Chris Kempes
speaker
209 appearances
1 recordings
1 series
first heard Jan 2025
last heard Jan 2025
Chris Kempes’s voice in public audio — every appearance, attributed to the second.
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So as organisms evolve and become more complicated, you can get new sorts of constraints. For example, why I already put the brain here And so if I'm going to put an eyeball somewhere, where should I put it? And that's that's a constraint that's related to an evolutionary decision that was made in the past. And we often call those sort of emergent constraints or physiological constraints.
But often when I'm saying physical constraints, I just mean the laws of physics, diffusion, gravity, fluid flow, electricity and magnetism. These are the sorts of things that we're thinking about.
Yeah, that's a great way to put it. And I often like to say that evolution according to physical constraints is sort of the ultimate convergence. It's the thing you can most rely on. It's the thing most likely to happen. And we wrote a paper where we said, you'll get optimization of traits if those traits depend on a very dominant physical constraint.
And what I mean is, to your point about quantum mechanics, Quantum mechanics certainly apply at the scale of entire trees. They're not a dominant constraint.
So many of the quantum mechanical effects have been averaged out over all of these different particles that I don't really need to use, say, the Heisenberg uncertainty principle to understand what's happening for a vascular plant or a large tree. However, that constraint is still there. But gravity is a dominant constraint.
And so if traits are connected to a dominant physical constraint, evolution will be able to see that and we'll get traits that look optimized according to those physics. And then those traits have to be independent enough of other traits. So there are many cases in biology where you would like to optimize something, but you can't for other reasons.
So a great example is our optic nerve that connects our eyeball to our brain is longer than it should be. And many people have talked about this optic nerve seems much longer than you would want in some optimal case. And part of that is likely due to the fact that you had brain structure and you develop this eye. And then it's really hard to remodel the brain.
It's really hard to move around different modules of the brain. And so you deal with a slightly longer optical nerve because it's just too hard to change everything else. And so that's what we mean by traits have to be independent enough of other traits in order for these physical constraints to be something that evolution can see.
Exactly. Yeah, exactly. And so what we often – in that same language, what we would say about some of these physical constraints is they create these huge valleys that are impossible to miss. Yeah, good. And so even though there's roughness on the surface, it's really like a –
gently undulating grassy hill that is all part of one big valley, one big watershed, where everything rolls to the bottom. And that's just because the physics is so strong, the physical constraints are so strong, you can't help but wind up in the bottom of the valley. But there could be emergent constraints that create local valleys that you can't get out of, as you were mentioning, and so forth.
Yeah.
Exactly. And there's a lot to say about that. I mean, so one really interesting thing is that often the only way to explain what you see in organism structure or function is from a consideration of multiple constraints.
And so what you do is you say we find some way to write down what in some fields would be called a global cost function, where you put all of the terms of different constraints in that same cost function. And maybe one trait interfaces with multiple different constraints. And then we just optimize over the whole thing and find some global optimum under multiple constraints.
This is an old idea in engineering. And that often is the best way to explain complicated structures. So for vascular plants, which I was mentioning, you know, the original work on that showed it wasn't just about gravity. It's also about fluid flow through the vessels of the plant.
And it's about trying to fill space with all the endpoints of the network so that you can either distribute leaves in space to uptake, you know, atmospheric space to uptake sunlight or to distribute cells in the body and feed them all with this vascular network. So that's a place where you sort of have three main constraints that you're optimizing over.
Absolutely. So I think we wouldn't – I'm willing to make bets about that actually. Okay, good. That if you get large multicellular organisms, they will have fractal-like vascular networks with a certain very specific fractal structure that have been outlined in the whole thread of work. that I was discussing before.
And so we have some evidence for that in that, you know, the vascular system in plants evolved independently of the vascular system in mammals, and yet they share a huge amount of the same structures. So that's convergent evolution with a huge evolutionary divergence in time One organism is motile, the other organism isn't.
One uses sunlight for energy, the other eats things that use sunlight for energy. One's warm-blooded, one's not. And yet the vascular systems we find share certain commonalities because they are roughly the same size and obey in the same physics.
So I think these are sort of the ultimate astrobiological convergence.
Yeah, so bacteria, we think of them as these tiny sacks of stuff, which in some ways they are. But even, as Sean was just saying, even then we get a factor of 10,000 in cell size, right? So that's four orders of magnitude from the smallest cell to the largest cell. And the smallest cell and the largest cell don't look like each other in lots of different ways that we can get into.
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