Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling

Abstract

-Neural networks and hybrid models were used to study substrate and product inhibition observed in the enzymatic hydrolysis of cellobiose at 40ºC, 50ºC and 55ºC, pH 4.8, using cellobiose solutions with or without the addition of exogenous glucose. Firstly, the initial velocity method and nonlinear fitting with Statistica ® were used to determine the kinetic parameters for either the uncompetitive or the competitive substrate inhibition model at a negligible product concentration and cellobiose from 0.4 to 2.0 g/L. Secondly, for six different models of substrate and product inhibitions and data for low to high cellobiose conversions in a batch reactor, neural networks were used for fitting the product inhibition parameter to the mass balance equations derived for each model. The two models found to be best were: 1) noncompetitive inhibition by substrate and competitive by product and 2) uncompetitive inhibition by substrate and competitive by product; however, these models' correlation coefficients were quite close. To distinguish between them, hybrid models consisting of neural networks and first principle equations were used to select the best inhibition model based on the smallest norm observed, and the model with noncompetitive inhibition by substrate and competitive inhibition by product was shown to be the best predictor of cellobiose hydrolysis reactor behavior.

Análise Do Potencial Fermentativo De Leveduras Isoladas De Latícinio Em Fontes De Carbono Alternativas

“…É conhecido que as enzimas glicosidases (β-glicosidases) convertem moléculas de celobiose em duas moléculas de glicose (Corazza et al, 2005;Zhang et al, 2006;Zhang e LYND, 2004). Logo, leveduras que possuam enzimas capazes de hidrolisar celobiose são promissoras para aplicações na fermentação de materiais lignocelulósicos, eliminando a etapa de hidrólise prévia à fermentação, pois o microrganismo mais frequentemente utilizado para a fermentação de etanol em processos industriais é a Saccharomycescerevisiae, que fermenta materiais lignocelulósicos desde que estes sejam previamente hidrolisados.…”

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

“…van Can et al (1996). In Corazza, Calsavara, Moraes, Zanin, and Neitzel (2005), the fact that the serial model will perform best when the provided structure is close to the "true" underlying structure is used to infer mechanistic knowledge.…”

“…The under-lying biological system -the cell, which houses highly complex chemical reaction networks and transport mechanisms, is usually modeled assuming lumped kinetics. Along with these, biomass is considered to be a catalyst to the reactions and then either specific kinetic rates are directly modeled by nonparametric techniques or after some knowledge has been incorporated only the "miss-ing" parts are represented by nonparametric models (Al-Yemni, 2003;Corazza et al, 2005;Fu & Barford, 1995a;Kasprow, 2000;Mazutti et al, 2010). The incorporation of additional knowledge can, as described above, improve the model properties, especially when the knowledge structures the model such as stoichiometric coefficients, wherefore current efforts reach out to integrate knowledge from systems biology .…”

Comparative Study between Neural Network Model and Mathematical Models for Prediction of Glucose Concentration during Enzymatic Hydrolysis

“…The advantages of enzymatic hydrolysis of cellulose over other hydrolysis methods such as acid hydrolysis are lower utility ( cooling water, gas, electricity ) and disposal costs and no corrosion issues for the equipments [2]. The enzyme system for conversion of cellulose to glucose comprise endo-1,4-βglucanase, cellobiohydrolase and β-glucosidase ( also called cellobiase), which act sequentially and cooperatively to degrade crystalline cellulose to glucose [3]. In approaching the process design for the enzymatic hydrolysis of celluloses, it is important to be able to predict processing outputs in response to different input variables, such as substrate initial concentration, enzyme activity and hydrolysis time.…”