Modeling signal transduction networks: a comparison of two stochastic kinetic simulation algorithms.
Computational efficiency of stochastic kinetic algorithms depend on factors such as the overall species population, the total number of reactions, and the average number of nodal interactions or connectivity in a network. These size measures of the network model can have a significant impact on computational efficiency. In this study, two scalable biological networks are used to compare the size scaling efficiencies of two popular and conceptually distinct stochastic kinetic simulation algorithms--the random substrate method of Firth and Bray (FB), and the Gillespie algorithm as implemented using the Gibson-Bruck method (GGB). The arithmetic computational efficiencies of these two algorithms, respectively, scale with the square of the total species population and the logarithm of the total number of active reactions. The two scalable models considered are the size scalable model (SSM), a four compartment reaction model for a signal transduction network involving receptors with single phosphorylation binding sites, and the variable connectivity model (VCM), a single compartment model where receptors possess multiple phosphorylation binding sites. The SSM has fixed species connectivity while the connectivity between species in VCM increases with the number of phosphorylation sites. For SSM, we find that, as the total species population is increased over four orders of magnitude, the GGB algorithm performs significantly better than FB for all three SSM compartment models considered. In contrast, for VCM, we find that as the overall species population decreases while the number of phosphorylation sites increases (implying an increase in network linkage) there exists a crossover point where the computational demands of the GGB method exceed that of the FB.
Pettigrew
Michel F
MF
Cell Systems Initiative, Department of Bioengineering, University of Washington, Seattle 98195-8070, USA. mpettigr@u.washington.edu
Resat
Haluk
H
D003198:Computer Simulation; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D009097:Multienzyme Complexes / Q000378:metabolism*; D010766:Phosphorylation; D010770:Phosphotransferases / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D013269:Stochastic Processes
D009097:Multienzyme Complexes; D020543:Proteome; D010770:Phosphotransferases