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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
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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
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Introduction
Mathematical model development
Models for Mammalian Cells
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
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Introduction
Mathematical model development
Models for Mammalian Cells
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).
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
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Introduction
Mathematical model development
Models for Mammalian Cells
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
I t d ti
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Introduction
Mathematical model development
Models for Mammalian Cells
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction
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Introduction
Mathematical model development
Models for Mammalian Cells
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction
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Introduction
Mathematical model development
Models for Mammalian Cells
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
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction Genome scale models
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Introduction
Mathematical model development
Models for Mammalian Cells
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).
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction Genome scale models
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Introduction
Mathematical model development
Models for Mammalian Cells
Final Remarks
Genome scale models
Resources
Physicochemical models
Reconstruction tools
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction Genome scale models
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Introduction
Mathematical model development
Models for Mammalian Cells
Final Remarks
Genome scale models
Resources
Physicochemical models
Reconstruction tools
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
Introduction Genome scale models
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Mathematical model development
Models for Mammalian Cells
Final Remarks
Resources
Physicochemical models
Reconstruction tools
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/.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction Genome scale models
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Mathematical model development
Models for Mammalian Cells
Final Remarks
Resources
Physicochemical models
Reconstruction tools
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction Genome scale models
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Mathematical model development
Models for Mammalian Cells
Final Remarks
Resources
Physicochemical models
Reconstruction tools
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction Genome scale models
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Mathematical model development
Models for Mammalian Cells
Final Remarks
Resources
Physicochemical models
Reconstruction tools
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction
M th ti l d l d l t
Genome scale models
R
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Mathematical model development
Models for Mammalian Cells
Final Remarks
Resources
Physicochemical models
Reconstruction tools
Web resources and databases for components and pathways
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction
Mathematical model development
Genome scale models
Resources
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Mathematical model development
Models for Mammalian Cells
Final Remarks
Resources
Physicochemical models
Reconstruction tools
Web resources and databases for components and pathways
for mammalian cellsContinued II
Name Web addressand description
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction
Mathematical model development
Genome scale models
Resources
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Mathematical model development
Models for Mammalian Cells
Final Remarks
Resources
Physicochemical models
Reconstruction tools
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
Introduction
Mathematical model development
Genome scale models
Resources
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Mathematical model development
Models for Mammalian Cells
Final Remarks
Resources
Physicochemical models
Reconstruction tools
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Genome scale modelsResources
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Mathematical model development
Models for Mammalian Cells
Final Remarks
Resources
Physicochemical models
Reconstruction tools
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Genome scale modelsResources
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Mathematical model development
Models for Mammalian Cells
Final Remarks
Resources
Physicochemical models
Reconstruction tools
Stages for the development of a mechanistic model
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Genome scale modelsResources
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p
Models for Mammalian Cells
Final Remarks
Physicochemical models
Reconstruction tools
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
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Genome scale modelsResources
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Models for Mammalian Cells
Final Remarks
Physicochemical models
Reconstruction tools
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Genome scale modelsResources
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Models for Mammalian Cells
Final Remarks
Physicochemical models
Reconstruction tools
Model SEEDHigh-throughput generation, optimization and analysis of genome-scale metabolic models
Henryet al. 2010, Nature Biotechnology 28:977
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Genome scale modelsResources
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Models for Mammalian Cells
Final Remarks
Physicochemical models
Reconstruction tools
Model SEEDHigh-throughput generation, optimization and analysis of genome-scale metabolic models
Henryet al. 2010, Nature Biotechnology 28:977
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
IntroductionMathematical model development
M d l f M li C ll
Genome scale modelsResources
Ph i h i l d l
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Models for Mammalian Cells
Final Remarks
Physicochemical models
Reconstruction tools
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Genome scale modelsResources
Physicochemical models
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Models for Mammalian Cells
Final Remarks
Physicochemical models
Reconstruction tools
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Genome scale modelsResources
Physicochemical models
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Models for Mammalian Cells
Final Remarks
Physicochemical models
Reconstruction tools
Modeling resources and databases for systems biology
research in mammalian cells
Name Web addressand description
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Genome scale modelsResources
Physicochemical models
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Models for Mammalian Cells
Final Remarks
Physicochemical models
Reconstruction tools
Modeling resources and databases for systems biology
research in mammalian cellsContinued
Name Web addressand description
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Steady state modelsDynamic models
Glycosilation
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Models for Mammalian Cells
Final Remarks
Glycosilation
Commercial models
Hybridoma ModelModeling Hybridoma Cell Metabolism Using a Generic Genome-Scale Metabolic Model ofMus
musculusSheikhet al. 2005, Biotechnol.Prog.21:112
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Steady state modelsDynamic models
Glycosilation
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Models for Mammalian Cells
Final Remarks
Glycosilation
Commercial models
Hybridoma ModelModeling Hybridoma Cell Metabolism Using a Generic Genome-Scale Metabolic Model ofMus
musculusSheikhet al. 2005, Biotechnol.Prog.21:112
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Steady state modelsDynamic models
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Steady state modelsDynamic models
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
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Steady state modelsDynamic models
Glycosilation
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Final Remarks Commercial models
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).
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Steady state modelsDynamic models
Glycosilation
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Final Remarks Commercial models
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Fi l R k
Steady state modelsDynamic models
Glycosilation
C i l d l
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Final Remarks Commercial models
CHO ModelComparative Metabolic Analysis of Lactate for CHO Cells in Glucose and Galactose
Wilkens et al. 2011, Biotechnology and Bioprocess Engineering 16:714
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Final Remarks
Steady state modelsDynamic models
Glycosilation
Commercial models
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Final Remarks Commercial models
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Final Remarks
Steady state modelsDynamic models
Glycosilation
Commercial models
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Final Remarks Commercial models
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Final Remarks
Steady state modelsDynamic models
Glycosilation
Commercial models
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Final Remarks Commercial models
Dynamic Model for HybridomaDynamic model of CHO cell metabolism
Nolanet al. 2011, Metabolic Engineering 13:108
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Final Remarks
Steady state modelsDynamic models
Glycosilation
Commercial models
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Final Remarks Commercial models
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Final Remarks
Steady state modelsDynamic models
Glycosilation
Commercial models
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Final Remarks Commercial models
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Final Remarks
Steady state modelsDynamic models
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Final Remarks
Steady state modelsDynamic models
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Final Remarks
Steady state modelsDynamic models
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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IntroductionMathematical model development
Models for Mammalian Cells
Final Remarks
Steady state modelsDynamic models
Glycosilation
Commercial models
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Virtual Liver
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
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.
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
IntroductionMathematical model development
Models for Mammalian Cells
Final Remarks
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Questions
Ziomara P. Gerdtzen From analysis to design: Models for metabolism and glycosylation
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