Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

A soft sensor approach is described for controlling metabolic overflow from mixed-acid fermentation and glucose overflow metabolism in a fed-batch cultivation for production of recombinant green fluorescence protein (GFP) in Escherichia coli. The hardware part of the sensor consisted of a near-infrared in situ probe that monitored the E. coli biomass and an HPLC analyzer equipped with a filtration unit that measured the overflow metabolites. The computational part of the soft sensor used basic kinetic equations and summations for estimation of specific rates and total metabolite concentrations. Two control strategies for media feeding of the fed-batch cultivation were evaluated: (1) controlling the specific rates of overflow metabolism and mixed-acid fermentation metabolites at a fixed pre-set target values, and (2) controlling the concentration of the sum of these metabolites at a set level. The results indicate that the latter strategy was more efficient for maintaining a high titer and low variability of the produced recombinant GFP protein.

[1]  P. Hobbs,et al.  Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters. , 2011, Bioresource technology.

[2]  Eduardo Gómez-Sánchez,et al.  Automatization of a penicillin production process with soft sensors and an adaptive controller based on neuro fuzzy systems , 2004 .

[3]  Carl-Fredrik Mandenius,et al.  Quality-by-design for biotechnology-related pharmaceuticals. , 2009, Biotechnology journal.

[4]  A J Morris,et al.  Enhancing bioprocess operability with generic software sensors. , 1992, Journal of biotechnology.

[5]  S Linko,et al.  Applying neural networks as software sensors for enzyme engineering. , 1999, Trends in biotechnology.

[6]  Alvaro R. Lara,et al.  Engineering Escherichia coli to improve culture performance and reduce formation of by‐products during recombinant protein production under transient intermittent anaerobic conditions , 2006, Biotechnology and bioengineering.

[7]  Rimvydas Simutis,et al.  Improving the batch-to-batch reproducibility in microbial cultures during recombinant protein production by guiding the process along a predefined total biomass profile , 2006, Bioprocess and biosystems engineering.

[8]  Luigi Fortuna,et al.  Soft sensors for product quality monitoring in debutanizer distillation columns , 2005 .

[9]  K. Kiviharju,et al.  Biomass measurement online: the performance of in situ measurements and software sensors , 2008, Journal of Industrial Microbiology & Biotechnology.

[10]  S. Enfors,et al.  Glucose overflow metabolism and mixed-acid fermentation in aerobic large-scale fed-batch processes with Escherichia coli , 1999, Applied Microbiology and Biotechnology.

[11]  K B Konstantinov,et al.  Monitoring and control of the physiological state of cell cultures. , 2000, Biotechnology and bioengineering.

[12]  Rimvydas Simutis,et al.  Control of cultivation processes for recombinant protein production: a review , 2008, Bioprocess and biosystems engineering.

[13]  T. Scheper,et al.  Comparison of polysialic acid production in Escherichia coli K1 during batch cultivation and fed-batch cultivation applying two different control strategies. , 2011, Journal of biotechnology.

[14]  Gerald Striedner,et al.  Evaluation of the GFP signal and its aptitude for novel on-line monitoring strategies of recombinant fermentation processes. , 2004, Journal of biotechnology.

[15]  D. Seborg,et al.  Estimating product composition profiles in batch distillation via partial least squares regression , 2004 .

[16]  U. Rinas,et al.  Simple technique for simultaneous on-line estimation of biomass and acetate from base consumption and conductivity measurements in high-cell density cultures of Escherichia coli. , 2000, Biotechnology and bioengineering.

[17]  Carl-Fredrik Mandenius,et al.  Evaluation of software sensors for on-line estimation of culture conditions in an Escherichia coli cultivation expressing a recombinant protein. , 2010, Journal of biotechnology.

[18]  Francisco Bolívar,et al.  Transcriptional and metabolic response of recombinant Escherichia coli to spatial dissolved oxygen tension gradients simulated in a scale-down system. , 2006, Biotechnology and bioengineering.

[19]  T. Bachinger,et al.  Monitoring cellular state transitions in a production-scale CHO-cell process using an electronic nose. , 2000, Journal of biotechnology.

[20]  Carl-Fredrik Mandenius,et al.  Recent developments in the monitoring, modeling and control of biological production systems , 2004, Bioprocess and biosystems engineering.

[21]  Carl-Fredrik Mandenius,et al.  Soft sensors in bioprocessing: a status report and recommendations. , 2012, Biotechnology journal.

[22]  Sing Kiong Nguang,et al.  Soft sensors for on-line biomass measurements , 2004, Bioprocess and biosystems engineering.

[23]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[24]  A Chéruy,et al.  Software sensors in bioprocess engineering , 1997 .

[25]  U. Rinas,et al.  Production of interferon-α in high cell density cultures of recombinant Escherichia coli and its single step purification from refolded inclusion body proteins , 2000, Applied Microbiology and Biotechnology.

[26]  Heléne Sundström,et al.  Software sensors for fermentation processes , 2008, Bioprocess and biosystems engineering.

[27]  C. Will Chen,et al.  Bounded water kinetic model of β-galactosidase in reverse micelles , 2004 .

[28]  T. Bachinger,et al.  Searching for process information in the aroma of cell cultures. , 2000, Trends in biotechnology.

[29]  Krist V Gernaey,et al.  On-line estimation of biomass, glucose and ethanol in Saccharomyces cerevisiae cultivations using in-situ multi-wavelength fluorescence and software sensors. , 2009, Journal of biotechnology.

[30]  Carl-Fredrik Mandenius,et al.  Off-line monitoring of bacterial stress response during recombinant protein production using an optical biosensor. , 2004, Journal of biotechnology.

[31]  Carl-Fredrik Mandenius,et al.  Process analytical technology (PAT) for biopharmaceuticals , 2011, Biotechnology journal.

[32]  Dale E. Seborg,et al.  DEVELOPMENT OF A SOFT SENSOR FOR A BATCH DISTILLATION COLUMN USING LINEAR AND NONLINEAR PLS REGRESSION TECHNIQUES , 2002 .

[33]  A C A Veloso,et al.  Monitoring of fed-batch E. coli fermentations with software sensors , 2009, Bioprocess and biosystems engineering.

[34]  Reiner Luttmann,et al.  A multi‐bioreactor system for optimal production of malaria vaccines with Pichia pastoris , 2011, Biotechnology journal.

[35]  Karl Bayer,et al.  Design of transcriptional fusions of stress sensitive promoters and GFP to monitor the overburden of Escherichia coli hosts during recombinant protein production , 2008, Bioprocess and biosystems engineering.

[36]  Sten Bay Jørgensen,et al.  A systematic approach for soft sensor development , 2007, Comput. Chem. Eng..