title: Discrete stochastic simulation of cell signaling: comparison of computational tools.
abstract: Several stochastic simulation tools have been developed recently for cell signaling. A comparative evaluation of the stochastic simulation tools is needed to highlight the current state of the development. In our study, we have chosen to evaluate three stochastic simulation tools: Dizzy, Systems Biology Toolbox, and Copasi, using our own MATLAB implementation as a benchmark. The Gillespie stochastic simulation algorithm is used in all tests. With all the tools, we are able to simulate stochastically the behavior of the selected test case and to produce similar results as our own MATLAB implementation. However, it is not possible to use time-dependent inputs in stochastic simulations in Systems Biology Toolbox and Copasi. The present study is one of the first evaluations of stochastic simulation tools for realistic signal transduction pathways.
authors.affiliation: Inst. of Signal Process., Tampere Univ. of Technol, PO Box FI-33101 Tampere, Finland.
authors.forename: Tiina
authors.initials: T
authors.lastname: Manninen
authors.forename: Eeva
authors.initials: E
authors.lastname: Mäkiraatikka
authors.forename: Antti
authors.initials: A
authors.lastname: Ylipää
authors.forename: Antti
authors.initials: A
authors.lastname: Pettinen
authors.forename: Kalle
authors.initials: K
authors.lastname: Leinonen
authors.forename: Marja-Linne
authors.initials: ML
authors.lastname: Linne
mesh_terms: D003198:Computer Simulation; D008954:Models, Biological; D011381:Programming Languages; D011493:Protein Kinase C / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D012984:Software; D012986:Software Validation; D013269:Stochastic Processes
chemical_list: D011493:Protein Kinase C