Mechanistic kinetic model for symmetric carboligations using benzaldehyde lyase

Zavrel, Michael; Schmidt, Timothy R.; Michalik, Claas; Ansorge‐Schumacher, Marion B.; Marquardt, Wolfgang; Büchs, Jochen; Spieß, Antje C.

doi:10.1002/bit.21867

Cited by 32 publications

(56 citation statements)

References 32 publications

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“…The lower and upper boundaries for the parameter values

k_{normali}

were 100 and 10,000 in respective units (min −1 or mM −1 min −1 ), and for the inactivation parameters 0.01 and 0.0001 in respective units (min −1 or mM −1 min −1 ). Thus, all microkinetic parameters describing a similar reaction system were within these boundaries. The residual sum of squares (RSS) of the reaction rates was calculated.…”

Section: Methodsmentioning

confidence: 89%

“…Model identification followed the strategy outlined in Figure . For all steps marked in dark gray, we established MATLAB routines.…”

Section: Methodsmentioning

confidence: 99%

“…The model equations were derived based on the proposed reaction mechanism of the Pf BAL‐catalyzed carboligation, an ordered bi‐uni‐mechanism, by setting up the mass balance of all reactants: substrate, product, free enzyme, and enzyme intermediates. Assuming steady‐state for the enzyme intermediates allows solving the equation system and obtaining reaction rate expressions.…”

Section: Methodsmentioning

confidence: 99%

“…In case of RSS values which were equal to the fourth decimal place, the parameters set with the best precision was chosen. The corresponding macrokinetic parameters of the best microkinetic model and their relative standard deviation was calculated using the error propagation equation:

σ_{K_{m a c r o}} = {(|, {false\sum}_{i = 1}^{p} {false\sum}_{j = 1}^{p} (|, \frac{\partial K_{m a c r o}}{\partial k_{i}} \frac{\partial K_{m a c r o}}{\partial k_{j}} v_{i j}))}^{1 / 2}

where

σ_{K_{m a c r o}}

is the standard deviation of the macro kinetic parameter K , and p is the number of microkinetic parameters k .…”

Section: Methodsmentioning

confidence: 99%

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Simultaneous identification of reaction and inactivation kinetics of an enzyme‐catalyzed carboligation

Ohs

Leipnitz

Schöpping

et al. 2018

Biotechnology Progress

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Thiamine diphosphate (ThDP)-dependent enzymes catalyze a broad range of reactions with excellent enantioselectivity. Among these reactions, carboligations of aldehydes are of particular interest since the products, chiral hydroxy ketones, are valuable building blocks in the pharmaceutical industry. However, the substrates, for example, benzaldehyde, inactivate the biocatalysts, for example the ThDP-dependent benzaldehyde lyase from Pseudomonas fluorescens (PfBAL). Because only few mechanistic kinetic models for carboligation and simultaneous inactivation are available today, we quantitatively determined the reaction kinetics and inactivation of the self-carboligation of benzaldehyde yielding the product (R)-benzoin catalyzed by PfBAL directly from progress curves using model-based experimental analysis. Discrimination of several inactivation models identified the substrate-dependent inactivation by benzaldehyde to be significant. Sensitivity analysis and optimal experimental design improved parameter precision significantly, to between 4 and 26% relative standard deviation while maintaining the necessary number of 13 experiments moderate. The developed mechanistic kinetic model will enable to perform a model-based process optimization to circumvent the substrate-dependent enzyme inactivation. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 2018 © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1081-1092, 2018.

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“…The lower and upper boundaries for the parameter values

k_{normali}

Section: Methodsmentioning

confidence: 89%

“…Model identification followed the strategy outlined in Figure . For all steps marked in dark gray, we established MATLAB routines.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

σ_{K_{m a c r o}} = {(|, {false\sum}_{i = 1}^{p} {false\sum}_{j = 1}^{p} (|, \frac{\partial K_{m a c r o}}{\partial k_{i}} \frac{\partial K_{m a c r o}}{\partial k_{j}} v_{i j}))}^{1 / 2}

where

σ_{K_{m a c r o}}

is the standard deviation of the macro kinetic parameter K , and p is the number of microkinetic parameters k .…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous identification of reaction and inactivation kinetics of an enzyme‐catalyzed carboligation

Ohs

Leipnitz

Schöpping

et al. 2018

Biotechnology Progress

Self Cite

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“…When properly validated, simplified mathematical models are capable of describing the dynamics of complex systems (Cruz Bournazou et al, 2012;Kokkalis et al, 2014;Legmann et al, 2009;Rosen et al, 2006;Zavrel et al, 2008). When properly validated, simplified mathematical models are capable of describing the dynamics of complex systems (Cruz Bournazou et al, 2012;Kokkalis et al, 2014;Legmann et al, 2009;Rosen et al, 2006;Zavrel et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Online optimal experimental re‐design in robotic parallel fed‐batch cultivation facilities

Bournazou

Barz

Nickel

et al. 2016

Biotech & Bioengineering

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We present an integrated framework for the online optimal experimental re-design applied to parallel nonlinear dynamic processes that aims to precisely estimate the parameter set of macro kinetic growth models with minimal experimental effort. This provides a systematic solution for rapid validation of a specific model to new strains, mutants, or products. In biosciences, this is especially important as model identification is a long and laborious process which is continuing to limit the use of mathematical modeling in this field. The strength of this approach is demonstrated by fitting a macro-kinetic differential equation model for Escherichia coli fed-batch processes after 6 h of cultivation. The system includes two fully-automated liquid handling robots; one containing eight mini-bioreactors and another used for automated at-line analyses, which allows for the immediate use of the available data in the modeling environment. As a result, the experiment can be continually re-designed while the cultivations are running using the information generated by periodical parameter estimations. The advantages of an online re-computation of the optimal experiment are proven by a 50-fold lower average coefficient of variation on the parameter estimates compared to the sequential method (4.83% instead of 235.86%). The success obtained in such a complex system is a further step towards a more efficient computer aided bioprocess development. Biotechnol. Bioeng. 2017;114: 610-619. © 2016 Wiley Periodicals, Inc.

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Organic Synthesis with Lyases

2022

Enzyme‐Based Organic Synthesis

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Mechanistic kinetic model for symmetric carboligations using benzaldehyde lyase

Cited by 32 publications

References 32 publications

Simultaneous identification of reaction and inactivation kinetics of an enzyme‐catalyzed carboligation

Simultaneous identification of reaction and inactivation kinetics of an enzyme‐catalyzed carboligation

Online optimal experimental re‐design in robotic parallel fed‐batch cultivation facilities

Organic Synthesis with Lyases

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