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10.1080/17513758.2015.1033022 http://scihub22266oqcxt.onion/10.1080/17513758.2015.1033022 C4408559!4408559 !25898769     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\25898769 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Biol+Dyn 2015 ; 9 (1 ): 125-46 Nephropedia Template TP {{tp|p=25898769 |t=2015. Bootstrapping least-squares estimates in biochemical reaction networks |pdf=|usr=}}{{25898769 }} gab.com Text Bootstrapping least-squares estimates in biochemical reaction networks JO: J Biol Dyn http://www.kidney.de/pget.php?&tw=1&pmid=25898769 #FOAvip The paper proposes new computational methods of computing confidence bounds for the least-squares estimates (LSEs) of rate constants in mass action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large-volume limit of a reaction network, to network's partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large-volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods. Twit Text FOAVip Bootstrapping least-squares estimates in biochemical reaction networks ????J Biol Dyn http://www.kidney.de/pget.php?&tw=1&pmid=25898769 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25898769 #FOAvip English Wikipedia <ref>{{Cite pmid|25898769 }}</ref> Bootstrapping least-squares estimates in biochemical reaction networks #MMPMID25898769 Linder DF ; Rempa?a GA J Biol Dyn 2015[]; 9 (1 ): 125-46 PMID25898769 show ga The paper proposes new computational methods of computing confidence bounds for the least-squares estimates (LSEs) of rate constants in mass action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large-volume limit of a reaction network, to network's partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large-volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods. |*Least-Squares Analysis [MESH] |Algorithms [MESH] |Biochemistry/methods [MESH] |Diffusion [MESH] |Kinetics [MESH] |Markov Chains [MESH] |Models, Biological [MESH] |Monte Carlo Method [MESH] |Normal Distribution [MESH]Bootstrapping least-squares estimates in biochemical reaction networks(epmc).pdf DeepDyve Pubget Overpricing