Extended Linear Response for Bioanalytical Applications Using Multiple Enzymes

Privman, Vladimir; Zavalov, Oleksandr; Simonian, Aleksandr

doi:10.1021/ac302998y

Cited by 7 publications

(19 citation statements)

References 42 publications

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“…Here, we develop a model, using rate equations that are typically used to model different chemical and biochemical processes, [16][17][18][19][20] to describe cellular dynamics during viral infection.…”

Section: Introductionmentioning

confidence: 99%

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

Domanskyi

Schilling

Gorshkov

et al. 2016

The Journal of Chemical Physics

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We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of "stiff" equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

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Section: Introductionmentioning

confidence: 99%

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

Domanskyi

Schilling

Gorshkov

et al. 2016

The Journal of Chemical Physics

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“…The author thanks his numerous colleagues, 1,2,3,11,22,23,[31][32][33][34][35][36][37]47,[52][53][54][55][56][57][58][59][76][77][78][79]91,99 notably, Prof. E. Katz, for collaborative team work. He gratefully acknowledges funding of the work reviewed here by the US National Science Foundation, most recently under grant CBET-1403208.…”

Section: Acknowledgementsmentioning

confidence: 99%

Theoretical modeling expressions for networked enzymatic signal processing steps as logic gates optimized by filtering

Privman

2016

International Journal of Parallel, Emergent and Distributed Sys

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We describe modeling approaches to a "network" of connected enzyme-catalyzed reactions, with added (bio)chemical processes that introduce biochemical filtering steps into the functioning of such a biocatalytic cascade. Theoretical expressions are derived that allow simple, few-parameter modeling of processes concatenated in such cascades, both with and without filtering. The modeling approach captures and explains features identified in earlier studies of enzymatic processes considered as potential "network components" for multi-step information/signal processing systems.

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“…The latter can be partially attributed to variations in and also degradation of the enzyme activity. This is typical for such enzymatic cascades, and underscores the value of developing few-parameter models and the preference [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] for a linear response, Scheme 1(b). Therefore, in our presentation of the results we focus on a single set of data, to minimize the effect of the enzyme degradation, taken continuously by varying (increasing) the input signal (glucose) first without the "filter" and then with it.…”

Section: Considerations For Data Fittingmentioning

confidence: 99%

“…Recent work has involved 30,31 data analysis to develop a theoretical understanding of how two enzymatic processes with different nonlinear responses can be combined to yield an extended linear response regime.…”

Section: Introductionmentioning

confidence: 99%

Kinetic Model for a Threshold Filter in an Enzymatic System for Bioanalytical and Biocomputing Applications

Privman¹,

Domanskyi²,

Mailloux³

et al. 2014

J. Phys. Chem. B

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A recently experimentally observed biochemical "threshold filtering" mechanism by processes catalyzed by the enzyme malate dehydrogenase is explained in terms of a model that incorporates an unusual mechanism of inhibition of this enzyme that has a reversible mechanism of action. Experimental data for a system in which the output signal is produced by biocatalytic processes of the enzyme glucose dehydrogenase are analyzed to verify the model's validity. We also establish that fast reversible conversion of the output product to another compound, without the additional inhibition, cannot on its own result in filtering.

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Extended Linear Response for Bioanalytical Applications Using Multiple Enzymes

Cited by 7 publications

References 42 publications

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

Theoretical modeling expressions for networked enzymatic signal processing steps as logic gates optimized by filtering

Kinetic Model for a Threshold Filter in an Enzymatic System for Bioanalytical and Biocomputing Applications

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