# Towards Controlling the Glycoform: A Model Framework Linking Extracellular Metabolites to Antibody Glycosylation

^{1}

^{2}

^{3}

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## Abstract

**:**

V | culture volume | L |

X_{V} | cell density | cells/L |

t | time | h |

μ | cell growth rate | h^{−1} |

k_{d} | cell death rate | h^{−1} |

F_{out} | flow rate out of culture | L/h |

Glc_{ext} | extracellular glucose concentration | mM |

Gln_{ext} | extracellular glutamine concentration | mM |

F_{in} | flow rate into culture | L/h |

Glc_{feed} | feed glucose concentration | mM |

K_{A} | activator species saturation coefficient | mM |

K_{M} | species saturation coefficient | mM |

k_{d,max} | maximum cell death rate | h^{−1} |

K_{d} | species depletion coefficient | mM |

q | species cellular production | mmol/(h-cell) |

Y | species yield | cell/mmol |

m | species cell maintenance term | mmol/(h-cell) |

mAb | Antibody product titer | mM |

Nuc | Intracellular nucleotide concentration | mM |

K_{TP} | Transport protein species saturation coefficient | mM |

V_{cell} | cell volume | L |

DNA_{f} | nucleotide fraction in DNA | dimensionless |

m_{DNA} | cellular DNA mass | mg/cell |

m_{RNA} | cellular RNA mass | mg/cell |

Mr | molecular species mass | mg/mmol |

RNA_{f} | nucleotide fraction in RNA | dimensionless |

k_{cat} | enzyme turnover rate | h^{−1} |

K_{i} | Species inhibition constant | mM |

E_{0} | Initial enzyme concentration | mM |

N_{gly,cell} | Number of glycans per cell | mmol/cell |

N_{NSD,gly} | NSDs consumed per host cell glycan | mmol/mmol |

N_{gly,mAb} | Number of glycans per antibody | mmol/mmol |

N_{NSD,mAb} | NSDs consumed per antibody | mmol/mmol |

F_{mAb} | Antibody production rate | mmol/h |

## 1. Introduction

#### Current Problems Resulting from Glycans and Causes of Variation

## 2. Mathematical Model Development

#### 2.1. Cell Culture Dynamics Model

_{V}in cells/L) is a function of growth rate (μ in h

^{−1}), death rate (k

_{d}in h

^{−1}) and the flow out of the reactor (F

_{out}in L/h):

_{M,glc}) and glutamine (K

_{M,gln}) all in units of mMol and the maximum growth rate μ

_{max}(h

^{−1}).

_{d,max,glc}in h

^{−1}) and glutamine (k

_{d,max,gln}in h

^{−1}), the species depletion coefficients for glucose (K

_{M,gln}in mmol) and glutamine (K

_{M,gln}in mmol). Parameter estimation using the gPROMS model building environment [18] showed that extracellular ammonia and lactate did not contribute significantly towards cell death or growth inhibition for this hybridoma cell line (not shown). Therefore, neither species features in other parts of the model.

_{glc}and q

_{gln}(mmol/(h-cell)) denote the specific glucose and glutamine cell uptake rate, respectively, and are defined by the equations shown below:

_{Xv}

_{/glc}and Y

_{Xv}

_{/gln}(cell/mmol) denote the biomass yield coefficient for glucose and glutamine, respectively. The m

_{glc}and m

_{gln}(mmol/(h-cell)) terms are the maintenance coefficients for glucose and glutamine, respectively. Lastly the product term and the specific productivity terms are represented by the following:

#### 2.2. Nucleotide Model

_{DNA}) and RNA monomer (Mr

_{RNA}) as well as the fraction of each nucleotide in DNA (DNA

_{f,nuc}) and RNA (RNA

_{f,nuc}) was based on the data used by Nolan and Lee [20]. The mass of DNA (m

_{DNA}) and RNA (m

_{RNA}) were taken as 7.05 and 28.55 pg/cell, respectively [21].

#### 2.3. Nucleotide Sugar Synthesis Model

_{m}), turnover rates (k

_{cat}), inhibitory terms (K

_{i}), enzyme activity and any Hill coefficients (Table S1). Rate equations are based on Michaelis-Menten kinetics, accounting for reported reaction mechanisms and inhibitory compounds and the Hill equation is used where data indicating deviation from Michaelis-Menten kinetics (i.e., independent binding) was available. Murine (Mus musculus) enzyme data was used where available and when lacking preference in data was based on most recent common ancestors as described in the molecular tree of mammals of the placental orders [24]. While the dissociation constant (K

_{m}) is an intrinsic parameter, V

_{max}is not, and is a function of the turnover rate constant, k

_{cat}, and the initial enzyme concentration used in a particular experimental condition [E

_{0}]. The following assumptions were made for the derivation of the rate of reaction expressions:

- Equilibrium is rapidly reached for all intermediate reactants;
- Rate-limiting steps are irreversible (Table S1);
- Where water is required for catalysis, full enzyme saturation is assumed due to the aqueous environment of the cytoplasm;
- Where more than one substrate is required for catalysis, a random order of substrate binding is assumed, unless reported otherwise;
- Rapid dissociation of reaction products from enzyme;
- Michaelis-Menten kinetics are assumed to hold true, unless reported otherwise;
- All enzyme and transport protein concentrations throughout the network are constant.

_{cat}turnover rate, E

_{0}initial enzyme concentration, and I denotes inhibitor concentrations. The derivations of rate of reaction equations for each type of mechanism including inhibitory terms are summarized more extensively in the supplementary information (Appendix 1). Each individual reaction and its corresponding mechanism are also listed in the supplementary information (Table S1). The equations for each type of kinetics are shown below.

^{5}cells as reported by Bonarius et al. [21] and an average amino acid monomer molecular weight of 127 μg/μmol as calculated by Nolan and Lee [20], an amino acid concentration per cell was found. Multiplying the cellular amino acid concentration with an O-linked glycan frequency of 0.00371 glycans per amino and an N-linked glycan frequency of 0.00400 glycans per amino acid for 64.9% of glycosylated host cell proteins [26], an O-linked and N-linked glycan concentration of 1.41 × 10

^{−8}and 1.31 × 10

^{−8}μmol/cell, respectively, was calculated for each glycan species. This in turn gives a total number of glycans per cell of 2.72 × 10

^{−8}μmol/cell (N

_{glyc,cell}). Furthermore, mass spectrometry data of the human mature B-cell glycoform was used to find the average compositions of O-linked and N-linked glycan structures based on the relative abundance of each species. The findings for each sugar species are summarized in Table 1 and the abundance of each individual identified species as well as the MS methodology on which the average glycoform are based are shown in Appendix 2. Thus, the system becomes constrained through the nine calculated outlet fluxes required to maintain cell growth. The glucose flux towards glycolysis is a function of the enzyme concentration (E

_{glyc}) for the reaction step from fructose 6-phosphate to fructose 1,6-bisphosphate and thus, is allowed to vary as part of the parameter estimation as well as with the concentration of its substrate fructose 6-phosphate. The kinetic data and reaction mechanism were obtained from Chen et al. [27]. This step is assumed to be irreversible and acts as the main outlet flux from the NSD network.

#### 2.4. Parameter Estimation

^{17}) and corresponding model outputs a metamodel was created upon which the GSA was performed thus, greatly reducing computational cost.

## 3. Model Performance and Discussion

_{M}value of 0.0024 mM for the UDP-Gal Golgi transporter [45]. This is well below the measured and estimated UDP-Gal concentrations and indicates saturation of the transport protein until later stages of the cell culture.

## 4. Materials and Methods

#### 4.1. Cell Culture, Metabolite Monitoring and Antibody Quantification

_{2}on an orbital shaking platform rotating at 125 rpm. Subculture was performed every three days and new cultures were seeded at a density of 2 × 10

^{5}cells/mL. Experiments were performed in 2 L vented Erlenmeyer flasks with a working volume of 400 mL. Cell concentration was determined using a Neubauer ruling haemocytometer and viability was estimated by the trypan blue dye exclusion method using light microscopy. Extracellular glucose, glutamine and lactate concentrations were measured using the BioProfile 400 (Nova Biomedical, Runcorn, UK). The antibody concentration in the supernatant was determined using an indirect sandwich enzyme-linked immunosorbent assay (ELISA) as described by Kontoravdi et al. [32].

#### 4.2. Intracellular Nucleotide and Nucleotide Sugar Extraction

#### 4.3. Characterization of Intracellular Nucleotides and Nucleotide Sugars

#### 4.4. Glycan Purification and Analysis

## 5. Conclusions

## Acknowledgments

## Appendix 1—Enzyme Mechanisms

#### A1.1. Single Substrate Michaelis-Menten Rate Equation

**Figure A1.**Single substrate Michaelis-Menten kinetics and competitive inhibition of species A through I.

Equation (1) Rate of reaction for Single substrate Michaelis-Menten kinetics | $v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A]}{{K}_{m}+[A]}$ |

Equation (2) Rate of reaction for Single substrate Michaelis-Menten kinetics including competitive inhibition of species A | $v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A]}{{K}_{m}\left(1+{\sum}_{i=1}^{NI}\frac{[{I}_{i}]}{{K}_{I,i}}\right)+[A]}$ |

Equation (3) Rate of reaction for Single substrate Michaelis-Menten kinetics including non-competitive inhibition | $v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A]}{({K}_{m}+[A])\hspace{0.17em}\left(1+\frac{[I]}{{K}_{I}}\right)},$ |

#### A1.2. Random Order Bi-Bi Kinetics

**Figure A2.**Random order bi-bi kinetics and competitive inhibition of species A through I and species B competing with J.

Equation (4) Rate of reaction for random order bi-bi kinetics | $v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A][B]}{{K}_{m,A}{K}_{m,B}+{K}_{m,A}[B]+{K}_{m,B}[A]+[A][B]}$ |

Equation (5) Rate of reaction for random order bi-bi kinetics including competitive inhibition of species A and B | |

$v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A][B]}{{K}_{m,A}{K}_{m,B}\left(1+{\sum}_{i=1}^{NI}\frac{[{I}_{i}]}{{K}_{I,i}}+{\sum}_{j=1}^{NJ}\frac{[{J}_{j}]}{{K}_{J,j}}\right)+{K}_{m,A}[B]\hspace{0.17em}\left(1+{\sum}_{i=1}^{NI}\frac{[{I}_{i}]}{{K}_{I,i}}\right)+{K}_{m,B}[A]\hspace{0.17em}\left(1+{\sum}_{j=1}^{NJ}\frac{[{J}_{j}]}{{K}_{J,j}}\right)+[A][B]}$ | |

Equation (6) Rate of reaction for random order bi-bi kinetics including non-competitive inhibition | |

$v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A][B]}{({K}_{m,A}{K}_{m,B}+{K}_{m,A}[B]+{K}_{m,B}[A]+[A][B])\hspace{0.17em}\left(1+\frac{[I]}{{K}_{I}}\right)},$ |

#### A1.3. Ordered Bi-Bi Kinetics

**Figure A3.**Ordered bi-bi kinetics and competitive inhibition of species A through I and species B competing with J.

Equation (7) Rate of reaction for ordered bi-bi kinetics | $v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A][B]}{{K}_{m,A}{K}_{m,B}+{K}_{m,B}[A]+[A][B]}$ |

Equation (8) Rate of reaction for ordered bi-bi kinetics including competitive inhibition of species A and B | $v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A][B]}{{K}_{m,A}{K}_{m,B}\left(1+{\sum}_{i=1}^{NI}\frac{[{I}_{i}]}{{K}_{I,i}}\right)+{K}_{m,B}[A]\hspace{0.17em}\left(1+{\sum}_{j=1}^{NJ}\frac{[{J}_{j}]}{{K}_{J,j}}\right)+[A][B]}$ |

Equation (9) Rate of reaction for ordered bi-bi kinetics including non-competitive inhibition | $v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A][B]}{({K}_{m,A}{K}_{m,B}+{K}_{m,B}[A]+[A][B])\hspace{0.17em}\left(1+\frac{[I]}{{K}_{I}}\right)},$ |

#### A1.4. Ping-Pong Bi-Bi Kinetics

**Figure A4.**Ping-pong bi-bi kinetics and competitive inhibition of species A through I and species B competing with J.

Equation (10) Rate of reaction for ping-pong bi-bi kinetics | $v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A][B]}{{K}_{m,A}[B]+{K}_{m,B}[A]+[A][B]}$ |

Equation (11) Rate of reaction for ping-pong bi-bi kinetics including competitive inhibition of species A and B | $v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A][B]}{{K}_{m,A}[B]\hspace{0.17em}\left(1+{\sum}_{i=1}^{NI}\frac{[{I}_{i}]}{{K}_{I,i}}\right)+{K}_{m,B}[A]\hspace{0.17em}\left(1+{\sum}_{j=1}^{NJ}\frac{[{J}_{j}]}{{K}_{J,j}}\right)+[A][B]}$ |

Equation (12) Rate of reaction for ping-pong bi-bi kinetics including non-competitive inhibition | $v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A][B]}{({K}_{m,A}[B]+{K}_{m,B}[A]+[A][B])\hspace{0.17em}\left(1+\frac{[I]}{{K}_{I}}\right)},$ |

#### A1.5. Ping-Pong Ter-Ter Kinetics

Equation (13) Rate of reaction for ping-pong ter-ter kinetics including competitive inhibition of species A |

$v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][A]{[B]}^{2}}{2{K}_{m,A}{K}_{m,B}[B]+2{K}_{m,B}[A][B]+{K}_{m,A}{[B]}^{2}\hspace{0.17em}\left(1+\frac{[I]}{{K}_{I}}\right)+{({K}_{m,B})}^{2}[A]+[A]{[B]}^{2}}$ |

#### A1.5. Hexokinase Rate of Reaction Expression

**Figure A6.**Hexokinase reaction scheme including competitive inhibition of species ATP through ADP, Glucose competing with other hexose species and un-competitive inhibition through Glucose-6P.

Equation (14) Rate of reaction for hexokinase based on the above reaction scheme |

$v=\frac{d[P]}{dt}=\frac{{k}_{cat}[{E}_{0}][ATP][Glc]}{{K}_{m,ATP}{K}_{m,Glc}\left(1+\frac{[ADP]}{{K}_{i,ADP}}+\frac{[G6P]}{{K}_{i,G6P,I}}\right)+[ATP]{K}_{m,Glc}\left(1+\frac{[G6P]}{{K}_{i,G6P,I}}+\frac{[G6P]}{{K}_{i,G6P,II}}+\mathrm{\Sigma}\frac{[{Hex}_{i}]}{{K}_{i,Hex}}\right)+[ATP][Glc]\hspace{0.17em}\left(1+\frac{[G6P]}{{K}_{i,G6P,I}}+\frac{[G6P]}{{K}_{i,G6P,II}}+\frac{[G6P]}{{K}_{i,G6P,III}}\right)}$ |

#### A1.6. Glycolysis

#### A1.7. Hill Coefficients

## Appendix 2—Relative Abundances of Activated Human B-Cell Glycans

**Table A1.**Summary of the relative abundances of N-linked glycan structures used to obtain an average glycan structure.

Glycan structure | Sugar frequency per glycan (mol/mol) | |||||
---|---|---|---|---|---|---|

Species abundance (%) | GlcNAc | Man | Gal | Fuc | CMP-Neu5Ac | |

7.37 | 2 | 5 | ||||

10.86 | 2 | 6 | ||||

1.75 | 4 | 3 | 1 | |||

13.40 | 2 | 7 | ||||

1.75 | 4 | 3 | 1 | 1 | ||

14.85 | 2 | 8 | ||||

0.69 | 4 | 3 | 2 | 1 | ||

0.47 | 5 | 3 | 1 | 1 | ||

17.69 | 2 | 9 | ||||

1.87 | 4 | 3 | 2 | 1 | ||

0.44 | 5 | 3 | 2 | 1 | ||

2.97 | 4 | 3 | 2 | 1 | 1 | |

0.49 | 4 | 3 | 2 | 2 | 1 | |

1.23 | 4 | 3 | 2 | 2 | ||

10.78 | 4 | 3 | 2 | 1 | 2 | |

2.59 | 5 | 3 | 3 | 1 | 1 | |

5.03 | 5 | 3 | 2 | 1 | 2 | |

0.34 | 6 | 3 | 3 | 1 | 1 | |

1.35 | 5 | 3 | 3 | 1 | 2 | |

0.59 | 6 | 3 | 4 | 1 | 1 | |

0.12 | 6 | 3 | 3 | 1 | 2 | |

0.10 | 7 | 3 | 4 | 1 | 1 | |

0.31 | 5 | 3 | 3 | 1 | 3 | |

0.68 | 6 | 3 | 4 | 1 | 2 | |

0.10 | 7 | 3 | 5 | 1 | 1 | |

0.09 | 7 | 3 | 4 | 1 | 2 | |

0.18 | 6 | 3 | 4 | 1 | 3 | |

0.36 | 7 | 3 | 5 | 1 | 2 | |

0.09 | 7 | 3 | 5 | 1 | 3 | |

0.09 | 8 | 3 | 6 | 1 | 2 |

**Table A2.**Summary of the relative abundances of O-linked glycan structures used to obtain an average glycan structure.

Glycan structure | Sugar frequency per glycan (mol/mol) | ||||
---|---|---|---|---|---|

Species abundance (%) | GlcNAc | GalNAc | Gal | CMP-Neu5Ac | |

30.34 | 1 | 1 | 1 | ||

2.18 | 1 | 1 | 2 | ||

53.64 | 1 | 1 | 2 | ||

11.42 | 1 | 1 | 2 | 1 | |

0.77 | 1 | 1 | 3 | ||

1.27 | 1 | 1 | 2 | 2 | |

0.38 | 2 | 1 | 3 | 1 |

## Appendix 3—Estimated Parameter Values and Non-Nucleotide Species

#### A3.1. NSD Metabolic Network Parameter Values

Parameter name | Parameter value |
---|---|

E15a | 2.5400E−05 |

E8a | 0.0000E+00 |

E26b | 0.0000E+00 |

E28a | 0.0000E+00 |

E34a | 0.0000E+00 |

E19a | 6.4400E−06 |

E21a | 6.8586E−06 |

Eglyc | 7.0800E−05 |

Ki15a_GDPFuc | 2.1200E−04 |

E29a | 1.3313E−03 |

E1c | 1.5096E−03 |

E1a | 1.8477E−03 |

Km17a_Man6P | 2.5300E−03 |

E23a | 3.7138E−03 |

E12a | 3.9000E−03 |

E32a | 3.9000E−03 |

E14a | 3.9000E−03 |

E16a | 3.9000E−03 |

E20a | 3.9000E−03 |

E2a | 3.9000E−03 |

E37a | 3.9000E−03 |

E3b | 3.9000E−03 |

E40a | 3.9000E−03 |

E4a | 3.9000E−03 |

E4b | 3.9000E−03 |

E17a | 3.9532E−03 |

E22a | 1.0000E−02 |

E3a | 1.4167E−02 |

E38a | 3.5128E−02 |

E13a | 3.9000E−02 |

E31a | 3.9000E−02 |

E33a | 3.9000E−02 |

E5a | 3.9000E−02 |

E6a | 3.9000E−02 |

Km26a_Glc6P | 9.3842E−02 |

Km22a_UDPGlc | 2.0697E−01 |

Km7b_UDPGlcNAc | 8.2129E−01 |

Gln_coef | 1.0000E+00 |

n29a_CMPNeu5Ac | 4.2000E+00 |

Ki_SA_Tra_UDPGlcNAc | 6.8885E+00 |

Km19a_Fru6P | 1.9700E+01 |

k5aB | 6.0000E+02 |

Ki34a_CMPNeu5Gc | 1.0000E+03 |

k26aF | 2.0972E+03 |

k6aB | 2.3900E+04 |

k17aF | 7.0928E+04 |

k22aB | 1.6151E+05 |

Ki29a_CMPNeu5Ac | 5.2433E+05 |

k21aB | 4.2900E+06 |

k21aF | 6.1200E+06 |

k19aF | 1.5900E+07 |

k27aF | 1.1200E+08 |

Parameter name | Parameter value |
---|---|

k_T_gln | 3.3800E−06 |

Kd_Glc_ext | 1.0051E−01 |

Kd_Gln_ext | 1.1872E−02 |

Km_Glc_ext | 2.6700E+00 |

Km_Gln_ext | 1.2000E+00 |

mu_d_max_glc | 3.9300E−01 |

mu_d_max_gln | 6.2053E−02 |

mu_g_max | 6.6745E−02 |

Y_ext_glc | 9.1600E+07 |

Y_ext_gln | 5.6400E+08 |

Y_mAb_mu | 0.0000E+00 |

Y_mAb_Xv | 1.1400E−09 |

Glc_in | 0.0000E+00 |

Gln_in | 0.0000E+00 |

F_in | 0.0000E+00 |

F_out | 0.0000E+00 |

Parameter name | Parameter value |
---|---|

Kdf_10_Gln | 2.6356E+00 |

Kdf_11_Glc | 2.1296E+00 |

Kdf_11_Gln | 1.3425E+00 |

Kdf_12_ATP | 1.1302E+01 |

Kdf_13_ADP | 3.9892E−04 |

Kdf_13_Glc | 4.7469E+00 |

Kdf_14_ADP | 2.5000E+01 |

Kdf_15_AMP | 1.1312E+01 |

Kdf_15_Glc | 2.3025E+00 |

Kdf_8_Glc | 1.2067E+00 |

Kdf_8_Gln | 2.4832E+00 |

Kdf_9_Gln | 2.1804E+00 |

Kdf_9_UTP | 5.0374E−03 |

Kdout_ATP | 1.0000E−03 |

Kdout_CTP | 1.0000E−03 |

Kdout_GTP | 1.0000E−03 |

Kdout_UTP | 1.0000E−03 |

kf_10 | 7.6621E+00 |

kf_11 | 6.7500E+00 |

kf_12 | 3.2344E+00 |

kf_13 | 2.1454E−01 |

kf_14 | 1.1649E+02 |

kf_15 | 8.6900E+01 |

kf_8 | 7.9486E+00 |

kf_9 | 1.3393E+00 |

Parameter name | Parameter value |
---|---|

KdiFucTA | 0.0000E+00 |

KdiFucTB | 0.0000E+00 |

KdiGalTa1A | 1.0709E+02 |

KdiGalTa1B | 7.2051E+00 |

KdiGalTa2A | 3.4573E+01 |

KdiGntII | 7.7067E+01 |

#### A3.2. Non-NSD Species

Intracellular species | Intracellular conc. (mM) | Source tissue |
---|---|---|

Acetyl Coenzyme A (ACoA) | 0.029 | Rat liver |

Coenzyme A (CoA) | 0.13 | Rat liver |

Glucose-1,6-biphosphate (Glc16PP) | 0.014 | Mouse liver |

Nicotinamide adenine dinucleotide (NAD) | 0.76 | Rat liver |

Nictotinamide adenine dinucleotide phosphate (NADP) | 0.067 | Rat liver |

Nictotinamide adenine dinucleotide phosphate, reduced (NADPH) | 0.30 | Rat liver |

Phosphoenolypyruvic acid (PEP) | 0.11 | Mouse liver |

Inorganic phosphate (PPi) | 3.37 | Rat liver |

Pyruvate (Pyr) | 0.18 | Mouse liver |

## Conflicts of Interest

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**Figure 1.**Structure of the mathematical model and the interactions of the individual model parts with a focus on the network for nucleotide sugar synthesis.

**Figure 4.**Experimental data compared with generated fits from the dynamic Monod growth model. Fits for extracellular glucose concentration (

**top left**), extracellular glutamine concentration (

**top right**), viable and dead cell density (

**bottom left**) and antibody titer (

**bottom right**).

**Figure 5.**Experimental data compared with generated fits from the nucleotide model. Fits for intracellular ADP concentration (

**top left**), intracellular AMP concentration (

**top right**), intracellular ATP concentration (

**center left**), intracellular CTP concentration (

**center right**), intracellular GTP concentration (

**bottom left**) and intracellular UTP concentration (

**bottom right**).

**Figure 6.**Experimental data compared with generated fits from the mechanistic bottom-up approach nucleotide sugar synthesis model. Fits for intracellular CMP-Neu5AC concentration (

**top left**), intracellular GDP-Fuc concentration (

**top right**), intracellular UDP-GalNAc concentration (

**center left**), intracellular UDP-Gal concentration (

**center right**) and intracellular UDP-GlcNAc concentration (

**bottom left**).

**Figure 7.**Simulated and experimentally determined distribution of the cumulative N-linked glycoform of the antibody Fc region for two time points during cell culture.

Glycan type | GlcNAc | GalNAc | Man | Gal | Neu5Ac | Fuc |
---|---|---|---|---|---|---|

N-linked glycan | 2.896 | 0 | 5.813 | 0.759 | 0.516 | 0.332 |

O-linked glycan | 0.156 | 1 | 0 | 1.156 | 1.543 | 0 |

Glycan average (N_{NSD,glyc}) | 1.579 | 0.481 | 3.018 | 0.950 | 1.010 | 0.173 |

© 2014 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

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**MDPI and ACS Style**

Jedrzejewski, P.M.; Del Val, I.J.; Constantinou, A.; Dell, A.; Haslam, S.M.; Polizzi, K.M.; Kontoravdi, C.
Towards Controlling the Glycoform: A Model Framework Linking Extracellular Metabolites to Antibody Glycosylation. *Int. J. Mol. Sci.* **2014**, *15*, 4492-4522.
https://doi.org/10.3390/ijms15034492

**AMA Style**

Jedrzejewski PM, Del Val IJ, Constantinou A, Dell A, Haslam SM, Polizzi KM, Kontoravdi C.
Towards Controlling the Glycoform: A Model Framework Linking Extracellular Metabolites to Antibody Glycosylation. *International Journal of Molecular Sciences*. 2014; 15(3):4492-4522.
https://doi.org/10.3390/ijms15034492

**Chicago/Turabian Style**

Jedrzejewski, Philip M., Ioscani Jimenez Del Val, Antony Constantinou, Anne Dell, Stuart M. Haslam, Karen M. Polizzi, and Cleo Kontoravdi.
2014. "Towards Controlling the Glycoform: A Model Framework Linking Extracellular Metabolites to Antibody Glycosylation" *International Journal of Molecular Sciences* 15, no. 3: 4492-4522.
https://doi.org/10.3390/ijms15034492