UCLA scientists envision the future of the biodata ‘revolution’

How do we integrate millions of data points to correctly diagnose complex diseases and identify therapies? Might it be possible to simulate clinical trials reliably, rather than put patients at risk? How do we assess a person’s immune health status to personalize vaccinations? Will we one day map the trillions of brain cells of neurology patients to inform treatment? 

Across campus and the global stage, the UCLA Institute for Quantitative and Computational Biosciences is leading the development of new analysis approaches and computational technologies that will answer these questions. To celebrate its first decade and look ahead to the world-changing innovations it seeks to spearhead in the next, QCBio faculty held a panel discussion Dec. 10 on campus.

“QCBio was created to catalyze and safeguard the transformation taking place in the biomedical sciences,” said Alexander Hoffmann, the institute’s director and a distinguished professor of microbiology, immunology and molecular genetics. “We’ve had the unique opportunity to unite top faculty and trainees from multiple areas across UCLA’s small, highly collaborative campus. Together, we are developing solutions to urgent challenges in health care, energy sustainability and beyond.”

Unique among its peers, QCBio encompasses more than 60 faculty members and houses five doctoral programs across the UCLA College’s divisions of life and physical sciences, the David Geffen School of Medicine at UCLA, the UCLA Samueli School of Engineering, the UCLA Fielding School of Public Health and the UCLA School of Dentistry. Internationally recognized experts in developing algorithmic solutions, software and computational models, these faculty conduct research spanning application areas from drug design to epidemic prevention.

Matteo Pellegrini, who directs the QCBio Collaboratory and holds appointments in the departments of molecular, cell and developmental biology, human genetics and medicine, spoke at the event. 

The Pellegrini lab recently developed algorithms to study modifications of genomic DNA and discovered that these change with age. The researchers’ big data analysis revealed that these processes constitute an epigenetic clock that predicts age with remarkably high accuracy, not just in humans, but in all mammals. By applying this analysis to different tissues, Pellegrini’s team can pinpoint the health status and health span of each organ — and assess the effectiveness of behavior modifications on extending the human lifespan.

“Ultimately, what we want to know is, can these processes be modified? We’ve known for many years that there are interventions that, at least in other species, can dramatically change aging rates, as well as these molecular changes to DNA,” Pellegrini said. “We believe these molecular observations will provide insights and motivation to continue the study of aging and how we can modify the rates of aging.”

Grace Xiao, professor of integrative biology and physiology, discussed her research on Alzheimer’s disease, which affects about 7 million Americans and is expected to afflict twice as many in the next 30 years. Using big data analysis and novel computational methods, her team focuses on what she calls a severely understudied area: abnormal RNA accumulation. 

“The goal of our research is to eventually develop biomarkers for early diagnosis of Alzheimer’s disease using these RNA species,” Xiao said. “And, of course, treatment strategies: Once we identify these novel RNA species, can we actually modulate them early, before the disease systems get worse?”

Jamie Lloyd-Smith, professor of ecology and evolutionary biology, studies the emergence of new virus infections and their pandemic potential. He explained that epidemiologists typically study transmission and immunity, and virologists and immunologists study the fundamental biology of what viruses do — but this work tends to happen in silos. Lloyd-Smith is integrating knowledge from both areas in mathematical modeling simulations that will have unprecedented predictive power, with the capacity to direct public health decision-making.

“This is the vision — to build a model which will predict the efficiency of transmission of a novel virus from things you can measure in the lab,” he said. “And this hopefully will enable us to assess the pandemic potential of the new virus before it’s out running in the world and give us a head start on prevention measures.”

Hoffmann, who also serves as UCLA’s Thomas M. Asher Professor of Microbiology, said QCBio’s work is driven by the faculty’s ability to not only foster research excellence and collaboration, but also develop the software and analysis tools necessary to interpret biological data. 

“QCBio leverages not only big data, but also big knowledge — that is, a deep understanding of biology that enables us to develop better, more precise predictions,” Hoffman said. “Our experimental labs have adopted technologies that produce big data faster than other research institutions, because we have removed the hurdles posed by data analysis.”

He outlined the institute’s commitment to building a broad, diverse pipeline of talent through intensive graduate training and undergraduate education initiatives. These include its Collaboratory, an ecosystem of graduate programs, the computational and systems biology major and related minors, the Bruins-In-Genomics Summer Program and the Life Science Education Core in Mathematics.

The institute’s decade of progress reflects the rapid pace of growth in a field in which massive data sets and computational methods have changed what it means to be a biology major.

“This development amounts to what can only be referred to as a revolution,” said Tracy Johnson, dean of the UCLA Division of Life Sciences, in remarks at the event. “Twenty years ago, computational biosciences occupied about 5% of biomedical research efforts; today, it amounts to about 50%. This complete change has altered not only the kind of science that we do, but also who is doing the science, and how we train the next generation of scientists to ask the really exciting and important questions.”

Hoffman expects these vast new avenues for research and education will continue to expand, along with the work of QCBio.

“UCLA is a leader in quantitative and computational biosciences teaching and research, and the potential of our scientific community is boundless,” he said. “Looking toward our next decade, it is exciting to imagine the new discoveries yet to be made.”