Multiple time scales in cellular chemical reaction systems often render the tau-leaping algorithm inefficient. Various model reductions have been proposed to accelerate tau-leaping simulations. However, these are often identified and deployed manually, requiring expert knowledge. This is time-consuming and prone to error. In previous work, we proposed a methodology for automatic identification and validation of model reduction opportunities for tau-leaping simulation. Here, we show how the model reductions can be automatically and adaptively deployed during the time course of a simulation. For multiscale systems, this can result in substantial speedups. (C) 2015 AIP Publishing LLC.
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