Name
Title | ||
|---|---|---|
Developing Itô stochastic differential equation models for neuronal signal transduction pathways. |
Abstract | ||
|---|---|---|
Mathematical modeling and simulation of dynamic biochemical systems are receiving considerable attention due to the increasing availability of experimental knowledge of complex intracellular functions. In addition to deterministic approaches, several stochastic approaches have been developed for simulating the time-series behavior of biochemical systems. The problem with stochastic approaches, however, is the larger computational time compared to deterministic approaches. It is therefore necessary to study alternative ways to incorporate stochasticity and to seek approaches that reduce the computational time needed for simulations, yet preserve the characteristic behavior of the system in question. In this work, we develop a computational framework based on the Itô stochastic differential equations for neuronal signal transduction networks. There are several different ways to incorporate stochasticity into deterministic differential equation models and to obtain Itô stochastic differential equations. Two of the developed models are found most suitable for stochastic modeling of neuronal signal transduction. The best models give stable responses which means that the variances of the responses with time are not increasing and negative concentrations are avoided. We also make a comparative analysis of different kinds of stochastic approaches, that is the Itô stochastic differential equations, the chemical Langevin equation, and the Gillespie stochastic simulation algorithm. Different kinds of stochastic approaches can be used to produce similar responses for the neuronal protein kinase C signal transduction pathway. The fine details of the responses vary slightly, depending on the approach and the parameter values. However, when simulating great numbers of chemical species, the Gillespie algorithm is computationally several orders of magnitude slower than the Itô stochastic differential equations and the chemical Langevin equation. Furthermore, the chemical Langevin equation produces negative concentrations. The Itô stochastic differential equations developed in this work are shown to overcome the problem of obtaining negative values. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.compbiolchem.2006.04.002 | Computational Biology and Chemistry |
Keywords | Field | DocType |
stochastic modeling,itˆ,stochastic differential equation model,neuronal signal transduction pathway,negative concentration,chemical species,stochastic approach,c signal transduction pathway,neuronal signal transduction,ito stochastic differential equation,different kind,ito stochastic differential,deterministic differential equation model,gillespie stochastic simulation algorithm,chemical langevin equation,itô stochastic differential equation,protein kinase c,stochastic model,stochastic differential equation,comparative analysis,signal transduction pathway,differential equation,time series,signal transduction,mathematical model | Stochastic simulation,Applied mathematics,Differential equation,Stochastic optimization,Mathematical optimization,Stochastic differential equation,Gillespie algorithm,Stochastic partial differential equation,Genetics,Neuronal signal transduction,Langevin equation,Mathematics | Journal |
Volume | Issue | ISSN |
30 | 4 | 1476-9271 |
Citations | PageRank | References |
12 | 0.98 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tiina Manninen | 1 | 75 | 7.29 |
| Marja-Leena Linne | 2 | 118 | 14.16 |
| Keijo Ruohonen | 3 | 151 | 22.20 |