title: Stiffness detection and reduction in discrete stochastic simulation of biochemical systems.
abstract: Typical multiscale biochemical models contain fast-scale and slow-scale reactions, where "fast" reactions fire much more frequently than "slow" ones. This feature often causes stiffness in discrete stochastic simulation methods such as Gillespie's algorithm and the Tau-Leaping method leading to inefficient simulation. This paper proposes a new strategy to automatically detect stiffness and identify species that cause stiffness for the Tau-Leaping method, as well as two stiffness reduction methods. Numerical results on a stiff decaying dimerization model and a heat shock protein regulation model demonstrate the efficiency and accuracy of the proposed methods for multiscale biochemical systems.
authors.affiliation: Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA. ypu@cs.vt.edu
authors.forename: Yang
authors.initials: Y
authors.lastname: Pu
authors.forename: Layne T
authors.initials: LT
authors.lastname: Watson
authors.forename: Yang
authors.initials: Y
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D001669:Biochemical Phenomena; D003198:Computer Simulation / Q000191:economics; D019281:Dimerization; D006360:Heat-Shock Proteins / Q000378:metabolism; D008954:Models, Biological; D008956:Models, Chemical; D013269:Stochastic Processes; D013997:Time Factors
chemical_list: D006360:Heat-Shock Proteins