Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics

@article{citeulike:1451588, abstract = {Exact methods are available for the simulation of isothermal, well-mixed stochastic chemical kinetics. As increasingly complex physical systems are modeled, however, these methods become difficult to solve because the computational burden scales with the number of reaction events. This paper addresses one aspect of this problem: the case in which reacting species fluctuate by different orders of magnitude. By partitioning the system into subsets of \"fast\" and \"slow\" reactions, it is possible to bound the computational load by approximating \"fast\" reactions either deterministically or as Langevin equations. This paper provides a theoretical background for such approximations and outlines strategies for computing these approximations. Two motivating examples drawn from the fields of particle technology and biotechnology illustrate the accuracy and computational efficiency of these approximations. \©2002 American Institute of Physics.}, author = {Haseltine, Eric L. and Rawlings, James B. }, citeulike-article-id = {1451588}, doi = {http://dx.doi.org/10.1063/1.1505860}, journal = {The Journal of Chemical Physics}, keywords = {gillespie\_algorithm, kinetics, simulation, stochastic}, number = {15}, pages = {6959--6969}, posted-at = {2007-12-11 22:31:52}, priority = {2}, publisher = {AIP}, title = {Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics}, url = {http://dx.doi.org/10.1063/1.1505860}, volume = {117}, year = {2002} }

TY - JOUR ID - citeulike:1451588 L3 - citeulike-article-id:1451588 TI - Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics JF - The Journal of Chemical Physics VL - 117 IS - 15 SP - 6959 EP - 6969 PB - AIP N2 - Exact methods are available for the simulation of isothermal, well-mixed stochastic chemical kinetics. As increasingly complex physical systems are modeled, however, these methods become difficult to solve because the computational burden scales with the number of reaction events. This paper addresses one aspect of this problem: the case in which reacting species fluctuate by different orders of magnitude. By partitioning the system into subsets of "fast" and "slow" reactions, it is possible to bound the computational load by approximating "fast" reactions either deterministically or as Langevin equations. This paper provides a theoretical background for such approximations and outlines strategies for computing these approximations. Two motivating examples drawn from the fields of particle technology and biotechnology illustrate the accuracy and computational efficiency of these approximations. ©2002 American Institute of Physics. KW - gillespie_algorithm KW - kinetics KW - simulation KW - stochastic AU - Haseltine, Eric AU - Rawlings, James PY - 2002/// UR - http://dx.doi.org/10.1063/1.1505860 ER -