Using Artificial Neural Network and
Leudeking-Piret Model in the Kinetic Modeling
of Microbial Production of Poly-β-
Hydroxybutyrate
Poly-β-hydroxybutyrate (PHB) is one of the most
famous biopolymers that has various applications in production of
biodegradable carriers. The most important strategy for enhancing
efficiency in production process and reducing the price of PHB, is the
accurate expression of kinetic model of products formation and
parameters that are effective on it, such as Dry Cell Weight (DCW)
and substrate consumption. Considering the high capabilities of
artificial neural networks in modeling and simulation of non-linear
systems such as biological and chemical industries that mainly are
multivariable systems, kinetic modeling of microbial production of
PHB that is a complex and non-linear biological process, the three
layers perceptron neural network model was used in this study.
Artificial neural network educates itself and finds the hidden laws
behind the data with mapping based on experimental data, of dry cell
weight, substrate concentration as input and PHB concentration as
output. For training the network, a series of experimental data for
PHB production from Hydrogenophaga Pseudoflava by glucose
carbon source was used. After training the network, two other
experimental data sets that have not intervened in the network
education, including dry cell concentration and substrate
concentration were applied as inputs to the network, and PHB
concentration was predicted by the network. Comparison of predicted
data by network and experimental data, indicated a high precision
predicted for both fructose and whey carbon sources. Also in present
study for better understanding of the ability of neural network in
modeling of biological processes, microbial production kinetic of
PHB by Leudeking-Piret experimental equation was modeled. The
Observed result indicated an accurate prediction of PHB
concentration by artificial neural network higher than Leudeking-
Piret model.
[1] P. R. Patnaik, "Neural network designs for poly-ß-hydroxybutyrate
production optimization under simulated industrial conditions",
Biotechnology Letters, 31 January 2005, pp. 409-415.
[2] Apostolis A. Koutinas, Yunji Xu, Ruohang Wang, Colin Webb,
"Polyhydroxybutyrate production from a novel feedstock derived from a
wheat-based biorefinery", Enzyme and Microbial Technology, 4 August
2006, pp. 1035-1044.
[3] Kwang-Min Lee, David F. Gilmore, "Formulation and process modeling
of biopolymer (polyhydroxyalkanoates: PHAs) production from
industrial wastes by novel crossed experimental design", Process
Biochemistry , 2005, pp. 229-246.
[4] Pornapa Suriyamongkol, Randall Weselake, Suresh Narine, Maurice
Moloney, Saleh Shah, "Biotechnological approaches for the production
of polyhydroxyalcanoates in microorganisms and plants", Biotechnology
Advances, 23 November 2006, pp. 148-175.
[5] M. Mahmoudi, M. Sharifzadeh Baei, G. D. Najafpour, F. Tabandeh, H.
eisazadeh, "Kinetic model for polyhydroxybutyrate (PHB) production
by Hydrogenophaga Pseudoflava and verification of growth
conditions", African Journal of Biotechnology, 24 May 2010, pp. 3151-
3157.
[6] Kwang-Min Lee., David F. Gilmore, "Formulation and process
modeling of biopolymer (polyhydroxyalcanoates: PHAs) production
from industrial wastes by novel crossed experimental design", Process
Biochemistry, 15 December 2003, pp. 229-246.
[7] Chung Ping Xu, Jong Won Yun, "A kinetic study for
exopolysaccharideproduction in submerged mycelia culture of an
entomopathogenic fungus paecilomyces tenuipes C240 ", Journal of Life
Science, 29 December 2004, pp. 15-20.
[8] K. Manikandan, V. Saravanan, "Kinetic studies on ethanol production
from banana peel waste using mutant strain of saccharomyces
cerivisiae", Indian Journal of biotechnology, January 2008, pp. 83-88.
[9] Faujan Bin H. Ahmad et al., "Artificial neural network modeling studies
to predict the yield of enzymatic synthesis of betulinic acid ester",
Electronic Journal of Biotechnology, Vol.13, 2010.
[10] A. Qaderi, "Modeling and optimization of microbial production of Poly-
β-hydroxybutyrate by use of artificial neural networks", MS Thesis,
Islamic Azad University Science and Research Branch, Faculty of
engineering, February 2011, pp. 26-89.
[11] Catia Bastioli, "Handbook of Biodegradable Polymers", Rapra
Technology Limited, 2005, pp. 189 , 220-241.
[12] Daniel Graupe, "Principles of Artificial Neural Networks", World
Scientific Publishing Co. Pte. Ltd.,2nd ed. Vol. 6, 2007, Ch. 4, 6.
[13] Michael A. Arbib, "The Handbook of Brain Theory and Neural
Networks", The MIT Press, 2nd ed. 2002.
[14] F. Mohammad, O. El-Tayeb and M. Aboulwafa, "Optimization of the
industrial production of bacterial a-amylase in Egypt. V. Analysis of
kinetic data for enzyme production by two strains of Bacillus
amyloliquefaciens", African Journal of Biotechnology Vol. 7 (24), 2007,
pp. 4537-4543.
[15] K Manikandan, V Saravanan and T Viruthagiri, "Kinetics studies on
ethanol production from banana peel waste using mutant strain of
saccharomyces cerevisiae", Indian Journal of Biotechnology, Vol. 7,
2007, pp. 83-88.
[1] P. R. Patnaik, "Neural network designs for poly-ß-hydroxybutyrate
production optimization under simulated industrial conditions",
Biotechnology Letters, 31 January 2005, pp. 409-415.
[2] Apostolis A. Koutinas, Yunji Xu, Ruohang Wang, Colin Webb,
"Polyhydroxybutyrate production from a novel feedstock derived from a
wheat-based biorefinery", Enzyme and Microbial Technology, 4 August
2006, pp. 1035-1044.
[3] Kwang-Min Lee, David F. Gilmore, "Formulation and process modeling
of biopolymer (polyhydroxyalkanoates: PHAs) production from
industrial wastes by novel crossed experimental design", Process
Biochemistry , 2005, pp. 229-246.
[4] Pornapa Suriyamongkol, Randall Weselake, Suresh Narine, Maurice
Moloney, Saleh Shah, "Biotechnological approaches for the production
of polyhydroxyalcanoates in microorganisms and plants", Biotechnology
Advances, 23 November 2006, pp. 148-175.
[5] M. Mahmoudi, M. Sharifzadeh Baei, G. D. Najafpour, F. Tabandeh, H.
eisazadeh, "Kinetic model for polyhydroxybutyrate (PHB) production
by Hydrogenophaga Pseudoflava and verification of growth
conditions", African Journal of Biotechnology, 24 May 2010, pp. 3151-
3157.
[6] Kwang-Min Lee., David F. Gilmore, "Formulation and process
modeling of biopolymer (polyhydroxyalcanoates: PHAs) production
from industrial wastes by novel crossed experimental design", Process
Biochemistry, 15 December 2003, pp. 229-246.
[7] Chung Ping Xu, Jong Won Yun, "A kinetic study for
exopolysaccharideproduction in submerged mycelia culture of an
entomopathogenic fungus paecilomyces tenuipes C240 ", Journal of Life
Science, 29 December 2004, pp. 15-20.
[8] K. Manikandan, V. Saravanan, "Kinetic studies on ethanol production
from banana peel waste using mutant strain of saccharomyces
cerivisiae", Indian Journal of biotechnology, January 2008, pp. 83-88.
[9] Faujan Bin H. Ahmad et al., "Artificial neural network modeling studies
to predict the yield of enzymatic synthesis of betulinic acid ester",
Electronic Journal of Biotechnology, Vol.13, 2010.
[10] A. Qaderi, "Modeling and optimization of microbial production of Poly-
β-hydroxybutyrate by use of artificial neural networks", MS Thesis,
Islamic Azad University Science and Research Branch, Faculty of
engineering, February 2011, pp. 26-89.
[11] Catia Bastioli, "Handbook of Biodegradable Polymers", Rapra
Technology Limited, 2005, pp. 189 , 220-241.
[12] Daniel Graupe, "Principles of Artificial Neural Networks", World
Scientific Publishing Co. Pte. Ltd.,2nd ed. Vol. 6, 2007, Ch. 4, 6.
[13] Michael A. Arbib, "The Handbook of Brain Theory and Neural
Networks", The MIT Press, 2nd ed. 2002.
[14] F. Mohammad, O. El-Tayeb and M. Aboulwafa, "Optimization of the
industrial production of bacterial a-amylase in Egypt. V. Analysis of
kinetic data for enzyme production by two strains of Bacillus
amyloliquefaciens", African Journal of Biotechnology Vol. 7 (24), 2007,
pp. 4537-4543.
[15] K Manikandan, V Saravanan and T Viruthagiri, "Kinetics studies on
ethanol production from banana peel waste using mutant strain of
saccharomyces cerevisiae", Indian Journal of Biotechnology, Vol. 7,
2007, pp. 83-88.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:60997", author = "A.Qaderi and A. Heydarinasab and M. Ardjmand", title = "Using Artificial Neural Network and
Leudeking-Piret Model in the Kinetic Modeling
of Microbial Production of Poly-β-
Hydroxybutyrate", abstract = "Poly-β-hydroxybutyrate (PHB) is one of the most
famous biopolymers that has various applications in production of
biodegradable carriers. The most important strategy for enhancing
efficiency in production process and reducing the price of PHB, is the
accurate expression of kinetic model of products formation and
parameters that are effective on it, such as Dry Cell Weight (DCW)
and substrate consumption. Considering the high capabilities of
artificial neural networks in modeling and simulation of non-linear
systems such as biological and chemical industries that mainly are
multivariable systems, kinetic modeling of microbial production of
PHB that is a complex and non-linear biological process, the three
layers perceptron neural network model was used in this study.
Artificial neural network educates itself and finds the hidden laws
behind the data with mapping based on experimental data, of dry cell
weight, substrate concentration as input and PHB concentration as
output. For training the network, a series of experimental data for
PHB production from Hydrogenophaga Pseudoflava by glucose
carbon source was used. After training the network, two other
experimental data sets that have not intervened in the network
education, including dry cell concentration and substrate
concentration were applied as inputs to the network, and PHB
concentration was predicted by the network. Comparison of predicted
data by network and experimental data, indicated a high precision
predicted for both fructose and whey carbon sources. Also in present
study for better understanding of the ability of neural network in
modeling of biological processes, microbial production kinetic of
PHB by Leudeking-Piret experimental equation was modeled. The
Observed result indicated an accurate prediction of PHB
concentration by artificial neural network higher than Leudeking-
Piret model.", keywords = "Kinetic Modeling, Poly-β-Hydroxybutyrate (PHB),
Hydrogenophaga Pseudoflava, Artificial Neural Network,
Leudeking-Piret", volume = "6", number = "1", pages = "93-8", }