It’s Like Google Maps, but for Your Cells

Human cells are highly complex networks, but single cell genomics is giving us "an unprecedented view of how the body is working".

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There’s an incredible amount of data on the biology of the cell, but researchers still don’t know how all those pieces fit together. Computational biologist Gary Bader believes that the key to understanding these complex systems lies in using computers to gather and assemble all the parts.

“In the past we had textbooks, but the rest of the world has moved much further along. We don’t use paper maps anymore, we have Google Maps,” says Bader. “What we need to do with biology is build a map of how all the pieces are connected.”

Bader is a professor of molecular genetics and computer science and researcher at the University of Toronto’s Medicine by Design group. Computers help him decipher human cells, which are incredibly complex networks of biomolecules like DNA, RNA, and proteins, and other small molecules like vitamins, fats, and sugars, each having various roles to play to keep cells healthy.

“The thing I’m most excited about these days is a new technology called single cell genomics,” says Bader. “Previously, when we were looking at the cell and how it works, we could take snapshots of one cell using a microscope, or we could use genomic technology to make thousands of millions of measurements, but only for millions and billions of cells all together.”

But when it comes to understanding an entire person, each cell is unique from its neighbours. Understanding each cell’s individual biology unlocks a better understanding of how diseases emerge, and how potential cures might restore health.

“The Human Genome Project gave us information about all the pieces of the cell, and now we’re trying to put that puzzle together and make a functioning machine,” adds Bader.

Single cell genomics provides that detailed individual information. It’s cutting edge technology that was invented only a few years ago, but it’s already being put to work in Bader’s lab.

“That’s giving us an unprecedented view of how the body is working,” adds Bader. “We’re getting tons of amazing data that are opening up huge new avenues of research that we didn’t think about before.”

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Gary Bader is a professor at The Donnelly Centre at the University of Toronto and an international leader in the field of computational biology. The Bader lab uses molecular interaction, pathway and ‘omics data to gain a ‘causal’ mechanistic understanding of normal and disease phenotypes. They are developing novel computational approaches that combine molecular interaction and pathway information with ‘omics data to develop clinically predictive models and identify therapeutically targetable pathways. This research, for instance, helped identify a histone methylation inhibitor as the first therapeutic candidate for pediatric ependymoma, a common childhood brain cancer, in collaboration with Michael Taylor at  SickKids.

Bader completed post-doctoral work in the group of Chris Sander in the Computational Biology Center (cBio) at Memorial Sloan-Kettering Cancer Center in New York. Gary developed the Biomolecular Interaction Network Database (BIND) during his PhD in the lab of Christopher Hogue in the Department of Biochemistry at the University of Toronto and the Samuel Lunenfeld Research Institute at Mount Sinai Hospital in Toronto. He completed a BSc in Biochemistry at McGill University in Montreal.

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