Name
Title | ||
|---|---|---|
A novel approach to model neuronal signal transduction using stochastic differential equations |
Abstract | ||
|---|---|---|
We introduce a new approach to model the behavior of neuronal signal transduction networks using stochastic differential equations. We present first a mathematical formulation for a stochastic model of protein kinase C pathway. Different kinds of numerical integration methods, including the explicit and implicit Euler-Maruyama methods, are used to solve the Ito@^ form of the stochastic model. Stochastic models may provide more realistic representations for the chemical species in signal transduction networks compared to deterministic models. Our approach has the advantage of being computationally less demanding in the context of large-scale stochastic simulations than other approaches where individual chemical interactions are simulated stochastically. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.neucom.2005.12.047 | Neurocomputing |
Keywords | Field | DocType |
protein kinase c,chemical species,euler–maruyama method,signal transduction,implicit euler-maruyama method,new approach,itoform,novel approach,neuronal signal transduction network,signal transduction network,large-scale stochastic simulation,different kind,individual chemical interaction,stochastic differential equation,euler-maruyama method,itô form,stochastic model,stochastic simulation,numerical integration | Applied mathematics,Stochastic optimization,Mathematical optimization,Pattern recognition,Numerical integration,Stochastic neural network,Stochastic differential equation,Artificial intelligence,Stochastic modelling,Neuronal signal transduction,Mathematics,Euler–Maruyama method | Journal |
Volume | Issue | ISSN |
69 | 10-12 | Neurocomputing |
Citations | PageRank | References |
4 | 0.44 | 3 |
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 |