Constrained Stochastic Space Search Method for Parameter Estimation in Biological Networks

Parameter estimation in genetic networks using a constrained stochastic space search method

Numerous stochastic search methods have been applied in parameter estimation problems in genetic network identification. In this work, a constrained stochastic space search (CSSS) method for parameter estimation is proposed and used to optimize the goal function for the difference between measured and estimated gene expression time series data. Both linear and nonlinear model formalism were use...

An S-System Parameter Estimation Method (SPEM) for Biological Networks

Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on th...

Proper orthogonal decomposition for parameter estimation in oscillating biological networks

Online State Space Model Parameter Estimation in Synchronous Machines

The purpose of this paper is to present a new approach based on the Least Squares Error method for estimating the unknown parameters of the nonlinear 3rd order synchronous generator model. The proposed method uses the mathematical relationships between the machine parameters and on-line input/output measurements to estimate the parameters of the nonlinear state space model. The field voltage is...

Parameter Estimation in Stochastic

Recent years have seen increasing popularity of stochastic chemical kinetic models due to their ability to explain and model several critical biological phenomena. Several developments in high resolution fluorescence microscopy have enabled researchers to obtain protein and mRNA data on the single cell level. The availability of these data along with the knowledge that the system is governed by...