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

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.