Stewart: Is this the same thing as discovery science and how that's different from traditional hypothesis-based science?
Hood: Yes. Discovery science is basically all about defining the elements in biological objects. The Human Genome Project is a classic example of discovery science because what it's really about is sequencing all 3 billion base-pairs in the 24 strings that constitute the human genome. Once you've done that discovery project, then you obviously open up a multitude of opportunities for hypothesis-driven science. It's why the genome project has revolutionized biology in virtually every way.
Another example of discovery science is analyzing the quantitative expression of all the messenger RNAs present in a particular cell type, or all the proteins present in a particular cell type. Again, the discovery is just the delineation of those elements in a quantitative sense; it doesn't say anything about science or biology. But once you have those things, then you can pose and formulate and integrate together the hypothesis-driven science with the discovery science. So systems biology actually mandates the integration of these two very different approaches to science, discovery science with hypothesis-driven science.
Stewart: You brought up the Human Genome Project, which I know you were involved with from the beginning. What do you see as the paradigm shifts that this project has unleashed on us?
Hood: I think the genome project has changed the practice of biology in medicine in a lot of different ways. The two fundamental paradigm changes I see coming out of it are first, this whole approach of systems biology, and second, in medicine it's going to make possible first predictive and then preventive medicine. So in 20 or 25 years we'll be able to take a snippet of your genome and analyze it and give you a probabilistic health history for the future. Then we'll be able to give you preventive medicines that circumvent the limitations of your genes. So we can keep people healthy, effective, and functional for not just 50 or 60 years, but 70 or 80 or 90 years. And, you know, that gets into very interesting social and ethical questions too.
Stewart: That's leads right into my next question. You've spoken about medicine evolving from a reactive science to a predictive and finally to a preventative science. As this is happening a whole host of new ethical issues arise. What do you consider the most important social and ethical questions our society will be faced with during this transition?
Hood: You know I don't think you can say "most important." I think there are a whole a series of important ones. If we do get to preventive medicine, you know we're going to have productive, creative individuals in their seventies and eighties and nineties, and society's going to have to restructure its attitudes toward older individuals to take advantage of their potential creativity and productivity. That's going to be a major change, just in retirement and all those kinds of things.
A second thing is we're really in the midst of dealing with issues of genetic privacy. If we can analyze your genome, then who has access to it? Insurance companies? Employers? Family? How do we deal with those issues? I think that's a tough one, and it's one that is terrifying in how badly it potentially could be done.
A third area that is somewhat related to things that are happening is this whole misunderstanding of stem cell research and stem cell biology. I think stem cells are one of the greatest potential preventive therapeutic agents in the world of medicine, and it's just ridiculous that the religious right has put these pathetic constraints on what we can do in sorting out how to deal with the human condition, and treat people, and everything. The whole confluence of the religious with science--and you see that in terms of creationism too--is something that America as a society, as an educated society, has responded to worse than any other educated society, by far. And it is due to a small minority of very vocal people, unfortunately.
Questions of germ line genetic engineering and whether humans should take a hand in their own evolution and direct the specification of intelligence, or of physical ability, or memory, or all of those kinds of things, are interesting questions. I think from opportunity always comes challenge, and what you have to do is balance the challenge and the opportunity. I think there are very rational ways for doing that, that don't inhibit and prevent, but our society can be pretty reactionary at times, and not deal well with these kinds of subjects.
Stewart: Do you see any computational research projects on the horizon that will have as dramatic ramifications as the Human Genome Project has had?
Hood: Not computational alone, but computational plus biology, yes. One of the things we're really interested in is can we use the systems approach and the computational approaches to understand how the innate and the adaptive immune system interacts to create vaccines? You know the fact is, in the 100 years since we started making vaccines no change has been made in how we do it, and that's because the systems interacting with one another are so complicated. Studies of one gene or one protein at a time were never ever revealing. With the systems approach we can fundamentally change this. Suppose that we can create at-will vaccines that are effective for emergent infectious diseases, and AIDS, and all of these kinds of things. I think that will have an enormous impact, in fact, in terms of what it will do for people throughout the world, a far more striking effect than the genome project, at least in the short term. There are things like that that I think are going to be really key.
Stewart: Although the ISB has been in existence for only a short while, a lot of exciting science has been produced there. What achievements at ISB are you most proud of?
Hood: One is how we took a simple model system in yeast and demonstrated that this whole systems approach, and the integration of these different levels of biological information, DNA to RNA to protein to interactions, is going to work absolutely beautifully. This was kind of the proof of principle up to that time, which had been largely lacking.
I think a second thing is how Ruedi Aebersold has really pushed the whole field of proteomics forward in a very dramatic fashion, developing two or three major new kinds of technologies for proteomics that will have an enormously broad impact. Also the work that Alan Aderem has done on the innate immune system in defining the nature of its receptors, things called the toll receptors, and how they communicate with one another, and beginning to define the nature of their signal transduction pathways so we can manipulate innate immunity. I think all of this is going to have just an enormous impact on, for example, how we deal with infectious diseases, and so forth. Finally, I would say we've developed a lot of very powerful computational methods for beginning to integrate the complexities that are required of systems biology, which I think are going to be tools generally effective for everyone. Those are four areas where I think we've made an enormous impact.
Stewart: You've been a prominent researcher in molecular biology and biotechnology for many years now, and you've witnessed firsthand the evolution of bioinformatics as a discipline. What do you see as the major or grand challenge facing bioinformatics? Are new approaches needed for analysis?
Hood: I think the grand challenge is how you effectively integrate bioinformatics with biology. I think that is going to be by far the hardest thing. I think in terms of new algorithms, in terms of databases that can integrate heterogeneous kinds of information, in terms of tools that can graphically display complexity, and ultimately model it, and so forth, I think we really can see how to do all of these kinds of things. With the enormous increase in hardware capacity I don't think there are going to be any technical limits on what we can do. So I think the grand challenge is the successful integration of computational biology, or bioinformatics, with biology itself.
Stewart: What kind of training is necessary for people entering the field? What kind of research do you think will be most critical in the next few years?
Hood: Well, I think it's great to have people come in from computer science and theoretical physics with these very strong computational tools, but the training that is really critical is they have to learn biology in a deep sense--what it's all about, and understand what relevant applications of computational biology can be pointed toward biology. For example, probably not this next summer, but by the following summer, we will be running summer courses on biology as an informational science where cross disciplinary scientists can come here and in two or three weeks get a grand introduction, not only didactically to biology as an informational science, but experimentally to what does it take to do biology? It's very important to give computational people a feel for experimental aspects of biology. We have all of our computational people do experimental work for a month or six weeks just so they gain some appreciation of the other side of the world.
Stewart: Do you feel the funding agencies are responding adequately to bioinformatics needs?
Hood: I think bioinformatics is kind of a gimmicky word right now. I think in general there's been a pretty good response in terms of the dollars that are out there. Whether they're being spent wisely or not is another question, but I think both at NIH and NSF, and even DARPA and DOE as well, there are dollars for bringing computational biology into biology.
Stewart: What is your opinion of the work being done on the Cardiome and Physiome projects? Do you think these attempts to create sophisticated computer models of entire organs are moving biological science in the right direction?
Hood: I think the ultimate question that you have to ask when you model biological complexity at the higher level of the whole organism, or even whole organ level, is whether you ultimately can tie those models back to the most fundamental elements, the genes, and the proteins, and things like that. I think as long as there's a gap between the whole organ modeling and the specifics of the molecular and transcriptional networks then you're not going to be able to obtain a really deep understanding. But I think it's very worthwhile to attempt to do these things, and then, very pressingly, to attempt to bridge the gap between downstream phenotypic descriptions and the detailed molecular descriptions.
Stewart: A lot of what we're talking about here with systems biology seems to boil down to this one question: Is it possible to produce a complete mathematical description of a complex biological system? Is that a yes or no question?
Hood: You know it isn't really a yes or no question. I think part of the reason is we'll be able to generate pretty effective mathematical descriptions of many of the complex biological systems in higher organisms. Where I'm less certain about in terms of how effectively we can do that is where the environment and stochastic processes play a major role in defining the informational media, and the brain is such an organ. Whether these more reductionistic methodologies can take you to a more complete, precise definition of how the brain works, and consciousness, and memory, and all those kinds of things, I don't really know and I don't think we'll probably know for awhile. But I do think we will be able to have enormously deep and sophisticated modeling insights and an understanding of the immune response, for example.
Stewart: Thank you so much for your time Dr. Hood, it's been fascinating. I look forward to your keynote at the upcoming Bioinformatics Technology Conference.
Hood: You're welcome.
Bruce Stewart is a freelance technology writer and editor.