Fermentation Growth Curves
A comparison of the typical growth curves of E. coli fermentations for the two models is shown in Figure 4. The measurements in picoFaraday/cm3 (pF/cm3 ) follow the same pattern as off-line OD and net weight (g/L) measurements (data not shown).
In both approaches, the cultures remained in the lag phase during the first six hours. In the stepwise model, after the lag
phase, the biomass increased and reached a specific growth rate of ~0.6/h (µmax) during the batch phase. Feeding was started once the glucose initially added to the medium was depleted (as determined by
pH or a DO peak). At this point, the specific growth rate increased gradually from 0.08/h to 0.15/h in response to a manual
increase in the feeding rate based on glucose consumption and biomass increases (Figure 4a).
To ensure good yield with this approach, the operator must regulate the flow of the feed solution to keep glucose levels in
the bioreactor below a critical value established for the process (in this case, 1 g/L). It should be taken into consideration
that the specific growth rate must not drop below 0.1/h before the point of induction.
In the automatically controlled feeding model, the biomass and feeding rate increased exponentially at µset until the induction point, when IPTG was added to the culture (Figure 4b).
As seen in Figure 4b, the µset during the exponential feeding period was 0.35/h, and the cells went through at least two generations of growth at that rate
until they were induced five hours later. It should be noted that no batch phase was observed, because there was no glucose
in the basal media.
IPTG was added at 60 g/L dry cell weight (corresponding to 5–6 pF/cm3 ) and the exponential feeding was programmed at a µset of 0.1/h during the rest of the fed batch period.
In comparing the two fermentation processes, we see that with the stepwise feeding strategy it took 24 h to reach 3 pF/cm3 whereas with the exponential feeding strategy, the system reached 6 pF/cm3 in 18 h. It took longer to reach a high cell density in the stepwise model because the glucose additions were not optimized.
Standard Deviation and Productivity
To compare the protein yield and robustness of the exponential feeding process at different specific growth rates, we repeated
the exponential fermentation strategy under the same operating conditions at least four times for each selected pre-induction
specific growth rate (0.30/h, 0.33/h, 0.35/h, 0.37/h, and 0.4/h).
We then calculated the standard deviation (SD) in the biomass concentration (as dry cell weight) at induction and harvest
(Figure 5), and the concentration of the recombinant protein generated (in g/L) by quantifying Coomassie stained gels.
Our results show that at a µset of 0.37/h, the SD decreased significantly during both induction and harvest compared to the other selected values. It is
important to note, however, that except in the case of the specific growth rate of 0.4/h, the SD of the other preselected
µ also were acceptable.
At a µset of 0.37/h, we also achieved a two-fold increase in protein production compared (9 g/L versus 4.5 g/L) to the stepwise feeding
process (Figure 6). The yield of product per cell mass also was 30% higher in the exponential feeding setup, with a recombinant
protein accumulation of 140 mg of protein/mg dry cell weight (data not shown).
It is important to note that despite the high µset of 0.37/h, acetate production value was kept below 800 mg/L during the process, which may explain the protein concentration
Because both overfeeding and underfeeding of nutrient in fed-batch operations is detrimental to cell growth and product formation,
the development of a suitable feeding strategy is critical in fed-batch cultivation processes at production scale.
Operator-controlled systems are bound to fail because a fermentation process must be carefully formulated and monitored. Because
the production of recombinant proteins requires the use of prokaryotic cells which are by nature a fluctuating biological
system, it is very important to implement a detailed system for controlling the growth medium, oxygen incorporation, pH, temperature,
and glucose additions.
Here, we suggest a robust model to overcome these problems while increasing protein production. In our particular case, productivity
was doubled during the exponential feeding period and the elapsed fermentation time was reduced by 25%, enabling the cost-effective
production of recombinant proteins.
Because exponential feeding allows cells to grow at a constant specific growth rate, acetate production can be minimized by
keeping the cell growth below critical µ values (which are unique for each strain and growth medium). Moreover, exponential
feeding allowed us to significantly decrease the standard deviation of the dry cell weight value during induction and harvest,
improving batch-to-batch reproducibility.
It is important to note that although the stepwise feeding rate did not show an alarming deviation from the mean, it was controlled
by the operator, which is far from ideal (Figure 7).
The automated system based on exponential feeding allowed us to achieve a reproducible, consistent, and operator-independent
process. The introduction of minimal changes in the growth rate resulted in significant increases in the quality and quantity
of the protein obtained.
These results suggest that to achieve high cell densities and production rates, it is important to have a controlled feeding
strategy that minimizes fluctuations while ensuring a complete understanding of the data gathered from the processes.
JULIETA AULICINO is a research scientist and MATILDE HERMIDA, RAŚL MEDINA, and LORENA SABAROTS are research assistants, all in cell line and bioprocess R&D; LUIS DUCREY is the head of the technology transfer department, EDUARDO ORTI is the R&D director, and FLORENCIA ROGERS is the head of cell line and bioprocess R&D, all at GemaBiotech, Buenos Aires, Argentina. +54.11.4825.8029; email@example.com
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