Methods
Cell culture experiments
Two production fed batch processes were used, Fed batch 1 and Fed batch
2. Both fed batch processes used chemically defined media and feeds over
the 12-day cell culture. Fed batch 1 used a glucose restricted fed batch
process called HiPDOG(Gagnon et al., 2011). Glucose concentration is
kept low during the initial phase of the process, Day 2-7, through
intermittent addition of feed medium containing glucose at the high end
of pH dead-band and then glucose was maintained above 1.5 g/L
thereafter, restricting lactate production without compromising the
proliferative capability of cells. In Fed batch 2 a conventional cell
culture process was used where glucose was maintained above 1.5 g/L
throughout the process.
For both process conditions, bioreactor vessels were inoculated at 2 x
106 viable cells/mL. The following bioprocess
characteristics were quantified daily using a NOVA Flex BioProfile
Analyzer (Nova Biomedical, Waltham, MA): viable cell density, average
live cell diameter and concentrations of glucose, lactate, glutamate,
and glutamine. Viable cell density data were converted to growth rates
by following equation to be compared to model-predicted growth rates.
\(\text{Growth}\ \text{rate}=\ \frac{1}{\text{vcd}}\ \frac{\Delta\text{vcd}}{\Delta\text{time}}\)
Flash-frozen cell pellets (106 cells) and supernatant
(1 mL) were collected from bioreactor runs for each sampling day.
Collected samples were sent to Metabolon (Metabolon Inc, Morrisville,
NC) for metabolomics analyses. Metabolomics measurements were used as
input data to the model by converting their units to model units of mmol
per gram of dry weight of cell per hour.
Metabolic network
modeling
We used a previously described metabolic network model that is tailored
to the investigated CHO clones(Schinn et al., 2020). Experimental
measurements for clone and culture day were used to constrain model
reactions for biomass production, monoclonal antibody secretion and
consumption of glucose, lactate, glutamate, and glutamine. Then, we
computed distributions of likely amino acid consumption rates by
stochastically sampling 5000 points within the model’s solution space
via a Markov chain Monte Carlo sampling algorithm, as described
previously(Megchelenbrink et al., 2014; Nam et al., 2012), usingoptGpSampler (Megchelenbrink et al., 2014) and COBRApy(Ebrahim et
al., 2013). Upon completion, the sampled distributions’ statistical
features were noted – that is, their mean, median, standard deviation,
25 percentile, and 75 percentile values.
Statistical methods
For each amino acid, the mean of the sample distribution was interpreted
as the likely consumption rates. These predicted consumption rates
deviated from experimental observations by a consistent fold amount.
Fold change error was also correlated with culture day, as the model
predicted the exponential growth phase better than the subsequent
stationary phase. Therefore, the model predictions were refined by a
regression model as follows, with growth rate and the predictions
themselves as explanatory variables.
\(\text{Corrected\ prediction}=\ \beta_{0}+\beta_{1}\bullet prediction+\beta_{2}\bullet growth\ rate\ \)
The time-course amino acid consumption profiles were described
mathematically by the Monod equation, as follows:
\(\text{Consumption\ rate}=\ \beta_{0}\bullet\frac{\text{time}}{\beta_{1}+time}\)
Here, β0 represents the minimum consumption rate which
the cells asymptotically approach during later stationary phase. The
variable β1 is the half-velocity constant, or the time
point at which the consumption rate reaches half of β0.
These analyses were carried out and visualized using COBRA Toolbox
2.0(Schellenberger et al., 2011) in MATLAB R2018b (MathWorks; Natick,
Massachusetts, USA)