### Abstract

Data matrices consisting of sample optical absorption as a function of wavelength and another variable, such as time, are decomposable using known matrix algebraic methods. The natural decomposition, based on the Beer - Lambert law, into the product of component absorbance and (time dependent) concentration matrices is usually not straightforward. Singular value decomposition yields orthonormal spectral and kinetic eigenvectors, with mathematical but not physical meaning. The connection of the two decompositions is explored with reference to the problem of the bacteriorhodopsin photocycle. The limitations and applicability of singular value decomposition with self-modeling is evaluated with known stoichiometric constraints on the intermediate kinetics and compared to other techniques applied to the same problem. The improved method of exponential fit assisted self-modeling is introduced and demonstrated on realistic simulated data.

Original language | English |
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Pages (from-to) | 4199-4209 |

Number of pages | 11 |

Journal | Journal of Physical Chemistry B |

Volume | 108 |

Issue number | 13 |

Publication status | Published - Apr 1 2004 |

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### ASJC Scopus subject areas

- Physical and Theoretical Chemistry

### Cite this

**Analysis of the Bacteriorhodopsin Photocycle by Singular Value Decomposition with Self-Modeling : A Critical Evaluation Using Realistic Simulated Data.** / Zimányi, L.

Research output: Contribution to journal › Article

}

TY - JOUR

T1 - Analysis of the Bacteriorhodopsin Photocycle by Singular Value Decomposition with Self-Modeling

T2 - A Critical Evaluation Using Realistic Simulated Data

AU - Zimányi, L.

PY - 2004/4/1

Y1 - 2004/4/1

N2 - Data matrices consisting of sample optical absorption as a function of wavelength and another variable, such as time, are decomposable using known matrix algebraic methods. The natural decomposition, based on the Beer - Lambert law, into the product of component absorbance and (time dependent) concentration matrices is usually not straightforward. Singular value decomposition yields orthonormal spectral and kinetic eigenvectors, with mathematical but not physical meaning. The connection of the two decompositions is explored with reference to the problem of the bacteriorhodopsin photocycle. The limitations and applicability of singular value decomposition with self-modeling is evaluated with known stoichiometric constraints on the intermediate kinetics and compared to other techniques applied to the same problem. The improved method of exponential fit assisted self-modeling is introduced and demonstrated on realistic simulated data.

AB - Data matrices consisting of sample optical absorption as a function of wavelength and another variable, such as time, are decomposable using known matrix algebraic methods. The natural decomposition, based on the Beer - Lambert law, into the product of component absorbance and (time dependent) concentration matrices is usually not straightforward. Singular value decomposition yields orthonormal spectral and kinetic eigenvectors, with mathematical but not physical meaning. The connection of the two decompositions is explored with reference to the problem of the bacteriorhodopsin photocycle. The limitations and applicability of singular value decomposition with self-modeling is evaluated with known stoichiometric constraints on the intermediate kinetics and compared to other techniques applied to the same problem. The improved method of exponential fit assisted self-modeling is introduced and demonstrated on realistic simulated data.

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UR - http://www.scopus.com/inward/citedby.url?scp=1842663302&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:1842663302

VL - 108

SP - 4199

EP - 4209

JO - Journal of Physical Chemistry B Materials

JF - Journal of Physical Chemistry B Materials

SN - 1520-6106

IS - 13

ER -