Application of Uncertainty and Sensitivity Analysis to a Kinetic Model for Enzymatic Biodiesel Production

Price, Jason Anthony; Nordblad, Mathias; Woodley, John M.; Huusom, Jakob Kjøbsted

doi:10.3182/20131216-3-in-2044.00060

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…Samukawa and co-workers found that they can increase the reuse of the immobilised enzyme (a clear indication of a reduction in enzyme deactivation), by using a stepwise feeding strategy. This kept the methanol content in the reactor below the concentration that gave the highest initial rate of FAME production (Samukawa et al 2000). Hence we wished to extend their work by actually being able to maintain the concentration of methanol in the reactor ({CH critical }) that gave the best initial rate, at each time increment t i , by minimizing the objective function in (4).…”

Section: Methanol Feeding Optimizationmentioning

confidence: 99%

“…As in our previous work (Price et al 2013), the Monte Carlo method was used to propagate the uncertainty of the kinetic parameters on the output (prediction) uncertainty of the model (Sin et al 2009). The confidence intervals from the parameter fitting is used to specify the input uncertainty in the parameter estimates and Latin hypercube sampling with correlation control is used for sampling of the parameters in the sample parameter space (Helton and Davis 2003).…”

Section: Uncertainty Analysismentioning

confidence: 99%

“…If the biocatalyst is to be reused, one challenge is mitigating the effects of inhibition and deactivation of the enzyme by the methanol substrate. To overcome the effects due to the methanol, researchers employ a stepwise feeding of methanol to the reactor (Samukawa et al 2000, Lv et al 2010, Du et al 2005. However the methods that are employed are far from optimal.…”

Section: Introductionmentioning

confidence: 99%

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Fed-Batch Feeding Strategies for Enzymatic Biodiesel Production

Price

Nordblad

Woodley

et al. 2014

IFAC Proceedings Volumes

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Abstract:In this work a kinetic model for the enzymatic transesterification of rapeseed oil using a solubilised lipase (Callera Trans L-Thermomyces lanuginosus) was developed from first principles. The model is based on a Ping-Pong Bi-Bi mechanism, with methanol inhibition, along with consideration of the differences in the interfacial and bulk concentrations of the enzyme. The model is then used to evaluate various feeding strategies to improve the enzymatic biodiesel production. The feeding strategies investigated, gave insight into how the methanol should be fed to potentially mitigate enzyme deactivation while improving the biodiesel yield. The best experimental results gave a yield of 703.76 g FAME L -1 and a reactor productivity of 28.12 g FAME L -1 h -1 . In comparison, to reach the same yield, the optimised two step feeding strategy took 6.25 hours less, which equates to an increase the reactor productivity of 36.9 %.

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Section: Methanol Feeding Optimizationmentioning

confidence: 99%

Section: Uncertainty Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fed-Batch Feeding Strategies for Enzymatic Biodiesel Production

Price

Nordblad

Woodley

et al. 2014

IFAC Proceedings Volumes

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“…The sample number was set at 1,000 (chosen for consistency with past studies; this was sufficient to obtain a good sampling rate whilst not overloading computing resources). For more information on the use of uncertainty and sensitivity analysis in process modelling please see the following references (76,77).…”

Section: Mathematical Modellingmentioning

confidence: 99%

Engineering a seven enzyme biotransformation using mathematical modelling and characterized enzyme parts

Finnigan

Cutlan

Šnajdrová

et al. 2019

Preprint

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Multi-step enzyme reactions offer considerable cost and productivity benefits. Process models offer a route to understanding the complexity of these reactions, and allow for their optimization. Despite the increasing prevalence of multi-step biotransformations, there are few examples of process models for enzyme reactions. From a toolbox of characterized enzyme parts, we demonstrate the construction of a process model for a seven enzyme, three step biotransformation using isolated enzymes. Enzymes for cofactor regeneration were employed to make this in vitro reaction economical. Good modelling practice was critical in evaluating the impact of approximations and experimental error. We show that the use and validation of process models was instrumental in realizing and removing process bottlenecks, identifying divergent behavior, and for the optimization of the entire reaction using a genetic algorithm. We validated the optimized reaction to demonstrate that complex multi-step reactions with cofactor recycling involving at least seven enzymes can be reliably modelled and optimized. Significance statementThis study examines the challenge of modeling and optimizing multi-enzyme cascades. We detail the development, testing and optimization of a deterministic model of a three enzyme cascade with four cofactor regeneration enzymes. Significantly, the model could be easily used to predict the optimal concentrations of each enzyme in order to get maximum flux through the cascade. This prediction was strongly validated experimentally. The success of our model demonstrates that robust models of systems of at least seven enzymes are † Present address: Manchester readily achievable. We highlight the importance of following good modeling practice to evaluate model quality and limitations. Examining deviations from expected behavior provided additional insight into the model and enzymes. This work provides a template for developing larger deterministic models of enzyme cascades. phosphotransferase (PAP), which allows the regeneration of ADP from AMP (24). The combination of PAP and PPK therefore allowed complete regeneration of ATP from AMP (25). In an alternative approach, adenylate kinase (which catalyzes the reversible phosphorylation of AMP by ATP) was used in place of PPK. Coupling of this enzyme with PAP pushes the equilibrium towards ATP regeneration ( Figure 1B) (26). The application of process modellingSynthetic biology has long promised the use of well-characterized parts for the rational design of new metabolic pathways (27)(28)(29). However the use of modelling to optimize these in vivo reactions is yet to be fully realized (30). In contrast, modelling of enzymes in vitro can be fairly robust and offers solutions for reaction engineering (31). This could allow the combination of enzymes, for which validated mathematical models exist, to be rapidly engineered and tested in silico (2).

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“…Nevertheless, these methods have turned out to be useful tools, sometimes resulting in fast solutions and they are already implemented in functions (e.g., in Matlab or R), which are easy to apply. They can be combined with statistical methods like Monte Carlo (MC) simulation, sensitivity, uncertainty, and/or identifiability analysis, in order simulate output uncertainty and to gain more information of the process (Hines, ; Price, Nordblad, Woodley, & Huusom, ; Raue et al, ; Sin et al, ).…”

Section: Introductionmentioning

confidence: 99%

Predicting industrial‐scale cell culture seed trains–A Bayesian framework for model fitting and parameter estimation, dealing with uncertainty in measurements and model parameters, applied to a nonlinear kinetic cell culture model, using an MCMC method

Rodríguez

Posch

Schmutzhard

et al. 2019

Biotech & Bioengineering

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For production of biopharmaceuticals in suspension cell culture, seed trains are required to increase cell number from cell thawing up to production scale. Because cultivation conditions during the seed train have a significant impact on cell performance in production scale, seed train design, monitoring, and development of optimization strategies is important. This can be facilitated by model‐assisted prediction methods, whereby the performance depends on the prediction accuracy, which can be improved by inclusion of prior process knowledge, especially when only few high‐quality data is available, and description of inference uncertainty, providing, apart from a “best fit”‐prediction, information about the probable deviation in form of a prediction interval. This contribution illustrates the application of Bayesian parameter estimation and Bayesian updating for seed train prediction to an industrial Chinese hamster ovarian cell culture process, coppled with a mechanistic model. It is shown in which way prior knowledge as well as input uncertainty (e.g., concerning measurements) can be included and be propagated to predictive uncertainty. The impact of available information on prediction accuracy was investigated. It has been shown that through integration of new data by the Bayesian updating method, process variability (i.e., batch‐to‐batch) could be considered. The implementation was realized using a Markov chain Monte Carlo method.

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Application of Uncertainty and Sensitivity Analysis to a Kinetic Model for Enzymatic Biodiesel Production

Cited by 4 publications

References 17 publications

Fed-Batch Feeding Strategies for Enzymatic Biodiesel Production

Fed-Batch Feeding Strategies for Enzymatic Biodiesel Production

Engineering a seven enzyme biotransformation using mathematical modelling and characterized enzyme parts

Predicting industrial‐scale cell culture seed trains–A Bayesian framework for model fitting and parameter estimation, dealing with uncertainty in measurements and model parameters, applied to a nonlinear kinetic cell culture model, using an MCMC method

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