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Better Algorithms, Better Models

  • Title: Assistant Professor of Biology, Massachusetts Institute of Technology; Core Member, Broad Institute
  • Education: PhD, Tel Aviv University, 2002
  • Recommended by: Jill Mesirov

Aviv Regev’s longstanding interest in how evolution has affected molecular networks began more than 10 years ago, while still in pursuit of her master’s degree of science at Tel Aviv University. During her PhD research, she developed a new language to represent biomolecular processes — and today continues to delve into how these processes came to be.

“I’ve always been interested in evolution and how complex systems evolve; it’s just that only in very recent years this has become an empirical problem rather than a theoretical problem,” Regev says. “And this is because of the genomic revolution.”

Currently, Regev’s lab is focused on applying computational approaches to two separate but related areas of molecular network research, the first of which is function and information processing for cells and evolution. She utilizes data from gene expression, protein-protein interactions, and protein-DNA interactions to help reconstruct networks — first in yeast and now in mammals. The second major line of Regev’s research is the reconstruction of evolutionary events of regulatory networks. Through a combination of experimental and computational approaches, her team has profiled more than a dozen species of yeast in order to develop algorithms that reconstruct what the ancestors of these species were doing based on their descendants’ responses, she says.

Regev says that despite advancements in tools for genomics, proteomics is still lacking. Any kind of application that would allow for high-content imaging to aid in cellular phenotyping to identify sets of proteins expressed in cells would be a boon to her work, she says. “I think that’s going to be transformative when it appears,” she says. “The nucleic acid side keeps getting better, but the protein technology does not advance at the same rate.”

Looking ahead

This young investigator would like to see a comprehensive model of gene regulation and information processing in mammals that would allow for more insight into pathways and gene response. By understanding how signals are integrated, she hopes to develop robust methods that use a variety of data from large-scale genomics experiments to construct a model sophisticated enough to explain why a particular expression profile is being observed, what transcription factors are involved, and which signal molecules control them.

“The type of data that is most missing is the understanding of protein interactions and signaling pathways. Lack of databases and resources and large-scale datasets of that particular system in the cell is actually making this problem very difficult to solve,” she says. “Computationally, the big challenge is building good integrative models that go beyond the hype — that deliver, rather than just promise. And I think that’s the challenge for all of us in the community.”

Publications of note

Regev and her colleagues published a paper in Nature earlier this year entitled “The Natural History and Evolutionary Principles of Gene Duplication in Fungi” in which they describe a new algorithm to resolve gene orthology. The tool addresses the longstanding computational problem of identifying orthologs and paralogs in an accurate and high-throughput manner so that they can be applied across multiple genomes simultaneously. Using a group of whole genome sequences of 17 fungal species, the group discovered that genes are not created equal when it comes to duplication, and that some genes seem to be much more likely to be copied than other genes.

And the Nobel goes to ...

Regev says that if she were to win, she wouldn’t be choosy about what it was for. “Anything that would better human life is a worthwhile endeavor; you never know what hides behind the corner.”

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