Currently, VCell supports exact Gillespie stochastic simulation of chemical reactions which are either defined as Mass Action kinetics, or are mathematically equivalent to Mass Action kinetics. This type of stochastic simulation capture accurate statistical measures expected for such reactions.
It has long been requested for VCell to also support stochastic simulation for reactions which have not be decomposed into elementary reactions, and are described by a net effective rate law (e.g. Michaelis Menten). The approach is to translate the net reaction rate into an effective forward reaction rate propensity, and if the net reaction is reversible then add a reverse step which captures negative net reaction rates as reverse reaction rate propensities.
In this approximate regime average behavior will be captured for some models, but the noise statistics and variance will be underrepresented. For some models which are sensitive to this difference in variance, qualitatively different predictions may occur. It is up to the user to interpret the applicability of their model to stochastic simulation.
Currently, VCell supports exact Gillespie stochastic simulation of chemical reactions which are either defined as Mass Action kinetics, or are mathematically equivalent to Mass Action kinetics. This type of stochastic simulation capture accurate statistical measures expected for such reactions.
It has long been requested for VCell to also support stochastic simulation for reactions which have not be decomposed into elementary reactions, and are described by a net effective rate law (e.g. Michaelis Menten). The approach is to translate the net reaction rate into an effective forward reaction rate propensity, and if the net reaction is reversible then add a reverse step which captures negative net reaction rates as reverse reaction rate propensities.
In this approximate regime average behavior will be captured for some models, but the noise statistics and variance will be underrepresented. For some models which are sensitive to this difference in variance, qualitatively different predictions may occur. It is up to the user to interpret the applicability of their model to stochastic simulation.