people have done, and I can't blame them, is that rather they've been obsessing about small biomarkers on, that we find on earth, amino acids, like single amino acids, or evidence of small molecules and these things, and looking for those run looking for complexity.
The beautiful thing about this is you can look for complexity without earth chemistry bias or earth biology bias. So assembly theory is just a way of saying, hey, complexity and abundance is evidence selection. That's how our universal life meter will work. Complexity and abundance is evidence of selection. Okay, so let's apply our life meter to earth.
So if we were just to apply assembly index measurements to Earth, what kind of stuff are going to be going to get? What's impressive about some of the complexity on Earth? So we did this a few years ago in that when I was trying to convince NASA and colleagues that this technique could work. And honestly, it's so funny because everyone's like, no, I'm going to work.
And it was just like, because the chemists were saying, of course there are complicated molecules out there you can detect that just form randomly. It was like, really, that's like, that was like, you know, it's a bit like a.
I don't know, someone's saying, of course, Darwin textbook was just written randomly by some monkeys and a typewriter. Just for me, it was like, really? And I've pushed a lot on the chemist now, and I think most of them are on board, but not totally. It really had some big arguments, but the copy number caught there, because I think I confused the chemist by saying one off, and then when I made clear about the copy number, I think that made it a little bit easier. Just to clarify.
Chemists might say that, of course, out there outside of Earth, there's complex molecules. Yes. Okay. And then you're saying, wait a minute, that's like saying, of course, there's aliens out there. Yeah, exactly that. Exactly. You clarify that that's actually a very interesting question, and we should be looking for complex molecules of which the copy number is two or greater.
Yeah, exactly. So on Earth, the coming back to Earth, what we did is we took a whole bunch of samples, and we were running prebiotic chemistry experiments in the lab. We took various inorganic minerals and extracted them, look at the volatile, because there's a special way of treating minerals and polymers and assembly theory. In this, in our life machine, we're looking at molecules. We don't care about polymers,
because they don't volatile, you can't hold them. If you can't discern that they're identical, then it's very difficult for you to work out if there's undergone selection or they're just a random mess. Same with some minerals, but we can come back to that. So basically what you do, we've got a whole lot of samples inorganic ones.
We got a load of, we got Scotch whiskey. And I also got, I took an odd bag, which is one of my favorite whiskeys, which is very petey. And another wisp. Petey means is like, so the way that on in Scotland in Isla, which is a little island, the scotch, the whiskey is let to mature in barrels. And it said that the peak, the complex molecules in the peat,
might find their way through into the whiskey. And that's what gives it this intense brown color and really complex flavor. It's literally molecular complexity that does that. And so, you know, Volcker is the complete opposite. It's just pure, right? What are the whiskey? The higher the semi index, the better the whiskey.
That's what, I mean, I really love deep PT Scottish whiskeys. Near my house, there is one of the lowland distilleries called Glengoin. It's still beautiful whiskey, but not as complex. So for fun, I cooked some Glengoin whiskey in our bag and put them into the mass spec and measure the assembly index. I also got Ecoli. So the way we do take the Ecoli, break the cell apart,
take it all apart, and also got some beer, and people were ridiculing us saying, oh, beer is evidence of complexity. One of the computational complexity people was just throwing.
Yeah, we kind of kind of is very vigorous in his disagreement of assembly theory was just saying, you know, you don't know what you're doing. Even beer is more complicated than human. We didn't realize is that it's not beer per se, it is taking the yeast extract, taking the extract, breaking the cells, extracting the molecules and just looking at the profile of the molecules, the others, anything over the threshold. And we also put in a really complex molecule taxo. So we took all of these, but also NASA gave us, I think, five samples.
And they wouldn't tell us what they are. They said, no, we don't believe you're going to get this to work. And they really gave us some super complex samples. And they gave us two fossils, one that was a million years old and one was at 10,000 years old. Something from Antarctica, seabed. They gave us an emergency and meter, right, and a few others. Put them through the system. So we took all the samples, treated them all identically, put them into mass spec, fragmented them, counted. And in this case,
implicit in the measurement was, in mass spec, you only detect peaks when you've got more than, say, let's say, 10,000 identical molecules. So the copy number is already baked in, but wasn't quantified, which is super important there. This is in the first paper, because I guess it's abundant, of course. And when you then took it all out, we found that the biological samples
gave you molecules that had an assembly index greater than 15, and all the A-bartic samples were less than 15, and then we took the NASA samples, and we looked at the ones that were more than 15 less than 15, and we gave them back to NASA, and they're like, oh gosh, yep, dead, living, dead, living, you got it.
And that's what we found on Earth. That's a success. Oh, yeah, resounding success. Can you just go back to the beer in the E. coli? So what's the assumption next on those?
So what you were able to do is the assembly index of we found high assembly index molecules originating from the beer sample and the E. coli sample. I didn't know which one was higher. We wouldn't really do any detail there because now we are doing that because one of the things we've done, it's a secret, but I can tell you.
No, nobody's listening. Well, is that we've just mapped the tree of life using assembly theory, because everyone said, oh, that you can't do it from biology. And what we're able to do is, so I think there's three ways, well, two ways of doing tree of life traffic, or three ways, actually. Yeah, what's the tree of life?
So the tree of life is basically tracing back the history of life on Earth, all the different species going back, who evolved from what? And it all goes all the way back to the first kind of life forms. And they branch off. And you have plant kingdom, the animal kingdom, the fungi, the kingdom, and different branches all the way up.
And the way this was classically done, and I'm no evolutionary biologist, evolutionary biologist, very tell me every day, at least 10 times. I want to be one though. I kind of like biology. It's kind of cool. But basically what Darwin and Mendeleev and all these people do is just they draw pictures, right? And they taxa. They were able to draw pictures and say, oh, these look like common classes.
They're artists really, they're just, you know. But they're able to find out a lot, right? And looking at Verberts and Verberts, Camera and Exposure and all this stuff. And then came the Genomic Revolution. And suddenly everyone used gene sequencing. And Craig Venter is a good example. I think he's gone around the world and he's just taking up samples, looking for new species, where he's just found new species of life, just from sequencing. It's amazing.
So you have taxonomy, you have sequencing, and then you can also do a little bit of kind of molecular kind of archaeology, like, you know, measure the samples and kind of form some inference. What we did is we were able to fingerprint
We took a load of random samples from all of biology, and we use mass spectrometry. And what we did now is not just look for individual molecules, but we looked for coexisting molecules where they had to look at their joint assembly space and where we were able to cut them apart and undergo recursion in the mass spec and infer some relationships. And we were able to recapitulate the tree of life using mass spectroscopy, no sequencing and no drawing.
All right, can you try to say that again with a little more detail? So recreating, what does it take to recreate the tree of life? What does the reverse engineering process look like here?
So what you do is you take an unknown sample, you pung it into the mass spec, because this comes from what you're asking, like, what do you see in E. coli? And so in E. coli, you don't just see it's not the most sophisticated cells on Earth make the most sophisticated molecules. It is the coexistence of lots of complex molecules above a threshold. And so what we realize is you could fingerprint different life forms. So fungi make really complicated molecules. Why? Because they can't move.
They have to make everything on site, whereas some animals are like lazy. They can just go eat the fungi. They don't need to make very much. And so what you do is you take, I don't know, the fingerprint, maybe the top number of high molecular weight molecules you find in the sample, you fragment them to get their assembly indices. And then what you can do is you can infer common origins of molecules. You can do a kind of molecular
When the reverse engineering of the assembly space, you can infer common routes and look at what's called a joint assembly space. But what let's translate that into the experiment. Take a sample, bung it in the mass spec, take the top say 10 molecules fragment them.
And then that gives you one fingerprint. Then you do it for another sample, you get another fingerprint. Now, the question is you say, hey, are these samples the same or different? And that's what we've been able to do. And by basically looking at the assembly space, these molecules create without any knowledge of assembly theory, you are unable to do it. With an knowledge of assembly theory, you can reconstruct the tree. How does knowing if they're the same or different give you the tree?
Let's go to two leaves on different branches on the tree, right? What you can do by counting the number of differences, you can estimate how far away that origin was. And that's all we do. And it just works. But when we realized you could even use assembly theory to recapitulate the tree of life from no gene sequencing, we were like,
So this, this is looking at samples that exist today in the world. We're about like things that are no longer exist. I mean, the tree contains information about the past. I would. Some of it is gone. Yeah. Yeah. Absolutely. I would love to get old fossil samples and apply assembly theory mass spec and see if we can find new forms of life that have, that are no longer amenable to gene sequencing because the DNA is all gone. Because DNA, DNA and RNA is quite unstable.
But some of the more complex molecules might be there might give you a hint something new or wouldn't it be great if you if you find a sample that's worth really persevering and doing you know doing the proper extraction to reek to you know PCR and so on and then sequence it and then put it together. So one thing dies you can still get some information about this complexity.
Yeah, and it appears that you can do some dating. Now, there are really good techniques. There's radiocarbon dating. There is longer dating, going looking at radioactive minerals and so on. And you can also in bone
you can look at what happens after something dies is you get what's called racemization, where the chirality in the polymers basically changes and you get decomposition. And the deviation from the pure
enantiomer to the mixture, you can have a time scale on it a half life. You can date when it died. I want to use assembly theory to see if I can date, use it and date death and things and trace the tree of life and also decomposition of molecules. Do you think it's possible?
Oh yeah, without doubt. It may not be better than what, because like the I was just at a conference where some brilliant people were looking at isotope and Richmond and looking at how life enriches isotopes and they're really sophisticated stuff that they're doing. But I think there's some fun to be had there because we give you another dimension of dating. How old is this molecule in terms of or more importantly, how long ago was this molecule produced by life?
The more complex the molecule, the more prospect for decomposition, oxidation, reorganization, loss of chirality, and all that jazz. But what life also does is it enriches as you get older, the amount of carbon 13 in you goes up.
Because of the, because of the way the metabol, because of the way the, the bonding is in, in carbon 13. So it has a slightly different strength, bond strength than you is called a kinetic isotope effect. So you can literally date how old you are, you know, uh, or when you stop metabolizing, so you could date someone as debt, how old they are, I think I'm making this up. This might be right. But I think it's roughly right. The amount of carbon 13 you have in you, you can kind of estimate how old you are.
How old living humans are? Yeah, you could say, oh, this person is 10 years old and this person is 30 years old because they've been metabolizing more carbon and they've accumulated it. That's the basic idea. It's probably completely wrong timescale. Signature of chemistry or fasting. So you've been seeing a lot of chemistry examples for assembly theory. What if we zoom out and look at a bigger scale of an object?
like really complex objects like humans or living organisms that are made up of millions or billions of other organisms. How do you try to apply somebody theory to that?
At the moment, we should be able to do this to morphology in cells, so we're looking at cell surfaces, and really I'm trying to extend further. We work so hard to get this paper
out and people to start discussing the ideas. But it's kind of funny because I think the penny is falling on this. I mean, the penny's dropped, right? Because a lot of people are like, it's rubbish, it's rubbish, you've insulted me, it's wrong. The paper got published on the 4th of October. It had 2.3 million engagements on Twitter.
Right and has been downloaded over a few hundred thousand times and someone actually said to me wrote to me and said this is an example of really bad writing and what not to do and I was like if all of my papers got read this much because that's the objective of I have a publishing of people to read it. I want to write that badly again. I don't know what's the deep inside here about the negativity in the space. I think it's probably the immune system of the scientific community.
Making sure that there's no bullshit that gets published. That's and then it can over fire can do a lot of damage it can shut down conversations in a way that's not productive. We go back coming on to your question about the hierarchy assembly but let's go back to the perception. People saying that people is badly written. I mean of course we could improve it we can always improve the clarity. Let's go there before we go to the hierarchy.
You know, it has been criticized quite a bit, the paper. What has been some criticism that you've found most powerful, like that you can understand and can you explain it?
Yes, the most exciting criticism came from the evolutionary biologists telling me that they thought that origin of life was a solved problem. And I was like, whoa, we're really onto something because it's clearly not. And then when you poke them on that, they just said, no, you don't understand evolution. And I said, no, no, I don't think you understand that evolution had to occur before biology. And there's a gap. That was really, for me,
that misunderstanding, and that did cause an immune response, which was really interesting. The second thing was the fact that physicists were actually really polite, right? Really nice about it. But they just said, huh, we're not really sure about the initial conditions thing, but this is a really big debate that we should certainly get into because the emergence of life was not encoded in the initial conditions of the universe.
And it can't and I think assembly theory shows why it can't be Okay, sure if you could say that again
The origin of the emergence of life was not and cannot, in principle, be encoded in the initial conditions of the universe. Just to clarify what we mean by life is like high assembly index objects. Yeah. And this goes back to your favorite subject. What's that? Time. Right. So why? So why? What does time have to do with it?
Probably we can come back to it later, but I think it might be if we have time. I think I now understand how to explain how lots of people got angry with the assembly paper, but also the ramifications of this is how time is fundamental in the universe.
and this notion of combinatorial spaces. And there are so many layers on this, but you have to become an intuitionist mathematician, and you have to ban and platonic mathematics, and also platonic mathematics is their physics astray, but there's a lot back there. So we can go to the atomic mathematics. Okay, it's okay. Evolution of biologists criticize because
the origin of life is understood and not, it doesn't require an explanation of the involves physics. That's their statement. Well, I mean, I think they said lots of confusing statements. Basically, I realized the evolutionary biology community that were vocal and some of them really rude, really spiteful and needlessly so, right? Because like, you know, I didn't,
People miss understand publication as well some of the people who said how dare this be published in nature this is you know how the terrible journal and nice and it really, what's the people look this is a brand new idea that's.
not only potentially going to change the way we look at biology, it's going to change the way we look at the universe. And everyone's like saying, how dare you? How dare you be so grandiose? I'm like, no, no, no, this is not hype. We're not saying we've invented some, I don't know, we've discovered an alien in a closet somewhere just for hype. We've genuinely mean this to genuinely have the impact or ask the question.
And the way people jumped on that was a really bad precedent for young people want to actually do something new because this makes a bold claim and the chances are.
that it's not correct. But what I wanted to do is a couple of things that I want to make a bold claim that was precise and testable and correctable, not a woolly, another woolly information in biology argument, information during machine, blah, blah, blah, blah, blah, a concrete series of statements that can be falsified and explored. And either the theory could be destroyed or built upon.
Well, what about the criticism of you're just putting a bunch of sexy names on something that's already obvious? Yeah, that's really good.
The assembly index of a molecule is not obvious. No one has thought to quantify selection complexity and copy number before in such a primitive quantifiable way. I think the nice thing about this paper, this paper is
is a tribute to all, well, not to all the people that understand that the biology does something very interesting. Some people call it neg entropy. Some people call it think about, you know, organizational principles that lots of people were not shocked by the paper because they'd done it before. A lot of the, a lot of the arguments we got, some people said, oh, it's rubbish. Oh, by the way, I had this idea 20 years before. I was like,
Which one? Is it the rubbish part or the really revolutionary part? So this kind of plucked two strings at once. There is something interesting that biology is as we can see around this, but we haven't quantified yet. And what this is, the first stab at quantifying that. So the fact that people said this is obvious, but it's also, so if it's obvious, why have you not done it?
Sure, but there's a few things to say there. One is
You know, this is in part of philosophical framework because, you know, it's not like you can apply this generally to any object in the universe. It's very chemistry focused. Yeah. Well, I think you will be able to. We just haven't got their robustly. So if we can say, how can we, let's go up a level. So if we go up from level, we go up, let's go up from molecules to cells because you jump to people and I jumped from motor cons and both are good and they will be assembled. Let's think with cells. Yeah. Let me go from it. If we go from
So if we go from molecules to assemblies, and let's take a cellular assembly, a nice thing about a cell is you can tell the difference between a eukaryote and a prokaryote, right? The organelles are specialized differently. When they look at the cell surface,
and the cell surface has different glycosylation patterns and these cells will stick together. Now, let's go up a level with multicellular creatures. You have cellular differentiation. Now, if you think about how embryos develop, you go all the way back, those cells undergo differentiation in a causal way that's biomechanically a feedback between the genetics and biomechanics. I think we can use assembly theory to apply to tissue types. We can even apply it to different cell disease types. So that's what we're doing next, but we're trying to walk
You know, the thing is I'm trying to leap ahead, I want to leap ahead to go, well, we apply it to culture, but clearly you can apply it to memes and culture. And we've also applied assembly theory to CAs. And not as you think.
Celia Thomas. Yeah, yeah, Celia Thomas. Not just do you think with different CA rules were invented by different people at different times. And one of my, one of my co workers very talented chap basically was like, oh, I can realize that different people had different ideas or different rules. And they copied each other and made slightly different bit, but different Celia Thomas rules. And they and public and looked at them online.
And so he was able to a third assembly index and copy number of rule whatever doing this thing, but I digress but it does show you can apply a higher scale. So what do we need to do to apply assembly theory to things we need to agree there's a common set of building blocks are in a cell well in a.
In a multicellular creature, you need to look back in time. There is the initial cell, which the creature is fertilized and then starts to grow, and then there is cell differentiation, and you have to then make that causal chain both on those.
development of the organism in time. Or if you look at the cell surfaces and the cell types, they've got different features on the cell, what walls and inside the cell. So we're building up, but obviously I want a leap to things like emoticons, language, mathematical theory. But that's a very large number of steps to get from a molecule to the human brain.
Yeah, and I think they are related, but in hierarchies of emergence, right? So you shouldn't compare them. I mean, the assembly index of a human brain, what does that even mean? Well, maybe we can look at the morphology of the human brain. So all human brains have these number of features in common.
If they have those numbers and then let's look at a brain in a whale, or a dolphin, or a chimpanzee, or a bird, say, okay, let's look at the assembly indices and number of features in these. And now the copy number is just a number of how many birds are there? How many chimpanzees are there? How many humans are there? Then you have to discover for that the features that you would be looking for. Yeah. And that means you need to have a, you need to have some idea of the anatomy. Is there an automated way to discover features?
I guess so, I mean, and I think this is a good way to apply machine learning and image recognition to the basic characterizing. So apply compression to it to see what emerges and then use the features used as part of the compression as the measurement of
as the thing that is searched for when you're measuring assembly index and copy and the compression has to be remember the assembly universe which is you have to go from assembly possible to assembly contingent and that jump from a set because assembly possible or possible brains or possible features all the time but we know that.
On the tree of life and also on the lineage of life, going back to Luca, the human brain just didn't spring into existence. Yesterday, it is a long lineage of brains going all the way back. And so if we could do assembly theory to understand the development, not just an evolutionary history, but in biological development, as you grow, we're going to learn something more. What would be amazing is if you can use assembly theory, this framework to show the increase in the assembly index.
associated with, I don't know, cultures or pieces of text like language or images and so on, and illustrate without knowing the data ahead of time, just kind of like you did with NASA, that you were able to demonstrate that it applies in those other contexts.
And that probably wouldn't at first, and you have to evolve the theory somehow. You have to change it, you have to expand it. I think so. But like that, I guess this is a paper at first step in saying, okay, can we create a general framework for measuring complexity of objects, for measuring life, the complexity of living organisms? Yeah. That's what this is reaching for.
That is the first step. And also to say, look, we have a way of quantifying selection and evolution in a fairly, not mundane, but a fairly mechanical way. Because before now, it wasn't very, the ground truth for it was very subjective.
Whereas here we're talking about clean observables. And there's going to be layers on that. I mean, we've collaborated right now. We already think we can do assembly theory on language. And not only that, wouldn't it be great if we can. So the if we can figure out how under pressure language is going to involve and be more efficient because you're going to want to transmit things. And again, it's not just about compression. It is about understanding how you can make the most of the in the architecture you've already built.
I think this is something beautiful that evolution does. We're reusing those architectures. We can't just abandon our evolutionary history. If you don't want to abandon your evolutionary history, and you know that evolution has been happening, then assembly theory works. I think that's a key comment I want to make, is that assembly theory is great for understanding what evolution has been used.
The next jump is when we go to technology, because of course, if you take the M3 processor, I want to buy, I haven't bought one yet, I can't justify it, but I want to at some point. The M3 processor, arguably, there's quite a lot of features, a quite large number. The M2 came before it, then the M1, all the way back. You can apply assembly theory to microprocessor architecture. It doesn't take a huge leap to see that.
I'm a Linux guy, by the way. So your examples go way over. Yeah. Well, whatever. Is that, is that like a, is that a fruit company or some sort? I don't even know. Yeah. There's a lot of interesting stuff to ask about language. Like you could look at how would that work? You could look at GPT one, GPT two, GPT three, three, five, four, and try to analyze the kind of language it produces. I mean, that's almost trying to look at assembly index of intelligence systems.
Yeah, I mean, I think the thing about large language models, and this is a whole hobby horse I have at the moment, is that obviously they're all about the evidence of evolution in the large language model comes from all the people that produced all the language. And that's really interesting. And all the corrections in the mechanical Turk.
right sure and and so that's part of the history part of the memory of the system exactly so you can you so so it would be really interesting to basically use an assembly-based approach to making language in a hierarchy right i think is it my guess is that
You could, we might be able to build a new type of large language model that uses assembly theory that it has more understanding of the past and how things were created. Basically, the thing with LLMs is they're like everything everywhere, all at once, splat, and make the user happy. So there's not much intelligence in the model. The model is how the human interacts with the model, but wouldn't it be great if we could understand how to embed more intelligence in the system?
What do you mean by intelligence there? He seemed to associate intelligence with history. Yeah. Well, I think selection produces intelligence.
You're almost implying that selection is intelligence. No, kind of. I would go out and live and say that, but I think it's a little bit more. Human beings have the ability to abstract and they can break beyond selection. And this is what like Darwinian selection, because a human being doesn't have to basically do trial and error. They can think about it and say, oh, that's a bad idea. I won't do that. And then technologies and so on.
We escaped Darwinian evolution, and now we're on to some other kind of evolution, I guess, higher level of evolution. And that assembly theory will measure that as well, right? Because it's all lineage. Okay, another piece of criticism, or by way of question, is how is assembly theory, or maybe assembly index, different from common graph complexity? So for people who don't know, common graph complexity of an object is the length of a shortest computer program that produces the object as output.
Yeah, I seem to, there seems to be a disconnect between the computational process. So, yeah, so a Comma-Goller-off measure requires a Turing machine, requires a computer.
And that's one thing. And the other thing is, assembly theory is supposed to trace the process by which life evolution emerged. There's a main thing there. There are lots of other layers. So common goal are of complexity. You can approximate common goal of complexity, but it's not really telling you very much about
the actual, it's really telling you about like your data set, compression of your data set. And so that doesn't really help you identify the turtle in this case is the computer. And so what assembly theory does is I'm going to say,
It's a trigger warning for anyone listening who loves complexity theory. I think that we're going to show that AIT is a very important subset of assembly theory because here's what happens. I think that assembly theory allows us to build, understand when we're selections occurring. Selection produces factories and things. Factories in the end produce computers and then algorithmic information theory comes out of that.
The frustration I've had with looking at life through this kind of information theory is it doesn't take into account causation. The main difference between assembly theory and all these complexity measures is there's no court or chain. I think that's the main... As the causal chain is at the core of assembly theory.
Exactly. And if you've got all your data in a computer memory, all the data is the same. You can access it in the same type way. You don't care. You just compress it. And you either look at the program runtime or the shortest program. And that, for me,
It is absolutely not capturing what it is, what its selection does. But assembly theory looks at objects. It doesn't have information about. The object history is going to try to infer that history.
by looking for the shortest history. The object doesn't have a Wikipedia page that goes with it about its history. I would say it does in a way, and it is fascinating. Look at it. So you've just got the objects.
And you have no other information about the object. What assembly theory allows you to do with just with the object is to, and the word infer is correct. I agree with infer. You're like, say, well, that's not the history, but something really interesting comes from this. The shortest path is inferred from the object. That is the worst case scenario if you have no machine to make it. So that tells you about the depth of that object in time.
And so what assembly theory allows you to do without considering any other circumstances to say from this object how deep is this object in time, if we just treat the object as itself without any other constraints. And that's super powerful because the shortest path then says, allows you to say, oh,
this object wasn't just created randomly, there was a process. And so assembly theory is not meant to, you know, one up AIT or to ignore the factory. It's just to say, it's just to say, hey, there was a factory. How big was that factory and how deep in time is it?
But it's still computationally very difficult to compute that history for complex objects. It is and becomes harder. But one of the things that's super nice is that it constrains your initial conditions, right? It constrains where you're going to be. So if you take, say, imagine, so one of the things we're doing right now is applying assembly theory to drug discovery.
Now, what everyone's doing right now is taking all the proteins and looking at the proteins and looking at molecules, doctor proteins. Why not instead take the molecules that are involved in interacting with the receptors over time rather thinking about and use the molecules evolve over time as a proxy for how the proteins evolved over time?
And then use that to constrain your drug discovery process. You flip the problem 180 and focus on the molecule evolution, rather than the protein. And so you can guess in the future what might happen. So you rather than having to consider all possible molecules, you know where to focus.
And that's the same thing if you're looking at in assembly spaces for an object where you don't know the entire history, but you know that, you know, in the history of this object, it's not going to have some other motif there that it doesn't apply. It doesn't appear in the past.
But just even for the drug discovery point you made, don't you have to simulate all of chemistry to figure out how to come up with constraints? No. The molecules. No. I mean, I don't know enough about protein. Well, this is another thing that I think causes, because this paper goes across 70 boundaries. So chemists have looked at this and said, this is not a react. This is not correct reaction. I was like, no, it's a graph.
Sure, there's assembly index and shortest path examples here on chemistry.
Yeah. And so, and what you do is you look at the minimal constraints on that graph. Of course, it has some mapping to the synthesis, but actually you don't have to know all of chemistry. You just have to understand, you can build up the constraint space rather nicely. But this is just at the beginning, right? There are so many directions that could go in, and I said it, it could all be wrong, but hopefully it's less wrong. What about the little criticism I saw of, do you,
by way of question. Do you consider the different probabilities of each reaction in the chain? So like that there could be different
When you look at a chain of events that led up to the creation of an object, doesn't it matter that some parts in the chain are less likely than others? No. It doesn't matter. No, no. Well, let's go back. So no, not less likely, but react. So no. So let's go back to what we're talking about. So the assembly index is the minimal path.
that could have created that object probabilistically. So imagine you have all your atoms in a plasma, you've got enough energy, you've got enough, there's collisions. What is the quickest way you could zip out that molecule with no reaction constraints? How do you define quickest there then?
It's just basically what a walk on a random graph. So we make an assumption that basically the timescale for forming the bonds. So no, I don't want to say that because it's going to have people getting obsessing about this point and your criticism is a really good one. What we're trying to say is like this puts a lower bound on something. Of course, some reactions are less possible than others, but actually, I don't think chemical reactions exist. Oh boy, what does that mean? Why don't chemical reactions exist?
I'm writing a paper right now that I keep being told I have to finish. And it's called the Origin of Chemical Reactions. And it merely says that reactivity exists as controlled by the laws of quantum mechanics. And reactions, we put, chemists put names on reactions, like, so you could have like, I don't know, the VITIC reaction, which is by, you know, VITIC. You could have the Suzuki reaction, which is by Suzuki.
Now, what are these reactions? So these reactions are constrained by the following. They're constrained by the fact that on planet Earth, 1G, 298 Kelvin, 1 bar. So these are constraints. They're also constrained by the chemical composition of Earth, oxygen, availability, all this stuff. And that then allows us to focus in our chemistry. So when a chemist does a reaction, that's a really nice compressed shorthand for constraint application, glass flask.
pure reagent, temperature, pressure, bomb, bomb, bomb, bomb, control, control, control, control, control. So of course, we have bond energies. So the bond energies are kind of intrinsic in a vacuum, if you say that. So the bond energy, you have to have a bond. And so for assembly theory to work, you have to have a bond, which means that bond has to give the molecules certain half life.
So you're probably going to find later on that some bonds are weaker and that you are going to miss in mass spectra. When you count, look at the assembly of some molecules, you're going to miss count the assembly of the molecule because it falls apart too quickly because the bonds just form. But you can solve that with looking at infrared. So when people think about the probability, they're kind of misunderstanding. Assembly theory says nothing about the chemistry.
Because chemistry is chemistry and the constraints are put in by biology there was no chemist in the origin of life bacon unless you believe in the chemist in the sky and they were you know it's like Santa Claus they had a lot of work to do, but chemical reactions do not exist.
in the constraints that allow chemical transformations to occur do exist. Okay. Okay. So it's constrained to applicants. So there's no chemical reactions. It's all constraint application, which enables the emergence of reactants.
What's the different word for chemical reaction? Transformation. Yeah, like a function. It's a function. But no, but I love chemical reactions in the shorthand. Yeah. And so the chemists don't all go mad. I mean, of course, chemical reactions exist on Earth. It's a shorthand. It's a shorthand for all these constraints. For it right. So assuming all these constraints that we've been using for so long, we just assume that that's always the case in natural language conversation.
Exactly. The grammar of chemistry, of course, emerges in reactions, and we can use them reliably. But I do not think the vitic reaction is accessible on Venus. Right. And this is useful to remember, you know, to frame it as constraint application is useful for when you zoom out to the bigger picture of the universe and looking at the chemistry of the universe and then starting to apply assembly theory. That's interesting. That's really interesting.
We've also pissed off the chemists now. They're pretty happy, but most of them. Everybody deep down is happy, I think. They're just sometimes feisty. That's how they show. That's how they have fun. Everyone is grumpy on some days when you challenge. The problem with this paper is it's almost like I went to a park. I do used to do this occasionally when I was young. I go to a meeting.
and just find a way to offend everyone at the meeting simultaneously. Even the factions that don't like each other, they're all unified in their hatred of you just defending them. This paper, it feels like the person that went to the party and offended everyone simultaneously, so stop fighting with themselves and just focus on this paper. Maybe just a little insider, interesting information. What were the editors of nature, like what their reviews and so on, how difficult was that process? This is a pretty big paper.
Yeah, I mean, so when we originally sent the paper, we sent the paper and the editor said that, you know, this was like, this is a quite a long process. We sent the paper and the editor gave us some feedback and said, you know, I don't think it's that interesting. It's not, you know, it's hard. It's hard concept. And we asked, and the editor gave us some feedback.
And Sarah and I took a year to rewrite the paper. Was the nature of the feedback very specific on the part of this part, or was it like, what do you guys smoke? Yeah, it was kind of the latter. What are you smoking? And you know... But polite and there's promise. Yeah, well, the thing is, the edit was really critical, but in a really professional way.
And i mean for me this was the way science should happen so when it came back you know we have too many equations in the paper if you look at the pre-print the just equations everywhere like twenty three equations and when i said to abhishek who's the first author we've got to remove all the equations but my assembly equation staying abhishek.
You know, no, we can't. I said, well, look, if we want to explain this to people, it's a real challenge. And so Sarah and I went through the, I think it was actually 160 versions of the paper, but we basically, we got to version 40 or something. We said, right, zero, it start again. So we wrote the whole paper again. We knew the entire, amazing. And we just went bit by bit by bit. And so what is it we want to say? And then we send the paper in.
And we expected it to be rejected and not even go to review. And then we got notification back, it had gone to review, and we were like, oh my god, it's so going to get rejected. How's it going to get rejected? Because the first assembly paper that was on the mass spec we sent to nature went through six rounds of review and rejected. And this by a chemist just said, I don't believe you, you must be committing fraud.
A long story probably a boring story, but in this case it went out to review the comments came back and the comments were incredibly. Oh, no, they were very there were very deep comments from all the reviewers there were. And but the but the none other but the nice thing was the reviewers were kind of.
Very critical, but not dismissive. They were like, oh, really? Explain this, explain this, explain this, explain this. Are you sure it's not comma-gold or off? Are you sure it's not this? And we went through, I think, three rounds of review pretty quick. And the editor went, yeah, it's in.
But maybe you could just comment on the whole process. You've published some pretty huge papers and all kinds of topics within chemistry and beyond. Some of them have some little spice in them, a little spice of crazy. Like Tom White says, I like my Tom with a little drop of poison. It's not a mundane paper. So what's it like psychologically to go through all this process to keep getting rejected?
to get reviews from people that don't get the paper or all that kind of stuff. Just from a question of a scientist. What is that like?
I think it's a, I mean this paper for me kind of, because this wasn't the first time we tried to publish assembly theory at the highest level, the nature communications paper on the mass spec, on the idea, went through, went to nature and got rejected, went through six rounds of review and got rejected.
And I just was so confused when the chemist said this can't be possible. I do not believe you can measure complexity using mass spec. And also, by the way, molecules, complex molecules can randomly form. And we're like, but look at the data. The data says, and they said, no, no, we don't believe you. And I just wouldn't give up.
Um, the other, and the other in the end, um, was just like, the different editors actually, right? Right. What's behind that never giving up is like, when you're sitting there, 10 o'clock in the evening, there's a melancholy feeling that comes over you and you're like, okay, this is rejection number five or it's not rejection, but maybe it feels like a rejection because the, you know, the, the, the comments or the, you totally don't get it. Like what gives you strength to keep going there? No, I don't know.
I don't normally get emotional about papers, but it's not about giving up because we want to get it published because we want the glory or anything. It's just like, why don't you understand? And so,
So what I would just try to be as rational as possible and say, yeah, you didn't like it. Tell me why. And then, sorry.
Silly. Never get emotional about papers normally, but I think what we do, you just compress like five years of angst from this. So it's been rough.
It's not just rough, it's like, it happened, you know, I came up with the assembly equation, you know, remote from Sarah in Arizona and the people SFI, I felt like a mad person, like, you know, the guy in depicted in, you know, in a beautiful mind, he was just like, not, not the actual genius part, but just the, the, the, the, yeah.
Because I kept writing expanded, and I have no mathematical ability or I was making these mathematical expansions, where I kept seeing the same motif again. I was like, oh, I think this is a copy number. The same string is going again and again. I kept, I couldn't do the math. And then I realized the copy number fell out of the equation and everything collapsed down. I was like, oh, that works kind of. So we submitted the paper and then when it was,
almost accepted, right? The mass spec one. And it was astrobiologist, a gray, you know, a mass spectroscopist, a gray and the chemist went nonsense, like biggest pile of nonsense ever fraud, you know. And I was like, but why fraud? And they just said, just because.
And I was like, well, and so, and the, and I could not convince the editor in this case. The edit was just sloped off because they see it as like a kind of, you know, a, a, you're wasting my time. And I would not give up. I wrote, I went and dissected, you know, all the parts. And I think, although, I mean, I got upset about it, you know, it was kind of embarrassing actually, but, but I guess it's beautiful.
But it was just trying to understand why they didn't like it. So they were part of me was like really devastated. And a part of me was super excited because I'm like, huh, they can't tell me why I'm wrong. And this kind of goes back to, you know, when I was at school, I was in a kind of learning difficulties class and I kept going to the teacher and say, you know,
you know, how, what do I do today to prove I'm smart? And they were like, nothing, you can't. I was like, give me a job, you know, give me something to do, give me a job to do, something to do is we. And I kind of felt like that a bit when I was arguing with the, and not arguing, there's no ad homin, I wasn't telling the editor, they were idiots or anything like this, or the reviewers, I kept it strictly like factual. And all I did is I just kept knocking it down bit by bit by bit by bit by bit.
It was ultimately rejected and it got published elsewhere. And then the actual experimental data. So this is kind of in this paper, the experimental justification was already published. So when we did this one and we went through the versions and then we sent it in and in the end it just got accepted, we were like, well, that's kind of cool, right? This is kind of like, you know, some days you had, you know, the students, sorry, the first author was like,
I can't believe we got accepted, like, nor am I. But it's great. It's like, it's good. And then when the paper was published, I was not expecting the backlash. I was expecting computational, well, what, no, actually, I was just expecting one person who'd been trolling me for a while about it just to carry on trolling. But I didn't expect the backlash. And then I wrote, I wrote to the editor and apologized. And the editor was like, well, you're apologizing for it was a great paper. Of course, it's going to get backlash. You said some controversial stuff.
But it's awesome. I think it's a beautiful story of perseverance. And the backlash is just a negative word for discourse, which I think is beautiful. I think you, as I said to, you know, when it got accepted and people were saying, we're kind of like hacking on it. I was like,
Papers are not gold medals. The reason I want to publish that paper in nature is because it says, hey, there's something before biological evolution. You have to have that if you're not a creationist, by the way. This is an approach. First time someone has put a concrete mechanism or sorry, a concrete quantification. And what comes next you're pushing on is a mechanism.
And that's what we need to get to is an auto-coloredic set, self-replicating molecules, some other features that come in. And the fact that this paper has been so discussed, for me, is a dream come true. It doesn't get better than that. If you can't accept a few people hating it, and the nice thing is, the thing that really makes me happy,
is that no one has attacked the actual physical content. You can measure the assembly index, you can measure selection now. So either that's right or it's, well, either that's helpful or unhelpful. If it's unhelpful, this paper will sink down and no one will use it again. If it's helpful, it'll help people scaffold on it and we'll start to converge to a new paradigm. So I think that that's the thing that I wanted to see
You know, my colleagues, authors, collaborators, and people were like, you've just published this paper. You're a chemist. Why have you done this? Like, who are you to be doing evolutionary theory? Like, well, I don't know. I mean, sorry. Did I need to cause anyone to do anything? Well, I'm glad you did. Let me just before coming back to origin of life and these kinds of questions, you mentioned learning difficulties. I didn't know about this. So what was it like?
I wasn't very good. It's cool, right? This is when you're very young. Yeah, yeah. But in primary school, my handwriting was really poor, and apparently I couldn't read, and my mathematics was very poor.
So they just said this is a problem, they identified it. My parents kind of at the time were confused because I was busy taking things apart, buying electronic junk from shop, trying to build computers and things. And then once I got out of, when I was, I think about the major transition in my stupidity, like, you know, everyone thought I wasn't that stupid. Well, basically everyone thought I was faking. I like stuff and I was faking wanting to be it. So I always want to be a scientist.
So five, six, seven years old would be a scientist, take things apart. And everyone's like, yeah, this guy wants to be a scientist, but he's an idiot. And so, and so, so everyone was really confused, I think at first that I wasn't smarter than I, you know, it was claiming to be. And then I just basically didn't do well in the test and I went down and down and down and down. And then, um, and I was kind of like, huh, this is really embarrassing. I really like,
maths and everyone says I can't do it. I really like kind of physics and chemistry and all that in science and people say you can't read and write. And so I found myself in a learning difficulties class at the end of primary school in the beginning of secondary school in the UK. Secondary school is like 11, 12 years old. And I remember being put in the remedial class and the remedial class was basically full of
There were two types, three types of people. There were people that had quite violent, right? And there were people who couldn't speak English. And there were people that really had learning difficulties.
The one thing I can objectively remember was, I mean, I could read. I like reading. I read a lot.
But something in me, I'm a bit of a rebel. I refused to read while I was told to read. And I found it difficult to read individual words in the way that I told. But anyway, I got caught one day teaching someone else to read. And they said, OK, we don't understand this.
I always know what to be a scientist, but didn't really know what that meant. And I realized you have to go to university and I thought, I can just go to university. It's like curious people like, no, no, no, you need to have these, you have to be able to enter these exams to get this grade point average. And the fact is the exam should be entered into you're not, you're, you're, you're just going to get C, D or E. You can't even get A, B or C, right? This is the UKG CSEs. I was like, Oh, shit.
And I said, can you just put me into the high exam? I said, no, no, you're going to fail. There's no chance. So my father intervened and said, you know, just let him go in the exams. And they said, he's definitely going to fail. It's a waste of time, waste of money. And he said, well, what have we paid?
So they said, well, OK, so you didn't actually have to pay to pay if I failed. So I took the exams and passed them, fortunately. I didn't get the top grades, but I got into A levels. But then that also kind of limited what I could do at A levels. I wasn't allowed to do A level maths.
That's what I mean, you weren't allowed to. Because I had such a bad math grade from my GCSE, I only had a C. But they wouldn't let me go into the ABC for math, because of some kind of coursework requirement back then. So the top grade I could have got was a C, so C new E, so I got a C. And then let me do kind of AS level maths, which is this half intermediate, but to go to university. But in the art I liked chemistry, I had a good chemistry teacher, so in the end I got to university to do chemistry.
So through that kind of process, I think, for kids in that situation, it's easy to start believing that you're not, oh, well, how do I put it? That you're stupid. And basically give up that you're just not good at math. You're not good at school.
So this is by way of advice for people for interesting people for interesting young kids right now experience in the same thing. Where was the place? What was the source of you not giving up there?
I have no idea other than, I was really, I really like not understanding stuff. For me, when I not understand something, I didn't understand, I feel like I didn't understand anything, but now, but back then, I was so, I remember when I was like, I don't know, I tried to build a laser when I was like eight, and I thought, how hard could it be?
And I basically, I was going to build a CO2 laser. And I was like, right, I think I need some partially coated mirrors and need some carbon dioxide. And I need a high voltage. So I kind of, and I was like, I didn't have a, and I was so stupid, right? I was kind of so embarrassed. I to make enough CO2, I actually set a fire and try to filter the flame.
I was like completely completely failed and I bent but half the
