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so yeah and thank you to all the speakers that went before me so what what I thought I would do today is give you a one person's perspective and it's a computational perspective on what we think is coming I think we all think
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this is coming the biology revolution and in thinking about this we can ask ourselves what insights allowed computer science to have these remarkable impact on our life and to drive the information
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technology revolution and I think three things can be pulled out one is this profound distinction between hardware and software and a focus on the control of information not just energy and matter and in particular the development
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of these things we call compilers which allow you to abstract away from low-level details and think about the the rational design of modular functionality and so this is sort of the progress in the field where we used to program by literally moving wires around
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so you have to think about the hardware the hardware was everything and then eventually we got this nice machine code still very much wedded to the hardware but eventually we have these modular sort of object-oriented languages where
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if you're writing things like this you really don't need to know very much at all about what's underneath and the reason I think this is relevant is because in biology we're getting good as you saw today we're getting really good at manipulating molecules and cells the
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hardware we're getting much better at this but we're really a long way from control of a large-scale form and function such as let's say regenerating an arm and so the question is can we work to move biology beyond a focus on
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the machine code and I'll just give you two simple examples we know a lot about DNA but here are two puzzles we have these planaria of which you'll see more in a minute but basically these guys reproduced by and these are flat worms
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they reproduce by tearing themselves in half and then regenerating okay so over the last let's say five hundred million years these guys have been practicing somatic inheritance every mutation that doesn't kill a neo blaster or stem cell
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makes it into the next population their genomes are a mess because they could they accumulate changes continuously and yet despite all of this they are perhaps the champions of regeneration every time they regenerate their Anatomy
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is spot-on perfect okay so we have this puzzle what is the relationship between this amazing patterning ability and and their genome which is I mean these animals are mix employed all the cells of different numbers of you know copies of the genome
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it's it's crazy and you can ask yourself another question if we take two species of planaria let's say with a flat head and a round head and you mix the stem cells one into the other so that there's now a combination and you cut this off
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what shape head are you going to get and we have no idea and the reason we have no idea is that while we are understand a lot about the molecules and the mechanisms that are required for some of these things we do not understand the algorithms that link large-scale shape
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and pattern to the underlying hardware and so the reason this is important is because if you look at some of the major problems of biomedicine has so it's a birth defects traumatic injuries cancer aging what do all of
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these things have in common well one of the things they have in common is control of anatomical structure errors of initial patterning loss or damage in adulthood defection of cells from the anatomical plan aging and so on
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and so the idea might be that if we could address not only the mechanisms but the algorithms of patterning such that we had control of anatomical growth and remodeling we could really make an impact on many areas of biomedicine so
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the first thing I want to talk to you about now is this amazing ability of at least some types of bodies to edit their own Anatomy so we all start life as a single fertilized egg cell this thing
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reliably self assembles into one of these remarkable morphologies but you know keep in mind stem cell differentiation this is crucial but it's not enough this thing is a teratoma it has bone and teeth and hair and various
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other structures but of course it's not it's not an organism because it's lacking the three-dimensional structure so this is development region or development itself is however is not hard-coded or hardwired so we know that
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we can split or actually join embryos of some species and what you get are perfectly normal in this case a monozygotic twins or slightly larger mice so this is a system that can make up for pretty drastic early deformations
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during adulthood here here these planaria there they can regenerate from any kind of cut so any cut you make the fragments will restore exactly what's missing and even vertebrates such as this axolotl that
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you've already heard about can regenerate many of its organs its limbs its eyes its jaws and so on so this is a regeneration is an amazing process that that that that repairs unpredictable
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perturbations and then stops at exactly the right moment and that actually is a really important issue is how does it stop so regeneration is not just for so-called lower animals even the Greeks knew that the liver the human liver was
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regenerative we don't know how they knew that by the way dear we know regenerate centimeters every day of new bone and and and skin when there regrowing even human children below about the age of
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eleven can regenerate their fingertips actually and this is the kind of example that that challenges us to think about information flow in this system if you transplant this is a really old
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experiments if you transplant a tail to the site of a limb over some amount of time even the cells at the tip of this tail get the message that there that this whole structure needs to be something different at this anatomical
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location and they slowly remodel right so this is this massive sort of anatomical surveillance and readjustment this is the work of dub black Aston and our postdoc in our group where he actually showed that this plasticity is
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not only structural but it's also functional so if the primary eyes are missing but you put in a topic I on the back of this tadpole you can use a machine like this that we built to train these animals to visual cues and these
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animals actually we have no problem seeing out of that eye they can see perfectly well so the brain adjusts its behavior of programs to a visual input that now comes from this weird patch of tissue on its back as opposed to where
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it could have expected to get it from for millions of years of frog evolution so an amazing degree of plasticity and this this this is another example of how
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we have to understand how these things process information this is the work of Danny Adams and Laura Vandenberg in our Center where normal tadpoles remodel their faces and become frogs but so do
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Picasso like tadpoles so if you make a temple where all the organs are in the raw place so the eyes are shifted the nostrils are all over the place okay so you do that and then these guys will just as well remodel move their organs around and eventually you get a pretty
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normal frog so as a computer scientist you you look at this and you say okay well fundamentally this is a computational problem these guys have to know what shape it's supposed to have they need to know what shape they have
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now and they need to know how to get from here to there and then they need to know when they could should stop growing so one way to think about this is as a property of general property of pattern homeostasis development regeneration all
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of this is a kind of pattern homeostasis where the system is trying to maintain and get back to a large-scale anatomical state so beyond this which we're all think of used to thinking about as
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you've got your gene regulatory networks your proteins interact in some way and then this marvelous Anatomy sort of emerges in in development we really now have to focus on this feedback loop where yes this is true but then after
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unpredictable perturbations this system is able to process information both in the gene regulatory circuits but also in the in the physics of it okay okay yeah
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this thing doesn't seem to work anybody to go to wait as it works everything so we die oh I don't need that I just need a pointer okay thank
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you how did you do this okay well this okay so yeah so so so we need to we need to focus on this on this information processing loop that allows tissues and organs to change position
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change shape and change gene expression as whatever is necessary until the correct shape is re-established and then they stop so since that's the case thank
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you perfect thanks ginger yeah excellent so the question is this if this is a homeostatic cycle the most important thing about a homeostatic cycle is that it has to store the setpoint
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everything from your thermostat on down if it's going to have homeostasis it has to store a memory of its set point in this case a pattern memory and if that's true this suggests a hypothesis can we target this pattern memory can we change
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it and force the homeostatic process to go to a different direction so what I'm going to tell you now about is our work on non-neuronal by electricity which is one probably of several media by which
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bodies accomplish this this task I don't have to go through this anymore you've heard a nice introduction where basically each cell has its own voltage gradient across its membrane the differential of volt at resting
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potentials across a field of cells such as this tap will hear form of what we call a bmm pattern or a snapshot of bioelectric activity and then there are a variety of by electrical signals that
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are temporal changes in this in this pattern so these electric pre patterns are in two forms there are your normal endogenous pre patterns such as this electric face first discovered by Danny
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Adams which shows you where the different organs and the different gene expressions and positions of the face are going to form in this temple and then you've got your pathological patterns like this where this tumor induced by a human oncogene is revealed
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by it's a Burin depolarisation even before it's morphologically apparent so these and then basically what we do technically is we are able to characterize largely
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using fluorescent dyes but increasingly using genetically encoded reporters were able to see these electrical conversations as they take place and then we're able to model them and this is this is the work of Alexis PI tech who's here in the audience who does a
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lot of this remarkable computational modeling that allows us to understand how this all works so beyond modeling and observing now these are our functional tools here's our electrical network as you heard
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today we're able to modify both the topology of this network by changing the way cells talk to each other through electrical synapses known as gap junctions and of course we can go in either with light or with drugs - or by
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miss expressing other channels to actually control individual voltage states of these cells so it's corresponding to synaptic plasticity and intrinsic plasticity if you are dealing with neurons so I'm going to show you
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now some examples of what you can what what we've done by by targeting this communication system among cells and so we're gonna explore these examples but I tell you that just to keep in mind and
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none of what I show you was done with applied electrical fields this is all molecular pharmacology or molecular genetics so one application of this is that we've shown that if you do put in a a tumor so let's say a chaos' or l3
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mutation normally in Xenopus it makes these tumors and they're marked in fluorescent with a fluorescent fusion protein but in fact if you do the same thing but you managed the electrical state appropriately such that these
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cells do not depolarize they keep an electrical contact with their neighbors even though the oncogenic mutation is strongly present in fact it's all over but really strong here there is no tumor so you can actually maintain manage the
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outcome of this and keep the cells sort of working towards their normal tissue plan despite the fact that this oncogenic mutation is there and we've done it in different ways including optogenetics another another example of restoring
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pattern and this is the work of iHub hi a postdoc in my lab this is a normal tat for brain you see here forebrain midbrain and hindbrain if you treat this animal with teratogen or else you
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introduce a mutation in a gene called notch which is a really important or a genesis gene the brain is completely disrupted you know basically you can see here total loss of structure forebrain is almost gone midbrain is a mess and so
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on but the remarkable thing is that even in the app even in the presence of these teratogen including the mutation in this critical gene called notch you can get largely a normal brain with proper gene
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expression proper Anatomy and proper memory they get there they get their IQs back actually if you mate if you superimpose ins and enforce a brain specific voltage pattern in in early
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development so the take-home message from the last two examples that I showed you is that despite for example mutations or various other disruptions we can enforce correct Anatomy by managing the bioelectric events towards
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a correct state and so here's here's another example where we're working on leg regeneration so of a frog unlike a salamander does not regenerate its limbs and so after amputation 45 days later
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you either get a spike or nothing and so what we can do is apply a cocktail that adjusts the the wound site to a particular by electrical state turns on regenerative early genes like MSX one
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and then the kick starts the whole process of leg regeneration now these legs eventually look pretty good you've got a toenail you've got some toes and you can see that this leg is touch sensitive and motile the animal can move on it with with this leg this this whole
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process takes months initially the the treatment with this ionophore cocktail lasted exactly one day so this is this is a kick starting event where you can trigger the whole module of leg building
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by very early stimulation and so with our collaborators in in David Kaplan and Annie and people in his lab were working on making bio reactors - and transitioning this to a to a
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rodent model to try and move move limb regeneration forward so I've showed you three examples now where we can try to reinforce or repair normal pattern now the question is beyond the repair of
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normal pattern how far can we go with this how much control over Anatomy can we get and I'm just just I tell you now I'm gonna show you some really weird creatures in a minute none of it is Photoshop these are real actual living
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beings that are in our lab so this is all it's all real things so so one thing we we found out how to do years ago was to induce specific organ formation so here's a tadpole the embryo has been
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injected with a particular ion channel into cells that are going to give rise to a portion of the gut and what happens is in on the gut you get this complete I and this is the work of a graduate student sherry how in my lab from years
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ago and also up high as well and these ectopic eyes have the same cell layers you know retina and optic nerve and all this as a normal eye would but they're made out of tissues that were previously thought to be incompetent to form I for
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example ended or in the case of planaria what we can do is we can cut off the head in the tail take this middle fragment and alter this normal electrical gradient that tells the animal where to put the head in the tail
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and thus you can get your two headed forms or your no headed forms and these guys are perfectly viable and now you can ask some really interesting questions about their IQs and behaviors and things like this but um but you see what's going on here this is this is
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reese pacification not of specific cell types but of whole organs so what we didn't do was have to micromanage this process to say where the you know the retina and all the components of the head are going to go we're literally
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dictating a large-scale anatomical features beyond that this is a project of a of an undergraduate student Maya Ellen's Bell who found out that what you can do is you can take this species of
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planaria with a triangular head cut off the head take the middle fragment perturb the electrical topology of that fragment and you can get worms with a flat head like these P phileas you can get round heads like this s
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mediterranean or you can get the normal triangular hit and note that you get not only changes in head shape and and we've analyzed this with morphometrics and so on so not only do you get the shape of these other species but you actually get
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the brain shape and the stem cell distributions appropriate to these other species these guys are about 150 million years distant from the planaria that that you started with and keep in mind -
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there's no genome editing here the genome the genomic sequence is completely normal has not been touched or no trans genes being introduced this is a purely electrical modulation of the network that gets you from this species
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specific morphology to a completely different species specific morphology you can go further than this and we've seen we've seen that various ozone so apparently planaria don't even have to be flat anymore you can take these these
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flat worms and you can get the spiky forms you can get little hats and you can get these tubes this is largely the work of Fallon Durant a grad student in the lab and other members of our of our
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worm team but really the this is starting to show that that a lot of aspects of the anatomy you can get from a single from from a single genome are really pretty pretty diverse and so I've
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showed you some examples of non genomic pattern control and so we start to wonder okay where is this pattern right to go from here from an early an early set of blastomeres to this really
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precisely is a cross-section of a human torso so to a really precisely patterned structure like this so where does this pattern come from and in we did this experiment though so this was this was
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Nestor Oviedo and jungeum or Akuma and various other people since then have we started with this question if you take a middle fragment and you treat it for just a couple of days you make this two-headed worm and then what we're
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gonna do is we're gonna take this middle fragment weeks later after all the reagents are gone we're gonna put it in plain water we're gonna cut it again and so the the current paradigm would say well the genome is still the same and
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you've gotten rid of this ectopic extra head tissue whatever if it was an organizer or something that's gone you're keeping just this nice normal gun what's gonna happen surely you're gonna have this and the answer is no what happens is in perpetuity every time you
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cut it this fragment will continue to regenerate as double-headed and in effect as of recently balanced work has shown how we can actually set it back we can take these guys and set it back to be two being normal
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so what we have here is the fact that the target morphology the shape to which this animal regenerates upon damage can be rewritten or edited in a permanent way and this transient change Reiss Pesa
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Faiz it in a stable way so this this is really starting to to push the the issue of memory because because this is stable and it's almost like a flip-flop in the sense that it can live in to permanent states it can be like this or like that
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and we can shift it back and forth and and we've started to think about this as as a the relationship of this kind of signaling to brain to brains and understanding of the nervous system and
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I point out this is one cell and this guy's sort of hunting for for his for his food here but this is one cell and I just point this out to remind us that complex behaviors and information processing were around long before
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brains showed up so you know brains and neurons evolved from much more simple cell types that did a lot of information processing and you don't have to be a neuron and you don't have to be a brain to have memory to have flexible dynamic
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behavior and to solve a lot of problems and so one of the most exciting recent stories and this is also falender ants work in biophysical journal just from a few months ago we have this remarkable
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finding that planaria which have so here's a flatworm here some gene expression showing where the anterior genes are and so these guys of course make single heads but we're able to generate animals that are anatomically
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normal histologically and molecularly normal they have the same markers and they have the same stem cell distribution and and all these things are the same but they're able to generate two-headed forms how is that possible and it turns out that the
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reason it's possible is because these anatomically normal worms are able to harbor a by electric distribution that is not normal in fact that is and you don't know anything is wrong with these guys until
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you check their electrical pattern memory and so the kind of amazing thing was two amazing things one is that the same the same body this is very much like memory and that the same structure
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can store distinct patterns inside but the patterns are not indicative of what the anatomy is now this by electrical state is not telling you what the anatomy of the animal is now it's telling you what is going to happen if
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it is cut at a future time this is an information structure that the animal is going to consult if it gets cut in the future but in the meantime the anatomy and the molecular histology are perfectly normal and it's not a surprise
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that this all starts to sound like the brain because brains use the same kind of scheme you've got your hardware which is these ion channels and electrical synapses that drive electrical behavior
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electrical ailment and dynamics that result in memory and cognition and behavior so this is a movie but zebrafish brain was zebrafish think about that whatever it is a zebrafish normally think about but we have a
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similar situation here where in fact all cells have the same ion channels gap junctions and so on and so here's this here's this movie again suggesting that much as people in the field of neuroscience at working on neural
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decoding so this idea that you will be able to read electrical states in the brain and then infer the cognitive structure infer the semantics of these electrical States like this person is thinking of a particular image we ought
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to be able to take readings of the electrical activity of these systems and be able to if we understood the by electric code infer what the pattern is going to be in the case of planaria it seems to be a fairly straightforward
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although even addis is much more complicated than than it seems but in the end it's going to be a quite a challenge but what the ideas that decode this and maybe even use that information to help out the neuroscientists in what
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probably is an even harder problem so another way to think about the role of by electrics in this is that in between the genome in the anatomy is this layer of physiology that's these bioelectric networks that have really
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dynamic flows of of and changes of voltage and that the the state space of these electrical circuits possess attractor States stable states that correspond to distinct anatomical
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outcomes we've only found a couple no doubt there are others that we haven't found yet but basically when the circuit lands and each one of these different stable States it is commands to the different cells and and and the various
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genetic circuits that build different structures and and this is well known in neuroscience that these kind of networks this is basically a map of individual memories in a kind of artificial neural network that could be used to store
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information each one of these things is literally of a memory that and this might be a good way to think about these things so I've told you is that all cells not just neurons form complex by electrical networks that targeting these
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networks allows rational control over large-scale anatomy and we're getting better and better at doing this that in some cases these network states can override what would otherwise be a genome default outcome in that
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activation of specific pattern memories provides modular control where we can call up eyes brains limbs and certain other organs without having to specify all the low-level details that we don't know how to specify anyway and so what I
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would suggest is perhaps a change of the hardware software metaphor where usually what you read is that the DNA the DNA is the software and if a cell is the hardware that interprets it but another way to think about it is that the DNA
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encodes the ion channels and thus the DNA is what specifies the hardware the software is actually all the electrical dynamics that run on the hardware that you get when a bunch of electrically connected cells are sitting next to each other and the reason that's important is
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because this software can be edited separately from the hardware you don't need too much like you can put different different algorithms and different memories in a set of chips without having to yank out the transistors the same thing is here you can keep your ion
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channels but if you knew how to how to alter the electrical dynam with let's say ion channel drugs specific compounds that would knock the not the the system into different attractor states you might be able to
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control what happens without without having to to change the hardware too much so in the last couple of minutes I want to talk about the role of computer science in this process you know when I
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talk about compilers and computers and everything one one thing to keep in mind is that the idea is not like what I'm not claiming is that biological systems are like today's computers because there are obviously massive differences what
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I'm saying though is a computation broadly understood is a fundamental feature of life this these are things like decision-making memory integrated information processing we have to decode the high level control algorithms that
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are going to allow us to modify form and function and after we do that we could even perhaps build computational media that are based on these principles so what if and this is a this is a
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hypothesis we don't know that this is true but what if cells are a kind of universal constructor in the sense that what if cells know how to build towards a specified plan and that perhaps may be a good strategy would be to rewrite the
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goal state and let the cells build to the spec instead of trying to directly micromanage the cell activities basically offload the computational complexity of something like building a limb or some other complex structure by
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rather than trying to micromanage the individual cells figure out how their how the goal state is encoded and and rewrite that and and I think this is a powerful strategy because biological systems at all scales from from chemical
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networks to sub cellular systems to whole animals to even grass warm swarm organisms like ants our fundamentally homeostatic goal-seeking computational systems if we can rewrite the goal we
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might be able to achieve a lot of progress that the currently eludes us and I must say we kind of got scooped on on this idea because this idea of goal states and micro management you probably know this guy from from his book the
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little prince but he also wrote this other book wisdom of the sands where he says if you want to build a ship don't get people together to collect wood and make them do all the specific things but but teach them to really drive for
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the goal right so the idea is that if the if the levels underneath are motivated to reach a particular outcome you're better off controlling at that level of a at that top level and that may be this provides an efficient level
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of control so for the future what I what I think we at some point are going to have is a kind of biological compiler where you can literally sit down and sort of design a an animal in a
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particular Anatomy and you're going to exploit the modularity and the innate goal seeking computation that living systems already know how to do and it's going to tell you how to it's going to fill in the the steps needed to give you
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a the creature that you want out of this so I think that if we understood the algorithms of dynamic pattern control basically we would have large allo made a large progress towards resolving these
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fundamental issues and of course for engineering we could create hybrid biological constructs with new structures and functions new computer architectures and so on so I think I think this this is really at the intersection of biology cognitive
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science and AI you know artificial intelligence so I want to thank all the people that have contributed to these antilles projects and our model systems who go through a lot of interesting
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perturbations in our lab and of course the funders who have made this possible and I will close by showing you as some of these forms the first time these these two-headed worms were obtained by cutting a cutting a middle fragment out
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of a two-headed worm and the first time I showed this at a at a conference and they set up and said that's impossible these animals shouldn't exist and so I like to I like to close with this because this is you know you can't argue with the animals themselves so thank you for listening
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[Applause] I great talk how do you envision these these gold states are encoded so these
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are self-organizing systems right so they're the cells are interacting with each other that leads to this sort of emergent structure do you think it's in the rules of the behave the rules of interaction between routine cells yes so
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basically we're in the process now of putting out or well putting together and in putting out some very specific computational models that show how the self-organizing properties of these
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electrical circuits result in stable large-scale shape so we're just just beginning to understand especially we have some quantitative now largely through our collaboration with with Alexis in our Center this is now starting to come online where we have
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actual models that show how it is that these electrical patterns stabilize into large-scale stable States and more importantly those things can now be interrogated to ask well if I want at a different pattern what would I have to do so we have other people working on
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inverting that process to say that if these are the rules but if I wanted a different shape how would I have to modify the system and we're going you know we're using a variety of techniques and everything from feedback training to
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bottom-up design to try to achieve that high if we look at the beginning you mentioned a few times when the animal will know to stop the green growth and I didn't mention anything about it later
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do you have some thought about if you have a regeneration how then even knows when to stop the regeneration when to stop is that was that the question yeah yeah that's a great question so my
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my current hypothesis on this is that what tissue or organs do is they they have a way to survey their own structure so what I think they're doing is they're constantly comparing themselves to this
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encoded memory of what it should be and basically the rule if you have to write down the pseudo code for it the rule is compare your current structure with the target morphology and if they're still it basically it's an error minimization
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scheme that if there's still distance do more and when that error goes down to a low enough level that's when you can stop and you can easily imagine that there are tech support that that's gonna go awry for example cancer for example you know
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there are other other types of overgrowth you could you the system can be fooled okay so it's certainly not foolproof but I think fundamentally it's it's a kind of error minimization scheme where it just is trying to minimize the distance between what's currently going
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on and what is encoded as the ideal low energy state I'm just curious what you
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think of the role of bioelectricity in cancer which is I think often thought of much more as a genetic disease but how how might that be encoded to drive tumor
00:32:04
formation yeah so we have a couple of I actually I took a bunch of notes - I'll talk to Len about this I'm one of the projects I didn't talk about it all has to do with melanoma in our frogs so it turns out you can by disrupting the
00:32:18
voltage of a particular cell population in in in tackles you can convert all the normal melanocytes to melanoma there's no genetic damage there's no oncogenes there's no carcinogen perfectly normal
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animal you wouldn't be able to tell it apart until later on it starts to turn on a bunch of known tumor markers but and there's no primary tumor by the way but basically all the melanocytes go completely crazy they over proliferate
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they make these crazy projections that lenses showed us they dig into the blood vessels and the neural tube they you know they go into all the vasculature I mean it looks like melanoma there is no genetic damage so I'm not going to say
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that that's the case for all cancers obviously but I think it is quite possible to derange cell communication to the point where cells abandon the anatomical plant and basically go
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unicellular you know a cell that whose computational boundaries shrinks to to the surface of its own you know if itself is only the one cell treats the rest of the organism as environment and at the expense of the environment you
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proliferate you dump entropy you dump waste products whatever it's going to be you do what you can and so my gut feeling is that that these cells in talking electrically but also chemically physically with physical forces that all of that
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Kelton cells need to talk to the rest of the body to beacon to abandon their unicellular you know every man for himself lifestyle and and join join the body and that process can be can go awry and that
00:33:44
process can go awry genetically it can also go awry physiologically the flipside of that aside from making melanoma which is interesting but obviously not not what anybody is looking for the the flipside of that is that as I as I showed you we can take
00:33:57
cells miss expressing a really bad you know nasty chaos mutations and basically force them to to participate in the in normal morphogenesis of the body and and this has been known actually not
00:34:11
electrically but the fact that you can inject a carcinoma cells into early mouse embryos and they basically become part of the normal embryo also regenerative systems are able to normalize cancer that's been known for
00:34:23
many decades that there are pattern in context where the patterning cues are really strong in some cases it can overcome what we think of as a cancer cell so in certain cases the genetics
00:34:35
are crucial in other cases it's going to be that the cell is not irrevocably damaged that what you have is is you know something somebody cause somebody said once that um if you're trying to understand a traffic jam then understanding the combustion engine it's
00:34:48
not gonna help you it's a system-level problem and i think for a certain class of cancers it's a systems-level problem and the system is mediated electrically that that's what i would say how is the
00:35:04
electrical external electric field interact with the bioelectric field all surround with that and it seems that it doesn't bother us much yeah right so so
00:35:18
it's actually pretty difficult to alter these the gradients that I showed you are not directly altered by weak electro medicine did by is electromagnetic fields most of the time and those people
00:35:31
have found effects of those for sure especially in vitro but those don't directly alter this kind of process the receptor mechanism for those is quite different so that that doesn't seem to be a problem for this however there are
00:35:43
other aspects of the environment which are a problem so for example of drugs that are ion channel drugs okay for example for um you know rythme is and things like this are teratogen there
00:35:55
are known to outages they would they would really be a problem in embryogenesis so I would say applied electrometallurgy magnetic fields are probably not a huge deal but there are certainly drugs that that you might come into contact with in pregnancy that are
00:36:07
a big deal Michael you use the word pseudo-code in one of your answers and that reminded me of one of my favorite features the way you're thinking about this software and
00:36:23
not worrying about the hardware well wonderful but when I think about how hard it was to make compilers and how
00:36:36
very very hard it is to make compilers that can deal with something like pseudocode I think of Charles Simone ease efforts for 20 years make what he
00:36:50
calls intentional software without much success yeah so your postulating another level
00:37:01
of design that has to evolve and I can't yet might see what the selection pressures are that are going
00:37:13
to help shape that level between the pseudo-code and the machine sure yeah well I would say two things on the level of difficulty so pseudocode would be nice I
00:37:27
would be happy with let's see at this point right so we don't have to go to pseudocode yet we have to what we for sure have to do for is go beyond the machine code where we're literally pushing around individual molecules or
00:37:38
even cells we have to mean when when a regenerative process stops or when it decides to turn a tail into a leg whatever it is it's measuring large-scale properties it's measuring things that are not defined at the level
00:37:51
of single cell this means that somewhere this is this is encoded we don't have to be able to write total pseudo code but we have to get to the point where we understand what is this thing measuring that is not a local state going beyond that Selective pressure that's that's a
00:38:04
great question and so we've actually started thinking about this in two ways number one if you actually look at the Tree of Life and the evolution of morphological complexity one of the things you can look at is the evolution
00:38:17
of different types of ion channels and some of these ion channels are incredibly interesting because some of the more advanced ion channels that appear when these anatomical innovations appear have the following property that they are not only determinants of
00:38:30
voltage but they are themselves voltage sensitive what this means is that you now have a transistor you have a voltage sensitive current and that allows you to so the hypothesis of this is you know we're just starting on all this but the
00:38:42
hypothesis is that without what the appearance of those kinds of the evolution discovered very early on that you can make feedback loops you can make logic circuits you can do all these things if you have a sort of self
00:38:54
referential building block like that and I think evolution found it early on and and the reason that that the evolutionary pressure is going to help things like this is that this thing has to be able to remodel at a high level
00:39:07
for the following reason think about the I on the back if every time we had an innovation in the body plan let's say the nervous system couldn't handle it and fell apart that kind of evolution would never get off the ground there has to be a degree of plasticity
00:39:19
and and modularity whereby there are circuits that make decisions about large things like hey my eye is over there I'm going to treat that as visual data that means you have to recognize an eye that means that if you know it's not just a
00:39:33
single cell kind of decision so so I think that's where the evolutionary pressure comes from it's the evolved ability and the and the ability to have plasticity - to deal with with accidents
00:39:45
with damage with the vagaries of the environment and I think evolution found this stuff very early on that has been exploiting this stuff I mean as you heard in the other talks this stuff is as you know bacteria have it fungi have
00:39:56
it I think it came along very early on [Applause] [Music] [Applause]
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