Permalink https://escholarship.org/uc/item/7q27b50x UC San Diego
UC San Diego Previously Published Works UC San Diego Title
iAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that 
can be used to simulate its physiological and patho-physiological states * Correspondence: palsson@ucsd.edu
Department of Bioengineering, University of California San Diego, 417
Powell-Focht Bioengineering Hall, 9500 Gilman Drive, La Jolla, CA, 92093-
0412, USA RESEARCH ARTICLE Open Access Open Access © 2011 Bordbar 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. Aarash Bordbar, Neema Jamshidi and Bernhard O Palsson* Aarash Bordbar, Neema Jamshidi and Bernhard O Palsson* Aarash Bordbar, Neema Jamshidi and Bernhard O Palsson* Abstract Background: The development of high-throughput technologies capable of whole cell measurements of genes,
proteins, and metabolites has led to the emergence of systems biology. Integrated analysis of the resulting omic
data sets has proved to be hard to achieve. Metabolic network reconstructions enable complex relationships
amongst molecular components to be represented formally in a biologically relevant manner while respecting
physical constraints. In silico models derived from such reconstructions can then be queried or interrogated
through mathematical simulations. Proteomic profiling studies of the mature human erythrocyte have shown more
proteins present related to metabolic function than previously thought; however the significance and the causal
consequences of these findings have not been explored. Results: Erythrocyte proteomic data was used to reconstruct the most expansive description of erythrocyte
metabolism to date, following extensive manual curation, assessment of the literature, and functional testing. The
reconstruction contains 281 enzymes representing functions from glycolysis to cofactor and amino acid
metabolism. Such a comprehensive view of erythrocyte metabolism implicates the erythrocyte as a potential
biomarker for different diseases as well as a ‘cell-based’ drug-screening tool. The analysis shows that 94 erythrocyte
enzymes are implicated in morbid single nucleotide polymorphisms, representing 142 pathologies. In addition,
over 230 FDA-approved and experimental pharmaceuticals have enzymatic targets in the erythrocyte. Conclusion: The advancement of proteomic technologies and increased generation of high-throughput proteomic
data have created the need for a means to analyze these data in a coherent manner. Network reconstructions
provide a systematic means to integrate and analyze proteomic data in a biologically meaning manner. Analysis of
the red cell proteome has revealed an unexpected level of complexity in the functional capabilities of human
erythrocyte metabolism. Powered by the California Digital Library
University of California eScholarship.org Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Background
h
d activities, little integrated functional analysis has been
performed to date. The advancement of high-throughput data generation
has ushered a new era of “omic” sciences. Whole-cell
measurements can elucidate the genome sequence
(genomics) as well as detect mRNA (transcriptomics),
proteins (proteomics), and small metabolites (metabolo-
mics) under a specific condition. Though these methods
provide a broad coverage in determining cellular Genome-scale network reconstructions are a common
denominator for computational analysis in systems biol-
ogy as well as an integrative platform for experimental
data analysis [1,2]. There are several applications of
reconstructions including: 1) contextualization of high-
throughput data, 2) directing hypothesis-driven discov-
ery, and 3) network property discovery [1]. Network
reconstruction involves elucidating all the known bio-
chemical transformations in a particular cell or organ-
ism and formally organizing them in a biochemically    Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Page 2 of 12 consistent format [3]. Genome sequencing has allowed
genome-scale reconstruction of numerous metabolic
networks of prokaryotes and eukaryotes [4-6]. In fact, a
genome-scale reconstruction of human metabolism has
been completed, called Recon 1. Recon 1 is a global
human knowledge-base of biochemical transformations
in humans that is not cell or tissue specific [7]. More
recently, Recon 1 has been adapted to study specific
cells and tissues with the help of high-throughput data,
including the human brain [8], liver [9,10], kidney [11],
and alveolar macrophage [12]. Proteomic based erythrocyte reconstruction Proteomic data has been successfully used for recon-
structions of Thermotoga maritima [25] and the human
mitochondria [26] and provides direct evidence of a
cell’s ability to carry out specific enzymatic reactions. One challenge in the measurement of proteomic data is
the depth of coverage, which is still known to be incom-
plete, even for studies aiming to obtain comprehensive
coverage. Another challenge is the possible contamina-
tion in the extract preparation with other blood cells
[21]. In addition, leftover enzymes from immature ery-
throid cells are possibly retained in mature red blood
cells. With multiple comprehensive proteomic studies
carried out in the last decade, the coverage for the red
cell has improved significantly but gaps and inaccurate
data still plague proteomic studies of the erythrocyte. Though many cell and tissue-specific models have
been reconstructed from Recon 1, the human erythro-
cyte has undeservedly received less attention as the
cell has been largely assumed to be simple. Background
h
d Histori-
cally, red cell metabolic models began with simple gly-
colytic models [13,14]. In a fifteen year period, the
original mathematical model was updated to include
the pentose phosphate pathway, Rapoport-Luebering
shunt, and adenine nucleotide salvage pathways
[15-18]. More recent metabolic models have been built
accounting for additional regulatory [19] and metabolic
components (e.g. glutathione [20]). However, in the
past decade, attempts to obtain comprehensive proteo-
mic coverage of the red cell have demonstrated a
much richer complement of metabolism than pre-
viously anticipated [21-24]. Modeling the unexpected
complexity of erythrocyte metabolism is critical to
further understanding the red cell and its interactions
with other human cells and tissues. Thus, in this study, we construct a full bottom-up
reconstruction of erythrocyte metabolism with rigorous
manual curation in which reactions inferred from pro-
teomically detected enzymes were cross-referenced with
existing experimental studies and metabolomic data as
part of the quality control measures to validate and gap
fill metabolic pathways and reactions (Figure 1). The final reconstruction, iAB-RBC-283, contains a
metabolic network that is much more expansive than
red blood cell models presented to date (Figure 2). The
reconstruction contains 292 intracellular reactions, 77
transporters, 267 unique small metabolites, and accounts
for 283 genes, suggesting that the erythrocyte has a
more varied and expansive metabolic role than pre-
viously recognized. Thus, we use available proteomics to develop the
largest (in terms of biochemical scope) in silico model
of metabolism of the human red cell to date. Though
comprehensive proteomic data provides an overview
of red cell proteins, we believe it does not provide a
full functional assessment of erythrocyte metabolism. Thus, we have also gathered 50+ years of erythrocyte
experimental studies in the form of 60+ peer-reviewed
articles and books to manually curate the final model. In order to objectively test physiological functionality,
we have put the final model through rigorous
simulation. A full bottom-up reconstruction of the human ery-
throcyte provides a functional interpretation of proteo-
mic data that is biochemically meaningful. Manual
curation provides experimental validation of metabolic
pathways, as well as gap filling. The data can be rigor-
ously and objectively analyzed through in silico
simulation. Functional assessment of iAB-RBC-283 In order to ascertain the functional capabilities of the
expanded erythrocyte reconstruction, iAB-RBC-283 was
converted into a mathematical model. The expanded
erythrocyte network was topologically and functionally
compared to a previous constraint-based model of ery-
throcyte metabolism [27] (see additional file 1). Predic-
tions made by this model could be recapitulated by iAB-
RBC-283. To determine new functionalities of the
expanded erythrocyte network, the system is assumed to
be at a homeostatic state and qualitative capacity/cap-
ability simulations are done to ascertain which reactions
and pathways can be potentially active in the in silico
erythrocyte. Flux variability analysis (FVA) was utilized
to determine the functional metabolic pathways of the Results and Discussion A A B Global Human Recon 1 Omic Database
curation Curated Metabolic Reconstruction 6pgl
pi
lac-L
g3p
atp
h2o
gtp
f6p
imp
h
pyr
6pgc
gsn
glu-L
adn
atp
h
ade
pi
r5p
nadph
r1p
adp
xmp
h
ppi
nadph
h2o
h
h2o
g6p
h
23dpg
adp
amp
h
dhap
h2o
h2o
atp
nh4
g3p
gmp
atp
pi
adp
ppi
imp
h
pi
gln-L
pep
nadh
nad
h
ppi
h
gdp
h
ins
prpp
nh4
amp
atp
atp
prpp
amp
nadh
fdp
nad
s7p
2pg
gua
nadph
adp
ru5p-D
co2
pi
glc-D
h
atp
atp
h
h
h2o
hxan
nadp
nadp
adp
35cgmp
prpp
pi
adp
ppi
nad
pi
h2o
h
nadh
xu5p-D
3pg
atp
e4p
f6p
h2o
gua
ppi
h2o
nadp
13dpg
nh4
h2o
r1p
adp
h
Curated Metabolic Reconstruction Protein/Metabolite
literature curation Uptake/secretion
literature curation iAB-RBC-283 Figure 1 (A) Workflow for building a comprehensive in silico erythrocyte metabolic network. The three major data types required are: the
human genome sequence, high-throughput data (specifically, proteomics for an enucleated cell), and primary literature. The global human
metabolic network, Recon 1, was constructed from the human genome sequence and annotation. To build the erythrocyte network, iAB-RBC-
283, proteomics was used to remove non-erythrocyte related open reading frames (ORFs) or genes. Detailed curation utilizing protein,
metabolite, and transport experimental literature was needed to build a high-quality metabolic reconstruction. (B) Without network
reconstruction and rigorous curation, the experimentally generated proteomic data is raw and difficult to interpret. The process detailed in panel
A provides a means to build a meaningful knowledge-base of available high-throughput data that can then be probed and tested. erythrocyte network (see Materials and Methods). FVA
determines the minimum and maximum allowable flux
through each metabolic reaction [28]. In short, the FVA
method defines the bounding box on network capabil-
ities. Reactions that had a non-zero minimum or maxi-
mum flux value were deemed to be functional. Galactose can also be shuttled to the pentose phosphate
pathway through glucuronate interconversions. Citric
acid cycle enzymes (fumarase, isocitrate dehydrogenase,
and malate dehydrogenase) are present, but we were
unable to fully understand their roles as full metabolic
pathways were not present. However, fumarate can be
shuttled into the model and exported as pyruvate
through conversions by fumarase and malic enzyme. Results and Discussion iAB-RBC-283 is a proteomic based metabolic recon-
struction and a biochemical knowledge-base, a func-
tional integration of high-throughput biological data and
existing experimentally verified biochemical erythrocyte
knowledge that can be queried through simulations and
calculations. We first describe the process and charac-
terize the new erythrocyte reconstruction and determine
the metabolic functionality. Then, we analyze the results
by mapping genetic polymorphisms and drug target
information onto the network. Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Page 3 of 12 Global Human Recon 1
Omic Database
curation
Remove non-RBC
ORFs based on
proteomic studies
Protein/Metabolite
literature curation
Uptake/secretion
literature curation
B
A Global Human Recon 1
Omic Database
curation
Remove non-RBC
ORFs based on
proteomic studies
Protein/Metabolite
literature curation
Uptake/secretion
literature curation
iAB-RBC-283
6pgl
pi
lac-L
g3p
atp
h2o
gtp
f6p
imp
h
pyr
6pgc
gsn
glu-L
adn
atp
h
ade
pi
r5p
nadph
r1p
adp
xmp
h
ppi
nadph
h2o
h
h2o
g6p
h
23dpg
adp
amp
h
dhap
h2o
h2o
atp
nh4
g3p
gmp
atp
pi
adp
ppi
imp
h
pi
gln-L
pep
nadh
nad
h
ppi
h
gdp
h
ins
prpp
nh4
amp
atp
atp
prpp
amp
nadh
fdp
nad
s7p
2pg
gua
nadph
adp
ru5p-D
co2
pi
glc-D
h
atp
atp
h
h
h2o
hxan
nadp
nadp
adp
35cgmp
prpp
pi
adp
ppi
nad
pi
h2o
h
nadh
xu5p-D
3pg
atp
e4p
f6p
h2o
gua
ppi
h2o
nadp
13dpg
nh4
h2o
r1p
adp
h
Curated Metabolic Reconstruction
B
A
Raw Erythrocyte Proteomic Data
Figure 1 (A) Workflow for building a comprehensive in silico erythrocyte metabolic network. The three major data types required are: the
human genome sequence, high-throughput data (specifically, proteomics for an enucleated cell), and primary literature. The global human
metabolic network, Recon 1, was constructed from the human genome sequence and annotation. To build the erythrocyte network, iAB-RBC-
283, proteomics was used to remove non-erythrocyte related open reading frames (ORFs) or genes. Detailed curation utilizing protein,
metabolite, and transport experimental literature was needed to build a high-quality metabolic reconstruction. (B) Without network
reconstruction and rigorous curation, the experimentally generated proteomic data is raw and difficult to interpret. The process detailed in panel
A provides a means to build a meaningful knowledge-base of available high-throughput data that can then be probed and tested. Results and Discussion Network level metabolic functional assessment showed
that iAB-RBC-283 accounts for additional pathways into
glycolysis through galactose, fructose, mannose, glucosa-
mine,
and
amino
sugars
(N-Acetylneuraminate). In addition, nucleotide metabolism and salvage path-
ways have been expanded from previous metabolic Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Page 4 of 12 Figure 2 Topological map of the human erythrocyte metabolic network (iAB-RBC-283). Utilizing proteomic data, a much expanded
metabolic network was reconstructed accounting for additional carbohydrate and nucleotide metabolism. In addition, the erythrocyte plays roles
in amino acid, cofactor, and lipid metabolism. Abbreviations: PPP - pentose phosphate pathway, Arg - arginine, Met - methionine. Figure 2 Topological map of the human erythrocyte metabolic network (iAB-RBC-283). Utilizing proteomic data, a much expanded
metabolic network was reconstructed accounting for additional carbohydrate and nucleotide metabolism. In addition, the erythrocyte plays roles
in amino acid, cofactor, and lipid metabolism. Abbreviations: PPP - pentose phosphate pathway, Arg - arginine, Met - methionine. Figure 2 Topological map of the human erythrocyte metabolic network (iAB-RBC-283). Utilizing proteomic data, a much expanded
metabolic network was reconstructed accounting for additional carbohydrate and nucleotide metabolism. In addition, the erythrocyte plays roles
in amino acid, cofactor, and lipid metabolism. Abbreviations: PPP - pentose phosphate pathway, Arg - arginine, Met - methionine. Figure 2 Topological map of the human erythrocyte metabolic network (iAB-RBC-283). Utilizing proteomic data, a much expanded
metabolic network was reconstructed accounting for additional carbohydrate and nucleotide metabolism. In addition, the erythrocyte plays roles
in amino acid, cofactor, and lipid metabolism. Abbreviations: PPP - pentose phosphate pathway, Arg - arginine, Met - methionine. Figure 2 Topological map of the human erythrocyte metabolic network (iAB-RBC-283). Utilizing proteomic data, a much expanded
metabolic network was reconstructed accounting for additional carbohydrate and nucleotide metabolism. In addition, the erythrocyte plays roles
in amino acid, cofactor, and lipid metabolism. Abbreviations: PPP - pentose phosphate pathway, Arg - arginine, Met - methionine. models to account for XMP, UMP, GMP, cAMP, and
cGMP metabolism. In particular, ATP and GTP can be
converted into cAMP and cGMP respectively as adeny-
late and guanylate cyclases were found to be present in
the proteomic data. Though the mature erythrocyte is unable to produce or
degrade fatty acids, the cell can uptake fatty acids from
the blood plasma to produce and incorporate diacyl-
glyercol into phospholipids for upkeep of its membrane
[30]. Results and Discussion A pseudo-carnitine shuttle in the cytosol is used to
create a buffer of CoA for the cell [31]. All major ery-
throcyte fatty acids (C16:0, C18:1, C18:2) and phospholi-
pids (phosphatidylcholine, phosphatidylethanolamine,
phosphatidylinositol) are explicitly modeled. The phos-
phatidylinositols can be converted into various forms of
myo-inositols, which play an extensive role in cell sig-
naling [32]. The erythrocyte uses amino acids to produce glu-
tathione for redox balancing, converts arginine to polya-
mines
and
a
byproduct
of
urea,
and
utilizes
homocysteine for methylation. It has been proposed that
Band III is the major methylated protein, particularly for
timing cell death [29]. This has also been included in
iAB-RBC-283. In addition, polyamine metabolism pro-
duces 5-methylthioadenosine which can be salvaged for
methionine recycling. Finally, FVA showed that the erythrocyte plays an
important role in cofactor metabolism. The reconstruc-
tion accounts for uptake, modification, and secretion of
multiple cofactors including vitamin B6, vitamin C, Another major expansion in metabolic capabilities
represented in iAB-RBC-283 is lipid metabolism. Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Page 5 of 12 involves determining the number of edges (reactions)
connected to every node (metabolite). riboflavin, thiamine, heme, and NAD. In addition,
human erythrocytes play a role in deactivating catecho-
lamines [33], hydrolyzing leukotriene [34], and detoxify-
ing acetaldehyde [35] which were confirmed in the
literature. We compared the in silico erythrocyte to the global
human metabolic network, Recon 1, as well as the sepa-
rate human organelles (Figure 3). A dotted line, linking
the minimum and maximum connectivities, is drawn on
the distributions as a reference for comparing the distri-
butions. A network with higher connectivity would have
many points above the dotted line, while lower connec-
tivity would result in points below the dotted line. Recon 1 has most points above the reference line (Fig-
ure 3, first panel). In fact, the metabolite node connec-
tivities for genome-scale reconstructions usually have a
distribution similar to Recon 1, highlighting the com-
plexity of these networks [37]. However, the organelles Metabolite connectivity BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Page 6 of 12 Page 6 of 12 iAB-RBC-283 explicitly accounts for the genetic basis
of the enzymes and transporters that it represents. To
determine the capability of the in silico erythrocyte as
a potential biomarker, we cross-referenced morbid
SNPs from the OMIM and nearly 4800 drugs from the
DrugBank database with the 281 enzymes accounted
for in iAB-RBC-283. 142 morbid SNPs were found in
90 of the erythrocyte enzymes. In addition, 232 FDA-
approved, FDA-withdrawn, and experimental drugs
have known protein targets in the human erythrocyte
(Figure 4). in Recon 1 usually have values below the reference line,
due mainly to a higher difficulty to annotate reactions
specific to an organelle. The separate organelle meta-
bolic networks are less connected. The exception is the
mitochondria (Figure 3, last panel), which is well studied
and has a very important and complex metabolic role. The metabolite connectivity of the erythrocyte net-
work is very similar to the less connected organelles in
Recon 1. This similarity is due to either 1) the erythro-
cyte biology or 2) the lack of complete proteomic profil-
ing. First, as the erythrocyte circulates in blood, it has
access to many types of metabolites. As the erythrocyte
is relatively simple, it is possible that the reconstructed
metabolic network is complete as different types of
metabolites do not have to be created and can easily be
transported into or out of the cell (e.g. amino and fatty
acids). However, the network simplicity of the in silico
erythrocyte model may also be attributed to the limita-
tions of proteomic techniques. As coverage of proteomic
data is typically not as complete as transcriptomics, the
reconstructed metabolic network may reflect this limita-
tion. Thus, deeper proteomic profiling could be of great
use to further elucidate the role of the erythrocyte in
systemic metabolism and the complexity of its own
metabolic network. Only 35 of the 142 morbid SNPs are related to pathol-
ogies unique to the erythrocyte, mainly dealing with
hemolytic anemia. The majority of the morbid SNPs
deal with pathologies that are peripheral to the red
blood cell and to the blood system. The remaining non-
erythrocyte related pathologies are classified in Table 1
using the Merck Manual [43]. Most of the observed
SNPs are causal and simple targeted assays could be
used as diagnostic tools. Metabolite connectivity We also cross-referenced the enzymes in iAB-RBC-
283 with the DrugBank’s known protein targets of phar-
maceuticals. 85 FDA-approved, 4 FDA-withdrawn, and
143 experimental drugs have known targets in the
human erythrocyte. These medications target enzymes
for the treatment of a wide range of diseases including In silico simulations show a greater physiological func-
tionality of erythrocyte metabolism. The additional func-
tionality is not evident from the proteomic data alone. However, metabolite connectivity analysis shows that
additional targeted proteomics are of interest. During
the manual curation steps we noted that portions of the
TCA cycle were detected but not functional. Further
studies for cysteine, folate, and phospholipid metabolism
are also of interest as some enzymes were detected in
the proteomic data but little or no conclusive experi-
mental literature was found. 85
143
4
Approved
Experimental
Withdrawn
13
59
17
15
31
non-RBC related pathology
RBC related pathology
7
Causal SNPs, Metabolic
Correlated SNPs
Causal SNPs, non-Metabolic
A
B
Drugs with known targets in erythrocyte
Erythrocyte enzymes with known morbid SNPs
Figure 4 To test the potential use of erythrocytes as
biomarkers, we identified the known morbid SNPs and drug
targets of erythrocyte enzymes account for in the
reconstructed network. 142 morbid SNPs were identified with the
majority dealing with non-erythrocyte related pathologies (see Table
1). In addition, over 230 FDA-approved, FDA-withdrawn, and
experimental drugs have known protein targets in the human
erythrocyte. 13
59
17
15
31
non-RBC related pathology
RBC related pathology
7
Causal SNPs, Metabolic
Correlated SNPs
Causal SNPs, non-Metabolic
A
B
Erythrocyte enzymes with known morbid SNPs A Correlated SNPs Metabolite connectivity In order to compare the network structure of the in
silico erythrocyte versus other similar metabolic net-
works, we calculated the connectivity of each metabolite
[36]. Simply, the connectivity is the number of reactions
that a metabolite participates in. As a metabolite can be
defined as a node in a network structure, the biochem-
ical reactions associated with a particular metabolite are
the edges of the network. Metabolite connectivity thus 10
0
10
2
10
4
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
Recon 1
iAB-RBC-283
Endoplasmic Reticulum
Golgi Apparatus
Lysosome
Mitochondria
Metabolite number
Number of reactions
Figure 3 The metabolite connectivity of iAB-RBC-283, Recon 1, and the similarly sized organelles in Recon 1. Recon 1 and the
mitochondria have high network connectivity, with many data points above the reference lines. The erythrocyte network and other organelles
have less metabolic connectivity denoted by data point being on or below the reference lines. This characteristic can be attributed to either 1)
an inherently ‘fragmented’ erythrocyte network, or 2) incomplete proteomic coverage. 10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
iAB-RBC-283
G l i A
t 10
0
10
2
10
4
10
0
10
1
10
2
10
3
Recon 1 10
0
10
1
10
2
10
3
10
0
10
1
10
2
10
3
Endoplasmic Reticulum
Number of reactions Endoplasmic Reticulum Endoplasmic Reticulum Number of reactions Golgi Apparatus Metabolite number Figure 3 The metabolite connectivity of iAB-RBC-283, Recon 1, and the similarly sized organelles in Recon 1. Recon 1 and the
mitochondria have high network connectivity, with many data points above the reference lines. The erythrocyte network and other organelles
have less metabolic connectivity denoted by data point being on or below the reference lines. This characteristic can be attributed to either 1)
an inherently ‘fragmented’ erythrocyte network, or 2) incomplete proteomic coverage. Bordbar et al. The human erythrocyte’s potential as a biomarker B Decades of models have described erythrocyte metabo-
lism to include principally glycolysis, the Rapoport-Lue-
bering shunt, the pentose phosphate pathway, and
nucleotide salvage pathways. Integration and compila-
tion of proteomic data, however, has surprisingly shown
evidence of a much richer metabolic role for the ery-
throcyte. Erythrocytes make contact with most portions
of the body and are one of the most abundant cells
(about 2 liters in volume in a typical adult). With such a
varied metabolic capacity, the erythrocyte can act as a
sink for and source of metabolites throughout the body. Erythrocytes have been previously studied as potential
biomarkers for riboflavin deficiency [38], thiamine defi-
ciency [39], alcoholism [40], diabetes [41], and schizo-
phrenia [42], however comprehensive systems level
analyses have not been performed to date. Decades of models have described erythrocyte metabo-
lism to include principally glycolysis, the Rapoport-Lue-
bering shunt, the pentose phosphate pathway, and
nucleotide salvage pathways. Integration and compila-
tion of proteomic data, however, has surprisingly shown
evidence of a much richer metabolic role for the ery-
throcyte. Erythrocytes make contact with most portions
of the body and are one of the most abundant cells
(about 2 liters in volume in a typical adult). With such a
varied metabolic capacity, the erythrocyte can act as a
sink for and source of metabolites throughout the body. Figure 4 To test the potential use of erythrocytes as
biomarkers, we identified the known morbid SNPs and drug
targets of erythrocyte enzymes account for in the
reconstructed network. 142 morbid SNPs were identified with the
majority dealing with non-erythrocyte related pathologies (see Table
1). In addition, over 230 FDA-approved, FDA-withdrawn, and
experimental drugs have known protein targets in the human
erythrocyte. Figure 4 To test the potential use of erythrocytes as
biomarkers, we identified the known morbid SNPs and drug
targets of erythrocyte enzymes account for in the Erythrocytes have been previously studied as potential
biomarkers for riboflavin deficiency [38], thiamine defi-
ciency [39], alcoholism [40], diabetes [41], and schizo-
phrenia [42], however comprehensive systems level
analyses have not been performed to date. g
y
y
y
reconstructed network. 142 morbid SNPs were identified with the
majority dealing with non-erythrocyte related pathologies (see Table
1). In addition, over 230 FDA-approved, FDA-withdrawn, and
experimental drugs have known protein targets in the human
erythrocyte. Bordbar et al. The human erythrocyte’s potential as a biomarker BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Page 7 of 12 Table 1 Morbid SNPs with non-erythrocyte related pathologies
Causal SNPs, Metabolic
Causal SNPs, Non-Metabolic
Correlated SNPs
Pediatric Disorders
28
6
4
Endocrine and Metabolic Disorders
10
2
8
Neurologic Disorders
5
2
1
Genitourinary Disorders
4
2
1
Eye Disorders
1
3
2
Musculoskeletal and Connective Tissue Disorders
3
-
1
None
1
-
3
Gastrointestinal Disorders
1
-
2
Hepatic and Billary Disorders
3
-
-
Hematology and Oncology Disorders
-
-
2
Nutritional Disorders
-
-
2
Psychiatric Disorders
-
-
2
Cardiovascular Disorders
1
-
-
Dermatologic Disorders
-
1
-
Ear, Nose, Throat, and Dental Disorders
-
1
-
Immunology, Allergic Disorders
1
-
-
Injuries, Poisoning
1
-
-
Pulmonary Disorders
-
-
1 Table 1 Morbid SNPs with non-erythrocyte related pathologies We were able to confidently detect in silico metabolic
flux changes in at least one exchange reaction for 75%
of the morbid SNPs and 70% of the drug treated condi-
tions (Figure 5B). On average, there were 12.6 and 9.9
differential activities of exchange reactions for morbid
SNPs and drug treated conditions respectively. The
average is skewed by some morbid SNPs and drug trea-
ted conditions that have over 45 affected exchange reac-
tions, as most differences are detected in between one
and ten exchange reactions (Figure 5B). seizures (topimarate), allergies (flunisolide), cancer
(topotecan), HIV (saquinavir), and high cholesterol
(gemfibrozil). Due to the availability of erythrocytes
from any individual, drugs can be easily screened and
optimized in vitro for individual patients where the
effect of the drug is known to occur in the erythrocyte. g
y
y
A comprehensive listing of all observed morbid SNPs
and drugs are provided in the Supplementary Material
(see additional files 2 and 3). The morbid SNPs with greater than 45 exchange reac-
tions with differential activity deal mostly with glycolytic
and transport enzymes that are known to cause anemias
and spherocytosis (see additional file 2 in Supplementary
Material). In addition, most of the drug treated condi-
tions with high numbers of differential exchange activity
correspond to drugs that have enzyme targets for the
same morbid SNPs that are known to cause hemolytic
diseases. Utilizing iAB-RBC-283 to develop biomarker studies An important application of metabolic reconstructions
and the resulting mathematical models is to predict and
compare normal and perturbed physiology. We used
iAB-RBC-283 to simulate not only normal conditions to
study the capacity of erythrocyte function, but also the
detected morbid SNPs and drug treated conditions for
drugs with known erythrocyte enzyme targets. Flux
variability analysis was used to characterize the
exchange reactions of the network for determining a
metabolic signature in the erythrocyte for the associated
perturbed conditions. We compared the minimum and
maximum fluxes through each reaction under normal
conditions versus all perturbed conditions and deter-
mined differential reaction activity (see Methods and
Figure 5A). Activated or suppressed flux from in silico
simulations provides a qualitative understanding into
which metabolites and reactions are perturbed, allowing
for experimental followup. An important metabolic enzyme found in erythrocytes
is catechol-o-methyltransferase (COMT) used to methy-
late cathecolamines. A morbid SNP of COMT gene has
been implicated in susceptibility to schizophrenia [44]. In silico simulations show that the associated morbid
SNP erythrocyte has lowered uptake of dopamine and
norepinephrine and lowered secretion of the methylated
counterparts. Though COMT has not been shown to be
causal for schizophrenia, the morbid SNP may have an
effect on the phenotype. Qualitative in silico simulation Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Page 8 of 12 Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 0
15
30
45
60
0
15
30
45
60
Exchange Fluxes
Secretion / +
Uptake / -
0
i) Active/Inactive
ii) Fixed Direction
iii) Magnitude Change
iv) Unaffected
A
Number of perturbed exchanges
Number of perturbed exchanges
Number of morbid SNPs
Number of Drugs
B
State A
State B
0
2
4
6
8
10
12
14
16
18
20
0
2
4
6
8
10
12
14
16
18
20
Figure 5 Flux variability of the exchange reactions can be used
to detect metabolic signatures of simulated morbid SNPs and
drug treated erythrocytes. (A) Exchange reactions are artificial
reactions that allow the mathematical model to uptake and secrete
metabolites into the extracellular space. Uptake of a metabolite into
the erythrocyte is expressed as a negative value and secretion is
expressed as a positive value. Utilizing iAB-RBC-283 to develop biomarker studies B 0
15
30
45
60
Number of perturbed exchanges
Number of morbid SNPs
B
0
2
4
6
8
10
12
14
16
18
20 0
15
30
45
60
0
15
30
45
60
Number of perturbed exchanges
Number of perturbed exchanges
Number of morbid SNPs
Number of Drugs
B
0
2
4
6
8
10
12
14
16
18
20
0
2
4
6
8
10
12
14
16
18
20 0
15
30
45
60
Number of perturbed exchanges
Number of Drugs
0
2
4
6
8
10
12
14
16
18
20 0
15
30
45
60
Number of perturbed exchanges Number of perturbed exchanges Figure 5 Flux variability of the exchange reactions can be used
to detect metabolic signatures of simulated morbid SNPs and
d
t
t d
th
t
(A) E
h
ti
tifi i l Figure 5 Flux variability of the exchange reactions can be used
to detect metabolic signatures of simulated morbid SNPs and
drug treated erythrocytes. (A) Exchange reactions are artificial
reactions that allow the mathematical model to uptake and secrete
metabolites into the extracellular space. Uptake of a metabolite into
the erythrocyte is expressed as a negative value and secretion is
expressed as a positive value. There are four major differences that
can occur for an exchange reaction in two different states: i) the
reaction is either active (non-zero minimum or maximum flux) or
inactive (zero minimum and maximum flux), ii) the exchange
becomes fixed in one direction (uptake or secretion only), iii) there
is a magnitude change in exchange, iv) the reaction is unaffected
and is the same for both states. (B) We detected 71% and 62% of
the morbid SNPs and drugs associated with the erythrocyte. The
distribution shows that most perturbed conditions have between
one to ten differentially active exchange reactions. Utilizing iAB-RBC-283 to develop biomarker studies There are four major differences that
can occur for an exchange reaction in two different states: i) the
reaction is either active (non-zero minimum or maximum flux) or
inactive (zero minimum and maximum flux), ii) the exchange
becomes fixed in one direction (uptake or secretion only), iii) there
is a magnitude change in exchange, iv) the reaction is unaffected
and is the same for both states. (B) We detected 71% and 62% of
the morbid SNPs and drugs associated with the erythrocyte. The
distribution shows that most perturbed conditions have between
one to ten differentially active exchange reactions. To further investigate the diagnostic capability of the
red blood cell, we assessed the uniqueness of the meta-
bolic signatures detected. We compared all the metabo-
lite signatures to see if some were shared between
different SNPs or drug treatments. In all, 67% of the
metabolic signatures are unique with most of the
remaining similar to only one other perturbed condition
(Table 2). Exchange Fluxes
Secretion / +
Uptake / -
0
i) Active/Inactive
ii) Fixed Direction
iii) Magnitude Change
iv) Unaffected
A
B
State A
State B A The in silico simulation results provide a method to
focus biomarker discovery experiments in the human
erythrocyte, as well as interpret global metabolomic pro-
filing. The flux variability shows that a large number of
morbid SNPs and drug effects can be detected in the
erythrocyte, with most having a unique metabolic signa-
ture. The differential activity in exchanges for the per-
turbed conditions allow for focusing experiments to
particular metabolites, exchanges, and associated path-
ways, allowing development of targeted assays. In addi-
tion, global metabolomic profiling of perturbed
conditions can be interpreted using the calculated meta-
bolic signatures and the erythrocyte reconstruction. A
full listing of all detected morbid SNPs and drug treated
conditions, as well as the corresponding exchange reac-
tions with differential activity and fluxes is provided in
the Supplementary Material (see additional files 2 and
3). Methods types of blood cells and inactive enzymes are passed
down the erythrocyte differentiation lineage. Thus, iAB-
RBC-283 is a knowledge-base of integrated high-
throughput and biological data, which can also be quer-
ied through simulations. Reconstructing the comprehensive erythrocyte network Metabolic network reconstruction has matured into a
methodological, systematic process with quality control
and quality assurance steps that can be carried out
according to standardized detailed protocols [3]. The
sequencing of the human genome enabled a comprehen-
sive, global reconstruction for all human cell types to be
carried out, with the caveat that in order to implement
any context, condition, or cell specific analysis, one
would need specific data for the particular human cell
or tissue of interest. Functional testing showed that the new reconstruction
takes into account historically neglected areas of carbo-
hydrate, amino acid, cofactor, and lipid metabolism. Traditionally, the erythrocyte is known for its role in
oxygen delivery, but the varied metabolism the cell exhi-
bits points towards a much more expanded metabolic
role as the cell can act as a sink or source of metabo-
lites, through interactions with all organs and tissues in
the body. Metabolic reconstructions contain all known meta-
bolic reactions of a particular system. The reactions are
charge and elementally balanced, with gene-protein-
reaction (GPR) annotations. GPRs mechanistically con-
nect the genome sequence with the proteome and the
enzymatic reactions. GPRs provide a platform for inte-
gration of high-throughput data to model specific condi-
tions. In this study, proteomic data was integrated with
Recon 1 to build a comprehensive erythrocyte network. Metabolite connectivity analysis showed that the ery-
throcyte metabolic network is relatively simple and is
similar to human organelles in network structure. This
could be due either to shortcomings of the high-
throughput data or the relatively simple metabolism of
red cells. From our manual curation steps, targeted pro-
teomic studies would be useful for a few metabolic path-
ways: including TCA cycle, cysteine, folate, and
phospholipid metabolism. p
y
y
iAB-RBC-283 was reconstructed in the following man-
ner. Proteomic data for erythrocytes from multiple
sources [21-24] were consolidated and cross-referenced
with Recon 1. The proteomic data was provided in the
IPI format and was converted to Entrez Gene Ids. The
Entrez Ids were linked with the Recon 1 transcripts,
including alternatively spliced variants, and used to gen-
erate a list of potential erythrocyte reactions. Methods Reactions
and pathways from Recon 1 that were present in the
proteomic data were used to build an automated draft
reconstruction. However, blood cell contamination and
remnant enzymes from immature erythroid cells
decrease the accuracy of algorithmically derived models
based on high-throughput data. In order to build the
most complete and accurate final model, the draft
reconstruction was rigorously and iteratively manually
curated. In brief, manual curation involves (1) resolving
all metabolite, reaction, and enzyme promiscuity, (2)
exploring existing experimental literature to determine
whether or not detected enzymes in the proteomics data
are correct, (3) determining and filling gaps in the pro-
teomic data through topological gap analysis and flux-
based functional tests. Steps 2 and 3 are an iterative
process where new biochemical data increases the scope
of the network, requiring additional gap and functional
analysis as well as additional literature mining. A metabolically rich and readily available erythrocyte
can be useful for clinical biomarkers. To determine
potential uses, we cross-referenced the enzymes in iAB-
RBC-283 with known morbid SNPs and enzymes that
are reported drug targets in DrugBank. There are 142
morbid SNPs detectable in erythrocyte enzymes with
the majority dealing with non-erythrocyte related
pathologies. In addition, over 230 pharmaceuticals in
the DrugBank have known protein targets in the human
erythrocyte. Utilizing iAB-RBC-283, we qualitatively detected
metabolic signatures for the majority of in silico per-
turbed conditions pertaining to the morbid SNPs and
drugs from DrugBank. The affected exchange reactions,
metabolites, and associated pathways can be used to
focus experiments for biomarker discovery as well as
interpret global metabolomic profiles. Taken together, with available proteomic data, a
comprehensive constraint-based model of erythrocyte
metabolism was developed. Genome-scale metabolic
reconstructions have been shown to be an important tool
for integrating and analyzing high-throughput data for
biological insight [8,12,45,46]. In this study, we show that
the comprehensive metabolic network of the erythrocyte
plays an unanticipated, varied metabolic role in human
physiology and thus has much potential as a biomarker
with clinical applications. As erythrocytes are readily avail-
able, the proteomics and metabolomics of normal and
pathological states of individuals can be easily obtained
and used for identifying biomarkers in a systems context. Thus, possible remnant enzymes, such as glycogen
synthase and aspartate aminotransferase, were properly
removed when experimental validation was not avail-
able. The manual curation process yielded 60+ peer
reviewed articles and books representing over 50 years
of erythrocyte research. Conclusion The mature, enucleated erythrocyte is the best studied
human cell for metabolism due to its relative simplicity
and availability. Still, the view of its metabolism is rather
limited. The advances in high-throughput proteomics of
the erythrocyte has enabled construction of a compre-
hensive in silico red blood cell metabolic reconstruction,
iAB-RBC-283. Proteomic data alone is not adequate for
generating an accurate, complete, and functional model. The reconstruction was rigorously curated and validated
by experimental literature sources as proteomic samples
are known to be incomplete, contaminated with other of this effect can help focus experimental design on
these metabolic pathways in erythrocyte screening. Table 2 Uniqueness of metabolic signatures
# of Metabolic
Signatures
# of conditions sharing same met. signature
Morbid
SNPs
Drug
Treated
1 (Unique)
19
19
2
8
3
3
3
1
4
1
1
5
0
1
7
1
0 In silico simulations show that the erythrocyte can
also be used as a diagnostic for drug treated conditions. For example, topimarate is a drug for treating seizures
and migraines and inhibits carbonic anhydrase. Inhibi-
tion of this enzyme during simulations showed a drastic
change in 54 exchange reaction fluxes pertaining to car-
bohydrate, cofactor (thiamine, pyridoxine, bilirubin),
lipid, and nucleotide metabolism. As individuals react
differently to pharmaceuticals and sometimes require
different dosages and types of drugs, our analysis shows
that the red blood cell can act as a readily available
diagnostic for personalizing drug therapies. Table 2 Uniqueness of metabolic signatures Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Page 9 of 12 Methods In addition, erythrocyte Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Page 10 of 12 lb < v < ub
(3) (3) literature sources from the Human Metabolome Data-
base [47] were used for validating existence of unique
metabolites. A full listing of literature sources for each
reaction, enzyme, and metabolite is provided in the Sup-
plementary Material (see additional file 4, iAB-RBC-283
in XLS format). lb < v < ub Thus, the network is studied under mass conservation
and thermodynamic constraints. In addition, constraints
are placed on fluxes that exchange metabolites with the
surrounding system (the blood plasma in this case),
based on existing literature of metabolite transport in
the human erythrocyte (see additional file 6 in Supple-
mentary Material). These reactions are called exchange
reactions and control the flow of metabolites into and
out of the in silico cell. Fatty acid chains were not represented as generic R-
groups as in Recon 1, but instead the three most com-
mon (by percent mass composition) fatty acids, palmitic,
linolenic, and linoleic (C16:0, C18:1, C18:2, respectively)
were used [48]. The final reconstruction is termed iAB-
RBC-283 for i (in silico), AB (the primary author’s initi-
als), RBC (red blood cell), 283 (number of open reading
frames accounted for in the network). iAB-RBC-283
consists of all the known metabolites, reactions, thermo-
dynamic directionality, and genetic information that the
detected erythrocyte metabolic enzymes catalyze. The
final reconstruction is provided in both an XLS and
SBML format in the Supplementary Material (additional
files 4 and 5) and can also be found at the BioModels
Database (id: MODEL1106080000). Flux balance analysis (FBA) is a well-established opti-
mization procedure [37] used to determine the maxi-
mum possible flux through a particular reaction in the
network based on the constraints on the network (Equa-
tions 2 and 3) without the need for kinetic parameters. A primer for using FBA and related tools is detailed by
Orth et al. [49]. Publicly available software packages
exist [50] In this work, a variant of FBA, called flux variability
analysis (FVA) [28], is used. FVA iteratively calculates
both the maximum and minimum allowable flux
through every reaction in the network. Reactions with a
calculated non-zero maximum or minimum have the
potential to be active and have a potential physiological
function. Constraint-based modeling and functional testing The network reconstruction can be represented as a
stoichiometric matrix, S, that is formed from the stoi-
chiometric coefficients of the biochemical transforma-
tions. Each column of the matrix represents a particular
elementally and charge balanced reaction in the net-
work, while each row corresponds to a particular meta-
bolite [37]. Thus, the stoichiometric matrix converts the
individual fluxes into network based time derivatives of
the concentrations (Equation 1). dx
dt = S · v
(1) dx
dt = S · v (1) As each reaction is comprised of only a few metabo-
lites, but there are many metabolites in a network, each
flux vector is quite sparse. The stoichiometric matrix is
sparse, with 1.3% non-zero elements, and has dimen-
sions (m × n); where m is the number of compounds
and n is the number of reactions. In the case of iAB-
RBC-283, m is 342 and n is 469. Calculating metabolite connectivity The stoichiometric matrices of the reconstructed ery-
throcyte network, Recon 1, and its constituent orga-
nelles were used to calculate metabolite connectivities
[36] of every species in each network. The number of
reactions each metabolite participates in was summed. For the organelle calculations, only the metabolites cor-
responding to the particular organelle of Recon 1 were
considered. The metabolite connectivity of each orga-
nelle as well as Recon 1 and iAB-RBC-283 was ranked
order from greatest to least connected to form a discrete
distribution (Figure 3). As in vivo kinetic parameters are difficult to obtain,
the system is assumed to be at a homeostatic state
(Equation 2), v = 0
(2)
wing for simulation without kinetic parameters. S · v = 0
(2) (2) S · v = 0 allowing for simulation without kinetic parameters. The network fluxes (v) are bounded by thermodynamic
constraints that limit the directionality of irreversible
catalytic mechanisms (lb = 0 for irreversible reactions)
as well as known vmax’s. Methods Thus, we use FVA to determine the capability/
capacity of the network reactions to determine meta-
bolic functionality. For a reaction to have a non-zero
flux, the reaction must be linked to other metabolic
reactions and pathways and plays a functional role in
the system. Thus, potentially active reactions are
deemed as functional. After determining which reactions
were functional, the reaction list was perused to deter-
mine pathway and subsystem functionality in the
network. Additional material 5. Reed JL, Vo TD, Schilling CH, Palsson BO: An expanded genome-scale
model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biology 2003,
4(9):R54.1-R54.12. Additional file 1: Comparison of iAB-RBC-283 and previous
constraint-based model of human erythrocyte. iAB-RBC-283 was
compared with the previous constraint-based model of the human
erythrocyte. Topological analysis showed that iAB-RBC-283 is a much
more expansive network that better describes human erythrocyte
metabolism. In addition, we recapitulated the randomized sampling
results of the previous network and showed that the new erythrocyte
model is more accurate, capturing all the correct predictions of the
previous model but also correcting its inaccurate predictions. 6. Forster J, Famili I, Fu PC, Palsson BO, Nielsen J: Genome-Scale
Reconstruction of the Saccharomyces cerevisiae Metabolic Network. Genome Research 2003, 13(2):244-53. 7. Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R,
Palsson BO: Global reconstruction of the human metabolic network
based on genomic and bibliomic data. Proc Natl Acad Sci USA 2007,
104(6):1777-82. 8. Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP,
Cheng JK, Patel N, Yee A, Lewis RA, Eils R, Konig R, Palsson BO: Large-scale
in silico modeling of metabolic interactions between cell types in the
human brain. Nat Biotechnol 2010, 28(12):1279-85. 8. Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP,
Cheng JK, Patel N, Yee A, Lewis RA, Eils R, Konig R, Palsson BO: Large-scale
in silico modeling of metabolic interactions between cell types in the
human brain. Nat Biotechnol 2010, 28(12):1279-85. Additional file 2: Detected SNPs and FVA results for SNP
perturbations. Tables containing the OMIM SNPs of enzymes that are in
iAB-RBC-283 with known pathologies, symptoms, metabolic subsystem,
and classification. In addition, exchange reactions determined by FVA to
be different in SNP perturbations are provided. 9. Gille C, Bolling C, Hoppe A, Bulik S, Hoffmann S, Hubner K, Karlstadt A,
Ganeshan R, Konig M, Rother K, Weidlich M, Behre J, Holzhutter HG:
HepatoNet1: a comprehensive metabolic reconstruction of the human
hepatocyte for the analysis of liver physiology. Mol Syst Biol 2010, 6:411. Additional file 3: Detected drug targets and FVA results for drug
effect perturbations. Tables containing the known drugs, from
DrugBank, that have targets in enzymes that are in iAB-RBC-283. In
addition, information is provided on the drug including name,
description, and classification. The exchange reactions determined by
FVA to be different in drug perturbations are also provided. 10. References 1. Oberhardt MA, Palsson BO, Papin JA: Applications of genome-scale
metabolic reconstructions. Mol Syst Biol 2009, 5:320. 2. Feist AM, Palsson BO: The growing scope of applications of genome-scale
metabolic reconstructions using Escherichia coli. Nat Biotech 2008,
26(6):659-667. 3. Thiele I, Palsson BO: A protocol for generating a high-quality genome-
scale metabolic reconstruction. Nat Protoc 2010, 5(1):93-121. 4. Edwards JS, Palsson BO: Systems properties of the Haemophilus
influenzae Rd metabolic genotype. Journal of Biological Chemistry 1999,
274(25):17410-6. Acknowledgements We would like to thank Nathan Lewis and Daniel Zielinski for insightful
conversations and suggestions for the manuscript. This work was supported
by National Institutes of Health Grant GM068837. Analyzing iAB-RBC-283 as a functional biomarker Analyzing iAB-RBC-283 as a functional biomarker The Morbid Map from the Online Mendelian Inheri-
tance in Man (OMIM) [51] and the DrugBank [52] were
downloaded from their respective databases (accession Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Page 11 of 12 date: 27/09/10). The enzyme names in iAB-RBC-283
were cross-referenced against the database entries to
determine morbid SNPs in erythrocyte proteins and
drugs with protein targets in the erythrocyte. The mor-
bid SNPs that did not have sole pathological effects in
the erythrocyte were classified using the Merck Manual
[43]. Additional file 6: Citations for exchanges in the human erythrocyte. Table containing citations used to determine exchange rates of
metabolites into the human erythrocyte. Additional file 7: Parameter sensitivity of threshold for FVA
simulations. Figure showing the average number of FVA-detected
exchange reactions for each perturbation and different thresholds. Thresholds were tested from 5-60% at intervals of 5%. The average
detected reactions were quite stable from 15-40% for both the SNP and
drug perturbations. A final 40% threshold was used in the study. Just as FVA can be used to assess the function of a
network under a particular set of constraints, it can also
be used to assess the changes in function and thus has
applications for characterizing disease states [53] and
identifying biomarkers [54]. When simulating a morbid
SNP or a drug inhibited enzyme, the lower and upper
bound constraints on the affected reaction is set to zero
as per Shlomi et al. FVA is then used to characterize
the exchange reactions under morbid SNP or drug trea-
ted conditions (Figure 5A) and then compared to the
normal state. A reaction was considered to be confi-
dently altered if the change in the minimum or maxi-
mum flux was 40% of the total flux span. The flux span
is defined as the absolute difference between the original
(unperturbed) maximum and minimum fluxes. Potential
thresholds from 5 - 60% were tested. Thresholds in the
15 - 40% range were very consistent while thresholds
above 40% had a dropoff (see additional file 7). Authors’ contributions AB reconstructed the network, performed the analyses, and drafted the
manuscript. NJ and BOP conceived the study and revised the manuscript. All
authors approved the content of the final manuscript. Received: 20 April 2011 Accepted: 12 July 2011 Published: 12 July 2011 Additional file 6: Citations for exchanges in the human erythrocyte.
Table containing citations used to determine exchange rates of
metabolites into the human erythrocyte.
Additional file 7: Parameter sensitivity of threshold for FVA
simulations. Figure showing the average number of FVA-detected
exchange reactions for each perturbation and different thresholds.
Thresholds were tested from 5-60% at intervals of 5%. The average
detected reactions were quite stable from 15-40% for both the SNP and
drug perturbations. A final 40% threshold was used in the study. Additional material Jerby L, Shlomi T, Ruppin E: Computational reconstruction of tissue-
specific metabolic models: application to human liver metabolism. Mol
Syst Biol 2010, 6:401. 11. Chang RL, Xie L, Bourne PE, Palsson BO: Drug off-target effects predicted
using structural analysis in the context of a metabolic network model. PLoS Comput Biol 2010, 6(9):e1000938. Additional file 4: iAB-RBC-283 Reconstruction (table format). The
reconstruction is provided in a table format with reactions, metabolites,
and gene-protein-reaction associations. In addition, information is
provided on whether the reaction was detected in the proteomic or
metabolomic data and citations are provided for reactions with existing
experimental evidence, implicating the reactions presence in the human
erythrocyte. 12. Bordbar A, Lewis NE, Schellenberger J, Palsson BO, Jamshidi N: Insight into
human alveolar macrophage and M. tuberculosis interactions via
metabolic reconstructions. Mol Syst Biol 2010, 6:422. 13. Rapoport TA, Heinrich R, Jacobasch G, Rapoport S: A linear steady-state
treatment of enzymatic chains. A mathematical model of glycolysis of
human erythrocytes. Eur J Biochem 1974, 42(1):107-20. Additional file 5: iAB-RBC-283 Reconstruction (SBML format). The
reconstruction is provided in the standardized SBML format. The XML file
can be loaded in to COBRA toolbox to perform in silico simulations. A
copy of the file is also available at the BioModels Database (id:
MODEL1106080000). Additional file 5: iAB-RBC-283 Reconstruction (SBML format). The
reconstruction is provided in the standardized SBML format. The XML file
can be loaded in to COBRA toolbox to perform in silico simulations. A
copy of the file is also available at the BioModels Database (id:
MODEL1106080000). 14. Brumen M, Heinrich R: A metabolic osmotic model of human
erythrocytes. Biosystems 1984, 17(2):155-69. 14. Brumen M, Heinrich R: A metabolic osmotic model of hum erythrocytes. Biosystems 1984, 17(2):155-69. 15. Joshi A, Palsson BO: Metabolic dynamics in the human red cell. Part I–A
comprehensive kinetic model. Journal of Theoretical Biology 1989,
141(4):515-28. Page 12 of 12 Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 Bordbar et al. BMC Systems Biology 2011, 5:110
http://www.biomedcentral.com/1752-0509/5/110 16. Mulquiney PJ, Bubb WA, Kuchel PW: Model of 2,3-bisphosphoglycerate
metabolism in the human erythrocyte based on detailed enzyme kinetic
equations: in vivo kinetic characterization of 2,3-bisphosphoglycerate
synthase/phosphatase using 13C and 31P NMR. Biochem J 1999, 342(Pt
3):567-80. 39. Additional material Baines M, Davies G: The evaluation of erythrocyte thiamin diphosphate
as an indicator of thiamin status in man, and its comparison with
erythrocyte transketolase activity measurements. Ann Clin Biochem 1988,
25(Pt 6):698-705. 40. Agarwal DP, Tobar-Rojas L, Harada S, Goedde HW: Comparative study of
erythrocyte aldehyde dehydrogenase in alcoholics and control subjects. Pharmacol Biochem Behav 1983, 18(Suppl 1):89-95. 17. Schauer M, Heinrich R, Rapoport SM: Mathematische Modellierung der
Glykolyse und des Adeninnukleotidstoffwechsels menschlicher
Erythrozyten. Acta Biological er Medica Germanica 1981, 40:1659-1682. 41. Murakami K, Kondo T, Ohtsuka Y, Fujiwara Y, Shimada M, Kawakami Y:
Impairment of glutathione metabolism in erythrocytes from patients
with diabetes mellitus. Metabolism 1989, 38(8):753-8. 18. Jamshidi N, Edwards JS, Fahland T, Church GM, Palsson BO: Dynamic
simulation of the human red blood cell metabolic network. Bioinformatics 2001, 17(3):286-7. 42. Prabakaran S, Wengenroth M, Lockstone HE, Lilley K, Leweke FM, Bahn S: 2-
D DIGE analysis of liver and red blood cells provides further evidence
for oxidative stress in schizophrenia. J Proteome Res 2007, 6(1):141-9. 19. Kinoshita A, Tsukada K, Soga T, Hishiki T, Ueno Y, Nakayama Y, Tomita M,
Suematsu M: Roles of hemoglobin Allostery in hypoxia-induced
metabolic alterations in erythrocytes: simulation and its verification by
metabolome analysis. J Biol Chem 2007, 282(14):10731-41. 43. Dadoly J: The Merck Manual. Medical Reference Services Quarterly , 18 2007,
26(2):113-114. y
20. Raftos JE, Whillier S, Kuchel PW: Glutathione synthesis and turnover in the
human erythrocyte: alignment of a model based on detailed enzyme
kinetics with experimental data. J Biol Chem 2010, 285(31):23557-67. 44. Shifman S, Bronstein M, Sternfeld M, Pisante-Shalom A, Lev-Lehman E,
Weizman A, Reznik I, Spivak B, Grisaru N, Karp L, Schiffer R, Kotler M,
Strous RD, Swartz-Vanetik M, Knobler HY, Shinar E, Beckmann JS, Yakir B,
Risch N, Zak NB, Darvasi A: A highly significant association between a
COMT haplotype and schizophrenia. Am J Hum Genet 2002,
71(6):1296-302. 21. Roux-Dalvai F, Gonzalez de Peredo A, Simo C, Guerrier L, Bouyssie D,
Zanella A, Citterio A, Burlet-Schiltz O, Boschetti E, Righetti PG, Monsarrat B:
Extensive analysis of the cytoplasmic proteome of human erythrocytes
using the peptide ligand library technology and advanced mass
spectrometry. Mol Cell Proteomics 2008, 7(11):2254-69. 45. Usaite R, Patil KR, Grotkjaer T, Nielsen J, Regenberg B: Global transcriptional
and physiological responses of Saccharomyces cerevisiae to ammonium,
L-alanine, or L-glutamine limitation. Appl Environ Microbiol 2006,
72(9):6194-203. 22. Additional material Pasini EM, Kirkegaard M, Mortensen P, Lutz HU, Thomas AW, Mann M: In-
depth analysis of the membrane and cytosolic proteome of red blood
cells. Blood 2006, 108(3):791-801. 46. Herrgard MJ, Lee BS, Portnoy V, Palsson BO: Integrated analysis of
regulatory and metabolic networks reveals novel regulatory mechanisms
in Saccharomyces cerevisiae. Genome Research 2006, 16(5):627-635. 23. Low TY, Seow TK, Chung MC: Separation of human erythrocyte
membrane associated proteins with one-dimensional and two-
dimensional gel electrophoresis followed by identification with matrix-
assisted laser desorption/ionization-time of flight mass spectrometry. Proteomics 2002, 2(9):1229-39. 47. Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, Hau DD,
Psychogios N, Dong E, Bouatra S, Mandal R, Sinelnikov I, Xia J, Jia L, Cruz JA,
Lim E, Sobsey CA, Shrivastava S, Huang P, Liu P, Fang L, Peng J, Fradette R,
Cheng D, Tzur D, Clements M, Lewis A, De Souza A, Zuniga A, Dawe M,
et al: HMDB: a knowledgebase for the human metabolome. Nucleic Acids
Res 2009, , 37 Database: D603-10. 24. Goodman SR, Kurdia A, Ammann L, Kakhniashvili D, Daescu O: The human
red blood cell proteome and interactome. Exp Biol Med (Maywood) 2007,
232(11):1391-408. 25. Zhang Y, Thiele I, Weekes D, Li Z, Jaroszewski L, Ginalski K, Deacon AM,
Wooley J, Lesley SA, Wilson IA, Palsson B, Osterman A, Godzik A: Three-
dimensional structural view of the central metabolic network of
Thermotoga maritima. Science 2009, 325(5947):1544-9. 48. Surgenor DM, Bishop CW: The red blood cell. New York: Academic Press;, 2
19742. 49. Orth JD, Thiele I, Palsson BO: What is flux balance analysis? Nat Biotechnol
2010, 28(3):245-8. 26. Vo TD, Greenberg HJ, Palsson BO: Reconstruction and functional
characterization of the human mitochondrial metabolic network based
on proteomic and biochemical data. J Biol Chem 2004, 279(38):39532-40. 50. Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ:
Quantitative prediction of cellular metabolism with constraint-based
models: The COBRA Toolbox. Nat Protocols 2007, 2(3):727-738. 27. Wiback SJ, Palsson BO: Extreme pathway analysis of human red blood
cell metabolism. Biophysical Journal 2002, 83(2):808-818. 51. Amberger J, Bocchini CA, Scott AF, Hamosh A: McKusick’s Online
Mendelian Inheritance in Man (OMIM). Nucleic Acids Res 2009, , 37
Database: D793-6. 28. Mahadevan R, Schilling CH: The effects of alternate optimal solutions in
constraint-based genome-scale metabolic models. Metab Eng 2003,
5(4):264-76. 52. Additional material Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B,
Hassanali M: DrugBank: a knowledgebase for drugs, drug actions and
drug targets. Nucleic Acids Res 2008, , 36 Database: D901-6. 29. Lou LL, Clarke S: Enzymatic methylation of band 3 anion transporter in
intact human erythrocytes. Biochemistry 1987, 26(1):52-9. 53. Sigurdsson MI, Jamshidi N, Jonsson JJ, Palsson BO: Genome-scale network
analysis of imprinted human metabolic genes. Epigenetics 2009, 4(1):43-6. 30. Allan D, Michell RH: Production of 1,2-diacylglycerol in human
erythrocyte membranes exposed to low concentrations of calcium ions. Biochim Biophys Acta 1976, 455(3):824-30. 54. Shlomi T, Cabili MN, Ruppin E: Predicting metabolic biomarkers of human
inborn errors of metabolism. Mol Syst Biol 2009, 5:263. 31. Arduini A, Mancinelli G, Radatti GL, Dottori S, Molajoni F, Ramsay RR: Role
of carnitine and carnitine palmitoyltransferase as integral components
of the pathway for membrane phospholipid fatty acid turnover in intact
human erythrocytes. J Biol Chem 1992, 267(18):12673-81. doi:10.1186/1752-0509-5-110
Cite this article as: Bordbar et al.: iAB-RBC-283: A proteomically derived
knowledge-base of erythrocyte metabolism that can be used to
simulate its physiological and patho-physiological states. BMC Systems
Biology 2011 5:110. 32. Berridge MJ, Irvine RF: Inositol trisphosphate, a novel second messenger
in cellular signal transduction. Nature 1984, 312(5992):315-21. 33. Tsukamoto T, Sonenberg M: Catecholamine regulation of human
erythrocyte membrane protein kinase. J Clin Invest 1979, 64(2):534-40. 34. McGee JE, Fitzpatrick FA: Erythrocyte-neutrophil interactions: formation of
leukotriene B4 by transcellular biosynthesis. Proc Natl Acad Sci USA 1986,
83(5):1349-53. 35. Inoue K, Ohbora Y, Yamasawa K: Metabolism of acetaldehyde by human
erythrocytes. Life Sci 1978, 23(2):179-83. 36. Becker SA, Price ND, Palsson BO: Metabolite coupling in genome-scale
metabolic networks. BMC Bioinformatics 2006, 7(111). metabolic networks. BMC Bioinformatics 2006, 7(111). 37. Palsson BO: Systems biology: properties of reconstructed networks. New
York: Cambridge University Press; 2006. 38. Graham JM, Peerson JM, Haskell MJ, Shrestha RK, Brown KH, Allen LH:
Erythrocyte riboflavin for the detection of riboflavin deficiency in
pregnant Nepali women. Clin Chem 2005, 51(11):2162-5. 38. Graham JM, Peerson JM, Haskell MJ, Shrestha RK, Brown KH, Allen LH:
Erythrocyte riboflavin for the detection of riboflavin deficiency in
pregnant Nepali women. Clin Chem 2005, 51(11):2162-5.