# Transcript — levin-bioelectric-morphogenesis-2024 Source: https://www.youtube.com/watch?v=qWrU2f1wMmY Language: en Origin: Auto-generated (YouTube) --- uh thank you uh to the organizers for setting up this remarkable meeting uh I'm incredibly pleased that this is happening at NIH and uh to also have the opportunity to speak to all of you uh and share some um some ideas what I think I'm going to do today is introduce we'll go back to the very beginning and ask the question of uh what is in fact uh cancer and uh introduce the idea of cancer as a kind of dissociative identity disorder of the morphogenetic intelligence and I'll explain um what all of that means Let's uh ask a kind of a strange question U why is cancer not a problem in the robotics field so what you can see here is that uh we have now fairly uh complex engineering uh structures here uh very functional um doing all sorts of interesting things but uh one thing that this this field does not have is a problem that is uh similar to cancer so why is that and what I want to point out is that uh at least for now uh the major uh type of architecture that we deal with in uh in engineering is very flat in other words the large scale system as a whole might have various problem solving capacities but they're made of Parts without agendas in other words the parts themselves are not smart uh and biology is not like that at all what we have in biology is what I call a multiscale competency architecture that is we are made of a material that all the way down from the genetic uh components to the subcellular organel tissues organs and all the way through groups and uh and and um you know colonies and so on uh have various competencies at every level so at each level so this is not just a structural set of Nest nested dolls but actually at each level um there are uh competencies of solving certain problems in different spaces and this uh this this has massive implications both for the remarkable uh plasticity and robustness of life but also for certain failure modes and so all of these are uh able to uh pursue specific states with various degrees of uh competency and Ingenuity in different spaces that I will mention momentarily now uh the classic developmental biologist wihelm Ru um wrote this this amazing monograph called the struct the struggle of the parts and what he was talking about is is precisely this idea that we are made of components that uh cooperate in some cases but also have their own individual identities and that uh keeping the organism together you know the sort of ship of feus that that is our body where where cells and and molecular materials must be constantly replaced and and and fit into the functional architecture that um a lot of these parts have autonomy and that autonomy must be harnessed in order for Global Health to take place so I'll just show you a couple of examples this is a flatworm uh called a planarian and one thing you don't see here is a little tube called the FX that they use to eat but if you uh and and and so this FX is quiescent most of the time but if you liberate that fings from the rest of the animal um uh here here here here they are and you will see that they have their own uh their own ability to move they have their own little little set of behaviors this is a piece of liver that they're going to try to eat now of course they're just tubes so what they end up doing is borrowing through it and the food comes out the other end but basically these components here are have independent activity that are normally harnessed by the rest of the body towards specific behaviors now here's another example this this little creature here if uh if you see this for the first time you might think that this is something that um we got from the bottom of a pond somewhere actually uh what it's made of are adult human tracheal epithelial cells this is what we call an anthr robot and gizm guska and her PhD work in our lab showed that if you uh take these out of an adult human patient under specific circumstances they join together to be a novel kind of uh moving construct with all sorts of fascinating behaviors its own transcripton that's quite different from that of the source tissue and they have interesting behaviors here they are assembling into a superbot um kind of cluster where what they'll be able to do in that configuration is heal neural scratch wound so this is a scratch wound through some neural tissue and these anthr robots will sit there and and and try to try to heal the Gap and so so we see that that uh we are made of a material that has all kinds of interesting and often unexpected um competencies and those project into different spaces so we as as humans are pretty primed to observe uh intelligent behavior in three-dimensional space so that's medium-sized objects moving at medium speeds in 3D space like like birds and primates and so on it may be an octopus or or a whale or something but uh there are all of these other spaces there's the space of possible transcriptional States uh the space of physiological States and the space of possible anatomical configurations which is the one we'll talk most about today in all of these spaces that are hard for us to visualize right we we don't normally think of this as Behavior as moving through these spaces as Behavior or in fact problem solving Behavior but in all of these spaces cells and tissues uh can do some very sophisticated things for example individual cell uh even even Gene regulatory networks never mind the whole cell but just just small Gene regulatory networks can do pavlovian conditioning and several other kinds of learning uh and and so on and so this is the sort of thing that we must understand if we're going to be able to um detect uh uh prevent and uh reverse defections from the this this coordinated activity and what happens in multicellularity both during our embryogenesis and during you know during during evolution is a a radical scale up of what we call the cognitive L cone so the cognitive L cone is simply the size both in space and time of the largest set point that uh any any kind of a system be it cells animals artificial systems whatever any kind of a set point that these um agents can pursue in a kind of homeostatic uh kind of a homeostatic process so so the bigger the bigger the ability of the system to have a memory going backwards in time anticipation forwards in time uh spatial uh uh spatial extent um that uh the the right kind of connectivity between subunits allows them to expand their cognitive lonee and use much larger States as the goals that they pursue in the these um homeostatic kinds of loops and what I mean by that is this this is this is a single cell so this this happens to be a free living organism known as a lacr areia and you can see uh it's here it is there's no brain there's no no no nervous system it's it's handling all of its uh all of its local goals uh within the scale of a single cell so the cognitive lyone of this of the system is roughly the size of the cell basically it's doing whatever it needs to do to manage the conditions inside of the cell the rest of the environment is doesn't matter it will dump entropy into the environment it will eat what it wants it will go where it wants what what happens during embryogenesis and uh and and through evolution is that uh cells individual cells get together and instead of pursuing very tiny physiological metabolic uh and so on uh goals they end up taking on these these massive grandiose construction projects so here is a group of cells making a salamander Limb and what happens is if you deviate them from this state meaning you cut the limb anywhere along this a they will very rapidly uh spring into action and rebuild and they stop when it's done so so what's happening here is this kind of anatomical homeostasis where all of these cells are perfectly aligned on what their goal is their goal is to rebuild this they we we know we know this because because if we deviate them from from this quiescent position they will they will build it again and then they will stop and they will do exactly what's needed no more no less to get back to this particular region of that anatomical morphos space so by scaling up their cognitive l individual cells are able to now uh have much larger set points as the target of these kind of homeostatic processes now that that system that um that ability to uh to to take on larger larger set points by joining together has an obvious failure mode it's inevitable uh unlike many other disease conditions which are very contingent on the details of of of evolution and Physiology uh cancer is is is is fundamental because this process of joining together toward large scale construction projects is going to break down occasionally for for a number of reasons and that is when we see cancer and so so this this happens to be um human gly blastoma uh but what's happened here is that the these cells are not unlike what's sometimes modeled in um Game Theory kinds of models of cancer the I I I don't think these cells are any more selfish than these cells they have smaller selves so what's happened here is that the boundary between self and world the size of the things you are actively trying to match manage here it's quite large here it's shrunk down back to the scale of individual cells at this point the rest of the body is just external environments to them all they're trying to do like their ancient unicellular ancestors is manage their own internal State and there's a lot of work that's been done on this kind of atavistic theory of cancer uh but uh but but I think I think what we need to do is is is pay attention to the uh to the to the policies that normally allow this kind of scale up to see if we can if we can reverse this this process so what is this cognitive glue you know in individual cells have these little homeostatic Loops like this there's there's a particular scaler that they might be uh managing let's say pH or hunger level or or or something like that and then by joining together so so here here's this here's this homeostatic kind of loop you measure something you compare it to a remembered set point and then if the error is more than some acceptable level then you act to reduce that error so individual cells are able to measure and control fairly small things by the time you have a large group of cells let's say a tissue or or you know an early embryo uh you're able to do things like this so what's critical here are these these connections the the the the policies and the information exchange that allows this tiny Loop to scale up to much bigger things that are then can be modeled via Landscapes whether the morphogenetic or transcriptional Landscapes uh pattern completion such as here when when part of this information is gone the cells will will rebuild so so there's a collective that that that uh knows what the whole thing is supposed to look like and is able to uh you know reduce reduce the air so so it becomes really important to understand what that what that cognitive glue is and uh we we know what it is in the brain so in the brain uh you have groups of neurons we also are more than uh just a pile of neurons because there is a an electrophysiological communication system that binds those neurons into uh memories goals preferences and various other capacities that do not belong to any of the individual neurons themselves and so uh in in the body um we have we have numerous uh kinds of signals chemical biomechanical and so on but today we're going to talk about um my my favorite which is the bioelectric layer and so I think I think it's not that the bioelectricity does everything by itself obviously these other things are quite important but I think the bioelectrical layer of this communication network has some very uh very useful and very interesting properties that that make it distinct and very attractive Target for regenerative medicine so let's compare this to um what actually happens in the brain because I think it's it's a it's a very good analogy to start with and then we have to break some of those assumptions to uh to really understand developmental or or cancer bioelectricity so so in the brain we have this um uh Hardware architecture that's specified genetically where you've got uh individual cells in their membrane they have they have ion channels these ion channels by virtue of letting ions in and out under specific rules uh will set up a voltage potential across this membrane that voltage potential may or may not be communicated to uh the neighbors through these electrical synapses known as Gap Junctions and um all of these both both uh many kinds of ion channels and certain kinds of Gap Junctions are themselves voltage sensitive so this uh enables a really interesting kind of historicity meaning what it's going to do now is dependent on whatever the physiological state was before that so that gives you a kind of memory um immediately and and feedback loops immediately already at the level of the Single Cell but of course things get much more complex in these networks and so so that's so that's the hardware and uh what what Neuroscience does is study how this this uh this network supports an interesting kind of software so the software is the physiological dynamics that uh operate uh within within the brain and nervous system here's an example this group made this amazing video of uh the electric activity of a living zebra fish brain as the fish is thinking about whatever it is that fish think about and um in Neuroscience there's there's this project of neural decoding so the commitment is that all of the animals memories preferences goals and and behavioral um competencies are in some way encoded in the electrophysiology that you're seeing here and so so the idea is that if we could record in the living State we record this electrophysiology we can uh translate it in some way we mine it for for and and for for the um the patterns that are there and we uh can now decode and be able to by reading the this electrophysiology we can tell what the animal has done before so we can read the memories and perhaps we can uh tell what it's going to do later meaning that we can understand what are the what are the goals and the uh the incipient behaviors of the system in three dimensional space right as it navigates through manipulation of of of muscle activity how it's going to navigate threedimensional space well it turns out that this system and this and this is this is a remarkable system as as as we all know but it turns out this system is incredibly ancient evolutionarily it actually shows up around the time of bacterial biofilms and so uh so pretty much every cell in your body has ion channels most cells have these electrical synapses or Gap Junctions to their to their neighbors and during Evolution uh We've we've been suggesting that that what's happened here is that uh Evolution took these uh these um these ancient ways of processing information in cellular networks which originally were were for processing decisions in anatomical space so deciding what the shape of the early embryo and and and you know the final uh uh the final body um Anatomy is going to be and it basically uh did two things first of all it pivoted into a new space so instead of navigating anatomical space uh once muscle and nerve came on the scene uh it it started to use that system also for navigating threedimensional space and of course It sped up the time so so instead of um instead of you know the kind of a long-term let's say hours long uh time scale activity here we went to milliseconds and enabled rapid motion but um there's a the there's a fundamental symmetry here between between neuroscience and developmental biology and its disorder such as such as cancer and so that means that we can take many of the tools and Concepts so both the Practical tools and the conceptual um kinds of ways of thinking about things from uh cognitive neuroscience and see what they allow us to do in in this field and so this is some some technology that Patrick McMillan has been pushing in our group which is which allows you now to look at uh non-neural cells in in real time both in Vivo and in vitro and characterize their bioelectrics and process them via interpretation pipelines similar to what neuroscientists are doing in the brain so um so now so now let's let's get specifically into cancer um what I what I've just told you is that all cells not just neurons communicate as electrical networks that process information that's important for cancer suppression so the continuous uh The Continuous essential process of taking new cells and integrating them into the anatomical structure of the body so that they can maintain healthy tissues and against aging against degenerative disease and against carcinogenic transformation and uh this leads to the hypothesis the hypothesis that we've been working on for for some years is that um cancer can be detected uh it should be able to be induced and perhaps even normalized by a uh by manipulation of bi electrical signaling that normally um enables uh cell cooperation towards anatomical goals and so uh to put the whole basically to put the whole talk in one in one slide uh what I'm going to show is that uh like the brain uh sematic tissues form electrical networks that make decisions about Anatomy that then we can Target this control system of large scale homeodynamics with many applications in cancer medicine so this this leads to a uh to a suggestion which is that perhaps what we can do is uh is uh normalize cancer uh by rebooting a patterning program basically by connect in cells to the uh set of cues that normally keep them harnessed toward some kind of morphogenesis this would be an alternative to uh necessarily killing those cells which might allow us to avoid a compensatory proliferation response evolution of tumor resistance and so on instead of trying to kill those cells we're going to try to reconnect them to the large scale um homeostatic set points that they used to have so uh so let's see let's see how we might um let's see how we might do that well one thing that's been known for a really long time is that uh if you just take measurements of cells in different states um proliferative highly proliferative embryonic cells stem cells and also cancer cells tend to be depolarized okay and your quiescent mature terminally differentiated cells tend to be hyperpolarized liver is an interesting exception of course highly regenerative but even the mature tissue hangs out around down here with with with this group so what what this what this has suggested is a um a kind of uh axis of plasticity where uh where where resting potential might actually um allow you to uh to to to move be you know from from this state to this state and vice versa and this is something that um Clarence con first kind of postulated back in the back in the 70s and if you if you track more more recent data so this is our met our meta analysis if you track more recent data the exact same cell types uh which are which can be quite um hyperpolarized in normal cells are actually depolarized um in cancer right so the exact the exact cell types so so since since then there's been a a large database of of results showing that um individual resting potential is responsible for controlling many things cell differentiation apoptosis migration changes in in cell shape but um as as as interesting as this is at the Single Cell level I actually think that um the the true import of bioelectricity and cancer is going to really shine at the multicellular scale as again as I was just talking about the the the role of these Potentials in being part of an electrical Network that makes decisions so in order to in order to study those things um we developed some tools uh these include uh just for example voltage die approaches uh to um be able to read the the the information that these cells are exchanging with each other um lots of uh computational modeling goes on in our lab so this is some um a simulator actually a bi electrical simulator made by Alexis pyac in our Center that allows us to take uh voltage information like this both slowly and the rapidly you know you can see a mix here of very rapid Dynamics but then also also slow ones um in this uh in this kind of voltage Imaging that that that Patrick has developed and so uh we we're able to now uh simulate it to understand why the patterns change in the way that they do now of course even more important than um simply uh characterizing these patterns we need functional tools so so what we've developed are ways to control the bioelectrical States uh without uh without having to use kind of old older tools such as electrodes so so we we don't use any electric field application we don't use uh there's no electromagnetics there are no EM waves nothing like that what we are doing is targeting the native interface that the cells are already using to communicate with each other so that would be the ion channels in the membrane and the Gap Junctions so um th this allows you to uh to change individual voltage states of cells and this this can be with uh with mutations it can be with pharmacology to open and close these channels it can be via optogenetics uh and that allows you to put down specific bioelectrical patterns and this this is more like um uh by by by by manipulating the the Gap Junctions what you're doing is changing the topology of the electrical Network which cells talk to which other cells so it's quite a quite a different uh type of change and then of course um you can go a little bit Downstream and look at uh for example uh some of the thing some of the signals that are actually Downstream of the second messengers like neuro like serotonin and other neurotransmitters so when you do this and I'm not going to take the time to show the the many examples I'll just showed you show you one um that uh when you ask the question okay so what can you actually achieve using this well what does bioelectricity control in the large scale uh in the large scale body besides individual cell properties what can you actually do by controlling bioelectricity so this is one um one example it's an old one from our lab and it's one of my favorites this was by Vi pi and Sher Al was discovered that um one one thing you can do is you can uh set up particular um voltage States uh in the uh in the in the body that uh correspond roughly to endogenous patterns that dictate the position of certain organs so if you inject uh specific potassium channels that set up a little a little voltage spot uh that's very similar to the ey spot and probably a Danny Adams when she gives a talk we'll show you the electric face and those um those eye spots but you can introduce that anywhere else in the body by injecting this RNA and when you do that it makes eyes and it can make eyes all over the place even in locations that the developmental biology textbook will tell you are not competent to um indu eyes so even outside the uh the anterior um uh Nector you can still form eyes if you use the right prompt so so this this bi electric State um if you section These Eyes uh with they can have normal uh all all the normal components so the the retina the optic nerve the lens um all all of that stuff and there's a couple of interesting things uh about this first of all uh it shows that the bioelectric state is instructive so this is not just about causing defects in existing structures you can call up you know entire new structures elsewhere in the body it's extremely modular so this is a very lwi information content stimulus we don't tell them how to build eyes I mean we have no idea how to how to build an eye with all of its many many components and and all of the genes that have to come on in all of the spatial relationships all we have to do is say build an eye here it's a it's a very high level almost a almost a sub routine call that takes tissues that in this case for example were going to be gut and assists to them build an eye and and everything else is handled below that in other words the shape of the eye we we don't we don't need to micromanage that um the other the other interesting thing about it is that actually again uh as as as I started out talking about cellular competencies that we can take advantage of um observe here that uh this is a section of a um of a lens is sitting out in the tail of a of a TPO somewhere and uh the blue cells are the ones that we actually injected but all of these other cells that are participating in this morphogenesis were never targeted by us What's happen here is that we tell these blue cells build an eye they determine that there's not enough of them to actually complete the task and they recruit through some sort of secondary instruction they recruit their neighbors to help them do this so that ability um other Collective intelligences do this of course ants and then termites and so on do exactly that um but but the idea is that that ability to scale uh your influence to the to the need a task and to communicate to the other cells that we as the regenerative medicine workers don't need to worry about is already in the material that is part of the competency of the material that you can harness when you use this bioelectrical interface okay so um the specific uh the specific claims here are are are basically these that that if bioelectrical signals were to be important for cancer uh you you can expect a few things first of all there should be molecular data that implicate uh Channel and pump proteins in cancer um uh and then we should be able to to use this as a diagnostic tool for incipient tumorogenesis we should be able to induce these kind of phenotypes in the absence of for example DNA damage by modulating the vmm signals and hopefully we can actually suppress or normalize them so let's let's look at those one at a time so actually there have been numerous ion channels and this is this is a fairly old list now there's there's there's many more now that um are known as uh as either on gen or tumor suppressors um and uh and so so uh you know for example from the work of Emily Bates and and many other people um have now been identified lots of channels that are that that contribute to this this phen type now one thing that we have to note is that this is uh this is a a serious underestimate because actually because of the the properties of these physiological networks that are very robust where many many different um uh channnel genes can compensate for each other uh the what what we see from genetic knockdowns for example and genetic screens are probably an underestimate of what's actually there you know you knock out one thing it's it often happens that other things other channels will actually take their their role in the physiological circuit and and we can miss that so I'm sure there are there are many others but but but this is a this is a good starting list and you can see for many of these things if you look through the Geo database that um through the progression through through cancer there are significant uh changes that that you can track already you can track this just just in the bioinformatics but but again I think that the bioinformatics is really a a a drastic underestimate of what's actually going on because these channels open and close posttranslationally that is you cannot infer from the presence of the gene or the RNA or the protein you cannot infer directly what the physiological state is going to be you can't guess the bi electrics just from their presence at least not not reliably so and that means that we have to go beyond the existing uh omix profiling of of molecular entities two physiics and the actual functional parameter which is the distribution of voltage so this is this is an example of the of the Diagnostics modality uh this was uh worked out by my grad student years ago Brooke Brook charet and what he found is that by injecting a variety of of of human and and other enogen they will they will form tumors the tumors will eventually this is this is in the tple model um the the the you know this this uh this this tasis process where they'll spread but but before the tumor even before the tumor becomes histologically apparent we can already see using a voltage sensitive fluorescent die technique uh we can already see uh that um the the region where the tumor is going to be already has a uh an abnormal voltage potential so so we start with the voltage monitoring techniques that uh that U Danny Adams worked out uh in our group uh a while back uh for looking at embryos and looking at how these voltage uh gradients change during embryogenesis and we can take that into tissue and organ maintenance and ask what does it look like when these cells uh contract their cognitive leg cones and basically start treating the rest of the body is just external environment and you can already see that that they depolarize uh and in fact not just the the tumor itself but there's actually plenty of other cells out here that are going to uh they're going to have this aberant behavior and so this is uh this is an obvious um uh beginning of of of of a diagnostic technology and this uh and and I and I point out this is this is an artist rendering so we don't actually have this working yet but but we're working on something like this where the idea uh is that uh you should be able to in real time using some sort of augmented reality goggles and this is actually surgeons already use this for various other indicators but we should be able to have a voltage indicating uh channel in there where uh the surgeon is going to be able to see where the margins are how much they need to take what are the straggler um cells they they might need to get and so on so the idea is of using using real time uh voltage bi electric voltage Imaging uh ultimately I think I think this technology is is coming and we'll be able to use it for Diagnostic applications anos surgical applications so the second uh thing that uh this the second prediction we talked about is the ability to uh induce a cancer-like state in the absence of carcinogens enogen um d damage and so on and uh this was uh this was found by Doug Blackiston and Maria leikin in my group where what we were able to what we were able to do is uh take take animals and with normal melanocytes so these are these little pigment cells here our normal normal melanocytes in this frog embryo and by targeting a specific ION channel in this case we targeted a chloride um a glycated chloride channel uh by targeting that chloride Channel we disrupted the ability of a population of cells this is actually a specific population we call them instructor cells because what they do is they talk to the melanocytes they normally keep the melanocytes in order and doing this when you silence uh the ability of those instructor cells to communicate um what happens is that the melanocytes the brakes are off these melanocytes and they go completely crazy they over proliferate they they enter these regions where they uh where they shouldn't be you can see the difference uh this is uh this is MMP dependent their migration is mm dependent and if you look at uh through in a section what's happening here so these are sections the neural tube here these are sections anterior and posterior through tpes this is what normal melanocytes are supposed to look like little round things there's quite few of them this is what uh Ivermectin which is an opener of these these chloride channels this is what um ierc treated animals look like the melanocytes first of all there's way too many of them second of all they have this this this crazy uh projection that you know they're they're long they're almost like neurons here and this is this is what they normally look like once once those signals uh are those bi electrical signals from the instructor cells are are squelched they transform and they do this and then they start digging into all the other uh tissues so here they are digging into the brain here they are in the neural tube the blood vessels here right and so what happens is that basically an animal wide metastatic melanoma phenotype which you can you can pick up in initially there are no enogen there is no DNA damage all there is is temporary uh interference with the normal bi electrical signals that keep order but once this all starts they turn on markers of uh epithelial meenal transition and all the other things you would see in cancer but that comes later the first the first event is physiological um not not genetic and um what's interesting here is that uh again uh I'll just point out the effect is not cell autonomous so this is a cross-section through that tle these are the cells right here this is an injected dominant negative ion Channel these are the cells here that we target but the melanos that that change that change their phenotype are at some distance and and we've actually worked out how this works and that that takes place through again serotonergic uh kind of signaling pathway but um what's what's apparent here is that it isn't the voltage of the cells themselves that determine what they're going to do it's a voltage change in the environment as a physiological switch towards metastatic behavior and in fact uh it's not even necessarily the micro environment because in in Vivo in a tadpole it only takes a few cells uh a few um targeted cells to Kickstart this and they can be quite far away in fact on the other side of the of the animal and we have all kinds of uh data looking at where you can you can inject and and where the this this this this this property uh you know turns on and and and the entire tat can be transformed by just a small number of cells that have an aberant electrophysiological signature at at at one location so this so this is a story of how you can Kickstart a cancer uh process uh with the physiology so that gives us some hope that gives us the idea that we should be able to uh reverse this uh prevent and and maybe even reverse this so so we tried this um and this again is the work of largely of Brook Chet when he was a PhD student in the postto in our group and what he did was once again he would inject uh enogen into various uh various plastomes they uh and they're labeled they're labeled with a with a tomato fluorescent Tracer so here you see it we we tried all kinds of onogen so so really nasty um Kass mutations p53 dominant negative p53 mutations all sorts of all sorts of things that that that cause these tumor structures and uh what we see is that uh they're they're quite efficient at causing this but if you co- inject an ion channel that will prevent the cells from depolarizing so one of the first things that enogen do is they disconnect cells and this has been known since since the 80s in from from malean cell studies that that enogen tend to disconnect the uh the the expressing cell from their neighbors um and this happens through a depolarization and so if you if you prevent artificially prevent that depolarization then so this is this is the same animal here's what happens the Ana protein is still blazingly expressed it's in fact it's all over the place but it's very strong at the site of injection um we don't we don't repair the the mutation we don't kill the mutated cells they're still here you can see them but there's no tumor and the the tumor incidence is is great suppressed um because these cells despite the the the the you know the genetic defect that they have um they remain physically connected to their neighbors and so instead of crawling off and doing their own thing they continue to operate as part of that Network which is uh which which has large scale set points it continues to make skin muscle uh whatever whatever other other organs and so so this tells you that you can override much like much like some of our work on um on birth defects it's it shows that some hardware issues such as the really the a dominant mutation uh in this in this protein some some hardware issues can be overridden quote unquote in software by manipulating the the voltage not repairing the original recording in progress and uh this also and so and so Brooke and Brooke and Danny did some work showing that this can actually also happen via uh via optogenetics um it doesn't have to be uh uh you know it doesn't it doesn't have to be any one modality it really is the voltage uh pattern that that does it and you can you can trigger this effect with a light that turns these channels on and off um the the the role of the environment in this is is is really important and it kind of goes against uh this this mainstream idea that that that basically these these phenotypes are very tightly um linked to um uh to the to the genetics and and ultimately uh to the clonal expansion of one particular one particular set of mutations um here's here's a here's a tple and this tple can have a tumor or an ectopic eye right and what sets the difference between having a tumor or an eye is the amount of sodium that's getting in and so this is this is following mustafa's work which I'm sure he'll talk about on this uh this this amazing nav Channel um but basically uh how much sodium is in your medium and how much of it is getting in and and some other subtleties will re uh uh switch between two very two radically different outcomes an ectopic organ or in fact a mutant so we have to start to understand the the the primary role of the physiology the collective decision- making uh in this process and and see these ion channels as a really important uh interface to that process and so so our our road map looks looks roughly like this what we would like to do is to uh scrape the existing uh profiling data on what channels and pumps and GAP Junctions exist in various human tissues so those are the that that defines the interface that shows us which are the control knobs that we can that we can use uh through uh extensive physiics which we we are just now um getting started on so this this largely uh these data largely do not exist except in a few cases in a few model systems but this really needs to be done um you know in a very uh very coherent um very coherent way and uh we can we can use simulators such as the original Betsy uh platform made by Alexis this is this is uh Danny's electric face uh preview so so a pattern like this where we say okay this is this is the pattern that we want and uh we can simulate which of these channels and pumps needs to be opened or closed and uh and and then then that enables us to choose uh to choose um these uh this specific reagents to to then get get what we want and there's a there's a review here that you can take a look at of uh existing links between various ion channels and the cancer phenotype that can be that can also be used by this and other kind of manual strategies so so so the cancer pipeline look and you can you can start to um play with this this is this is online of course uh it's not not remotely finished yet but there's pieces of it already here where you can start to choose specific uh specific tissues and specific T cell types it will tell you what are the the the knobs that you have to play with so so what are the channels and pumps that are there and using the simulator to ask if we know what electric State we want which of these do we need to uh do we need to trigger and this is this is collaborative work with Jack tazinski and the software was built by Philip winter we can then we can then begin to choose electrocuting or novel uh uh drugs targeting ion channels that can have specific effects predicted by this computational platform and so our latest and this is this is some work by oneita Matthews and there's much more um coming shortly uh is is now moving all of this from from the from the Frog model into human cells first in 2D culture and now in in cancer spheroids and eventually of course in in in Vio uh starting to look at in this case G gasta we're also looking at um uh colon cancer and uh and and breast cancer and there's some really nice data on not only affecting uh the the individual cell behaviors but but even even looking for uh signatures of of normalization the idea that when we use when we use these drugs not only do these cells stop as many of their cancer like behaviors but they actually start to turn on some some markers of of normal multicellular tissues so um just a couple of things left to kind of look into the look into the future I've made the claim that um that cancer is not just a genetic disease but actually a disorder of the scaling of cellular competencies in navigating anatomical space and I think that's fundamentally an important way to to look at this to look at this problem I've suggested that bioelectric properties can be used to detect induce and normalize neoplastic cell Behavior Uh we now know that the behavior of these electric circuits can be uh modified in in useful ways uh just like we do in the nervous system with stimuli that is not with Hardware rewiring necessarily but with uh with various kinds of pharmacological physiological and and probably many other kinds of uh kinds of stimuli and uh I think I think the future involves uh pharmacological Optical and other strategies Guided by computational simulation platforms and so one of the one of the important um uh uh things that needs to happen in the future is is this uh this these these uh uh the the the knowledge of which bioelectrical states occur in different uh you know in different disease States and what are the healthy States um that we want and so much like you know I I always like this uh this this um history of Imaging this is what Pluto looked like in '96 and this is what it you know what looked like a few years ago and who knows what what they could do now but the idea is that Imaging these Imaging Technologies are very important and um the the the the breakthroughs that that um that we had made with with Danny's work back in the back in the day starting to get for the first time get an actual video you know the first the first pictures of voltage in embryos were done by Thor Leaf thorland around 2000 in Ken Robinson's lab but but but then the first um uh time-lapse movies of actually watching all the cells uh interact with each other electrically and watching the patterns such as this electric face that Danny will show that um um presses where and determines actually where all the organ such as the eye and so on where it's going to be we can now uh you know people like Patrick McMillan and our group are working on novel ways to uh to track in real time many different parameters cytoskeletal voltage um and so on and so this is this is what that same that same region now looks like this so we can we can start to get much much more complex patterns and hopefully get enough uh get enough data here to to deploy uh really uh state-of-the-art uh machine learning tools to try to um infer these patterns and apply um all kinds of interesting metrics from computational Neuroscience to understand how to how to how to manage this this this Behavior so um I think I think in the future um what I what I would like to see is uh better better technology and better data for uh developing these uh physiological um sign signatures uh we're working towards contr methods in in mammals because we'd like to move this into patient soon and the big idea of basically cracking this bi Electric Code and using normalization via electral as stimuli to the cellular Collective Guided by computational tools um and I will just just a reminder for all of us Mustafa Jos and I um edit the bioelectricity journal so if you have any papers uh that that are forthcoming on any of this I encourage you to submit to our um Journal so what I'd like I'd like to do at this point is to thank all of you for listening and thank the people who did the work so Wanita Matthews who's doing all of the human cancer bi electricity in our group and Patrick McMillan who is studying the the bioelectrics of cell collectivity and also um uh developing uh ways to understand how these electric properties relate to single cell versus group cell behavior um Brooke and Maria here did all of the early info work on the cancer that I just showed you uh this is gizm mus who show who's uh who did the the anthr robots and and that's a that's a model that um we are going to be using um in the cancer field shortly um Danny Adams and her um pivotal early work on looking at bioelectric patterns in in real embryos um lots of lots of other students um lots of support staff here without whom we couldn't have done this work many many collaborators uh here's here are some some funders that that supported some of this work uh I need to do a disclosure so uh so astonishing Labs is a is a um that supports a lot of this work in our group and we we're moving forward together towards uh various therapeutic hopefully therapeutic Avenues in in in in patients and the last thing I'll just I'll just if I have 30 seconds I'll just pitch kind of the the the the very end of this is this idea that um I I really think that uh the way to think about bi electricity is not as another piece of biophysics that we micromanage the way we do with with transcription factors and in Pathways I I think when we study bi electricity what we're really looking at is a Communications interface it's a it's an interface to the root of the problem which is the boundary that active Agents Set between themselves and the outside world and so this is kind of um go now now pulling back Beyond cancer this is uh this is this is kind of a larger view of of of how I see biom medicine developing and whereas this is where currently most of the progress has been around these bottom up kinds of interventions I think I think there is massive room for uh top down and these are you know many of these things we're doing and other people are doing in their groups looking at uh Behavior shaping and taking advantage of the various competencies of um of of of cells and tissues uh various kinds of agential implants such as such as anthr robots for healing in the body um different kinds of um um morphos cutical uh and and uh and electral and so the cancer uh the cancer problem I think is going to be uh really uh powerfully addressed in some of these ways if we use new techniques both both in terms of uh in terms of AI and things that are pop powerful um Concepts that are being developed in computational Neuroscience to address the cancer problem in this context and some of the some of the details are here so um I think that's it again I'll I'll just thank all of these people who did this amazing work and I'll thank you for listening