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Harvard Med School Licenses GNS Healthcare's Big Data Analytics Platform

NEW YORK (GenomeWeb News) – Big data analysis firm GNS Healthcare announced today that Harvard Medical School is licensing its REFS analytics platform for the characterization and elucidation of signaling and transcriptional events in biological systems.

Researchers at the school are investigating the mechanisms and pathways of cell differentiation and control in embryonic development as well as how drug treatments affect such mechanisms. Using whole-genome sequencing, RNA sequencing, and high-throughput protein measurements, researchers anticipate generating "huge" amounts of data that will be collected at different time points in the presence and absence of different drugs.

The collected data will be analyzed with REFS "to build computational models of cell differentiation pathways, how they are controlled, and how they are affected by drug treatment," Cambridge, Mass.-based GNS said.

REFS, which stands for Reverse Engineering and Forward Simulation, is a scalable supercomputer-enabled framework for "discovering new knowledge directly from data," the company said. It "automates the discovery and extraction of causal network models from observational data" and generates the new knowledge using high-throughput simulations.

"Reverse engineering is a key challenge for systems biology," Marc Kirschner, a professor and chair of the Department of Systems Biology at Harvard Medical School. "Achieving a synergy between the design of experiments and reverse engineering methods will be defining for how we understand biological mechanism in the next century."

This study is part of the larger Initiative in Systems Pharmacology at the university, in which biologists, chemists, computer scientists, physicists, and mathematicians are studying how drugs work in the body to design better treatments.

Leon Peshkin, principal investigator and senior research scientist in the systems biology department at Harvard, said that the REFS platform "will allow us to build computational models directly from large biological data sets in an automated, hypothesis-free way, revealing cause-and-effect relationships and furthering our understanding of the fundamental aspects of biological systems."

Financial details of the five-year licensing deal were not disclosed.

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