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Báo cáo y học: "Parameter estimation in biochemical systems models with alternating regression"

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: Parameter estimation in biochemical systems models with alternating regression | Theoretical Biology and Medical Modelling BioMed Central Research Parameter estimation in biochemical systems models with alternating regression I-Chun Chou1 Harald Martens2 and Eberhard O Voit 1 Open Access Address The Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University 313 Ferst Drive Atlanta GA 30332 USA and 2CIGENE Norwegian U. of Life Sciences P.O.Box 5003 N - 1432 Ảs Norway Email I-Chun Chou - gtg392p@mail.gatech.edu Harald Martens - harald.martens@matforsk.no Eberhard O Voit - eberhard.voit@bme.gatech.edu Corresponding author Published 19 July 2006 Received 27 April 2006 Theoretical Biology and Medical Modelling 2006 3 25 doi 10.1186 1742-4682-3-25 Accepted 19 July 2006 This article is available from http www.tbiomed.cOm content 3 1 25 2006 Chou et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background The estimation of parameter values continues to be the bottleneck of the computational analysis of biological systems. It is therefore necessary to develop improved methods that are effective fast and scalable. Results We show here that alternating regression AR applied to S-system models and combined with methods for decoupling systems of differential equations provides a fast new tool for identifying parameter values from time series data. The key feature of AR is that it dissects the nonlinear inverse problem of estimating parameter values into iterative steps of linear regression. We show with several artificial examples that the method works well in many cases. In cases of no convergence it is feasible to dedicate some computational effort to identifying suitable start values and search settings because the method is fast in comparison to