Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

An ecologic comparison study of the impact of economic disadvantage on Texas Assessment of Knowledge and Skills performance, graduation rates, and readiness for higher education for students attending public and charter schools in Texas from 2004 to 2006

Purpose. No Child Left Behind aimed to "improve the academic achievement of the disadvantaged." The primary research question considered how academic achievement of those from economic disadvantage compared to those not from disadvantage? ^ Economically disadvantaged students can potentially have added academic disadvantage. Research shows low academic achievement can potentially result in drug abuse, youth violence, and teen pregnancy. ^ Methods. To compare the student populations, measures included TAKS results and academic indicator data collected by the Texas Education Agency. ^ Results. T-test analyses showed a significant difference between the economically and non-economically disadvantaged student populations in meeting the TAKS passing standard, graduation, and preparation for higher education.^ Conclusions. The achievement gap between students remained as indicated by the Texas testing program. More research and time are needed to observe if the desired impact on those from economic disadvantage will be reflected by academic achievement data.^