title: Hybrid modeling and simulation of stochastic effects on progression through the eukaryotic cell cycle.
abstract: The eukaryotic cell cycle is regulated by a complicated chemical reaction network. Although many deterministic models have been proposed, stochastic models are desired to capture noise in the cell resulting from low numbers of critical species. However, converting a deterministic model into one that accurately captures stochastic effects can result in a complex model that is hard to build and expensive to simulate. In this paper, we first apply a hybrid (mixed deterministic and stochastic) simulation method to such a stochastic model. With proper partitioning of reactions between deterministic and stochastic simulation methods, the hybrid method generates the same primary characteristics and the same level of noise as Gillespie's stochastic simulation algorithm, but with better efficiency. By studying the results generated by various partitionings of reactions, we developed a new strategy for hybrid stochastic modeling of the cell cycle. The new approach is not limited to using mass-action rate laws. Numerical experiments demonstrate that our approach is consistent with characteristics of noisy cell cycle progression, and yields cell cycle statistics in accord with experimental observations.
authors.affiliation: Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, USA. zhenliu@vt.edu
authors.forename: Zhen
authors.initials: Z
authors.lastname: Liu
authors.forename: Yang
authors.initials: Y
authors.lastname: Pu
authors.forename: Fei
authors.initials: F
authors.lastname: Li
authors.forename: Clifford A
authors.initials: CA
authors.lastname: Shaffer
authors.forename: Stefan
authors.initials: S
authors.lastname: Hoops
authors.forename: John J
authors.initials: JJ
authors.lastname: Tyson
authors.forename: Yang
authors.initials: Y
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D002453:Cell Cycle; D003198:Computer Simulation; D005057:Eukaryotic Cells / Q000737:chemistry / Q000378:metabolism*; D008954:Models, Biological; D013269:Stochastic Processes