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10.1016/j.mbs.2016.08.004 http://scihub22266oqcxt.onion/10.1016/j.mbs.2016.08.004 C5706552!5706552 !27590775     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27590775 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Math+Biosci 2017 ; 287 (ä): 130-146 Nephropedia Template TP {{tp|p=27590775 |t=2017. Nonparametric dynamic modeling |pdf=|usr=}}{{27590775 }} gab.com Text Nonparametric dynamic modeling JO: Math Biosci http://www.kidney.de/pget.php?&tw=1&pmid=27590775 #FOAvip Challenging as it typically is, the estimation of parameter values seems to be an unavoidable step in the design and implementation of any dynamic model. Here, we demonstrate that it is possible to set up, diagnose, and simulate dynamic models without the need to estimate parameter values, if the situation is favorable. Specifically, it is possible to establish nonparametric models for nonlinear compartment models, including metabolic pathway models, if sufficiently many high-quality time series data are available that describe the biological phenomenon under investigation in an appropriate and representative manner. The proposed nonparametric strategy is a variant of the method of Dynamic Flux Estimation (DFE), which permits the estimation of numerical flux profiles from metabolic time series data. However, instead of attempting to formulate these numerical profiles as explicit functions and to optimize their parameter values, as it is done in DFE, the metabolite and flux profiles are used here directly as a scaffold for a library from which values are interpolated and retrieved for the simulation of the differential equations describing the model. Beyond simulations, the proposed methods render it possible to determine steady states from non-steady state data, perform sensitivity analyses, and estimate the Jacobian of the system at a steady state. Twit Text FOAVip Nonparametric dynamic modeling ????Math Biosci http://www.kidney.de/pget.php?&tw=1&pmid=27590775 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27590775 #FOAvip English Wikipedia <ref>{{Cite pmid|27590775 }}</ref> Nonparametric dynamic modeling #MMPMID27590775 Faraji M ; Voit EO Math Biosci 2017[May]; 287 (ä): 130-146 PMID27590775 show ga Challenging as it typically is, the estimation of parameter values seems to be an unavoidable step in the design and implementation of any dynamic model. Here, we demonstrate that it is possible to set up, diagnose, and simulate dynamic models without the need to estimate parameter values, if the situation is favorable. Specifically, it is possible to establish nonparametric models for nonlinear compartment models, including metabolic pathway models, if sufficiently many high-quality time series data are available that describe the biological phenomenon under investigation in an appropriate and representative manner. The proposed nonparametric strategy is a variant of the method of Dynamic Flux Estimation (DFE), which permits the estimation of numerical flux profiles from metabolic time series data. However, instead of attempting to formulate these numerical profiles as explicit functions and to optimize their parameter values, as it is done in DFE, the metabolite and flux profiles are used here directly as a scaffold for a library from which values are interpolated and retrieved for the simulation of the differential equations describing the model. Beyond simulations, the proposed methods render it possible to determine steady states from non-steady state data, perform sensitivity analyses, and estimate the Jacobian of the system at a steady state. |*Metabolic Networks and Pathways [MESH] |*Models, Biological [MESH] |*Statistics, Nonparametric [MESH]Nonparametric dynamic modeling (epmc).pdf DeepDyve Pubget Overpricing