Profiles in Computer Science Courage Part I: Reflections on the rewards of plunging into biomedicine

Interviews with Leonidas Guibas, Ron Shamir, Michael Black, David Haussler, Daphne Koller, Erin Halperin, Gene Myers, Paul Groth and Bruce Donald

To a computer scientist, the fields of biology and medicine can seem like the vast Pacific Ocean, says Leonidas Guibas, PhD, professor of computer science at Stanford University. “You go to the edge and stare out for thousands of miles. How do you know where to go in? It’s scary.”


And Guibas is talking about how it feels today. That vast sea must have seemed quite daunting thirty years ago when the field of computational biology barely existed. But a few pioneers from computer science saw an opportunity to bring their skills and intuitions to bear in a new arena—an arena that could impact human health while also advancing the field of computer science. So they dove in: They learned the language of biomedicine; adjusted to a different peer-review and publishing system; and successfully developed a new field.


Today, many universities offer not only graduate degrees in computational biology, but undergraduate majors as well. Yet the field of biomedicine still presents tremendous opportunities to the pure computer scientist who knows little about the area.


The people profiled here provide a sampling of those opportunities. Some were pioneers thirty years ago; others are relative newcomers. Some now dedicate their careers to biomedicine while others still maintain a computer science focus. Their skills span a variety of computational techniques including computational algorithms, imaging, knowledge representation, robotics, machine learning, and computer vision. And they are applying their skills to biomedicine’s vast sea: genomic sequencing, molecular biology, phenotyping, drug design, epidemiology, neuroscience and more.


For researchers contemplating following in these scientists’ footsteps, it’s clear that each person must find his or her own path. Yet the stories and advice of these role models should prove reassuring: “The challenges in this space are never-ending and there’s always a need for smart people to look at the data and figure out how to extract the most information from them,” says Daphne Koller, PhD, professor of computer science at Stanford University.


Ron Shamir: A Gradual Transition

Ron Shamir, PhD, professor in the School of Computer Science, Raymond and Beverly Sackler Chair of Bioinformatics and head of Edmond J. Safra Bioinformatics Program, Tel Aviv UniversityDuring the early years of his academic career, Ron Shamir had no idea that he would ever develop an interest in biology. He never took biology in school; his PhD thesis in operations research at the University of California, Berkeley in 1984 was completely theoretical; and his research after grad school focused on graph algorithms and optimization, with no biological applications. But around 1990, after presenting some research on temporal reasoning—an area of artificial intelligence—at Rutgers, an audience member (the late Gene Lawler from Berkeley) commented that it was a beautiful model for the physical mapping of DNA. The same approach—which determines whether the time periods for a group of overlapping events can be arranged to satisfy a set of constraints—could be used to study overlapping clones along the chromosome.


The comment sparked Shamir’s curiosity and he started reading biology texts. His wife, a biologist, helped him with the basics, and the newly launched human genome project fanned his fascination. “It was just serendipity,” he says. He counts himself lucky to have been involved when things really took off. “It was evident that there would be a need for a good deal of computing. Otherwise the human genome project wouldn’t fly.”


In the late 1980s, 100 percent of Shamir’s work involved optimization and graph algorithms. By the mid-1990s, 50 percent of that had been replaced by biologically motivated problems, and more recently, the vast majority of his work became driven by biology. Making the shift was pretty risky, he says. “I was moving into a discipline that had no name and none of my colleagues knew what I was talking about,” he says. He also had to bridge a large cultural gap and language barrier, which have both shrunk a lot since. “But I didn’t make a 90-degree turn. You do it gradually and build your confidence in the field over time.”


David Haussler: The Chance to Address Great Scientific Questions

David Haussler, PhD, professor of biomolecular engineering at the University of California, Santa CruzDavid Haussler got a taste of biology when he worked in his older brother’s biology lab at the University of Arizona in the 1970s and again when working with his PhD advisor, Andrzej Ehrenfeucht, in the 1980s. “He was a real polymath, interested in all aspects of science,” Haussler says. It was before the field of bioinformatics really existed, Haussler says, but Ehrenfeucht led discussions of how to analyze DNA using computer algorithms.


But it wasn’t until the 1990s that Haussler began using his computer expertise for biological applications. He was interested in artificial neural networks and hidden Markov models—trying to get a handle on what was learnable by a machine. Then one day, recalling his happy days with Ehrenfeucht, Haussler proposed to his postdoc, Anders Krogh (now a professor at the University of Copenhagen), that they should apply neural nets and hidden Markov models to protein and DNA sequences. “So we tried it and it worked perfectly,” Haussler says. Their paper on hidden Markov models is now a mainstay of bioinformatics. “It was one of those magic moments where things took off and very rapidly we were revising the earlier work and pulling together a unified viewpoint for the field,” Haussler says.


Since then, Haussler’s research has gradually become completely focused on biology. After more than ten years as a professor of computer science, he became a professor of bio-molecular engineering in 2004—reflecting his shift. He now supervises both experimental and computational biological research.


What draws him to apply computer science to biomedicine? Two things, he says: First, the chance to address some of the great scientific questions. “The questions we look at are among the greatest. How did we become human? How does the cell work? How did life come to be?” he says.


Second, he says, the chance to really affect medicine. Haussler works on the cancer genomics and cancer genome atlas projects, which apply large-scale analysis to find all of the mutations in a tumor and determine which ones are driving the cancer.  
Haussler also heads the Genome 10K project, which is dedicated to sequencing the genomes of 10,000 vertebrate species. The goal is to map out the evolutionary changes that produced the amazing diversity of life on this planet, he says. “The computer science challenges are nothing short of enormous. This is an incredibly exciting time to be alive.”


Michael Black: Changing Lives with Computation

Michael Black, PhD, professor of computer science at Brown UniversityMichael Black has a longstanding interest in human perception. He contemplated a graduate degree in cognitive science (but was advised to stick with computer science because “you’ll make more money”) and later enjoyed hanging out with cognitive scientists at NASA/Ames while working on his PhD on optical flow estimation. Yet Black’s career remained firmly rooted in computer science until he described his computer vision research to his wife’s French-Canadian grandmother. According to Black, she shook her head and said, “That’s all a lot of excess baggage. I’ve got my garden, my health, and my family. I’ve put away vegetables in the cellar for the winter. That’s all I need.” And Black thought, “She’s right!”


On the flight home from Canada, Black considered whether he might be able to use his skills to help people do the most basic and important things. And he sketched out an idea for a brain-machine interface (BMI) to help paralyzed people gain back some of their independence. When initial support for these ideas evaporated at Xerox PARC where he worked at the time, he put it aside for a while. But when he landed a job at Brown University in 2000, he confided his interest in BMIs to a colleague. “I was sort of embarrassed because it sounded kind of crazy,” Black says. But he was told, “that’s not crazy—there’s a guy here working on that.” Thus was launched a successful collaboration between Black and John Donoghue, a neuroscientist at Brown.


Ten years in, Black says, patients are using the brain-machine interface systems he helped develop. As a result, he’s driven less by computational elegance than by the patients’ needs and what’s practical for them. “It’s not just a scientific question anymore; it’s a usability question,” he says.


Although Black still does basic computer science work, his experience with biology has changed him, he says. Computer scientists are trained to think like engineers or mathematicians rather than experimentalists, he says. “Learning to think like a biologist has made me a better computer scientist.” He’s also developed a drive to work on problems that could change someone’s life. “I’m a little addicted to finding some of that in everything I do. It doesn’t have to be a biological impact, but I want to somehow affect peoples’ lives outside the academic realm.”


Daphne Koller: Hammer Looking for a Nail (at first)

Daphne Koller, PhD, professor of computer science at Stanford UniversityAbout ten years ago, Daphne Koller was working on a project to extract meaningful networks of relationships from complex heterogeneous data. She tested it on a dataset of scientific papers and authors and also on a database of movies, actors and directors, but wanted to try it on something even more complex. “I basically had a hammer and was looking for a nail,” she says. And because biological datasets were rich and readily available, she decided to see if her techniques would be valuable in biological analyses. She says, “Over the course of the first few months of working on the problems, I became more interested in the nail than the hammer.”


Koller’s hammer was useful for studying networks both in the clinical and molecular setting. Initially, she used her tools to study networks of tuberculosis patients in San Francisco. Koller has worked partially in computational biology ever since, while still researching hard-core machine learning and other computer science problems as her mainstay.


Koller likes the fact that her biological research can have a much more direct effect on peoples’ lives than can much of her computer science research. For example, she’s developed a tool to evaluate a neonate’s risk of developing major complications. Using only noninvasive data collected by a heart rate monitor during the first 3 hours of life, it calculates a risk score that is considerably more accurate than any other risk score previously proposed. She also developed a tool that finds pathways in cancer, the first step in identifying new drugs or personalizing cancer treatments. “I also really like the puzzle nature of trying to figure out how to take a new problem that no one has looked at computationally and thinking about how to model it, what’s the right way of thinking about it, what’s the right algorithmic approach. That’s very satisfying.”


Eran Halperin: Having an Impact

Eran Halperin, PhD, Senior Lecturer, Blavatnik School of Computer Science, and the Department of Molecular Microbiology and Biotechnology, Tel-Aviv UniversityEran Halperin began his academic career as a computer scientist working on purely mathematical problems with little regard for applications. But, while working on his PhD, he joined a bioinformatics company. It completely changed his view. Compared with designing a new theoretical algorithm, if you find a new gene or potentially new treatment, he says, “you feel the impact on society much more strongly.”


When he moved on to postdoctoral research, Halperin gradually changed the focus of his research to the study of applications of computer science to biology. With Halperin's background in theoretical computer science, a natural way to choose the research problems would be to look for problems that are based on computational interest. However, Halperin doesn't choose his research problem this way. “It's not the driver,” he says. He chooses based on the potential impact of the project and whether his background provides some kind of advantage.


“What you learn in math and computer science is a way of thinking about a problem and how to attack it,” he says. That’s something he brings to the table. Halperin is perfectly willing to set his ego aside to serve the needs of biomedical research. “Everything we do is service,” he says. “Eventually it serves the purpose of advancing science.”


Gene Myers: Seeking Uncharted Territory

Gene Myers, PhD, Group Leader, HHMI Janelia Farm Research Campus, Ashburn, VirginiaIn the early 1980s, biology was primarily a descriptive field rather than a quantitative one, Gene Myers says. “Most biologists could not compute. And this created the opportunity.”


Myers was drawn to apply his computer science skills to biology—primarily in gene sequencing—because “it was a cool source of problems,” he says. “I was being challenged to extract interesting variations on traditional problems. I’m a big fan of that.”
He is also a fan of working in a field without much competition. “It was nice to be one of the few researchers in the field because you had a lock on a niche.” In 2005, viewing the sequencing field as “crowded,” and in some ways “passé,” Myers made another switch—from sequencing to microscopy imaging, a relatively new niche. “It means I’m back in the 1980s,” he says. “It’s a really small club. I’m having a great time.”


Myers believes that the most interesting computational work in molecular biology in the next 10 to 15 years will involve using microscopy to understand phenotype. “The genotype/phenotype correlation is not going to yield itself just by looking at genotype, which is what the DNA sequencing people are doing,” he says. Microscopy yields rich, high-dimensional data for phenotyping, which will help researchers get the “most bang for the buck” from genomic data, he says. “So I realized that if I want to be in it, I’ve got to be an imaging guy.”


Because computer scientists divide themselves by technique, he says, “it’s very hard to get people to go from sequences to images.” In addition, most academicians at his career stage are “walking this tightrope of seeking funding and managing a large group of students,” he says. “They are basically supertankers, and changing the direction of the supertanker is hard.” But Myers made the switch anyway. “I was lucky to come to Janelia Farm and really have a chance to retread myself.”


When he moved to Janelia Farm, Myers says he felt like a postdoc for a few years as he got up to speed on imaging methods and developed an intuition about what techniques should work to solve computer vision problems. Now, Myers is exactly where he wants to be. He’s addressing imaging problems that have requirements no one has addressed before, he says. “It’s great. I’m in new territory.” 


Leonidas Guibas: Feeding on Biology’s Abstractions

Leonidas Guibas, PhD, professor of  computer science at Stanford University Leonidas Guibas is driven to understand abstractions. He deals in the mathematics and algorithms for describing the shape and motion of things. For many years, he taught a course on geometric modeling in computer science graphics that covered only manufactured shapes such as car hoods, airplane fuselages and the like—geometric forms that people have designed. To take on the challenge of applying these same ideas to biological shapes, in 2003 he moved from Stanford University’s computer science building to the Bio-X program at Stanford’s Clark Center, where interdisciplinary research is encouraged.


For Guibas, biology offered the opportunity to study imprecise shapes such as protein surfaces, which have electrons floating around them. Studying proteins requires fundamentally different kinds of tools than those used to model the shape of a car or airplane, Guibas says. “That’s feeding me something interesting to work on.”


Guibas enjoys his interactions with biologists, but he’s clear where his interests differ from theirs. “I care about computation as an object of study by itself,” he says. “The biologists are interested in proteins because they are essential to life. I don’t have this predilection. I study proteins as something that has geometry to it. I’m interested in something more abstract—something with shape and motion that can help me develop mathematical tools and representations that are appropriate to proteins but may also have many other uses.”


Paul Groth: Biology Drives Interesting Computation

Paul Groth, PhD, postdoc in the artificial intelligence department  at the Free University in Amsterdam, the Netherlands. Paul Groth’s career has been built around e-Science, computationally intensive science carried out over a network. “In e-Science you get really difficult computer science problems as well as really simple ones,” Groth says. And many of the more complex ones come from biology. “That’s the key interest for me as a computer scientist,” he says.


In any kind of scientific research, it’s essential to know where the data come from—i.e., their provenance—Groth says. In biology, where vast storehouses of remote data continue to grow and change, figuring out how to connect provenance information to the data themselves has become an interesting area of computer science research involving both knowledge representation (how to describe where results come from) and distributed systems (if data are coming from many different places, how do we capture and store that information?).


“Biology drove this computer science problem,” Groth says, “because biologists were the first to really use publicly available data provided by web services that they didn’t control and that could be updated remotely.”


Often, Groth says, biologists might ask for a solution to a simple problem. “As the computer scientist, you have to ask what they would really want,” he says. “You end up discovering the bigger computer science problem behind the little problem.”


For example, Groth worked with a bioinformatician who wrote many different scripts but would then forget which version was the one that produced his results. “This sounds like a simple thing of being more organized,” Groth says, “but in the end, the question was how to help him automatically determine what he did, which turns out to be not a simple problem.” And when the researcher moved from a desktop to a supercomputer, Groth had to address the more complex provenance questions raised by a supercomputer consisting of multiple machines where the mechanics could fail in many different ways. Solving this problem for one researcher led to software that could then be used by others. “The common representation we helped develop, called the open provenance model, is now becoming widely deployed,” Groth says, as are several other systems developed by other groups. “Biology is a very good example of why you need this sort of provenance model.”


Often, Groth says, the computer scientist has to be clear that he or she is not a programmer for the biologist. “I’m not here to design perhaps the program that helps you immediately,” he says. “I might do that because I’m a nice guy and it helps me understand the collaboration. But in the end, I’m looking for the computer science research challenges that will help you eventually.”


Bruce Donald: End-to-End Computational Biology

Bruce Donald, PhD, professor in the computer science  and biochemistry departments at Duke University When Bruce Donald began his career in robotics in the early 1980s, he was excited about the opportunity to do what he calls “end-to-end” work. In robotics, a researcher could go from math, to algorithms, to software, to simulations, to actually making metal or silicon move.


In 1998-99, when Donald turned his attention to using computer science for structural biology, “end-to-end” work remained his predilection. While his lab focuses on developing mathematical and highly sophisticated algorithms, they don’t stop there. They will also engage in a substantial software project, implement it, and test it experimentally. That might mean, for example, performing nuclear magnetic resonance on certain proteins, developing algorithms to determine a realistic structure that captures real properties of the proteins (for example, its flexibility), predicting algorithmically how that structure would interact with a library of possible drugs, and then testing that prediction experimentally to find a drug with the desired characteristics.


To make that start-to-finish approach a reality, Donald collaborates with experimentalists. But, more recently, he gathers together the necessary experimental techniques in his own lab. “That’s what I’m most excited about and proud of,” he says. “A real algorithmic accomplishment is one that when applied to real data and real protein systems, really works and produces some insight in biomedical research.”


And working on problems with relevance to human health has another benefit, Donald says. “I don’t really have to ask myself why it’s important. It’s manifestly important and manifestly interesting as well.”


For more, see Advice on Taking the Plunge

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