Toolbox for analyzing finite two-state trajectories

In many experiments, the aim is to deduce an underlying multisubstate on-off kinetic scheme (KS) from the statistical properties of a two-state trajectory. However, a two-state trajectory that is generated from an on-off KS contains only partial information about the KS, and so, in many cases, more...

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Bibliographic Details
Main Authors: Flomenbom, O. (Author), Silbey, Robert J. (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Chemistry (Contributor)
Format: Article
Language:English
Published: American Physical Society, 2010-02-03T14:38:04Z.
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Online Access:Get fulltext
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042 |a dc 
100 1 0 |a Flomenbom, O.  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Chemistry  |e contributor 
100 1 0 |a Silbey, Robert J.  |e contributor 
100 1 0 |a Silbey, Robert J.  |e contributor 
700 1 0 |a Silbey, Robert J.  |e author 
245 0 0 |a Toolbox for analyzing finite two-state trajectories 
260 |b American Physical Society,   |c 2010-02-03T14:38:04Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/51348 
520 |a In many experiments, the aim is to deduce an underlying multisubstate on-off kinetic scheme (KS) from the statistical properties of a two-state trajectory. However, a two-state trajectory that is generated from an on-off KS contains only partial information about the KS, and so, in many cases, more than one KS can be associated with the data. We recently showed that the optimal way to solve this problem is to use canonical forms of reduced dimensions (RDs). RD forms are on-off networks with connections only between substates of different states, where the connections can have nonexponential waiting time probability density functions (WT-PDFs). In theory, only a single RD form can be associated with the data. To utilize RD forms in the analysis of the data, a RD form should be associated with the data. Here, we give a toolbox for building a RD form from a finite time, noiseless, two-state trajectory. The methods in the toolbox are based on known statistical methods in data analysis, combined with statistical methods and numerical algorithms designed specifically for the current problem. Our toolbox is self-contained-it builds a mechanism based only on the information it extracts from the data, and its implementation is fast (analyzing a 106 cycle trajectory from a 30-parameter mechanism takes a couple of hours on a PC with a 2.66 GHz processor). The toolbox is automated and is freely available for academic research upon electronic request. 
546 |a en_US 
655 7 |a Article 
773 |t Physical Review E