BACKGROUND
the gram-negative bacterium e. coli is one of the best-studied microorganisms.
this bacterial species includes pathogenic strains that cause disease in various tissues in mammalian and other vertebrate hosts.
some of the more common diseases associated with pathogenic e. coli strains are caused by bacteria found in the gastrointestinal tract or urinary tract, and is a major cause of human morbidity and mortality worldwide.
e. coli infections cost the healthcare industry over a billion dollars annually with the enterohemorrhagic  and uropathogenic  e. coli strains alone responsible for more than  <dig>  and  <dig> , <dig> illnesses annually in the united states, respectively  <cit> .
a number of genome sequences for these pathovars exist, and comparative analysis between commensal and pathogenic strains has revealed different virulence strategies  <cit> .
however, the metabolic properties that differentiate these strains have not been thoroughly investigated.
the metabolic content of the genomes of these strains is complex with each strain predicted to contain over  <dig>  genes encoding metabolic enzymes and transporters  <cit> .
one method to investigate the complexity of genome-scale metabolic networks is through the construction of computational models.
computational modeling of bacterial metabolism offers a promising approach to predict strain-to-strain variation in metabolic capabilities and microbial strategies used during host association.
the number of available genome-scale metabolic models  has grown recently, and they capture the metabolic capabilities of numerous microbial taxa important to human health, biotechnology and bioengineering  <cit> .
systems biology combines computational and experimental approaches to study the complexity of biological networks at a systems level, where the cellular components and their interactions lead to complex cellular behaviors.
genome-scale biological networks have proven useful for interpreting high-throughput data and generating computational models.
mathematical models are constructed from network reconstructions, and they include variables, parameters, and equations to describe the potential behavior of these networks.
numerous types of genome-scale biological networks have been constructed including metabolic, regulatory, and transcriptional and translational machinery for e. coli k- <dig>  <cit> .
to date, gems have been constructed for only two commensal strains of e. coli, e. coli k- <dig>  and e. coli w  <cit> .
the e. coli k- <dig> gem has been used to engineer strains to increase valuable product formation  <cit> , facilitate enzyme function discoveries  <cit> , provide insight into the genome evolution of other enterobacteria  <cit> , and improve the understanding of the connectivity of metabolic reactions within the cell  <cit> .
furthermore, computational metabolic models can be validated and refined by comparing in silico predictions with experimental data, where the discovery of disagreements or incorrect in silico predictions can lead to improvements and/or hypotheses about component interactions and unknown network components.
an iterative process thus develops where the models are used to analyze experimental data and discrepancies lead to improved models and additional biological discovery.
such approaches have proven successful for updates to the e. coli models for regulation and metabolism  <cit> .
currently the construction of metabolic networks relies primarily on information derived from genome annotations, enzymatic/pathway databases, and published literature.
by combining these resources, the elementally- and charged-balanced reactions catalyzed by enzymes associated with a given gene can be identified  <cit> .
these reactions incorporate pertinent information such co-factors, substrates, products, reversibility, stoichiometry, and subcellular location.
a genome-scale metabolic network contains a list of reactions, as well as the gene to protein to reaction  associations, and is used to formulate constraint-based gems.
by comparing gems for pathogenic and non-pathogenic e. coli strains, metabolic differences can be identified that may lead to the development of new control strategies for e. coli associated disease.
here we describe the construction of a detailed gem for the pangenome of the species e. coli, and the use of this gem to rapidly generate six strain-specific gems to compare genome-scale metabolism between four strains from two pathogenic lineages with two commensal k- <dig> strains.
in addition, an ancestral e. coli core gem was constructed consisting of only those metabolic reactions associated with genes that are conserved across  <dig> e. coli genomes.
the metabolic potential of this ancestral core was also examined.
experiments were performed to iteratively refine and validate the six strain-specific gems under aerobic and anaerobic conditions.
once strain-specific gems were validated, the properties and metabolic differences distinguishing these pathogenic and commensal e. coli strains were computationally investigated, revealing that some pathogenic e. coli strains are more metabolically efficient than other strains in some environmental conditions.
the e. coli gems generated in this work provide new tools for investigating the evolutionary and metabolic differences of these strains in conditions reflecting those environments encountered in human hosts.
this is the first study to examine the metabolic properties of numerous strains of such a phylogenetically related group of microorganisms, and provides insight into the evolution of metabolism for the species e. coli.
methods
bacterial strains and growth conditions
six e. coli strains and one salmonella strain were used in this study .
frozen cultures were streaked onto luria bertani  agar plates and grown overnight at 37°c.
isolated colonies were then used to inoculate mops  minimal media  and incubated overnight with shaking  at 37°c, and then overnight cultures were used to inoculate batch cultures grown with continuous sparging aerobically  or anaerobically  as previously described  <cit> .
for carbon plate utilization assays, isolated colonies were used to inoculate sheep blood agar plates  and incubated at 37°c overnight aerobically or anaerobically in sealed whirl-pak® long-term sample retention bags  saturated with an anaerobic gas mixture  as described  <cit> .
anaerobic conditions were confirmed using an obligate aerobic bacterium that exhibited no growth and no respiration in any of the anaerobic conditions examined.
cells were collected and used to inoculate biolog pm <dig> plates following the manufacturers recommendations with a minor modification of adding a top layer of mineral oil to each well for anaerobic culture conditions.
aenterohemorrhagic e. coli
buropathogenic e. coli
updates to the e. coli k- <dig> mg <dig> metabolic network
prior to generating a pangenome gem, additional genes in the genome of e. coli k- <dig> mg <dig> were evaluated as possible updates to the most recent e. coli gem  <cit> .
the annotations for e. coli k- <dig> mg <dig> were obtained and examined from the asap, ecogene, kegg, and ecocyc databases  <cit> .
orfs encoding enzymes that were not included in iaf <dig> were further investigated to develop elementally and charge-balanced reactions and to assign the reaction to a subcellular location based on psort predictions  <cit> .
in some instances new orfs were added as isozymes to existing reactions and the gene-to-protein-to-reaction associations updated.
this resulted in the addition of  <dig> new orfs to the iaf <dig> gem to create ieco1339_mg <dig> .
of the new  <dig> orfs , 62% were based on experimental data from the literature for e. coli strains  <cit>  and the rest were based on sequence homology to enzymes already existing in e. coli metabolic networks or to experimentally characterized enzymes from other enterobacteria.
these gene additions resulted in  <dig> new reactions,  <dig> new isozymes, and  <dig> new metabolites to the in silico model for e. coli k- <dig> mg <dig>
generation of an e. coli pangenome metabolic network
draft and complete enterobacterial genomes in the asap database have been continually updated using new publicly accessible genomes since the database's inception  <cit> .
there are more than  <dig> genomes of enterobacteria in the asap database ,  <dig> of which are e. coli genomes.
of these e. coli genomes,  <dig> are completely finished, and we have used the information from these genomes and that of salmonella typhimurium lt <dig>  to generate an e. coli pangenome metabolic network based on metabolic enzymes present in the union of  <dig>  orfs.
each orf was assigned in the asap database to an ortholog cluster group , and the  <dig>  orfs map to  <dig>  ocgs.
this reduced the number of genes that had to be evaluated for inclusion in the metabolic network and allowed generation of strain-specific gprs to rapidly be formulated .
not all of these ocgs play a metabolic role and/or have sufficient experimentally determined details for metabolic network inclusion.
for example only  <dig> % of orfs in the total genome  are accounted for in the updated gem for e. coli k- <dig> mg <dig> .
all of the gene to protein to reaction association information from the e. coli k- <dig> mg <dig>  and the salmonella lt <dig>  <cit>  gems were mapped to the ocgs that contained the respective orfs.
the annotations for the genes composing the remaining ocgs were analyzed for additional new metabolic reactions and isozyme additions  leading to the generation of an e. coli pan-gem named ieco1712_pan .
all eight sbml files generated in this work were checked for syntax and internal consistency using the validation tool  and found to conform to all specifications of sbml through level  <dig> version  <dig> core .
aenterohemorrhagic e. coli
benteroinvasive e. coli
curopathogenic e. coli
denteropathogenic e. coli
eenterotoxigenic e. coli
favian pathogenic e. coli
gincluded for comparative purposes
flux balance analysis
fluxes through metabolic network reactions can be predicted using flux balance analysis   <cit> .
in fba, fluxes are constrained by steady-state mass balances, enzyme capacities and reaction directionality.
these constraints yield a solution space of possible flux values, and fba uses an objective function to identify flux distributions that maximize  the physiologically relevant predicted solution.
cellular growth rate  is often used as an objective function for fba  <cit> , and was used for fba analyses performed in this study.
the same biomass equation, growth  and non-growth  associated atp requirement values, and po  ratio were used for all e. coli developed models, and were the same as that in iaf <dig> <cit> .
for fba and dynamic simulations the reported  <cit>  wildtype biomass was used and for determination of essential reactions the core biomass was used.
using fba, in silico predictions of growth yield, growth rate, and carbon source utilization were compared to experimentally determined values for all six e. coli strains and for salmonella lt <dig> in both aerobic and anaerobic conditions .
for carbon source utilization and gene deletion simulations, a maximum uptake rate of  <dig> mmol per gram of dry weight per hour  was used.
fba was also used to predict essential reactions by constraining reactions to have zero flux and maximizing growth rate.
if the resulting maximum predicted growth rate  was zero then the reaction was considered to be essential.
reaction deletion simulations were evaluated under both aerobic and anaerobic conditions.
batch growth experiments and simulations
we performed dynamic fba simulations of batch growth  <cit>  and compared these results with experimental data .
in the laboratory, cells were grown overnight in mops minimal medium with the addition of glucose  as the sole carbon source, and used to inoculate batch cultures to an optical density  of ~ <dig> .
batch growth under aerobic and anaerobic conditions was conducted at 37°c and spectrophotometric measurements, viable cells/ml, and biomass  were determined at each time point.
samples were collected every hour and passed through a  <dig>  μm syringe filter, and then frozen at -70°c for subsequent uplc analysis.
the filtered supernatants were then analyzed to determine glucose concentrations at each time point using a uplc following manufacturers recommendations .
growth rates, growth yields, and glucose specific uptake rates were determined from experimental data using the linear least squares estimate as described  <cit> .
biomass to od <dig> conversion values were also calculated for each strain .
these conversion values were used to estimate initial biomass  values using the initial od <dig> measurements  for each experiment.
the t <dig> values were used as parameters for dynamic fba simulations of batch growth to determine the exponential growth rates , biomass yields , and times  needed to reach stationary phase for the corresponding experimental conditions .
for each strain, experimental values from three biological replicates were then compared to those calculated from three computational simulations with matching starting biomass values under aerobic and anaerobic conditions.
uplc analysis
glucose analysis was conducted using an acquity uplc equipped with an acquity beh glycan column .
a mobile phase  was used at a flow rate of  <dig>  ml/min to separate small molecules on a waters acquity uplc equipped with an evaporative light scattering detector and photodiode array.
phylogenetic analysis
a maximum parsimony phylogenetic analyses of seven taxa were conducted in mega <dig>  <cit>  using a concatenated protein sequence data set of acna, gapa, icda, mdh, mtld, pgi, and proa with s. typhimurium lt <dig> used as the outgroup species.
these genes were chosen since they have been successfully used for phylogenetic analyses of enterobacteria  <cit> .
the alignment for this data set and subsequent maximum likelihood phylogenetic analyses was performed in mega <dig> using default parameters.
RESULTS
the metabolic model for e. coli k- <dig> mg <dig> was developed  <dig> years ago and has undergone numerous improvements and updates.
it is now a sophisticated compartmentalized gem containing over  <dig>  genes and  <dig>  reactions.
it has been used extensively for biotechnology and discovery applications.
here we generated a gem for the pangenome of e. coli, and used the information from this larger metabolic network to generate strain-specific e. coli gems for two pathogenic lineages and an ancestral core gem containing reactions conserved across all e. coli strains.
using this new collection of gems we validated strain-specific models by comparing predictions to experimental data, conducted a comparison of strain-specific gems from three e. coli lineages , and examined the metabolic networks of numerous e. coli strains in an evolutionary perspective based on phenotypic traits.
updating the e. coli k- <dig> mg <dig> metabolic model
the contents of the e. coli k- <dig> mg <dig> genome were surveyed for new genes/reactions to add to the existing gem .
this effort added a total of  <dig> genes to iaf <dig>  of which  <dig> encoded proteins with significant similarity to proteins with characterized enzymatic activity,  <dig> were added based on orthology to genes found in the s. typhimurium lt <dig> gem , and  <dig> were added based on experimental evidence from the scientific literature .
three of the new genes were linked to metabolic reactions that were already included in iaf <dig>  but whose associated genes were previously unknown .
the  <dig> new genes added  <dig> new metabolic reactions and  <dig> new metabolites to the gem.
exchange/transport reactions to permit cis-dihydrodiol-phenylacetyl-coa utilization were also added resulting in an updated gem for e. coli k- <dig> mg <dig> designated as ieco1339_mg <dig> composed of  <dig>  genes,  <dig>  metabolites, and  <dig>  reactions .
this includes eight new reactions for phenylacetate metabolism that were added following our observation that e. coli k- <dig> mg <dig> can grow in minimal media with phenylacetate as a sole carbon source .
the  <dig> new reactions in ieco1339_mg <dig> were classified into  <dig> metabolic subsystems .
there were  <dig> genes added to the gem that likely encode isozymes that participate in  <dig> existing reactions across  <dig> metabolic subsystems .
a total of  <dig> reactions in ieco1339_mg <dig> contain multiple isozymes.
generation of an e. coli pangenome metabolic model
understanding the evolution of metabolism for the species e. coli requires comparing genome-scale metabolic content among different strains of e. coli and its relatives.
to faciltate these comparisons we mined the contents of  <dig> e. coli genomes to identify reactions that could be added to ieco1339_mg <dig> to generate a pangenome metabolic network representing all metabolic reactions associated with genes present in any one of the e. coli genomes .
all genes from the sixteen e. coli and the s. typhimurium lt <dig> genomes  were classified into orthologous cluster groups  based on ortholog relationships from the asap database  <cit> .
this analysis resulted in a total of  <dig>  ocgs with  <dig>  representing the e. coli pangenome and  <dig>  ocgs unique to s. typhimurium lt <dig>  of the  <dig>  e. coli pangenome ocgs,  <dig>  are found in all sixteen e. coli genomes,  <dig>  are shared by two or more e. coli strains, and  <dig>  are unique to individual e. coli genomes.
each additional genome added on average  <dig> new genes; however this number decreased as more genomes were analyzed .
a gem for the pangenome was then constructed.
existing gene to protein to reaction  associations from gems of ieco1339_mg <dig> and s. typhimurium lt <dig>  were mapped to their corresponding ocgs.
the remaining ocgs were analyzed to see if they could be added to the pangenome metabolic network by adding additional isozymes or new reactions.
this led to the addition of  <dig> ocgs and  <dig> new reactions beyond those found in the updated e. coli k- <dig> mg <dig> gem , resulting in a pan-gem, ieco1712_pan , consisting of  <dig>  genes,  <dig> metabolites, and  <dig>  reactions .
each additional e. coli genome added to the pangenome metabolic analysis resulted on average added  <dig> new metabolic genes,  <dig> isozymes, and approximately  <dig> new metabolic reactions to the pan-gem .
the  <dig> reactions added to ieco1712_pan fall into  <dig> metabolic subsystems , with the majority being related to alternate carbon metabolism  and oxidative phosphorylation .
other added ocgs resulted in addition of isozymes associated with  <dig> metabolic subsystems, with the most abundant being alternate carbon metabolism , cell envelope biosynthesis , oxidative phosphorylation , nitrogen metabolism , glutamate metabolism , and the remaining  <dig> subsystems consisted of a single reaction addition .
generation of an e. coli core metabolic model
e. coli strains are thought to have diverged from a common ancestor ~ <dig> million years ago   <cit>  and it is of interest in understanding how strain-specific metabolism has evolved over time to have an estimate of the metabolic capabilities of an ancestral core for the species e. coli.
we assume that genes conserved across the genomes of all strains represents a conservative estimate of the core genome of the ancestor of modern e. coli strains and used this collection of  <dig>  conserved genes to construct an ancestral core gem named ieco1053_core .
there are  <dig>  of these genes in the e. coli k- <dig> mg <dig> gem .
the gem for the e. coli ancestral core was made by removing ocgs and their associated reactions from the ieco1339_mg <dig> gem if one or more of the sixteen e. coli genomes did not have a gene assigned to the ocg .
if removing a reaction prevented biomass production for anaerobic growth on glucose  then the reaction was added back to the metabolic reconstruction without a gene associated with it and this occurred  <dig> times .
using this approach  <dig> orfs associated with  <dig> reactions and  <dig> isozymes were removed from ieco1339_mg <dig> resulting in an e. coli ancestral core gem  consisting of a total of  <dig>  orfs and  <dig>  reactions , and these  <dig> reactions we removed were classified based on metabolic subsystem .
anecessary reactions without an associated gene for in silico models
characteristics of five new e. coli strain specific models
the pan-gem was used to expedite the process of generating five new strain-specific e. coli gems, since the pangenome has reactions connected to cluster groups, a given strains genome contents were analyzed to identify what cluster groups its genes belong to and those associated reactions were included .
the e. coli strains we selected include an additional e. coli k- <dig> strain , two enterohemmoraghic e. coli o157:h <dig> strains , and two uropathogenic strains .
comparisons to ieco1339_mg <dig>  including the total number of strain-specific gene additions and deletions and the corresponding metabolic reactions are shown in table  <dig>  the two k- <dig> strains are laboratory strains derived from the same isolate and not surprisingly their gems, were very similar with the sole difference being removal of a few isozymes and a gene associated with galactitol transport from the w <dig> gem named ieco1335_w <dig>  due to a w3110-specific is insertion in the gata gene  <cit> .
we built the five new e. coli gems named ieco1335_w <dig> , ieco1344_edl <dig> , ieco1345_sakai , ieco1288_cft <dig> , and ieco1301_uti <dig>  by deleting genes and reactions from the pan-gem when missing from the genome under consideration .
if removing a reaction prevented biomass production for anaerobic growth on glucose  then the reaction was added back to the metabolic reconstruction without a gene associated with it.
the number of these reactions without associated genes varied from  <dig> to  <dig> for each of the pathogenic e. coli strains .
five of these reactions without associated genes were required in all four pathogenic e. coli gems .
of the remaining five reactions, two were required for strains edl <dig> and sakai, one was required for strains edl <dig>  sakai, and uti <dig>  and the remaining three are specific to e. coli strain cft <dig>
even though the genomes of the four pathogenic e. coli strains contain between 700- <dig>  genes not present in the genome of e. coli k- <dig> mg <dig>  relatively few pathogen-specific metabolic genes were added to each gem .
eight new reaction additions were unique to the gems of the ehec strains  and consisted of urease, udp-n-acetylglucosamine 4-epimerase, salicylate hydroxylase, gentisate  <dig> ,-dioxygenase, sucrose transport, tellurite reduction, fucose synthetase, and perosamine synthetase reactions.
the two upec strain gems  shared only one lineage-specific reaction addition for propionate coa-transferase and each has a single strain-specific reaction addition unique to each strain; galactose isomerase activity for ieco1288_cft <dig> and hydroxypyruvate reductase activity for ieco1301_uti <dig>
in contrast to the relatively small number of gene and reaction additions there were a large number of reaction deletions for the pathogenic strain gems compared to ieco1339_mg <dig> .
for ehec strain gems, ieco1344_edl <dig> and ieco1345_sakai, there were  <dig> genes found in the e. coli k- <dig> mg <dig> gem  that had no orthologous gene in the genomes of the two ehec strains.
these missing genes resulted in  <dig> reaction deletions in both ieco1344_edl <dig> and ieco1345_sakai, and these were classified based on metabolic subsystem .
there was only one additional reaction deletion unique to ieco1345_sakai for d-cysteine desulfhydrase, whereas the orf encoding this enzyme was still intact in the edl <dig> strain.
for each of the two upec strains there were  <dig> reactions that were missing in both ieco1288_cft <dig> and ieco1301_uti <dig> compared to ieco1339_mg <dig>  and these were further classified into metabolic subsystems .
each of the two upec strains also contained numerous reaction deletions unique to each strain .
when gems for all four pathogens  were compared to those of the two k- <dig> strains ,  <dig> shared reaction deletions were common to all four pathogenic e. coli strains, and they were categorized into the metabolic subsystems of alternate carbon metabolism, cell envelope biosynthesis, inorganic ion transport and metabolism, lipopolysaccharide biosynthesis, methionine metabolism, nitrogen metabolism, inner membrane transport, and outer membrane transport .
assessment and validation of models for carbon source utilization
to evaluate the accuracy of the gems for all six e. coli strains, we examined each strain's ability to use different carbon sources under aerobic and anaerobic conditions using biolog phenotypic arrays.
there were numerous strain-specific differences in carbon source utilization in both aerobic  and anaerobic conditions .
these experimental results were then compared to fba predictions of growth using different carbon sources.
for those compounds included in the biolog plates that have transporters in the model, fba was used to predict if they could be used for growth as sole carbon source.
this included  <dig> potential carbon sources for the six e. coli strains and  <dig> potential carbon sources for salmonella lt <dig>  if fba calculated a zero growth rate then the compound was predicted not to be usable as a sole carbon source, while positive calculated growth rates indicated that the model predicted the compound could be used as the sole carbon source.
of the  <dig> compounds, there were  <dig>  and  <dig>  carbon sources where model predictions and experiments agreed for all six e. coli models .
the  <dig> and  <dig> carbon sources with discrepancies between in silico and experimental results in at least one model  fall into two categories i) when strains did not grow and the model predicted growth , and ii) instances where strains grew and the model predicted no growth .
for aerobic carbon source utilization, the number of false positives varied from one to four, and the most accurate models  were for e. coli k- <dig> w <dig>  and uti <dig>  .
the number of aerobic false negatives was greater than false positives and ranged from three to seven with the least observed for ieco1339_mg <dig> and the most observed for ieco1288_cft <dig>  of these model-data discrepancies, there were some carbon sources that led to inaccurate predictions by all six e. coli strain specific models such as the utilization of d-aspartic acid  and m-inositol and tyramine .
the two pathogenic lineages  exhibited some lineage-specific false negatives for alpha-keto-butyric acid and acetoacetic acid utilization  or d-melibiose .
fba predictions using a mixture of l- and r- isomers of  <dig>  propanediol  resolved aerobic false negative discrepancies for  <dig>  propanediol for five e. coli strains, without introducing any new false positives .
for anaerobic carbon source utilization, there were no false positives observed for three e. coli strains  and either one or two false positives observed for carbon sources such as d-threonine , d-aspartic acid , and l-lyxose .
in contrast to the aerobic results, there were generally more false negative than false positive predictions, with the number of false negatives ranging from  <dig>  to  <dig>  for the six e. coli strains .
of these compounds associated with anaerobic false negatives, there were  <dig> that led to inaccurate growth predictions for all six e. coli strains  .
the biolog phenotype assay uses reduction of a colorimetric tetrazolium dye to measure microbial respiration.
our initial fba predictions used an objective function that relates to the ability of the bacterium to convert a particular carbon source into biomass.
there may be carbon compounds that the bacteria are able to metabolize but which do not result in measurable growth, thus leading to false negatives.
to see if changing the objective function from biomass production to indicator dye reduction improves the fba predictions under both aerobic and anaerobic conditions, two additional reactions were added to each of the models representing the movement of electrons from reduced quinones to the indicator dye used in biolog plates .
fba was used again but a new objective function, equal to the sum of flux through these two new reactions, was maximized.
if the maximum sum of fluxes was zero then the model predicted the carbon source could not be metabolized, while a positive sum of fluxes indicated a carbon source could be metabolized.
these new fba predictions  significantly reduced the number of anaerobic false negatives to between two  and eight  , while not affecting the number of false positives.
aenterohemorrhagic e. coli
buropathogenic e. coli
cexperimental = y
dexperimental = n
eexperimental = n and in silico = y
fexperimental = y and in silico = n
overall, once the fba objective was changed from biomass to respiration, all e. coli models exhibited a statistically significant relationship between model predictions and experimental growth phenotypes  for both aerobic  and anaerobic  conditions .
the carbon sources m-inositol and tyramine still led to false negative predictions for all e. coli models examined under both aerobic and anaerobic conditions, which may indicate that missing reactions or gaps may exist in pathways for utilization of these carbon compounds.
when considering both the aerobic and anaerobic conditions the overall accuracy for individual strain-specific models was ieco1301_uti <dig> , ieco1335_w <dig> , ieco1339_mg <dig> , ieco1345_sakai , ieco1344_edl <dig> , and ieco1288_cft <dig> .
batch growth predictions
to further evaluate model predictions, dynamic fba was used to predict time-courses , exponential growth rates  and biomass yields  for aerobic and anaerobic batch cultures in mops minimal media with the addition of glucose as the sole carbon and energy source.
batch culture experiments were conducted for each strain, and conversion factors for optical density to biomass, optical density to viable cell concentration, and biomass per viable cell values  were determined for each strain in both anaerobic and aerobic conditions.
these conversion values were then used to approximate starting biomass values used in the dynamic fba simulations.
for each e. coli strain, the maximum glucose uptake rates used for dynamic fba were those reported for e. coli k- <dig> strain w <dig>  <cit>  .
the predicted growth rates and biomass yields from the model were then compared to experimental results of batch culture of each strain under anaerobic or aerobic conditions.
for both aerobic or anaerobic growth conditions, the calculated growth rate  for each e. coli strain was compared to the experimentally determined values  and the agreement between in silico and experimental values was strong and significant , yet when viewed separately for aerobic or anaerobic conditions, the correlation was not as strong and was not significant .
in addition, the growth yields were calculated under aerobic or anaerobic growth conditions in mops minimal media for both aerobic and anaerobic growth conditions, and the in silico growth yields for each strain were compared to those determined experimentally  and the agreement between in silico and experimental values was strong and significant .
amaximum oxygen uptake rates  and glucose uptake rates for aerobic  and anaerobic  conditions were used for in silico batch simulations in this work were those previously determined for e. coli w <dig> from batch culture in m <dig> minimal media  <cit>
bexperimentally determined glucose uptake rate values from this work used for in silico batch simulations for e. coli k- <dig> , ehec  or upec .
for the core and pangenome models the average experimentally determined glucose uptake rate values from this work was used .
the maximum glucose uptake rate from one e. coli strain from each lineage k- <dig> , ehec , upec  was determined from experimental data for both aerobic and anaerobic conditions.
these results revealed that the uptake rates for e. coli k- <dig> mg <dig> were not similar to previously published values for e. coli k- <dig> w <dig> .
when compared to the two pathogenic lineages, the two e. coli k- <dig> strains appear to have significantly higher glucose uptake rates in aerobic conditions but significantly lower glucose uptake rates in anaerobic conditions.
the dynamic fba simulations were repeated using the measured lineage-specific glucose uptake rates as parameters.
the recalculated growth rates , still showed a moderately strong correlation overall between in silico and experimental values , yet when viewed separately, resulted in a decreased correlation for aerobic conditions, and a increased correlation between in silico and experimental values for anaerobic conditions .
figure  <dig> shows a phylogenetic tree of the strain relationships plotted along with the growth rate data, displaying the aerobic and anaerobic growth rates calculated without the uptake rate correction.
additionally we determined the amount of time needed to reach stationary phase experimentally and computationally using dynamic fba for each strain in each condition.
evaluation of these results reveal that some of the pathogenic lineages attain final biomass in less time compared to the e. coli k- <dig> strains .
analysis of reaction essentiality
to further explore the metabolic differences and similarities between all six e. coli strain-specific gems, we compared reaction essentiality predictions for in silico conditions simulating aerobic and anaerobic growth in glucose minimal media.
the number of predicted essential reactions shared in common for all six e. coli strains and also the e. coli ancestral core in both aerobic and anaerobic growth conditions was determined  , and their corresponding reactions were further classified by metabolic subsystem .
additionally for all six e. coli strains and the e. coli ancestral core, there were  <dig> additional conserved essential reactions predicted to be required under anaerobic conditions and these involve reactions assigned to subsystems for anaplerotic reactions , citric acid cycle , cofactor and prosthetic group biosynthesis , glycolysis/gluconeogenesis , purine and pyrimidine biosynthesis , inner membrane transport , and outer membrane transport .
in addition to the shared predicted essential reactions for all e. coli strains examined, there were two lineage-specific reactions predicted as essential under both aerobic and anaerobic conditions for both ehec strains , and the corresponding reactions were for fumarate reductase and glycolate oxidase.
for the e. coli ancestral core , there were five additional reactions predicted as essential that were not predicted for any of the six e. coli strains , and these involve reactions assigned to subsystems for alanine and aspartate metabolism, glutamate metabolism, inner membrane transport, glycolate exchange, and outer membrane porin transport.
discussion
this study describes the generation of gems representing the union  and also the intersection  of all identifiable metabolic reactions contained in sixteen genomes of e. coli.
we used the e. coli pan-gem to rapidly construct six e. coli strain-specific gems.
a comparison between model growth predictions and biolog phenotypes measured in the laboratory demonstrated an accuracy of more than 88%, including those under anaerobic conditions additional quantitative data was generated for each strain and used to validate the correlation between model predictions and experimental physiology of the strains in the laboratory.
these new e. coli gems serve as a framework to examine genome-scale metabolic similarities and differences between strains in an evolutionary context with respect to the commensal, ehec, and upec lineages.
the two e. coli k- <dig> strains  are widely used laboratory strains that are believed to have diverged from the same parental strain  approximately  <dig> years ago  <cit> .
the sole identified metabolic differences between the two e. coli k- <dig> strains based on genome comparison include the gata gene that is involved in galactitol transport, dcua and dcuc involved in c4-dicarboxylate transport metabolism, and also tnab thought to be involved in the utilization of tryptophan as a carbon and/or nitrogen source  <cit> .
of these four metabolic gene differences, only inactivation of gata leads to a loss of a reaction in ieco1335_w <dig>  compared to ieco1339_mg <dig> since dcua, dcuc and tnab have other isozymes.
the gata gene contains an insertion sequence  element in e. coli w <dig>  which suggests a phenotypic loss for galactitol utilization as a carbon source, yet experimental data  reveals that the strain can still use this substrate as sole carbon source, indicating that other transporters may permit galactitol transport for e. coli w <dig>  although the two e. coli k- <dig> strains  exhibited no differences in their gems, quantitative and strain-specific differences were observed during batch growth in minimal media with glucose as the sole carbon source.
while in silico predictions for growth yield were similar for ieco1339_mg <dig> and ieco1335_w <dig>  experimental data reveal that in both aerobic and anaerobic conditions, strain mg <dig> had higher growth yields, higher growth rate, and attained the final biomass value in less time than strain w <dig> .
therefore, although the in silico models for these two strains are nearly indistinguishable, strain specific differences in complex traits such as biomass composition  <cit> , atp requirements, po ratios, and glucose uptake rates may account for these experimental differences.
previous studies have shown that despite their nearly identical genomes and very similar growth patterns in a bioreactor, w <dig> and mg <dig> have many significant differences in their transcriptomes and proteomes.
these include differential expression of pathways affecting central metabolism and the generation of precursor metabolites and energy  <cit>  suggesting that future models for even these very similar strains will need to account for subtle genetic differences between strains to accurately predict phenotypic traits in simulated culture conditions.
previous analyses of the e. coli pangenome estimated that on average each new e. coli genome sequence added about  <dig> unique genes to the pangenome  <cit> , and among these unique genes, we found each additional e. coli genome resulted in  <dig> metabolic gene additions corresponding to about  <dig> new metabolic reactions and  <dig> isozymes suitable for inclusion in the pan-gem .
clearly some of the metabolic differences between e. coli strains are due to the addition of genes with new metabolic activity.
however, our ability to add new reactions to the metabolic reconstructions is severely limited by the paucity of experimental characterization of the metabolic genes, proteins, and reactions unique to pathogenic strains.
since the strain-specific portions of the genomes remain largely uncharacterized, our current understanding of the metabolic functions they encode is dominated by the presence and absence of genes encoding functions represented in the ieco1339_mg <dig> gem.
many of the genes included in this model are not universally conserved among the genomes we examined; resulting in strain-specific gems with an average of  <dig> fewer genes than ieco1339_mg <dig> .
this observation is also consistent with draft gems generated using the model seed  <cit>  where the gem for e. coli mg <dig> contained more genes  and reactions  than the draft gems for all four pathogenic e. coli strains examined in this work .
although carbon source utilization has become a standard method to assess the validity of computational metabolic model predictions, this study was the first to examine this procedure under anaerobic conditions.
initially, the accuracy of predictions for carbon source utilization during anaerobic conditions was less than those determined during aerobic conditions.
we account this difference to comparisons between biolog carbon source assays, which examine the ability of a microbial strain to generate energy from each sole carbon source, to in silico analysis that determines growth as a positive flux value for the biomass reaction.
one possible explanation for experimental and in silico data discrepancies may be that a microbial strain may be able to generate energy from a given carbon source, but that the carbon source is not suitable to sustain growth .
therefore, rather than maximize the objective value for the biomass equation, we added two reactions to monitor the ability to generate energy through electron transfer to quinones, and in many cases this analysis resolved discrepancies between in silico predictions and experimental data, especially for anaerobic conditions.
although this methodology of examining carbon source utilization seems trivial, validation for accurate carbon source utilization is important for modeling complex environments such as those encountered in a host, as  <dig> of the  <dig> carbon sources tested here were used to simulate the conditions reflecting invasion of a human cell to study s. typhimurium lt <dig> infection  <cit> .
therefore, the validation of these strain-specific metabolic models for carbon source utilization will prove useful for future computational modeling of pathogenic e. coli strains in conditions encountered in the gastrointestinal tract or in other locations such as the urinary tract in mammalian hosts.
with the generation of the first gems for pathogenic e. coli strains, two ehec strains and two upec strains, properties of these genome-scale metabolic networks were investigated to identify differences that may play a role in human disease.
we analyzed two e. coli o157:h <dig> strains associated with foodborne outbreaks, strain edl <dig> isolated from ground beef in the u.s in  <dig> and strain sakai isolated from contaminated radish sprouts that sickened thousands in japan in  <dig>  strains cft <dig> and uti <dig>  which cause human disease outside of the intestine, were isolated from patients with acute urinary tract infections.
a comparison of reaction deletions between the ehec and upec metabolic networks reveals that the ehec strains have more missing genes corresponding to reactions for inner membrane transport in comparison to the upec strains.
in addition, the reaction deletions that occur in both pathogenic lineages relative to e. coli k- <dig> strains are mainly associated with genes involved in lipopolysaccharide biosynthesis/recycling and alternate carbon utilization.
it seems likely that some of these missing reactions are the result of acquisition of genes during the evolution of the k- <dig> lineage.
perhaps some of the reactions missing from both pathogen lineages arise from parallel deletions arising from selective pressures common to both pathogens.
batch growth experiments were conducted to compare growth yields, growth rates, and the amount of time to attain final biomass among strains.
we were surprised that edl <dig>  sakai and cft <dig> have significantly higher growth rates than mg <dig> during aerobic growth conditions yet the in silico predictions reveal little to no differences.
we sought to determine if strain-specific glucose uptake rates may improve in silico growth rate predictions.
experimentally determined glucose uptake rates were actually lower for edl <dig> and cft <dig> than for mg <dig>  and did not improve in silico predictions.
the growth yield values we measured in the laboratory also showed significantly  higher yields for edl <dig> and cft <dig> than the two k- <dig> strains, but in silico predictions showed only minor strain-to-strain variations.
dynamic fba using the strain specific e. coli gems predicts a similar growth rate from all models including the model for the ancestral core of e. coli.
yet the actual growth rates determined experimentally vary significantly between strains suggesting that our models are not accounting for some strain-specific factors such as oxygen uptake rates, biomass composition, atp requirement parameters, or additional uncharacterized reactions.
the length of time required to attain final biomass was significantly  shorter for the four pathogens suggesting that they may be more efficient at biomass production during glucose catabolism, and dynamic fba analysis accurately predicted this phenotypic difference among the strains.
in anaerobic batch growth conditions there were also differences between strains.
all pathogenic strains have higher growth rates than the k- <dig> strains.
the fba predictions for ehec strains both reflect this phenotype, but the in silico growth rate predictions for the upec strains did not reflect this trend.
the experimentally determined glucose uptake rates are higher for both pathogenic lineages than k- <dig>  and these organism-specific parameters improved the fba predictions.
the growth yields determined experimentally are significantly  higher for the four pathogens than the k- <dig> strains.
the length of time required to attain final biomass predicted by fba and determined experimentally was significantly  shorter for the ehec strains than the k- <dig> strains.
overall, for anaerobic glucose catabolism, all four pathogens appear to grow better than both e. coli k- <dig> strains.
even though the metabolic networks of each e. coli strain differ, there were relatively few strain-to-strain differences in reactions predicted as essential for the two growth conditions examined.
while there were some identified for all strains that were unique for anaerobic growth in comparison to aerobic, there were relatively few differences between all strains.
the two reactions  predicted as essential for the e. coli o157:h <dig> strains, play essential metabolic roles for glycolate recycling and the reoxidation of menaquinol, and represent new targets for control strategies that may help to prevent and treat human ehec illness.
the comparison of the pan- and core-gems reveals that a substantial fraction of the reactions in our current pan-gem are also in the ancestral core-gem .
however, our knowledge of the detailed biochemistry of the pangenome is likely incomplete since many of the genes in other e. coli strains have unknown functions.
one reason why the number of reactions in the core- and pan-gems are so similar is because the genes that have been well-characterized biochemically in e. coli tend to be the genes that are conserved and likely ancestral.
while the pathogenic e. coli strains are of great interest medically, they are not typically the focus of intense biochemical study to uncover the functions of their novel metabolic genes.
overall, when data for aerobic conditions is viewed phylogenetically , there is no clear trend specific to the two pathogenic lineages, yet it appears that e. coli cft <dig> has evolved with a similar growth rate in comparison to the e. coli ancestral core predictions, where as all other strains have evolved with higher growth rates and yields .
in contrast, in anaerobic conditions , higher growth yields and faster batch growth performance were observed for both ehec e. coli strains , and the insight derived from e. coli ancestral core in silico predictions suggest that the upec and k- <dig> lineages have evolved with less efficient anaerobic glucose catabolism then the ehec lineage.
one possible explanation for this behavior may be that the k- <dig> and upec strains do not routinely encounter the selective pressure from anaerobic conditions, whereas the ehec strains may have evolved for improved growth in anaerobic conditions enabling their growth in both bovine and mammalian gi tracts, thus suggesting that many ehec strains may have a better-suited anaerobic metabolism for glucose utilization.
these findings suggest that e. coli k- <dig> strains could be engineered to be more efficient for anaerobic batch growth and that other e. coli strains not examined in this work may yield similar results, yet additional studies are warranted to examine more e. coli strain-specific gems, quantitative parameters, and catabolism of additional substrates other than glucose.
CONCLUSIONS
here we have presented an update to the e. coli k- <dig> mg <dig> gem and an extensive new collection of gems for five e. coli strains including the first for two pathogenic lineages.
these models have been validated through experimental data for aerobic and anaerobic conditions.
this work demonstrated a new approach for validation of carbon source utilization, yielding accuracies of >88% for aerobic and anaerobic conditions for all six e. coli strains examined.
in addition new lineage-specific quantitative data were generated and led to validation of the correlation between in silico predictions and experimental batch culture data for glucose catabolism during aerobic and anaerobic growth conditions.
thus, the ieco1339_mg <dig>  ieco1335_w <dig>  ieco1344_edl <dig>  ieco1345_sakai, ieco1288_cft <dig>  and ieco1301_uti <dig> gems provide new suitable platforms for computing cellular phenotypes in conditions reflecting those encountered in mammalian hosts such as the intestine or urinary tracts and for further integration of high throughput data generated from these bacterial strains during the course of infection in animal models.
distinctive lineage-specific differences in the gems were identified and reveal that the main delineating metabolic factors between pathogenic and commensal e. coli strains are due to numerous gene/reaction deletions and not additions, and this observation was consistent with the number of genes and reactions contained in draft gems for all six e. coli strains generated using model seed.
historically many researchers have noticed that some pathogenic e. coli strains grow faster in comparison to commensal strains such as k- <dig>  yet this phenotype has remained unexplained.
these strain-specific models offer new tools for further investigation to determine precisely what combination of gene/reaction deletions account for the faster and more efficient biomass production observed experimentally for some of the pathogenic strains, thus providing new insight for bioengineering of industrial e. coli strains.
the generation of an e. coli pan-gem  consisting of all metabolic genes and reactions from  <dig> e. coli genomes, represents a new framework to rapidly generate additional e. coli strain/lineage-specific gems consisting of >  <dig>  genes and > <dig>  metabolic reactions.
finally, this study is the first to use a "paleo systems biology" approach to generate a gem for an ancestral core of e. coli  providing the first insight to metabolic traits of an e. coli relative that may have existed ~ <dig> mya, and demonstrated the use of an ancestral model to examine a closely related phylogenetic group of e. coli strains in the context of evolution.
authors' contributions
db constructed the pangenome, core, and the six strain-specific gems and performed all in silico analyses.
db and rp obtained all of the experimental data.
db, jg, and np designed the study.
db and jr analyzed and interpreted the data and performed the statistical analysis.
db, jg, jr, and np wrote the manuscript.
db generated all eight sbml model files.
all authors approve the content of this manuscript.
supplementary material
additional file 1
genome-scale metabolic model for e. coli k- <dig> strain mg <dig>  sbml format of ieco1339_mg <dig> for distribution and use in other modeling environments.
click here for file
additional file 2
gene to protein to reaction  updates for e. coli k- <dig> mg <dig> in the final version of the reconstruction.
this file contains two tables, the first contains all new gpr information added to the previous e. coli k- <dig> mg <dig> gem , and the second contains the final gpr information for ieco1339_mg <dig>
click here for file
additional file 3
orthologous gene cluster groupings for all orfs in  <dig> e. coli genomes and one salmonella genome.
this file contains the mapping of orthologous cluster group identifiers to each asap feature identifier and locus tag for all orfs contained in the genomes of  <dig> strains of e. coli and one salmonella genome.
click here for file
additional file 4
e. coli pangenome orthologous cluster group identifier  to protein to reaction information for the e. coli pan-gem.
this file contains four tables, the first contains all gene additions, the second contains all metabolite additions, and the third contains all reaction additions to ieco1339_mg <dig> to construct the e. coli pan-gem .
the fourth table contains the final ocg to protein to reaction information for eco1712_pan.
click here for file
additional file 5
pangenome-scale metabolic model representing the e. coli pangenome.
sbml format of ieco1712_pan for distribution and use in other modeling environments.
click here for file
additional file 6
quantitative experimental batch growth data for six e. coli and one salmonella strain for aerobic and anaerobic conditions.
this file contains  <dig> tables, the first contains the biomass data , the second contains glucose data , and the third contains the optical density data  for experimental batch growth of six e. coli and one salmonella strain for aerobic or anaerobic conditions.
values highlighted in yellow reflect the timepoints used to determine lineage-specific glucose uptake rates and strain-specific growth rates.
click here for file
additional file 7
experimental and in silico carbon source utilization data.
this file contains two tables, the first contains experimental and in silico carbon source utilization data during aerobic conditions, and the second contains experimental and in silico carbon source utilization data during anaerobic conditions for six e. coli and a salmonella strain.
values highlighted in blue represent false negatives and those highlighted in magenta represent false positives.
click here for file
additional file 8
genome-scale metabolic model representing the ancestral core of e. coli.
sbml format of ieco1053_core for distribution and use in other modeling environments.
click here for file
additional file 9
necessary orphan reactions required for biomass production.
list of metabolic reactions without corresponding genes necessary for each e. coli strain-specific gem for biomass production in minimal media with glucose added as the sole carbon source.
click here for file
additional file 10
genome-scale metabolic model for e. coli k- <dig> strain w <dig>  sbml format of ieco1335_w <dig> for distribution and use in other modeling environments.
click here for file
additional file 11
genome-scale metabolic model for enterohemorrhagic e. coli o157:h <dig> strain edl <dig>  sbml format of ieco1344_edl <dig> for distribution and use in other modeling environments.
click here for file
additional file 12
genome-scale metabolic model for enterohemorrhagic e. coli o157:h <dig> strain sakai.
sbml format of ieco1345_sakai for distribution and use in other modeling environments.
click here for file
additional file 13
genome-scale metabolic model for uropathogenic e. coli strain cft <dig>  sbml format of ieco1288_cft <dig> for distribution and use in other modeling environments.
click here for file
additional file 14
genome-scale metabolic model for uropathogenic e. coli strain uti <dig>  sbml format of ieco1301_uti <dig> for distribution and use in other modeling environments.
click here for file
additional file 15
deleted reactions for strain-specific e. coli gems and the e. coli ancestral core gem.
this file contains three tables; the first contains all gene and corresponding reactions deleted for five e. coli strains in comparison to eco1339_mg <dig>  the second contains all deleted genes corresponding to isozymes for five e. coli strains in comparison to eco1339_mg <dig>  the third contains all reactions deleted from eco1339_mg <dig> to generate the e. coli ancestral core gem .
click here for file
additional file 16
reactions corresponding to essential gene predictions for all six strain-specific e. coli gems and for the e. coli ancestral core gem.
this file contains three tables, the first contains all predicted essential reactions during both aerobic and anaerobic conditions, the second contains all anaerobic-specific predicted essential reactions, the third contains predicted strain- or core-specific essential reactions for all six strain-specific e. coli gems and for the e. coli ancestral core e. coli gem.
click here for file
acknowledgements and funding
we are grateful and would like to acknowledge the following for provision of bacterial strains used in this study: prof. scott hultgren  for provision of e. coli strain uti <dig>  prof. patricia kiley  for provision of e. coli k- <dig> strain mg <dig>  and prof. diana downs  for provision of salmonella lt <dig>  we would also like to thank the great lakes bioenergy research center for use of the uplc instrument for analysis.
this work was funded by the us department of energy genomics:gtl and scidac programs  for the bacter institute post-doctoral fellowship  and also the national library of medicine, national institutes of health, grant no.
5t15lm <dig> to the computation and informatics in biology and medicine training program for a post-doctoral traineeship  and in part by nih grant #nih/gnm gm62994- <dig>  finally, we would like to thank dr. eric cabot and christopher tervo for assistance in generation of the sbml model files.

 the gram-negative bacterium e. coli is one of the best-studied microorganisms. this bacterial species includes pathogenic strains that cause disease in various tissues in mammalian and other vertebrate hosts. some of the more common diseases associated with pathogenic e. coli strains are caused by bacteria found in the gastrointestinal tract or urinary tract, and is a major cause of human morbidity and mortality worldwide. e. coli infections cost the healthcare industry over a billion dollars annually with the enterohemorrhagic and uropathogenic e. coli strains alone responsible for more than and, illnesses annually in the united states, respectively. a number of genome sequences for these pathovars exist, and comparative analysis between commensal and pathogenic strains has revealed different virulence strategies. however, the metabolic properties that differentiate these strains have not been thoroughly investigated. the metabolic content of the genomes of these strains is complex with each strain predicted to contain over genes encoding metabolic enzymes and transporters. one method to investigate the complexity of genome-scale metabolic networks is through the construction of computational models. computational modeling of bacterial metabolism offers a promising approach to predict strain-to-strain variation in metabolic capabilities and microbial strategies used during host association. the number of available genome-scale metabolic models has grown recently, and they capture the metabolic capabilities of numerous microbial taxa important to human health, biotechnology and bioengineering. systems biology combines computational and experimental approaches to study the complexity of biological networks at a systems level, where the cellular components and their interactions lead to complex cellular behaviors. genome-scale biological networks have proven useful for interpreting high-throughput data and generating computational models. mathematical models are constructed from network reconstructions, and they include variables, parameters, and equations to describe the potential behavior of these networks. numerous types of genome-scale biological networks have been constructed including metabolic, regulatory, and transcriptional and translational machinery for e. coli k-. to date, gems have been constructed for only two commensal strains of e. coli, e. coli k- and e. coli w. the e. coli k- gem has been used to engineer strains to increase valuable product formation, facilitate enzyme function discoveries, provide insight into the genome evolution of other enterobacteria, and improve the understanding of the connectivity of metabolic reactions within the cell. furthermore, computational metabolic models can be validated and refined by comparing in silico predictions with experimental data, where the discovery of disagreements or incorrect in silico predictions can lead to improvements and/or hypotheses about component interactions and unknown network components. an iterative process thus develops where the models are used to analyze experimental data and discrepancies lead to improved models and additional biological discovery. such approaches have proven successful for updates to the e. coli models for regulation and metabolism. currently the construction of metabolic networks relies primarily on information derived from genome annotations, enzymatic/pathway databases, and published literature. by combining these resources, the elementally- and charged-balanced reactions catalyzed by enzymes associated with a given gene can be identified. these reactions incorporate pertinent information such co-factors, substrates, products, reversibility, stoichiometry, and subcellular location. a genome-scale metabolic network contains a list of reactions, as well as the gene to protein to reaction associations, and is used to formulate constraint-based gems. by comparing gems for pathogenic and non-pathogenic e. coli strains, metabolic differences can be identified that may lead to the development of new control strategies for e. coli associated disease. here we describe the construction of a detailed gem for the pangenome of the species e. coli, and the use of this gem to rapidly generate six strain-specific gems to compare genome-scale metabolism between four strains from two pathogenic lineages with two commensal k- strains. in addition, an ancestral e. coli core gem was constructed consisting of only those metabolic reactions associated with genes that are conserved across e. coli genomes. the metabolic potential of this ancestral core was also examined. experiments were performed to iteratively refine and validate the six strain-specific gems under aerobic and anaerobic conditions. once strain-specific gems were validated, the properties and metabolic differences distinguishing these pathogenic and commensal e. coli strains were computationally investigated, revealing that some pathogenic e. coli strains are more metabolically efficient than other strains in some environmental conditions. the e. coli gems generated in this work provide new tools for investigating the evolutionary and metabolic differences of these strains in conditions reflecting those environments encountered in human hosts. this is the first study to examine the metabolic properties of numerous strains of such a phylogenetically related group of microorganisms, and provides insight into the evolution of metabolism for the species e. coli. methods bacterial strains and growth conditions six e. coli strains and one salmonella strain were used in this study. frozen cultures were streaked onto luria bertani agar plates and grown overnight at 37°c. isolated colonies were then used to inoculate mops minimal media and incubated overnight with shaking at 37°c, and then overnight cultures were used to inoculate batch cultures grown with continuous sparging aerobically or anaerobically as previously described. for carbon plate utilization assays, isolated colonies were used to inoculate sheep blood agar plates and incubated at 37°c overnight aerobically or anaerobically in sealed whirl-pak® long-term sample retention bags saturated with an anaerobic gas mixture as described. anaerobic conditions were confirmed using an obligate aerobic bacterium that exhibited no growth and no respiration in any of the anaerobic conditions examined. cells were collected and used to inoculate biolog pm plates following the manufacturers recommendations with a minor modification of adding a top layer of mineral oil to each well for anaerobic culture conditions. aenterohemorrhagic e. coli buropathogenic e. coli updates to the e. coli k- mg metabolic network prior to generating a pangenome gem, additional genes in the genome of e. coli k- mg were evaluated as possible updates to the most recent e. coli gem. the annotations for e. coli k- mg were obtained and examined from the asap, ecogene, kegg, and ecocyc databases. orfs encoding enzymes that were not included in iaf were further investigated to develop elementally and charge-balanced reactions and to assign the reaction to a subcellular location based on psort predictions. in some instances new orfs were added as isozymes to existing reactions and the gene-to-protein-to-reaction associations updated. this resulted in the addition of new orfs to the iaf gem to create ieco1339_mg. of the new orfs, 62% were based on experimental data from the literature for e. coli strains and the rest were based on sequence homology to enzymes already existing in e. coli metabolic networks or to experimentally characterized enzymes from other enterobacteria. these gene additions resulted in new reactions, new isozymes, and new metabolites to the in silico model for e. coli k- mg generation of an e. coli pangenome metabolic network draft and complete enterobacterial genomes in the asap database have been continually updated using new publicly accessible genomes since the database's inception. there are more than genomes of enterobacteria in the asap database, of which are e. coli genomes. of these e. coli genomes, are completely finished, and we have used the information from these genomes and that of salmonella typhimurium lt to generate an e. coli pangenome metabolic network based on metabolic enzymes present in the union of orfs. each orf was assigned in the asap database to an ortholog cluster group, and the orfs map to ocgs. this reduced the number of genes that had to be evaluated for inclusion in the metabolic network and allowed generation of strain-specific gprs to rapidly be formulated. not all of these ocgs play a metabolic role and/or have sufficient experimentally determined details for metabolic network inclusion. for example only % of orfs in the total genome are accounted for in the updated gem for e. coli k- mg. all of the gene to protein to reaction association information from the e. coli k- mg and the salmonella lt gems were mapped to the ocgs that contained the respective orfs. the annotations for the genes composing the remaining ocgs were analyzed for additional new metabolic reactions and isozyme additions leading to the generation of an e. coli pan-gem named ieco1712_pan. all eight sbml files generated in this work were checked for syntax and internal consistency using the validation tool and found to conform to all specifications of sbml through level version core. aenterohemorrhagic e. coli benteroinvasive e. coli curopathogenic e. coli denteropathogenic e. coli eenterotoxigenic e. coli favian pathogenic e. coli gincluded for comparative purposes flux balance analysis fluxes through metabolic network reactions can be predicted using flux balance analysis. in fba, fluxes are constrained by steady-state mass balances, enzyme capacities and reaction directionality. these constraints yield a solution space of possible flux values, and fba uses an objective function to identify flux distributions that maximize the physiologically relevant predicted solution. cellular growth rate is often used as an objective function for fba, and was used for fba analyses performed in this study. the same biomass equation, growth and non-growth associated atp requirement values, and po ratio were used for all e. coli developed models, and were the same as that in iaf. for fba and dynamic simulations the reported wildtype biomass was used and for determination of essential reactions the core biomass was used. using fba, in silico predictions of growth yield, growth rate, and carbon source utilization were compared to experimentally determined values for all six e. coli strains and for salmonella lt in both aerobic and anaerobic conditions. for carbon source utilization and gene deletion simulations, a maximum uptake rate of mmol per gram of dry weight per hour was used. fba was also used to predict essential reactions by constraining reactions to have zero flux and maximizing growth rate. if the resulting maximum predicted growth rate was zero then the reaction was considered to be essential. reaction deletion simulations were evaluated under both aerobic and anaerobic conditions. batch growth experiments and simulations we performed dynamic fba simulations of batch growth and compared these results with experimental data. in the laboratory, cells were grown overnight in mops minimal medium with the addition of glucose as the sole carbon source, and used to inoculate batch cultures to an optical density of ~. batch growth under aerobic and anaerobic conditions was conducted at 37°c and spectrophotometric measurements, viable cells/ml, and biomass were determined at each time point. samples were collected every hour and passed through a μm syringe filter, and then frozen at -70°c for subsequent uplc analysis. the filtered supernatants were then analyzed to determine glucose concentrations at each time point using a uplc following manufacturers recommendations. growth rates, growth yields, and glucose specific uptake rates were determined from experimental data using the linear least squares estimate as described. biomass to od conversion values were also calculated for each strain. these conversion values were used to estimate initial biomass values using the initial od measurements for each experiment. the t values were used as parameters for dynamic fba simulations of batch growth to determine the exponential growth rates, biomass yields, and times needed to reach stationary phase for the corresponding experimental conditions. for each strain, experimental values from three biological replicates were then compared to those calculated from three computational simulations with matching starting biomass values under aerobic and anaerobic conditions. uplc analysis glucose analysis was conducted using an acquity uplc equipped with an acquity beh glycan column. a mobile phase was used at a flow rate of ml/min to separate small molecules on a waters acquity uplc equipped with an evaporative light scattering detector and photodiode array. phylogenetic analysis a maximum parsimony phylogenetic analyses of seven taxa were conducted in mega using a concatenated protein sequence data set of acna, gapa, icda, mdh, mtld, pgi, and proa with s. typhimurium lt used as the outgroup species. these genes were chosen since they have been successfully used for phylogenetic analyses of enterobacteria. the alignment for this data set and subsequent maximum likelihood phylogenetic analyses was performed in mega using default parameters.  the metabolic model for e. coli k- mg was developed years ago and has undergone numerous improvements and updates. it is now a sophisticated compartmentalized gem containing over genes and reactions. it has been used extensively for biotechnology and discovery applications. here we generated a gem for the pangenome of e. coli, and used the information from this larger metabolic network to generate strain-specific e. coli gems for two pathogenic lineages and an ancestral core gem containing reactions conserved across all e. coli strains. using this new collection of gems we validated strain-specific models by comparing predictions to experimental data, conducted a comparison of strain-specific gems from three e. coli lineages, and examined the metabolic networks of numerous e. coli strains in an evolutionary perspective based on phenotypic traits. updating the e. coli k- mg metabolic model the contents of the e. coli k- mg genome were surveyed for new genes/reactions to add to the existing gem. this effort added a total of genes to iaf of which encoded proteins with significant similarity to proteins with characterized enzymatic activity, were added based on orthology to genes found in the s. typhimurium lt gem, and were added based on experimental evidence from the scientific literature. three of the new genes were linked to metabolic reactions that were already included in iaf but whose associated genes were previously unknown. the new genes added new metabolic reactions and new metabolites to the gem. exchange/transport reactions to permit cis-dihydrodiol-phenylacetyl-coa utilization were also added resulting in an updated gem for e. coli k- mg designated as ieco1339_mg composed of genes, metabolites, and reactions. this includes eight new reactions for phenylacetate metabolism that were added following our observation that e. coli k- mg can grow in minimal media with phenylacetate as a sole carbon source. the new reactions in ieco1339_mg were classified into metabolic subsystems. there were genes added to the gem that likely encode isozymes that participate in existing reactions across metabolic subsystems. a total of reactions in ieco1339_mg contain multiple isozymes. generation of an e. coli pangenome metabolic model understanding the evolution of metabolism for the species e. coli requires comparing genome-scale metabolic content among different strains of e. coli and its relatives. to faciltate these comparisons we mined the contents of e. coli genomes to identify reactions that could be added to ieco1339_mg to generate a pangenome metabolic network representing all metabolic reactions associated with genes present in any one of the e. coli genomes. all genes from the sixteen e. coli and the s. typhimurium lt genomes were classified into orthologous cluster groups based on ortholog relationships from the asap database. this analysis resulted in a total of ocgs with representing the e. coli pangenome and ocgs unique to s. typhimurium lt of the e. coli pangenome ocgs, are found in all sixteen e. coli genomes, are shared by two or more e. coli strains, and are unique to individual e. coli genomes. each additional genome added on average new genes; however this number decreased as more genomes were analyzed. a gem for the pangenome was then constructed. existing gene to protein to reaction associations from gems of ieco1339_mg and s. typhimurium lt were mapped to their corresponding ocgs. the remaining ocgs were analyzed to see if they could be added to the pangenome metabolic network by adding additional isozymes or new reactions. this led to the addition of ocgs and new reactions beyond those found in the updated e. coli k- mg gem, resulting in a pan-gem, ieco1712_pan, consisting of genes, metabolites, and reactions. each additional e. coli genome added to the pangenome metabolic analysis resulted on average added new metabolic genes, isozymes, and approximately new metabolic reactions to the pan-gem. the reactions added to ieco1712_pan fall into metabolic subsystems, with the majority being related to alternate carbon metabolism and oxidative phosphorylation. other added ocgs resulted in addition of isozymes associated with metabolic subsystems, with the most abundant being alternate carbon metabolism, cell envelope biosynthesis, oxidative phosphorylation, nitrogen metabolism, glutamate metabolism, and the remaining subsystems consisted of a single reaction addition. generation of an e. coli core metabolic model e. coli strains are thought to have diverged from a common ancestor ~ million years ago and it is of interest in understanding how strain-specific metabolism has evolved over time to have an estimate of the metabolic capabilities of an ancestral core for the species e. coli. we assume that genes conserved across the genomes of all strains represents a conservative estimate of the core genome of the ancestor of modern e. coli strains and used this collection of conserved genes to construct an ancestral core gem named ieco1053_core. there are of these genes in the e. coli k- mg gem. the gem for the e. coli ancestral core was made by removing ocgs and their associated reactions from the ieco1339_mg gem if one or more of the sixteen e. coli genomes did not have a gene assigned to the ocg. if removing a reaction prevented biomass production for anaerobic growth on glucose then the reaction was added back to the metabolic reconstruction without a gene associated with it and this occurred times. using this approach orfs associated with reactions and isozymes were removed from ieco1339_mg resulting in an e. coli ancestral core gem consisting of a total of orfs and reactions, and these reactions we removed were classified based on metabolic subsystem. anecessary reactions without an associated gene for in silico models characteristics of five new e. coli strain specific models the pan-gem was used to expedite the process of generating five new strain-specific e. coli gems, since the pangenome has reactions connected to cluster groups, a given strains genome contents were analyzed to identify what cluster groups its genes belong to and those associated reactions were included. the e. coli strains we selected include an additional e. coli k- strain, two enterohemmoraghic e. coli o157:h strains, and two uropathogenic strains. comparisons to ieco1339_mg including the total number of strain-specific gene additions and deletions and the corresponding metabolic reactions are shown in table the two k- strains are laboratory strains derived from the same isolate and not surprisingly their gems, were very similar with the sole difference being removal of a few isozymes and a gene associated with galactitol transport from the w gem named ieco1335_w due to a w3110-specific is insertion in the gata gene. we built the five new e. coli gems named ieco1335_w, ieco1344_edl, ieco1345_sakai, ieco1288_cft, and ieco1301_uti by deleting genes and reactions from the pan-gem when missing from the genome under consideration. if removing a reaction prevented biomass production for anaerobic growth on glucose then the reaction was added back to the metabolic reconstruction without a gene associated with it. the number of these reactions without associated genes varied from to for each of the pathogenic e. coli strains. five of these reactions without associated genes were required in all four pathogenic e. coli gems. of the remaining five reactions, two were required for strains edl and sakai, one was required for strains edl sakai, and uti and the remaining three are specific to e. coli strain cft even though the genomes of the four pathogenic e. coli strains contain between 700- genes not present in the genome of e. coli k- mg relatively few pathogen-specific metabolic genes were added to each gem. eight new reaction additions were unique to the gems of the ehec strains and consisted of urease, udp-n-acetylglucosamine 4-epimerase, salicylate hydroxylase, gentisate,-dioxygenase, sucrose transport, tellurite reduction, fucose synthetase, and perosamine synthetase reactions. the two upec strain gems shared only one lineage-specific reaction addition for propionate coa-transferase and each has a single strain-specific reaction addition unique to each strain; galactose isomerase activity for ieco1288_cft and hydroxypyruvate reductase activity for ieco1301_uti in contrast to the relatively small number of gene and reaction additions there were a large number of reaction deletions for the pathogenic strain gems compared to ieco1339_mg. for ehec strain gems, ieco1344_edl and ieco1345_sakai, there were genes found in the e. coli k- mg gem that had no orthologous gene in the genomes of the two ehec strains. these missing genes resulted in reaction deletions in both ieco1344_edl and ieco1345_sakai, and these were classified based on metabolic subsystem. there was only one additional reaction deletion unique to ieco1345_sakai for d-cysteine desulfhydrase, whereas the orf encoding this enzyme was still intact in the edl strain. for each of the two upec strains there were reactions that were missing in both ieco1288_cft and ieco1301_uti compared to ieco1339_mg and these were further classified into metabolic subsystems. each of the two upec strains also contained numerous reaction deletions unique to each strain. when gems for all four pathogens were compared to those of the two k- strains, shared reaction deletions were common to all four pathogenic e. coli strains, and they were categorized into the metabolic subsystems of alternate carbon metabolism, cell envelope biosynthesis, inorganic ion transport and metabolism, lipopolysaccharide biosynthesis, methionine metabolism, nitrogen metabolism, inner membrane transport, and outer membrane transport. assessment and validation of models for carbon source utilization to evaluate the accuracy of the gems for all six e. coli strains, we examined each strain's ability to use different carbon sources under aerobic and anaerobic conditions using biolog phenotypic arrays. there were numerous strain-specific differences in carbon source utilization in both aerobic and anaerobic conditions. these experimental results were then compared to fba predictions of growth using different carbon sources. for those compounds included in the biolog plates that have transporters in the model, fba was used to predict if they could be used for growth as sole carbon source. this included potential carbon sources for the six e. coli strains and potential carbon sources for salmonella lt if fba calculated a zero growth rate then the compound was predicted not to be usable as a sole carbon source, while positive calculated growth rates indicated that the model predicted the compound could be used as the sole carbon source. of the compounds, there were and carbon sources where model predictions and experiments agreed for all six e. coli models. the and carbon sources with discrepancies between in silico and experimental results in at least one model fall into two categories i) when strains did not grow and the model predicted growth, and ii) instances where strains grew and the model predicted no growth. for aerobic carbon source utilization, the number of false positives varied from one to four, and the most accurate models were for e. coli k- w and uti. the number of aerobic false negatives was greater than false positives and ranged from three to seven with the least observed for ieco1339_mg and the most observed for ieco1288_cft of these model-data discrepancies, there were some carbon sources that led to inaccurate predictions by all six e. coli strain specific models such as the utilization of d-aspartic acid and m-inositol and tyramine. the two pathogenic lineages exhibited some lineage-specific false negatives for alpha-keto-butyric acid and acetoacetic acid utilization or d-melibiose. fba predictions using a mixture of l- and r- isomers of propanediol resolved aerobic false negative discrepancies for propanediol for five e. coli strains, without introducing any new false positives. for anaerobic carbon source utilization, there were no false positives observed for three e. coli strains and either one or two false positives observed for carbon sources such as d-threonine, d-aspartic acid, and l-lyxose. in contrast to the aerobic results, there were generally more false negative than false positive predictions, with the number of false negatives ranging from to for the six e. coli strains. of these compounds associated with anaerobic false negatives, there were that led to inaccurate growth predictions for all six e. coli strains. the biolog phenotype assay uses reduction of a colorimetric tetrazolium dye to measure microbial respiration. our initial fba predictions used an objective function that relates to the ability of the bacterium to convert a particular carbon source into biomass. there may be carbon compounds that the bacteria are able to metabolize but which do not result in measurable growth, thus leading to false negatives. to see if changing the objective function from biomass production to indicator dye reduction improves the fba predictions under both aerobic and anaerobic conditions, two additional reactions were added to each of the models representing the movement of electrons from reduced quinones to the indicator dye used in biolog plates. fba was used again but a new objective function, equal to the sum of flux through these two new reactions, was maximized. if the maximum sum of fluxes was zero then the model predicted the carbon source could not be metabolized, while a positive sum of fluxes indicated a carbon source could be metabolized. these new fba predictions significantly reduced the number of anaerobic false negatives to between two and eight, while not affecting the number of false positives. aenterohemorrhagic e. coli buropathogenic e. coli cexperimental = y dexperimental = n eexperimental = n and in silico = y fexperimental = y and in silico = n overall, once the fba objective was changed from biomass to respiration, all e. coli models exhibited a statistically significant relationship between model predictions and experimental growth phenotypes for both aerobic and anaerobic conditions. the carbon sources m-inositol and tyramine still led to false negative predictions for all e. coli models examined under both aerobic and anaerobic conditions, which may indicate that missing reactions or gaps may exist in pathways for utilization of these carbon compounds. when considering both the aerobic and anaerobic conditions the overall accuracy for individual strain-specific models was ieco1301_uti, ieco1335_w, ieco1339_mg, ieco1345_sakai, ieco1344_edl, and ieco1288_cft. batch growth predictions to further evaluate model predictions, dynamic fba was used to predict time-courses, exponential growth rates and biomass yields for aerobic and anaerobic batch cultures in mops minimal media with the addition of glucose as the sole carbon and energy source. batch culture experiments were conducted for each strain, and conversion factors for optical density to biomass, optical density to viable cell concentration, and biomass per viable cell values were determined for each strain in both anaerobic and aerobic conditions. these conversion values were then used to approximate starting biomass values used in the dynamic fba simulations. for each e. coli strain, the maximum glucose uptake rates used for dynamic fba were those reported for e. coli k- strain w. the predicted growth rates and biomass yields from the model were then compared to experimental results of batch culture of each strain under anaerobic or aerobic conditions. for both aerobic or anaerobic growth conditions, the calculated growth rate for each e. coli strain was compared to the experimentally determined values and the agreement between in silico and experimental values was strong and significant, yet when viewed separately for aerobic or anaerobic conditions, the correlation was not as strong and was not significant. in addition, the growth yields were calculated under aerobic or anaerobic growth conditions in mops minimal media for both aerobic and anaerobic growth conditions, and the in silico growth yields for each strain were compared to those determined experimentally and the agreement between in silico and experimental values was strong and significant. amaximum oxygen uptake rates and glucose uptake rates for aerobic and anaerobic conditions were used for in silico batch simulations in this work were those previously determined for e. coli w from batch culture in m minimal media bexperimentally determined glucose uptake rate values from this work used for in silico batch simulations for e. coli k-, ehec or upec. for the core and pangenome models the average experimentally determined glucose uptake rate values from this work was used. the maximum glucose uptake rate from one e. coli strain from each lineage k-, ehec, upec was determined from experimental data for both aerobic and anaerobic conditions. these results revealed that the uptake rates for e. coli k- mg were not similar to previously published values for e. coli k- w. when compared to the two pathogenic lineages, the two e. coli k- strains appear to have significantly higher glucose uptake rates in aerobic conditions but significantly lower glucose uptake rates in anaerobic conditions. the dynamic fba simulations were repeated using the measured lineage-specific glucose uptake rates as parameters. the recalculated growth rates, still showed a moderately strong correlation overall between in silico and experimental values, yet when viewed separately, resulted in a decreased correlation for aerobic conditions, and a increased correlation between in silico and experimental values for anaerobic conditions. figure shows a phylogenetic tree of the strain relationships plotted along with the growth rate data, displaying the aerobic and anaerobic growth rates calculated without the uptake rate correction. additionally we determined the amount of time needed to reach stationary phase experimentally and computationally using dynamic fba for each strain in each condition. evaluation of these results reveal that some of the pathogenic lineages attain final biomass in less time compared to the e. coli k- strains. analysis of reaction essentiality to further explore the metabolic differences and similarities between all six e. coli strain-specific gems, we compared reaction essentiality predictions for in silico conditions simulating aerobic and anaerobic growth in glucose minimal media. the number of predicted essential reactions shared in common for all six e. coli strains and also the e. coli ancestral core in both aerobic and anaerobic growth conditions was determined, and their corresponding reactions were further classified by metabolic subsystem. additionally for all six e. coli strains and the e. coli ancestral core, there were additional conserved essential reactions predicted to be required under anaerobic conditions and these involve reactions assigned to subsystems for anaplerotic reactions, citric acid cycle, cofactor and prosthetic group biosynthesis, glycolysis/gluconeogenesis, purine and pyrimidine biosynthesis, inner membrane transport, and outer membrane transport. in addition to the shared predicted essential reactions for all e. coli strains examined, there were two lineage-specific reactions predicted as essential under both aerobic and anaerobic conditions for both ehec strains, and the corresponding reactions were for fumarate reductase and glycolate oxidase. for the e. coli ancestral core, there were five additional reactions predicted as essential that were not predicted for any of the six e. coli strains, and these involve reactions assigned to subsystems for alanine and aspartate metabolism, glutamate metabolism, inner membrane transport, glycolate exchange, and outer membrane porin transport. discussion this study describes the generation of gems representing the union and also the intersection of all identifiable metabolic reactions contained in sixteen genomes of e. coli. we used the e. coli pan-gem to rapidly construct six e. coli strain-specific gems. a comparison between model growth predictions and biolog phenotypes measured in the laboratory demonstrated an accuracy of more than 88%, including those under anaerobic conditions additional quantitative data was generated for each strain and used to validate the correlation between model predictions and experimental physiology of the strains in the laboratory. these new e. coli gems serve as a framework to examine genome-scale metabolic similarities and differences between strains in an evolutionary context with respect to the commensal, ehec, and upec lineages. the two e. coli k- strains are widely used laboratory strains that are believed to have diverged from the same parental strain approximately years ago. the sole identified metabolic differences between the two e. coli k- strains based on genome comparison include the gata gene that is involved in galactitol transport, dcua and dcuc involved in c4-dicarboxylate transport metabolism, and also tnab thought to be involved in the utilization of tryptophan as a carbon and/or nitrogen source. of these four metabolic gene differences, only inactivation of gata leads to a loss of a reaction in ieco1335_w compared to ieco1339_mg since dcua, dcuc and tnab have other isozymes. the gata gene contains an insertion sequence element in e. coli w which suggests a phenotypic loss for galactitol utilization as a carbon source, yet experimental data reveals that the strain can still use this substrate as sole carbon source, indicating that other transporters may permit galactitol transport for e. coli w although the two e. coli k- strains exhibited no differences in their gems, quantitative and strain-specific differences were observed during batch growth in minimal media with glucose as the sole carbon source. while in silico predictions for growth yield were similar for ieco1339_mg and ieco1335_w experimental data reveal that in both aerobic and anaerobic conditions, strain mg had higher growth yields, higher growth rate, and attained the final biomass value in less time than strain w. therefore, although the in silico models for these two strains are nearly indistinguishable, strain specific differences in complex traits such as biomass composition, atp requirements, po ratios, and glucose uptake rates may account for these experimental differences. previous studies have shown that despite their nearly identical genomes and very similar growth patterns in a bioreactor, w and mg have many significant differences in their transcriptomes and proteomes. these include differential expression of pathways affecting central metabolism and the generation of precursor metabolites and energy suggesting that future models for even these very similar strains will need to account for subtle genetic differences between strains to accurately predict phenotypic traits in simulated culture conditions. previous analyses of the e. coli pangenome estimated that on average each new e. coli genome sequence added about unique genes to the pangenome, and among these unique genes, we found each additional e. coli genome resulted in metabolic gene additions corresponding to about new metabolic reactions and isozymes suitable for inclusion in the pan-gem. clearly some of the metabolic differences between e. coli strains are due to the addition of genes with new metabolic activity. however, our ability to add new reactions to the metabolic reconstructions is severely limited by the paucity of experimental characterization of the metabolic genes, proteins, and reactions unique to pathogenic strains. since the strain-specific portions of the genomes remain largely uncharacterized, our current understanding of the metabolic functions they encode is dominated by the presence and absence of genes encoding functions represented in the ieco1339_mg gem. many of the genes included in this model are not universally conserved among the genomes we examined; resulting in strain-specific gems with an average of fewer genes than ieco1339_mg. this observation is also consistent with draft gems generated using the model seed where the gem for e. coli mg contained more genes and reactions than the draft gems for all four pathogenic e. coli strains examined in this work. although carbon source utilization has become a standard method to assess the validity of computational metabolic model predictions, this study was the first to examine this procedure under anaerobic conditions. initially, the accuracy of predictions for carbon source utilization during anaerobic conditions was less than those determined during aerobic conditions. we account this difference to comparisons between biolog carbon source assays, which examine the ability of a microbial strain to generate energy from each sole carbon source, to in silico analysis that determines growth as a positive flux value for the biomass reaction. one possible explanation for experimental and in silico data discrepancies may be that a microbial strain may be able to generate energy from a given carbon source, but that the carbon source is not suitable to sustain growth. therefore, rather than maximize the objective value for the biomass equation, we added two reactions to monitor the ability to generate energy through electron transfer to quinones, and in many cases this analysis resolved discrepancies between in silico predictions and experimental data, especially for anaerobic conditions. although this methodology of examining carbon source utilization seems trivial, validation for accurate carbon source utilization is important for modeling complex environments such as those encountered in a host, as of the carbon sources tested here were used to simulate the conditions reflecting invasion of a human cell to study s. typhimurium lt infection. therefore, the validation of these strain-specific metabolic models for carbon source utilization will prove useful for future computational modeling of pathogenic e. coli strains in conditions encountered in the gastrointestinal tract or in other locations such as the urinary tract in mammalian hosts. with the generation of the first gems for pathogenic e. coli strains, two ehec strains and two upec strains, properties of these genome-scale metabolic networks were investigated to identify differences that may play a role in human disease. we analyzed two e. coli o157:h strains associated with foodborne outbreaks, strain edl isolated from ground beef in the u.s in and strain sakai isolated from contaminated radish sprouts that sickened thousands in japan in strains cft and uti which cause human disease outside of the intestine, were isolated from patients with acute urinary tract infections. a comparison of reaction deletions between the ehec and upec metabolic networks reveals that the ehec strains have more missing genes corresponding to reactions for inner membrane transport in comparison to the upec strains. in addition, the reaction deletions that occur in both pathogenic lineages relative to e. coli k- strains are mainly associated with genes involved in lipopolysaccharide biosynthesis/recycling and alternate carbon utilization. it seems likely that some of these missing reactions are the result of acquisition of genes during the evolution of the k- lineage. perhaps some of the reactions missing from both pathogen lineages arise from parallel deletions arising from selective pressures common to both pathogens. batch growth experiments were conducted to compare growth yields, growth rates, and the amount of time to attain final biomass among strains. we were surprised that edl sakai and cft have significantly higher growth rates than mg during aerobic growth conditions yet the in silico predictions reveal little to no differences. we sought to determine if strain-specific glucose uptake rates may improve in silico growth rate predictions. experimentally determined glucose uptake rates were actually lower for edl and cft than for mg and did not improve in silico predictions. the growth yield values we measured in the laboratory also showed significantly higher yields for edl and cft than the two k- strains, but in silico predictions showed only minor strain-to-strain variations. dynamic fba using the strain specific e. coli gems predicts a similar growth rate from all models including the model for the ancestral core of e. coli. yet the actual growth rates determined experimentally vary significantly between strains suggesting that our models are not accounting for some strain-specific factors such as oxygen uptake rates, biomass composition, atp requirement parameters, or additional uncharacterized reactions. the length of time required to attain final biomass was significantly shorter for the four pathogens suggesting that they may be more efficient at biomass production during glucose catabolism, and dynamic fba analysis accurately predicted this phenotypic difference among the strains. in anaerobic batch growth conditions there were also differences between strains. all pathogenic strains have higher growth rates than the k- strains. the fba predictions for ehec strains both reflect this phenotype, but the in silico growth rate predictions for the upec strains did not reflect this trend. the experimentally determined glucose uptake rates are higher for both pathogenic lineages than k- and these organism-specific parameters improved the fba predictions. the growth yields determined experimentally are significantly higher for the four pathogens than the k- strains. the length of time required to attain final biomass predicted by fba and determined experimentally was significantly shorter for the ehec strains than the k- strains. overall, for anaerobic glucose catabolism, all four pathogens appear to grow better than both e. coli k- strains. even though the metabolic networks of each e. coli strain differ, there were relatively few strain-to-strain differences in reactions predicted as essential for the two growth conditions examined. while there were some identified for all strains that were unique for anaerobic growth in comparison to aerobic, there were relatively few differences between all strains. the two reactions predicted as essential for the e. coli o157:h strains, play essential metabolic roles for glycolate recycling and the reoxidation of menaquinol, and represent new targets for control strategies that may help to prevent and treat human ehec illness. the comparison of the pan- and core-gems reveals that a substantial fraction of the reactions in our current pan-gem are also in the ancestral core-gem. however, our knowledge of the detailed biochemistry of the pangenome is likely incomplete since many of the genes in other e. coli strains have unknown functions. one reason why the number of reactions in the core- and pan-gems are so similar is because the genes that have been well-characterized biochemically in e. coli tend to be the genes that are conserved and likely ancestral. while the pathogenic e. coli strains are of great interest medically, they are not typically the focus of intense biochemical study to uncover the functions of their novel metabolic genes. overall, when data for aerobic conditions is viewed phylogenetically, there is no clear trend specific to the two pathogenic lineages, yet it appears that e. coli cft has evolved with a similar growth rate in comparison to the e. coli ancestral core predictions, where as all other strains have evolved with higher growth rates and yields. in contrast, in anaerobic conditions, higher growth yields and faster batch growth performance were observed for both ehec e. coli strains, and the insight derived from e. coli ancestral core in silico predictions suggest that the upec and k- lineages have evolved with less efficient anaerobic glucose catabolism then the ehec lineage. one possible explanation for this behavior may be that the k- and upec strains do not routinely encounter the selective pressure from anaerobic conditions, whereas the ehec strains may have evolved for improved growth in anaerobic conditions enabling their growth in both bovine and mammalian gi tracts, thus suggesting that many ehec strains may have a better-suited anaerobic metabolism for glucose utilization. these findings suggest that e. coli k- strains could be engineered to be more efficient for anaerobic batch growth and that other e. coli strains not examined in this work may yield similar results, yet additional studies are warranted to examine more e. coli strain-specific gems, quantitative parameters, and catabolism of additional substrates other than glucose.  here we have presented an update to the e. coli k- mg gem and an extensive new collection of gems for five e. coli strains including the first for two pathogenic lineages. these models have been validated through experimental data for aerobic and anaerobic conditions. this work demonstrated a new approach for validation of carbon source utilization, yielding accuracies of >88% for aerobic and anaerobic conditions for all six e. coli strains examined. in addition new lineage-specific quantitative data were generated and led to validation of the correlation between in silico predictions and experimental batch culture data for glucose catabolism during aerobic and anaerobic growth conditions. thus, the ieco1339_mg ieco1335_w ieco1344_edl ieco1345_sakai, ieco1288_cft and ieco1301_uti gems provide new suitable platforms for computing cellular phenotypes in conditions reflecting those encountered in mammalian hosts such as the intestine or urinary tracts and for further integration of high throughput data generated from these bacterial strains during the course of infection in animal models. distinctive lineage-specific differences in the gems were identified and reveal that the main delineating metabolic factors between pathogenic and commensal e. coli strains are due to numerous gene/reaction deletions and not additions, and this observation was consistent with the number of genes and reactions contained in draft gems for all six e. coli strains generated using model seed. historically many researchers have noticed that some pathogenic e. coli strains grow faster in comparison to commensal strains such as k- yet this phenotype has remained unexplained. these strain-specific models offer new tools for further investigation to determine precisely what combination of gene/reaction deletions account for the faster and more efficient biomass production observed experimentally for some of the pathogenic strains, thus providing new insight for bioengineering of industrial e. coli strains. the generation of an e. coli pan-gem consisting of all metabolic genes and reactions from e. coli genomes, represents a new framework to rapidly generate additional e. coli strain/lineage-specific gems consisting of > genes and > metabolic reactions. finally, this study is the first to use a "paleo systems biology" approach to generate a gem for an ancestral core of e. coli providing the first insight to metabolic traits of an e. coli relative that may have existed ~ mya, and demonstrated the use of an ancestral model to examine a closely related phylogenetic group of e. coli strains in the context of evolution. authors' contributions db constructed the pangenome, core, and the six strain-specific gems and performed all in silico analyses. db and rp obtained all of the experimental data. db, jg, and np designed the study. db and jr analyzed and interpreted the data and performed the statistical analysis. db, jg, jr, and np wrote the manuscript. db generated all eight sbml model files. all authors approve the content of this manuscript. supplementary material additional file 1 genome-scale metabolic model for e. coli k- strain mg sbml format of ieco1339_mg for distribution and use in other modeling environments. click here for file additional file 2 gene to protein to reaction updates for e. coli k- mg in the final version of the reconstruction. this file contains two tables, the first contains all new gpr information added to the previous e. coli k- mg gem, and the second contains the final gpr information for ieco1339_mg click here for file additional file 3 orthologous gene cluster groupings for all orfs in e. coli genomes and one salmonella genome. this file contains the mapping of orthologous cluster group identifiers to each asap feature identifier and locus tag for all orfs contained in the genomes of strains of e. coli and one salmonella genome. click here for file additional file 4 e. coli pangenome orthologous cluster group identifier to protein to reaction information for the e. coli pan-gem. this file contains four tables, the first contains all gene additions, the second contains all metabolite additions, and the third contains all reaction additions to ieco1339_mg to construct the e. coli pan-gem. the fourth table contains the final ocg to protein to reaction information for eco1712_pan. click here for file additional file 5 pangenome-scale metabolic model representing the e. coli pangenome. sbml format of ieco1712_pan for distribution and use in other modeling environments. click here for file additional file 6 quantitative experimental batch growth data for six e. coli and one salmonella strain for aerobic and anaerobic conditions. this file contains tables, the first contains the biomass data, the second contains glucose data, and the third contains the optical density data for experimental batch growth of six e. coli and one salmonella strain for aerobic or anaerobic conditions. values highlighted in yellow reflect the timepoints used to determine lineage-specific glucose uptake rates and strain-specific growth rates. click here for file additional file 7 experimental and in silico carbon source utilization data. this file contains two tables, the first contains experimental and in silico carbon source utilization data during aerobic conditions, and the second contains experimental and in silico carbon source utilization data during anaerobic conditions for six e. coli and a salmonella strain. values highlighted in blue represent false negatives and those highlighted in magenta represent false positives. click here for file additional file 8 genome-scale metabolic model representing the ancestral core of e. coli. sbml format of ieco1053_core for distribution and use in other modeling environments. click here for file additional file 9 necessary orphan reactions required for biomass production. list of metabolic reactions without corresponding genes necessary for each e. coli strain-specific gem for biomass production in minimal media with glucose added as the sole carbon source. click here for file additional file 10 genome-scale metabolic model for e. coli k- strain w sbml format of ieco1335_w for distribution and use in other modeling environments. click here for file additional file 11 genome-scale metabolic model for enterohemorrhagic e. coli o157:h strain edl sbml format of ieco1344_edl for distribution and use in other modeling environments. click here for file additional file 12 genome-scale metabolic model for enterohemorrhagic e. coli o157:h strain sakai. sbml format of ieco1345_sakai for distribution and use in other modeling environments. click here for file additional file 13 genome-scale metabolic model for uropathogenic e. coli strain cft sbml format of ieco1288_cft for distribution and use in other modeling environments. click here for file additional file 14 genome-scale metabolic model for uropathogenic e. coli strain uti sbml format of ieco1301_uti for distribution and use in other modeling environments. click here for file additional file 15 deleted reactions for strain-specific e. coli gems and the e. coli ancestral core gem. this file contains three tables; the first contains all gene and corresponding reactions deleted for five e. coli strains in comparison to eco1339_mg the second contains all deleted genes corresponding to isozymes for five e. coli strains in comparison to eco1339_mg the third contains all reactions deleted from eco1339_mg to generate the e. coli ancestral core gem. click here for file additional file 16 reactions corresponding to essential gene predictions for all six strain-specific e. coli gems and for the e. coli ancestral core gem. this file contains three tables, the first contains all predicted essential reactions during both aerobic and anaerobic conditions, the second contains all anaerobic-specific predicted essential reactions, the third contains predicted strain- or core-specific essential reactions for all six strain-specific e. coli gems and for the e. coli ancestral core e. coli gem. click here for file acknowledgements and funding we are grateful and would like to acknowledge the following for provision of bacterial strains used in this study: prof. scott hultgren for provision of e. coli strain uti prof. patricia kiley for provision of e. coli k- strain mg and prof. diana downs for provision of salmonella lt we would also like to thank the great lakes bioenergy research center for use of the uplc instrument for analysis. this work was funded by the us department of energy genomics:gtl and scidac programs for the bacter institute post-doctoral fellowship and also the national library of medicine, national institutes of health, grant no. 5t15lm to the computation and informatics in biology and medicine training program for a post-doctoral traineeship and in part by nih grant #nih/gnm gm62994- finally, we would like to thank dr. eric cabot and christopher tervo for assistance in generation of the sbml model files.
despite the availability of numerous complete genome sequences from e. coli strains, published genome-scale metabolic models exist only for two commensal e. coli strains. these models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for e. coli strains could enhance these efforts. we used the genomic information from e. coli strains to generate an e. coli pangenome metabolic network by evaluating their collective orfs. comparative analysis of all six strain-specific e. coli models revealed that some of the pathogenic e. coli strains possess reactions in their metabolic networks enabling higher biomass yields on glucose. such pangenome and strain-specific models can be further used to engineer metabolic phenotypes of interest, such as designing new industrial e. coli strains. finally the lineage-specific metabolic traits were compared to the ancestral core model predictions to derive new insight into the evolution of metabolism within this species. all six e. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with % glucose. all remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model. our findings demonstrate that a pangenome-scale metabolic model can be used to rapidly construct additional e. coli strain-specific models, and that quantitative models of different strains of e. coli can accurately predict strain-specific phenotypes. each of these orfs was assigned to one of ortholog groups including orfs associated with reactions in the most recent metabolic model for e. coli k- for orthologous groups that contain an orf already represented in the mg model, the gene to protein to reaction associations represented in this model could then be easily propagated to other e. coli strain models. the pangenome model included reactions from a metabolic model update for e. coli k- mg and enabled development of five additional strain-specific genome-scale metabolic models. when compared to the e. coli k- models, the metabolic models for the enterohemorrhagic and uropathogenic strains contained numerous lineage-specific gene and reaction differences. an ancestral genome-scale metabolic model based on conserved ortholog groups in all e. coli genomes was also constructed, reflecting the conserved ancestral core of e. coli metabolism. these additional models include a second k- strain and four pathogenic strains.
 despite the availability of numerous complete genome sequences from e. coli strains, published genome-scale metabolic models exist only for two commensal e. coli strains. these models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for e. coli strains could enhance these efforts.  we used the genomic information from e. coli strains to generate an e. coli pangenome metabolic network by evaluating their collective orfs. each of these orfs was assigned to one of ortholog groups including orfs associated with reactions in the most recent metabolic model for e. coli k- for orthologous groups that contain an orf already represented in the mg model, the gene to protein to reaction associations represented in this model could then be easily propagated to other e. coli strain models. all remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model. the pangenome model included reactions from a metabolic model update for e. coli k- mg and enabled development of five additional strain-specific genome-scale metabolic models. these additional models include a second k- strain and four pathogenic strains. when compared to the e. coli k- models, the metabolic models for the enterohemorrhagic and uropathogenic strains contained numerous lineage-specific gene and reaction differences. all six e. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with % glucose. an ancestral genome-scale metabolic model based on conserved ortholog groups in all e. coli genomes was also constructed, reflecting the conserved ancestral core of e. coli metabolism. comparative analysis of all six strain-specific e. coli models revealed that some of the pathogenic e. coli strains possess reactions in their metabolic networks enabling higher biomass yields on glucose. finally the lineage-specific metabolic traits were compared to the ancestral core model predictions to derive new insight into the evolution of metabolism within this species.  our findings demonstrate that a pangenome-scale metabolic model can be used to rapidly construct additional e. coli strain-specific models, and that quantitative models of different strains of e. coli can accurately predict strain-specific phenotypes. such pangenome and strain-specific models can be further used to engineer metabolic phenotypes of interest, such as designing new industrial e. coli strains. 