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Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models


Three different models: the unstructured mechanistic black-box model, the input–output neural network-based model and the externally recurrent neural network model were used to describe the pyruvate production process from glucose and acetate using the genetically modified Escherichia coli YYC202 ldhA::Kan strain. The experimental data were used from the recently described batch and fed-batch experiments [ Zelić B, Study of the process development for Escherichia coli-based pyruvate production. PhD Thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, Croatia, July 2003. (In English); Zelić et al. Bioproc Biosyst Eng 26:249–258 (2004); Zelić et al. Eng Life Sci 3:299–305 (2003); Zelić et al Biotechnol Bioeng 85:638–646 (2004)]. The neural networks were built out of the experimental data obtained in the fed-batch pyruvate production experiments with the constant glucose feed rate. The model validation was performed using the experimental results obtained from the batch and fed-batch pyruvate production experiments with the constant acetate feed rate. Dynamics of the substrate and product concentration changes was estimated using two neural network-based models for biomass and pyruvate. It was shown that neural networks could be used for the modeling of complex microbial fermentation processes, even in conditions in which mechanistic unstructured models cannot be applied.

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Fig. 6


c A :

acetate concentration (g L−1)

c A,0 :

acetate concentration in the feed (g L−1)

c G :

gucose concentration (g L−1)

c G,0 :

glucose concentration in the feed (g L−1)

c P :

pyruvate concentration (g L−1)

c P,MAX :

critical pyruvate concentration above which reaction cannot proceed (g L−1)

c X :

biomass concentration (g L−1)

K P :

inhibition constant of Jerusalimsky (g L−1)

K S A :

monod growth constant for acetate (g L−1)

K S G :

monod growth constant for glucose (g L−1)

m A :

Maintenance coefficient for growth on acetate (g g−1 h−1)

m G :

maintenance coefficient for growth on glucose (g g−1 h−1)

q V :

volumetric flow rate (L h−1)

q VA :

volumetric flow rate of acetate (L h−1)

q VG :

volumetric flow rate of glucose (L h−1)

r A :

specific rate of acetate consumption (g g−1 h−1)

r G :

specific rate of glucose consumption (g g−1 h−1)

r P :

specific rate of pyruvate production (g g−1 h−1)

t :

time (h)

V :

reaction (broth) volume (L)

Y P/G :

yield coefficient pyruvate from glucose (g g−1)

Y X/A :

yield coefficient biomass from acetate (g g−1)


maximum yield coefficient biomass from acetate (g g−1)

Y X/G :

yield coefficient biomass from glucose (g g−1)


maximum yield coefficient biomass from glucose (g g−1)

u :

input variables (–)

x :

current output state (–)


specific growth rate (h−1)

μMAX :

maximum specific growth rate (h−1)


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All experiments were performed at the Institute of Biotechnology 2, Research Center Jülich, Germany. The authors are indebted to Prof. Dr Christian Wandrey and Dr Ralf Takors for generous support of this work. This work was partially funded by the Croatian Ministry of Science, Education and Sport, contract grant number 0125 021.

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Correspondence to B. Zelić.

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Zelić, B., Bolf, N. & Vasić-Rački, Đ. Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models. Bioprocess Biosyst Eng 29, 39–47 (2006).

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  • Pyruvate
  • Escherichia coli
  • Unstructured “black-box” model
  • Input–output neural network-based model
  • Externally recurrent neural network model