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The above equations have been provided by [57] as a stochastic version of the deterministic quasi-steady state assumption Similar equations have been obtained in [40] for partitioning according to fast and slow reactions, hence essentially based on the partial equilibrium assumption Closely related to both approaches is the slow-scale stochastic simulation algorithm [17] where the partitioning is according to fast and slow species and an alternative approximate equation for the slow species is derived An extension to three time scales is proposed in [14] Given any of these sets of assumptions and equations, once a partitioning is de ned the submodels can be tackled by any suitable solver In fact, subsequently to the work in [40, 57], various algorithmic versions appeared that mainly differ in the techniques used to solve and combine the submodels, either purely simulative or hybrid Quite often, the slow submodel is solved by simple direct stochastic simulation and only the fast submodel is subject to different solvers Advanced stochastic simulation techniques such as tau-leaping (see Section 116) are applied, or the submodel is treated by ODE solvers for the deterministic reaction rate equations Hence, the submodels are sometimes even handled with different modeling paradigms It is beyond our scope to go into the details and we refer the reader to the original papers, [14, 54, 62, 78] to mention but a few All these algorithms are quite young Case studies and applications are limited to a few relatively simple models Analyses of their accuracy or statistical robustness for general model classes are still lacking, and the important issues of checking the validity of the underlying assumptions and automated partitioning require further investigation Also, almost all algorithms proposed so far apply stochastic simulation at least to parts of the model, though the reduced size and complexity render non-simulative solutions possible In the conclusion of [57], Rao and Arkin emphasize that they believe the true strength of the quasi-steady-state assumption is as a tool for model reduction We fully agree and note that the same holds for all partitioning techniques Concerning solution techniques, purely numerical, non-simulative methods, if ef ciently applicable, should be clearly preferred as they eliminate the statistical uncertainty from the results so that the question of the robustness of estimators becomes obsolete One step in this direction is [15] where a stiff enzymatic reaction set is numerically solved via aggregation, which has been demonstrated to be much faster than the slow-scale stochastic simulation algorithm Further investigations are forthcoming [67] Undoubtedly, integrating the knowledge from the numerical solution of Markovian models is very promising More generally, combining methods from different domains which is essentially the goal of systems biology is highly desirable and offers a great potential for signi cant advances in stochastic chemical kinetics. java pdf 417 reader Topic: pdf417 · GitHub
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