Using math to understand and define life

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00:00:12:07 Hi, everyone, my name is Dr. Alyssa Adams,
00:00:15:01 and I'm a physicist who fell in love with biology.
00:00:18:01 For my PhD, I studied the question
00:00:20:00 of what makes living systems
00:00:22:02 different than not living systems?
00:00:24:11 And more specifically,
00:00:26:08 is there a mathematics that we can use
00:00:28:10 to be able to understand and define life
00:00:31:06 and how it's exactly different
00:00:33:03 than nonliving systems?
00:00:34:29 Now, this is an interesting question,
00:00:36:26 because today, we're always looking at the stars
00:00:39:11 and we're always wondering,
00:00:40:15 "Is there a life outside of Earth,
00:00:43:22 outside of our solar system?
00:00:46:17 Is there any way that we'll be able to make contact
00:00:48:28 with even something that's like intelligent alien life?"
00:00:52:18 But even a more simple example,
00:00:54:06 "Could we make contact with something
00:00:55:21 that might not even be intelligent?
00:00:57:22 What would life look like even at all?"
00:01:00:14 So, let's imagine that we're astronauts
00:01:03:12 and not just any astronaut,
00:01:05:15 but we're the astronauts
00:01:07:04 that are going to be the first team
00:01:09:15 to visit a planet outside of our solar system
00:01:12:23 in the search for extraterrestrial life.
00:01:15:10 So, we already know that this planet is dark, it's cold,
00:01:18:09 and it's frozen just from what we know
00:01:20:14 by looking at astronomical data.
00:01:22:21 But as we get there and our ship lands,
00:01:25:04 we notice that the planet
00:01:26:19 is completely covered in this weird, black substance
00:01:30:02 that's very strange.
00:01:31:25 So, we get out of our ship, and we get some samples,
00:01:34:26 and we put it in our test tube,
00:01:36:10 and it looks completely different than what we would expect.
00:01:39:04 It moves in strange ways and it might turn different colors.
00:01:42:14 When we get back to the ship
00:01:43:19 and we bring our samples back home to Earth,
00:01:45:26 we wanna know, "Is this life?"
00:01:48:11 and more specifically,
00:01:49:23 is this life that doesn't have any chemistry
00:01:52:28 that we're used to seeing on Earth?
00:01:54:24 Because it's frozen, there's no water on this planet,
00:01:58:05 there's no oxygen, it's cold.
00:02:00:17 So, what would life have to look like in that environment?
00:02:04:15 So, chemistry is really just a way
00:02:08:02 that matter transforms over time
00:02:11:09 and we formalized those rules
00:02:13:05 and we know how those things look like
00:02:14:27 as they change over time,
00:02:16:10 thanks to chemistry.
00:02:17:22 But another way that things change over time
00:02:20:02 is by physically acting on a physical system.
00:02:24:16 Now, in this picture, we have a sculptor
00:02:26:25 who's physically doing a process
00:02:29:07 on something that's not alive.
00:02:31:09 Now, we all know that rocks aren't alive.
00:02:33:22 But in this example,
00:02:35:10 because the sculptor who is alive
00:02:37:26 is acting and doing a process on this nonliving rock,
00:02:42:07 we can say that the whole system together of the sculptor,
00:02:45:18 and the rock,
00:02:47:02 and the actual process of chiseling
00:02:50:20 is life.
00:02:52:01 So, how can we understand these physical transformations
00:02:56:22 and think about like,
00:02:58:10 what do we see and what were the processes
00:03:01:17 that got us there even though it's not alive?
00:03:04:09 So, we know that there's a difference
00:03:05:16 between life and alive
00:03:07:21 and just because something isn't alive
00:03:09:25 doesn't necessarily mean that it's not life.
00:03:12:26 So, another example is thinking about these rocks
00:03:15:11 that we find on Mars.
00:03:16:23 Now, there's a lot of things out there in space
00:03:18:29 or like other planets and the moon
00:03:20:14 that you look at it and it's like,
00:03:22:03 "Oh, that looks like a shape
00:03:23:12 that we would see here on Earth."
00:03:25:04 So, for this example, it just sort of looks like
00:03:27:05 a weird Pokemon hybrid between something,
00:03:29:29 and I guess an iguana.
00:03:31:10 That's what I see anyways.
00:03:33:02 So, our intuition is to say,
00:03:34:24 "Oh, that looks like an iguana.
00:03:36:05 We see iguanas on Earth.
00:03:37:12 We know that iguanas are alive, this must be alive too."
00:03:40:11 But it could be that the process
00:03:42:06 that got us to this rock shape
00:03:44:13 might look completely different
00:03:46:09 than what is actually something that's life and alive.
00:03:49:28 It could just be something like a boring geological process
00:03:52:18 or maybe an asteroid hit Mars
00:03:54:05 and it caused a funny shape to go off and roll down a hill,
00:03:57:05 and then that's what we get today.
00:03:58:26 So, it's not necessarily about the actual state
00:04:02:22 that we're currently looking at,
00:04:04:20 but in order to understand if it's life versus not life,
00:04:08:11 what we have to do is we have
00:04:09:27 to think about the processes that got us there.
00:04:13:04 And processes of things that change over time
00:04:16:19 is also known as evolution.
00:04:18:23 It's beyond chemical composition.
00:04:20:27 It's how things are changing over time.
00:04:24:01 Now, there's two kinds of evolution, I think,
00:04:27:10 that we're used to thinking about,
00:04:29:04 and the first one is more biological
00:04:31:17 and we think of, like, Darwinian Evolution.
00:04:33:22 Here's a diagram of the evolution of whales
00:04:37:17 over millions of years.
00:04:39:16 So, first, it sort of looks like a pig
00:04:41:22 and then it's sort of like maybe turned
00:04:43:20 into something that looks like an otter or something.
00:04:46:24 And eventually, over millions of years,
00:04:48:17 we have the whales that we see today.
00:04:50:28 So, that's a way that a physical system,
00:04:53:12 which is a living system, changes over time.
00:04:56:28 But as a physicist,
00:04:58:05 I also know that there's a different kind of evolution,
00:05:00:18 which is just this generalized notion
00:05:02:17 that things change over time,
00:05:04:10 like the evolution of our planets
00:05:06:28 going around our solar system.
00:05:09:06 This is two completely different kinds of evolution
00:05:13:12 where one is about biology and it's also open-ended as well.
00:05:18:16 And what it means by open-ended
00:05:20:07 is it means that it's constantly changing,
00:05:23:04 it's not repeating itself exactly over time,
00:05:25:15 and it's always coming up with new things.
00:05:27:20 But as a physicist,
00:05:28:28 we don't have any notion of how to capture that
00:05:32:14 in terms of mathematical equations.
00:05:35:16 So, what is the mathematics
00:05:37:08 of the system that uses open-ended evolution
00:05:39:25 instead of regular evolution?
00:05:41:10 Because we know a lot about mathematics of the evolution,
00:05:45:02 about how planets go around the solar system,
00:05:47:12 and we know a lot about the evolution
00:05:49:16 of a ball swinging in a pendulum
00:05:52:12 because these things are things
00:05:53:22 that repeat themselves over time, they have symmetries.
00:05:56:14 And no matter where you're at in space,
00:05:59:07 the laws of gravity
00:06:00:27 and all of those laws of physics are the same,
00:06:04:02 but it doesn't necessarily seem like that's the case
00:06:06:16 with biological evolution.
00:06:09:21 So, as an example,
00:06:11:08 I wanted to talk about my favorite game when I was a kid,
00:06:14:10 which is ""Conway's Game of Life."
00:06:16:17 Now, I first saw this "Game of Life" in 1996
00:06:20:12 with our brand-new Windows computer.
00:06:22:25 And basically, the way it works
00:06:24:23 is you are introduced with a grid
00:06:27:22 and if you're a white square
00:06:29:10 and you have a bunch of different squares around you,
00:06:32:06 depending on that configuration
00:06:34:04 of different colors of squares,
00:06:35:09 it's gonna tell you in the next time step,
00:06:38:00 if you are a white square or if you are a black square.
00:06:41:18 Now, this game got really, really famous
00:06:43:24 because it has really simple rules,
00:06:46:04 but you see all sorts of really cool emergent phenomena:
00:06:50:04 persistent structures,
00:06:51:24 things that move around the screen
00:06:54:06 in all sorts of interesting ways.
00:06:55:26 And people were really excited
00:06:57:11 because they thought, "Maybe life works like this too."
00:07:00:26 But is this system open-ended?
00:07:02:29 And unfortunately, for this case, it's actually not.
00:07:06:20 And I'm gonna tell you why.
00:07:08:19 In open-ended evolution, we have a system
00:07:11:27 that doesn't repeat itself exactly over time,
00:07:14:26 it grows in complexity.
00:07:16:15 "Conway's Game of Life" also grows in complexity as well,
00:07:20:06 but it doesn't do something that's known as innovating,
00:07:23:14 which means it creates something new over time.
00:07:25:28 So, you can have structures that may look new,
00:07:29:06 but as they grow, and they evolve, and they change,
00:07:32:07 they're not necessarily doing anything
00:07:34:15 that isn't completely predictable.
00:07:37:06 And biology is changing its rules over time.
00:07:40:06 And it's also changing its rules
00:07:42:02 according to the environment.
00:07:43:28 So, the natural selection of a bacteria
00:07:47:04 would look completely different
00:07:48:12 depending on what environment it's in.
00:07:51:00 But in terms of "Conway's Game of Life,"
00:07:53:14 that stuff doesn't matter at all.
00:07:55:13 The rules never change
00:07:56:28 depending on where they're at on the screen,
00:07:59:17 they're always gonna be the same everywhere.
00:08:02:27 So, can we get open-ended evolution
00:08:05:07 from simple computer programs like "Conway's Game of Life"?
00:08:09:06 Now, "Conway's Game of Life"
00:08:10:23 is a example of a two-dimensional cellular automata.
00:08:14:07 But in order to
00:08:15:29 make this a little bit simpler for ourselves,
00:08:18:13 instead of looking at the two-dimensional version
00:08:20:20 of cellular automata,
00:08:21:24 let's focus on the one-dimensional version
00:08:23:24 of cellular automata.
00:08:25:06 These have a special name
00:08:26:21 and they're called elementary cellular automata.
00:08:30:15 And what they do is there's 256 different kinds,
00:08:34:22 and the kinds are described according to the rule.
00:08:38:14 So, in this example,
00:08:39:15 we have one of those kinds of elementary,
00:08:41:20 one-dimensional cellular automata, and it's called Rule 110.
00:08:45:25 And you can see the rule table right here
00:08:48:14 and what the rule table is gonna tell us
00:08:50:07 is how everything is going to change
00:08:52:19 from one state into the next state.
00:08:55:00 So, I also have an initial state,
00:08:56:28 which is just my initial configuration
00:08:59:01 of white squares and black squares.
00:09:01:02 It could really look like anything I want.
00:09:03:12 But when I'm looking at the rule table,
00:09:06:01 I'm asking myself this.
00:09:07:22 "For every square that's in my state, what color am I?
00:09:11:00 And also, what color
00:09:12:04 are my two adjacent neighbors to myself?"
00:09:15:06 So, we can look up this for
00:09:17:07 the one black square that's there.
00:09:19:15 So, according to this Rule 110 rule table,
00:09:23:05 I have my one black square
00:09:25:02 that is surrounded by two white squares.
00:09:27:02 So, I know that in the next time step,
00:09:29:01 that square is going to stay a black square.
00:09:31:22 But there's also other squares on the board too.
00:09:34:20 So, there's also the white square
00:09:36:13 next to the black square on the right,
00:09:37:29 and there's also the one on the left,
00:09:39:17 and there's also the white squares
00:09:41:16 that are completely surrounded by other white squares.
00:09:44:08 So, when we put all of this together,
00:09:46:22 we get our next step, and this is what it looks like.
00:09:50:12 So, we continue doing this over time
00:09:53:07 and even though it looks like
00:09:54:12 a two-dimensional cellular automata,
00:09:56:11 it's not the same thing as "Conway's Game of Life"
00:09:58:25 because time is going downwards like this.
00:10:01:03 It's still one-dimensional,
00:10:02:18 but you can still just see the whole evolution
00:10:04:29 on the same picture,
00:10:06:01 which makes it really handy to do.
00:10:08:23 So, what happens when we get to the very end here?
00:10:11:14 Well, when we get to the end,
00:10:13:04 the boundary is just going to wrap around itself.
00:10:15:19 So, it's not an infinite plane
00:10:17:04 that's stretching off into infinity, it could be.
00:10:19:23 But for this example
00:10:20:28 in the cellular automata that we studied,
00:10:22:23 it's more like a Coke can.
00:10:24:14 So, once it goes all the way to the end,
00:10:26:08 it's simply just gonna pop out on the other side.
00:10:28:25 And each of these cellular automata
00:10:30:22 can look completely different
00:10:32:11 depending on what rules that we're using.
00:10:34:27 So, that was Rule 110, but this is rule 30.
00:10:38:05 So, you could see that the rule table is slightly different.
00:10:40:21 But as a result,
00:10:41:19 the pattern that you get from having an initial state
00:10:44:15 is going to look completely different as well.
00:10:47:23 So, what's an even easier way
00:10:50:08 to look at cellular automata?
00:10:52:11 Well, an even easier way
00:10:53:29 is just to think about how the states
00:10:55:26 are changing into other states
00:10:57:20 in a state space transition diagram.
00:11:01:18 So, the way that we do this
00:11:02:26 is we look at all the individual unique states
00:11:06:08 and we're gonna take that state
00:11:08:00 and we're going to represent it as a circle.
00:11:12:18 But, according to this rule,
00:11:13:24 it's going to evolve
00:11:14:21 and it's gonna turn into a next state
00:11:16:14 and that's like this purple state here.
00:11:18:10 And the way that we would represent that is a yellow dot
00:11:21:14 and an arrow pointing towards the next state.
00:11:24:11 Because what we're saying
00:11:25:17 is that this state leads to this next state,
00:11:29:13 according to this particular cellular automata rule.
00:11:33:11 Now, we continue to do this for all the different states.
00:11:36:29 And remember how we're not having an infinite plane
00:11:40:07 instead, we just have like this
00:11:41:23 constrained, Coke-can world.
00:11:44:15 And because it's like this wraparound boundary,
00:11:47:04 this pattern is eventually going to repeat itself over time.
00:11:50:23 So, what that's gonna look like
00:11:52:14 is it's going to look like something
00:11:53:22 that has a cycle in its state space topology diagram.
00:11:58:10 This is just a cartoon example.
00:11:59:26 If I were to actually write this out for Rule 30,
00:12:02:10 this would actually look much bigger.
00:12:05:04 Now, all elementary cellular automata
00:12:07:17 are going to repeat themselves exactly
00:12:09:24 and that's not very helpful for open-endedness
00:12:12:02 because what we want for open-ended evolution
00:12:14:10 is something that doesn't repeat itself exactly over time.
00:12:17:10 But this is how they repeat themselves,
00:12:19:15 just so that you know.
00:12:21:05 So, if you start in any state
00:12:23:01 on the state space diagram,
00:12:25:01 what's gonna happen is you just simply follow the arrows
00:12:27:26 to know which state you're gonna get to next.
00:12:30:08 So, if I start here, I follow my arrow into...
00:12:33:12 I'm in the next state, and then I'm gonna follow that,
00:12:36:02 and I go to the next state, and so on.
00:12:38:29 But you'll notice that for every cellular automata,
00:12:42:06 you get stuck in a cycle
00:12:44:24 and you're going to repeat yourself
00:12:46:26 after a certain number of states.
00:12:49:16 So, we can look at all of the different
00:12:52:03 elementary cellular automata rules
00:12:54:00 and all the different initial states.
00:12:55:29 And what we get is we get these unique maps.
00:13:00:23 If I'm in Rule 240,
00:13:02:25 it turns out there's these like distinct islands
00:13:05:05 and once I'm stuck in one of those islands,
00:13:07:04 I can't necessarily visit any of the states
00:13:09:12 according to this rule.
00:13:10:20 And that's a really big problem for open-endedness
00:13:13:27 because open-endedness requires
00:13:15:20 that something has to continuously innovate
00:13:18:01 and it continuously has to reach new states.
00:13:20:17 So, if I'm stuck in a rule where it forces me into islands,
00:13:25:02 then that doesn't seem very open-ended at all.
00:13:28:26 So, what's it gonna take in order to explore new states?
00:13:31:21 What's gonna happen if I'm stuck in one of these islands
00:13:34:02 according to some sort of rule?
00:13:35:22 Because it could be that...
00:13:37:11 Well, the laws of physics are sort of like rules.
00:13:41:04 They tell us how states change over time.
00:13:43:19 Are we stuck on an island
00:13:45:20 according to the laws of physics?
00:13:47:17 And how are we going to break out of that?
00:13:50:00 So, say, you are a bacteria
00:13:53:11 and you're in an environment
00:13:55:24 that has an abundance of all sorts of foods,
00:13:58:22 but you're not able to digest any of the food
00:14:01:16 because you don't have any of the proper enzymes.
00:14:03:24 What you want to do as the bacteria is you want to thrive.
00:14:06:27 You're in a state that you're not thriving
00:14:08:25 and you want to go into a state
00:14:10:17 where you are thriving and taking advantage
00:14:12:21 of all the food in the environment.
00:14:14:18 So, what you do is you go through this process
00:14:18:16 of random mutations and natural selection
00:14:21:04 and after some point,
00:14:22:16 you find yourself the ability to make this new enzyme
00:14:26:08 and to be able to digest this food.
00:14:28:17 So, the rule that was able to take you there
00:14:31:09 in this biological example
00:14:33:02 is random mutations and natural selection.
00:14:36:06 But that random mutation and natural selection,
00:14:39:24 these evolutionary pressures,
00:14:42:00 might look completely different
00:14:43:21 depending on what environment you're in as an organism.
00:14:48:05 And also, it might be completely different
00:14:50:05 depending on what state you're currently in as an organism.
00:14:53:07 So, if I'm already in a state
00:14:54:22 that I can already process this food,
00:14:57:13 there's absolutely no reason for me
00:14:59:07 to change into another state at all.
00:15:01:23 So, the exact rules and the exact mechanisms
00:15:04:22 that drive the actual rule
00:15:07:00 that takes you from one state to another state
00:15:09:01 can change completely differently.
00:15:11:10 So, I represented that by having different-colored arrows.
00:15:15:09 So, it seems as if biology
00:15:17:20 doesn't necessarily look like this,
00:15:19:16 where if you were to draw or imagine
00:15:22:05 all the possible states that biology could be in,
00:15:25:11 the transitions between those states
00:15:27:13 would not look like something that is only one rule.
00:15:31:12 So, for cellular automata,
00:15:33:02 these elementary cellular automata
00:15:34:20 only evolve according to one rule.
00:15:36:23 So, in this example, this is Rule 30.
00:15:39:02 So if I have Rule 30,
00:15:40:22 there might be an island out there
00:15:42:13 of a state that I can't reach according to Rule 30.
00:15:45:24 But if I look at Rule 110, I can reach that.
00:15:49:12 But then, even if I shift to a new rule
00:15:51:20 that's completely new,
00:15:52:27 I'm gonna be losing states in the meantime.
00:15:55:14 So, what am I gonna do
00:15:56:28 in order to reach all of the states?
00:15:59:21 And the answer is to use many rules;
00:16:02:24 not just one rule, but many rules.
00:16:04:28 So, maybe, to go from one state to another state,
00:16:07:15 you use Rule 110,
00:16:09:16 and then going from another state
00:16:10:25 to another state that's unreachable,
00:16:12:26 you can use Rule 30 in order to do so.
00:16:16:18 So, this brings up an interesting question
00:16:19:26 of, "What's picking the rule?"
00:16:23:05 So, if we think that biology
00:16:25:20 is acting like a cellular automata system
00:16:29:08 that is able to pick its new rule to get to new states,
00:16:33:01 how is it doing that?
00:16:34:20 And what we know from biology
00:16:36:16 is that it seems that it's doing it from the environment,
00:16:40:09 it's getting information about its environment;
00:16:42:12 and it's getting information
00:16:43:18 about its current state that it's in,
00:16:45:17 like its current configuration;
00:16:47:04 and it's using that information
00:16:48:29 to drive how it picks the new rule
00:16:51:25 and to create the new rules.
00:16:55:05 So, let's step back from cellular automata for a second,
00:16:57:24 just for a second, we'll get right back to it.
00:16:59:24 But we're gonna think about a whale in an ocean.
00:17:03:22 So, going back to our whales.
00:17:06:01 They're evolving and they're changing over time,
00:17:08:21 but they're also in this ocean
00:17:10:17 and we don't necessarily think of the ocean
00:17:13:04 as being particularly open-ended.
00:17:15:16 We just kind of think of it as the environment.
00:17:18:03 But we also know that the environment
00:17:20:05 also has like little things embedded in it
00:17:23:01 that are living systems.
00:17:24:05 So, we could even say
00:17:25:17 that the environment is open-ended too,
00:17:27:28 but for now, we're just gonna be really simple.
00:17:29:23 We're gonna say that we have an open-ended biological system
00:17:34:12 embedded in something that is not open-ended
00:17:37:24 and it's something that's sort of like regular evolution.
00:17:43:13 So, what we did is we decided to explore
00:17:46:08 that kind of system.
00:17:47:15 Like, what if we took that idea
00:17:49:11 and we turned it into a cellular automata model?
00:17:52:04 And so, we have two different cellular automata
00:17:54:29 to represent both of those things.
00:17:57:27 So, we have one that represents like a whale system
00:18:00:18 where it's a biological system
00:18:03:06 that's capable of changing its rules over time.
00:18:05:26 And then we also have this other system
00:18:08:13 that is not changing its rules over time,
00:18:11:29 it's just doing regular evolution,
00:18:13:17 a regular cellular automata, like the "Game of Life."
00:18:16:06 But that's the environment that the thing is in.
00:18:18:26 So, we had it set up such that
00:18:22:16 as these two things evolve at the same time in parallel,
00:18:26:28 but they're separate, they're kept separate,
00:18:29:07 what the whale system, the biological system,
00:18:31:24 is going to do is at every time step,
00:18:34:14 it's going to ask itself,
00:18:36:10 "What is my current state
00:18:38:02 and what is the current state of the environment in?"
00:18:40:28 And it's gonna take those two things
00:18:42:26 and put it into an arbitrary function that we just made up
00:18:46:04 in order to pick its new rule.
00:18:48:19 So, we do recognize that when we did this,
00:18:51:05 we were just arbitrarily picking like a function,
00:18:54:16 but we tried many different functions.
00:18:57:01 But the point is that we just wanted to see
00:18:58:24 that if there is a function that takes in those two things
00:19:03:07 in order to pick a new rule, do we get something?
00:19:06:14 Do we get a cellular automata system
00:19:08:16 that actually looks more like biology
00:19:10:24 than something that "Conway's Game of Life" does?
00:19:13:08 And it turns out that, yeah, we do.
00:19:16:03 So, here are these whale systems.
00:19:18:02 So, these are our open-ended cellular automata on the left
00:19:21:28 and you can see that they look really different
00:19:25:07 and you can see where the rules
00:19:26:27 have been changing over time.
00:19:28:23 And on the right side,
00:19:29:19 you can compare these to the regular cellular automata
00:19:33:03 that don't change the rules over time.
00:19:35:02 They're not open-ended,
00:19:36:05 we know that they're not open-ended,
00:19:37:16 they're not like biology.
00:19:39:00 But what we do have on the left
00:19:40:16 is something that is actually starting to look like biology
00:19:43:11 because it's taking these biological principles of,
00:19:46:07 "Oh yeah, we can change a rule
00:19:47:29 based off of the environment
00:19:49:05 and we can change a rule based off of our current state."
00:19:52:04 And what we get is something
00:19:53:19 that we have never seen before in cellular automata,
00:19:56:10 which is extremely exciting.
00:19:59:05 So, the lesson to be learned here
00:20:00:29 is after we looked at this
00:20:02:04 and we thought about it is that open-endedness
00:20:04:15 happens in the smaller part of a whole system.
00:20:07:20 But that actually makes a lot of sense, right?
00:20:10:00 Because when we think about ourselves on this planet,
00:20:12:28 in the middle of the solar system,
00:20:14:23 we're not thinking about the open-endedness of the universe,
00:20:17:28 we're just thinking about the open-endedness
00:20:20:02 of ourselves and biology.
00:20:22:11 So, it really made sense
00:20:25:03 and we felt really good about those results because of that.
00:20:29:02 But, of course, there's still more to explore.
00:20:31:17 So, we wanna understand,
00:20:33:16 "Okay, how does structure equal function
00:20:36:12 and how can we put that into mathematical sense too?"
00:20:39:23 So if we're thinking about something like proteins;
00:20:43:17 one of the common things that's said in biology
00:20:45:19 is structure equals function.
00:20:46:24 If it looks like scissors, it probably is scissors.
00:20:49:04 It's gonna cut something.
00:20:50:29 But what is the mathematics behind that
00:20:53:00 and how is that driving the ability for a system
00:20:56:03 to create new rules
00:20:57:14 and new laws of physics to evolve itself over time?
00:21:02:09 What about things like viruses?
00:21:04:17 Can we apply this knowledge
00:21:07:08 to understand how viruses
00:21:09:00 are going to evolve and change over time?
00:21:11:07 Because they're made of proteins
00:21:12:29 and proteins are a lot like computer systems
00:21:15:19 because they have this really great ability to...
00:21:19:00 As they change and they evolve like this,
00:21:22:18 they're also changing their function.
00:21:24:28 They're changing the rule as well in order to interact.
00:21:28:18 So, how can we take that and understand things like viruses?
00:21:31:28 What we really want in the end
00:21:33:16 is we really want something that looks like a calculus,
00:21:36:05 but for biological evolution
00:21:38:00 because when we get off the planet
00:21:40:11 and we're saying goodbye to our brand-new planet
00:21:43:16 that we just collected our samples from,
00:21:45:28 we are really happy that we're able to
00:21:49:19 use our information about the laws of physics
00:21:52:07 to get our spaceship all the way back home,
00:21:54:28 and to slingshot around other planets, and stuff like that.
00:21:57:29 But we don't have those same mathematical equations
00:22:01:21 to be able to look at a sample of viruses or bacteria,
00:22:06:00 and to have a set of equations
00:22:07:24 to understand what it's going to look like
00:22:09:25 in a particular context over some amount of time.
00:22:14:26 But that's what we're working for
00:22:16:10 and that's what we're really excited to see in the future.
00:22:20:02 So, I wanna give a special thanks to everybody
00:22:22:18 who's worked with me
00:22:24:07 and has been amazing colleagues;
00:22:27:02 everybody from my PhD program
00:22:30:07 and everybody who is a part of
00:22:33:03 my current postdoc position at UW.
00:22:36:03 So, I wanna give a special thank you to everybody there
00:22:39:02 and special thank you to you for listening to this talk.
00:22:43:09 Thank you so much.

This Talk

Speaker: Alyssa Adams

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Recorded: November 2020

Talk Overview

Is it possible to define life using math? Dr. Alyssa Adams’s research seeks to answer precisely this question. In their Share Your Research talk, Dr. Adams introduces the concept of open-ended evolution, and describes how they have developed mathematical models to help us understand how biological systems can innovate within a changing environment. These studies could help us to identify living systems beyond our planet that may be vastly different from those we recognize on Earth!

Speaker Bio

Alyssa Adams

Alyssa Adams got their PhD in Physics at Arizona State University, where they studied the difference between living systems and non-living ones. Alyssa is currently a postdoctoral fellow at the Morgridge Institute and the University of Wisconsin-Madison where they study interactions between viruses and hosts they infect. These interactions might help us understand how biological… Continue Reading

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Brittany Anderton (iBiology): Producer
Eric Kornblum (iBiology): Videographer and Editor
Chris George (iBiology): Graphics
Maggie Hubbard (iBiology): Graphics

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