tailieunhanh - Báo cáo hóa học: " Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí sinh học đề tài :Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models | Baker et al. EURASIP Journal on Bioinformatics and Systems Biology 2011 2011 7 http content 2011 1 7 s EURASIP Journal on Bioinformatics and Systems Biology a SpringerOpen Journal RESEARCH Open Access Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models Syed Murtuza Baker C Hart Poskar and Bjorn H Junker Abstract In systems biology experimentally measured parameters are not always available necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter UKF rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison. 1. Introduction The focus of systems biology is to study the dynamic complex and interconnected functionality of living organisms 1 . To have a systems-level understanding of these organisms it is necessary to integrate experimental and computational techniques to form a dynamic model 1 2 . One such approach to dynamic models is the modeling of metabolic fluxes by their underlying enzymatic reaction rates. These enzymatic reaction rates or enzyme kinetics are described by a kinetic rate law. .