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Genome Technology in Industrial Cell Bioprocessing

Toward QbD in Cell Culture Biologics

From analysis to design:

Models for metabolism and glycosylation

Ziomara P. Gerdtzen [email protected]

Departament of Chemical Engineering and Biotechnology

University of Chile

October 16, 2011
http://[email protected]/http://[email protected]/

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Introduction

Mathematical model development

Models for Mammalian Cells

Final Remarks

Systems Biology

Mammalian Cells

Systems biology overview

There has been a growing interest in addressing the study of biochemical

networks and cellular processes in a comprehensive way.

A systems approach assumes that the behavior of a system can be

described as a direct consequence of the interactions of its components,

and considers all the components and reactions involved in a particular

cellular process. Those reactions, ranging from DNA, RNA, protein

synthesis and their regulation to biochemical conversions, form complexinteraction networks.

Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
mailto:[email protected]:[email protected]

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Introduction



Final Remarks

Systems Biology

Mammalian Cells

Systems biology overview

Modeling, computation and experimental research data from

metabolomics, genomics and proteomics can be combined to generate a

meaningful description of a cellular process or biochemical network.


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Introduction



Final Remarks

Systems Biology

Mammalian Cells

Systems biology overviewDifferent levels of information

A set of genes, metabolites,

proteins, with a specific copy

number or concentration definesthe physiology an organism (level

1). These components form

genetic- regulatory motifs or

metabolic pathways (level 2),

which are the building blocks of

functional modules (level 3).Nested modules generate a

hierarchical architecture (level 4).


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Introduction



Final Remarks

Systems Biology

Mammalian Cells

Mammalian Cells

In the pharmaceutical industry, nearly 70% of the glycoproteins with

pharmacologic application are produced in mammalian cell cultures using

systems such asChinese hamster ovary cells (CHO)

Mouse myeloma (NS0)

Baby hamster kidney (BHK)

Human embryo kidney cells (HEK-293)

since they provide clear advantages over other hosts such as bacteria,yeast or fungi.


I t d ti

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Introduction



Final Remarks

Systems Biology

Mammalian Cells

Summary

The paradigm for the analysis of cellular systems has shifted from a focus

on individual processes to comprehensive descriptions that consider the

interactions of metabolic, genomic and signaling networks.

Different types of models can be used for this purpose, depending on the

type and amount of information available for the specific system.

The development of detailed models for mammalian cell metabolism has

only recently started to grow more strongly, due to the intrinsiccomplexities of mammalian systems, and the limited availability of

experimental information and adequate modeling tools.


Introduction

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Introduction



Final Remarks

Systems Biology

Mammalian Cells

The system: Mammalian cells

Complex compartmentalized cellular

systems that perform a large number of

biochemical reactions simultaneously,

subject to a series of thermodynamic

constraints.

Crowded intracellular environment where

many interactions take place.

Internal spatial complexity,non-homogeneous distribution.

Parameters are a function of growth rate.


Introduction

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Introduction



Final Remarks

Systems Biology

Mammalian Cells

The system: Mammalian cellsCharacteristics of animal cells vs. E. coli

Characteristic Mammalian cell E. coli

Diameter 18-20m 0.8-2.0m

Volume 10,000m3 1m3

Cell generation time 20h - non dividing 30min to h

Genome size 3.0 109 bp 4.6 106 bp

Number of genes 25000-30000 3200

RNA content 25g/106cell 20-211g/109cell

RNA lifetime 2-5 min 10 min-10 h

DNA content 10g/106cell 7.6-18g/109cell

Protein content 250g/106cell 25-130g/109cell

Proteins/cell 41010 4106

Diffusion time of protein across cell 100sec 0.1sec

Carbohydrate content 150g/106cell 4.4-26g/109cell

Lipid content 120g/106cell 3-17.3g/109cell

Compartmentalization Yes No


Introduction Genome scale models

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Introduction



Final Remarks

Genome scale models

Resources

Physicochemical models

Reconstruction tools

Developing a mathematical model

Identification of components of the system to

be modeled.

Assessment of interactions and directionality.

Systems network assembly (data repositories,automated reconstruction).

Construction of a mathematical description or

model fitting.

Experimental measurement of parameters, or

use of literature and database sources.

Model allows organization of information

available at different levels (genomic,

proteomic, metabolomic).



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Introduction



Final Remarks

Genome scale models

Resources



Genome scale model reconstructionA protocol for generating a high-quality genome scale metabolic reconstruction

Thiele and Palsson 2010, Nature Protocols 5(1):93-121

Bottomup metabolic network reconstructions represent structured

knowledge bases that abstract pertinent information on the biochemical

transformations taking place within specific target organisms.Conversion of a reconstruction into a mathematical format facilitates

computational biological studies, including evaluation of network content,

hypothesis testing and generation, analysis of phenotypic characteristics

and metabolic engineering.

Genome-scale metabolic reconstructions for more than 50 organisms

have been published and this number is expected to increase rapidly.

However, these reconstructions differ in quality and coverage that may

minimize their predictive potential and use as knowledge bases.



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Introduction



Final Remarks

Genome scale models

Resources



Genome scale model reconstructionA protocol for generating a high-quality genome scale metabolic reconstruction

Thiele and Palsson 2010, Nature Protocols 5(1):93-121

Hybridoma Comprehensive protocol describing steps for building a high-quality genome-scale metabolic

reconstruction.Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation


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Final Remarks

Resources



Reconstructed models for mammalian cellsComputational prediction of human metabolic pathways from the complete human genome

Romeroet al. 2004, Genome Biology 6:R2

Computational pathway analysis of the human genome that assigns

enzymes encoded therein to predicted metabolic pathways.2,709 human enzymes asigned to 896 bioreactions.

Predicted pathways closely match the known nutritional requirements of

humans.

203 pathway holes on genome annotation.

Predicted metabolic map described by the Pathway/Genome Database

called HumanCyc, available at http://HumanCyc.org/.



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Final Remarks

Resources



Reconstructed models for mammalian cellsComputational prediction of human metabolic pathways from the complete human genome

Romeroet al. 2004, Genome Biology 6:R2

Elucidated the human metabolic map. The database places many human genes in a pathway context, thereby

facilitating analysis of gene expression, proteomics, and metabolomics datasets through Cellular Overview.



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Final Remarks

Resources



Reconstructed models for mammalian cellsModeling Hybridoma Cell Metabolism Using a Generic Genome-Scale Metabolic Model ofMus

musculusSheikhet al. 2005, Biotechnol.Prog.21:112

Reconstructed cellular metabolic network ofMus musculus, based on

annotated genomic data, pathway databases, and currently available

biochemical and physiological information

Collect and characterize the metabolic network of a mammalian cell on

the basis of genomic data.

Generic reaction network. Attempts to capture the carbon, energy, and

nitrogen metabolism of the cell. Compartmentalization and transport

included.

The reaction list consists of 872 internal metabolites involved in a total of

1220 reactions.



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Final Remarks

Resources



Web resources and databases for components and pathways

for mammalian cellsName Web addressand description

AfCS www.afcs.org/index.html- Protein-protein interactions and modifications.

APID bioinfow.dep.usal.es/apid- Open access web application where experimentally val-

idated protein-protein interactions are unified.

BioCyc biocyc.org- Collection of genome and metabolic pathways for a large number of

organisms, including mammalian.BioGRID thebiogrid.org - Public repository with genetic and protein interaction data from

model organisms and humans.

BOND bond.unleashedinformatics.com - Comprehensive information on small molecule

and protein interactions.

BRENDA www.brenda-enzymes.org- Freely available comprehensive collection of biochem-

ical,molecular and metabolic information on all classified enzymes, based on liter-

ature.

DIP dip.doe-mbi.ucla.edu- Experimentally determined protein-protein interactions.

GeneNet wwwmgs.bionet.nsc.ru- Integrates databases on gene network components and

data processing tools for structure and function of DNA, RNA, and proteins.

GenMAPP www.genmapp.org - Computer application for analysis and visualization of

genome-scale data for rapid interpretation.


Introduction

M th ti l d l d l t

Genome scale models

R

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Final Remarks

Resources




for mammalian cellsContinued

Name Web addressand description

HPID wilab.inha.ac.kr/hpid- Integrates information from other databases on proteins and

their interactions. Predicts potential interactions between human proteins.

IBIS www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi- Protein interactions, both experimen-

tally determined and inferred by homology.iHOP www.ihop-net.org- Protein association network built by literature mining.

Ii2D ophid.utoronto.ca- Database with known, experimental and predicted PPIs for five

model organisms and human.

IntAct www.ebi.ac.uk/intact- Open source database system and analysis tools for protein

interaction data.

KDBI xin.cz3.nus.edu.sg/group/kdbi/kdbi.asp- Kinetic data of interactions between pro-

teins, RNA, DNA and ligands, or reaction events.KEGG BRITE www.genome.jp/kegg/brite - Contains functional hierarchies and binary relation-

ships between biological objects.

KEGG PATHWAY www.genome.jp/kegg/pathway.html- Collection of pathway maps for a large num-

ber of organisms.

LOCATE locate.imb.uq.edu.au- Curated database describing membrane organization and

sub cellular localization of proteins from mouse and human.


Introduction


Genome scale models

Resources

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Final Remarks

Resources




for mammalian cellsContinued II


MINT mint.bio.uniroma2.it/mint- Contains experimentally verified protein-protein interac-

tions.

MIPS mips.helmholtz-muenchen.de/proj/ppi- Collection of manually curated high-quality

PPI data from the scientific literature.MetaCyc metacyc.org- Experimentally determined pathways and enzymes.

Predictome visant.bu.ed- Predicted functional associations among genes and proteins.

PRIME prime.ontology.ims.u-tokyo.ac.jp- Contains integrated gene/protein information au-

tomatically extracted.

PSIbase psibase.kobic.re.kr- Molecular interaction database.

Sigmoid www.sigmoid.org- Cellular signaling and metabolic pathways models.

SGMP www.signaling-gateway.org/molecule- Proteins involved in cellular signaling.SIMPATHWAY www.helios-bioscience.com/technologies-molecular.php - Commercial molecular

interaction database. Includes an integrated intracellular signal transduction net-

work, query and data representation software.

STRING string.embl.de- Database of known and predicted protein interactions.

ResNet www.ariadnegenomics.com/products/databases- Commercial database of molec-

ular interactions for human, rat and mouse.


Introduction


Genome scale models

Resources

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Final Remarks

Resources



Developing a mathematical modelContinued

There are a number of different representations of metabolism that can be

used, which vary in the level of of detail, the level of available information,

and the type of predictive capacity of the model.

Essential to define beforehand the purpose and scope of the model,

assumptions and restrictions

The modeling strategy to be followed depends on the final objective of the

analysis, the characteristics of the experimental data and the availabledetails of the structure of the network.


Introduction


Genome scale models

Resources

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Final Remarks

Resources



Physicochemical and Statistical modelsPhysicochemical Models

Describe the different kinds of molecular transformations in biological

systems (association, translocation and modification through reactions).

Each equation in the model can be associated to a specific process in the

system and each parameter assigned a physical interpretation.

For well studied organisms, a high level of detail is available in terms of

pathway components and interactions. In this case the mathematical

model is only a translation of the physical description of the system toequations.


IntroductionMathematical model development

Genome scale modelsResources

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Final Remarks

Resources



Physicochemical and Statistical modelsStatistical Models

Abundant but intricate data, and a scarce amount of information available

about the structure of the system, data can be used to obtain some

insight about the system using data-driven statistical modeling.




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Final Remarks

Resources



Stages for the development of a mechanistic model




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p


Final Remarks



General form of a dynamic mass balance for metabolites j

dCj

dt = Sr(C,p)

Cis the concentrations vector (n1)

Cj is the concentration of metabolitej

ris the vector of metabolic reaction rates (m1) which is a function of the metabolites

concentration vector (n1) and a parameter vector p,

Sis the stoichiometric matrix that contains the stoichiometric coefficients that relate the n species

in them reactions that form the network. This generates a system of n differential equations and

munknown time-dependent fluxes.

Under steady state assumption

Sr(C,p) = 0




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Final Remarks



Model SEEDHigh-throughput generation, optimization and analysis of genome-scale metabolic models

Henryet al. 2010, Nature Biotechnology 28:977

Genome-scale metabolic models valuable for predicting organism

phenotypes from genotypes.

Model SEED: accelerate new models development with genome

sequencing.

Web-based resource for high-throughput generation, optimization and

analysis of genome-scale metabolic models. Automated from assembled

genome sequence.Applied to generate 130 genome-scale metabolic models. 22 validated

against available data, average model accuracy of 87% after optimization.




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Final Remarks








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Final Remarks





Research question Unique capability of Model SEED

What are the essential genes in my

newly sequenced organism?

Functioning draft models enable essential genes to

be predicted.

What defined culture conditions will

my organism grow in?

Functioning metabolic models enable culture condi-

tions to be predicted.

What are some global trends in mi-

crobial metabolic behavior?

Functioning draft models for many diverse microbes

enable the exploration of such trends.

How accurate are the annotations for

my organism of interest?

Functioning models convert annotations into predic-

tions of experimentally observable phenotypes.

What are the knowledge gaps in

genome annotation in general?

Recurring annotation gaps can be identified by com-

paring gaps found in every model.

What alternative pathways are

present in an organisms metabolic

reaction network?

Comprehensive reaction database, functional role

mappings and updated annotations enable identifica-

tion of alternative pathways.

How can I identify and fill the gaps in

my genome annotations?

Directed searches may be performed for functions

added during model auto-completion and optimiza-

tion.Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation


M d l f M li C ll


Ph i h i l d l

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Final Remarks



Pathway ToolsPathwayTools version 13.0: integrated software for pathway/genome informatics and systems

biologyKarp et al. 209, Briefings in Bioinformatics 11:40

Software environment for creating a Pathway/Genome Database (PGDB).

PGDB such as EcoCyc integrates the understanding of genes, proteins,

metabolic network and regulatory network of an organism.

Multiple computational inferences: prediction of metabolic pathways,

metabolic pathway hole fillers and operons.

Interactive editing of PGDBs by DB curators. It supports web publishing of

PGDBs, and provides query and visualization tools.

More than 800 PGDBs have been created using Pathway Tools, including

curated DBs for model organisms. PGDBs can be exchanged using a

peerto- peer PGDB Registry DB sharing system.






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Final Remarks








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Final Remarks



Modeling resources and databases for systems biology

research in mammalian cells


BioModels biomodels.net- Model collection in SML format. Includes data resources and publica-

tion references. Supports model visualization.

CellDesigner www.celldesigner.org - Diagram editor for drawing gene-regulatory and biochemical

networks. Models are stored in SBML format.

Cellerator www.cellerator.org- Mathematica package for automatic generation of differential equa-

tions of biochemical networks.

CellNetAnalyzer www.mpi-magdeburg.mpg.de/projects/cna/cna.html - MATLAB based software pack-

age for structural and functional analysis of networks based on their topology.

CellWare www.bii.a-star.edu.sg/achievements/applications/cellware - Grid based tool for model-

ing, simulation, parameter estimation and optimization.

COBRA systemsbiology.ucsd.edu/downloads/COBRAToolbox - Matlab package for quantitative

prediction of cellular behavior using a constraint-based approach.COPASI www.copasi.org - Supports models in the SBML standard. Uses ODEs or Gillespies

stochastic simulation algorithms.

DOQCS doqcs.ncbs.res.in- Database of Quantitative Cellular Signaling. Repository of models

of signaling pathways. It includes reaction schemes, concentrations, rate constants, as

well as annotations on the models.

E-Cell www.e-cell.org- Platform for modeling, simulation and analysis of complex systems.






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Final Remarks



Modeling resources and databases for systems biology

research in mammalian cellsContinued


JDesigner www.sys-bio.org- Visual network design tool for systems biology.

Metatool pinguin.biologie.uni-jena.de/bioinformatik/networks - Program for calculating elemen-

tary modes, compatible with octave and Matlab. Distributed with CellNetAnalyzer.

OptFlux www.optflux.org - Tools for in silicometabolic engineering.

Pathway

Analyser

sourceforge.net/projects/pathwayanalyser - Flux based analyses and simulations on

SBML Models.

SBRT www.ieu.uzh.ch/wagner/software/SBRT - Systems Biology Research Tool. Software

platform for analyzing stoichiometric networks.

SNA www.bioinformatics.org/project/?group_id=546 - Toolbox for steady state analysis of

metabolic networks.

STOCKS www.sysbio.pl/stocks- Software for stochastic simulation of biochemical processes withGillespie algorithm. Supports SBML.

Virtual Cell www.vcell.org- Web-based environment for modeling and simulation of cell biology.

WebCell webcell.kaist.ac.kr- Integrated simulation environment for the analysis of cellular net-

works over the web.

YANAsquare yana.bioapps.biozentrum.uni-wuerzburg.de - Software package for the analysis of

metabolic networks. It incorporates database extraction and visualization tools.




Steady state modelsDynamic models

Glycosilation

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Final Remarks

Glycosilation

Commercial models

Hybridoma ModelModeling Hybridoma Cell Metabolism Using a Generic Genome-Scale Metabolic Model ofMus


Few works associated with metabolic models of mammalian cellsdue to the

complexity of mammalian cell metabolism, reduced availability of information

about the system and difficulties in measuring in vivo metabolic fluxes in

mammalian cells.

A reconstruction of the cellular metabolic network ofMus musculuswas

presented recently by Sheikhet al.

Based on annotated genomic data, pathway databases, and currently

available biochemical and physiological information from KEGGIt captures carbon, energy, and nitrogen metabolism in a

compartmentalized setting, including transport reactions between the

compartments and the extracellular medium.

It considers 872 internal metabolites and 1220 reactions.





Glycosilation

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Final Remarks

Glycosilation

Commercial models

Hybridoma ModelModeling Hybridoma Cell Metabolism Using a Generic Genome-Scale Metabolic Model ofMus


MFA for underdetermined system.

Linear programming considering three objective functions: maximization

of cell growth, minimization of substrate uptake rate, and maximization of

production of monoclonal antibody.

Predicts growth, lactate, and ammonia production given glucose, oxygen,

and glutamine uptake, but it fails to predict alanine production, illustrating

the limitations of the model.Improved by Selvarasuet al. including biomass and mAb synthesis as

well as updated lipid, amino acids and nucleotide metabolic pathways.

Strategies for increased cell density and mAb productivity identified.





Glycosilation

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Final Remarks

y

Commercial models

Improved Hybridoma ModelGenome-scale modeling and in silico analysis of mouse cell metabolic network

Selvarasuet al. 2010, Mol.BioSyst. 6:152

Improved by Selvarasuet al. including additional information on

geneprotein-reaction association, and improved network connectivity

through lipid, amino acid, carbohydrate and nucleotide biosyntheticpathways. Considers biomass and mAb synthesis.

Strategies for increased cell density and mAb productivity identified.

In silicomouse model can be exploited for understanding and

characterizing cellular physiology, identifying potential cell engineeringtargets for the enhanced production of recombinant proteins and

developing diseased state models for drug targeting.





Glycosilation

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Final Remarks Commercial models

Improved Hybridoma ModelGenome-scale modeling and in silico analysis of mouse cell metabolic network

Selvarasuet al. 2010, Mol.BioSyst. 6:152





Glycosilation

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CHO ModelA detailed metabolic flux analysis of an underdetermined network of CHO cells

Zamorano et al. 2010, Journal of Biotechnology 150:497

MFA for CHO cells recently was performed by Zamorano et al., 2010.

Network involving 100 reactions.

Used to assess the efficiency of flux analysis when using a small set of

extracellular measurements (underdetermined mass balance system)

Narrow intervals found for most fluxes.

Confirmed by Goudaret al. (Metabolic flux analysis of CHO cells in perfusion culture by metabolite balancing

and 2D [13C,1H] COSY NMR spectroscopy, 2010 Metabolic Engineering 12:138). The fluxes at the pyruvatebranch point were almost equally distributed between lactate and the TCA

cycle (55% and 45%, respectively).





Glycosilation

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CHO ModelMetabolic Flux Analysis of CHO Cell Metabolism in the Late Non-Growth Phase

Senguptaet al. 2011, Biotechnology and Bioengineering 108:82

Stoichiometric model for CHO cells used in combination with steady-state

isotopomer balancing to evaluate flux distribution in the late non-growth

phase.

Almost all of the consumed glucose is diverted towards PPP with a high

NADPH production. Almost all of the pyruvate produced from glycolysis

entered the TCA cycle with little or no lactate production.

Similar conclusion from Ahn et al. (Metabolic flux analysis of CHO cells at growth and non-growth phases

using isotopic tracers and mass spectrometry, 2011 Metabolic Engineering 13:598) for CHO cells at the growthphase and early stationary phase in fed-batch culture, using a isotopic

tracers and a compartmentalized metabolic network model of CHO cell

metabolism. Changes in metabolic fluxes were identified.




Fi l R k


Glycosilation

C i l d l

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CHO ModelComparative Metabolic Analysis of Lactate for CHO Cells in Glucose and Galactose

Wilkens et al. 2011, Biotechnology and Bioprocess Engineering 16:714




Final Remarks


Glycosilation

Commercial models

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Dynamic Model for HybridomaModeling Amino Acid Metabolism in Mammalian Cells-Toward the Development of a Model Library

Kontoravdi et al. 2007, Biotechnol.Prog.23:1261

Single unstructured model structure for describing the cell growth kinetics

and metabolism of HEK-293 and CHO cells.

The network considered is consistent with the information available in

literature sources and the pathways available in KEGG.




Final Remarks


Glycosilation

Commercial models

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Dynamic Model for HybridomaDynamic model of CHO cell metabolism

Nolanet al. 2011, Metabolic Engineering 13:108

Detailed model for CHO cells and a framework for simulating the

dynamics of metabolic and biosynthetic pathways of CHO cellsin

fed-batch.

It Considers the effects of temperature shift, seed density, specificproductivity, and metabolite concentrations on viable cell density (VCD),

antibody, lactate, asparagine, and the redox state.

The model defines a subset of intracellular reactions with kinetic rate

expressions , which are used to calculate pseudo-steady state flux

distributions and extracellular metabolite concentrations at discrete timepoints.

The model provides time profiles for all metabolites in the reactor and

successfully predicts the effects of several process perturbations on cell

growth and product titer.




Final Remarks


Glycosilation

Commercial models

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Dynamic Model for HybridomaDynamic model of CHO cell metabolism

Nolanet al. 2011, Metabolic Engineering 13:108




Final Remarks


Glycosilation

Commercial models

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Protein Glycosilation

Protein glycosylation is a post-translational modification of high

importance towards the function, immunogenicity, and efficacy of

recombinant glycoprotein therapeutics. Affects pharmacokinetics and

protein physiochemical characteristics.

Obtaining a consistent glycoform profile in production is desired due to

regulatory concerns.

Protein glycosylation control needs to be studied on a case-by-case basis

since there are often conflicting results with respect to a control variable

and the resulting glycosylation.

Gene expression analysis and systems biology have been used for a

multivariate interpretation of potential control variables.




Final Remarks


Glycosilation

Commercial models

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GlycosilationOptimal and consistent protein glycosylation in mammalian cell culture

Hossleret al. 2009, Glycobiology 19:936

Studies have shown some degree of protein glycosylation control in

mammalian cell culture, through cellular, media, and process effects.




Final Remarks


Glycosilation

Commercial models

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Protein GlycosilationGene expression analysis and systems biology

Gene expression analysis has provided information as to how

glycosylation pathway genes both respond to culture environment and

facilitate changes in the glycoform profile.Systems biology has allowed researchers to model the glycosilation

pathway as well defined, inter-connected systems, allowing for the in

silico testing of pathway parameters that would be difficult to test

experimentally.

Important tools for the production of optimal glycoform profiles on a

consistent basis.




Final Remarks


Glycosilation

Commercial models

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Glycosilation ModelSystems Analysis of N-Glycan Processing in Mammalian Cells

Hossleret al. 2007, PLoS ONE 2(8):e713

Glycosilation pathway: network in which a relatively small number of

enzymes give rise to a large number of N-glycans as the reaction

intermediates and terminal products.

Mathematical model of glycan biosynthesis in the Golgi and analysis of

reaction variables on the resulting glycan distribution.

Four continuous mixing-tanks (4CSTR) and four plug-flow reactors

(4PFR) in series.

A sufficient holding time is needed to produce terminally-processed

glycans. Altering enzyme concentrations has a complex effect on the final

glycan distribution, as many reaction steps in the network are affected.




Final Remarks


Glycosilation

Commercial models

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Glycosilation modelSystems Analysis of N-Glycan Processing in Mammalian Cells

Hossleret al. 2007, PLoS ONE 2(8):e713

Model was used to assess whether a homogeneous glycan profile can be

created through metabolic engineering.

Results indicate that by the spatial localization of enzymes to specific

compartments, all terminally processed N-glycans can be synthesized as

homogeneous products with a sufficient holding time in the Golgi

compartments.Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation

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Final Remarks


Glycosilation

Commercial models
mailto:[email protected]

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Virtual Liver




Final Remarks

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Final Remarks

Development of dynamic and genome based models for mammalian cells

require abundant high quality experimental data, software, algorithms and

visualization techniques.

Mathematical models can assist on strain design and complement

metabolic methods in order to increase productivity and improve process

and product quality. Available models are limited due to the scope of the

solutions, and availability and accuracy of the data used on their

construction.

The metabolic networks modeling field will continue to grow. Importantapplications in predicting mammalian cell behavior for application in

productive processes as well as on the medical field.




Final Remarks

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Questions


qbd models 2011

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