Entelos Joins Pfizer Diabetes Consortium; Signs Agreement with Lilly
In two separate announcements this week, Entelos said that it has joined a consortium funded by Pfizer to identify new drug targets for diabetes and obesity and that it has signed an agreement with Eli Lilly to use its PhysioLab platform for diabetes research.
In the agreement with Pfizer, Entelos will work with the pharma and four universities to apply computational approaches to identify new drug targets relevant to diabetes and obesity. Pfizer’s initial funding for the consortium, called the Insulin Resistance Pathways, or IRP, Project, is $14.4 million over the next three years, with an option to extend for an additional two years.
The consortium includes researchers from the University of California, Santa Barbara; the California Institute of Technology; the Massachusetts Institute of Technology; and the University of Massachusetts.
The IRP Project aims to collect experimental data about insulin pathways within specific human cell types and then use mathematical models to better understand the biological mechanisms of these pathways. The consortium partners will then use Entelos’ PhysioLab model of whole-body metabolism to predict human clinical response to potential insulin-resistance drugs.
In the Lilly agreement, Entelos will apply its Metabolism PhysioLab platform to conduct research in the field of diabetes.
Financial terms were not disclosed.
James Karis, president and CEO of Entelos, said in a statement that Lilly was already a “long-time customer of Entelos' predictive toxicology systems and other information products.”
SGI Systems Support Papaya Sequencing Project
SGI said this week that researchers at the University of Hawaii used its technology to analyze data for the recently completed sequence of the papaya genome.
Last week, the International Papaya Genome Consortium published its analysis of the papaya genome in Nature. The collaboration involved researchers at 22 research institutions in the US and China and was led by Maqsudal Alam, the director of the University of Hawaii’s Advanced Studies in Genomics Proteomics and Bioinformatics program.
The collaborators sequenced the disease-resistant SunUp papaya, making it the first fruit and the first transgenic crop to be sequenced.
The team sequenced 75 percent of the entire papaya genome and about 90 percent of its genetically active euchromatin sequence at three times coverage using 2.8 million whole-genome shotgun sequencing reads. Based on their predictions, the 372 megabase papaya genome contains roughly 23,000 to 25,000 genes.
The UH team used an SGI Altix 350 system, 16 terabytes of SGI InfiniteStorage, and Qube! scheduling software from PipelineFX to analyze the data.
In a statement, Alam said that the researchers chose the SGI Altix “because of the configuration of the memory system, how quickly we can use the random memory, and also the scalability of the system, and of course, the price/performance.”
SGI announced separately this week that a number of other academic centers are using its systems for bioinformatics research, including Michigan State University, Université de Montréal, the University of Arizona, the Translational Genomics Research Institute, the Malaysia Genome Institute in Selangor, and the China National Human Genome Center.
NCI Using Tranche Network to Disseminate Mouse Proteomics Data
The National Cancer Institute will release proteomic data from its Mouse Proteomic Technologies Initiative to the public via the Tranche file sharing network and data warehouse, the Tranche project announced this week.
Tranche is a free and open source file-sharing tool that allows collections of computers to share scientific data sets. It is a program of the University of Michigan Medical School Department of Biological Chemistry.
The MPTI collects tissue and serum measurements from mouse models of different types of cancers using analytical techniques such as mass spectrometry. Tranche researchers, along with University of Michigan researcher Philip Andrews, deposited nearly a terabyte of MPTI raw data into the Tranche network, where it can be shared between participating researchers.
The dataset is now being released in publicly accessible formats as well and is available to others in the research community. Because of the encryption used on the site, data on Tranche can be privately used by labs with access to the information until it is ready to be released to the public.