RESEARCH ARTICLE Open Access
Further developments towards a genome-scale
metabolic model of yeast
Paul D Dobson1†, Kieran Smallbone2,3*†, Daniel Jameson2,4, Evangelos Simeonidis2,5, Karin Lanthaler1,2, Pınar Pir6,
Chuan Lu7, Neil Swainston2,4, Warwick B Dunn1,2, Paul Fisher4, Duncan Hull1, Marie Brown1, Olusegun Oshota2,5,8,
Natalie J Stanford2,5,8, Douglas B Kell1, Ross D King7, Stephen G Oliver6, Robert D Stevens4, Pedro Mendes2,4,9
Abstract
Background: To date, several genome-scale network reconstructions have been used to describe the metabolism
of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven
reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid
metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway
connectivity.
Results: We have expanded the yeast network reconstruction to incorporate many new reactions from the
literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction
comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than
any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268
enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved,
with more than 90% of metabolites now reachable from the growth medium constituents. The present updates
allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results
from in vivo experiments and to those of previous reconstructions.
Conclusions: We report the development of the most complete reconstruction of yeast metabolism to date that is
based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is
available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-
bio.org/yeastnet/.
Background
A central goal of integrative systems biology is the accurate
representation of molecular interaction networks. Ulti-
mately, such networks can be used to underpin mathemati-
cal models, consisting of stochastic or ordinary differential
equations that permit the simulation of biological beha-
viour. The first step in generating such models is construct-
ing a network of biochemical reactions and interactions
between molecular components of the system to form a
qualitative (unparameterised) model. Several groups have
reconstructed the metabolic network of baker’s yeast from
genomic and literature data [1-3]. Variation in the
approaches used, and contradictory interpretations of the
available literature, mean that most reconstructions differ
considerably. To resolve these problems, a cohort of the
yeast systems biology community collaborated to create a
consensus reconstruction. In April 2007, a large focused
meeting brought together experts from various groups and
disciplines in order to resolve discrepancies between the
various reactions and metabolites described by other avail-
able reconstructions and form a consensus. The resultant
reconstruction [4], subsequently referred to as “Yeast 1.0”,
removed the ambiguities inherent in its predecessors
through the use of principled and computer-readable anno-
tations. Whilst previous reconstructions had defined enti-
ties using subjective names, which lacked precision and
resulted in ambiguities, Yeast 1.0 directly referenced chemi-
cal and protein descriptions to persistent databases or used
standardised, database-independent, computer-readable
* Correspondence: kieran.smallbone@manchester.ac.uk
† Contributed equally
2Manchester Centre for Integrative Systems Biology, The University of
Manchester, 131 Princess Street, Manchester, M1 7DN, UK
Full list of author information is available at the end of the article
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© 2010 Dobson et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.

representations. This removed the ambiguities and allowed
the new reconstruction to be used effectively as the basis
for automated analyses.
A limitation of Yeast 1.0 came about through the very
generation of the consensus; the network became consid-
erably fragmented as reactions that could not be readily
annotated (due to the presence of structural ambiguities)
were removed. This led to underrepresentation of a num-
ber of pathways, particularly those involved in lipid bio-
synthesis. Since Yeast 1.0, many improvements have been
made to the reconstruction. The latest release, described
here, is considerably larger (in terms of numbers of meta-
bolites and reactions), of higher quality (by reference to
literature evidence), exhibits greater coverage of known
metabolic enzymes, and is better connected than all pre-
vious efforts.
The reconstruction is described and made available in
Systems Biology Markup Language (SBML) [5], an estab-
lished community XML format for the mark-up of bio-
chemical models. With the introduction of SBML Level 2,
specific model entities, such as species or reactions, can be
annotated using ontological terms. These annotations,
encoded using the resource description framework (RDF)
[6], provide the facility to assign definitive terms to indivi-
dual components, allowing the software to identify such
components unambiguously and thus link model compo-
nents to existing data resources [7]. Minimum Information
Requested in the Annotation of Models (MIRIAM) [8]
-compliant annotations have been used to identify compo-
nents unambiguously by associating them with one or
more terms from publicly available databases registered in
MIRIAM Resources [9]. An example of such an annota-
tion is presented in Figure 1, where an enzyme is identified
by MIRIAM-compliant references to the UniProt [10],
SGD [11], and PubMed [12] databases. Metabolites are
annotated with reference to the ChEBI (Chemical Entities
of Biological Interest) database [13]. Whilst SBML is the
primary format for dissemination of the reconstruction,
we also make the reconstruction available in an online
database [14], B-Net, that enables easy searching of the
content. B-Net [15] is able to represent all of the SBML
features utilised in the current reconstruction. Searches
can be performed using synonyms and the user is also
able to navigate through the network from any point (e.g.
a metabolite, reaction or enzyme) to its connected neigh-
bours. Query results can also be exported in SBML and
this is an effective mechanism to extract subsets of the
entire model in this exchange format.
Results and Discussion
Improvements in the representation of yeast metabolism
in this release as compared to Yeast 1.0 primarily consist
of its enhanced representation of lipid metabolism and
greater connectivity, thereby permitting constraint-based
flux analyses. Many of the extensions to Yeast 1.0 are reac-
tions garnered from the literature, which are entirely novel
to any genome-wide yeast metabolic reconstruction. Data
were also incorporated, when backed up by traceable evi-
dence, from two other reconstructions: iMM904 [16] and
iIN800 [17]. The resulting consensus network (reported in
Additional File 1) consists, in decompartmentalised form,
of 1102 metabolic reactions involving 924 metabolites and
924 proteins (Table 1) and is therewith larger in scope
than any previous reconstruction.
Careful curation does not simply involve increasing the
scope of the reconstruction. Indeed, 32 enzymes from
Yeast 1.0 were considered insufficiently evidenced and
have been removed, whilst a number of metabolites were
relocalised to a different compartment. A typical example
of an enzyme removed from the reconstruction is Gpm2p;
whilst a homologue of Gpm1p, its phosphoglycerate
mutase activity could not be evidenced and may be non-
functional [18]. Four reconstructions are compared in
Figure 2 in terms of enzymes present. In addition to the
32 enzymes removed, the reactions of a further 37
enzymes from iMM904 and iIN800 have not been added
for lack of supporting evidence. In total, the new recon-
struction considers 124 more enzymes than its predeces-
sor, with half of these (61) being retrieved manually from
the literature and therefore new to all reconstructions.
Lipid metabolism
The correct and complete representation of lipid meta-
bolism is important, not only to meet the ultimate goal
of genome-scale coverage, but also because understand-
ing and engineering lipid metabolism through systems
and synthetic biology is likely to play a major role in the
replacement of fossil energy sources and chemical feed-
stocks with biofuels and bioplastics [19]. In Yeast 1.0,
lipid metabolism was poorly captured. To move towards
a better representation, the literature, database annota-
tions and homology relationships were used to identify
the set of lipid-related yeast enzymes. Homology with
mouse and human enzymes reported in LipidMaps [20],
and with enzymes from all organisms reported in KEGG
lipid pathways [21], indicated lipid enzymes in yeast
(homology relationships predefined by Ensembl [22]).
Further enzymes were added to the set manually by
examination of SGD and Ensembl annotations. A total
of 268 yeast enzymes were identified as likely to be part
of lipid metabolism. Although the boundaries of this set
are unavoidably subjective, it appears to capture the
majority of lipid-related genes in yeast.
With reference to this set of lipid enzymes, the iIN800
reconstruction of Nookaew et al. improved upon the
original community reconstruction (Yeast 1.0) by
increasing set coverage from 48% to 62% (with at least
one reaction being associated with each enzyme). In the
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present release set coverage has further improved to
87%. Coverage of the lipid enzyme set by the various
reconstructions is summarised in Figure 3. From iIN800
and iMM904, 56 lipid enzymes were added to Yeast 1.0,
while three enzymes from these sources were not added.
The current reconstruction describes activities for 49
enzymes that no other reconstruction has ever consid-
ered. Combining these, the reconstruction extends the
Yeast 1.0 description of lipid metabolism by a total of
105 new enzymes, extends iMM904 by 59 enzymes, and
iIN800 by 70 enzymes. This is by far the most compre-
hensive reconstruction of yeast lipid metabolism to date.
The 34 remaining lipid enzymes (in figure 3 these are
31 not found in any reconstruction, plus three found in
both iMM904 and iIN800) from the set are either too
poorly characterised functionally to be included or can-
not be represented within the current description of the
cell’s compartmentalisation. Flippases, for example,
require a more detailed description of membrane faces
to capture their role in membrane asymmetry. Improv-
ing compartmental representation will be a goal for
future releases.
Connectivity
Structural improvement was a major focus of the
advancements made to the reconstruction by identifying
Figure 1 SBML example. Simplified example of MIRIAM-compliant SBML, whereby an enzyme is annotated with reference to the databases
UniProt, SGD and PubMed, respectively.
Table 1 Reconstruction scope
iMM904 iIN800 Yeast 1.0 Yeast 4.0 change (%)
reactions 1050 907 962 1102 14.6
metabolites 872 812 813 924 13.7
proteins 904 707 832 924 11.0
compartments 8 4 15 16 6.7
Comparison of the scope of reconstructions (Yeast 4.0 being the version
number of the current reconstruction). Metabolites and reactions in different
intracellular compartments are considered one, as are reactions with the same
stoichiometry (isoenzymatic). This renders reconstructions with differing
granularity comparable.
Figure 2 Comparison of reconstructions in terms of enzymes
present. The reconstruction presented here contains 124 more
enzymes than Yeast 1.0, 61 of which have not been considered by
any of the other reconstructions. Yeast 1.0 was also improved upon
through better curation leading to the removal of (2 + 9 + 21 =) 32
enzymes. A further (6 + 13 + 18 =) 37 enzymes from iMM908 and
iIM800 were not added to the reconstruction.
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and rectifying unconnected regions of the network. Two
measures were used to describe connectivity. First, we
identified clusters of unreachable metabolites; that is,
clusters of metabolites that are disconnected from the
extracellular medium, in a graph-theoretic sense, and
thus cannot ever be produced by the reaction network.
Secondly, we used flux variability analysis [23] to iden-
tify reactions that, by mass balancing, must have zero
flux, for example because of dead-end metabolites (pro-
ducts that are not the substrates of another reaction).
Led by these analyses, which are explained graphically
in Figure 4, we looked for literature evidence describing
these missing elements of our network. By targeting
unreachable clusters and those reactions whose recon-
nection has the most influence on the network’s con-
nectivity, we maximised the impact of literature curation
on modelling. By both measures, the present release
improves both upon the previous release and particu-
larly upon iMM904 and iIN800 (Table 2). More than
90% of metabolites can be reached from the extracellu-
lar medium and only 12.7% of reactions must have zero
flux.
Our approach towards structural improvement is also
an example of the iterative “cycle of knowledge”
approach [24], where the model is first used to guide
biological research and can subsequently be updated
and improved as specific new knowledge becomes avail-
able. In this case the iteration consisted of discovery and
collation of experimental evidence previously obtained
but which had never been identified in this context.
Such discovery of knowledge was informed by the pre-
vious models and was unlikely to have happened in
their absence.
Constraint-based analysis
New reconstructions are often validated through con-
straint-based approaches like Flux Balance Analysis
(FBA) [25] to assess their ability to predict experimental
results. While there is clear utility in deploying such
methods to explore biochemical capacity, using improved
agreement with experimental observations to determine
whether the reconstruction is, in some sense, ‘better’
than previous efforts is potentially misleading. In the cur-
rent release, non-inferred reactions are supported by evi-
dence from the literature and it is in this sense that the
reconstruction is validated and improved. That said, the
updates improved the connectivity considerably and
together with the inclusion of a reaction describing bio-
mass composition now allows FBA to be performed. The
availability of the model in SBML means that it is accessi-
ble through many generic and systems-biology-specific
software packages, including the COBRA (COnstraint-
Based Reconstruction and Analysis) toolbox [26].
The model was used to predict single knockout viability
through flux balance analysis (FBA). Growth conditions
Figure 3 Comparison of the coverage of lipid metabolism
enzymes by the different reconstructions. At least one reaction
in a reconstruction is catalyzed by each enzyme. On top of
extending Yeast 1.0 by (1 + 9 + 46 =) 56 enzymes from iMM904
and iIN800, a further 49 enzymes uniquely appear in this latest
reconstruction. Three reactions common to iMM904 and iIN800, plus
31 others, have not been incorporated for lack of evidence.
Figure 4 Visualisation of connectivity analysis. Metabolites that
are unreachable (in red) were identified with a graphical analysis, by
locating metabolites that are disconnected from the extracellular
medium. Flux variability analysis identified reactions that must have
zero flux (in blue) because they lead to dead-end metabolites.
Table 2 Network connectivity
iMM904 iIN800 Yeast 1.0 Yeast 4.0
intracellular metabolites 708 681 658 758
unreachable 440 468 108 75
% 62.2 68.7 16.4 9.9
metabolic reactions 1050 907 962 1102
zero flux 225 282 153 140
% 21.4 31.1 15.9 12.7
As in Table 1, decompartmentalised models were used to generate these
data.
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exactly followed those set out in iMM904, namely a glu-
cose-limited minimal medium. Cellular biomass was
defined as in iIN800 (carbon-limited version), due to its
high level of detail regarding lipid composition. As the
reaction producing biomass does not represent a real
metabolic process it is semantically annotated as such
using SBO (Systems Biology Ontology) [27] identifiers
and GO (Gene Ontology) [28] evidence codes to ensure
this distinction is maintained (therefore allowing one to
easily remove this reaction based on its annotation).
Simulations were performed using COBRA (which is reli-
ant on libSBML [29] and the GNU linear programming
kit [30]). The simulation predictions were compared to a
list of lethal gene knockouts. This list was generated by
considering results from viability experiments under both
rich [31] and glucose minimal growth medium condi-
tions [32]. Results demonstrate similar performance to
that of previous reconstructions in terms of the accuracy
of prediction of single gene knockout viability (Table 3).
Closer inspection of predictions reveals that relatively
subtle network variations often underlie prediction dif-
ferences. Four experimentally lethal knockouts were not
initially predicted as such by the new reconstruction,
but are correctly predicted using iMM904. Three of
these genes encode enzymes that are essential to ribofla-
vin biosynthesis. The capacity of iMM904 to predict
lethality correctly is due to its biomass definition includ-
ing a small contribution from riboflavin, whereas this
was not part of the initial iIN800 or current network’s
biomass definition. Subsequent addition of riboflavin to
the (empirical) biomass description has resolved these
differences. Note that this is not therefore a reflection of
the quality of the underlying network but only of the
empirical biomass estimation, which is itself dependent
on the growth conditions.
In places, the added richness of the new reconstruc-
tion combines with certain known limitations to defeat
total agreement with experiment. An example is seen by
knocking out the acs2 gene, encoding acetyl-coA
synthetase (Acs2p). By experiment this should be lethal,
yet in the current network the cytoplasmic reaction is
also catalysed by Acs1p, consistent with experimental
data [33]. When the Acs2p-catalysed reaction is elimi-
nated, flux simply re-routes through the Acs1p reaction.
Importantly, it is only the fortuitous incompleteness of
iMM904, lacking the cytosolic Acs1 isozyme that reveals
the inviability of the acs2 knockout. The proper basis of
the inviability of the acs2 mutant is that ACS1 is tran-
scriptionally repressed in the high glucose conditions of
viability experiments and so is unable to compensate for
the loss of ACS2 [34]. Transcriptional control is not
captured in the metabolic network and thus cannot be
captured in metabolic reconstructions of this type.
Both these examples highlight the caution required
when using approaches such as FBA to validate recon-
structions. The added detail in the present network can
naturally lead to an increase in false positive outcomes:
in silico knockouts that are overcome by alternative
routings in the network but are actually lethal in vivo.
This is, however, tempered by a decrease in false nega-
tive outcomes (i.e. knockouts that appear lethal compu-
tationally but are viable in vivo, as presented in Table 3).
Uncharacterised enzymes
Despite the much-increased coverage of the current
reconstruction, 451 genes probably encode metabolic
enzymes that still have no associated reaction (Additional
file 2). For the majority of these, very little is known
about their function and further characterisation is
required. From the viewpoint of furthering systems biol-
ogy reconstruction efforts, these enzymes are important
targets for reductionist molecular biology studies, includ-
ing, for instance, systematic analyses using the Robot
Scientist approach [35]. Their listing here is a motivation
for further iterations on the cycle of knowledge.
Conclusions
The development of high quality, well annotated, gen-
ome-scale, metabolic networks is an ambitious, challen-
ging, but necessary step towards the realisation of
integrative systems biology. While networks predicted
through bioinformatics approaches are useful, particu-
larly for the extension of systems biology approaches to
less well-studied organisms, reconstructions built upon
solid biochemical evidence provide a gold standard
upon which predictions can be reliably based. For meta-
bolic reconstructions, where the goal is to capture maxi-
mally our current understanding of metabolism, these
problems are primarily of data integration and quality. It
has proven essential to involve the extended systems
biology and yeast communities in this process, both to
establish the mechanisms and structures for acquiring
and representing information, and also to tap into
expert knowledge from the various sub-disciplines of
biology and biochemistry. In the recent very large-scale
Table 3 Gene knockout analysis
iMM904 iIN800 Yeast 4.0
number of genes 904 707 924
true positive (%) 75.0 69.7 74.8
true negative (%) 5.1 6.9 5.3
false positive (%) 9.3 10.6 11.1
false negative (%) 10.6 12.7 8.8
Results of in silico viability prediction of deletion strains of S. cerevisiae.
“Positive” and “negative” refer to the ability and inability to grow, respectively.
Following earlier studies, the knockout simulation was conducted in a
glucose-limited minimal medium, and compared to experimental knockout
data [30,31].
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reconstruction of the yeast molecular interaction net-
work by Aho et al. [36], genomic, transcriptomic, pro-
teomic and metabolomic data were integrated. These
authors note that incorporating the higher quality data
of Yeast 1.0 (and therefore even more of this contribu-
tion) would considerably improve their reconstruction
over the metabolic information extracted from KEGG,
and also that standards compliance is essential to this
integration task.
Yeast 1.0 set standards and amalgamated existing net-
works, enhancing annotation and removing less reliable
data. In this latest reconstruction, we have made signifi-
cant headway on the process of filling gaps in the network.
There is still some way to go before realising the goal of at
least one reaction for each putative metabolic enzyme and,
if one also considers enzyme promiscuity [37,38], even this
will represent an incomplete picture of metabolism. This
latest reconstruction is a considerable improvement on
previous releases, particularly in describing lipid metabo-
lism and addressing gaps in the original reconstruction
that hindered modelling efforts. Information from other
reconstructions since Yeast 1.0 has been incorporated,
although not indiscriminately, and very many reactions
not found in other reconstructions have been garnered
from the literature. It is considerably larger than all pre-
vious efforts, while maintaining compliance with commu-
nity-defined standards.
While Yeast 1.0 represented a major advance, particu-
larly through the definition of standards and by the invol-
vement of the wider yeast community, a major flaw was
that it was not amenable to constraint-based analysis. The
current reconstruction rectifies this, mostly by filling in
gaps but also by inclusion of an appropriately annotated
“biomass” reaction, without compromising the strict evi-
dence requirements of its predecessor. When compared to
experimental knockout data, this reconstruction did not
identify certain lethal knockouts that other yeast recon-
structions correctly predicted, but proves better than them
in recognising viable deletions. This is a direct result of
the richness of the model; as with the example of the
acetyl-coA synthetases (above), addition of isoenzymes of
specific reactions that do not exist in earlier reconstruc-
tions can reduce the predictive power of the model. None-
theless, such enzymes are included due to literature
support. This reconstruction continues the shifting focus,
started with the consensus model Yeast 1.0, toward realis-
tic representation and proof-based selection of reactions,
rather than creating a reconstruction with simulation in
mind. Reactions with a lower level of confidence (e.g. bio-
mass definition) are characterised with specialised evi-
dence codes and SBO terms, allowing the easy extraction
of subsets of the network from the SBML code for specific
purposes.
To facilitate further improvements, we encourage the
community to provide information and/or corrections
to the current release. We have set up a dedicated
point-of-contact to this end network.reconstruction@
manchester.ac.uk. We also highlight gaps in the network
that cannot be resolved from current literature, as well
as the little-studied enzymes for which we have not yet
identified any function (see Additional File 2). These
represent potentially important research opportunities
for the community and we welcome efforts towards an
improved understanding of their functions.
Additional material
Additional file 1: Yeast SBML files. ZIP file containing the latest
reconstruction in SBML format. The metabolic network reconstruction is
described using MIRIAM-compliant SBML, compatible with many Systems
Biology software packages, including the COBRA toolbox. The model is
also available in decompartmentalised form, and in an old SBML format
(level 2, version 1) for backward compatibility.
Additional file 2: Poorly characterised genes. Excel spreadsheet. The
network is built upon intensive literature mining to identify reactions.
Many genes still do not have detailed literature describing the functions
of their products, yet (by what little is known or through sequence
analysis) they appear likely to be involved in metabolism. The attached
list describes these genes.
Acknowledgements
The Manchester groups thank the UK Biotechnology and Biological Sciences
Research Council (BBSRC) and the Engineering and Physical Sciences
Research Council (EPSRC) for financial support (grants BB/C008219/1 and BB/
F006012/1). The Cambridge group acknowledges BBSRC grant BB/C505140/
2. The Manchester, Aberystwyth and Cambridge groups all acknowledge
support from the European Union FP7 project UNICELLSYS (Grant
agreement no.: 201142) and from SysMO (MOSES). We thank Mike Hucka for
advice on formatting SBML annotations, Rasmus Ågren for providing the
iIN800 reconstruction and Steve Turner for help with ChEBI submissions. This
is a contribution from the Manchester Centre for Integrative Systems Biology
and the Cambridge Systems Biology Centre.
Author details
1School of Chemistry, The University of Manchester, Manchester M13 9PL,
UK. 2Manchester Centre for Integrative Systems Biology, The University of
Manchester, 131 Princess Street, Manchester, M1 7DN, UK. 3School of
Mathematics, The University of Manchester, Oxford Road, Manchester M13
9PL, UK. 4School of Computer Science, Kilburn Building, The University of
Manchester, Oxford Road, Manchester M13 9PL, UK. 5School of Chemical
Engineering and Analytical Science, The University of Manchester, Oxford
Road, Manchester M13 9PL, UK. 6Cambridge Systems Biology Centre &
Department of Biochemistry, University of Cambridge, 80 Tennis Court Road,
Cambridge CB2 1GA, UK. 7Department of Computer Science, Aberystwyth
University, SY23 3DB, UK. 8Doctoral Training Centre for Integrative Systems
Biology, The University of Manchester. 9Virginia Bioinformatics Institute,
Virginia Tech, Washington Street 0477, Virginia 24061, USA.
Authors’ contributions
PDD, KS, DJ, ES, KL, PP, NS, WBD, DH, MB, OO, NJS and PM contributed to
literature curation to identify new reactions. KS and NS prepared and
curated the SBML. PF collated relevant literature for curation. PDD, KS, DJ, ES,
DBK and PM wrote the manuscript. CL, DBK, RDK, SGO, RDS and PM
supervised work and/or contributed to discussions. All authors read,
improved, and approved the final manuscript.
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Received: 2 June 2010 Accepted: 28 October 2010
Published: 28 October 2010
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doi:10.1186/1752-0509-4-145
Cite this article as: Dobson et al.: Further developments towards a
genome-scale metabolic model of yeast. BMC Systems Biology 2010 4:145.
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