 If it weren't about 5.30 in the morning my time, I might have something special to say to Mark after that introduction, but I have. It's one of the things that I'm struck by being here is how different this is in its atmosphere and its mission from the last early genomics meeting, which had a frightful amount of focus surrounding questions like, should we do a draft genome sequence? And I remember with some trepidation becoming convinced that that was a good idea and actually feel pretty good about it now because I don't know about all the people out there in the audience, but I've certainly found it a wonderful thing to have and have been equally pleased that the commitment of NHGRI to actually finish it to high quality sequence has not, as was once feared, wavered. And so I'm grateful on two counts. But now on to the charge that Francis has given, which was actually just ever so slightly more focused than to talk about all of biology, which is really hubris beyond belief. And this is almost hubris beyond belief, is to talk about the interface of biology with genomics with a 20-year time horizon. So that's all a very nice idea and the notion of trying to think about what we will have learned by 2020 is something that I wish I could think about with any kind of thought that I was going to be even vaguely accurate. But in fact, I'm shamelessly clueless. And I want to own up to that. What there is out there that will have happened that is profound and important, I'm sure that I can't and possibly even most people here wouldn't dare to guess, which is perhaps the most wonderful thing about being a biologist. The really great stuff comes crawling out from places and observations that you just can't predict. With this in mind, I'd like to just get this huge 20-year thing kind of wrestled down into something that a smaller mind can deal with, namely mine. If we can agree on the cosmic, maybe it's a little easier to get down to things that are a little less cosmic. So the cosmic, in my view, would be to link the entire genome plan that we find writ in ATGC with the heritable properties of the organism. I think if we're able to do that, we can all go home, but I think it's fairly safe to say that most of us in this room won't have that happen before we're moldering in our graves. I'm optimistic, but I'm not that optimistic. And the other thing that you'd really like to know that's cosmic is to understand how genomes have evolved. And so what I really want to try to find a framework for people to talk about in the rest of this meeting is how we approach that cosmic. And to my mind, oh, here, actually, I couldn't resist the temptation of pointing to just two random cases. You can pull these out of any of our friendly neighborhood journals by just picking up an issue, the things you don't expect. And I want to make a point here about the interface, not of all of biology, but particularly of what we think of as classical genetics with genomics. And what I'm showing here is a figure out of one paper out of a group of three that were recently in Nature that are about small, untranslated RNAs that are found in C. elegans and they're also found in close relatives and conserved as in Briggsier. They're found also in Drosophila in humans. I mean, these are serious, they're conserved. And one of the things is you can't go out and find them by looking with our old algorithms. And so these were kicked off in the old-fashioned way by forward genetics finding something called let7, which was one of them. And then these various investigators went on to enumerate a large fraction of what's probably in the elegans genome, and maybe there's now enough known about them to be able to go forward and plumb other genomes where there may very well be more with something like a knowledgeable algorithm. And so it's this interplay of particularly classical genetics and genomics, and in fact, what you might call classical molecular biology and genomics, that for my money is a good thing to think about as the future of genomics itself. There's a second case that arises differently and backwards. That is, it arises first from pure genomics. And this comes from recent microbial sequencing. And in the adventure that I think the DOE has sponsored, which was its microbial month of last year, in which it did, I don't know how many, Ari, how many microbial genomes? 17. Okay, big number of microbial genomes. Not quite one a day, but a lot. And in doing those, when people sat down and started to look at the genomes, they found something that I think was entirely unexpected. And that is that very large fractions of genomes from disparate organisms that are not close relatives are found apparently exchanged or zorched up. And so you get to a really interesting idea, which may or may not pan out, which is at least in the microbial world of evolution by large pieces of genomes, even bigger than pathways for metabolism and pathways for pathogenesis, which had already been seen, actually exchanging and moving around so that one might eventually have to think of sort of a phylo genome, the genes available to an entire phylum to swap around. It's more than the kind of lateral transmission, at least, that I had ever thought of. And this you can pick up looking in J-Random Journal today. And so for this reason, I think projecting some of the most interesting things that may turn out to be the most profound is indeed a thankless task. But now on to maybe how we set our trajectory. So I guess the first pass is to figure out a little bit about where we are now. And I think Francis talked to some extent about where NHGRI is with things that are supported in their genomic program, but where are we scientifically and what intermediate steps do we need to take that are non-negotiable, the ones we know we need to take that are perhaps worthy of our talk and attention over the next day and a half. So what we wouldn't want to do in this next day and a half is miss an opportunity of the form of what if we hadn't committed to the human genome project over a decade ago, or what if when we had, it had committed only to the human genome and not to the key model organisms. Now it seems impossible to consider that either of those things might not have happened or run amuck from the perspective of now, but I think from the perspective of then it was actually a pretty hot and heavy debate. So I guess we need to think up are there equivalents right now and what needs special cultivation. And there seemed to me to be three broad categories. The first one is the people, and this is going to be kind of my saw for today. Thinking about the people that we're training right now and how we're training them and how that fits in with what genome science is bringing to biology and how it's going to change biology. Of course there are technologies and there's going to be an entire talk where Jeff DeWick is going to talk about the interface of technologies of all kinds and of new approaches to biology. And finally there are the issues of public data sets, clone resources and other things that might specifically be the kinds of things that individual laboratories or even centers cannot do and that call for some kind of community consensus. Oh and then there's access to the basement of the Federal Reserve where they keep all the gold. Francis I hope is getting a key. So this starts and ends this framework with students and I think that's appropriate because one shocking fact about projecting to the year 2020 is that unless things about scientists developing their research programs change dramatically the leaders of genome biology and biology at large are in our labs right now and so thinking about what it's going to be like 20 years ago is a lot more concrete and material to me when I look at the people in the lab and realize that they are it and what it is that they're learning to do now or what it is maybe that they're not learning to do right now is going to affect our trajectory perhaps more than any single data gathering project that we can now think up. Then there are the special properties of genomics and the two that strike me as being singular and particularly important and I feel it so strongly as an armchair aficionado of yeast biology is first the property of completeness. It is so different to be addressing the organism at the level of the full set of genes that it has to offer or even in the case of mice and humans most of it and not quite all of it perhaps and the further companion to that the two C's are combinatorics. The degree to which what's going on in almost any process that you delve into deeply is deeply deeply combinatoric and this is what means to me that 30,000 genes is more than enough if that turns out to be what we have. The novel combinations that one can put together given 30,000 genes and a significant fraction of that in the regulatory spaghetti of signal transduction and regulation of transcription and regulation of protein turnover is just absolutely mind bending and so I think looking from this perspective of completeness to judge how it is that genomics is going to have its impact and how we should select perhaps the next to gathering things that we do how we should address the question of when is completeness of the next genome and the next genome and the next genome as important as was completeness in the first of the model organisms and then there are a couple of overarching questions one I simply abbreviate by calling the arrows the arrows are those nasty arrows and I'll show a few just as a frame of reference they connect things in pathways and they connect things in pathways based largely on genetic evidence and now genomic evidence to some degree not so much yet and biochemical evidence but if we're ever going to figure out what's actually written in the genome and how it is that it specifies all the heritable traits and behaviors of an organism then figuring out in a meaningful way what those arrows actually mean I think is really key this is a challenge for biology not just for genome biology it's an extraordinary challenge the fact that genomics has been one of the first items to drag the larger community kicking and screaming toward some degree of computation is probably an incredibly good collateral it's the opposite of collateral damage it's a collateral good effect of genomics but it is just the beginning and it's something that I think we all think needs a lot of attention and finally there's the other big overarching question that comes with natural variation and evolution I'm not going to delve deeply into that there are people here that are vastly more qualified to talk about it in the breakout sessions than I am except to say that if there's something deeply profound to be understood understanding natural variations it's degrees and what it is that it brings not only to humans but to all organisms and to their evolution seems deep and core and so if we can somehow do something serious about those overarching questions in what it is that we plan out then I think we will have done a good thing and at the end of the day it's what do these students of ours need in terms of intellectual tools and technical tools in order to do this so I'm just going to flesh out a little bit my rant about the students of today and the lead scientists of 2020 so that it can't possibly get lost at the end and the most obvious thing that's different about growing biologists today than even 5 years ago or 10 years ago is that instead of being computationally oriented as a subset and perhaps numerically a minor subset of biologists now being able to use computational tools as something other than a black box and even invent them or at least knowing what you're doing with them is absolutely critical and so this means more math skills and computational tools it's not clear to me that it's critical that everybody be able to write code but it sure is clear to me that you need to be able to talk to people who do and that's not a trivial art form and I'm not sure most of us biologists have mastered it yet even those of us who are trying there's the usual genetics biochemistry and molecular biology and I think it's interesting for this group to consider how those relate to genomics itself and how genomics fits into all of this personally I'll carry my rant a little farther and see that I don't so much see genomics emerging as a pure specialty unhinged from all the others nor do I see it completely turning into a toolkit like restriction enzymes were once thought of as molecular biology there really is a subject here which is in a certain sense the global central dogma and that reflects itself in needing to know about forward genetics biochemistry, molecular biology and genomics as a group and then there's phylogenetic flexibility and I think that's a done deal that doesn't need to be argued in this room as one approaches any problem one of the most powerful tools and some of the most interesting stuff you can learn comes from walking across the phyla and seeing how whatever phenomenon you're studying is varying and what one can tell you about the other we've all experienced this it's just there for completeness for someone who's not going to visualize themselves as a sort of genome specialist but an average genome transcriptome proteome user how do they differ and I'd like to suggest as an arguing point they don't differ so much they need to change curriculum as well and a change set of experiences come back my computer was ruffed at length before this and so it has some new characteristics for the average user I think it ain't so different from number one and that you simply add or superimpose those key elements of biological problem where you're not focusing on how genomes evolve and specific things about genomes as the central focus but rather how an organism develops according to what's laid out in the genome neurobiological everything from development to behavior the heritable aspects thereof through environmental microbiology whatever and so to terminate my front-ramp rant on the generation of tomorrow it probably begins with undergraduates and it's time at most places to harden the curriculum and include more quantitative skills early on at the graduate level fusing things as I've discussed at the postdoc level these days there probably has to be extra time given for backfilling courses to learn what they didn't learn as undergraduates and graduate students and finally building these interdisciplinary teams that have people with serious computational skills interacting with the biologist is something that isn't done in a day no matter how much you want it to be done in a day and I think the experience of people who are doing it is that it's sometimes frustrating not only because the computational side folks are very expensive in normal biology budgets but also that just sort of our normal funding cycle doesn't fit very well with the team building cycle and often the degree of assurance that people on that side are used to and want to have as they go forward this is a problem that might only be solved by that you know getting into the basement of the Federal Reserve but I think it is something to think about and I know that NHGRI and staff have worried already about this and have programs that try to address various aspects of this so it's not as if it hasn't been thought of before but I think we're really at a point now where it doesn't refer to just a few specialists but it refers to most of the people in most of our labs so now on to this notion of completeness and a little more science there was a profound change I became keenly aware of the completeness business when an email missive arrived and my recollection is that it's attributable to PRB Paul Revere Botstein and it went something like yes Virginia there is no mick in yeast and for people who had spent two months, two years five years of their time or student time trying to do the phylogenetic dance from species to species to figure out what something like CMIC, a gene of some interest at the time was doing this ability to say it's not there was actually, I mean, sounds vaguely amusing and sounds sort of trivial right now but the ability to say not only what is there but to say definitively what is not there is a very, very powerful thing there's a little more to it than that and it extends pretty quickly to in yeast people and now in other organisms first going after all the targets of a given transcription factor and then all transcription factors to help define genetic regulatory elements asking the impact of every at least viable loss of function mutation under diverse conditions you can look at the binding of a given transcription factor to all possible binding sites of vivo in the organism and these are all things that have already been done there are things that are in progress that people are just sneaking up on but in a very serious way in yeast and that will or may come to other organisms like different forms of asking about the interaction of every protein with every other protein and there are some interesting debates to be had about making protein interaction maps and what you can infer from them is that there can come of two hybrid based maps compared with maps that might be generated based on assaying whole complexes by mass spec and somebody might want to get to that in breakout sessions or going forward as a matter of the scientific agenda but all of these things have this quality of either completeness or an attempt at pseudo completeness and what's interesting as you move out is that while a genome can really be known pretty much completely other things are in varying degrees pseudo complete and we have some sort of philosophic and operational decisions to be made and those have to do with what kinds of projects of this sort one might actually do going forward and one that I'm familiar with thinking about a little bit because the folks at DOE have put serious time into thinking about it is what happens if you go forward and try to map an entire proteome for the content of all of its multi-protein complexes and what you get is this pseudo completeness problem where you start to have to ask more complicated questions than for the genome itself so for example there are many complexes and the one cartoon here is involved in regulated protein turnover it's called an SCF complex and it targets particular things for ubiquitin addition and then going to the 26S proteasome for degradation and much has been learned about it especially in yeast but also in mammalian systems and what is found is that there's a core similarity to this complex but the so-called F-box component as well as the Cullen components vary there are few Cullen components and that gives you combinations of things and lots of different F-boxes for the specificity for recognition of substrate is and so while you can define the machine fairly quickly as containing a Cullen and F-box in these other components defining each and every machine and then attaching it to its function is not so small a trick so what this means is that not only are there components that are variable but that what comes out of this machine and I suspect out of many others is that there are both generic functions in this case targeting something to the proteasome via ubiquitin addition acting as an E3 ligase and very specific functions I mean there's a specific one of these that mediates notch signaling in one pathway there's another one that mediates various stages of cell cycle progression and so on and so on and so on they're turning up all over the place there's yet a different kind of E3 ligase that I've run into in muscle biology which I study that has to do with muscle atrophy they're turning up all over the map and so when you think about mapping them and then a map is important only when you attach it to function how you figure out the functions the question of what is complete starts to be a pretty hairy question so that leaves us with grappling for these future activities about when are our activities best done to pull out completion when are they best done selectively as in a new genome where you only look for protein interactions of say novel genes for example and one might offer from the experience of looking at the multiple microbial genomes for example the notion that letting go of completeness should be done with great care because it is such a valuable quality on the other hand we live in a real world of finite differences and so what we cannot solve by much better much cheaper much faster technology that lets us be complete but at much less scientific peril and for many fewer dollars then we will have to make choices and how those choices get made is a question that I'd like to lay on the table I have no answer for it and then finally the obvious let's solve it by driving technology to the ever cheaper faster better mode then somehow succeed in generating the forces of whatever economic and other that are needed to maximize academic access and Jeff may have something to say about this later but certainly a worry is of a divergent and a two prong genetic world or genomic world forming in the future in which particularly some of these expensive proteomic kinds of approaches are relatively inaccessible to the problems of the day to day user and there are lots of other things that will be invented and may only be used at the level of the pharmaceutical company so pondering how to work our way through that thicket over and over again even as it was tried to be worked through early on and with some success over access to microarray gene ships is going to be an ongoing problem at the interface of genomics and biology so what this is designed to point out what I'm showing you is a famous signal transduction pathway this is one that has been illuminated by studies the form that's shown here is the RAS pathway and in this case it's been illuminated as a problem in developmental biology beginning with a cell identity question and leading laboratories these figures are from a review by Paul Sternberg a colleague of mine at Caltech and it reviews work from his laboratories to Kim's laboratory and a number of others and there are parallel stories in Drosophila that are equally elegant but my point in bringing it up here is first to go from a cellular pathway on the right which is showing cells that will eventually become the vulva of a C. elegans to a biochemical and genetic pathway on the left where most of the biochemistry is by inference from other organisms and the majority of what has been deduced comes from classical forward genetics and a point that comes out of the dissection of this pathway I think is important for considering another kind of full out complete activity that has been done now with yeast and is contemplated with other organisms and that is make the full collection of null mutations that you can make by some form of knockout or use of interfering RNA whatever and what can you learn from that well to a first approximation you can learn a whole lot so it's not a non-starter by any means but the point I want to make here is that in this case and I think in many other pathways null mutations provide a really bare beginning particularly when you're working in a multicellular organisms has to do with early lethal phenotypes and things like that and so what they were able to learn was very much learned by more sophisticated screens off of weak alleles and gain of functional alleles and suppressors of various of these alleles and this isn't going to stop just because we've identified the genes and get to the null mutation what will often have to happen is a deep partnership with what we hope will be a faster form of classical genetics further comparative genomics could not have the comparative genomics that can get you the pathway it can show you RAS sitting up at the top linked to various receptors it can show you a map kinase cascade downstream and it can show you transcription factor targets downstream of the map kinase cascade that kind of connection you can often get from comparative genomics and comparative pathway analysis but often these things are directed toward very different ends in the animal there are not so many that are like a pathway that I'm going to show you in a moment because I'm familiar with it because I work on it which is muscle biogenesis which is actually perhaps unduly remarkably ancient and unduly remarkably conserved and unduly remarkably over simple probably and having said all of that I still love it but the point of this is that you can get connections but you can't get what it does for the animal without dealing with the animal ok so this is I'm exiting the parts as parts part of the interface of biology and genomics and I'm going to spend a little time in the parts to pathways business which was just indicated by the last slide and talking about connectivity and then getting really to where do we have to go to understand what all those arrows mean I mean those arrows mean x is upstream of y in an epistatic sense those arrows mean x reacts with or phosphorylates y but what really does that have to do with the function that eventually is read out so if we want to go from genome to how you make a worm you have to pass through the pathway but we have to know something more than what its components are and what their order is so here's an example of a very simple metazoan pathway and it's an ancient thing if you're going to be an animal and you're going to figure out how to eat you often have to move and you often have to have a few nerves connected to you in order to move and this is on the moving side this is how you make muscle and what's graphed here is a simplified version of how it happens in vertebrates and how it happens which is not so far what happens in a host of other organisms invertebrates is a multi-potential progenitor cell that can give rise to other fates but when it goes through a switch, a cell state switch as it were, it turns and becomes a committed myoblast which is a cell that can divide and it knows what it's going to become it will under appropriate environmental circumstances become muscle and only muscle and under most environmental circumstances that anybody's thought to present it with it can't become any of those other fates and there's now a molecular correlate of that commitment of end and then there's the differentiation switch where it actually goes to become differentiated muscle and express all the genes that are characteristic of it my reason for bringing it up other than my love of it is that it represents a kind of pathway that at least developmental biologists would like to understand to completion so we'd like to bring this completeness to understanding what each of these three cell states is and even ask are there really just three discreet cell states or have we been kidding ourselves all this time are there really two very discreet, rapid bi-stable switch-like switches that separate them or do they ooze and even though they ultimately seem to go in a unidirectional way do they dither and what does that tell us about the actual process that's going on that's sitting underneath those arrows so this is another way of representing that pathway and you're not expected to or even I would be loath to invite you to try and make heads or tails out of that but each and every one of those negative things indicated by a red blocker or those black things indicated by a arrow indicate respectively a negative or interfering like interaction or a positive interaction and for a large we're looking at what is mainly a group of transcription factors and these transcription factors talk to each other, they talk to themselves and they talk in tiers moving from tier 1 to tier 2 to tier 3 to tier 4 and down to tier 4 there was what was thought to be a fairly homogeneous group regulated by those guys sitting in tier 3 and for several of these there is rather direct knowledge and evidence that a tier 3 protein sits on a tier 4 gene so this is a part of a genetic regulatory network probably not atypical of many in metasomes perhaps even a little oversimplified and it doesn't contain in the graph all of the signaling that leads in to turn these guys on it contains mainly their discourse and discussion with each other and all of that is required to do this relatively simple pathway through what we think are 3 cell states and 2 transitions so this is just to amplify a little more than I actually intended to that even that bottom subgroup can be subdivided and this is a particular subset of things that have to do with getting straight how the cell cycle is regulated and what the arrested state is in the final differentiated myocyte which you're looking at is a sum of data that's been extracted from some chip analysis and as close as you'll get to seeing a chip but what it tells us is that even this group which was thought to be a rather simple set of genes reporting to the upstream regulators are diverse enough and probably because of differences that we're now starting to look at in their cis-regulatory apparatus and the details of the proteins that interact on the DNA that their kinetics reveal that they are not identically regulated in this case the kinetics are probably attributable principally to differential transcription and so all of this says that even when you've got a connection made and even when it seems like you know something about it and you bring out some strong differences probably with some fairly large implications for how the system actually does what it does so in Francis' email handout he said that genetic regulatory networks were one of the things that he wanted people to think about and there are lots of different levels at which one can describe such networks and different kinds of information you need if you're really going to know what such a network consists of and so this is what one regulatory module on one of these downstream genes looks like and all of those nasty little geometric shapes in different colors represent proteins that we know interact directly with the DNA often with each other as heterodimers or homodimers or other kinds of looser interactions and proteins that interact off the DNA to help activate transcription downstream etc. etc. I'm not going to take you through all of this to illustrate and to have appreciated by people who don't think about these aspects of gene regulatory networks is that even after you've connected a pattern of expression with sequences on the DNA and the proteins that bind there there is a measure of complexity in the system that gives subtle but important differences sometimes not so subtle differences in how the downstream genes are regulated moreover it's trivial to pick these guys out and much can be done with comparative genetics but much requires very we hope in the future high throughput rapid and simple assays for how they actually function yep okay and so I will finish up in a few minutes I will simply point out that gene regulatory networks in the MetaZoa also include elements that operate at very considerable distance making sorting them out an interesting task and again the comparative genomics aspect is helpful but not entirely revealing until you've got bioassay where I want to go and spend the last few minutes is at converting arrows and blockers into dynamic behavior and I want to show a little case that comes from the work of Michael Ellowitz and Stan Leibler and this is the little cartoon up there indicates three repressors in this case they're all from prokaryotic systems, lambda, tet and lack so if you've got the diagram at the top A represses B, B represses C and C represses A, what does it do and I'm not sure that all of us would instantaneously have said oh it oscillates but indeed it does and there's a lovely paper reporting the first part of this work and they've the further investigations and I certainly can't do the entire work justice but I just want to show you a little bit of both theoretical prediction that prompted them to actually set up this system and what came out of it so the top right is a system that has a reporter gene on the right that reports what one of these repressors is doing and on the left the system which is sitting on one plasmid and it causes the expression of three different repressors regulated by each other and what's down in the lower left is a prediction of what each one of those repressors ought to be doing it's a computational prediction of what they should be doing given a particular set of initial conditions and turnover times and on the right is data for what the reporter is doing and so and the paper I mean you really should visit the paper and not take this very superficial accounting of it to say all because there's much more to say, there's much to say about whether one treats the system stochastically or not there's much to say about how different cells behave and how cells in a lineage behave over time which is that there's a lot of it's not a perfect oscillator but it's amazing how well it works and so one view I think is that you understand even a simple little system like that when you can build it and it does what you predict and most of us, for most of our problems are nowhere near that level of understanding of our arrows we're still putting the arrows together in order, we're still collecting the parts, we're still finding connectivity at its most basic and even sometimes I think base kinds of levels and to move to a higher level of understanding of dynamically what these things are doing to actually build a cell much less an organism seems to me to be the real push for biology and its interface with genomics. There's also the question of whether there will be a limited number of stereotypical cassette circuits that are equivalent and I'm particularly attracted to this oscillator because I have a problem that I try to study which I'm going to skip by for a moment, we know some of the genes the point is that there's a lovely transcriptional oscillator that is operating in segmenting the vertebrate body making those segments that eventually become your ribs for example and getting to the bottom of that makes us wonder can we take a hint is it going to have a cycle of repressors well probably there's more than one way to build an oscillator, in fact we already know there are other ways which I won't get into but if there are only a few if there are stereotype strategies it's going to make describing the world in this way a lot easier if every game is an all new game and an all new strategy then our world is going to be really difficult and it's a task for much more than 20 years so this student of ours to come back to that student is going to need computational modeling plus increasingly sophisticated genetic manipulations with respect to combinatorics the ability to move around whole circuits and groups of genes seems to me as if it's going to be necessary on the horizon single cell assays that will let you look at discreet events so that you're not confused by the averaging of populations single molecule assays in the same way so that you're not confused and able to really understand what's going on and move out of the level of the kinds of diagrams that we've been drawing for so long and with that I'm going to skip to one last slide so since Francis kind of brought it up in his email I brought out one last thought there have been prior meetings like this at least one that HHMI co-sponsored a while back and it ended up being this incredible hair tearing event in which people were struggling with well what's genomics and what's the boundary and isn't it just all of biology and as if that were the bad news I couldn't understand what the horrible problem was yet from my perspective of course and mine was different than a lot of people in the room who saw the ship dissipating and becoming unfocused and fragmenting and turning into a bunch of from a big iceberg into a bunch of tiny little bits of ice and melting in the great ocean of NIH but I think times have changed at least I hope they have and they've changed partly because genome biology itself I think is in the process of growing up and well there may be an argument about whether one should call it a discipline and frankly I'm still a little confused about what disciplines are is it how you do the science or is it the question we've got both kinds I don't really care what I care about is that it has enough center of gravity and enough problems of its own to evolve and at this interface with its what should be its dearest friend classical genetics that it has plenty of center of gravity to interact with the rest of biology without being lost and so I would at least put on the table great optimism for embracing the interface rather than spending a great deal of time trying to find the boundary and if that's something that Francis and staff have to do and if they had some later time dealing with practicalities we'll let them do it on their own time so that was perhaps a little harsh but I needed a place to end and you know when you got to go you got to go thank you I think she may have ended but we're going to keep her up here and ask her some questions I'm only putting the questions on the table I'm not something that you mentioned at the end which is very important and relates to what you had in the beginning of your talk a lot of current diagrams of biological systems you have all the arrows just coming one way and I think a lot of the complexity of the system is lost in that fashion can you comment on that a little bit and how we can capture the full complexity of how the downstream elements influence what's above them yeah so the question is about all of these sort of one way arrows and how many of these interactions either molecular or cellular are actually unidirectional and how much is in the eye of the beholder and I guess I may be rambling off point but I'm reminded particularly of what has always seemed to me to be and has proved to be an unwise asymmetry where we always feel safer when we're looking at synthesis than when we're looking at decay and yet regulated decay is every bit as interesting and every bit is information rich both at the protein level and at the transcriptional level as is regulated synthesis similarly when you look at these regulatory pathways there's an awful lot of feedback going on I mean Adam Ark can better comment on all these different kinds of pathways and how they function than I can do but my impression just from the ones I'm really familiar with is that indeed arrows going in both directions whether directly or indirectly I think indirect happens a lot is clearly a big part of the game and the question is how much we've had our lenses and sunglasses tuned to looking for certain kinds of things if I were going to ramble further which I probably shouldn't but in mammals for example in looking at cis regulatory elements because of the nature of the assay extraordinary bias there has been looking for elements that cause something to turn on as opposed to the parts of those elements that repress and in organisms that don't carry some of the just experimental infrastructure and cost infrastructure that has caused people to relentlessly pursue the positive element because then you can write the paper and move on to the next one and it's very clear that negative regulation is at least as important as positive so yeah I like to weigh arrows it's a wonderful talk and it prompted me to think about two things trying to think about something out in 2020 do we need completeness of data resources or techniques for tissues and for organisms and for siat against of course we're very lucky for the work that was done before us but for I think big fluffy things like us we need probably to have that set of complete resources for organisms and I think that's quite interesting I hadn't really verbalized it internally to say to myself but what actually do we need to do to make that complete set of resources happen in terms of that that's one comment you might want to comment back the other thing about your education which is very very true about getting the people right I think we may miss a trick if we don't steal just directly from our computational colleagues so we don't have to like oh theft is encouraged theft is definitely encouraged but I think we should perhaps have an active thieving policy on computational and physics yes but it has to be thieving with a possible return policy and I have a bioassay and the bioassay is when you think you have successfully stolen someone from the computational or physical world you give them a copy of the latest edition of the molecular biology of the cell and you make sure it's a very fresh new copy and you give it to them and then you go look and see if it's been opened and if after a certain amount of time acts where acts should not exceed three months and it's not dog-eared then it's time for the return policy and by which I mean there are people who come over who wonderfully not only like the problems but are willing to if not become biologists are willing to and enjoy learning about the problems so that when you're talking to them you're not speaking in Swahili I always think of these New Yorker cartoons of a cat that's you know it's master is talking to it little zigzags well that can happen too okay well I don't know but I guess it is interesting to have a a thieving policy on computational side I don't know whether there's a route to do it I don't even know which part of the American system is the computational side you go and say we want to professionally nick people from you consistently over the next ten years I'm not sure we should tell them I think we should just do it I mean engineering departments are interesting places applied math which sometimes exists attached to engineering sometimes it exists attached to math computer science there are a whole host of them but very interesting things do happen even on a campus like ours which prides itself I think justifiably on encouraging interdisciplinary work between the physical sciences and biology even there when you actually get into the down and dirty of what happens in a graduate program when there will be two courses two years of courses about algorithms information theory and building computers and if you try to superimpose the biology on that it starts to look like you know making an mdphd at least so I have a question that deals with the issue of completeness or pseudo completeness at the level of the genome sequence itself and of course sort of the dirty little secret of the flying human genome projects is that about 25% of the genome namely the heterochromatin is currently excluded from our definition of completeness and these are regions that are important not only for inheritance but for understanding genome evolution especially and so we have these wonderful tools in terms of proteins and transcripts etc sort of what level of priority would you place on actually completing the genome sequence it requires you know technology development it will be some time before it can be done and so sort of where should our resources be placed do we really want to have a complete genome sequence or not other people than I should probably take a swing at that but I don't know whether it should be Dr. Lander who can think about whether he really wants to go through and finish sequence of all that quality or whether it should be people at NHGRI I mean I guess the argument about we know there's gold in them of our hills we know there's gold there because there are functions served evolutionary functions and there are genes buried in there I think I remember that you know the principle Mapkinase in Drosophila was sitting in an island of otherwise stuff that would not have been seen and wasn't even cloneable that was one of the arguments for the benefits of whole genome shotgun if I recall correctly so it's clearly a good thing the finishing it down to the last nucleotide is wonderful but it's a cost benefit analysis and what you'd hope is that by incrementally moving the definition of completeness at the same time that you're willing to invest in technology to go after the hard stuff that you'll be able to continue to move and not just decide one day well 15% is left and we're tired and we're going to put all our money somewhere else and I think that's how most genome oriented people think about it time for one more this is just a quick comment Barbara I think some of the points you made about education and culture and combinatoriality in figuring out how genomic information actually explains biology could be more widely promulgated and I think people in this room should be aware that current policy in the biology literature is to make sure that the combinatorial aspects of any system are published last rather than first and that essentially one is constrained by editorial policy good journals quote unquote to start by publishing papers about how one molecule has an input to a system and another paper on how another molecule has an input and I think the genomics perspective has not been heard by many of these organs and it would be very nice to get this word out to other people yeah I think point well made and maybe it's a redefinition of what comprises elegance it's it always seems easier with one gene one input, one relationship to feel at ease with an elegant bit of truth even if it's only transient in its elegance and when you put it all together it starts to get messy again it's that thing that sends our physics friends running away from us yelling and screaming and going oh you people you live in a muddle of confusion and to the extent that biology builds its systems sometimes in a not entirely parsimonious way or that what we think is elegant parsimony actually there's a more elegant thing going on I mean I heard a story which I'm not going to be too specific about because I think it's not published but except to say it's a story of three transcription factors that appear to be entirely redundant and why were the three transcription factors in the cell and indeed until you blew them all out you didn't do something awful to them but if you got down and actually look at the dynamics of the decision making they were actually doing this marvelous dance in which first the first one did something and then the second one did something and then the third one did something but it was also finally resolved they actually had to get into the computational modeling game of the predictions to test what was underlying the redundancy and I think as we see more and more of those cases come up people will become maybe more and more skeptical of real simple one molecule one arrow things Thank you Barbara