Minimum Entropy Parameter Estimation: Application to the RKIP Regulated ERK Signaling Pathway

摘要

Parameter estimation plays an important role in Systems Biology in helping to understand the complex behavior of signal transduction networks. The problem becomes more intense as the inherent stochasticity of the signaling mechanism involves noise components of non-Gaussian nature. A novel stochastic parameter estimation method has been developed where the aim is to obtain the optimal parameters corresponding to a lower entropy measure on the residual joint probability density function. The residual joint PDF is approximated using Kernel Density Estimation methods and the method is designed to handle general multivariable dynamic ODE systems where the measurement noise is not necessarily Gaussian. The analysis on the proposed minimum entropy parameter estimation involves an application to the RKIP regulated ERK pathway where the demonstrated simulation results clearly indicate its effectiveness.