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Gene Network Sciences Breaks New Ground


Systems biology pioneer Gene Network Sciences (GNS) will use its cancer expertise and computational modeling expertise to help choose optimal therapies for cancer patients at the Mary Crowley Medical Research Center (MCMRC) in Dallas. This is the first time biosimulation will be used to test treatments for actual cancer patients based on their individual gene expression data, says GNS.

 

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To perform the biosimulations, GNS will take patient information such as gene expression response and pharmacological information from MCMRC and input the data into computer models. The models then run on a cluster of supercomputers, and the results are used to group MCMRC patients into biologically similar subsets who can undergo similar treatments. The data from these subsets are then run through the simulation again to predict which therapies will have the greatest efficacy and least toxicity.

The deal is an important milestone for systems biology in general and modeling in particular. As part of the agreement, GNS will be compensated on a per-patient basis, representing another advance for systems biology providers, who have struggled to develop profitable business strategies. An Israeli company, Optimata, also uses biosimulation to choose optimal therapies, but it is less focused on incorporating patient gene expression data (see Optimizing Optimata, November 2005 Bio-IT World).

“We are very pleased to enter into this strategic relationship with Gene Network Sciences,” said David Shanahan, president of the Mary Crowley Medical Research Center, in a press release. “This technological innovation will allow us a new means of developing safer and more-effective cancer treatments for our patients.”  GNS models will be used on a range of cancers.  MCMRC treats many types of cancers, including breast, colon, lung and prostate.

MCMRC will use GNS computer models to help improve clinical trial success rates and advance patient care. GNS’ biosimulation platform allows for the testing of efficacy and toxicity of compounds before they are introduced into a patient—something that previously was not possible. Recently, the dramatic growth of biological knowledge has combined with affordable high-performance computing to make biosimulation more attractive. The GNS computer lab occupies 150 sq. ft. of office space and is equipped with a 32-CPU supercomputing cluster (32 AMD Athlon MP 2000 CPUs) with a total of 16 GB of RAM and over 1 Terabyte of disk storage and 5 single- and dual-processor Windows 2000 and Linux servers. In addition, there is a 48-CPU cluster (48 Mac G5 Power PC CPU’s) with a total of 24GB of RAM and 4.4TB of disk storage

“By using clinical data to predict who will benefit most from therapy, our models arm the researchers at Mary Crowley Medical Research Center with the power to improve clinical trial success rates and expand treatment options,” said Colin Hill, CEO of GNS.

The MCMRC’s primary mission is to explore investigational vaccine, gene, and cellular therapies with the goal of expanding treatment options for all cancer patients. By first filtering out drug candidates based on their efficacy in computer models fed with patient data, MCMRC specialists will be able to more accurately pinpoint drugs that will have an optimal effect on the patient.

Recognized as a national leader in cancer research with a particular focus on targeted therapies, MCMRC has performed hundreds of Phase I and Phase II clinical trial protocols, treated more than 3,000 patients, explored more than 100 new agents, and accomplished more than 140 FDA-approved clinical trials. The organization was established in 1992.

Gene Network Sciences is an early mover in the systems biology space and has developed a model of a “virtual” colon cancer cell. First focused on developing predictive mechanistic models, GNS has recently begun emphasizing bottom-up inference approaches that analyze large data sets to develop models. With this partnership, MCMRC gains the unique advantage of being able to use GNS computer simulations to test which therapies will have the best clinical outcomes for patients.

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