The Mary Crowley Medical Research Center (MCMRC) announces its partnership with Gene Network Sciences (GNS) to provide a new innovative service for cancer patients. MCMRC will utilize computer models from GNS to help improve clinical trial success rates and advance patient care. As part of the agreement, GNS will be compensated on a per-patient basis. The Mary Crowley Medical Research Center's primary mission is to explore investigational vaccine, gene and cellular therapies with the goal of expanding treatment options for all cancer patients. Gene Network Sciences is dedicated to processing massive amounts of biological data to develop computer simulations of cells, tissues and organs for the purpose of improving cancer drug research and patient treatment. This marks the first time biosimulation will be used to test treatments for actual cancer patients based on their individual gene expression data. "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. "This technological innovation will allow us a new means of developing safer and more effective cancer treatments for our patients." 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. 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. "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 Gene Network Sciences. "It's truly motivating to work with such a progressive, highly regarded team and to witness the front-line application of our technology." In order to perform biosimulations, GNS takes patient information such as gene expression response and pharmacological information from MCMRC and inputs it into computer models. The models then run on a supercomputing cluster and the results are used to group MCMRC patients into biologically similar subsets who can undergo similar treatments. These subsets are then run through the simulation again to predict which therapies have the greatest efficacy and least toxicity. Pharmaceutical and research groups have become increasingly aggressive in their pursuit of technologies able to deliver safer and more effective drugs to the right patient groups. GNS's biosimulation platform allow for the testing of efficacy and toxicity of compounds before they are introduced into a patient, something that was previously not possible. However, with the exponential growth of both biological knowledge and computational power, biosimulation has become a cost-effective method of pharmaceutical testing.