Welcome to Science Talk, the weekly podcast of Scientific American for the seven days starting July 11th. I am Steve Mirsky. This week on the podcast ...
Hood: So, I remember when I went to Caltech in 1970 and started developing a series of five different instruments that basically became the foundation for modern molecular biology.
Steve: That's one of America's preeminent biologists, Leroy Hood, co-inventor of the automated DNA sequencer. Leroy Hood was described in Scientific American magazine in 1994 as not just one of the most influential molecular immunologists of the age, [but] he has also made a name for himself as a charismatic and astute business man who has helped launch several companies' commercializing advances in biotechnology. We'll talk to Hood and we'll test your knowledge about some recent science in the news.
Leroy Hood was at Caltech from 1970 until 1992, when Bill Gates gave the University of Washington 12 million dollars to [bring] Leroy Hood there as head of a new interdisciplinary department of molecular biology; and in 2000, Hood founded the Seattle-based Institute for Systems Biology. So what is systems biology? Hood an ex-college football player once described systems biology with a gridiron metaphor, "You never understand the game of football, he said, by defining what the end or fullback or even the quarterback does, it's understanding how the team plays together and how the other teams plays against them. It's systems within systems and the interactions between them", or as the journal Nature Biotechnology put it, "Systems biology ideally seeks to understand complex biological systems in their entirety by integrating all levels of functional information into a cohesive model". That's rather than looking at one gene or one protein at a time. Leroy Hood recently dropped by the offices of Scientific American where a group of the magazine's editors sat down with him for a chat. Here are some of the highlights of that conversation.
First I asked Hood about that football analogy.
Hood: You know, I'll give you a much better analogy, if that's okay. I mean, one that anybody can understand is, suppose you were an engineer and you wanted to figure out how a radio converted radio waves into sound waves—what would you do? First thing you do is you do a parts assessment. You list all the parts of the radio. That's what molecular cell biology has done for 40 years. They've only actually studied half the parts or less evenin that time, but what the genome project did, that was really key for systems biology, is it gave us the complete list or relatively complete list, okay. So, what's the second thing you do then? You put the parts into their circuits and you try and understand how the circuits individually and collectively moved electrons around and what that inference had for this conversion process. And systems biology in humans is exactly the same; and for biological information it is handled by these various types of biological networks and the key to understanding biological complexities is understanding how this information is processed by these networks dynamically.
Steve: When I do this here what happens all the way over here?
Hood: Yeah! Exactly, that's exactly what—but even more than that you want to say, when you do this here at time one, what happens over here; but if you do it at time two, how
is[does] what happens over here change, because the architecture as well as the quantitative aspects of the components in the network have all changed. And if it's proteins we are talking about, the qualitative aspects of the proteins change with modification, with processing, with moving from the cytoplasm to the nucleus, all these other kinds of components.
Steve: SciAm editor Gary Stix asked Hood if the concept of systems biology is making inroads in the biological community and whether systems biology is being carried out in the way Hood thought it would be.
Hood: So I would say two things. One, systems biology has been around for a hundred-plus years because, you know, if you think about physiologists and homeostasis at the turn of the nineteenth century, that's systems biology; or neurobiology or immunology are—those are all systems. But so I would say what distinguishes
the systems biology, I think about are really four ideas. So one is the idea that where possible you try and do global measurements—and by global I mean you look at all DNA, all genes, all messages and principle of all proteins—we can't do it yet and so forth, but you don't look at just a subset of things, you try and look at everything. Then I think the second thing that's really key if you are to do a quantitative modeling is those measurements should be as quantitative as possible. I think the third thing that is really fundamental; well, let me just say that what the essence of systems biology is really looking at [is] biology as an informational science; and by that I mean two different things. So one is the idea that there are two fundamental types of biological information: There is the digital genome that is ultimately knowable right down to the last base pair in principle, and that's what distinguishes biology from all of the other scientific disciplines. There is this core of certainty from which we start building living organisms that is totally accessible. And then the second type is the environmental information that impinges upon and changes, modulates the digital information. So I see the role of systems biology is[as] being able to assess the relative contributions of those two types of biological information to—one, development; two, physiological responses; and three, disease. That's the big [one], I mean, in health sciences anyway; we can talk about energy and other things differently. And I would say the second basic idea in looking at biology as an informational science is that all biological information is processed by biological networks. So they can be networks within cells, they can be cells themselves that are networks, or they can be networks of living organisms and ecologies that interact; so there are these different hierarchical levels of information. And to understand most systems, what you have to be able to do—and this is the third imperative—is integrate as many of these levels of information as they change dynamically across development, physiology or disease. And again the fundamental aspect is networks and their dynamic changing and the ability to integrate different types of information; and by integration what you really mean is how you can take DNA and RNA in proteins and interactions and so forth and put them together so that you can extract and separate out what is digital information from what is environmental information; as really, and you'll never understand systems if you try and study them in just one level of dimensionality, like genomics, okay?. That doesn't begin to get at the rich diversity of information that is generated. So those four things, and the ability to study things globally, to study things quantitatively, to study things dynamically, like capturing these networks dynamically, and finally the ability to integrate the levels of information—those are the four attributes that I think separate the systems biology, I think, that from a lot of other things out there that ha[ve] s been defined in different ways. Look, systems biology— , it's all the either(unclear 8:35) you can define it any way you want there. Most of the people who claim to be doing systems biology are really studying simple and complex molecular machines and how they function and that is an aspect of systems biology; but it isn't. It's the networks that really capture and store and transmit and integrate and modulate and finally end up executing the biological information as it is. So the heart of our systems biology is really understanding these biological networks and their various dimensions.
Steve: Scientific American Editor, Steve Ashley ...
Ashley: So there you mentioned that the dynamics is always an expression to allow the digital genome in the environmental and the timing of these environmental impacts to just simply quantify the immediate thing that is common. That is going to be the hardest part to get at?
Hood: I'll tell you what I think the really hard problems in systems biology are right now. So, I think one is figuring out how to integrate different types of information; that is really challenging. I think, two, it's taking enormous amounts of data and my prediction—and maybe we'll talk about it later—is in ten years in medicine, we'll have billions of data bits on every single individual and the key question for medicine is how are we going to reduce that data dimensionality to simple hypothesis about health and disease. So this reduction in data dimensionality to biology or to medicine is really a key and challenging problem. I think a third problem that a lot of people in [the] field don't realize is data space is utterly infinite, and if you want to understand an aspect of a system, you have to figure out how to query the right part of data space, to get information that is actually relevant to the biology you are interested in; and one of the enormous dangers of all these high through-put data sets that exist out there in the literature—apart from the fact they are error prone to the nth degree—is the fact that many of them were captured in the wrong data space and they can be very misleading about the biology you might be interested in. So you have to be sophisticated about those kinds of things.
Ashley: What sounds (unclear 11:03)
Hood: You mean in an example of data that might have been gathered in the wrong data space? I'll give you a really simple example. One of the things that we have studied with Eric Davidson at Caltech is the development of sea urchins, okay?. And sea urchins go through two separate phases of development—they develop as a larva over a period of 72 hours or so, where they are mobile and they even have a, kind of a notochord like thing; and then in the adult stage, they become sessile and sit down and they become the plant. So, I would argue information that's taken in the larval stage about how development is occurring would be utterly irrelevant to information captured in the sessile adult stage—that is, they're solving completely different problems yet they are the same organism and it's looking at them in different time dimensions in this case. But so that's the kind of example of something that could be very, very misleading. I mean in another one is how do we visually represent the dynamics of networks. I mean first of all, networks are really quite complicated; and second, they do change and they change quantitatively in the nodal components of the networks, but they change in the architecture of the network as well—so how can we visualize the dynamics? So we are actually working with a group that is one of the world's experts in visualization at the University of Utah.
Ashley: You mean there are different levels and things that create new branches.
Hood: Exactly! That's the kind of thing that you can think about, but, you know, it's one question. Let's say that we could capture data at fine enough time points that we could have data representations for—and it's a lot of data, so you'd need quantitative representation of messenger or proteins or whatever you are looking at.
Ashley: At [the] same spatial place basically.
Hood: But you'd also need measurements that told you who was interacting with whom and how those interactions change when you modify the proteins, so when you accelerated their turnover or, when you know, what are all the different ways that we can think about doing this? So we don't have good ways of handling that and being able to visualize—visualization of information is really key for this, trying to reduce it's dimensionality to things that we can understand. So we have to figure out how to do that. We were not very formal.
Steve: Hood then discussed the future of biomedical science.
Hood: I think one of the really interesting questions is if you think about where all these things are going to go in medicine, I would argue in 10 to 20 years, we'll have a medicine that is predictive, highly personalized. [It]
Is, well, [it will] start to be preventive in the next 15 years or so, and ultimately it's going to become very participatory because there will be so much knowledge out there; and I would argue that, that will transform the business plans of every single major component in the healthcare industry—drug companies, pharma, insurance companies, HMOs, IT, medical schools. I mean you know medical schools are teaching their physicians many of the wrong things now. How are you ever going to get them to change in a major way for this new kind of medicine? Those are all really interesting questions, and there are two possibilities. One, you can set up entities within other entities that have enormous independence and are not blocked by the conservatism, either of the scientist or of the bureaucrats; or two, you start just brand new things. Then start out with a mission to do this thing completely in the right way and of course to start out new medical schools these days will be pretty tough, I think. I mean somebody like Gates would have to put in, you know, a billion dollars to really get something very minimal.
Steve: What is the consequence of all this knowledge
is for the vast majority of people? You have to eat more healthfully and get more exercise,(laughs)rather than ...
Hood: So, I'll tell you my attitude
is after I look at the billions of dollars we've spent trying to persuade people not to smoke— is the only way in the end you will probably be able to deal with those issues is say, here's a pill. You can eat as much as you want, but if you take this pill, you will be okay. Or here's a pill, you can smoke as much as you want but if you take this pill, you will be okay. I mean that's slightly cynical but either that or psychologists are going to have to learn some fundamental new principles to convince. See I think the real problem is it's easy to persuade young kids of particular kinds of ideas because they are flexible. I think it's virtually impossible to get adults to change their ideas, and any major reason—you know, look at debates on religion, intelligent design and creationism.
Steve: Leroy Hood is also a member of the genomics X PRIZE advisory board—that's a group within the X PRIZE foundation that will award a ten million dollar prize to whoever can put together a system for sequencing one hundred human genomes in ten days—and for more on Hood's institute for systems biology, go to www.systemsbiology.org.
We'll be right back.
Male voice: Scientific American's RSS feeds they help you keep up with the latest science trends, chose from a variety of topic feeds at SciAm.com/rss.
Steve: Now it's time to play TOTALL.......Y BOGUS. Here are four science stories, but only three are true. See if you know which story is TOTALL.......Y BOGUS.
Story number 1: NASA has purchased the world's most expensive toilet.
Story number 2: A multi-institutional study has confirmed that a diet rich in lycopene—a compound found in high amounts in tomatoes—wards off prostate cancer.
Story number 3: Researchers found evidence for the cultivation of at least 10 different kinds of chili peppers in Mexico, possibly as long as 1,500 years ago.
And Story number 4: The super rare butterfly known as the El Segundo Blue has suddenly been found all over a couple of beaches in southern California.
Time is up.
Story number 4 is true. The tiny El Segundo Blue—an endangered species of butterfly—has emerged in strong numbers in Redondo Beach in Torrance, California, the LA Times reports. Apparently, the key to the appearance was the replacement on the beaches of a nonnative plant with a buck weed that the El Segundo prefers to feed on. A few colonies of the butterflies had been kept alive on private properties, but as soon as buck weed came back to the beaches, some of those sequestered butterflies have apparently made their way to the suddenly greener pastures.
Story number 3 is true. The desiccated remains of 10 different cultivated varieties of chili peppers were found in a couple of caves in Mexico. The find[s] also show that ancient Mexican peoples were using the chilies both fresh and dried. For more, check out the July 10th episode of the daily SciAm podcast, 60-Second Science.
And Story number 1 is true. NASA spent 19 million dollars on a Russian-built toilet for the international space station. A photo of the toilet shows what appeared to be straps for your feet, which is a great idea given, Newton's third law.
All of which means that Story number 2 about a diet rich in lycopene preventing prostate cancer is unfortunately TOTALL.......Y BOGUS. Because a study of 28,000 men by the Fred Hutchinson Cancer Research Center in Seattle and the National Cancer Institute found that lycopene and other antioxidants exerted no beneficial effects against prostate cancer. You can read more about that in the update section of the August issue of Scientific American on page 16. And despite this news, I plan to continue to eat tomato sauce in copious amounts, especially in pizza.
Well that's it for this edition of the weekly Scientific American podcast. You can write to us at podcast@SciAm.com, check out news articles at our Web site, www.SciAm.com, the daily SciAm podcast, 60-Second Science is at the Web site and at iTunes. For Science Talk, the weekly podcast of Scientific American, I am Steve Mirsky. Thanks for clicking on us.