BACKGROUND
the advancement of high-throughput data generation has ushered a new era of "omic" sciences.
whole-cell measurements can elucidate the genome sequence  as well as detect mrna , proteins , and small metabolites  under a specific condition.
though these methods provide a broad coverage in determining cellular activities, little integrated functional analysis has been performed to date.
genome-scale network reconstructions are a common denominator for computational analysis in systems biology as well as an integrative platform for experimental data analysis  <cit> .
there are several applications of reconstructions including: 1) contextualization of high-throughput data, 2) directing hypothesis-driven discovery, and 3) network property discovery  <cit> .
network reconstruction involves elucidating all the known biochemical transformations in a particular cell or organism and formally organizing them in a biochemically consistent format  <cit> .
genome sequencing has allowed genome-scale reconstruction of numerous metabolic networks of prokaryotes and eukaryotes  <cit> .
in fact, a genome-scale reconstruction of human metabolism has been completed, called recon  <dig>  recon  <dig> is a global human knowledge-base of biochemical transformations in humans that is not cell or tissue specific  <cit> .
more recently, recon  <dig> has been adapted to study specific cells and tissues with the help of high-throughput data, including the human brain  <cit> , liver  <cit> , kidney  <cit> , and alveolar macrophage  <cit> .
though many cell and tissue-specific models have been reconstructed from recon  <dig>  the human erythrocyte has undeservedly received less attention as the cell has been largely assumed to be simple.
historically, red cell metabolic models began with simple glycolytic models  <cit> .
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  <cit> .
more recent metabolic models have been built accounting for additional regulatory  <cit>  and metabolic components .
however, in the past decade, attempts to obtain comprehensive proteomic coverage of the red cell have demonstrated a much richer complement of metabolism than previously anticipated  <cit> .
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, we use available proteomics to develop the largest  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.
RESULTS
iab-rbc- <dig> is a proteomic based metabolic reconstruction and a biochemical knowledge-base, a functional 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 characterize 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.
proteomic based erythrocyte reconstruction
proteomic data has been successfully used for reconstructions of thermotoga maritima  <cit>  and the human mitochondria  <cit>  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 incomplete, even for studies aiming to obtain comprehensive coverage.
another challenge is the possible contamination in the extract preparation with other blood cells  <cit> .
in addition, leftover enzymes from immature erythroid 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.
thus, in this study, we construct a full bottom-up reconstruction of erythrocyte metabolism with rigorous manual curation in which reactions inferred from proteomically 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 .
the final reconstruction, iab-rbc- <dig>  contains a metabolic network that is much more expansive than red blood cell models presented to date .
the reconstruction contains  <dig> intracellular reactions,  <dig> transporters,  <dig> unique small metabolites, and accounts for  <dig> genes, suggesting that the erythrocyte has a more varied and expansive metabolic role than previously recognized.
a full bottom-up reconstruction of the human erythrocyte provides a functional interpretation of proteomic data that is biochemically meaningful.
manual curation provides experimental validation of metabolic pathways, as well as gap filling.
the data can be rigorously 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- <dig> was converted into a mathematical model.
the expanded erythrocyte network was topologically and functionally compared to a previous constraint-based model of erythrocyte metabolism  <cit>  .
predictions made by this model could be recapitulated by iab-rbc- <dig>  to determine new functionalities of the expanded erythrocyte network, the system is assumed to be at a homeostatic state and qualitative capacity/capability simulations are done to ascertain which reactions and pathways can be potentially active in the in silico erythrocyte.
flux variability analysis  was utilized to determine the functional metabolic pathways of the erythrocyte network .
fva determines the minimum and maximum allowable flux through each metabolic reaction  <cit> .
in short, the fva method defines the bounding box on network capabilities.
reactions that had a non-zero minimum or maximum flux value were deemed to be functional.
network level metabolic functional assessment showed that iab-rbc- <dig> accounts for additional pathways into glycolysis through galactose, fructose, mannose, glucosamine, and amino sugars .
galactose can also be shuttled to the pentose phosphate pathway through glucuronate interconversions.
citric acid cycle enzymes  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.
in addition, nucleotide metabolism and salvage pathways have been expanded from previous metabolic 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 adenylate and guanylate cyclases were found to be present in the proteomic data.
the erythrocyte uses amino acids to produce glutathione for redox balancing, converts arginine to polyamines 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  <cit> .
this has also been included in iab-rbc- <dig>  in addition, polyamine metabolism produces 5-methylthioadenosine which can be salvaged for methionine recycling.
another major expansion in metabolic capabilities represented in iab-rbc- <dig> is lipid metabolism.
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 diacylglyercol into phospholipids for upkeep of its membrane  <cit> .
a pseudo-carnitine shuttle in the cytosol is used to create a buffer of coa for the cell  <cit> .
all major erythrocyte fatty acids  and phospholipids  are explicitly modeled.
the phosphatidylinositols can be converted into various forms of myo-inositols, which play an extensive role in cell signaling  <cit> .
finally, fva showed that the erythrocyte plays an important role in cofactor metabolism.
the reconstruction accounts for uptake, modification, and secretion of multiple cofactors including vitamin b <dig>  vitamin c, riboflavin, thiamine, heme, and nad.
in addition, human erythrocytes play a role in deactivating catecholamines  <cit> , hydrolyzing leukotriene  <cit> , and detoxifying acetaldehyde  <cit>  which were confirmed in the literature.
metabolite connectivity
in order to compare the network structure of the in silico erythrocyte versus other similar metabolic networks, we calculated the connectivity of each metabolite  <cit> .
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 biochemical reactions associated with a particular metabolite are the edges of the network.
metabolite connectivity thus involves determining the number of edges  connected to every node .
we compared the in silico erythrocyte to the global human metabolic network, recon  <dig>  as well as the separate human organelles .
a dotted line, linking the minimum and maximum connectivities, is drawn on the distributions as a reference for comparing the distributions.
a network with higher connectivity would have many points above the dotted line, while lower connectivity would result in points below the dotted line.
recon  <dig> has most points above the reference line .
in fact, the metabolite node connectivities for genome-scale reconstructions usually have a distribution similar to recon  <dig>  highlighting the complexity of these networks  <cit> .
however, the organelles in recon  <dig> usually have values below the reference line, due mainly to a higher difficulty to annotate reactions specific to an organelle.
the separate organelle metabolic networks are less connected.
the exception is the mitochondria , which is well studied and has a very important and complex metabolic role.
the metabolite connectivity of the erythrocyte network is very similar to the less connected organelles in recon  <dig>  this similarity is due to either 1) the erythrocyte biology or 2) the lack of complete proteomic profiling.
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 .
however, the network simplicity of the in silico erythrocyte model may also be attributed to the limitations of proteomic techniques.
as coverage of proteomic data is typically not as complete as transcriptomics, the reconstructed metabolic network may reflect this limitation.
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.
in silico simulations show a greater physiological functionality of erythrocyte metabolism.
the additional functionality 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 experimental literature was found.
the human erythrocyte's potential as a biomarker
decades of models have described erythrocyte metabolism to include principally glycolysis, the rapoport-luebering shunt, the pentose phosphate pathway, and nucleotide salvage pathways.
integration and compilation of proteomic data, however, has surprisingly shown evidence of a much richer metabolic role for the erythrocyte.
erythrocytes make contact with most portions of the body and are one of the most abundant cells .
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  <cit> , thiamine deficiency  <cit> , alcoholism  <cit> , diabetes  <cit> , and schizophrenia  <cit> , however comprehensive systems level analyses have not been performed to date.
iab-rbc- <dig> 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  <dig> drugs from the drugbank database with the  <dig> enzymes accounted for in iab-rbc- <dig>   <dig> morbid snps were found in  <dig> of the erythrocyte enzymes.
in addition,  <dig> fda-approved, fda-withdrawn, and experimental drugs have known protein targets in the human erythrocyte .
only  <dig> of the  <dig> morbid snps are related to pathologies 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  <dig> using the merck manual  <cit> .
most of the observed snps are causal and simple targeted assays could be used as diagnostic tools.
we also cross-referenced the enzymes in iab-rbc- <dig> with the drugbank's known protein targets of pharmaceuticals.
<dig> fda-approved,  <dig> fda-withdrawn, and  <dig> experimental drugs have known targets in the human erythrocyte.
these medications target enzymes for the treatment of a wide range of diseases including seizures , allergies , cancer , hiv , and high cholesterol .
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.
a comprehensive listing of all observed morbid snps and drugs are provided in the supplementary material .
utilizing iab-rbc- <dig> 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- <dig> 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 determined differential reaction activity .
activated or suppressed flux from in silico simulations provides a qualitative understanding into which metabolites and reactions are perturbed, allowing for experimental followup.
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 conditions .
on average, there were  <dig>  and  <dig>  differential activities of exchange reactions for morbid snps and drug treated conditions respectively.
the average is skewed by some morbid snps and drug treated conditions that have over  <dig> affected exchange reactions, as most differences are detected in between one and ten exchange reactions .
the morbid snps with greater than  <dig> exchange reactions with differential activity deal mostly with glycolytic and transport enzymes that are known to cause anemias and spherocytosis .
in addition, most of the drug treated conditions 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.
an important metabolic enzyme found in erythrocytes is catechol-o-methyltransferase  used to methylate cathecolamines.
a morbid snp of comt gene has been implicated in susceptibility to schizophrenia  <cit> .
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 of this effect can help focus experimental design on these metabolic pathways in erythrocyte screening.
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.
inhibition of this enzyme during simulations showed a drastic change in  <dig> exchange reaction fluxes pertaining to carbohydrate, cofactor , 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.
to further investigate the diagnostic capability of the red blood cell, we assessed the uniqueness of the metabolic signatures detected.
we compared all the metabolite 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 .
the in silico simulation results provide a method to focus biomarker discovery experiments in the human erythrocyte, as well as interpret global metabolomic profiling.
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 signature.
the differential activity in exchanges for the perturbed conditions allow for focusing experiments to particular metabolites, exchanges, and associated pathways, allowing development of targeted assays.
in addition, global metabolomic profiling of perturbed conditions can be interpreted using the calculated metabolic signatures and the erythrocyte reconstruction.
a full listing of all detected morbid snps and drug treated conditions, as well as the corresponding exchange reactions with differential activity and fluxes is provided in the supplementary material .
CONCLUSIONS
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 comprehensive in silico red blood cell metabolic reconstruction, iab-rbc- <dig>  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 types of blood cells and inactive enzymes are passed down the erythrocyte differentiation lineage.
thus, iab-rbc- <dig> is a knowledge-base of integrated high-throughput and biological data, which can also be queried through simulations.
functional testing showed that the new reconstruction takes into account historically neglected areas of carbohydrate, amino acid, cofactor, and lipid metabolism.
traditionally, the erythrocyte is known for its role in oxygen delivery, but the varied metabolism the cell exhibits points towards a much more expanded metabolic role as the cell can act as a sink or source of metabolites, through interactions with all organs and tissues in the body.
metabolite connectivity analysis showed that the erythrocyte 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 proteomic studies would be useful for a few metabolic pathways: including tca cycle, cysteine, folate, and phospholipid metabolism.
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- <dig> with known morbid snps and enzymes that are reported drug targets in drugbank.
there are  <dig> morbid snps detectable in erythrocyte enzymes with the majority dealing with non-erythrocyte related pathologies.
in addition, over  <dig> pharmaceuticals in the drugbank have known protein targets in the human erythrocyte.
utilizing iab-rbc- <dig>  we qualitatively detected metabolic signatures for the majority of in silico perturbed 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  <cit> .
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 available, the proteomics and metabolomics of normal and pathological states of individuals can be easily obtained and used for identifying biomarkers in a systems context.

 the advancement of high-throughput data generation has ushered a new era of "omic" sciences. whole-cell measurements can elucidate the genome sequence as well as detect mrna, proteins, and small metabolites under a specific condition. though these methods provide a broad coverage in determining cellular activities, little integrated functional analysis has been performed to date. genome-scale network reconstructions are a common denominator for computational analysis in systems biology as well as an integrative platform for experimental data analysis. there are several applications of reconstructions including: 1) contextualization of high-throughput data, 2) directing hypothesis-driven discovery, and 3) network property discovery. network reconstruction involves elucidating all the known biochemical transformations in a particular cell or organism and formally organizing them in a biochemically consistent format. genome sequencing has allowed genome-scale reconstruction of numerous metabolic networks of prokaryotes and eukaryotes. in fact, a genome-scale reconstruction of human metabolism has been completed, called recon recon is a global human knowledge-base of biochemical transformations in humans that is not cell or tissue specific. more recently, recon has been adapted to study specific cells and tissues with the help of high-throughput data, including the human brain, liver, kidney, and alveolar macrophage. though many cell and tissue-specific models have been reconstructed from recon the human erythrocyte has undeservedly received less attention as the cell has been largely assumed to be simple. historically, red cell metabolic models began with simple glycolytic models. 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. more recent metabolic models have been built accounting for additional regulatory and metabolic components. however, in the past decade, attempts to obtain comprehensive proteomic coverage of the red cell have demonstrated a much richer complement of metabolism than previously anticipated. 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, we use available proteomics to develop the largest 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.  iab-rbc- is a proteomic based metabolic reconstruction and a biochemical knowledge-base, a functional 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 characterize 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. proteomic based erythrocyte reconstruction proteomic data has been successfully used for reconstructions of thermotoga maritima and the human mitochondria 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 incomplete, even for studies aiming to obtain comprehensive coverage. another challenge is the possible contamination in the extract preparation with other blood cells. in addition, leftover enzymes from immature erythroid 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. thus, in this study, we construct a full bottom-up reconstruction of erythrocyte metabolism with rigorous manual curation in which reactions inferred from proteomically 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. the final reconstruction, iab-rbc- contains a metabolic network that is much more expansive than red blood cell models presented to date. the reconstruction contains intracellular reactions, transporters, unique small metabolites, and accounts for genes, suggesting that the erythrocyte has a more varied and expansive metabolic role than previously recognized. a full bottom-up reconstruction of the human erythrocyte provides a functional interpretation of proteomic data that is biochemically meaningful. manual curation provides experimental validation of metabolic pathways, as well as gap filling. the data can be rigorously 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- was converted into a mathematical model. the expanded erythrocyte network was topologically and functionally compared to a previous constraint-based model of erythrocyte metabolism. predictions made by this model could be recapitulated by iab-rbc- to determine new functionalities of the expanded erythrocyte network, the system is assumed to be at a homeostatic state and qualitative capacity/capability simulations are done to ascertain which reactions and pathways can be potentially active in the in silico erythrocyte. flux variability analysis was utilized to determine the functional metabolic pathways of the erythrocyte network. fva determines the minimum and maximum allowable flux through each metabolic reaction. in short, the fva method defines the bounding box on network capabilities. reactions that had a non-zero minimum or maximum flux value were deemed to be functional. network level metabolic functional assessment showed that iab-rbc- accounts for additional pathways into glycolysis through galactose, fructose, mannose, glucosamine, and amino sugars. galactose can also be shuttled to the pentose phosphate pathway through glucuronate interconversions. citric acid cycle enzymes 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. in addition, nucleotide metabolism and salvage pathways have been expanded from previous metabolic 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 adenylate and guanylate cyclases were found to be present in the proteomic data. the erythrocyte uses amino acids to produce glutathione for redox balancing, converts arginine to polyamines 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. this has also been included in iab-rbc- in addition, polyamine metabolism produces 5-methylthioadenosine which can be salvaged for methionine recycling. another major expansion in metabolic capabilities represented in iab-rbc- is lipid metabolism. 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 diacylglyercol into phospholipids for upkeep of its membrane. a pseudo-carnitine shuttle in the cytosol is used to create a buffer of coa for the cell. all major erythrocyte fatty acids and phospholipids are explicitly modeled. the phosphatidylinositols can be converted into various forms of myo-inositols, which play an extensive role in cell signaling. finally, fva showed that the erythrocyte plays an important role in cofactor metabolism. the reconstruction accounts for uptake, modification, and secretion of multiple cofactors including vitamin b vitamin c, riboflavin, thiamine, heme, and nad. in addition, human erythrocytes play a role in deactivating catecholamines, hydrolyzing leukotriene, and detoxifying acetaldehyde which were confirmed in the literature. metabolite connectivity in order to compare the network structure of the in silico erythrocyte versus other similar metabolic networks, we calculated the connectivity of each metabolite. 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 biochemical reactions associated with a particular metabolite are the edges of the network. metabolite connectivity thus involves determining the number of edges connected to every node. we compared the in silico erythrocyte to the global human metabolic network, recon as well as the separate human organelles. a dotted line, linking the minimum and maximum connectivities, is drawn on the distributions as a reference for comparing the distributions. a network with higher connectivity would have many points above the dotted line, while lower connectivity would result in points below the dotted line. recon has most points above the reference line. in fact, the metabolite node connectivities for genome-scale reconstructions usually have a distribution similar to recon highlighting the complexity of these networks. however, the organelles in recon usually have values below the reference line, due mainly to a higher difficulty to annotate reactions specific to an organelle. the separate organelle metabolic networks are less connected. the exception is the mitochondria, which is well studied and has a very important and complex metabolic role. the metabolite connectivity of the erythrocyte network is very similar to the less connected organelles in recon this similarity is due to either 1) the erythrocyte biology or 2) the lack of complete proteomic profiling. 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. however, the network simplicity of the in silico erythrocyte model may also be attributed to the limitations of proteomic techniques. as coverage of proteomic data is typically not as complete as transcriptomics, the reconstructed metabolic network may reflect this limitation. 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. in silico simulations show a greater physiological functionality of erythrocyte metabolism. the additional functionality 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 experimental literature was found. the human erythrocyte's potential as a biomarker decades of models have described erythrocyte metabolism to include principally glycolysis, the rapoport-luebering shunt, the pentose phosphate pathway, and nucleotide salvage pathways. integration and compilation of proteomic data, however, has surprisingly shown evidence of a much richer metabolic role for the erythrocyte. erythrocytes make contact with most portions of the body and are one of the most abundant cells. 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, thiamine deficiency, alcoholism, diabetes, and schizophrenia, however comprehensive systems level analyses have not been performed to date. iab-rbc- 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 drugs from the drugbank database with the enzymes accounted for in iab-rbc- morbid snps were found in of the erythrocyte enzymes. in addition, fda-approved, fda-withdrawn, and experimental drugs have known protein targets in the human erythrocyte. only of the morbid snps are related to pathologies 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 using the merck manual. most of the observed snps are causal and simple targeted assays could be used as diagnostic tools. we also cross-referenced the enzymes in iab-rbc- with the drugbank's known protein targets of pharmaceuticals. fda-approved, fda-withdrawn, and experimental drugs have known targets in the human erythrocyte. these medications target enzymes for the treatment of a wide range of diseases including seizures, allergies, cancer, hiv, and high cholesterol. 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. a comprehensive listing of all observed morbid snps and drugs are provided in the supplementary material. utilizing iab-rbc- 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- 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 determined differential reaction activity. activated or suppressed flux from in silico simulations provides a qualitative understanding into which metabolites and reactions are perturbed, allowing for experimental followup. 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 conditions. on average, there were and differential activities of exchange reactions for morbid snps and drug treated conditions respectively. the average is skewed by some morbid snps and drug treated conditions that have over affected exchange reactions, as most differences are detected in between one and ten exchange reactions. the morbid snps with greater than exchange reactions with differential activity deal mostly with glycolytic and transport enzymes that are known to cause anemias and spherocytosis. in addition, most of the drug treated conditions 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. an important metabolic enzyme found in erythrocytes is catechol-o-methyltransferase used to methylate cathecolamines. a morbid snp of comt gene has been implicated in susceptibility to schizophrenia. 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 of this effect can help focus experimental design on these metabolic pathways in erythrocyte screening. 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. inhibition of this enzyme during simulations showed a drastic change in exchange reaction fluxes pertaining to carbohydrate, cofactor, 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. to further investigate the diagnostic capability of the red blood cell, we assessed the uniqueness of the metabolic signatures detected. we compared all the metabolite 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. the in silico simulation results provide a method to focus biomarker discovery experiments in the human erythrocyte, as well as interpret global metabolomic profiling. 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 signature. the differential activity in exchanges for the perturbed conditions allow for focusing experiments to particular metabolites, exchanges, and associated pathways, allowing development of targeted assays. in addition, global metabolomic profiling of perturbed conditions can be interpreted using the calculated metabolic signatures and the erythrocyte reconstruction. a full listing of all detected morbid snps and drug treated conditions, as well as the corresponding exchange reactions with differential activity and fluxes is provided in the supplementary material.  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 comprehensive in silico red blood cell metabolic reconstruction, iab-rbc- 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 types of blood cells and inactive enzymes are passed down the erythrocyte differentiation lineage. thus, iab-rbc- is a knowledge-base of integrated high-throughput and biological data, which can also be queried through simulations. functional testing showed that the new reconstruction takes into account historically neglected areas of carbohydrate, amino acid, cofactor, and lipid metabolism. traditionally, the erythrocyte is known for its role in oxygen delivery, but the varied metabolism the cell exhibits points towards a much more expanded metabolic role as the cell can act as a sink or source of metabolites, through interactions with all organs and tissues in the body. metabolite connectivity analysis showed that the erythrocyte 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 proteomic studies would be useful for a few metabolic pathways: including tca cycle, cysteine, folate, and phospholipid metabolism. 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- with known morbid snps and enzymes that are reported drug targets in drugbank. there are morbid snps detectable in erythrocyte enzymes with the majority dealing with non-erythrocyte related pathologies. in addition, over pharmaceuticals in the drugbank have known protein targets in the human erythrocyte. utilizing iab-rbc- we qualitatively detected metabolic signatures for the majority of in silico perturbed 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. 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 available, the proteomics and metabolomics of normal and pathological states of individuals can be easily obtained and used for identifying biomarkers in a systems context. 
analysis of the red cell proteome has revealed an unexpected level of complexity in the functional capabilities of human erythrocyte metabolism. 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. 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 erythrocyte enzymes are implicated in morbid single nucleotide polymorphisms, representing pathologies. the development of high-throughput technologies capable of whole cell measurements of genes, proteins, and metabolites has led to the emergence of systems biology.
 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.  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 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 erythrocyte enzymes are implicated in morbid single nucleotide polymorphisms, representing pathologies. in addition, over fda-approved and experimental pharmaceuticals have enzymatic targets in the erythrocyte.  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. 