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Cellular reaction systems are often multiscale in nature due to wide variation in the species abundance and reaction rates. Traditional deterministic or stochastic modeling of such systems, which do not exploit this multiscale behavior, will be computationally expensive for simulation or inference purposes. This necessitates developing simplified hybrid models combining both stochastic and deterministic...
In cellular reaction systems, events often happen at different time and abundance scales. It is possible to simulate such multi-scale processes with exact stochastic simulation algorithms, but the computational cost of these algorithms is prohibitive due to the presence of high propensity reactions. This observation motivates the development of hybrid models and simulation algorithms that combine...
Stochastic reaction systems with discrete particle numbers are usually described by a continuous-time Markov process. Realizations of this process can be generated with the stochastic simulation algorithm, but simulating highly reactive systems is computationally costly because the computational work scales with the number of reaction events. We present a new approach which avoids this drawback and...
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