Center to Seek New Therapeutics by Integrating Gene, Protein Databases
Mount Sinai researchers awarded one of the Big Data to Knowledge (BD2K) Centers of Excellence grants
A Mount Sinai research team today received a $20 million grant from the National Institutes of Health (NIH) to create a center that will integrate databases and build computer models that glean new insights on how human cells react to drugs and toxins. The goal is to accelerate the discovery of new therapies and diagnostics by mining data.
With advances in inexpensive computing power and new methods of data collection, biomedical research has entered the era of “big data”. Researchers can now design algorithms that identify previously unrecognized molecular networks and their role in disease from integrative analysis of many databases. Medical research is becoming ever more data-driven, and researchers need a common framework to bring analyses together.
“We believe that our new Center will help many labs to better map the molecular pathways in human cells in response to thousands of drugs as we become more capable of predicting which drugs will be most effective for treating complex diseases at the individual patient level,” said Avi Ma’ayan, PhD, Associate Professor in the Department of Pharmacology and Systems Therapeutics within the Icahn School of Medicine at Mount Sinai, and a principal investigator for the Center grant. “We have much work to do in harmonizing, analyzing and visualizing the masses of data collected by many NIH-funded centers, but the combined effort promises to drive synergistic discovery.”
In a separate NIH grant, researchers in the same Mount Sinai department last month received $12 million to create one of six centers that will feed data into the data integration Center announced today. Specifically, the latest grant will help to establish a Data Coordination and Integration Center (DCIC) for the Library of Integrated Network-based Cellular Signatures (LINCS) program, part of the Big Data to Knowledge (BD2K) initiative established by the Office of the NIH Director.
LINCS signatures are confirmed sets of genetic and protein global responses within a type of cell to a drug, drug combination or other factors that affect human health (e.g. environmental toxins). The data integration center will create a computing environment and a web portal where data from many sources can be shared and combined. It will also conduct research into how data is generated, stored, gathered, analyzed and build an array of computer models to tease out patterns about drug response in human cells. This in turn promises to advance human health by bridging the gap between clinical data and molecular networks.
The Center will also design new ways to visualize data. One existing example of this, developed by the Mount Sinai team, is the prototype tool called the LINCS Canvas Browser. It can show clustering analysis of 200,000 experiments all at once on a globe display, with color coding that enables rapid identification of the relationships between groups of genes and prior biological knowledge about cell regulatory networks.
The LINCS project originated with the Broad Institute of MIT over ten years ago, where researchers created a database of molecular signatures by treating four human cancer cell lines with over 1000 drugs, and then measuring gene expression in the presence of each drug. This dataset proved to be useful for discovering the functions of new drugs by comparing their signatures with existing drugs. The excitement surrounding this first study, called the Connectivity Map, led to the establishment of the LINCS program.
Along with Dr. Ma’ayan, the new Center will bring together a veteran team of computational experts, including Stephan Schürer, PhD, from the Center for Computational Science at the University of Miami, and Mario Medvedovic, PhD, from the Laboratory for Statistical Genomics and Systems Biology at the University of Cincinnati, both also principal investigators. The Center will also support projects led by principal investigators from Carnegie Mellon, the University of Washington and Cell Signaling Technology, Inc. The new grant will provide 2.5 million in funding for the first 7 months, and then 4.3 million each year for four years.
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