Simulating an Evolving Microcosmos: The Path to Multicellularity

foreign to fish and to humans all of the living organisms that you can see around you exist thanks to evolution by natural selection yet these Origins lie hidden in an unseen microcosm of the cellular world I've been interested in these processes for a while but as a computer scientists and not a biologist I've been exploring Evolution through a lens of programming in this video I'm going to show you a simulation I've been working on and introduce you to some of the interesting emerging dynamics of this world as with any computer simulation of a real world process we'll be making vast simplifications of the system we're studying for me this has been a great excuse to learn some biochemistry and I decided that what I was most interested in is the evolutionary Bridge from single-celled organisms called protozoa to multicellular systems this was a key transition on the path from the microcosm the world of animals and other large organisms what you're seeing on screen now is an evolved multicellular structure in my simulation but before I explain how this all worked let's rewind to the start [Music] the event that you just witnessed was the birth of a simulated protozoan-like creature the result of the parent cell splitting into multiple new copies of itself but with the chance for a random mutation over the course of this video we'll dive into how these organisms can see reproduce generate complex molecules and engage in cellular construction projects before going into too many details of how everything works I think it's instructive to just start by watching this protozoan as it develops in the UI we're tracking it as it moves around this environment controlling its speed and angle of turning via a no network controller that's evolved we can see this network visualized on the right hand side of the screen with its neurons and synapses dancing in flux with the activations of its various input sensors in order to survive and reproduce first and foremost to protozoan must collect mass and energy from its surroundings the simplest way to do so is by harvesting it from the green plant cells that are floating around in the environment plateaus themselves can have their own complex Dynamics and must also grow and reproduce but in the current situation they do not have volatile traits starting out with the basics there were two key morphological traits that the protozoa can evolve to help them survive firstly they can grow spikes that extrude from their surface and damage other cells that they come into contact with secondly they can develop a light sensitive retina that can help them navigate by facilitating vision throughout this video we'll go into more detail about how these systems work but for now we can see that the life of this protozoa is coming to a close as this managed to survive to the point where it can split into new child cells let's rewind to the beginning of the simulation to get a more holistic view of the environment we're looking at a zoomed out view showing the entire world of the processor I've paused the simulation so that we can focus on some key on-screen elements first the jagged rock-like structures over the map these are procedurally generated rigid boundaries that restrict the movement of cells by keeping populations somewhat separated we provide the potential for speciation along separate evolutionary branches in evolutionary biology this is called allopatric speciation one of the most striking ways in which this can happen is through the movement of confidence atop shifting to Tonic plates as land masses separate from one another new viable ecological niches may arise then when they come back into contact new developments are in effect tested against one another the next thing to note is the clusters of initial populations we have three centers of various sizes in which groups of plants and protozoa spawn from this Mountain's point it's hard to see many of the individual cells but if we play this time lapse we can get a sense of the Dynamics that set in motion evolution we can see that the plants are slowly growing and spreading across the map the simulation is built on top of a physics engine that I coded to handle collisions and the movements of cells as you can see plant cells tend to Clump together as they exert an attractive force on one another when they're close by this keeps the plants from spreading out too thinly across the environment as well as providing more concentrated resource sites for protozoa to compete over however often fast-moving plants escape the attractive forces of their neighbors and are launched away from the group these Intrepid explorers act as spores set out to populate new areas and pave the way for protozoa that require their presence to thrive you'll also notice a glowing effect emanating from the plant cells and trails of green blooming behind them like the tales of comets these are pheromones being released into the fluids of the environment and they're detectable by protozoa in the area [Music] from this closer point of view we can see how the lives of the protozoa begin these featureless colorful cells that you see are the initial protozoa are bumbling amongst the plants almost all of these first experiments are short-lived yet among the failures there are some successes the protozoan that we're now watching is one of Evolution's early tinkerings with vision we can see a single retina cell facing outwards along the direction of this travel and mirroring the object in its field of view let us turn briefly to go into slightly more detail about how the retina mechanics are implemented the image on screen shows a protozoa with a highly developed retina consisting of eight cells arranged across a 90 degree field of view these cells work according to a raycasting algorithm used in many computer Graphics systems [Music] returning to the simulation you may notice that the retina of this protozoa appeared Stark and faint this is because it's underdeveloped in order to understand the process that facilitates Vision we first need to turn to a more detailed look at how cell growth works in the simulation in this diagram we're going to lay out the flow of energy and mass to facilitate cell growth beyond the straightforward processes of growing larger and repairing damage a cell can engage in various projects the projects here are retina growth Spike growth and cell adhesion the last of which is a more complicated aspect that we'll get into shortly however a key ingredient in most projects is complex molecules for instance in order to grow retina a cell must have a supply of the molecule retinal available thus another way in which a cell can use mass and energy is the production of these molecules a key part of this process is that when approaches Owen dies its supply of complex molecules can be scavenged by another protester allowing for them to bypass the need to make it themselves this opens up a possible Avenue for predation to take hold in an ecosystem next we're rejoining the simulation after over a thousand Generations has passed and we can see around 700 000 protozoa have lived and died what we're seeing here is the emergence of multicellular structures starting to perform these structures are able to exist due to the presence of cell adhesion molecules being produced by the cells cell adhesion is an important part of biology and it's the key bridge between the microscopic and the macroscopic worlds there are many ways in which cells combine together each with their own particular functions for instance some bindings are purely structural and keep cells together but others can allow for cells to share important molecules or resources to one another and even communicate I've implemented two types of cell binding so far the first are anchoring Junctions simply hold cells together the second a channel forming Junctions that allow cells to transfer mass energy and complex molecules to each other the multicellular structure that we're watching is held together with Channel forming bindings which is allowing cells that are not in contact with any plants to still receive nutrients to maintain themselves and grow interestingly with the introduction of cell binding though emerges a new form of asexual reproduction this being the reproduction of a multicellular structure through the kinds of fission that we're witnessing here the colony of cells gets too large and either through individual cells splitting or dying the overall structure splits having now outlined how most of the key mechanics of the simulation work let's now turn to understanding The evolutionary processes taking place firstly we need a way to visualize the Dynamics on this graph of plotting a hypothetical distribution of a trait across population at some point in time in order to get a hand on the problem of understanding Evolution we're starting here with an idealized example rather than diving straight into Data from the simulation to this end we're also not considering any trait in particular but for reference in the simulation this could correspond to something like the size maximum speed color or any number of features for approach 7. the code that we're looking at is a gaussian distribution centered around the mean denoted with mu sub 1 where the subscript 1 is telling us that we're looking at the first generation suppose that most of the individuals in the population die and fail to reproduce but those in this green region of the graph I.E those with a sufficiently high value for the trait are able to survive and pass on their genes this means that in the Next Generation we'll see a new distribution over the traits as indicated by this new yellow gaussian in this example we're seeing a very large selection pressure for the traits as only individuals in this critical region are able to survive and the distribution is shifted dramatically very quickly but let's take a step back and ask how do we even measure evolutionary change this question famously goes back to the evolutionary biologist JBS holdain who is considered to be one of the founding scientists of our modern statistical conception of evolution by natural selection and an all-round fascinating character as such there's an important measure of the rate of evolutionary change named after haldane despite this unit actually having been defined later by another biologist called Philip Gingrich on the screen now we have the equation for computing Holdings firstly I want to draw your attention to this term in the denominator this is the number of generations between measures which in our case is just going to be one so we can remove it next we have this somewhat unusual notation for the other term in the denominator this is the pooled standard deviation of the log values of the trait between Generations so let's unpack that statement firstly a pooled standard deviation is actually very simple it's just the weighted mean of the standard deviations where more weight is given to larger populations the intuition behind this can be demonstrated with a couple examples let's construct an example population here with protozoa of different redness now the population of the second generation is large and has very small variance this is an indication that the individuals with that trait near the mean in the second generation have been disproportionately successful on the other hand rewinding to the previous generation suppose that everything else is constant but we have a much smaller second population in this case the smaller the initial variance the more pressure must have been exerted to only select the individuals far from the mean finally you may be wondering what those logarithms are doing in the equation these are here in order to make the whole day dimensionless as the difference between two logarithms is dimensionless the advantage of this is that we're more able to cross-compare The evolutionary rates of different traits now turning to some data in the simulation itself here we're looking at how the network depth trade changes over time on the left we have the distribution of the trait represented again as a normal distribution on the right we're seeing how the mean changes over time as we can see throughout the simulation the neural network controllers approaches over tends to increase next let's look at the rates of evolutionary change for a couple of the traits throughout the simulation going from least to most we can see that the Hub of all Factor trait decreases slightly over time this trade controls how the processor imbalances the trade-off between extracting food from meat or plant cells I.E when it's higher they get more out of plants and vice versa for meat however in this case what we're seeing is a little misleading as in fact we can see from this graph that the trait varied quite a lot throughout the simulation this goes to show the importance of considering the scale at which we measure evolutionary change moving on to the next trait here we have genetic size this trade controls how large the maximum size of the Bush's Owen is in this case we can see the change is basically zero but again there was variation throughout the Run not captured here next is Max turning this is the first trait of this sample to have clear evolutionary change Max turning controls how swiftly approaches Owen can turn its body to move or see in a new direction we see they're clearly gradually increases over time finally we have Network depth and network size these both grew throughout the simulation the most we see the network size outstrip depth which corresponds to adding more neurons in parallel rather than in sequence this could perhaps be due to how adding sequential neurons increases the time between inputs and actions in effect the protozoa has slower reflexes [Music] in the last section of this video I'd like to talk a little bit about my further plans for this project some of which have already started implementing the code as touched upon previously a mostly interested in multicellular systems however I'm yet to mention some of the most interesting ideas regarding how this is thought to have evolved in real life to understand this we first have to talk about cell specialization and gene expression specialization is the process that occurs during development of multicellular systems where cells take on Specialized roles for instance at first to developing multicello organism mostly consists of what's called stem cells these are cells without a unique function but rather can transform into other kinds of cells such as liver cells or neurons we can imagine the space of possible cells as a plane with these different types of cells placed at different locations now let's give each point a height value according to What's called the cell's blower potency this is the cell's potential to become other cells or other kinds of cells the resultant surface is what we call waddington's Landscape named for its originator Conrad Waddington another eclectic scientists in the history of evolutionary biology Waddington imagined that throughout development cells descend the slopes of this landscape in order to end up in its valleys for instance here we have the trajectory from a stem cell to a neuron the visualization highlights that cells with low plural potency tend to remain in their developed form even when we give this marble in the neuron value substantial nudge we see that it falls back into place for those of you familiar with the area of mathematics called dynamical systems theory specialized cells are fixed point attractors on the other hand through a potent stem cells end up in vastly different places depending on the initial conditions represented here as the initial velocity of the marble so that's cell specialization but the next piece of the puzzle for multicellular systems is regulated gene expression in Biochemistry gene expression is the translation of DNA into a special kind of molecule called RNA and RNA into proteins these proteins are then we'll go on to give the solid specific traits this process is so important that it's called the central dogma of molecular biology it's here that we see how Expressions regulated I.E how the effects of genes are turned on and off in response to various conditions thus giving a cell specialized functions according to the situation it finds itself in this conditional behavior is a key player in evolution it's something that benefits single-celled organisms and thus creates the bridge in evolution to multicellular organisms now here's where things could delightfully elegant and self-referential some of the conditions of Gene expressions are themselves encoded in genes in other words if we have a sequence of genes the activation of one gene can deactivate another Gene and perhaps the first Gene itself was regulating another Gene that was regulating the first leading to a cycle of Regulation we could represent the set of all these interdependencies with a gene regulatory Network each node represents a gene and we can color the nodes according to whether or not they're activated or not in other words whether or not they're transcribing DNA to make proteins the edges represent the regulatory dependencies that genes have on one another and these put in motion the Dynamics of the system what you're seeing on screen is a random Boolean Network model of Gene regulation but we'll get into that in the next video where I show you how I'm going about encoding these processes in my simulation so that's going to be it for this video hopefully you've learned something about Evolution and biochemistry and if you're interested in seeing how this project goes then subscribe to this channel for updates find all of the code for the simulation on my GitHub but I should know that it's not very optimized and requires quite a powerful computer to run the code for the GUI is also a terrible mess so apologies to anyone who dares to look at it I started this project quite a long time ago and recently came back to it so yeah I definitely wish I had taken a different approach of the graphics anyways thank you for watching I'm Gonna Leave You some footage from me just exploring simulation [Music] foreign [Music] foreign [Music] foreign

Channel: Dylan Cope

Views: 157,486

Keywords: simulation, programming, coding, evolution, natural selection, protozoa, multicellular, emergence, advanced, biology, biochemistry, java, ray casting, physics simulation, game development, science, AI, genetics, algorithms, computer science, genetic algorithms, gene regulatory networks, waddington's landscape, gene expression, gene regulation, multicellularity, graphics, complexity, dynamics

Id: fEDqdvKO5Y0

Length: 19min 3sec (1143 seconds)

Published: Wed Jan 04 2023

Reddit Comments

[u/No-Seaworthiness2985]

I actually saw this in my recommended (amazing vid btw). It’s funny seeing it here

[u/10buy10]

I watched it and goddamn what a good video! I really wish more people made evolution sims like Thrive or the one in the video.