CHANGE DETECTION IN THE DYNAMICS OF AN INTRACELLULAR PROTEIN SYNTHESIS MODEL USING NONLINEAR KALMAN FILTERING

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 (Derivativefree nonlinear Kalman Filter) and of statistical change detection methods. I'he intracellular protein synthesis dynamic model is described by a set of coupled nonlinear differential equations. It is shown that such a dynamical system satisfies differential flatness properties and this allows to transform it, through a change of variables (diffeornorphism), to the so-called linear canonical form. For the linearized equivalent of the dynamical system, state estimation can be performed using the Kalman Filter recursion. Moreover, by applying an inverse tI ansformation based on the previous diffeomorphism it becomes also possible to obtain estimats of the state variables of the initial nonlinear model. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted dynamical model) with measurements obtained from the monitored protein synthesis rs:,,,,stern, a sequence of differences (residuals) is obtained. 1.'he statistical processing of the residuals with the use of X2 change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indi('ations about the appearance of specific diseases (e.g. malignancies)