A novel approach to model neuronal signal transduction using stochastic differential equations

Authors:
Tiina Manninen;Marja-Leena Linne;Keijo Ruohonen
Affiliations:
Institute of Mathematics, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland and Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tam ...;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland;Institute of Mathematics, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland
Venue:
Neurocomputing
Year:
2006

Citing 2
Cited 1

An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations

SIAM Review
Review: Stochastic approaches for modelling in vivo reactions

Computational Biology and Chemistry

Developing Itô stochastic differential equation models for neuronal signal transduction pathways

Computational Biology and Chemistry

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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.