Application of artificial neural networks to modelling of starch hydrolysis by glucoamylase

Bryjak, Jolanta; Murlikiewicz, K.; Zbiciński, Ireneusz; Stawczyk, J.

doi:10.1007/s004499900170

Cited by 15 publications

(9 citation statements)

References 19 publications

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“…1). Calculating changes in the concentration of the intermediate product and predicting ®nal product concentration using dierential equation is much more complicated for multienzymatic and multisubstrate systems (Bryjak et al, 2000), e.g., industrial process of starch liquefaction by endoamylases and sacchari®sation by exoamylases. Therefore, in some cases, in describing the reaction, it is easier to apply the stochastic method instead of analytical procedures (Kurtz, 1972).…”

Section: Introductionmentioning

confidence: 99%

Iteration model of starch hydrolysis by amylolytic enzymes

Wojciechowski¹,

Kozioł²,

Noworyta³

2001

Biotech & Bioengineering

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An elaborate computer program to simulate the process of starch hydrolysis by amylolytic enzymes was been developed. It is based on the Monte Carlo method and iteration kinetic model, which predict productive and non-productive amylase complexes with substrates. It describes both multienzymatic and multisubstrate reactions simulating the "real" concentrations of all components versus the time of the depolymerization reaction the number of substrates, intermediate products, and final products are limited only by computer memory. In this work, it is assumed that the "proper" substrate for amylases is the glucoside linkages in starch molecules. Dynamic changes of substrate during the simulation adequately influence the increase or decrease of reaction velocity, as well as the kinetics of depolymerization. The presented kinetic model, can be adapted to describe most enzymatic degradations of a polymer. This computer program has been tested on experimental data obtained for alpha- and beta-amylases.

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Section: Introductionmentioning

confidence: 99%

Iteration model of starch hydrolysis by amylolytic enzymes

Wojciechowski¹,

Kozioł²,

Noworyta³

2001

Biotech & Bioengineering

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“…Therefore, methods that use an algorithm of artificial neural network are used in i.e. modelling of non-linear processes in which it is difficult to provide an accurate mathematical equation (or equations) to describe a given process [35] . From over 50 architectures of artificial neural networks [36] in the simulation, the model of training of neural network with the use of Levenberg-Marqardt algorithm with five neurons in the hidden layer was used (Fig.…”

Section: Resultsmentioning

confidence: 99%

Bioaccumulation of Cr(III) ions by Blue Green-alga Spirulina sp. Part II. Mathematical Modeling

Chojnacka¹,

Wojciechowski²

2007

American J. of Agricultural and Biological Sciences

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In the present paper bioaccumulation of Cr(III) ions by blue-green algae Spirulina sp. is discussed. We found that the process consisted of two stages: passive in which Cr(III) ions are bound to the surface of cells, identical with biosorption and active, metabolism-dependent, in which Cr(III) ions are transported into the cellular interior. The passive stage occurs in both living and non-living cells and the active -only in living biomass. Two distinctive mathematical models of the process were proposed. The first was physical model basing on the identified mechanism of the process. In the second model, artificial neural networks were proposed.

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“…Combination of mass balances with the prediction capability of NNs may provide hybrid models able to capture relationships between different variables that affect enzyme kinetics-such as temperature and pH [21,22]. Enzymatic depolymerization reactions, which inherently have complex substrates/products, have also been modeled by NNs [23]. In particular, multilayer perceptrons (MLPs) NNs [24,25] have been successfully applied to solve this kind of problem.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Inference of Molar Mass Distributions of Peptides during Tailor-Made Enzymatic Hydrolysis of Cheese Whey: Effects of pH and Temperature

Pinto

Giordano

2007

Appl Biochem Biotechnol

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The fine-tuning of the enzymatic hydrolysis of proteins may provide a pool of peptides with predefined molar mass distributions. However, the complex mixture of molecules (peptides and amino acids) that results after the proteolysis of cheese whey turns unfeasible the assessment of individual species. In this work, a hybrid kinetic model for the proteolysis of whey by alcalase, multipoint-immobilized on agarose, is presented, which takes into account the influence of pH (8.0-10.4) and temperature (40-55 degrees C) on the activity of the enzyme. Five ranges of peptides' molar mass have their reaction rates predicted by neural networks (NNs). The output of NNs trained for constant pH and temperatures was interpolated, instead of including these variables in the input vector of a larger NN. Thus, the model complexity was reduced. Coupled to differential mass balances, this hybrid model can be employed for the online inference of peptides' molar mass distributions. Experimental kinetic assays were carried out using a pH-stat, in a laboratory-scale (0.03 L) batch reactor. The neural-kinetic model was integrated to a supervisory system of a bench-scale continually stirred tank reactor (0.5 L), providing accurate predictions during validation tests.

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Application of artificial neural networks to modelling of starch hydrolysis by glucoamylase

Cited by 15 publications

References 19 publications

Iteration model of starch hydrolysis by amylolytic enzymes

Iteration model of starch hydrolysis by amylolytic enzymes

Bioaccumulation of Cr(III) ions by Blue Green-alga Spirulina sp. Part II. Mathematical Modeling

Neural Network Inference of Molar Mass Distributions of Peptides during Tailor-Made Enzymatic Hydrolysis of Cheese Whey: Effects of pH and Temperature

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