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Big data in biosciences and health care is focus of new UCLA research center

Institute for Quantitative and Computational Biosciences will advance basic and applied biomedical sciences

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Alexander Hoffmann
Reed Hutchinson/UCLA

Alexander Hoffmann and his colleagues will collaborate with mathematicians to make sense of a tsunami of biological data.

A new research institute at UCLA may eventually provide doctors with tools to more accurately tailor medicines for individual patients, which could both improve quality of care and minimize the side effects associated with today’s medicine.

The Institute for Quantitative and Computational Biosciences will employ multidisciplinary research to study how molecules and genes interact. Its goal: unlocking the biological basis of health and disease by tapping the power of big data and computational modeling.

“UCLA’s Institute for Quantitative and Computational Biosciences will have a major, positive impact on human health,” said UCLA Chancellor Gene Block. “It will engage exceptional faculty from the life sciences and physical sciences, and our David Geffen School of Medicine and Henry Samueli School of Engineering and Applied Science to ensure that UCLA is at the forefront of research that will help usher in a new era of personalized health care, and to transform research and education in the biosciences.”

The institute is led by Alexander Hoffmann, professor of microbiology, immunology and molecular genetics in the UCLA College, whose research aims to understand how our genes interact to ensure health or produce disease — and the roles played by such factors as food, environmental stresses, infectious agents and pharmaceuticals. Among the diseases for which Hoffmann’s research may lead to significant progress are cancer and immune disorders, because they are caused by errors in cellular decision-making.

Hoffmann says that biology’s million-dollar question is how genes and environment interact to ensure health or cause disease, he said. As UCLA researchers work to answer that question, they will collaborate with UCLA mathematicians who will create mathematical models that help them make sense of a tsunami of biological data.

“Biology is entering a new phase,” Hoffmann said. “So far, biology has been much less math-based than the other sciences. Since the sequencing of the human genome in the early 2000s, there has been an irreversible change in the way biology and biomedical research are being done. At UCLA, we will lead research in that direction and connect basic and applied sciences in an unprecedentedly productive collaboration.”

Victoria Sork, dean of the UCLA Division of Life Sciences, said the institute’s approach represents the “new life sciences” and predicts that the new center will accelerate discovery and translational application in many areas, including medicine, the environment, energy, and food production and food safety.

“Technological breakthroughs are enabling scientists to analyze not only one gene at a time, but how hundreds or thousands of genes work together,” Sork said. “Combined with big data, new knowledge of critical gene networks will lead us to a better understanding of what makes humans healthy.”

The road to “precision medicine”

Dr. A. Eugene Washington, vice chancellor of UCLA Health Sciences and dean of the David Geffen School of Medicine at UCLA, said the new era of personalized medicine will offer higher-quality health care — and possibly lower-cost care — because genetic information will give health providers better knowledge about individual patients.

“We are likely to see significant change in health care in the coming years as genetic data for individuals become more widely available,” Washington said.

In fact, big data already has begun to transform health care. In the past, doctors treating people with a certain disease might have relied solely on their own or their colleagues’ experience treating others with the same disease. Now, instead of relying on a small number of case studies, physicians can turn to mountains of data to guide their approach.

“We haven’t yet begun to fully tap into the knowledge we have about how we have treated millions of patients,” said Dr. Steven Dubinett, director of the UCLA Clinical and Translational Science Institute, and UCLA’s senior associate dean for translational research and associate vice chancellor for research.

Victor Cartagena
Dr. Steven Dubinett

“Now, with the rise of big data, we have the capability to utilize a network of brains in a highly sophisticated manner so that all our experience at UCLA, in the University of California system and the many other hospitals with which we share data can be brought to bear on patient treatment in a way that was not possible before.”

The result may be not only personalized health care, but “precision medicine”—the ability for doctors to accurately predict positive health outcomes for patients, Dubinett said.

The move to big data also is dramatically changing the skill sets required for life sciences and biomedical researchers: Increasingly, backgrounds in mathematics, computer science and physics will be highly sought after. Already, UCLA is planning new programs through which computational scientists will train clinicians so they can understand how to work with large sets of data and apply the insights they gain to treating patients.

In addition, UCLA has established a doctoral program in bioinformatics, and the Clinical and Translational Science Institute, in which UCLA is one of four partner institutions, is at the forefront of utilizing big data in clinical care — including developing new pharmaceuticals and bringing important new discoveries into the community.

Much of the data UCLA faculty will work with will come from the University of California Research eXchange, which manages an extremely large repository of clinical data — more than 12 million patient records. Dubinett said UCReX is in the process of adding millions of additional records through partnerships with other Los Angeles medical institutions and, eventually, other academic medical centers in California and throughout the U.S. (Patients’ identities are not released to researchers.)

Dubinett said UCLA will be a national leader in this revolution in personalized health care, in part because UCLA’s medical center is part of its main campus — something that is not the case at many other research universities. That close proximity makes it easier for doctors to collaborate with experts in biomedical informatics and other fields, and has been a lure for many of the exceptional scientists joining the effort.

To strengthen the new institute, UCLA has hired nine faculty members since July 2011 and has plans to hire additional faculty in the next several years. One of the new hires was Leonid Kruglyak, who came to UCLA from Princeton University in 2013. Kruglyak uses big data in his genetics research and, according to Sork, is a “brilliant superstar of the highest stature.”

Among the other outstanding faculty members UCLA has hired, Sork said, are two at the cutting edge of computational biology: Matteo Pellegrini, professor of molecular, cell and developmental biology, and Xinshu (Grace) Xiao, an associate professor of integrative biology and physiology. Both are in the UCLA College.

From individual genes to entire ecologies

Pellegrini, co-director of the institute, said the move to big data also will enable scientists to significantly broaden the scope of their research.

“We’re going from a paradigm where scientists studied individual genes to one in which they will study organisms and even entire ecologies — sequencing the genomes of communities of organisms and understanding how they interact,” Pellegrini said. “Technology is making science very exciting, presenting enormous opportunities to revolutionize our understanding of biology at the genome-wide level and to apply these techniques to answer all kinds of questions.”

Hoffmann said that in the past, one of the major challenges in biology research was generating data. “Now, the challenge is how to make sense of a tsunami of scientific data, to discover the critical patterns and to tell the signal from the noise,” he said. “The opportunities to develop accurate predictions are unprecedented.”

“These examples are just the tip of the iceberg,” said Hoffmann. “The power of combining big data computational tools with computational modeling based on hard basic science is leading a revolution in the bio- and health sciences that provides unimagined opportunities to humanity.”

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