At first, Xuebing Wu , PhD, was on track to pursue a research career in computer engineering. After taking a course by Dr. Yanda Li, a pioneer of bioinformatics, Dr. Wu’s interest quickly shifted and he soon got hooked on genomics research and computational biology.

Xuebing Wu, PhD

“Around that time—2003 to 2004—the human genome project had just been completed, and there had been lots of enthusiasm about using computational approaches to decipher the human genome,” he said. “I was excited to dive into this field that seemed wide open for research possibilities.”

Dr. Wu joined Columbia University’s Department of Systems Biology in the fall of 2018, with a joint appointment in the Department of Medicine’s Cardiology Division . He also is a member of the Herbert Irving Comprehensive Cancer Center at NewYork-Presbyterian/Columbia and the Columbia Data Science Institute , and his lab straddles basic science and computational biology. Dr. Wu and collaborators often consider how their work can make an impact in novel therapeutics. 

At the center of his interests is understanding the fundamental principles of gene regulation in human cells through integrative genomics approaches. His previous work has uncovered important roles of RNA sequence and structure signals in controlling the expression and evolution of the mammalian genome. His lab currently studies RNA-centric gene regulation, focusing on mRNA structures and mRNA translation. Dr. Wu and his team are increasingly turning their attention to the development of genomic technologies such as the revolutionary CRISPR/Cas system and a high throughput analysis technology called massively parallel reporter assays (MPRA), as well as novel computational tools and deep learning models to study gene regulation at a global scale. 

Dr. Raul Rabadan coauthors new book that introduces techniques of topological data analysis, a rapidly growing subfield of mathematics. (Cambridge University Press)

The deluge of data in the diverse field of biology comes with it the challenge of extracting meaningful information from large biological data sets. A new book, Topological Data Analysis for Genomics and Evolution, introduces central ideas and techniques of topological data analysis and aims to explain in detail a number of specific applications to biology.

“High-throughput genomics has profoundly transformed the field of modern biology and has made it possible for scientists to make rapid scientific advances,” says the book’s co-author Dr. Raul Rabadan, professor of systems biology and founding director of Columbia University’s Program for Mathematical Genomics. “The explosion of data has hit biology, and as a result, we need new, more innovative analytical and computational tools to make sense of it all.”

Co-authored with Andrew J. Blumberg, PhD, professor of mathematics at University of Texas at Austin, the new book discusses techniques of topological data analysis, a rapidly developing subfield of mathematics that provides a methodology for analyzing the shape of data sets. The book offers several examples of these techniques and their use in multiple areas of biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes.