Change detection in the dynamics of an intracellular protein synthesis model using nonlinear Kalman Filtering

Abstract

A method for early diagnosis of parametric changes in intracellular protein synthesis models (e.g. the p53 protein - mdm2 inhibitor model) is developed with the use of a nonlinear Kalman Filtering approach (Derivative-free nonlinear Kalman Filter) and of statistical change detection methods. By applying a diffeomorphism that is based on differential flatness theory a linearized form of such models can be obtained. For the linearized equivalent models, state estimation can be performed using the Kalman Filter recursion. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted protein synthesis model) with measurements obtained from the monitored protein synthesis system, a sequence of differences (residuals) is obtained. The statistical processing of the residuals with the use of χ2 change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indications about the appearance of specific diseases (e.g. malignancies).