“The first attempt at an integrative view of the effects of
cancer mutations, long missing in molecular biology.”
Turbine's services help bring effective treatments
to cancer patients faster
8million leads screened in a day on a cancer cell line or patient tumor
4Weeks to test every combination of FDA approved cancer therapies
3Years potential extension to drug lifetime with in silico trial design
5Patients' sequenced data needed to simulate any cancer type
Turbine wins grant with Csaba Bodor’s leading onco-hematological lab to research CLL
October 2016 | In order to tackle relapse in patients, the Bodor Lab aims to understand the transformation of chronic disease into acute cancer in CLL patients. The collaboration includes designing personalized therapies with AI based on samples from tumors and lymph nodes.
Turbine's AI realistically models therapy
response on millions of cells
The Turbine Simulated Cell
Turbine's Simulated Cell is made of an expert-curated network of 1100+ nodes, including proteins and their physical and conditional relations. The network describes all hallmarks of cancer, contains a significant number of known cancer driver proteins, and was built through years of curating scientific findings about the wiring diagram of human cells. The Simulated Cell is the largest network ready for dynamic analysis and simulation, 10x the size of the second largest, published network.
The past century’s biochemical knowledge is built into our Simulated Cell, a network of more than 1100 well-described signaling and cancer related proteins, including all hallmarks of cancer.Learn more
Customizing the Simulated Cell to disease or patient
As the base of the Simulated Cell is a universal signaling network of human cells, it can be customized with multiple OMICS layers to simulate any cancer type. Inputs can be genomic, transcriptomic and proteomic datasets specific to a tumor, tissue or patient.
Customizing cells to disease or patient
Simulated Cells are tailored to cell line, tissue or patient with several OMICS layers. This includes mutational and expression data to ensure in silico cells exhibit the characteristic behaviors of live ones.Learn more
Designing interventions with Turbine's artificial intelligence
The Simulated Cell is treated with all likely treatment combinations in silico. Turbine's algorithms are so fast that they enable feeding each result into a self-learning artificial intelligence. Simulation results are compiled into so-called attractor landscapes. These show the most likely states of the system given certain mutations or interventions. This approach ensures predictions are much more robust, and thus accurate than other statistical or simulational methods. What's more, simulations are easily testable and reproducible, as all parameters are controlled - unlike preclinical in vitro experiments, where results can be influenced by a multitude of factors.
Designing interventions with AI
Simulated Cells are treated with all likely treatment combinations in silico by an artificial intelligence. The AI analyzes so-called attractor landscapes, which show the most likely cell states – like apoptosis - after certain combination treatments.Learn more