Identification of biomolecule mass transport and binding rate parameters in living cells by inverse modeling
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Identification of biomolecule mass transport and binding rate parameters in living cells by inverse modeling Kouroush Sadegh Zadeh*, Hubert J Montas and Adel Shirmohammadi Address: Fischell Department of Bioengineering, University of Maryland, College Park, Maryland 20742, USA Email: Kouroush Sadegh Zadeh* - [email protected]; Hubert J Montas - [email protected]; Adel Shirmohammadi - [email protected] * Corresponding author
Published: 11 October 2006 Theoretical Biology and Medical Modelling 2006, 3:36
doi:10.1186/1742-4682-3-36
Received: 29 August 2006 Accepted: 11 October 2006
This article is available from: http://www.tbiomed.com/content/3/1/36 © 2006 Sadegh Zadeh et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract Background: Quantification of in-vivo biomolecule mass transport and reaction rate parameters from experimental data obtained by Fluorescence Recovery after Photobleaching (FRAP) is becoming more important. Methods and results: The Osborne-Moré extended version of the Levenberg-Marquardt optimization algorithm was coupled with the experimental data obtained by the Fluorescence Recovery after Photobleaching (FRAP) protocol, and the numerical solution of a set of two partial differential equations governing macromolecule mass transport and reaction in living cells, to inversely estimate optimized values of the molecular diffusion coefficient and binding rate parameters of GFP-tagged glucocorticoid receptor. The results indicate that the FRAP protocol provides enough information to estimate one parameter uniquely using a nonlinear optimization technique. Coupling FRAP experimental data with the inverse modeling strategy, one can also uniquely estimate the individual values of the binding rate coefficients if the molecular diffusion coefficient is known. One can also simultaneously estimate the dissociation rate parameter and molecular diffusion coefficient given the pseudo-association rate parameter is known. However, the protocol provides insufficient information for unique simultaneous estimation of three parameters (diffusion coefficient and binding rate parameters) owing to the high intercorrelation between the molecular diffusion coefficient and pseudo-association rate parameter. Attempts to estimate macromolecule mass transport and binding rate parameters simultaneously from FRAP data result in misleading conclusions regarding concentrations of free macromolecule and bound complex inside the cell, average binding time per vacant site, average time for diffusion of macromolecules from one site to the next, and slow or rapid mobility of biomolecules in cells. Conclusion: To obtain unique values for molecular diffusion coefficient and binding rate parameters from FRAP data, we propose conducting
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