Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài:Identification of metabolic system parameters using global optimization methods | Theoretical Biology and Medical Modelling BioMed Central Research Open Access Identification of metabolic system parameters using global optimization methods Pradeep K Polisetty1 Eberhard O Voit2 and Edward P Gatzke 1 Address Department of Chemical Engineering University of South Carolina Swearingen Engineering Center 301 Main Street Columbia SC 29208 USA and 2The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University 313 Ferst Drive Suite 4103 Atlanta GA 30332 USA Email Pradeep K Polisetty - POLISETT@ Eberhard O Voit - Edward P Gatzke - gatzke@ Corresponding author Published 27 January 2006 Received 26 November 2005 Theoretical Biology and Medical Modelling2006 3 4 doi 1742-4682-3-4 Accepted 27 January 2006 This article is available from http content 3 1 4 2006Polisetty et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background The problem of estimating the parameters of dynamic models of complex biological systems from time series data is becoming increasingly important. Methods and results Particular consideration is given to metabolic systems that are formulated as Generalized Mass Action GMA models. The estimation problem is posed as a global optimization task for which novel techniques can be applied to determine the best set of parameter values given the measured responses of the biological system. The challenge is that this task is nonconvex. Nonetheless deterministic optimization techniques can be used to find a global solution that best reconciles the model parameters and measurements. Specifically the paper employs branch-and-bound principles to identify the best set of model
