Study of kinetic parameters in a mechanistic model for enzymatic hydrolysis of sugarcane bagasse subjected to different pretreatments

Neto, João Moreira; Garcia, Danielle dos Santos; Rueda, Sandra Marcela Gómez; Costa, Aline Carvalho da

doi:10.1007/s00449-013-0930-6

Cited by 19 publications

(10 citation statements)

References 28 publications

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“…1-5) was integrated using the routine IVPRK (initial value problem, Runge-Kutta method) to obtain the concentration profiles of GLC, 5-HMF and LA. The same routine has already been used in a previous work (Neto et al 2013). Estimation of kinetic parameters using GA, coupled to the model, was made in a Intel Ò Core TM i7-4790 CPU@3.60 GHz.…”

Section: Fomentioning

confidence: 99%

Kinetic insights into the lignocellulosic biomass-based levulinic acid production by a mechanistic model

et al. 2020

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In this work, a mechanistic model was developed to simulate the kinetics of the production of levulinic acid (LA) from sugarcane bagasse (SCB), rice husk (RH) and soybean straw (SS). The production of LA from those agro-industrial wastes followed the methodology of biorefining in three stages. Experimental data from the third stage (catalytic depolymerization of cellulose) obtained under a wide range of operating conditions were used to estimate the parameters of the model. An optimization procedure based on a genetic algorithm was used to determine the optimal parameter values. The prediction of the concentrations of glucose, 5-HMF and LA using the mechanistic model was particularly accurate, as demonstrated by R 2 and RMSE. Thereby, a satisfactory concordance was reached between the high yields of LA of 61.1 mol%, 67.7 mol% and 61.4 mol% calculated by the model, and the experimental yields of 60.5 ± 2.1 mol%, 65.2 ± 2.9 mol% and 61.5 ± 4.0 mol% (under optimum conditions of 190°C, 7.0% w/v of H 2 SO 4 , 75 min) for SCB, RH and SS, respectively. The biorefining of the agro-industrial wastes under optimum operating conditions allowed a satisfactory catalytic depolymerization of cellulose, regardless of the degree of crystallinity. The estimation of yields led to suggesting a strategy from the point of view of process synthesis and design, integrating SCB, RH and SS to supply their off-season and, thus, to ensure the supply of raw materials in the production of LA.

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Section: Fomentioning

confidence: 99%

Kinetic insights into the lignocellulosic biomass-based levulinic acid production by a mechanistic model

et al. 2020

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“…The enzymatic parameters of Cel3A and Cel3B were obtained experimentally (Nelson, Rogowski, et al, 2017) and the rest of the parameters in the model were inferred computationally. We developed an inference methodology based on genetic algorithms—an heuristic optimization approach popular for the optimization of metabolic models (López‐Pérez, Puebla, Velázquez Sánchez, & Aguilar‐López, 2016; Neto, Dos Reis Garcia, Rueda, & Da Costa, 2013; Shadbahr, Zhang, Khan, & Hawboldt, 2018)—and a dynamic simulator of metabolic models including multiple substrates and gene deletions. We employed this algorithm to infer a single set of parameters that could recapitulate the microbial growth and metabolite changes from a series of batch cultures that used different carbon sources for both wild‐type and mutant strains.…”

Section: Resultsmentioning

confidence: 99%

Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β‐glucosidase function in Cellvibrio japonicus

Hwang

Hari

Cheng

et al. 2020

Biotech & Bioengineering

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Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in β-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industriallyuseful strains. Importantly, the model is able to explain the role of the different β-glucosidases and their behavior under genetic perturbations. This integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.

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“…These models could be used to enable the implementation of suitable operating strategies to achieve high operational performance . Most of the mechanistic models are based on inhibition of the Michaelis–Menten models . Models showing the enzyme adsorption step, enzyme deactivation and adsorption of the enzyme by lignin were also proposed.…”

Section: Introductionmentioning

confidence: 99%

“…8 Most of the mechanistic models are based on inhibition of the Michaelis-Menten models. [9][10][11] Models showing the enzyme adsorption step, 9,[12][13][14][15] enzyme deactivation 16 and adsorption of the enzyme by lignin 9,12,17 were also proposed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of overall glucose yield in hydrolysis of pretreated sugarcane bagasse using a single artificial neural network: good insight for process development

Tovar

Rivera

Mariano

et al. 2017

J of Chemical Tech & Biotech

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BACKGROUND In this work a single artificial neural network (ANN) was used to model the overall yield of glucose (YGLC) as a function of a wide range of operating conditions of both pretreatment and enzymatic hydrolysis. RESULTS The model was validated experimentally and presented good predictions of YGLC. Sensitivity analysis using the ANN model indicated that most of the operating parameters, except for pretreatment time, were statistically significant (P‐value <0.05). Experiments showed that the processing of sugarcane bagasse (in natura) results in a satisfactory glucose yield of 69.34% when pretreated for 60 min with low initial biomass concentration and acid concentration (10% and 1.0% w/v), and followed by enzymatic hydrolysis for 72 h with 3.0% w/v substrate loading and 60 FPU per gWIS enzyme concentration. CONCLUSION This study demonstrated how pretreatment and enzymatic hydrolysis data can be used to parameterize a single ANN model. Acceptable predictions of YGLC are achieved in terms of RSD, MSE and R2. Supported by the model, this study provided a good insight for process development. © 2017 Society of Chemical Industry

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Study of kinetic parameters in a mechanistic model for enzymatic hydrolysis of sugarcane bagasse subjected to different pretreatments

Cited by 19 publications

References 28 publications

Kinetic insights into the lignocellulosic biomass-based levulinic acid production by a mechanistic model

Kinetic insights into the lignocellulosic biomass-based levulinic acid production by a mechanistic model

Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β‐glucosidase function in Cellvibrio japonicus

Prediction of overall glucose yield in hydrolysis of pretreated sugarcane bagasse using a single artificial neural network: good insight for process development

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