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10.1016/j.bpj.2017.01.011 http://scihub22266oqcxt.onion/10.1016/j.bpj.2017.01.011 C5355488!5355488 !28297646     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 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\28297646 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biophys+J 2017 ; 112 (5 ): 868-880 Nephropedia Template TP {{tp|p=28297646 |t=2017. Inferring Mechanistic Parameters from Amyloid Formation Kinetics by Approximate Bayesian Computation |pdf=|usr=}}{{28297646 }} gab.com Text Inferring Mechanistic Parameters from Amyloid Formation Kinetics by Approximate Bayesian Computation JO: Biophys J http://www.kidney.de/pget.php?&tw=1&pmid=28297646 #FOAvip Amyloid formation is implicated in a number of human diseases, and is thought to proceed via a nucleation-dependent polymerization mechanism. Experimenters often wish to relate changes in amyloid formation kinetics, for example, in response to small molecules to specific mechanistic steps along this pathway. However, fitting kinetic fibril formation data to a complex model including explicit rate constants results in an ill-posed problem with a vast number of potential solutions. The levels of uncertainty remaining in parameters calculated from these models, arising both from experimental noise and high levels of degeneracy or codependency in parameters, is often unclear. Here, we demonstrate that a combination of explicit mathematical models with an approximate Bayesian computation approach can be used to assign the mechanistic effects of modulators on amyloid fibril formation. We show that even when exact rate constants cannot be extracted, parameters derived from these rate constants can be recovered and used to assign mechanistic effects and their relative magnitudes with a great deal of confidence. Furthermore, approximate Bayesian computation provides a robust method for visualizing uncertainty remaining in the model parameters, regardless of its origin. We apply these methods to the problem of heparin-mediated tau polymerization, which displays complex kinetic behavior not amenable to analysis by more traditional methods. Our analysis indicates that the role of heparin cannot be explained by enhancement of nucleation alone, as has been previously proposed. The methods described here are applicable to a wide range of systems, as models can be easily adapted to account for new reactions and reversibility. Twit Text FOAVip Inferring Mechanistic Parameters from Amyloid Formation Kinetics by Approximate Bayesian Computation ????Biophys J http://www.kidney.de/pget.php?&tw=1&pmid=28297646 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28297646 #FOAvip English Wikipedia <ref>{{Cite pmid|28297646 }}</ref> Inferring Mechanistic Parameters from Amyloid Formation Kinetics by Approximate Bayesian Computation #MMPMID28297646 Nakatani-Webster E ; Nath A Biophys J 2017[Mar]; 112 (5 ): 868-880 PMID28297646 show ga Amyloid formation is implicated in a number of human diseases, and is thought to proceed via a nucleation-dependent polymerization mechanism. Experimenters often wish to relate changes in amyloid formation kinetics, for example, in response to small molecules to specific mechanistic steps along this pathway. However, fitting kinetic fibril formation data to a complex model including explicit rate constants results in an ill-posed problem with a vast number of potential solutions. The levels of uncertainty remaining in parameters calculated from these models, arising both from experimental noise and high levels of degeneracy or codependency in parameters, is often unclear. Here, we demonstrate that a combination of explicit mathematical models with an approximate Bayesian computation approach can be used to assign the mechanistic effects of modulators on amyloid fibril formation. We show that even when exact rate constants cannot be extracted, parameters derived from these rate constants can be recovered and used to assign mechanistic effects and their relative magnitudes with a great deal of confidence. Furthermore, approximate Bayesian computation provides a robust method for visualizing uncertainty remaining in the model parameters, regardless of its origin. We apply these methods to the problem of heparin-mediated tau polymerization, which displays complex kinetic behavior not amenable to analysis by more traditional methods. Our analysis indicates that the role of heparin cannot be explained by enhancement of nucleation alone, as has been previously proposed. The methods described here are applicable to a wide range of systems, as models can be easily adapted to account for new reactions and reversibility. |*Models, Molecular [MESH] |*Protein Multimerization [MESH] |Amyloid/*chemistry [MESH] |Bayes Theorem [MESH] |Kinetics [MESH] |Peptide Fragments/chemistry [MESH] |Protein Structure, Secondary [MESH]Inferring Mechanistic Parameters from Amyloid Formation Kinetics by Ap(epmc).pdf DeepDyve Pubget Overpricing