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Dumontier Lab |
Towards Personalized Medicine |
Cell SimulationAn ambitious, long term goal of computational cell biology is to simulate a biological cell with as many components as we currently know about, and predict those that we don't know anything about. This task has two major challenges: 1) building accurate biological models and 2) developing scalable simulation architectures. Efforts towards realizing these objectives are expected to support therapeutic drug development against human diseases by increasing our understanding of biological systems. Molecular systems are inherently stochastic and spatially dependent such that they require compatible simulation methods. Stochasticity arises from the fact that i) molecules exhibit Brownian dynamics in solution and that certain cellular processes are controlled with but hundreds or thousands of molecules. Moreover, the idealization of a “well mixed” system does not reflect the biological reality of a densely populated cell that is heavily compartmentalized. Howver, the simulation of discrete, stochastic, spatially-dependent molecular systems is extremely computationally expensive and most current simulators do not support all of these functionalities. Our collaborative work aims to build agent-based models and scalable, hardware accelerated simulators towards whole cell simulation. Current work is focused on developing more sophisticated, knowledge enhanced models and powerful user interfaces to explore and simulate models. Below is a screenshot of GridCell, a stochastic, discrete particle simulator developed in collaboration with Dr. Warren Gross at McGill University. GridCell
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