OPTIMISATION METHODS FOR IMPROVING FED-BATCH CULTIVATION OF E. COLI PRODUCING RECOMBINANT PROTEINS

Two optimisation techniques for the fed-batch cultivation of high cell density Escherichia coli producing recombinant proteins were compared. An unstructured model for the growth, based on the General State Space Dynamical Model [1] was used to represent the four major metabolic pathways: oxidative growth on glucose, fermentative growth on glucose, oxidative growth on acetate, and maintenance. The dilution rate (dependent on the substrate feed rate) was chosen as the input variable. Recombinant protein production is known to be proportional, in our system, to the biomass concentration. Thus, biomass productivity was chosen as the criterion to be maximized. The two methods compared were a first order gradient method based on Pontryagin’s minimum principle and a stochastic method based on the biological principle of natural evolution, using a genetic algorithm. The former method revealed less efficient concerning to the computed maximum, and dependence on good initial values.

[1]  Eugénio C. Ferreira,et al.  On-line simultaneous monitoring of glucose and acetate with FIA during high cell density fermentation of recombinant E. coli , 2002 .

[2]  Bong Hyun Chung,et al.  Adaptive optimization of fed-batch culture of yeast by using genetic algorithms , 2002 .

[3]  S K Nguang,et al.  Optimisation of fed-batch culture of hybridoma cells using genetic algorithms. , 2001, ISA transactions.

[4]  Eugénio C. Ferreira,et al.  An integrated system for advanced monitoring and control of fed-batch fermentations of recombinant E. coli. , 2001 .

[5]  Sunwon Park,et al.  Control of fed-batch fermentations. , 1999, Biotechnology advances.

[6]  Johannes Andries Roubos,et al.  An evolutionary strategy for fed-batch bioreactor optimization; concepts and performance , 1999 .

[7]  Ian David Lockhart Bogle,et al.  Modelling the effects of glucose feeding on a recombinant E. coli fermentation , 1999 .

[8]  R. Simutis,et al.  Model based design of a biochemical cultivation process , 1998 .

[9]  J. Shiloach,et al.  Proposed mechanism of acetate accumulation in two recombinant Escherichia coli strains during high density fermentation. , 1998, Biotechnology and bioengineering.

[10]  Thomas F. Coleman,et al.  Optimization Toolbox User's Guide , 1998 .

[11]  F.-S. Wang,et al.  Optimal feed policy for fed-batch fermentation of ethanol production by Zymomous mobilis , 1997 .

[12]  G. van Straten,et al.  Comparison of optimization methods for fed-batch cultures of hybridoma cells , 1997 .

[13]  S. Lee,et al.  High cell-density culture of Escherichia coli. , 1996, Trends in biotechnology.

[14]  W. Fred Ramirez,et al.  Optimal fed‐batch control of induced foreign protein production by recombinant bacteria , 1994 .

[15]  Hartmut Pohlheim,et al.  Genetic and evolutionary algorithm toolbox for use with matlab , 1994 .

[16]  Michel Perrier,et al.  Fed-batch culture of hybridoma cells: comparison of optimal control approach and closed loop strategies , 1993 .

[17]  H C Lim,et al.  Acetic acid formation in escherichia coli fermentation , 1992, Biotechnology and bioengineering.

[18]  A. Johnson.  Optimization of Fed-batch Biotechnical Processes , 1992 .

[19]  S. Shioya.  Optimization and Control for Maximum Production in Fed-batch Culture , 1992 .

[20]  D. Leštan,et al.  The Sequential Algorithm for Dynamic Optimization of Bioprocesses , 1992 .

[21]  D. Dochain,et al.  On-Line Estimation and Adaptive Control of Bioreactors , 2013 .

[22]  T. Ritch,et al.  Production of human alpha consensus interferon in recombinant Escherichia coli , 1986 .

[23]  Tsuneo Yamane,et al.  Fed-batch techniques in microbial processes , 1984 .