BACKGROUND
genome-scale metabolic models  are increasingly becoming available for an expanding range of organisms.
there exists at least forty completed bacterial and thirteen eukaryotic metabolic reconstructions with many more under development  <cit> .
in the past decade, several studies  <cit>  have demonstrated a variety of uses ranging from strain optimization  <cit>  pathogen drug target identification  <cit> , bacterial community metabolic interactions  <cit>  and identification of human disease biomarkers  <cit> .
in contrast to the extensive interest devoted towards bacterial and eukaryotic metabolism reconstruction, efforts to construct archaeal metabolic models have been noticeably limited  <cit> .
this is partly due to the current relative paucity of -omics datasets available for species from the archaea domain.
this dearth of data, however, is likely to change in the near future as recent interest in methanogenic archaea has led to several sequencing efforts  <cit> , as well as transcriptomic and proteomic analyses  <cit> .
furthermore, it is increasingly becoming apparent that archaeal metabolism has significant implications to the earth's climate  <cit>  thus motivating the need to globally assess their true metabolic capabilities by reconstructing high quality metabolic models.
methanogens  constitute the largest group of the archaea domain of life  <cit> .
methanogens are terminal organisms in anaerobic microbial food chains  that break down organic matter to methane in diverse anaerobic environments in a process that helps regulate the global carbon flux  <cit> .
the process plays a surprisingly significant role in global warming accounting for about one billion tons of the annual methane production  <cit> .
recently, cheng and coworkers used a consortia of methanogens to convert electricity into methane thereby paving the way for converting electric current generated by renewable energy sources into renewable biofuels  <cit> .
on the evolutionary front, methanogens are amongst the most ancient form of life on earth and their role as the progenitors of the first eukaryotic cell has been suggested under two separate hypotheses  <cit> .
in addition, unique biochemical properties such as broad substrate specificity, participation of novel coenzymes in the methanogenesis pathways and the presence of unique lipids in their cell wall set these organisms apart from the bacterial and eukaryotic branches of life  <cit> .
therefore, the reconstruction of archaeal methanogen metabolic models could help paint a more complete picture of life's metabolic processes.
feist and coworkers first developed a genome-scale model   <cit>  for the fresh-water methanogen, methanosarcina barkeri using a draft version of its genome.
in this paper, we reconstruct a genome-scale metabolic model for the marine methanogen, methanosarcina acetivorans.
m. acetivorans is an aceticlastic methanogen that was first isolated from marine microbial communities that degrade kelp into methane  <cit> .
at over  <dig>  million base pairs  <cit> , it has the largest reported genome of all sequenced archaea  alluding to an expanded metabolic repertoire.
this repertoire includes unique methanogenic pathways, broader substrate specificity than other methanogens and a large number of duplicate genes  <cit> .
recent studies have shown that m. acetivorans uniquely exhibits both methanogenic and acetotrophic growth on carbon monoxide  <cit> .
all these unique metabolic characteristics and planet-wide carbon balance impact  <cit>  provide the motivation to globally assess its metabolic capabilities.
draft metabolic reconstructions generated using homology-based comparisons unavoidably contain some omissions and misclassifications.
these errors are manifested either as unreachable metabolites or as in silico predictions that are in contrast with observed in vivo behavior  <cit> .
in response to these challenges, suthers et al., proposed a computational workflow to generate and curate the metabolic models and applied it to the metabolic reconstruction of mycoplasma genitalium  <cit> .
the proposed workflow makes use of two separate model correction procedures.
gapfind and gapfill identify and subsequently restore connectivity to unreachable metabolites  <cit>  and growmatch that reconciles in silico growth predictions with in vivo growth data  <cit> .
in this paper, we streamline this workflow for the generation of an archaeal metabolic model and customize it to the available types of data.
we first generated a draft reconstruction of m. acetivorans using homology comparisons with the published model  <cit>  of the fresh-water methanogen, m. barkeri.
we then deployed a modified version of the workflow presented in suthers et al., by combining the gapfind/gapfill and growmatch steps of the procedure  <cit> .
the completed model accounts for  <dig> genes,  <dig> reactions and  <dig> metabolites.
the model also predicted substrate specific phenotypes of m. acetivorans and captured unique bioenergetics exhibited by the organism across different conditions.
RESULTS
the metabolic model reconstruction workflow consists of four steps.
step  <dig> refers to the draft model generation using bidirectional blast  and database/literature searches.
step  <dig> involves model modifications to ensure biomass formation for growth under all known substrates.
step  <dig> applies growmatch  <cit>  to restore growth prediction inconsistencies and step  <dig> applies gapfind and gapfill  <cit>  to restore connectivity.
step 1: generating draft model
bbh searches for each of the  <dig> genes included in the iaf <dig> model were conducted against the latest genome sequence of methanosarcina acetivorans c2a strain  <cit> .
at this stage of the reconstruction process, we included only open reading frames  that have e-values  of at most 10- <dig>  this process yields an initial conservative model for m. acetivorans that has  <dig> genes.
based on the primary tigr annotation of m. acetivorans  <cit>  this accounts for  <dig> %  of all orfs in the m. acetivorans genome.
the mapping of the metabolic genotypes between these two very closely related organisms is surprisingly complex.
specifically,  <dig> one-to-one mappings,  <dig>  one-to-many mappings and  <dig>  many-to-many mappings  were generated.
the large number of one-to-many and many-to-many mappings is consistent with the incidence of a high number of gene duplicates in the m. acetivorans genome  and accounts for the additional  <dig> genes in ivs <dig> over iaf <dig>  <cit> .
we use multiple sources to annotate the remaining  <dig>  orfs in the genome.
specifically, we preferentially assigned metabolic annotation to seven genes based on the information available at an organism-specific annotation effort for m. acetivorans  <cit> ,  <dig> genes based on seed annotations  <cit>  and  <dig> genes based on tigr annotations.
interestingly out of these  <dig> genes as many as  <dig> code for isozymes.
predicted or hypothetical proteins account for the remaining  <dig>  orfs not included in the model after the annotation step.
approximately 44% of all genes in m. acetivorans  were present in the draft metabolic model.
the methanogenesis pathways in the m. acetivorans model were modified to account for known differences documented in the literature.
specifically, m. acetivorans and m. barkeri use different electron transport chains to generate atp when they grow on acetate.
the electron transport chain in m. barkeri consists of a pair of hydrogenases, ech and vho that couple hydrogen production/oxidation to proton translocation outside the membrane  <cit> .
in m. acetivorans, ech and vho are absent and instead it is hypothesized that an electron transfer complex rnf  oxidizes reduced ferredoxin to generate a sodium ion gradient which is then exchanged for a proton gradient by the multiple resistance/ph regulation na+/h+ antiporter   <cit> .
m. acetivorans grows on carbon monoxide as a substrate in the absence of hydrogen using both the electron transport chain  pathway) and substrate level phosphorylation  pathway).
alternatively, it has been proposed that m. barkeri grows on co only in the presence of hydrogen and oxidizes co to co <dig> and uses the resulting energy to produce hydrogen that is then reoxidized using the hydrogenases  to produce electrons needed to reduce co to methane  <cit> .
on c <dig> compounds such as methanol and methylamines, both organisms have a methylotrophic pathway that disproportionates the carbon to form carbon dioxide and methane  <cit> .
interestingly, one mole of substrate is oxidized to generate reducing equivalents required to produce three moles of methane.
in contrast with other archaeal models  <cit> , we delineated methyltransferase specificity  <cit>  for different substrates of m. acetivorans .
we also generated the gene-protein-reaction mappings for the m. acetivorans model using as a starting point the iaf <dig> model based on the autograph method developed by notebaard and coworkers  <cit> .
all exchange reactions and non-gene associated intracellular reactions available in the iaf <dig> model were also appended to the model, as we did not find any evidence to the contrary .
upon conclusion of step  <dig>  a draft model with  <dig> genes,  <dig> reactions and  <dig> metabolites was generated.
step 2: model correction to enable biomass formation
we determine the metabolic capabilities of the assembled draft model to grow on known methanogenic substrates by first specifying the biomass equation and then specifying the composition of the minimal medium.
the first requirement is addressed by assuming that the set of components that compose the biomass equation in m. acetivorans is identical to the one used in the iaf <dig> model.
the stoichiometric coefficients of the nucleotide components  were modified to reflect the difference in the g/c contents of the two organisms .
the utilization of the same biomass component set is supported by experimental data on the minimal medium .
the minimal growth medium contained six additional vitamins and trace elements  over the one used in iaf <dig>  <cit> .
we chose to exclude them from our model as no metabolic role for them was identified based on literature searches or gleaned by the model.
equipped with the biomass composition and the minimal medium, we tested the capability of the draft model to enable growth on the following known methanogenic substrates: carbon monoxide, acetate, methanol and monomethylamine, dimethylamine and trimethylamine  <cit> .
the draft model did not exhibit growth on any of these substrates motivating the use of gapfind  <cit>  to identify the biomass precursor metabolites that could not be produced using these substrates in a minimal medium.
gapfind revealed that the same precursor metabolite adenosylcobalamin-hbi could not be produced for all substrate choices in the draft model.
we used gapfill  <cit>  to restore flow through this metabolite.
this was achieved under all aforementioned substrate conditions by adding an exporter for the cofactor, tetrahydrosarcinapterin.
while the export of the cofactor could be an in silico response to an imbalance of cofactors and there is no evidence in the literature for the presence of a tetrahydrosarcinapterin exporter, it is possible that an enzyme outside the cell wall may utilize the cofactor as a substrate.
step 3: evaluating and improving model performance using growmatch
after ensuring in silico growth on a defined medium across different substrates, we further examined the model by testing for growth prediction agreement with experimental data across different genetic/environmental perturbations.
using literature searches, we assembled a dataset consisting of in vivo growth data for  <dig> different conditions .
as shown in table  <dig>  growth data was assembled for eighteen genetic perturbations for growth on methanol, thirteen on acetate as carbon substrates, nine on carbon monoxide as carbon and energy source, and  <dig> on methylamines as carbon substrates.
not surprisingly, most of these gene deletions are in methanogenesis pathways  indicative of the significant attention this pathway has received before.
in line with previous approaches  <cit>  the growth cutoff for classifying a mutant as a "growth" or a "no-growth" mutant was chosen to be 1/3rd of average growth across the dataset.
using this cutoff and the terminology introduced in the growmatch procedure  <cit>  we arrive at  <dig> gg  thirteen gng  and eight ngng  cases.
notably, the incidence of only gng model/experimental discrepancies indicates that the draft model tends to over-predict the metabolic capabilities of the organism when in error.
a closer examination reveals that in  <dig> out of  <dig> gg cases the deleted genes correspond to isozymes while six correspond to deletions of methyltransferases.
in all these cases both the model and the experiment agree that the deleted genes are non-essential.
of the nine gng cases that could be resolved, eight were resolved by conditionally suppressing one additional reaction and one was resolved by carrying out a single global suppression .
in the second case ), deleting atp synthase results in a gng mutant when the organism grows on methanol as the sole carbon and energy source  <cit> .
this deletion negates proton-coupled generation of energy via methanogenesis leaving substrate level generation of energy via acetogenesis.
growmatch suggests restoring consistency to this mutant by suppressing the sodium proton antiporter .
suppressing this reaction in this mutant metabolic network deactivates flux in the sodium-dependent reaction methyl-thspt:coenzyme m methyltransferase  which results in no flux in the acetogenesis pathway ).
step 4: network connectivity analysis and restoration
after evaluating and improving the model using in vivo gene deletion data, we used the automated procedures gapfind and gapfill  <cit>  to identify and rectify any remaining network connectivity inconsistencies.
using gapfind, we identify  <dig> metabolites  that cannot be produced for any choice of carbon substrate.
not surprisingly, none of the  <dig> no production metabolites were present in the methanogenesis pathway alluding to the completeness of its reconstruction.
interestingly, of the  <dig> metabolites present in the m. acetivorans model but absent in iaf <dig>   <dig> can be produced whereas  <dig> have blocked production routes.
notably, gapfind revealed that  <dig> out of these  <dig> metabolites could also not be produced in the iaf <dig> model of m. barkeri.
flow restoration to all  <dig> metabolites was attempted using gapfill by adding reactions from kegg  <cit> .
in this step, we restored consistency to only two of the  <dig> no production metabolites.
flow through these metabolites was restored by treating two existing reactions alamin-hbi adenosyltransferase and hydroxyethylthiazole kinase) as reversible.
model characteristics for ivs941
we compared flux values through the methane forming reaction catalyzed by methyl coenzyme reductase and the biomass equation to ascertain the extent of coupling between flux in the methanogenesis pathway and in silico growth rates.
we identified the range of methane production flux by maximizing and minimizing flux through the mcr reaction for different values of biomass formation.
conversely, we identified the range of biomass production for different values of required methane production.
figure  <dig> shows these plots for growth on methanol, acetate and carbon monoxide.
as shown in figure  <dig> and  <dig>  a positive biomass flux implies a non-zero mcr flux for growth in methanol and acetate but not the reverse.
using the terminology introduced in  <cit> , this implies that the flux in biomass reaction is directionally coupled to the flux in mcr.
this is consistent with the indispensability of the methanogenic branch when m. acetivorans grows on acetate and methanol  <cit> .
moreover, the maximum biomass formation is reached at when the flux through mcr is fixed at 74% of its maximum value for growth on methanol and 86% for growth on methanol.
at maximum biomass production, the ratio of biomass to methane production is  <dig>  gdw/mmol and  <dig>  gdw/mmol for growth on methanol and acetate, respectively.
this higher biomass yield is qualitatively consistent with the higher energetic yield per mole of methanol observed for m. acetivorans  <cit> .
CONCLUSIONS
metabolic reconstruction technology has been used extensively to document the metabolic repertoire of organisms in the bacteria and the eukarya domains  <cit> .
here, we take advantage of the increased availability of experimental datasets and genomic information for archaeal organisms to build the metabolic model, called ivs <dig>  of the archaeon with the largest known genome, methanosarcina acetivorans.
the ivs <dig> model is constructed using a systematic procedure that enables sequential evaluation and improvement of model capabilities.
the model consists of  <dig> reactions,  <dig> metabolites and  <dig> genes; the latter accounting for about 44% of all orfs in m. acetivorans with putative annotations  <cit> .
the completed model has metabolites  that can be produced and it has a high agreement of  <dig> % against published in vivo growth data across environmental and genetic perturbations  with specificity of 81%  and selectivity of  <dig> % .
additionally, the model recapitulates substrate-specific energetic characteristics such as atp synthase indispensability for growth on acetate/methanol and its dispensability for growth on carbon monoxide.
the number of reactions included in the draft model under step  <dig> is quite sensitive to the adopted blast cutoff.
the number of gene entries increases to  <dig>  when the cutoff is relaxed to 10- <dig> from the  <dig> entries for the adopted cutoff of 10- <dig>  this more stringent cutoff was chosen to ensure that the draft model did not contain any falsely added functionalities.
we have found that it is much easier to find and add missing functionalities than correctly identifying and removing erroneous ones.
interestingly, all but one reaction in the methanogenesis pathway known to occur in m. acetivorans were included in the draft model using the most stringent cutoff.
reaction ech hydrogenase which is known to occur in m. barkeri but not in m. acetivorans was excluded from the draft model.
this constructed ivs <dig> model represents the most comprehensive up-to-date effort to catalogue methanogenic metabolism.
given the attention methanogenic consortia have received and the growing amount of metagenomic data  <cit> , this model can be used to assess the biological impact on carbon balance of methanogenic communities.
this organism-specific compilation of the metabolic repertoire of m. acetivorans can serve as the framework for fusing additional experimental data on methanogens as they become available.
the model is available in sbml format to enable dissemination .

 genome-scale metabolic models are increasingly becoming available for an expanding range of organisms. there exists at least forty completed bacterial and thirteen eukaryotic metabolic reconstructions with many more under development. in the past decade, several studies have demonstrated a variety of uses ranging from strain optimization pathogen drug target identification, bacterial community metabolic interactions and identification of human disease biomarkers. in contrast to the extensive interest devoted towards bacterial and eukaryotic metabolism reconstruction, efforts to construct archaeal metabolic models have been noticeably limited. this is partly due to the current relative paucity of -omics datasets available for species from the archaea domain. this dearth of data, however, is likely to change in the near future as recent interest in methanogenic archaea has led to several sequencing efforts, as well as transcriptomic and proteomic analyses. furthermore, it is increasingly becoming apparent that archaeal metabolism has significant implications to the earth's climate thus motivating the need to globally assess their true metabolic capabilities by reconstructing high quality metabolic models. methanogens constitute the largest group of the archaea domain of life. methanogens are terminal organisms in anaerobic microbial food chains that break down organic matter to methane in diverse anaerobic environments in a process that helps regulate the global carbon flux. the process plays a surprisingly significant role in global warming accounting for about one billion tons of the annual methane production. recently, cheng and coworkers used a consortia of methanogens to convert electricity into methane thereby paving the way for converting electric current generated by renewable energy sources into renewable biofuels. on the evolutionary front, methanogens are amongst the most ancient form of life on earth and their role as the progenitors of the first eukaryotic cell has been suggested under two separate hypotheses. in addition, unique biochemical properties such as broad substrate specificity, participation of novel coenzymes in the methanogenesis pathways and the presence of unique lipids in their cell wall set these organisms apart from the bacterial and eukaryotic branches of life. therefore, the reconstruction of archaeal methanogen metabolic models could help paint a more complete picture of life's metabolic processes. feist and coworkers first developed a genome-scale model for the fresh-water methanogen, methanosarcina barkeri using a draft version of its genome. in this paper, we reconstruct a genome-scale metabolic model for the marine methanogen, methanosarcina acetivorans. m. acetivorans is an aceticlastic methanogen that was first isolated from marine microbial communities that degrade kelp into methane. at over million base pairs, it has the largest reported genome of all sequenced archaea alluding to an expanded metabolic repertoire. this repertoire includes unique methanogenic pathways, broader substrate specificity than other methanogens and a large number of duplicate genes. recent studies have shown that m. acetivorans uniquely exhibits both methanogenic and acetotrophic growth on carbon monoxide. all these unique metabolic characteristics and planet-wide carbon balance impact provide the motivation to globally assess its metabolic capabilities. draft metabolic reconstructions generated using homology-based comparisons unavoidably contain some omissions and misclassifications. these errors are manifested either as unreachable metabolites or as in silico predictions that are in contrast with observed in vivo behavior. in response to these challenges, suthers et al., proposed a computational workflow to generate and curate the metabolic models and applied it to the metabolic reconstruction of mycoplasma genitalium. the proposed workflow makes use of two separate model correction procedures. gapfind and gapfill identify and subsequently restore connectivity to unreachable metabolites and growmatch that reconciles in silico growth predictions with in vivo growth data. in this paper, we streamline this workflow for the generation of an archaeal metabolic model and customize it to the available types of data. we first generated a draft reconstruction of m. acetivorans using homology comparisons with the published model of the fresh-water methanogen, m. barkeri. we then deployed a modified version of the workflow presented in suthers et al., by combining the gapfind/gapfill and growmatch steps of the procedure. the completed model accounts for genes, reactions and metabolites. the model also predicted substrate specific phenotypes of m. acetivorans and captured unique bioenergetics exhibited by the organism across different conditions.  the metabolic model reconstruction workflow consists of four steps. step refers to the draft model generation using bidirectional blast and database/literature searches. step involves model modifications to ensure biomass formation for growth under all known substrates. step applies growmatch to restore growth prediction inconsistencies and step applies gapfind and gapfill to restore connectivity. step 1: generating draft model bbh searches for each of the genes included in the iaf model were conducted against the latest genome sequence of methanosarcina acetivorans c2a strain. at this stage of the reconstruction process, we included only open reading frames that have e-values of at most 10- this process yields an initial conservative model for m. acetivorans that has genes. based on the primary tigr annotation of m. acetivorans this accounts for % of all orfs in the m. acetivorans genome. the mapping of the metabolic genotypes between these two very closely related organisms is surprisingly complex. specifically, one-to-one mappings, one-to-many mappings and many-to-many mappings were generated. the large number of one-to-many and many-to-many mappings is consistent with the incidence of a high number of gene duplicates in the m. acetivorans genome and accounts for the additional genes in ivs over iaf. we use multiple sources to annotate the remaining orfs in the genome. specifically, we preferentially assigned metabolic annotation to seven genes based on the information available at an organism-specific annotation effort for m. acetivorans, genes based on seed annotations and genes based on tigr annotations. interestingly out of these genes as many as code for isozymes. predicted or hypothetical proteins account for the remaining orfs not included in the model after the annotation step. approximately 44% of all genes in m. acetivorans were present in the draft metabolic model. the methanogenesis pathways in the m. acetivorans model were modified to account for known differences documented in the literature. specifically, m. acetivorans and m. barkeri use different electron transport chains to generate atp when they grow on acetate. the electron transport chain in m. barkeri consists of a pair of hydrogenases, ech and vho that couple hydrogen production/oxidation to proton translocation outside the membrane. in m. acetivorans, ech and vho are absent and instead it is hypothesized that an electron transfer complex rnf oxidizes reduced ferredoxin to generate a sodium ion gradient which is then exchanged for a proton gradient by the multiple resistance/ph regulation na+/h+ antiporter. m. acetivorans grows on carbon monoxide as a substrate in the absence of hydrogen using both the electron transport chain pathway) and substrate level phosphorylation pathway). alternatively, it has been proposed that m. barkeri grows on co only in the presence of hydrogen and oxidizes co to co and uses the resulting energy to produce hydrogen that is then reoxidized using the hydrogenases to produce electrons needed to reduce co to methane. on c compounds such as methanol and methylamines, both organisms have a methylotrophic pathway that disproportionates the carbon to form carbon dioxide and methane. interestingly, one mole of substrate is oxidized to generate reducing equivalents required to produce three moles of methane. in contrast with other archaeal models, we delineated methyltransferase specificity for different substrates of m. acetivorans. we also generated the gene-protein-reaction mappings for the m. acetivorans model using as a starting point the iaf model based on the autograph method developed by notebaard and coworkers. all exchange reactions and non-gene associated intracellular reactions available in the iaf model were also appended to the model, as we did not find any evidence to the contrary. upon conclusion of step a draft model with genes, reactions and metabolites was generated. step 2: model correction to enable biomass formation we determine the metabolic capabilities of the assembled draft model to grow on known methanogenic substrates by first specifying the biomass equation and then specifying the composition of the minimal medium. the first requirement is addressed by assuming that the set of components that compose the biomass equation in m. acetivorans is identical to the one used in the iaf model. the stoichiometric coefficients of the nucleotide components were modified to reflect the difference in the g/c contents of the two organisms. the utilization of the same biomass component set is supported by experimental data on the minimal medium. the minimal growth medium contained six additional vitamins and trace elements over the one used in iaf. we chose to exclude them from our model as no metabolic role for them was identified based on literature searches or gleaned by the model. equipped with the biomass composition and the minimal medium, we tested the capability of the draft model to enable growth on the following known methanogenic substrates: carbon monoxide, acetate, methanol and monomethylamine, dimethylamine and trimethylamine. the draft model did not exhibit growth on any of these substrates motivating the use of gapfind to identify the biomass precursor metabolites that could not be produced using these substrates in a minimal medium. gapfind revealed that the same precursor metabolite adenosylcobalamin-hbi could not be produced for all substrate choices in the draft model. we used gapfill to restore flow through this metabolite. this was achieved under all aforementioned substrate conditions by adding an exporter for the cofactor, tetrahydrosarcinapterin. while the export of the cofactor could be an in silico response to an imbalance of cofactors and there is no evidence in the literature for the presence of a tetrahydrosarcinapterin exporter, it is possible that an enzyme outside the cell wall may utilize the cofactor as a substrate. step 3: evaluating and improving model performance using growmatch after ensuring in silico growth on a defined medium across different substrates, we further examined the model by testing for growth prediction agreement with experimental data across different genetic/environmental perturbations. using literature searches, we assembled a dataset consisting of in vivo growth data for different conditions. as shown in table growth data was assembled for eighteen genetic perturbations for growth on methanol, thirteen on acetate as carbon substrates, nine on carbon monoxide as carbon and energy source, and on methylamines as carbon substrates. not surprisingly, most of these gene deletions are in methanogenesis pathways indicative of the significant attention this pathway has received before. in line with previous approaches the growth cutoff for classifying a mutant as a "growth" or a "no-growth" mutant was chosen to be 1/3rd of average growth across the dataset. using this cutoff and the terminology introduced in the growmatch procedure we arrive at gg thirteen gng and eight ngng cases. notably, the incidence of only gng model/experimental discrepancies indicates that the draft model tends to over-predict the metabolic capabilities of the organism when in error. a closer examination reveals that in out of gg cases the deleted genes correspond to isozymes while six correspond to deletions of methyltransferases. in all these cases both the model and the experiment agree that the deleted genes are non-essential. of the nine gng cases that could be resolved, eight were resolved by conditionally suppressing one additional reaction and one was resolved by carrying out a single global suppression. in the second case ), deleting atp synthase results in a gng mutant when the organism grows on methanol as the sole carbon and energy source. this deletion negates proton-coupled generation of energy via methanogenesis leaving substrate level generation of energy via acetogenesis. growmatch suggests restoring consistency to this mutant by suppressing the sodium proton antiporter. suppressing this reaction in this mutant metabolic network deactivates flux in the sodium-dependent reaction methyl-thspt:coenzyme m methyltransferase which results in no flux in the acetogenesis pathway ). step 4: network connectivity analysis and restoration after evaluating and improving the model using in vivo gene deletion data, we used the automated procedures gapfind and gapfill to identify and rectify any remaining network connectivity inconsistencies. using gapfind, we identify metabolites that cannot be produced for any choice of carbon substrate. not surprisingly, none of the no production metabolites were present in the methanogenesis pathway alluding to the completeness of its reconstruction. interestingly, of the metabolites present in the m. acetivorans model but absent in iaf can be produced whereas have blocked production routes. notably, gapfind revealed that out of these metabolites could also not be produced in the iaf model of m. barkeri. flow restoration to all metabolites was attempted using gapfill by adding reactions from kegg. in this step, we restored consistency to only two of the no production metabolites. flow through these metabolites was restored by treating two existing reactions alamin-hbi adenosyltransferase and hydroxyethylthiazole kinase) as reversible. model characteristics for ivs941 we compared flux values through the methane forming reaction catalyzed by methyl coenzyme reductase and the biomass equation to ascertain the extent of coupling between flux in the methanogenesis pathway and in silico growth rates. we identified the range of methane production flux by maximizing and minimizing flux through the mcr reaction for different values of biomass formation. conversely, we identified the range of biomass production for different values of required methane production. figure shows these plots for growth on methanol, acetate and carbon monoxide. as shown in figure and a positive biomass flux implies a non-zero mcr flux for growth in methanol and acetate but not the reverse. using the terminology introduced in, this implies that the flux in biomass reaction is directionally coupled to the flux in mcr. this is consistent with the indispensability of the methanogenic branch when m. acetivorans grows on acetate and methanol. moreover, the maximum biomass formation is reached at when the flux through mcr is fixed at 74% of its maximum value for growth on methanol and 86% for growth on methanol. at maximum biomass production, the ratio of biomass to methane production is gdw/mmol and gdw/mmol for growth on methanol and acetate, respectively. this higher biomass yield is qualitatively consistent with the higher energetic yield per mole of methanol observed for m. acetivorans.  metabolic reconstruction technology has been used extensively to document the metabolic repertoire of organisms in the bacteria and the eukarya domains. here, we take advantage of the increased availability of experimental datasets and genomic information for archaeal organisms to build the metabolic model, called ivs of the archaeon with the largest known genome, methanosarcina acetivorans. the ivs model is constructed using a systematic procedure that enables sequential evaluation and improvement of model capabilities. the model consists of reactions, metabolites and genes; the latter accounting for about 44% of all orfs in m. acetivorans with putative annotations. the completed model has metabolites that can be produced and it has a high agreement of % against published in vivo growth data across environmental and genetic perturbations with specificity of 81% and selectivity of %. additionally, the model recapitulates substrate-specific energetic characteristics such as atp synthase indispensability for growth on acetate/methanol and its dispensability for growth on carbon monoxide. the number of reactions included in the draft model under step is quite sensitive to the adopted blast cutoff. the number of gene entries increases to when the cutoff is relaxed to 10- from the entries for the adopted cutoff of 10- this more stringent cutoff was chosen to ensure that the draft model did not contain any falsely added functionalities. we have found that it is much easier to find and add missing functionalities than correctly identifying and removing erroneous ones. interestingly, all but one reaction in the methanogenesis pathway known to occur in m. acetivorans were included in the draft model using the most stringent cutoff. reaction ech hydrogenase which is known to occur in m. barkeri but not in m. acetivorans was excluded from the draft model. this constructed ivs model represents the most comprehensive up-to-date effort to catalogue methanogenic metabolism. given the attention methanogenic consortia have received and the growing amount of metagenomic data, this model can be used to assess the biological impact on carbon balance of methanogenic communities. this organism-specific compilation of the metabolic repertoire of m. acetivorans can serve as the framework for fusing additional experimental data on methanogens as they become available. the model is available in sbml format to enable dissemination. 
based on the size of the genome-scale metabolic reconstruction and extent of validated predictions this model represents the most comprehensive up-to-date effort to catalogue methanogenic metabolism. by following a systematic workflow we reconstruct a genome-scale metabolic model for m. acetivorans. the model also correctly recapitulates metabolic pathway usage patterns of m. acetivorans such as the indispensability of flux through methanogenesis for growth on acetate and methanol and the unique biochemical characteristics under growth on carbon monoxide. methanosarcina acetivorans, with a genome size of ~ mb, is the largest sequenced archaeon methanogen and unique amongst the methanogens in its biochemical characteristics. the model achieves % prediction agreement with in vivo growth data across different substrates and multiple gene deletions. the reconstructed model is available in spreadsheet and sbml formats to enable dissemination.
 methanogens are ancient organisms that are key players in the carbon cycle accounting for about one billion tones of biological methane produced annually. methanosarcina acetivorans, with a genome size of ~ mb, is the largest sequenced archaeon methanogen and unique amongst the methanogens in its biochemical characteristics. by following a systematic workflow we reconstruct a genome-scale metabolic model for m. acetivorans. this process relies on previously developed computational tools developed in our group to correct growth prediction inconsistencies with in vivo data sets and rectify topological inconsistencies in the model.  the generated model ivs accounts for genes, reactions and metabolites. the model achieves % prediction agreement with in vivo growth data across different substrates and multiple gene deletions. the model also correctly recapitulates metabolic pathway usage patterns of m. acetivorans such as the indispensability of flux through methanogenesis for growth on acetate and methanol and the unique biochemical characteristics under growth on carbon monoxide.  based on the size of the genome-scale metabolic reconstruction and extent of validated predictions this model represents the most comprehensive up-to-date effort to catalogue methanogenic metabolism. the reconstructed model is available in spreadsheet and sbml formats to enable dissemination. 