CAMBRIDGE, Mass. - May 1, 2013 - GNS Healthcare announced today that Harvard Medical School has licensed its REFS™ (Reverse Engineering and Forward Simulation) Big Data analytics platform. Under the five-year license, Harvard researchers will use REFS™ to characterize and understand signaling and transcriptional events in biological systems. Financial terms of the agreement were not disclosed.
"Reverse engineering is a key challenge for systems biology," said Marc Kirschner, 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."
The focus of the research at Harvard will be the mechanisms and pathways of cell differentiation and control in embryonic development and on how drug treatment affects these mechanisms. Harvard researchers will generate huge amounts of data using whole genome sequencing, RNA sequencing, and high-throughput protein measurements, collected at different points in time, in the presence and absence of different drugs. This data will then be analyzed using REFS™ to build computational models of cell differentiation pathways, how they are controlled, and how they are affected by drug treatment.
"We are very excited to gain access to the Big Data analytics capabilities of the GNS platform," said Leon Peshkin, Principal Investigator and Senior Research Scientist in the Harvard Medical School Department of Systems Biology. "The 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."
This research is part of the larger Initiative in Systems Pharmacology at Harvard, which brings together biologists, chemists, computer scientists, physicists, and mathematicians to study how drugs work in the body and how to use this information design better therapies. The project aims to understand diseases as biological systems and to understand how drugs work in these systems to treat the diseases.
Computational models of cell differentiation built using REFS™ could allow researchers to better understand how cancers develop and how to treat them. "Cancer is ultimately a problem of cell identity. Something goes wrong in the signals a cell receives, causing the cells to grow out of control and form a tumor," said Kirschner. "If we can figure out how to regulate the mechanisms by which cells send signals and coordinate growth throughout the body, we can create better treatments for cancer."
"Licensing the REFS™ platform to Harvard complements the Big Data analytics work we are doing with pharmaceutical and biotechnology companies, health plans and hospitals," said Colin Hill, CEO of GNS Healthcare. "Harvard researchers will use the platform to make fundamental discoveries in biology, which will drive healthcare innovation in the private sector. Harvard is an ideal partner for bringing this kind of innovation to life."
REFS™ (Reverse Engineering and Forward Simulation) is GNS Healthcare's scalable, supercomputer-enabled framework for discovering new knowledge directly from data. REFS™ automates the discovery and extraction of causal network models from observational data and uses high-throughput simulations to generate new knowledge.
About GNS Healthcare
GNS Healthcare is a Big Data analytics company that has developed a scalable approach for the discovery of what works in healthcare, and for whom. Our analytics solutions are being applied across the healthcare industry: from pharmaceutical and biotechnology companies, health plans and hospitals, to integrated delivery systems, pharmacy benefits managers, and accountable care organizations. REFS™ is GNS Healthcare's scalable, supercomputer-enabled framework for discovering new knowledge directly from data. REFS™ automates the discovery and extraction of causal network models from observational data and uses high-throughput simulations to generate new knowledge.
MacDougall Biomedical Communications