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Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes.

Wechselberger P, Sagmeister P, Herwig C - Bioprocess Biosyst Eng (2012)

Bottom Line: The quantification of biomass in the induction phase of a recombinant bioprocess is not straight forward, since biological burden, caused by protein expression, can have a significant impact on the cell morphology and physiology.Results were compared with "state of the art" methods to estimate the biomass concentration and the specific growth rate µ.This method allows for variable model coefficients such as yields in contrast to other process models, hence does not require prior experiments.

View Article: PubMed Central - PubMed

Affiliation: Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna University of Technology, Gumpendorfer Straße 1a, 1060 Vienna, Austria. pwechsel@mail.tuwien.ac.at

ABSTRACT
The real-time measurement of biomass has been addressed since many years. The quantification of biomass in the induction phase of a recombinant bioprocess is not straight forward, since biological burden, caused by protein expression, can have a significant impact on the cell morphology and physiology. This variability potentially leads to poor generalization of the biomass estimation, hence is a very important issue in the dynamic field of process development with frequently changing processes and producer lines. We want to present a method to quantify "biomass" in real-time which avoids off-line sampling and the need for representative training data sets. This generally applicable soft-sensor, based on first principles, was used for the quantification of biomass in induced recombinant fed-batch processes. Results were compared with "state of the art" methods to estimate the biomass concentration and the specific growth rate µ. Gross errors such as wrong stoichiometric assumptions or sensor failure were detected automatically. This method allows for variable model coefficients such as yields in contrast to other process models, hence does not require prior experiments. It can be easily adapted to a different growth stoichiometry; hence the method provides good generalization, also for induced culture mode. This approach estimates the biomass (or anabolic bioconversion) in induced fed-batch cultures in real-time and provides this key variable for process development for control purposes.

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Related in: MedlinePlus

Induction phase of E. coli culture with significant product with μinitial = 0.10 (h−1); a inputs for the soft sensor based on elemental balancing; b measured biomass concentration and estimated biomass concentration by soft sensor based on elemental balancing; c specific growth rate calculated from interpolated offline biomass samples (μ off-line), off-gas rates (μ tCER and μ tOUR, together with the respective yields: Yco2/x and Yo2/x), capcaitance probe (μ Cap) and based on cumulative elemental balancing (μ soft-sensor); d elemental balances for off-line biomass concentrations and h value for soft sensor
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Fig3: Induction phase of E. coli culture with significant product with μinitial = 0.10 (h−1); a inputs for the soft sensor based on elemental balancing; b measured biomass concentration and estimated biomass concentration by soft sensor based on elemental balancing; c specific growth rate calculated from interpolated offline biomass samples (μ off-line), off-gas rates (μ tCER and μ tOUR, together with the respective yields: Yco2/x and Yo2/x), capcaitance probe (μ Cap) and based on cumulative elemental balancing (μ soft-sensor); d elemental balances for off-line biomass concentrations and h value for soft sensor

Mentions: The applicability of the soft-type sensor based on cumulative elemental balancing was evaluated by estimation of biomass and the specific growth rate in a culture with significant extracellular product and variable yields. Results were compared with a Luedeking–Piret-type approach, a capacitance probe (“Capacitance probe”) and conventional off-line sampling. Two different fed-batch experiments were evaluated. In one experiment the initial specific growth rate was adjusted from μ = 0.15 (h−1) to a μinitial = 0.10 (h−1) (Fig. 3), while in the other experiment the initial specific growth rate was adjusted from μ = 0.15 (h−1) to a μinitial = 0.06 (h−1) (Fig. 4).Fig. 3


Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes.

Wechselberger P, Sagmeister P, Herwig C - Bioprocess Biosyst Eng (2012)

Induction phase of E. coli culture with significant product with μinitial = 0.10 (h−1); a inputs for the soft sensor based on elemental balancing; b measured biomass concentration and estimated biomass concentration by soft sensor based on elemental balancing; c specific growth rate calculated from interpolated offline biomass samples (μ off-line), off-gas rates (μ tCER and μ tOUR, together with the respective yields: Yco2/x and Yo2/x), capcaitance probe (μ Cap) and based on cumulative elemental balancing (μ soft-sensor); d elemental balances for off-line biomass concentrations and h value for soft sensor
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3755222&req=5

Fig3: Induction phase of E. coli culture with significant product with μinitial = 0.10 (h−1); a inputs for the soft sensor based on elemental balancing; b measured biomass concentration and estimated biomass concentration by soft sensor based on elemental balancing; c specific growth rate calculated from interpolated offline biomass samples (μ off-line), off-gas rates (μ tCER and μ tOUR, together with the respective yields: Yco2/x and Yo2/x), capcaitance probe (μ Cap) and based on cumulative elemental balancing (μ soft-sensor); d elemental balances for off-line biomass concentrations and h value for soft sensor
Mentions: The applicability of the soft-type sensor based on cumulative elemental balancing was evaluated by estimation of biomass and the specific growth rate in a culture with significant extracellular product and variable yields. Results were compared with a Luedeking–Piret-type approach, a capacitance probe (“Capacitance probe”) and conventional off-line sampling. Two different fed-batch experiments were evaluated. In one experiment the initial specific growth rate was adjusted from μ = 0.15 (h−1) to a μinitial = 0.10 (h−1) (Fig. 3), while in the other experiment the initial specific growth rate was adjusted from μ = 0.15 (h−1) to a μinitial = 0.06 (h−1) (Fig. 4).Fig. 3

Bottom Line: The quantification of biomass in the induction phase of a recombinant bioprocess is not straight forward, since biological burden, caused by protein expression, can have a significant impact on the cell morphology and physiology.Results were compared with "state of the art" methods to estimate the biomass concentration and the specific growth rate µ.This method allows for variable model coefficients such as yields in contrast to other process models, hence does not require prior experiments.

View Article: PubMed Central - PubMed

Affiliation: Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna University of Technology, Gumpendorfer Straße 1a, 1060 Vienna, Austria. pwechsel@mail.tuwien.ac.at

ABSTRACT
The real-time measurement of biomass has been addressed since many years. The quantification of biomass in the induction phase of a recombinant bioprocess is not straight forward, since biological burden, caused by protein expression, can have a significant impact on the cell morphology and physiology. This variability potentially leads to poor generalization of the biomass estimation, hence is a very important issue in the dynamic field of process development with frequently changing processes and producer lines. We want to present a method to quantify "biomass" in real-time which avoids off-line sampling and the need for representative training data sets. This generally applicable soft-sensor, based on first principles, was used for the quantification of biomass in induced recombinant fed-batch processes. Results were compared with "state of the art" methods to estimate the biomass concentration and the specific growth rate µ. Gross errors such as wrong stoichiometric assumptions or sensor failure were detected automatically. This method allows for variable model coefficients such as yields in contrast to other process models, hence does not require prior experiments. It can be easily adapted to a different growth stoichiometry; hence the method provides good generalization, also for induced culture mode. This approach estimates the biomass (or anabolic bioconversion) in induced fed-batch cultures in real-time and provides this key variable for process development for control purposes.

Show MeSH
Related in: MedlinePlus