  Biological reaction networks often contain what might be called 'hub
molecules', which are involved in many reactions. For example, ATP is commonly
consumed and produced. When reaction networks contain molecules like ATP, they
are difficult to efficiently simulate, because every time such a molecule is
consumed or produced, the propensities of numerous reactions need to be
updated. In order to increase the speed of simulations, we developed 'Lazy
Updating', which postpones some propensity updates until some aspect of the
state of the system changes by more than a defined threshold. Lazy Updating
works with several existing stochastic simulation algorithms, including
Gillespie's direct method and the Next Reaction Method. We tested Lazy Updating
on two example models, and for the larger model it increased the speed of
simulations over eight-fold while maintaining a high level of accuracy. These
increases in speed will be larger for models with more widely connected hub
molecules. Thus Lazy Updating can contribute towards making models with a
limited computing time budget more realistic by including previously neglected
hub molecules.
