Michael Eisen, Part 1 of 2

Spectrum

In part 1, investigator with the Howard Hughes Medical Institute Michael Eisen talks about his research, the field, and both experimental and computational biology. Eisen is Associate Professor of Genetics, Genomics, and Development in UC Berkeley's Dept. of Molecular Biology.

Transcript

Speaker 1: Spectrum's next

Speaker 2: [inaudible]. Welcome to [inaudible]

Speaker 1: section, the Science and technology show on k a l x Berkeley, a biweekly 30 minute program bringing you interviews [00:00:30] featuring bay area scientists and technologists as well as a calendar of local events and news.

Speaker 3: Good afternoon. My name is Brad Swift. Thanks for tuning in. Today we are presenting part one of two interviews with Michael Eisen and associate professor of genetics, genomics and development in UC Berkeley's department of molecular biology. Iceland employs a combination of experimental and computational methods to the study of gene regulation [00:01:00] using the fruit fly Drosophila melanogaster as a model system. Isen and his colleagues have pioneered genomic approaches in modern molecular biology and our leaders in the emerging field of computational biology. In part one, Michael talks about how he got started in biology and how his research has evolved onto the interview. Michael Isen, welcome to spectrum. Thank you. My pleasure. Would you give us a narrative of how you initiated your research and how your research has [00:01:30] changed to what it is currently?

Speaker 4: Okay. Actually, I grew up in a family of scientists. My parents were both biologists, so I always had an interest in biology. But as a kid, my talents were primarily in math and I was a heavy duty math geek and went to college expecting to be a mathematician and took this freshman calculus class and all the hardcore math geeks tuck. And I did fine. I did well in the class, but [00:02:00] there were several people in the class who were clearly a notch better than me in a way that I think you only can realize and you know, basketball and mathematics at the age of 18 that you're not destined to be the best. And I think math is a field where if you're not the best, it's just kind of boring. And so I stayed as a mathematician and math major in college, but I started increasingly taking a lot of biology classes and had more or less, you know, realized that biology was what really captured my, my attention and [00:02:30] my heart.

Speaker 4: And so I went to graduate school but had the idea that I'm interested in biology, but I'm really good at math. So there must be some way of combining these two things. And so I entered a graduate program in biophysics, which is sort of a place where people who are interested in biology maybe haven't taken all the prereqs for a normal biology department but also have a quantitative background go cause. And so, you know, in the way that people sort of drifted into things, I drifted into working on protein structure and [00:03:00] did my phd studying the evolution of the proteins on the surface of flu viruses and using a combination of experimental work and I would hesitate to call it mathematics. It was really just sort of kind of physics and it's, it's a lot of data. You generate a lot of raw data, you generate a lot of data on the coordinates of individual protein molecules and things that they might bind to.

Speaker 4: And so it was very natural to start using computers in that work. You know, my background was not in computer science. I programmed as a kid [00:03:30] because my grandfather bought me a computer and I taught myself how to program and I wrote programs to, you know, keep track of baseball statistics and other things like that. In College, I basically never programmed anything in the math department I was in. It was considered not math that you were touching a computer. And so I didn't really do anything with computers until I got to graduate school when you started seeing all this data coming down the pipe. But I wasn't particularly interested in structural biology and I discovered that through six years from graduate school that [00:04:00] although I liked doing it, it wasn't intellectually satisfying, was too small. You're working on one sugar bound to one protein in one virus and I was having trouble seeing how that would expand into something grand and whatever.

Speaker 4: You know, the ambitions of, uh, of a graduate student wanting to do something big. And I got lucky in the way that often happens in that my advisor had a colleague he knew from an advisory board. He sat on and he was coming into town because his brother was getting some honorary degree [00:04:30] and I met him in his hotel room, Austin. And he had with him, uh, glass microscope slide onto which had been spotted down little pieces of DNA, each of which corresponded to one gene in the yeast genome. So it's about 6,000 genes in the yeast genome. And you could see them because there was still salt in the spots, but it was a very evocative little device. You could sort of hold it up in front of the sun and you could see the sun sort of glittering on all these little spots.

Speaker 4: You could just see the grandness of [00:05:00] the device. Didn't know how people were using them. I didn't know what they would be used for. I didn't know what I would do with them, but I was sort of drawn in by the scale of it all. The idea that you could work on everything at once and you didn't have to choose to work on just one little thing and disappear into a little corner and study. Just that. And so my advisor said, oh, you really should go do this. They need someone who's, you know, understands biology, but can deal with the computational side of things. It's clear that this was going to generate a lot of data [00:05:30] and that, you know, he was right. I mean this was a field that really was in great need of people who understood the biology but could work well in the quantitative computational side of things.

Speaker 4: So I packed up and moved to Stanford with a short stint as a minor league baseball announcer in between. Really it was just a very fortuitous time to have gotten into this new field. I mean, the field was really just beginning. So this was in 1996 the first genomes been sequenced, they were microbes, there's bacteria and yeast [00:06:00] and so forth. And we were just getting our first glimpse of the scale of the kind of problems that we were going to be facing in genomics. But what I loved about this device, which is a DNA microarray, it's the sort of became a very hot tool in biology for a number of years was that it wasn't just a computer, it wasn't just data in a computer. It actually you were doing to do experiments with this. I'm interested in biology cause I liked living things. I like doing experiments, I like seeing things and I didn't want to just disappear with someone else's data and [00:06:30] analyze it.

Speaker 4: So I went to Stanford to work on these and it really was just this awesome time and we were generating huge amounts of data in the lab and not just me. There were, you know, dozens of people generating tons of different types of experiments and so forth. And we lacked any kind of framework for looking at that data constructively. You couldn't look at those experiments and figure out by looking line by line in an excel spreadsheet at what gene was expressed, at what level and what condition. It just wasn't [00:07:00] the way to do it. And so my main contribution to the field at the time was in bringing tools for organizing the information and presenting it visually and being able to interact with that kind of incredibly complicated data in a way that was intuitive for people who understood the biology and allowed them to go back and forth between the experiment in the computer and the data and really try to make sense of what was a huge amounts of data with huge amounts of information, but something nobody had really been trained to [00:07:30] look at. And so it was there that I really realized kind of the way I like to do science, which is this constant back and forth between experiments on the computer. In my mind and in what I try to teach people in my lab. There's no distinction between doing experiments on the bench or in the field or in a computer that they're just different ways of looking at biology.

Speaker 3: This is spectrum line KALX Berkeley. Today, Michael [00:08:00] I's associate professor at UC Berkeley explains his research in developmental biology.

Speaker 4: On the basis of that time at Stanford, I got a job at Berkeley and what I did when I started my lab at Berkeley was really tried to focus on one problem. I mean I had been working on a million different problems at Stanford where we had a huge group and a million different people working on, and I was sort of moving around from problem the problem and helping out people with their data or thinking of different experiments. And when I came to Berkeley, I really [00:08:30] wanted to focus on one problem. And the problem that had intrigued me from the beginning of working on the microarray stuff was figuring out how it is that an animal's genome, which is the same essentially in every cell in the body, how it instructs different cells to behave differently, to turn on different genes and to acquire different properties. And so partly because of the influence of people here at Berkeley who were working on fruit flies, I switched my research program to work on [inaudible] when I started my lab at Berkeley, the genome of that [00:09:00] had just been sequenced and I liked working with animals.

Speaker 4: I like having something that moves around and you know, had some behaviors and so the lab started to work on flies and pretty much since then that's what we've worked on. That's sort of the story of how I got to where I am. So your research then is you're looking at flies over time? Yeah, I mean, I mean I see how the genes are expressed. I'd say we're looking at classified more as developmental biology in the sense that we're looking at how genes are expressed over time during the lifespan of a lie. To this day, [00:09:30] we can't look at a newly sequenced genome and say, oh well this is what the animal's going to look like. That is, I couldn't tell you except sort of by cheating and knowing, comparing it to other genomes. If I, you gave me a fly genome, I look at it, I wouldn't know it was a fly or a worm or a tree or it's just the way in which the organism acquires it.

Speaker 4: Things that make them interesting, their form, their appearance, their function. We have just the tiniest scratch of understanding of how that works. And so it's, for me, the most [00:10:00] interesting problem in biology is how do you get in a complicated structure like an animal out of a single cell. And how is that encoded in a genome sequence? I mean it's a fascinating mystery that I thought, you know, when I first started doing this I thought we'd have solved that problem by now. Not Easily. You know, because we had all this new data, we had the genome sequences we could measure. And a lot of what my lab does is actually measure which genes come on when, during development and try to understand for individual genes where that's been encoded in the genome [00:10:30] and how that happens. And I just sort of figured, well, you know, the problem for all these years was not that the problem was that hard.

Speaker 4: We just didn't have the right data to look at this problem. And now we can do these experiments. I can sequence the genome of a fly and in a day I can characterize which genes are turned on when during development. And I sort of naively thought, well, we'll just sort of put it into a computer and shake things up and be clever and we'll figure out how these things are related to each other. And I mean now it's laughable that I would've ever thought that, but it was a very, very complicated thing. It's a process that's [00:11:00] executed by very complicated molecular machines operating in a very complicated environment or the nucleus and it, you know, we really don't understand it very well. We've learned a lot, but it's not a problem. We really understand. And so what is it that you've accumulated in terms of knowledge in that regard?

Speaker 4: What do you think you've learned? A small amount of this is coming from my lab, but this is a whole field of people looking at this. But that we know the basic way in which that information is encoded in the genome. [00:11:30] We know that there are tuneable switches that can turn genes on and off in different conditions. And we know basically what molecular processes are involved in doing that in the sense that we know that there are proteins that can bind DNA in a sequence specific manner. So they will stick only to pieces of DNA that contain a motif or a particular code that distinct for each of these factors. In flies, there's several hundred of these factors and for humans that are several thousand of these factors that bind DNA in a [00:12:00] sequence specific manner, and they basically translate the nucleotide sequence of the genome into a different kind of code, which is the code of proteins bound to DNA.

Speaker 4: And we know from a million different experiments that it's the action of those proteins binding to DNA that triggers the differential expression of genes in different conditions. So if you have a particular proteins, these are called transcription factors. If you have one in a cell at high levels than the genes [00:12:30] that are responding to that factor will be turned on in that cell. And if there's another cell where that protein isn't present, the set of genes that responds to it won't be turned on. So we know that as a general statement, but working out exactly how those proteins function, what it is that they actually do to turn a gene on and off, how they interact with each other, what conditions are necessary for them to function. All of those things are, I wouldn't say we know nothing about it, but they're very, [00:13:00] very poorly understood.

Speaker 4: A lot of this sort of simple ideas that people had of there being a kind of regulatory code that looked something like the protein code that we're, you know, amino acid code that people are familiar with, right, that there'll be a genetic code for gene regulation. The idea that that's true is long disappeared from our thinking in the sense that it's much more like a very, very complicated problem with hundreds of different proteins that all interact with each other in a dynamic way. Something bind recruits, something else. [00:13:30] The thing it recruits changes the coding on the DNA and essence to a different state and then that allows other proteins to come in and that somehow or another that we still really don't understand. You eventually reach a state where the gene is turned on or turned off depending on what these factors are doing and you know, while there's lots of models for how that might function, they're all still tentative and we're getting better. The techniques for doing these kinds of experiments get better all the time. We can take individual pieces of or Sophala embryo [00:14:00] and sequence all the RNA contains and get a really complete picture of what's turned on when the technology is improving to the point where we can do a lot of this by imaging cells as amazing things we can do, but still the next level of understanding the singularity in our understanding of transcriptional regulation is still before us.

Speaker 3: Spectrum is on KALX, Berkley alternating Fridays today. Michael [inaudible], associate professor at UC Berkeley [00:14:30] is our guest. In the next section, Michael describes the challenges his research poses

Speaker 4: and is the task then the hard work of science and documenting everything's, yeah. Mapping a little bit about just observing. I mean, I'm a big believer in observational science that what's limited us to this has been just our poor tools for looking at what's going on. I mean we still hard to visualize the activity of individual molecules within cells, although we're on the precipice [00:15:00] of being able to do that better. So yeah, it's looking and realizing when the paradigms we have for thinking about this thing are clearly just not sufficient. And I think the fields get trapped sometimes in a way of thinking about how their system works and they do experiments that are predicated on some particular idea. But you know, usually when you have an idea and you pursue it for quite a long time and it doesn't pan out, it's because the idea is wrong.

Speaker 4: And not always, but I think the transcriptional regulation field has been slow to adapt [00:15:30] to new sort of models for thinking. Although that is changing, I think that there's a lot of activity now and thinking about the dynamics of DNA and proteins within the nucleus. You know, we tend to think about DNA as kind of a static thing that sits in the nucleus and it's a, it's sort of read out by proteins, but really much more accurate as to think of it as a living kind of warned me like thing in the nucleus that gets pulled around to different parts of the nucleus and where it is in the nucleus is one way in which you control what's turned on and off. And I think people are really [00:16:00] appreciating the importance of this sort of three-dimensional architecture of the nucleus as a key facet and controlling the activity that there's, the nucleus itself is not a homogeneous place.

Speaker 4: There is active and inactive regions of the nucleus and it's really largely from imaging that we're learning how that's functioning and you know, we as the whole field and are there lots of collaborators and people who are doing work? Yeah, I mean I'd say oh yeah. I mean it's a, it's an active feeling. Pay Attention to [00:16:30] oh yeah. So it's an active, if not huge field and not just in flies. I mean, I think it's transcriptional regulations of big field and in particular in developmental biology where amongst scientists we're interested in how animals develop. It's long been clear that gene regulation is sort of sits at the center of understanding development and so people interested in developmental biology and have long been interested in transcriptional regulation and I think everybody's got their own take on it here. But yeah, it's a very active field with lots of people, including several other people at Berkeley who are doing really [00:17:00] fascinating stuff.

Speaker 4: So it's not out in the wilderness. This is not the hinterlands of science, but it's um, it's a nice field to work in about appropriate size. Our annual meetings only have a thousand, a few thousand people. It's not like some of these fields with 25,000 people. I can realistically know all the people who are working on problems related to ours and I literally know them and I know what they're doing and we sort of exchange ideas. So I like it. It's, it's nice community of people. [00:17:30] Is the field driving a lot of tool development? Absolutely. I say, this is something I really try to encourage people in my lab and people I trained to think, which is when you have a problem, you should be thinking not what am I good at? What can I apply to this problem? What technique has out there that would work here?

Speaker 4: But what do I need to do? What is the right way to solve this problem? And if someone else has figured out how to do it, great, do it. But if they haven't, then do it yourself. And I think that this applies sort of very specifically [00:18:00] to doing individual experiments, but also to this broader issue we were talking about before with this interplay between computation and experiment. I think too many people come into science graduate school or wherever, thinking, well, I'm an experimentalist or I'm a computational biologist or whatever. And then they ask a question and then the inevitably hit the point where the logical path and pursuing their question would take them across this self-imposed boundary. Either you're an experimentalist who generated data and you're not [00:18:30] able to get at it in the right way and therefore, you know what you really need to be doing is sitting at a computer and playing around with the data.

Speaker 4: But if you view that as a boundary that you're not allowed to cross or you're incapable of crossing, you'll never solve it because it almost never works. You almost never can find somebody else no matter how talented they are. Who's as interested in the problem that you're working on as you are. And I think that's a general rule. Scientists should feel as uninhibited about pursuing new things even if they're bad at it. It's certainly been a mantra [00:19:00] I've always tried to convey to the people in my lab, which is, yeah, sure, you come in with a computer science background and you know you're a coder and you've never picked up a pipette or grown a fly. But that's why the first thing you should do in the lab is go grow flies and vice versa. For the people who come in perfectly good in the lab but unable to do stuff in the computer, the first thing you should do is start playing around with data on the computer and it doesn't always work and not everybody sort of successfully bridges that gap, but the best scientists in my mind are ones who don't [00:19:30] circumscribe what they're good at.

Speaker 4: They have problems and they pursue them. When something like visualization, is that a bridge too far to try to embrace that kind of technology? I've always done that. I mean I almost every time I do an analysis in the computer, I reduce it to picture some way or another. You know, because of the human brain, no matter how fancy your analysis is, the human brain is just not good at assimilating information as numbers. What we're good at as thinkers is looking at patterns, [00:20:00] finding patterns and things, looking at looking at images, recognizing when patterns are interesting and important, and there's a crucial role for turning data into something the human brain can pull in. And that's always, for me, one of the most fun things is taking data that is just a string of numbers and figuring out how to present it to your brain in a way that makes some sense for it and the refinement of it so that it's believable.

Speaker 4: Yeah, and so then you can do it over and over and over and get the same result. Yeah, and all, I mean it is one of the dangers [00:20:30] you deal with when you're working with, when you're relying on human pattern recognition is we're so good at it that we recognize patterns even when they don't exist. There's a lot of statistics that gets used in modern biology, but often people I think use it incorrectly and people think that statistics is going to tell them what things are important, what things they should be paying attention to. For me, we almost entirely used statistical thinking to tell us when we've fooled ourselves into thinking something's interesting, you know, with enough data and enough things going on, you're going [00:21:00] to find something that looks interesting there and having a check on that part of your brain that likes to find patterns and interesting things is also crucial.

Speaker 4: You know, I think people understand that if you flip a coin three times, it's not that we are trying to land on heads, but they have much, much harder time thinking about what happens if you flip a coin a billion times. We're struggling with this in biology, this transformation from small data to big data, it taxes people's ability to think clearly about what kinds of phenomena are interesting and aren't interesting. [00:21:30] Big Data is sort of the promise land now for a lot of people. Yeah. I'm a big believer in data intrinsically. If you're interested in observing things and interested in understanding how they work, the more you can measure about them better. It's just that's not the end of the game. Right? Just simply measuring things that doesn't lead to insight. Going from observing something to understanding it. That's where the challenges and that's true. Whether you're looking at the movement of DNA in a nucleus or you're [00:22:00] looking at people by a target, right? Like the same. It's the same problem.

Speaker 3: This concludes part one of our interview with Michael [inaudible]. On the next spectrum, Michael Eisen will explain the Public Library of science, which he [inaudible]. He will give his thoughts on genetically modified organisms and a strategy for labeling food. He discusses scientific outreach and research funding. Don't miss him now. Our calendar of science and technology [00:22:30] events happening locally over the next few weeks. Rick Karnofsky and Renee Rao present the calendar

Speaker 5: tomorrow, February 9th from noon to one wild Oakland presents nature photography basics at lake merit. Meet in front of the Rotary Nature Center at 600 Bellevue Avenue at Perkins in Oakland. For this free event, learn to get more out of the camera you currently have and use it to capture beautiful photos of Oakland's jewel lake merit. [00:23:00] Bring your camera and you'll learn the basics of composition, camera settings, but photography and wildlife photography. Okay. Your instructor will be Dan. Tigger, a freelance photographer that publishes regularly in Bay Nature and other magazines. RSVP at Wild Oakland dot o r G. UC Berkeley

Speaker 6: is holding its monthly blood drive. This February 12th you are eligible to no-name blood if you are in good health way, at least 110 pounds and are 17 years or older. You can [00:23:30] also check out the eligibility guidelines online for an initial self screening if you're not eligible or you prefer not to donate blood. There are other ways to support campus blood drives through volunteering, encouraging others and simply spreading the word. You can make an appointment online, but walk ins are also welcome. The blood drive will be on February 12th and the alumni house on the UC Berkeley campus will last from 12 to 6:00 PM you can make an appointment or find more information at the website. [00:24:00] Red Cross blood.org using the sponsor code you see be February 13th Dr. Bruce Ames, senior scientist at the Children's Hospital Oakland Research Institute will speak at a colloquium on the effects that an inadequate supply of vitamins and minerals has on aging.

Speaker 6: Dr Ames posits that the metabolism responds to a moderate deficiency of an essential vitamin or mineral by concentrating on collecting the scarce proteins [00:24:30] to help short term survival and reproductive fitness, usually at the expense of proteins important for longterm health. This is known as triaged theory. Dr Ian Discuss ways in which the human metabolism has evolved to favor short term survival over longterm health. He will also present evidence that this metabolic trade-off accelerates aging associated diseases such as cancer, cognitive decline, and cardiovascular disease. The colloquium will be on February 13th from 12 [00:25:00] to 1:15 PM on the UC Berkeley campus in five one oh one Tolman hall February 16th the Monthly Science at Cau Lecture series will hold a talk focusing on the emerging field of synthetic biology, which applies engineering principles to biology to build sales with new capabilities. The Speaker, John Dabber is a mentor in the international genetically engineered machines competition or ai-jen and a UC Berkeley professor, [00:25:30] Dr Debra. We'll discuss the new technique created in J key's link's lab to make low cost drugs to treat malaria. He will also introduce student members of the UC Berkeley Igm team who will discuss their prize winning project. The free public event will be on February 16th from 11:00 AM to 12:00 PM will be held on the UC Berkeley campus in room one oh five of Stanley hall

Speaker 5: on Tuesday the 19th how long now and Yearbook Buenos Center for the Arts Presents. Chris Anderson's talk [00:26:00] on the makers revolution. He describes the democratization of manufacturing and the implications that that has. Anderson himself left his job as editor of wired magazine to join a 22 year old from Tijuana and running a typical makers firm. Three d robotics, which builds is do it yourself. Drones, what based collaboration tools and small batch technology such as cheap 3d printers, three d scanners, laser cutters and assembly. Robots are transforming manufacturing. [00:26:30] Suddenly large scale manufacturers are competing, not just with each other on multi-year cycles are competing with swarms of tiny competitors who can go from invention to innovation to market dominance. In a weeks today, Anderson notes there are nearly a thousand maker spaces shared production facilities around the world and they're growing at an astounding rate. The talk is seven 30 to 9:00 PM at the Lam Research Theater at the Yerba Buena Center for the arts at 700 Howard Street in San Francisco.

Speaker 5: [00:27:00] Tickets are $15 for more information, visit long now.org now to new stories presented by Renee and Rick. The Federal Communication Commission has released a proposal to create super wifi networks across the nation. This proposal created by FCC Chairman Julius Jenna Koski, is it global first, and if approved, could provide free access to the web in every metropolitan area and many rural areas. The powerful new service could even allow people [00:27:30] to make calls for mobile phones using only the Internet. A robust public policy debate has already sprung up around the proposal, which has drawn aggressive lobbying on both sides. Verizon wireless and at t, and t along with other telecommunications companies have launched a campaign to persuade lawmakers. The proposal is technically and financially unfeasible. Meanwhile, tech companies like Google and Microsoft have championed the ideas sparking innovation and widening access to an [00:28:00] increasingly important resource. We can add this to the growing list of public policy debate over our changing and complex relationship with the Internet.

Speaker 5: A team at McMaster university as reported in the February 3rd issue of nature chemical biology that they have found the first demonstration of a secreted metabolite that can protect against toxic gold and cause gold. Biomineralization. That's right. Bacterium Delphia, [00:28:30] a seat of [inaudible] take solutions continuing dissolve the gold and creates gold particles. This helps protect the bacteria from absorbing harmful gold ions, but it also might be used to harvest gold. The researchers found genes that cause gold, precipitation, engineered bacteria that lack these jeans and observed that these bacteria had stunted growth and that there was no gold precipitation. They also extracted the chemical responsible [00:29:00] for the gold mineralization naming it delftibactin a, the molecule creates metallic gold within seconds in Ph neutral conditions at room temperature. Gold exists in extremely dilute quantities in many water sources and the bacteria or the metabolite might be used to extract gold from mine. Waste in the future.

Speaker 3: [inaudible] the music her during the show is by Luciana, David [00:29:30] from his album foam and acoustic, released under a creative Commons license, 3.0 attribution. Thank you for listening to spectrum. If you have comments about show, please send

Speaker 1: them to us. Our email address is spectrum dot k a l [e-mail] join us in two weeks at this same time.

Speaker 2: [inaudible].

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