Vol 13, Issue 2, 244-253, February 2003
LETTER
Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network
Jochen Förster1,3,4,
Iman Famili2,4,
Patrick Fu2,
Bernhard Ø. Palsson2 and
Jens Nielsen1,5
1Center for Process Biotechnology, BioCentrum-DTU,
Technical University of Denmark, DK-2800 Lyngby, Denmark;2
Department of Bioengineering, University of California San
Diego, La Jolla, California 92093, USA
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ABSTRACT
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The metabolic network in the yeast Saccharomyces cerevisiae
was reconstructed using currently available genomic, biochemical, and
physiological information. The metabolic reactions were
compartmentalized between the cytosol and the mitochondria, and
transport steps between the compartments and the environment were
included. A total of 708 structural open reading frames (ORFs) were
accounted for in the reconstructed network, corresponding to 1035
metabolic reactions. Further, 140 reactions were included on the basis
of biochemical evidence resulting in a genome-scale reconstructed
metabolic network containing 1175 metabolic reactions and 584
metabolites. The number of gene functions included in the reconstructed
network corresponds to  16% of all characterized ORFs in S.
cerevisiae. Using the reconstructed network, the metabolic
capabilities of S. cerevisiae were calculated and compared
with Escherichia coli. The reconstructed metabolic network is
the first comprehensive network for a eukaryotic organism, and it may
be used as the basis for in silico analysis of phenotypic functions.
[Supplemental material is available online at www.genome.org. The
detailed genome-scale reconstructed model of
Saccharomyces cerevisiae can be found at
http://www.cpb.dtu.dk/models/yeastmodel.html or
http://geneticcircuits.ucsd.edu/organisms/yeast.html.]
Baker's yeast, Saccharomyces cerevisiae,
was the first eukaryotic genome that was fully
sequenced, annotated, and made publicly available. (Goffeau 1997 ).
Along with its industrial importance, S. cerevisiae serves as
a model organism for understanding and engineering eukaryotic cell
function (Dujon 1996; Botstein et al. 1997). There have been many
studies aiming to unravel the function of orphan genes in the genome
(Oliver 1998; Entian et al. 1999; Winzeler et al. 1999; Hughes et al.
2000), and various functional genomics techniques were first
implemented in S. cerevisiae. The first genome-wide cDNA array
study was designed for S. cerevisiae (DeRisi et al. 1997),
which subsequently resulted in a large number of studies on expression
profiling (Hughes et al. 2000). Large-scale studies have been conducted
to investigate the protein–protein interactions (Uetz et al. 2000),
including the use of two-hybrid systems (Ito et al. 2001). These
studies and a large body of biochemical literature now enable us to
functionally integrate the wealth of available genetic, molecular, and
biochemical information for S. cerevisiae.
Integration of knowledge at different levels in the cascade from genes
to protein and further to metabolic fluxes in a genome-scale network
will be pivotal for understanding how the individual components in the
system interact and influence overall cell function. The approach of
analyzing a complex process at different levels was illustrated in a
recent study in which expression profiles in different mutants were
compared with protein levels in order to unravel the structure of the
complex galactose (GAL)–regulon (Ideker et al. 2001). This coordinated
and multilevel effort may have significant influence on designing
metabolic engineering strategies (Østergaard et al. 2000a,b). These
interactions must now be quantified through the use of a mathematical
framework—something that involves a significant research effort, but
which is believed to lead to fundamental new insights into cellular
function (Schilling et al. 1999; Endy and Brent 2001). To gain insight
into cell synthesis and the metabolic capability through mathematical
modeling, a natural first step is to reconstruct the underlying
metabolic network, as this is responsible for the synthesis capacity of
the cell, and, as well, it allows detailed analysis of the interactions
between the individual pathways functioning in the cell. Recently,
genome-scale metabolic networks were reconstructed for prokaryotic
cells (Edwards and Palsson 1999; Covert et al. 2001), and it was
demonstrated how such reconstructed metabolic models allow direct
correlation between the genomic information and metabolic activity at
the flux level. In these reconstructed metabolic networks, which
consist of several hundred reactions and several hundred metabolites,
it was possible to simulate the phenotypic behavior under different
genetic conditions and physiological environments (Edwards et al.
2001).
Here, we present the reconstruction of the metabolic network of S.
cerevisiae, the first genome-scale in silico metabolic network for
a eukaryotic cell. Characteristics of eukaryotic cells, such as
compartmentation of reactions and involvement of transport steps across
cellular membranes, were considered in the network. The structure and
metabolic capabilities of the metabolic network of S.
cerevisiae were compared with a genome-scale reconstructed
metabolic network of Escherichia coli (Edwards and Palsson
2000).
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RESULTS AND DISCUSSION
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Genome-Scale Metabolic Reconstruction
A genome-scale reconstruction of a metabolic network is currently a
nonautomated and iterative decision-making process that can easily
require up to one man-year to assemble a satisfying reaction list
specifically collected for one organism. Once a metabolic network is
reconstructed, mathematical methods, such as convex analysis and linear
programming, can be applied to analyze structural properties, such as
connectivity, etc., and simulation of cellular behavior under different
genetic and physiological conditions can be conducted. For example,
results may be used for the development of metabolic engineering
strategies for the construction of strains with desired and improved
properties. The present work focuses on the reconstruction of the
metabolic network of the yeast S. cerevisiae. Some structural
properties of the network and capabilities for biomass precursor and
amino acid production by the network were evaluated.
During the reconstruction process, a number of decisions on each
reaction needed to be taken (Fig. 1). Is an
enzyme present in the organism? Which reaction catalyzes the enzyme,
and what is the stoichiometry of that reaction? If cofactors are
involved, is the reaction, for example, reduced nicotinamide-adenine
dinucleotide (NADH) or reduced nicotinamide-adenine dinucleotide
phosphate (NADPH) dependent, or can the enzyme use both cofactors? Is
the reaction reversible or irreversible? Where is the reaction
localized? Furthermore, for modeling purposes, information on the
biomass composition, and on growth and nongrowth-dependent
adenosine-5'-diphosphate (ATP) requirements has to be available. Once
the metabolic pathways have been reconstructed or a complete and
satisfying reaction list is available, the model can be used to
simulate metabolic behavior. However, before it can be applied, the
validity of the reaction list for modeling purposes has to be tested.
For model validation, we compared computed results of anaerobic and
aerobic chemostat cultivation at a dilution rate of 0.1 h–1
to experimental results from Nissen et al. (1997) and Overkamp et al.
(2000) (data not shown).
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Figure 1. Reconstruction of the metabolic network of S. cerevisiae.
Based on the available information from the genome annotation,
biochemical pathway databases, biochemistry textbooks, and recent
publications, a genome-scale metabolic network of S.
cerevisiae was designed. Additional physiological constraints were
considered and modeled, such as growth and nongrowth-dependent ATP
requirements. Compartmentation was included, and cofactor requirements
of all model reactions were inspected carefully, thereby, reactions
that created a net transhydrogenic effect were additionally
constrained. Regulatory information was not included. The picture of
the pathway map was taken from the KEGG database (www.genome.ad.jp).
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A genome-scale reconstruction is based on a thorough literature
examination in order to extract the current state of the art on known
metabolic reactions. Here, online pathway databases, biochemistry
textbooks, and the annotated genome sequence have to be consulted, and,
especially, extraction of information on metabolic reactions from
journal publications is essential.
In detail, the reconstruction process for S. cerevisiae was
initiated by downloading a gene catalog from the KEGG metabolic
pathways database
(http://kegg.genome.ad.jp/dbget-bin/get_htext?S.cerevisiae.kegg+-f+T+w+C).
The information contained in this gene catalog is organized into
traditional pathways, such as glycolysis, pentose phosphate pathway,
any amino acid biosynthetic pathway, etc., and was used throughout the
reconstruction process. Details are presented for the ORF name, gene
name, enzyme name, EC number (if available), and SWISS-PROT entry name,
and the KEGG metabolic chart may be used to reconstruct specifically
the metabolic network for S. cerevisiae. The information on EC
numbers was used to search for the stoichiometry of the reactions using
the Enzyme nomenclature database (http://www.expasy.ch/enzyme/). To
create a comprehensive reaction list, the present information in the
reconstructed network was checked for whether genes were missing using
the MIPS Comprehensive Yeast Genome Database (CYGD)
(http://mips.gsf.de/proj/yeast/) and Saccharomyces Genome
Database (SGD) (http://genome-www.stanford.edu/Saccharomyces/). The
stoichiometry of a reaction in the Enzyme nomenclature database and
also in many pathway databases is often presented in a general form,
such as, for example, for NADH-dependent alcohol dehydrogenase, as
alcohol dehydrogenases generally accept a wide range of different
aldehydes or alcohols as substrate. Hence, the stoichiometry is found
as follows: An aldehyde + NADH <–> An alcohol + NAD_upper (+).
For reconstruction and modeling purposes, this information is
insufficient, and through an additional database and literature search,
the S. cerevisiae-specific substrates and products were
identified, here, acetaldehyde and ethanol. Also, a large number of
reactions involve cofactors utilization, and for many of these
reactions, the cofactor requirements have not yet been completely
elucidated, for example, whether reactions only require either NADH or
NADPH as a cofactor or whether the enzyme can use both cofactors. Some
reactions are known to involve both cofactors. For example, the
mitochondrial aldehyde dehydrogenase encoded by ALD4 may use
both NADH and NADPH as a cofactor (Remize et al. 2000). In such cases,
two reactions were included in the reconstructed metabolic network.
Concerning localization, all reactions were localized into the two main
compartments, cytosol and mitochondria, as most of the common metabolic
reactions in S. cerevisiae take place in these compartments.
Reactions located in vivo in other compartments, or reactions for which
no information is presently available on the localization, were assumed
to be cytosolic. Information on localization was mainly extracted from
CYGD and YPD. All corresponding metabolites were assigned appropriate
localization, and a link between cytosol and mitochondria was
established through either known transport and shuttle systems or
through inferred reactions to meet metabolic demands. To differentiate
metabolites in the mitochondria and cytosol, metabolites that were
located in the mitochondria for a specific reaction end with a small m
(see Web links).
Whether reactions are irreversible or reversible was extracted from
pathway databases and additional literature (see Methods). When no
information was available, reactions were initially defined to be
reversible.
For enzyme complexes such as succinate dehydrogenase, fatty acid
synthase, and complexes of the electronic transport chain, a single
reaction for the corresponding genes was defined.
Further considerations were taken into account to preserve some unique
features of the S. cerevisiae metabolism. S.
cerevisiae lacks a gene that encodes the enzyme transhydrogenase.
Insertion of a corresponding gene from Azetobacter vinelandii
in S. cerevisiae has a major impact on its phenotypic
behavior, especially under anaerobic conditions (Nissen et al. 2001).
As a result, reactions that create a net transhydrogenic effect in the
model were either constrained to zero or forced to become irreversible.
For instance, the flux carried by NADH-dependent glutamate
dehydrogenase (Gdh2p) was constrained to zero to avoid the appearance
of a net transhydrogenase activity through coupling with the
NADPH-dependent glutamate dehydrogenases (Gdh1p and Gdh3p).
Decisions also needed to be made as to whether a reaction should be
present in the reconstructed metabolic model, although no corresponding
confirmed gene function is available. Many reactions have shown
experimentally that they must be present in S. cerevisiae or
they must simply be present to allow the formation of biomass. A
typical example for the former case is the oxidative branch of the
pentose phosphate pathway, which is the main supplier of cytosolic
NADPH. It is not currently known whether the second step in the pentose
phosphate is driven nonenzymatically or enzymatically by
6-phosphogluconolactonase. Because the oxidative pathway has to be
active in S. cerevisiae and because the S. cerevisiae
genome contains at least four possible sites for a
6-phosphogluconolactonase (SOL1, SOL2, SOL3, SOL4), the
corresponding reactions were included in the model.
The reconstruction process led to a set of biochemical reactions that
might be used in constructing stoichiometric models of metabolism using
metabolite balancing (Stephanopoulos et al. 1998; Edwards et al. 1999;
Gombert and Nielsen 2000). These models simply rely on mass balances
around metabolic intermediates and allow simulation of steady state
behavior, without inclusion of information on regulatory and dynamics
information. Further to the information obtained on the stoichiometry,
localization, and reversibility of the reactions as described before,
knowledge on the biomass composition needed to be computed as a drain
of precursors or building blocks into biomass. Table
1 shows the biomass composition that was
considered in the stoichiometric model and details on the calculation
can be found at the mentioned Internet links. Even though the biomass
composition changes under different physiological conditions, it may be
assumed constant, as it has been demonstrated that a change in biomass
composition merely changes the simulation results (Varma et al. 1993).
Furthermore, information on the growth-associated ATP requirements
(Stouthamer 1979) (maintenance of membrane potentials, turn-over of
macromolecules, etc.) and on the ATP cost that is required for the
polymerization of amino acids and nucleotides needed to be available.
The polymerization cost was calculated by Verduyn et al. (1991) to be
23.92 mmole ATP/g DW, and the growth-associated ATP maintenance was
found by fitting the reconstructed model to the experimentally
determined biomass yield of 0.51 g DW/g glucose (Verduyn 1991). Hereby,
this contribution was estimated to be 35.36 mmole ATP/g DW. Thus, the
sum of these two contributions is 59.28 mmole ATP/g DW, which was
included in the stoichiometric model of S. cerevisiae. The ATP
costs for the synthesis of building blocks, which could be derived
directly from the model, was found to be 9.89 mmole ATP/g DW and, thus,
the total ATP requirement for biomass growth was estimated to be 69.2
mmole ATP/g DW, which falls into the range of experimentally measured
values (Verduyn et al. 1990; Verduyn 1991). Finally, a
nongrowth-associated ATP requirement of 1 mmole/g DW/h was assumed to
be required (Stouthamer 1979; Verduyn et al. 1990).
At this step, a first model was designed that could be applied to
linear programming to simulate cellular behavior. The model was used to
minimize the glucose uptake rate at a dilution rate of 0.1
h–1 for aerobic and anaerobic conditions, and results were
compared with experimental results (data not shown). Initially, no
agreement between computed and experimental results could be found.
However, through a rigorous investigation of flux distributions and
shadow price analyses, it was possible to adjust and correct the
initial reaction list until simulated results were in agreement for
both cases. At this step, the reconstruction process was considered to
be finished.
Characteristics of the Reconstructed Network
The metabolic reconstruction process resulted in a network that
consisted of 1175 metabolic reactions and 584 metabolites (Table
2). A total of 708 metabolic ORFs were
included in the reconstructed network, to which 1035 reactions were
assigned. Some 595 metabolic ORFs contained at least one enzyme
commission (EC) number. This corresponded to 54% of all ORFs that
were assigned an EC number in the MIPS database (595 of 1098 ORFs;
Mewes et al. 1997). The remaining 46% correspond mainly to protein
kinases, protein phosphates, peptidases, and proteases, which have not
been included. Most of the enzymes are monofunctional, with 179 enzymes
being multifunctional. Currently, the number of protein-coding genes in
the S. cerevisiae genome is estimated by YPD (Costanzo et al.
2001) to be 6281, of which 4127 corresponding proteins were
characterized by genetics or biochemistry, and an additional 252
proteins were assigned functions by homology searches. The total number
of genes included in the reconstructed metabolic network corresponded
to 16% of all characterized ORFs.
On the basis of the protein complex catalog of MIPS (Mewes et al.
1997), 26 protein complexes, which catalyzed 88 reactions, were
identified in the reconstructed metabolic network. The metabolic
network contained 193 ORFs coding for isoenzymes, which catalyzed 239
reactions.
A total of 140 reactions were included on the basis of biochemical
evidence or physiological considerations, but currently with no
annotated ORF. More than 85% of these reactions were transport
reactions over the cytoplasmic or mitochondrial membrane, other
reactions were mainly involved in amino acid, nucleotide, and vitamin
metabolism (Table 3). A total of 349
transport reactions were included in the model, of which 287 were
involved in transporting metabolites in or out of the cell, and 62
transport reactions were involved in interchanging metabolites between
the cytosol and the mitochondria. Reversibility and irreversibility of
reactions was carefully accounted for in the reconstruction process, so
that approximately two-thirds of the reactions were assumed to be
irreversible.
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Table 3. Distribution of 140 Metabolic Reactions of S.
cerevisiae With Currently No Annotated ORF That Were Included
in the Reconstruction Process on the Basis of Physiological and
Biochemical Evidence
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A complete list of all included reactions can be downloaded at
http://www.cpb.dtu.dk/models/yeastmodel.html or
http://gcrg/organisms/yeast.html.
The most frequently used metabolic intermediates in the reconstructed
network are presented in Table 4, showing
that the most connected metabolites were involved in energy metabolism,
such as ATP, etc., in redox metabolism, such as NADPH, and in nitrogen
metabolism, such as glutamine and glutamate. The most frequently used
metabolite was proton, due to its participation in a high number of
proton-coupled transport reactions in the network. The number of
reactions involving proton was much higher than in metabolic networks
of prokaryotic microorganism reconstructed previously (Schilling 2000).
This difference was mainly due to the larger number of proton-driven
transport systems in S. cerevisiae—both in the cytosolic and
in the mitochondrial membrane. For comparison, the metabolic
connectivity of three prokaryotic organisms was examined (Table 4). In
all 4 reconstructed networks, the 12 most connected metabolites
represented the key intermediates of high-energy metabolism, redox
carriers, nitrogen metabolism, and 2- and 3-carbon intermediates.
Another important topological property of the reconstructed network was
the number of metabolites that participate in each reaction (Fig.
2). For all four networks, the most common
number was 4, representing the conversion of a substrate to a product
concomitant with the conversion of a coupled cofactor from one form to
another. Most frequently, this conversion involved ATP and ADP or the
translocation of H+.
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Table 4. Connectivity of Metabolites in Saccharomyces cerevisiae,
Escherichia coli, Haemophilus influenzae, and Helicobacter
pylori
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A total of 184 metabolites were not connected to the overall metabolic
network, showing that either reactions linking these metabolites to the
overall metabolic network have not been identified yet, proteins may
have been assigned wrong functions in the annotation process, or
S. cerevisiae has lost some of its metabolic functions during
evolution. This result shows that the information on the metabolic
network in S. cerevisiae is currently still incomplete,
however, the presented reconstructed metabolic network may be useful in
guiding the assignment of orphan ORFs (Förster et al. 2002) or
the identification of erroneous assignments. An example of an unlinked
metabolite was formaldehyde. It appeared only in one reaction in the
metabolic network through the inclusion of the gene SFA1,
which codes for a formaldehyde dehydrogenase. It is as yet unknown
which role formaldehyde plays in the natural environment of S.
cerevisiae. From the observation that S. cerevisiae
contains a formaldehyde as well as formate dehydrogenases, it may be
concluded that it either encounters these C1 compounds in its natural
environment or generates them in its metabolic network (J. Pronk, pers.
comm.).
The Reconstructed Metabolic Network of S. cerevisiae Versus MIPS Entries and Enzyme Commission Assignments
Throughout the reconstruction process, 595 ORFs have been assigned
an EC number, corresponding to 850 reactions. The most abundant enzyme
class is transferases followed by oxidoreductases, hydrolases, lyases,
ligases, and isomerases (Fig. 2.) A similar tendency was found for a
reconstructed metabolic network of E. coli (Edwards and
Palsson 2000), but this network contains more lyases than hydrolases.
The comparison of the number of ORFs with the number of reactions in
each enzyme category suggests that in S. cerevisiae,
isomerases, and transferases are less substrate specific (ratio of
number of reactions to number of ORFs) than any of the other enzyme
classes, whereas in E. coli transferases and hydrolases are
the least substrate-specific enzyme classes (Fig.
3).
The number of ORFs included in the metabolic reconstruction of S.
cerevisiae was also compared with the functional categories as
defined by MIPS. Not surprisingly, most of the ORFs fall into two main
classes, such as metabolism and energy, followed by the classes of
transport facilitation and cellular transport and transport mechanism.
Furthermore, for 430 ORFs, information about localization is
available as characterized by the functional category, cellular
organization (Table 5).
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Table 5. ORFs in the Saccharomyces cerevisiae Metabolic Network
Sorted Into Functional Categories According to the Munich Information
Center for Protein Sequences (MIPS)
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The large functional classes of metabolism and energy were investigated
in more detail (Fig. 4). Analyzing the
functional category metabolism (Fig. 4A) revealed that most ORFs are
involved in C-compound and carbohydrate metabolism, followed by amino
acid metabolism, lipid, fatty acid and isoprenoid metabolism, and
nucleotide metabolism. Comparison with MIPS entries showed that the
number of ORFs included in the reconstructed network is different from
the MIPS database. This difference is either due to exclusion of ORFs,
which are involved in regulation, such as ORFs encoding activators or
negative regulators, or exclusion of ORFs, which have assigned function
based on similarity searches. The second largest functional category
classified by the MIPS database is that of the lipid, fatty acid, and
isopreniod metabolism. However, during the metabolic reconstruction
process, more ORFs were included in the functional category, amino-acid
metabolism, than in the functional category, lipid, fatty acid, and
isoprenoid metabolism, based on a high number of ORFs that have been
assigned function using similarity searches. This result is consistent
with the fact that the amino acid metabolism is currently still better
understood than the much more complex lipid metabolism.
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Figure 4. ORFs sorted into the functional categories of MIPS, metabolism
(A) and energy (B). (AA) Amino-Acid Metabolism;
(N2/S) nitrogen and sulphur metabolism; (N) nucleotide metabolism; (P)
phosphate metabolism; (C) C-compound and carbohydrate metabolism; (L)
lipid, fatty-acid and isoprenoid metabolism; (V) metabolism of
vitamins, cofactors, and prosthetic groups; (S) secondary metabolism;
(EMP) glycolysis and gluconeogenesis; (PPP) pentose-phosphate pathway;
(TCA) tricarboxylic-acid pathway; (RES) respiration; (FER)
fermentation; (ER) metabolism of energy reserves (glycogen, trehalose);
(GLYC) glyoxylate cycle; (OX) oxidation of fatty acids; (O) other
energy generation activities.
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Details of the functional class energy metabolism are shown in Figure
4B, elucidating the fact that traditional pathways of the primary
metabolism, such as glycolysis, gluconeogenesis, pentose-phosphate
pathway, TCA cycle, and glyoxylate cycle are very well described. The
functional classes of respiration, fermentation, etc., contain a higher
number of proteins that are involved in regulation and transport. In
addition, these categories contain a high number of ORFs that have been
assigned function by similarity searches and for many cases, the
function has not been fully elucidated.
Biosynthesis of Amino Acid and Precursor Metabolites—Metabolic Capabilities of S. cerevisiae
All building blocks needed for synthesis of macromolecules
constituting cell mass can be generated from a set of 12 precursor
metabolites (Stephanopoulos et al. 1998). The capability of the
reconstructed genome-based S. cerevisiae and E. coli
networks to produce these precursor metabolites using glucose as the
sole carbon source was computed by use of linear optimization (Fell and
Small 1986; Varma and Palsson 1993). Similarly, the maximum production
of the 20 common amino acids was calculated for both organisms. In both
cases, S. cerevisiae was found to be more efficient in
producing precursor metabolites and amino acids (Fig. 5A,B).
This result is somewhat surprising, as
E. coli has been recognized and is widely used as a host for
industrial amino acid production. Investigation of the corresponding
flux distributions shows that the difference is caused by the higher
ATP maintenance requirements in E. coli. If ATP maintenance
requirements are not considered, the S. cerevisiae and E.
coli networks generate similar systemic yields, except for
acetyl-CoA, glutamate, glutamine, and glycine. Thus, the analysis shows
that S. cerevisiae may be a suitable host for industrial amino
acid production.
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Figure 5. Maximum precursor and amino acid production in S. cerevisiae
and E. coli including nongrowth-specific ATP maintenance
(A,B) (in mole/mole glucose). (3PG) 3-Phospho
glycerate; (ACCOA) acetyl-CoA; (AKG) 2-Oxoglutarate; (ALA) alanine;
(ARG) arginine; (ASN) asparagine; (ASP) aspartate; (CYS) cysteine;
(E4P) eErythrose 4 -phosphate; (F6P) fructose 6-phosphate; (G6P)
glucose 6-phosphate; (GLN) glutamine; (GLU) glutamate; (GLY) glycine;
(HIS) histidine; (ILE) isoleucine; (LEU) leucine; (LYS) lysine; (MET)
methionine; (OA) oxaloacetate; (PEP) phosphoenolpyruvate; (PHE)
phenylalanine; (PRO) pProline; (PYR) pyruvate; (R5P) ribose
5-phosphate; (SER) serine; (SUCCOA) succinyl-CoA; (T3P1) glyceraldehyde
3-phosphate; (THR) threonine; (TRP) tryptophane; (TYR) tyrosine; (VAL)
valine.
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In conclusion, the metabolic network of S. cerevisiae was
reconstructed using a procedure based on information from genomic
databases, reaction databases, and a comprehensive literature search on
S. cerevisiae. Although it is incomplete, given the number of
orphan ORFs, it is a first step toward cataloging and characterizing
the entire metabolic portfolio of a eukaryotic organism. This
conclusion is supported by the myriad of specific and insightful
information derived from the list of metabolic reactions. The potential
of the reconstructed model may further be used for the analysis of
phenotypic behavior under different genetic and physiological
conditions (I. Famili, J. Förster, J. Nielsen, and B.Ø. Palsson,
in prep.) The reconstructed metabolic network of S. cerevisiae
represents a strong platform for reconstruction of metabolic networks
of higher organisms, such as plants, animal, and human. Such
reconstructed metabolic networks will serve an important role in
systems biology, as the analysis of reconstructed metabolic networks
will facilitate the exploration of metabolism for drug targets
(Schuster et al. 1999), enable the design of microbial strains with
improved characteristics through metabolic engineering (Nielsen 2001),
and serve as a tool in functional annotation (Selkov et al. 2000).
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METHODS
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Metabolic Reconstruction Process
The reconstruction process is shown in Figure 1 and is described in
detail in the main text. In brief, the reconstruction process involves
the collection of all known enzymatic reactions in the metabolic
pathways of S. cerevisiae. Tables 6 and
7 contain
information on the online genome and pathway databases and key
references used for the reconstruction process. Furthermore, journal
publications were used to identify specific information on the
reactions.
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Table 7. Key references consulted additionally to online resources for the
reconstruction of the metabolic network of
Saccharomyces cerevisiae
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Linear Programming
The reactions of the reconstructed metabolic model were formulated
as a stoichiometric model S · v = 0, as, for example, described in
Edwards et al. (1999) or Stephanopoulos et al. (1998). This model
describes cellular behavior under pseudo steady-state conditions, and S
is defined as the stoichiometric matrix that contains the
stoichiometric coefficients of internal (balanced) metabolite i in the
jth reactions and v is the flux vector that corresponds to
the flux of the jth reaction. The stoichiometric model was
solved using linear programming, an approach often referred to as flux
balance analysis (Edwards et al. 1999).
The linear programming problem was formulated by defining an objective
function Z:
in which a was a row vector containing weights of the individual
variables specifying the influence of the individual fluxes on the
objective function Z. The elements of the flux vector v were
constrained for the definition of reversible and irreversible
reactions, vi,rev and vi,irr,
respectively. Uptake was defined for glucose, sulfate,
ammonia, phosphate, oxygen (for aerobic growth), and ergosterol and
zymosterol (for anaerobic growth). Secretion was defined for all major
metabolic products, such as ethanol, glycerol, succinate, acetate,
pyruvate, and for all amino acid, organic acids (see supplementary
material).
The consistency of the model was checked at anaerobic and aerobic
conditions at a dilution rate of 0.1 h–1 (objective function
Z = µ) and compared with experimental results from Nissen et al.
(1997) and Overkamp et al. (2000), respectively.
For the maximization of precursors or building blocks of biomass, an
additional reaction was defined in the model and maximized for. The
general format of the additional reaction was as follows: precursor 
precursorOut, and the objective was the maximization of that particular
reaction.
All calculations were carried out using the commercially available
software Lindo (Lindo Systems Inc.).
Shadow Prices
Shadow prices are derived from the dual variable of a linear
programming problem (see, for example, Bertsimas and Tsitsiklis 1997).
Its' definition is:
in which bi corresponds to a potential uptake or
secretion rate of metabolite i. Negative shadow prices
describe metabolites that are demanded by the metabolic network and
positive shadow prices identify metabolites that the metabolic network
would like to excrete in order to improve the objective value Z.
Preliminate versions of the reconstructed models were unable to model
cellular behavior; either the model did not allow growth or growth
reached infinity. In such cases, three strategies were considered for
identifying the missing or incorrect information in the model during
the reconstruction process. First, investigation of the flux
distribution, second, investigation of shadow prices, and third,
definition of new linear programming problems, such as maximization of
precursors or building blocks that are necessary to synthesize biomass.
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Acknowledgements
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Research activities in the field of functional genomics at the
Center for Process Biotechnology are financially supported by the
Danish Biotechnology Instrument Center (DABIC). We thank the Whitaker
Foundation for their support through the Graduate Fellowship in
Biomedical Engineering to I.F., the National Science Foundation through
grant nos. 9873384 and 9814092, and the National Institutes of Health
through grant no. GM57089.
The publication costs of this article
were defrayed in part by payment of page charges. This article must
therefore be hereby marked "advertisement" in accordance with 18
USC section 1734 solely to indicate this fact.
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Footnotes
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3 Present address: Fluxome Sciences A/S, Soltofts Plads,
Building 223, Technical University of Denmark, DK-2800 Lyngby, Denmark 
4 These authors contributed equally to this work.
5 Corresponding author.
E-MAIL jn{at}biocentrum.dtu.dk; FAX 45-45-88-41-48.
Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.234503.
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Received March 3, 2002;
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13:244-253 © by 2003 Cold Spring Harbor Laboratory Press ISSN 1088-9051/03 $5.00
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ABSTRACT
RESULTS AND DISCUSSION
