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\begin{document}

\title{A genome-scale metabolic network model and machine learning predict
amino acid concentrations in Chinese Hamster Ovary cell cultures}



\author[1]{Song-Min Schinn}%
\author[2]{Carly Morrison}%
\author[2]{Wei Wei}%
\author[2]{Lin Zhang}%
\author[3]{Nathan Lewis}%
\affil[1]{University of California San Diego}%
\affil[2]{Pfizer Inc}%
\affil[3]{University of California, San Diego}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\selectlanguage{english}
\begin{abstract}
The control of nutrient availability is critical to large-scale
manufacturing of biotherapeutics. However, the quantification of
proteinogenic amino acids is time-consuming and thus is difficult to
implement for real-time in situ bioprocess control. Genome-scale
metabolic models describe the metabolic conversion from media nutrients
to proliferation and recombinant protein production, and therefore are a
promising platform for in silico monitoring and prediction of amino acid
concentrations. This potential has not been realized due to unresolved
challenges: (1) the models assume an optimal and highly efficient
metabolism, and therefore tend to underestimate amino acid consumption,
and (2) the models assume a steady state, and therefore have a short
forecast range. We address these challenges by integrating machine
learning with the metabolic models. Through this we demonstrate accurate
and time-course dependent prediction of individual amino acid
concentration in culture medium throughout the production process. Thus,
these models can be deployed to control nutrient feeding to avoid
premature nutrient depletion or provide early predictions of failed
bioreactor runs.%
\end{abstract}%



\sloppy


\section*{Short Communication}

{\label{short-communication}}

Chinese Hamster Ovary (CHO) cells are widely used to manufacture complex
biotherapeutic molecules at large scales. Industrial bioprocesses ensure
high product yield and quality by maintaining favorable growth
conditions in cell culture environments, which requires careful
monitoring and control of nutrient availability. Chemically-defined
serum-free media can contain dozens or \textgreater{}100
components(Ritacco et al., 2018), but key nutrients include
proteinogenic amino acids, which are direct substrates and
regulators(Duarte et al., 2014; Fomina-Yadlin et al., 2014) of
proliferation and protein synthesis. Unfortunately, conventional methods
for amino acid quantification based on liquid chromatography and mass
spectrometry are time-consuming and difficult to use for decision making
and control of cell culture. Alternate spectroscopic approaches have
been sensitive to a limited number of amino acid species(Bhatia et al.,
2018). Here we present a computational method to forecast time-course
amino acid concentrations from routine bioprocess measurements,
facilitating a timely and anticipatory control of the bioprocess (Figure
1).

At the foundation of our method is a genome-scale metabolic network
model, which accounts for the complex conversion from media nutrients to
biomass and recombinant protein production. Such models have been
increasingly utilized for CHO cells(Hefzi et al., 2016; Calmels et al.,
2019; Huang \& Yoon, 2020) and bioprocess applications(Sommeregger et
al., 2017; Zhang \& Hua, 2016), such as predicting clonal
performances(Popp et al., 2016), identifying metabolic
bottlenecks(Zhuangrong \& Seongkyu, 2020), and optimizing media
formulation(Fouladiha et al., 2020; Traustason et al., 2019). Metabolic
network models can also estimate amino acid uptake rates necessary to
experimentally support observed proliferation and productivity(Chen et
al., 2019). However, challenges have limited their practical
application.

First, metabolic network models are typically highly complex but
under-constrained, and therefore are easy to overfit. This is mitigated
by training the model on a variety of bioprocess conditions and
metabolic phenotypes. Second, metabolic network models assume that cells
operate at some metabolic optimum, and thus tend to describe an
idealized metabolism specifically fit to the assumed objective (e.g.,
biomass production(Feist \& Palsson, 2010; Szeliova et al., 2020),
minimization of redox(Savinell \& Palsson, 1992)). Third, for the
present purpose, these models need to predict amino acid consumption
fluxes, typically on the order of
10\textsuperscript{-3}mmol\selectlanguage{ngerman}·g\textsubscript{DW}\textsuperscript{-1}·hr\textsuperscript{-1}(see
Methods), from input data that are multiple magnitudes larger, such as
growth rate and glucose consumption (10\textsuperscript{-1} to
10\textsuperscript{-2}mmol·g\textsubscript{DW}\textsuperscript{-1}·hr\textsuperscript{-1}).
The preceding two challenges increase prediction error. Lastly,
metabolic network models assume a steady state, which reduces the range
of forecast. Typically, input data from one day are used to make
predictions for the same day. However, such predictions cannot be
extended to multiple days or subsequent culture phases, as
cross-temporal shifts in metabolism would violate the steady state
assumption. Thus, model predictions of amino acid concentrations can be
overfit, ideal, and near-sighted -- all of which dilutes their
practicality for industrial bioprocess control. Here we demonstrate that
these weaknesses can be addressed in a data-driven manner by coupling a
metabolic network model with machine learning.

We developed this hybrid approach on a diverse set of 10 CHO clones with
different growth and productivity profiles from two different fed-batch
production processes. These CHO clones were subject to different
bioprocess conditions and recombinant antibody identities (see Methods),
resulting in a variety of phenotypes and productivity performances (Fig.
S1). For example, several high-performing clones were exceptionally
proliferative or productive, suggesting an efficient conversion from
nutrients to biomass or recombinant protein product. Other clones
performed these conversions at lower rates, suggesting attenuated
metabolic activity or inefficient resource utilization. The CHO cells
adjusted their nutrient uptake according to these various metabolic
phenotypes, leading to diverse amino acid consumption patterns (Fig,
S2). For example, the consumption of glucose and serine differed by
several fold across conditions and time. Furthermore, different clones
varied in their consumption or secretion of key metabolites such as
lactate, alanine, glycine, and glutamine.

We sought to predict these diverse consumption behaviors using a
tailored model of CHO metabolism(Schinn et al., 2020). As input
information, we utilized the following routinely measured industrial
bioprocess data: (1) viable cell density and titer measurements, from
which growth rate and specific productivity are calculated (Methods,
equation 1), and (2) bioreactor concentrations of glucose, lactate,
glutamate and glutamine, from which their respective consumption rates
are calculated. These measurements were used as boundary conditions by
constraining the fluxes of biomass production, recombinant protein
synthesis and consumption of the four metabolites to observed values.
Subsequently, we used Markov chain Monte Carlo sampling of metabolic
fluxes(Schellenberger et al., 2011) to sample the range and magnitude of
all reaction fluxes to calculate the likely uptake fluxes of the
remaining 18 proteinogenic amino acids (see Methods). These predictions
were applied to the CHO clones across 8 days of a 12-day production run
(days 4 to 11), resulting in a total of 80 individual predictions.

We evaluated the resulting model predictions in two ways. First, we
examined the differences in model predictions and experimental
measurements of amino acid uptake and secretion (Figure 2A). For most
amino acids, this difference was small compared to the scale of input
data, suggesting that metabolic models can describe the conversion from
nutrients to biomass and recombinent proteins. Second, we examined the
fold changes between model predictions and experimental measurements.
These fold change errors are summarized in Figure 2B by their mean and
variance across the 80 observations. Overall, fold change error varied
significantly across amino acids. For example, the model predicted some
essential amino acids consistently well -- e.g. phenylalanine, cysteine
and tryptophan (fold change \selectlanguage{english}[?] 1), but predicted others poorly -- e.g.
alanine, lysine, glycine, and methionine (fold change \selectlanguage{english}[?] 0). Overall, the
sizeable fold change errors for many amino acids confirm the difficulty
of using metabolic network models alone to predict amino acid
consumption.

Notably, the model systematically underestimated consumption rates for
almost all amino acids (fold change \textless{} 1). This is likely
because the model doesn't consider certain metabolic inefficiencies --
e.g. CHO cells consume more amino acids than needed for the observed
production of biomass and recombinant protein, and catabolize them as
byproducts(Mulukutla et al., 2017). Furthermore, the variance of fold
change error was relatively low (\selectlanguage{english}[?]1) for most amino acids. This suggests
that the difference between model ideality and biological reality
remained consistent across many clones and conditions.

We hypothesized that this consistent gap could be bridged with data and
statistical modeling. We constructed a series of linear regression
models to `correct' the predictions from metabolic modeling, using
growth rate and the predictions from the metabolic model as explanatory
variables (Methods, equation 2). The 80 observations were randomly
divided into a training dataset and validation dataset, consisting of 48
and 32 observations, respectively. The regression coefficients were
first estimated from the training dataset and then applied to the
validation dataset. According to validation results, the regression
models substantially improved predictions, as fold change error
approached unity for most amino acids (Fig. 3B). As exceptions,
predictions for alanine, glycine and histidine were not reliably
improved (Fig. 3, red). These results were replicated in additional
validation studies involving four distinct clones (Supplementary
Document).

These results show that our hybrid modeling approach estimates amino
acid consumptions well for a small timescale of 1 day, when the steady
state assumption holds true. This assumption is not valid at larger
timescales of multiple days, where nutrient consumption declines
asymptotically as cellular metabolism shifts from exponential growth
phase to stationary phase. However, we found this limitation could be
remedied by modeling the multi-phase decline in amino acid consumption
with a simple sigmoid function (Methods, equation 3; line in Fig. 4),
which can be fitted from only a few datapoints. Specifically, we further
adapted our hybrid modeling approach by first predicting amino acid
consumption rates of several early culture days as heretofore described.
Then, these datapoints were used to fit a sigmoid function that
described the entire consumption profile, including later culture days
(Fig. 4A). Using this approach, we accurately predicted the time-course
consumption rates of 13 out of 18 amino acids (Spearman \selectlanguage{greek}ρ \selectlanguage{english}\textgreater{}
0.65; Fig. 4B), with only few amino acids remaining difficult to predict
(alanine, glycine, and histidine). Notably, our approach accurately
predicted the consumption profiles of amino acids that are highly
abundant in recombinant antibodies (e.g. serine, valine, and
leucine)(Fan et al., 2015), or that complicate media formulation due to
low solubility (e.g. tyrosine). These results highlight the method's
value in monitoring and forecasting the bioreactor environment.

In summary, the presented modeling workflow forecasted the entire amino
acid consumption profile from early bioprocess measurements,
facilitating anticipatory and \emph{in situ} control of bioreactor
nutrient availability. This was realized by a novel combination of
metabolic and statistical models. A metabolic network model estimated
amino acid uptake rates necessary for observed proliferation and
productivity, assuming an ideally efficient metabolism and steady state
conditions. Two subsequent regression models refined these predictions
by offsetting prediction errors empirically and by describing the
time-course relationship of individual predictions. Our efforts are part
of a growing trend of synergizing metabolic network models with machine
learning methods(Zampieri et al., 2019), and demonstrates the power of
hybrid modeling for on-line control of bioprocesses.

\section*{Methods}

{\label{methods}}

\subsubsection*{Cell culture experiments}

{\label{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
10\textsuperscript{6} 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 (10\textsuperscript{6} 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.

\subsubsection*{Metabolic network
modeling}

{\label{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),
using\emph{optGpSampler} (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.

\subsubsection*{Statistical methods}

{\label{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, \selectlanguage{greek}β\selectlanguage{english}\textsubscript{0} represents the minimum consumption rate which
the cells asymptotically approach during later stationary phase. The
variable \selectlanguage{greek}β\selectlanguage{english}\textsubscript{1} is the half-velocity constant, or the time
point at which the consumption rate reaches half of \selectlanguage{greek}β\selectlanguage{english}\textsubscript{0}.
These analyses were carried out and visualized using COBRA Toolbox
2.0(Schellenberger et al., 2011) in MATLAB R2018b (MathWorks; Natick,
Massachusetts, USA)

\section*{References}

{\label{references}}

Bhatia, H., Mehdizadeh, H., Drapeau, D., \& Yoon, S. (2018). In-line
monitoring of amino acids in mammalian cell cultures using raman
spectroscopy and multivariate chemometrics models. \emph{Engineering in
Life Sciences} , \emph{18} (1), 55--61.
https://doi.org/10.1002/elsc.201700084

Calmels, C., McCann, A., Malphettes, L., \& Andersen, M. R. (2019).
Application of a curated genome-scale metabolic model of CHO DG44 to an
industrial fed-batch process. \emph{Metabolic Engineering} , \emph{51} ,
9--19. https://doi.org/10.1016/j.ymben.2018.09.009

Chen, Y., McConnell, B. O., Gayatri Dhara, V., Mukesh Naik, H., Li,
C.-T., Antoniewicz, M. R., \& Betenbaugh, M. J. (2019). An
unconventional uptake rate objective function approach enhances
applicability of genome-scale models for mammalian cells. \emph{NPJ
Systems Biology and Applications} , \emph{5} .
https://doi.org/10.1038/s41540-019-0103-6

Duarte, T. M., Carinhas, N., Barreiro, L. C., Carrondo, M. J. T., Alves,
P. M., \& Teixeira, A. P. (2014). Metabolic responses of CHO cells to
limitation of key amino acids. \emph{Biotechnology and Bioengineering}
,\emph{111} (10), 2095--2106. https://doi.org/10.1002/bit.25266

Ebrahim, A., Lerman, J. A., Palsson, B. O., \& Hyduke, D. R. (2013).
COBRApy: COnstraints-Based Reconstruction and Analysis for
Python.\emph{BMC Systems Biology} , \emph{7} (1), 74.
https://doi.org/10.1186/1752-0509-7-74

Fan, Y., Val, I. J. D., M\selectlanguage{ngerman}üller, C., Sen, J. W., Rasmussen, S. K.,
Kontoravdi, C., Weilguny, D., \& Andersen, M. R. (2015). Amino acid and
glucose metabolism in fed-batch CHO cell culture affects antibody
production and glycosylation. \emph{Biotechnology and Bioengineering}
,\emph{112} (3), 521--535. https://doi.org/10.1002/bit.25450

Feist, A. M., \& Palsson, B. O. (2010). The biomass objective
function.\emph{Current Opinion in Microbiology} , \emph{13} (3),
344--349. https://doi.org/10.1016/j.mib.2010.03.003

Fomina\selectlanguage{english}-Yadlin, D., Gosink, J. J., McCoy, R., Follstad, B., Morris, A.,
Russell, C. B., \& McGrew, J. T. (2014). Cellular responses to
individual amino-acid depletion in antibody-expressing and parental CHO
cell lines. \emph{Biotechnology and Bioengineering} , \emph{111} (5),
965--979. https://doi.org/10.1002/bit.25155

Fouladiha, H., Marashi, S.-A., Torkashvand, F., Mahboudi, F., Lewis, N.
E., \& Vaziri, B. (2020). A metabolic network-based approach for
developing feeding strategies for CHO cells to increase monoclonal
antibody production. \emph{BioRxiv} , 751347.
https://doi.org/10.1101/751347

Gagnon, M., Hiller, G., Luan, Y.-T., Kittredge, A., DeFelice, J., \&
Drapeau, D. (2011). High-End pH-controlled delivery of glucose
effectively suppresses lactate accumulation in CHO Fed-batch
cultures.\emph{Biotechnology and Bioengineering} , \emph{108} (6),
1328--1337. https://doi.org/10.1002/bit.23072

Hefzi, H., Ang, K. S., Hanscho, M., Bordbar, A., Ruckerbauer, D.,
Lakshmanan, M., Orellana, C. A., Baycin-Hizal, D., Huang, Y., Ley, D.,
Martinez, V. S., Kyriakopoulos, S., Jimenez, N. E., Zielinski, D. C.,
Quek, L.-E., Wulff, T., Arnsdorf, J., Li, S., Lee, J. S., \ldots{}
Lewis, N. E. (2016). A Consensus Genome-scale Reconstruction of Chinese
Hamster Ovary Cell Metabolism. \emph{Cell Systems} , \emph{3} (5),
434-443.e8. https://doi.org/10.1016/j.cels.2016.10.020

Huang, Z., \& Yoon, S. (2020). Integration of Time-Series Transcriptomic
Data with Genome-Scale CHO Metabolic Models for mAb
Engineering.\emph{Processes} , \emph{8} (3), 331.
https://doi.org/10.3390/pr8030331

Megchelenbrink, W., Huynen, M., \& Marchiori, E. (2014). optGpSampler:
An Improved Tool for Uniformly Sampling the Solution-Space of
Genome-Scale Metabolic Networks. \emph{PLOS ONE} , \emph{9} (2), e86587.
https://doi.org/10.1371/journal.pone.0086587

Mulukutla, B. C., Kale, J., Kalomeris, T., Jacobs, M., \& Hiller, G. W.
(2017). Identification and control of novel growth inhibitors in
fed-batch cultures of Chinese hamster ovary cells. \emph{Biotechnology
and Bioengineering} , \emph{114} (8), 1779--1790.
https://doi.org/10.1002/bit.26313

Nam, H., Lewis, N. E., Lerman, J. A., Lee, D.-H., Chang, R. L., Kim, D.,
\& Palsson, B. O. (2012). Network Context and Selection in the Evolution
to Enzyme Specificity. \emph{Science} , \emph{337} (6098), 1101--1104.
https://doi.org/10.1126/science.1216861

Popp, O., Muller, D., Didzus, K., Paul, W., Lipsmeier, F., Kirchner, F.,
Niklas, J., Mauch, K., \& Beaucamp, N. (2016). A hybrid approach
identifies metabolic signatures of high-producers for chinese hamster
ovary clone selection and process optimization. \emph{Biotechnology and
Bioengineering} , \emph{113} (9), 2005--2019.
https://doi.org/10.1002/bit.25958

Ritacco, F. V., Wu, Y., \& Khetan, A. (2018). Cell culture media for
recombinant protein expression in Chinese hamster ovary (CHO) cells:
History, key components, and optimization strategies.\emph{Biotechnology
Progress} , \emph{34} (6), 1407--1426. https://doi.org/10.1002/btpr.2706

Savinell, J. M., \& Palsson, B. O. (1992). Network analysis of
intermediary metabolism using linear optimization. I. Development of
mathematical formalism. \emph{Journal of Theoretical Biology}
,\emph{154} (4), 421--454. https://doi.org/10.1016/s0022-5193(05)80161-4

Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D.,
Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian,
S., Kang, J., Hyduke, D. R., \& Palsson, B. O. (2011). Quantitative
prediction of cellular metabolism with constraint-based models: The
COBRA Toolbox v2.0. \emph{Nature Protocols} , \emph{6} (9), 1290--1307.
https://doi.org/10.1038/nprot.2011.308

Schinn, S.-M., Morrison, C., Wei, W., Zhang, L., \& Lewis, N.
(2020).\emph{Systematic evaluation of parameterization for genome-scale
metabolic models of cultured mammalian cells} .

Sommeregger, W., Sissolak, B., Kandra, K., von Stosch, M., Mayer, M., \&
Striedner, G. (2017). Quality by control: Towards model predictive
control of mammalian cell culture bioprocesses. \emph{Biotechnology
Journal} , \emph{12} (7). https://doi.org/10.1002/biot.201600546

Szeliova, D., Ruckerbauer, D., Galleguillos, S., Petersen, Hanscho, M.,
Troyer, Causon, Schoeny, Christensen, Lee, D. Y., Lewis, N. E.,
Koellensperger, Hann, Nielsen, L. K., Borth, N., \& Zanghellini, J.
(2020). What CHO is made of: Variations in the biomass composition of
Chinese hamster ovary cell lines. \emph{Metabolic Engineering} .

Traustason, B., Cheeks, M., \& Dikicioglu, D. (2019). Computer-Aided
Strategies for Determining the Amino Acid Composition of Medium for
Chinese Hamster Ovary Cell-Based Biomanufacturing
Platforms.\emph{International Journal of Molecular Sciences} , \emph{20}
(21), 5464. https://doi.org/10.3390/ijms20215464

Zampieri, G., Vijayakumar, S., Yaneske, E., \& Angione, C. (2019).
Machine and deep learning meet genome-scale metabolic
modeling.\emph{PLoS Computational Biology} , \emph{15} (7).
https://doi.org/10.1371/journal.pcbi.1007084

Zhang, C., \& Hua, Q. (2016). Applications of Genome-Scale Metabolic
Models in Biotechnology and Systems Medicine. \emph{Frontiers in
Physiology} , \emph{6} . https://doi.org/10.3389/fphys.2015.00413

Zhuangrong, H., \& Seongkyu, Y. (2020). Identifying metabolic features
and engineering targets for productivity improvement in CHO cells by
integrated transcriptomics and genome-scale metabolic
model.\emph{Biochemical Engineering Journal} , 107624.
https://doi.org/10.1016/j.bej.2020.107624

\section*{Figures}

{\label{figures}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1: Overview of method} . A novel combination of a
metabolic and statistical models forecast the time-course amino acid
consumption profiles in CHO cell cultures, as follows: (1) Routine
bioprocess measurements are used as input data. (2) A metabolic model
initially estimates early amino acid consumption rates.(3) A regression
model refines these predictions. (4) These refined predictions are fit
to a curve describing the time-course profile. (5) This would allow for
anticipatory control of amino acid availability in bioreactors.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2: Metabolic network model shows moderate accuracy in
estimating amino acid consumption} . (a) Model predictions compared well
to experimental observations, given the scale of input data such as the
consumption rates of glucose and lactate (upper right, filled circles).
(b) The fold change of model predictions and experimental measurements
was also explored. The mean and variance of fold change across all 10
clones and 8 timepoints are shown. Prediction accuracy was particularly
good for phenylalanine, tryptophan and cysteine, whose fold change
approached unity (red line, x-axis). However, for many amino acids,
model predictions were prone to significant fold change errors. Notably,
the relatively low variance in fold change error (y-axis) suggests that
predictions could be improved empirically.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3: Statistical learning refines predictions of the
metabolic model.} A regression model was devised for each amino acid.
The variables \selectlanguage{greek}μ, \selectlanguage{english}x, and x' represent growth rate, predicted consumption
rate and revised consumption rate, respectively. The regression
coefficients were fitted from the training dataset. (a) Here we show the
prediction qualities by fold change for a validation dataset before
refinement by regression. The mean and variance of fold changes are
comparable to the entire dataset (Figure 2b). (b) The prediction values
were transformed by the regression model. Prediction of nearly all amino
acids approach unity (red line). The datapoints are colored by the
variance in fold change after transformation. Amino acids with high
variance in fold change (y [?] 1, red) -- alanine, glycine, arginine and
histidine -- failed to be reliably corrected by the regression model.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
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\textbf{Figure 4: Curve fitting forecasts amino acid profiles} . (A)
Bioprocess data from days 4-7 are used to estimate tyrosine consumption
rates (empty circles). These estimations are used to parameterize a
sigmoid curve (line; see Methods, equation 3) that describes the
consumption profile for the entire culture duration. This predicted
time-course consumption profile agreed well with experimental
measurements (filled circle). Time-course profiles of other amino acids
are provided in Supplementary Figures. (B) Overall, model predictions
and experimental measurements agreed fairly well for all amino acids
(Spearman \selectlanguage{greek}ρ \selectlanguage{english}= 0.54).

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