r12350 - /website/conferences/ -- January 20, 2011 - 10:50

Author: bugman
Date: Thu Jan 20 10:50:03 2011
New Revision: 12350

URL: http://svn.gna.org/viewcvs/relax?rev=12350&view=rev
Log:
My abstract was a text file and not a PDF.

See http://www.nmr-relax.com/events.html.


Added:
    website/conferences/edward_abstract_ICMRBS_2006_Goettingen_Germany.txt
      - copied unchanged from r12348, 
website/conferences/edward_abstract_ICMRBS_2006_Goettingen_Germany.pdf
Removed:
    website/conferences/edward_abstract_ICMRBS_2006_Goettingen_Germany.pdf

Removed: 
website/conferences/edward_abstract_ICMRBS_2006_Goettingen_Germany.pdf
URL: 
http://svn.gna.org/viewcvs/relax/website/conferences/edward_abstract_ICMRBS_2006_Goettingen_Germany.pdf?rev=12349&view=auto
==============================================================================
--- website/conferences/edward_abstract_ICMRBS_2006_Goettingen_Germany.pdf 
(original)
+++ website/conferences/edward_abstract_ICMRBS_2006_Goettingen_Germany.pdf 
(removed)
@@ -1,11 +1,0 @@
-The program relax -- improving and adding some new twists to model-free 
analysis.
-
-Model-free analysis is a technique for studying the pico to nanosecond 
dynamics of individual atoms of a macromolecule.  It is used to convert the 
R_1 and R_2 relaxation rates and steady-state NOE value into physically 
interpretable numbers.  Through parametric restrictions a number of 
model-free models are created.  Starting with an initial estimate of the 
Brownian rotational diffusion tensor the data analysis steps are:  
optimisation of each model-free model (with the diffusion tensor fixed), 
selection of the best model, and then optimisation of the diffusion tensor.  
Using the optimised diffusion tensor as a starting point these three steps 
are iterated until convergence.
-
-The program relax (http://nmr-relax.com) is designed to implement every 
component of model-free analysis from the initial input of peak intensities 
to the final presentation of results.  This includes relaxation curve-fitting 
for obtaining the R_1 and R_2 rates (using Monte Carlo simulations for error 
analysis); calculation of the NOE and its error; optimisation of the 
model-free parameters, diffusion parameters, or both simultaneously; and 
model selection.  Reduced spectral density mapping is also implemented.  All 
results can be visualised by the creation of Molmol macros, 2D Grace plots, 
or 3D OpenDX maps.  The commonly used software packages Modelfree and Dasha 
can be used as drop-in optimisation engines.  The program relax, released 
under the GPL open source licence, has been written as a flexible, modular 
collection of data analysis tools.
-
-One improvement relax offers is in optimisation.  The Lorentzian spectral 
density functions, their linear recombinations to obtain the relaxation 
rates, the ratio of two rates to obtain the NOE, and the quadratic chi^2 
equation itself cause convolutions in the model-free chi-squared space.  
Providing numerous high quality minimisation algorithms and enabling high 
precision optimisation, relax allows the single minimum to be reached in all 
situations.  By comparing results a number of optimisation failures are 
identified in Modelfree and Dasha:  the singular matrix failure of the 
Levenberg-Marquardt minimisation algorithm, low precision, the inability to 
slide along limits, and a bug in the Modelfree code.
-
-Noise can destroy the true minimum of certain model-free models creating a 
new minimum at infinite correlation times.  The program relax implements a 
new mathematical modelling step between optimisation and model selection in 
which these failed models are removed -- model elimination.  For model 
selection all of the advanced techniques in d'Auvergne and Gooley (2003) 
JBNMR, 25, 25-39 are available.
-
-Finding the dynamics of the entire protein is much more complex than 
optimising the model-free parameters as the values are intricately linked to 
the Brownian rotational diffusion of the molecule, mathematically through the 
autocorrelation function and statistically through model selection.  A new 
methodology is presented which approaches the problem from the reverse 
direction -- rather than starting with the diffusion tensor this procedure 
begins by optimising the model-free parameters free of any global parameters, 
selecting between the model-free models, and finally optimising the diffusion 
tensor.  This methodology avoids all the problems of current approaches.