Neural networks as `software sensors' in enzyme production

Linko, Susan; Luopa, J.; Zhu, Y.-H.

doi:10.1016/s0168-1656(96)01650-1

Cited by 62 publications

(29 citation statements)

References 19 publications

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“…11 and 12, the accuracy of parameter estimation by the MCMC method was based on the fit of the simulated and measured values of the six variables in the training data-set. A simple regression analysis was used to assess the accuracy for each variable, 46 of which 91 pairs of the simulated and measured values (7 cell culture runs Â 13 days) were compared. To evaluate the applicability, the developed model was then applied to seven different cell culture runs in 5,000 L-bioreactors to predict the time profiles of the six variables (Validation 1).…”

Section: Reliability Assessments Of the Kinetic Modelmentioning

confidence: 99%

Modeling kinetics of a large‐scale fed‐batch CHO cell culture by Markov chain Monte Carlo method

Xing

Bishop

Leister

et al. 2009

Biotechnology Progress

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Markov chain Monte Carlo (MCMC) method was applied to model kinetics of a fed-batch Chinese hamster ovary cell culture process in 5,000-L bioreactors. The kinetic model consists of six differential equations, which describe dynamics of viable cell density and concentrations of glucose, glutamine, ammonia, lactate, and the antibody fusion protein B1 (B1). The kinetic model has 18 parameters, six of which were calculated from the cell culture data, whereas the other 12 were estimated from a training data set that comprised of seven cell culture runs using a MCMC method. The model was confirmed in two validation data sets that represented a perturbation of the cell culture condition. The agreement between the predicted and measured values of both validation data sets may indicate high reliability of the model estimates. The kinetic model uniquely incorporated the ammonia removal and the exponential function of B1 protein concentration. The model indicated that ammonia and lactate play critical roles in cell growth and that low concentrations of glucose (0.17 mM) and glutamine (0.09 mM) in the cell culture medium may help reduce ammonia and lactate production. The model demonstrated that 83% of the glucose consumed was used for cell maintenance during the late phase of the cell cultures, whereas the maintenance coefficient for glutamine was negligible. Finally, the kinetic model suggests that it is critical for B1 production to sustain a high number of viable cells. The MCMC methodology may be a useful tool for modeling kinetics of a fed-batch mammalian cell culture process.

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Section: Reliability Assessments Of the Kinetic Modelmentioning

confidence: 99%

Modeling kinetics of a large‐scale fed‐batch CHO cell culture by Markov chain Monte Carlo method

Xing

Bishop

Leister

et al. 2009

Biotechnology Progress

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“…NN modeling has been used in the prediction of the thermal conductivity of bakery products Sablani et al, (2002) dough rheological properties (Ruan et al, 1995), physical properties of ground wheat Fang et al, (1998) thermal conductivity of fruits and vegetables (Hussain and Rahman, 1999) antioxidant capacity of cruciferous sprouts (Bucinski et al, 2004) and more recently esterification of anthranilic acid with methanol (Manohar and Divakar, 2005). There are papers about the use of neural networks in enzymology (Linko and Zhu, 1992;Linko et al, 1997;Zhu et al, 1996). (Baş et al, 2007) investigated the use of artificial neural networks (ANN) for the estimation of the enzymatic reaction rate for amyloglucosidase hydrolysed maltose.…”

Section: Introductionmentioning

confidence: 99%

Prediction of a model enzymatic acidolysis system using neural networks

Ciftci¹,

Fadı́loğlu²,

Göğüş³

et al. 2008

Grasas y Aceites

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RESUMEN Predicción de un modelo de acidólisis enzimática usando redes neuronalesEn este estudio se presenta un modelo para la acidólisis de la trilinoleina y el ácido palmítico mediante la catálisis con una lipasa específica sn-1,3 inmovilizada. Un modelo basado en redes neuronales (NN) ha sido desarrollado para la predicción de la concentración de los principales productos de esta reacción (1-palmitoil-2,3-oleoil-glicerol (POO), 1,3-dipalmitoil-2-oleoil-glicerol (POP) y trioleina (OOO)). Se han usado como parámetros de entrada: la proporción del sustrato (SR), la temperatura de reacción (T) y el tiempo de reacción (t). La arquitectura óptima del modelo de NN propuesto, que consiste en una capa de entrada con tres entradas, una capa oculta con siete neuronas y una capa de salida con tres salidas, fue capaz de predecir la concentración de los productos de reacción con un error cuadrático medio (MSE) de menos de 1.5 y una R 2 de 0.999 . Se presenta una formulación explícita del modelo NN propuesto. Se obtienen muy buenos resultados en la predicción de la reacciones de acidólisis mediante el uso de las redes neuronales.PALABRAS CLAVE: Acidólisis -Lipasa específica sn-1 -Modelo explícito -Redes neuronales. SUMMARY Prediction of a model enzymatic acidolysis system using neural networksA model for the acidolysis of triolein and palmitic acid under the catalysis of immobizied sn-1,3 specific lipase was presented in this study. A neural networks (NN) based model was developed for the prediction of the concentrations of the major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO), 1,3-dipalmitoyl-2-oleoyl-glycerol (POP) and triolein (OOO)). Substrate ratio (SR), reaction temperature (T) and reaction time (t) were used as input parameters. The optimal architecture of the proposed NN model, which consists of one input layer with three inputs, one hidden layer with seven neurons and one output layer with three outputs, was able to predict the reaction products concentration with a mean square error (MSE) of less than 1.5 and R 2 of 0.999. An explicit formulation of the proposed NN model is presented. Considerable good performance is achieved in modelling the acidolysis reaction using neural networks.KEY-WORDS: Acidolysis -Explicit modeling -Neural networks -sn-1,3 Specific lipase.

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“…The literature concerning on-line estimators for bioreactors is very extensive, for instance, see [25,26]. Nevertheless, the test and comparison of different algorithms, employing real on-line data, is always worthwhile.…”

Section: Introductionmentioning

confidence: 99%

Comparing the performance of multilayer perceptrons networks and neuro-fuzzy systems for on-line inference of Bacillus megaterium cellular concentrations

Nucci

Silva

Souza

et al. 2007

Bioprocess Biosyst Eng

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Penicillin G acylase (PGA) is one of the most important enzymes for the pharmaceutical industry. Bacillus megaterium has the advantage of producing extra-cellular PGA. This work compares two neural networks (NNs) architectures for on-line inference of B. megaterium cell mass in an aerated stirred tank bioreactor, during the production of PGA. Nowadays, intelligent computing tools such as artificial NNs and fuzzy logic are commonly applied for state inference and modeling of bioreactors. Combining these two approaches in hybrid, neuro-fuzzy systems, may be advantageous. Our results indicate that a neuro-fuzzy inference system showed a better performance to infer cell concentrations, when compared to multilayer perceptrons networks.

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Neural networks as `software sensors' in enzyme production

Cited by 62 publications

References 19 publications

Modeling kinetics of a large‐scale fed‐batch CHO cell culture by Markov chain Monte Carlo method

Modeling kinetics of a large‐scale fed‐batch CHO cell culture by Markov chain Monte Carlo method

Prediction of a model enzymatic acidolysis system using neural networks

Comparing the performance of multilayer perceptrons networks and neuro-fuzzy systems for on-line inference of Bacillus megaterium cellular concentrations

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