Recent technological advances in accurate mass spectrometry and data analysis have revolutionizedmetabolomics experimentation. Activity-based and global metabolomic profiling methods allowsimultaneous and rapid screening of hundreds of metabolites from a variety of chemical classes,making them useful tools for the discovery of novel enzymatic activities and metabolic pathways. Byusing the metabolome of the relevant organism or close species, these methods capitalize onbiological relevance, avoiding the assignment of artificial and non-physiological functions. Thisreview discusses state-of-the-art metabolomic approaches and highlights recent examples of their usefor enzyme annotation, discovery of new metabolic pathways, and gene assignment of orphan metabolicactivities across diverse biological sources. The functional annotation of the wealth of genetic information generated during the genomic erais currently considered one of the grand challenges in molecular biology. Although bioinformaticsand modelling have contributed considerably to the functional assignment of proteins, significantportions of sequenced genomes  in an unbiased manner usingcovalent active-site-directed probes A significant proportion of unannotated or misannotated genes encode enzymes, the catalyticactivity of which is usually fundamental to their physiological function. Techniques that candirectly exploit or monitor the native activity of a candidate enzyme are therefore powerful toolsin accurate functional assignment of unannotated gene products: several approaches that havehistorically been used to annotate enzyme function, as well as newly developed techniques, aredescribed in Metabolites constitute the substrates and products of enzymatic reactions, and the study of thetotal metabolite pool of a given organism or cell type is known as metabolomics. As metabolitesrepresent the final outcome of gene expression and activity, the metabolome can be perceived as theultimate readout of the biochemical and physiological state of a cell, that is, a direct linkbetween mechanistic biochemistry and cellular phenotype The diversity and quantity of metabolites that comprise a metabolome varies depending on theorganism, and can range from several hundred (bacteria) to several thousand  uniquelow-molecular-weight (< 1000 Da) chemical entities Escherichia coli. Consequently, the discovery of novel metabolic pathways is anongoing effort A significant impediment to large-scale assignation of enzymatic function is our incompleteunderstanding of primary and secondary metabolism, even among well-studied organisms such as13C and15N) is an essential component of most studies utilizing discovery metabolomictechniques. Two types of information can be uniquely gathered from well-designed SIL experiments:(a) metabolic fate, and (b) metabolic pace.Stable isotope labelling or SIL into succinate, there are at least two known potential metabolic pathways in which this enzyme couldserve . The firMetabolic pace refers to the flux (rate) of biochemical reactions and pathways in living cells. Time-coet al recently used ametabolomics approach to delineate several novel isoflavonoid and phenylpropanoid pathways in theleguminous plant species Medicago truncatula after stimulation with methyljasmonate and yeast elicitor, two agents known to influence plant secondary metabolite synthesisOur current understanding of central metabolism has been influenced by studies performed in asmall number of model organisms under a limited set of test conditions, suggesting thatconditionally induced pathways are poorly represented in metabolic charts. Varying growthconditions, or applying abiotic or biotic stimuli to cultures of interest, are therefore usefultactics for the elucidation of cryptic pathways. Farag Geobacter metallireducensR-)stereoisomer of citrate in the Krebs cycle of Desulfovibrio vulgarisMethylobacterium extorquensSimilarly, new pathways and functions are likely to be found in non-model organisms, particularlythose inhabiting unique physical environments—such as thermophiles and halophiles.Importantly, non-canonical metabolic processes that occur in these organisms, or those able tooperate under non-mesophilic conditions, are often highly sought after as biotechnological orindustrial tools. Stable-isotope probing combined with metabolomics and fluxomics was recently usedto uncover an unusual isoleucine biosynthesis pathway in E.coli glycolysis and the tricarboxylic acid (TCA) cycle are essential for growth understandard laboratory conditions etal recently analysed the biochemical mechanisms of pathway compensation in E.coli strains deficient in primary enzymes of central carbon metabolism pfkA). Thecombination of genetic tools and metabolomics has also been used to identify a previouslyunrecognized uridine monophosphate (UMP) degradation pathway in E. coli strainsthat are deficient in a negative feedback regulator of pyrimidine homeostasis Alternatively, molecular genetics can be used in combination with metabolomics as an effectivehypothesis-generating tool to discover new metabolic functions: the disruption of a pathway ofinterest might lead to the upregulation of alternative pathways to compensate for the lostfunction(s). For example, despite the essentiality of central carbon metabolism in all livingsystems, studies have shown that just 4 of the more than 70 enzymes involved in Arabidopsis,respectively The understanding of novel metabolic activities and pathways is incomplete without theidentification of the genes responsible. As already mentioned, untargeted investigations of newbiochemical pathways can rarely associate metabolic reactions with the enzymes that catalyse themwithout subsequent genetic intervention or recombinant protein studies. One method that can overcomethis is co-expression analysis. This transcriptomics-based method allows the identification of genesinvolved in a defined metabolic pathway based on their co-expression with genes of known function,through the assumption that genes involved in the same biochemical pathway are co-regulated In vitro studies with purified recombinant protein often provide the mostdefinitive proof of a bona fide activity for a specific enzyme, as consumption ofsubstrate and generation of product can be monitored directly in an isolated system, free ofcontaminating species. The disadvantages of this approach are that putative substrates need to betested individually  and are often not commercially available, or, if so,prohibitively expensive. A possible solution is to engage the metabolome of the parent organism as asubstrate library substitute. The metabolome represents a chemically diverse and rich mixture ofphysiologically relevant compounds, including those that are unknown or not commercially available,and is easily and cheaply obtained in large quantities (depending on the culturability of the parentorganism). After the incubation of a recombinant enzyme of interest with a cellular metabolomeextract (including any required co-factors or co-substrates), mass changes can be monitored by massspectrometry to identify putative native substrates, which decrease in abundance, and products,which increase in abundance -MS on mixtures of recombinant enzyme and E. colismall molecular extract to identify the phosphatase and phosphotransferase activities of twoE. coli uncharacterized enzymes, YbhA and YbiV ance Fig . Furtheret al have extensively characterized the substrate specificity of hP450 enzymes,with successful application of metabolome-based in vitro assays in many cases invitro reactions to facilitate the positive identification of substrates and products afteranalysis by MS 18O and 16O-labelled oxygen gas, causing theresulting enzymatic product to emit a 1:1 ratio of native (M) and isotopically labelled (M +2) m/zs on a mass spectrum plot. Although this approach is applicable to enzymesthat have other common co-substrates, such as ATP, NAD(P)H, or SAM , the prerequisite of knowing the identity of the co-substrate limits itsapplication to putative enzymes of known functional class.The human cytochrome P450 monooxygenase (hP450) family of enzymes comprise more than 50 membersand have significant roles in the normal physiological metabolism of a variety of lipids, sterols,vitamins and xenobiotics in vitro metabolomic methods are particularly useful for characterizingenzyme function. For example, although genetic tools are relatively well developed for the humanpathogen Mycobacterium tuberculosis, the slow growth rate (~20 h doubling time) ofthis organism makes gene knockout protocols cumbersome. Consequently, we have used invitro metabolomic methods to identify the functions of two uncharacterized M.tuberculosis gene products. One is a 2-hydroxy-3-oxoadipate synthase that was previouslyannotated as an oxoglutarate decarboxylase component of the Krebs cycle oxoglutarate dehydrogenasecomplex Genetic techniques are powerful tools in functional genomics, allowing target gene disruption andsubsequent assessment of the resulting phenotype or metabolic status to infer important informationon enzymatic function. However, genetic methods are not always applicable, such as when thecandidate gene is essential for growth or the host organism is not genetically tractable. In thesecases, in vitro technologies are generally insufficient, in isolation, to attain anaccurate and detailed picture of the physiological function of a candidate gene. Instead,in or ex vivo approaches are becoming increasingly popular asprimary or complementary screens in enzyme function or pathway discovery, with metabolomics at theforefront of these advances. As already mentioned, ex vivo metabolomics has longbeen a major tool in the delineation of cellular metabolic circuitry and is still a key experimentalplatform in pathway and metabolite discovery ex vivo metabolomics to conditional knock-down, stable geneticmutants, over-expression strains or to chemical knock-downs of a candidate gene product allows amore precise and specific assessment of the function of a gene in cellular metabolism and polyketide synthase (PKS) pathways, the products of which are of substantial biomedical interestdue to their pharmacological properties and roles in virulence. In these cases, bioinformatics canreliably predict gene clusters likely to code for NRPS or PKS operons, and subsequent metabolomicsof individual gene mutants provides the necessary means of both identifying the final metabolite andassigning each of the chemical steps involved in its biosynthesis to individual gene products etal recently used genetics and metabolomics to identify the catalytic roles of each of theeight gene products responsible for the production of a new siderophore and virulence factor,hexahydroastechrome, in the filamentous fungus Aspergillus fumigatusex vivo metabolomics need not require genetic disruptionof the gene of interest. This is particularly useful in cases where gene function is essential forcell survival, the host organism does not have readily available genetic tools, or where geneticinactivation of the candidate gene does not cause significant metabolomic changes due to inherentgenetic robustness of the test organism et al recently used a mechanism-based inhibitor to inactivate andinterrogate the physiological function of an integral membrane hydrolase (KIAA1363) that had beenshown to be upregulated in certain aggressive cancers in situ and subsequent metabolomics has been used by Cravatt etal to investigate the function of a series of unannotated human serine hydrolase familyproteins In addition to the canonical approach described above, several groups have demonstrated that genefunctional assessment by The experimental workflows and examples discussed above implemented a ‘reversegenetics’ approach, whereby the coding gene of interest is initially chosen by bioinformaticmeans and the cognate function subsequently sought with activity-based metabolomics. Although thisis a logical process for assigning function to unannotated genes, the reverse process, that is,assigning gene sequences to predetermined orphan metabolic activities or pathways, is largelyexcluded.et al overcame this problem by initially selectingfor mutants with a known phenotype that was predicted to correlate with the metabolic pathway ofinterest, in their case impaired starch metabolism in Arabidopsis thaliana.Subsequent metabolomics was then used to compare, by principal components analysis, mutants of knownstarch metabolizing genes with the experimentally derived mutants and subsequently to discriminatebetween strains with a bona fide deficiency in starch metabolism and thosecontaining mutations in alternative pathways that coincidentally had pleiotropic effects on starchmetabolism Orphan metabolic activities are an alternative and growing problem within functional genomics, aconsequence of many decades of pre-genomics enzymology research, as well as the recent and rapidrise in untargeted metabolomics data accumulation. For example, recent studies found that30–40% of all enzymatic activities currently assigned an enzyme commission (EC) numberare not associated with a defined genetic locus How can we best prioritize large-scale metabolomic-based functional genomics efforts?Which organisms have a large number of orphan enzymes and are therefore good models for thediscovery of new enzymatic functions and new metabolic pathways?Which are the most complementary methods to confirm metabolomic results?Which enzyme classes present the larger potential for new catalytic functions?et al recently used a technique termed ‘metabolic footprinting’to substantially enhance the throughput of metabolomic analysis of a library of randomly mutagenizedbacterial strains of interest, for the identification of genes involved in the metabolism of testcompounds E.coli and Shewanella oneidensis  were grown inmedia containing compounds of interest, and a shortened LC-MS protocol (2 min per sample) was thenused to analyse the supernatant from spent cultures. This streamlined methodology was possible dueto the substantially reduced quantity and diversity of metabolites present in the supernatantrelative to cell lysates. Strains that had reduced metabolism (higher remaining levels) of the testcompound of interest were analysed further to confirm the role of the target gene in the predictedmetabolic pathway(s). This approach allowed the authors to assign ergothioneine histidase activityto the S. oneidensis gene SO3057.Defined phenotypic traits are not always associated with the presence or absence of metabolicactivities of interest, nor are the global metabolic pathways to which they belong commonly known.Baran in vitro and ex vivo metabolomicexperiments is the loss of information regarding the subcellular and cellular localization of themetabolites/pathways under scrutiny. For example, compartmentalized metabolite pools  and/or cellular heterogeneity among groups of cells ina tissue cannot be differentiated in a typical metabolomic experiment. In practical terms, thechanges in abundance of putative substrates and products are diminished due to dilution ofnon-reacting pools of other cellular or tissue compartments. Imaging mass spectrometry (IMS) is apowerful technique that can complement traditional metabolomic experiments and provide informationabout spatial distribution, in addition to mass and abundance data  imaging and secondary ion mass spectroscopy (SIMS) imaging. MALDI imagingcurrently has a spatial resolution of 5–50 μm, allowing analysis of single cellswithin tissue samples, for example. A lateral resolution under 50 nm can be achieved using SIMS, andup to five different ions can be detected simultaneously, making SIMS-based ‘ionmicroscopy’ a unique technique to follow the fate and organization of metabolic pathwaysinside and outside cells. However, with the available technology, only mono- or diatomic ions can bedetected, in contrast to entire molecular ion detection obtained with MALDI imaging. Directmonitoring of new enzymatic activities with subcellular resolution is a goal that is yet to beachieved, but holds the promise to improve our understanding of cellular physiology and enzymefunction at their correct compartment within cells.An intrinsic disadvantage of in vitro or ex vivo. The future ofmetabolomics as a tool in enzyme functional assessment is inextricably linked to the future of MS asan analytical platform. As the technology improves, its reliability and accuracy as a functionalgenomics tool will follow suit. Similarly, methodological improvements in how metabolomicsexperiments are executed are constantly being sought, including increases in the breadth ofmetabolic diversity detected with a single analytical method Enzymes control metabolism and are responsible for the formation of many of the most elaborateand interesting chemical structures in the natural world. Tools that enable rapid and unbiasedfunctional assignment of unannotated enzymes are therefore valuable commodities in almost everyfield of natural science research. Metabolomics has recently emerged as a leading technology infunctional genomics, where in combination with modern genetic tools and recombinant proteintechniques, it has enabled a unique perspective on the identification of the mechanistic propertiesof test enzymes either


E. coli and Synechocystis sp. andvalidated via existing networks for these species. Then, it is applied to reconstructgenome-scale metabolic network models for 36 sequenced cyanobacteria amenable forconstraint-based modeling analysis and specifically for metabolic engineering. Thereconstructed network models are supplied via standard SBML files.Genome-scale metabolic network reconstructions are considered a key step inquantifying the genotype-phenotype relationship. We present a novel gap-fillingapproach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifiesmissing network reactions by integrating metabolic flux analysis and functionalgenomics data. MIRAGE's performance is demonstrated on the reconstruction ofmetabolic network models of  Genome-scale metabolic network reconstructions are considered a key step in quantifyingthe genotype-phenotype relationship . While tThe two major computational challenges in metabolic network reconstruction are (i) theidentification of missing reactions in a metabolic network, and (ii) the association ofgenes with network reactions. The identification of missing reactions, referred to as'gap-filling', is commonly performed based on a pre-defined metabolic capability thatthe network is expected to be able to fulfill. For example, the capability to generateessential biomass products under various genetic and environmental conditions -25, the Escherichia coli and the cyanobacteria Synechocystis sp. PCC 6803,validated via existing networks for these species. Then, it is applied to reconstructgenome-scale metabolic network models for 36 sequenced cyanobacteria (supplied viastandard Systems Biology Markup Language (SBML) files [44]), amenablefor constraint-based modeling analysis and specifically for metabolic engineering. Todemonstrate the utility of the reconstructed cyanobacteria networks, a strain designmethod was applied to predict gene knockouts whose implementation is expected tosignificantly elevate the production rate of an important nutritional product,astaxanthin.Here, we present a novel approach, MetabolIc Reconstruction via functionAl GEnomics(MIRAGE), for reconstructing metabolic network models and specifically addressing theproblem of gap-filling, by searching for missing reactions whose presence is supportedby various functional genomic data. Specifically, to reconstruct a metabolic networkmodel for an organism of interest, MIRAGE starts from a core set of reactions, whosepresence is established via strong genomic evidence, and identifies missing reactionsthat are required to activate the latter core reactions (in addition to biomassrequirement) by identifying additional reactions, whose presence is further supported byphylogenetic profiles and gene expression data. The performance of MIRAGE, in comparisonto previous methods, is demonstrated on the reconstruction of network models forMIRAGE is a functional genomics-based model reconstruction approach that aims togenerate a genome-scale metabolic network model for an organism of interest, given acore set of reactions that are known to exist in its network, and optionally, adefinition of a biomass reaction. The core set of reactions can be automaticallyderived strictly from genomic data, based on strong sequence similarity with knownenzyme-coding genes in other species. The method then aims to find missing reactions), supported by functional genomics data,whose addition to the network would lead to a functional model. The method follows atwo-step procedure, starting with the utilization of functional genomics data toestimate the probability of including each reaction from the universal database inthe reconstructed network, and then, metabolic flux analysis that selects the mostlikely set of reactions whose addition to the network would satisfy the abovedescribed objectives.For the first step, we utilize two functional-genomics data sources to estimate thelikelihood that a reaction from a universal reactions database should be included inthe target metabolic network: (i) enzymes' phylogenetic profiles, and (ii) geneexpression. Specifically, we define a weight for each reaction in the universaldatabase ,based on the functional similarity between neighboring enzymes, in terms ofresemblance of phylogenetic profiles, and correlation in gene expression of theenzyme-coding genes .Enzyme phylogenetic profiles were extracted from KEGG, representing a pattern ofenzyme presence or absence across an available collection of species. For eachreaction in KEGG, we computed a phylogenetic weight, representing the likelihood forits inclusion in the network reconstruction. Specifically, the phylogenetic weight ofa certain reaction is calculated based on the maximal Jaccard coefficient between itsphylogenetic profile and the corresponding profiles of its neighboring core reactionsin the network . Similarly, an expression weight for eachreaction was calculated by evaluating gene expression profiles (measured in thetarget organism) of potential enzyme-coding genes , compared with the expression profiles of known genes associatedwith neighboring core reactions. The sum of the phylogenetic and expression weightsafter proper normalization was used as input for the second reconstruction step.de novo synthesis of all metabolites that exist inthe network) [The second reconstruction step aims to find a set of high weight gap-fillingreactions that satisfy the objectives described above. Towards this goal, we employedthe following reaction pruning procedure. Starting from a metabolic network modelconsisting of all reactions in the universal reaction database, we iteratively removepotential gap-filling reactions, as long as the removal does not affect theconsistency of the model. In each iteration, the probability of choosing a certainreaction for removal is inversely proportional to its weight - that is, low weightreactions have a higher probability to be chosen first for removal. The modelconsistency check procedure involves the usage of constraint-based modeling to verifythat the remaining network (i) enables each core reaction to carry non-zero metabolicflux within a stoichiometrically balanced flux distribution, accounting for reactiondirectionality constraints, (ii) enables the production of all essential biomassconstituents, and (iii) accounts for the growth-associated dilution of all networkmetabolites  . Since tet al. [Notably, the presented method extends upon the Model Building Algorithm (MBA) ofJerby et al.  that waset al. [et al. resulted from the markedly large size andcomplexity of the universal reaction database in comparison to the human networkmodel used by Jerby et al., and the additional reconstruction objectivespreviously not accounted for.The described method is computationally demanding since each trial of the randomreaction pruning procedure , requires eliminating each reaction from the universalreactions set in turn, and checking the consistency of the resulting model.Implementing the speedup heuristic suggested by Jerby et al. , which aTo overcome this, we implemented the following additional speed-up techniques. First, the model consistency check procedure is based onidentifying a set of flux distributions in which all core reactions are activated, and is applied following the removal of each reactionin the reaction pruning procedure. The first speed-up involved the utilization offlux distributions computed in one call to the model consistency check procedure insubsequent calls to this procedure  to avoid time-consuming linear programmingoptimizations. Second, to further minimize the number of performed linearoptimizations, the latter are now formulated with the objective of minimizing fluxthrough subsequent gap-filling reactions in the pruning order. These two speed-uptechniques, significantly elaborated upon in Additional file 1, provide a 100-foldimprovement in running time.Figure E. coli, for which a comprehensively curated model(iAF1260) is already available for validation [E. coli reactions to be used by MIRAGE, we considered KEGGreactions annotated as existing in E. coli and also belonging to iAF1260,plus the known biomass and all exchange reactions from iAF1260. Then we removeddead-end reactions that cannot be activated within a feasible flux distribution whenconsidering the entire universal reactions set, yielding a core set of 812 reactions.Performing standard flux variability analysis [To evaluate the performance of MIRAGE, we applied it to reconstruct a metabolicnetwork model for lidation . Towardsanalysis  when focE. coli with iAF1260shows a predictive precision of 41.9% and recall of 24.3%, which is significantlybetter than random sampling of gap-filling reactions . Without utilizing functionalgenomics data, the predictive performance was also markedly lower, with a precisionof 27.5% and recall of 20.6% . Using only geneexpression [E. coli, without prior knowledgeof exchange reactions , finding an overall similar predictive performance, showing an improvementof MIRAGE compared to other approaches .Comparison of MIRAGE's reconstructed network model for pression  or phyloE. coli with that of Model SEED [E. coli is close , the number of predicted gap-filling reactions byMIRAGE was 62, in comparison to only 10 by SEED. This results from MIRAGE's aim toresolve all gap-filling problems instead of just enabling biomass production asperformed by SEED. The precision of MIRAGE's predictions was significantly higherthan that of SEED, reaching 41.9% for MIRAGE versus 10% for SEED. Re-running MIRAGEgiven the very same definition of a biomass reaction used in the SEED reconstructionof E. coli's model (rather than the biomass definition taken from iAF1260)still resulted in a higher number of 76 predicted gap-filling reactions, with asignificantly higher precision of 34.2% than that achieved by SEED.Comparing the predictive performance of MIRAGE on reconstructing the metabolicnetwork of del SEED  has showSynechocystis, Synechococcus,Cyanobacteria, Prochlorococcus, Anabaena, and so on [Synechocystis sp. PCC 6803 [2 is the sole carbon source. Due to lack ofcomprehensive gene expression for most cyanobacteria species, we utilized here onlyphylogenetic data  to define reaction weights.To demonstrate the utility of MIRAGE, we applied it to reconstruct genome-scalemetabolic network models for 36 cyanobacteria for which genomic data are available todefine core reactions sets. Our analysis spans all cyanobacteria for which enzymeannotations are available in KEGG, including nd so on . For allPCC 6803 , assuminThe average size of a core reactions set for a cyanobacteria network is 570 reactionsFigure , out of Synechocystis sp. PCC 6803 withthe manually curated models of Knoop et al. [Synechocystismodel by further utilizing gene expression data obtained from Tu et al. [et al. model [P-values are 2.99 × 10-27 and 3.59 ×10-31 for Knoop et al.'s model and iSyn811, respectively).Again, we find that the predictive performance of either the functional genomics dataor the flux analysis alone is far worse . Moreover,20.3% of predicted reactions showed E-values below 10-100, compared to9.7% of randomly sampled reactions, testifying the overall correctness of thepredicted set of reactions.To evaluate the performance of MIRAGE in reconstructing cyanobacteria models, wecompared a reconstructed network model for p et al.  and iSynp et al. ,52. In tu et al.  as part l. model  and precl. model ,52. These Figure . AcompaSynechocystis model, we appliedit to predict gene knockout lethality data provided by [et al. : out of 39 genesknown to be non-essential, Knoop et al. correctly predicted 35, while ourmodel correctly predicts 38. Out of 11 known essential genes, Knoop et al.correctly predicted 7, while our model correctly predicts 6. The fact that ourautomatically generated model reaches a similar level of prediction performance tothat of a manually curated model demonstrates the applicability and importance of ourmodel reconstruction approach.As a further evaluation of our reconstructed vided by . We findTo demonstrate the applicability of the reconstructed cyanobacteria networks, weapplied a computational metabolic engineering approach called Optknock  on these-1 h-1 inProchlorococcus marinus 9601, representing a carbon utilization of 40% forastaxanthin production (considering a CO2 uptake rate of 0.22 mmolgDW-1 h-1 [2 to produce astaxanthin is predictedto reduce growth rate by 57% relative to the wild-type Prochlorococcus strain (TableThe application of Optknock for astaxanthin production identified double geneknockouts in 15 species that are expected to lead to astaxanthin secretion (TableDW-1 h-1 ). This uE. coli [Synechocystis sp. PCC 6803 [Our paper presents a novel method, MIRAGE, for reconstructing metabolic network modelsby integrating metabolic flux analysis and functional genomics data to resolve networkgaps. MIRAGE was validated based on a comparison of its predictions with manuallycurated metabolic networks for E. coli  and SynePCC 6803 -52. ThenOur results show that functional genomics data enable the marked improvement ofgap-filling in metabolic networks. Furthermore, we show that the integration of morethan one type of functional genomics data can further improve the performance of MIRAGE.Naturally, MIRAGE can be extended to account for additional functional genomics data,including protein-protein interactions and genomic context data, which were previouslyused for the identification of missing gene annotations in metabolic networks . MetabolSeveral existing gap-filling methods work by searching for a minimal set of missingreactions that would enable the network to perform a certain task . MIRAGE Metabolic models generated by automated methods such as MIRAGE should be regarded asfirst draft models, requiring further manual curation to bring them up to comparablelevel with standard manually curated models. The growing interest in reconstructingmetabolic network models for hundreds of species raises the challenge of developingimproved such gap-filling approaches that could speed up the reconstruction process,while the approach presented here shows a marked improvement in this direction over thestate-of-the-art, supporting the advantage of integrating functional genomic data aspart of model reconstruction. We expect MIRAGE to be used for automatic reconstructionsof many other species, leading to a significant boost in the understanding of theirmetabolism.Binary vectors describing reaction phylogenetic profiles were acquired from KEGG. Aphylogenetic weight for each non-core reaction is defined as the maximal Jaccardsimilarity with a phylogenetic profile of a core reaction that shares a metabolitesubstrate with the reaction at hand. The Jaccard values are normalized based on thefrequency of appearance of the shared metabolite in the universal reaction database.An expression weight for a given non-core reaction is computed by evaluating thecorrelation between profiles of genes that may potentially code for an enzymecatalyzing the reaction at hand and expression profiles of genes associated withneighboring reactions. Specifically, for each gene in the genome of the targetspecies, we compute the average Pearson correlation between its expression profileand profiles of genes associated with neighboring reactions, with the expressionweight defined as the maximal such correlation obtained. All Pearson correlations arenormalized by the frequency of appearance of the connecting metabolites (as doneabove).The distribution of phylogenetic and expression weights are normalized to having thesame mean and standard deviation. Final edge weights are defined based on the sum ofnormalized phylogenetic and expression weights. Reactions for which either weight ismissing are assigned the median normalized value.First, we create a random reaction pruning list by iteratively sampling the nextreaction with probability proportional to its weight normalized by the sum of weightsof the remaining non-sampled reactions. Next, we scan through the obtained reactionlist and try to remove each reaction in turn from the model, as long as the resultingmodel remains consistent. The consistency check involves verifying that: (i) eachcore reaction can carry non-zero flux under steady-state and reaction directionalityconstraints; (ii) there can be non-zero flux through the biomass reaction; and (iii)the growth-dilution of each metabolite in the network is accounted for. Once wefinish scanning through the pruning list, we are left with a minimal functionalmodel. We repeat the random pruning procedure 500 times and count the number of timesthat each non-core reaction appeared in the final model. Finally, we order thenon-core reactions based on their frequencies (from low to high) and repeat thepruning step to obtain the final model.The details of the above and the implementation of the various speed-up techniquesthat makes this algorithm computationally tractable are described in Additional file1. The implementation of MIRAGE is available at .KEGG, Kyoto Encyclopedia of Genes and Genomes; MBA, Model Building Algorithm; MIRAGE,MetabolIc Reconstruction via functionAl Genomics; SBML, Systems Biology MarkupLanguage.EV and TS conceived the research and wrote the paper. EV performed the computationalanalysis. Both authors read and approved the final manuscript.


The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.http://identifiers.org/biomodels.db/MODEL1603150001).Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (The online version of this article (doi:10.1007/s11306-016-1051-4) contains supplementary material, which is available to authorized users. Metabolic processes are implicated in many important aspects of human health. Models are critical in the exploration and understanding of the complexity underlying human metabolism. Genome-scale models of human metabolism . The use of R-groups is especially prevalent in Recon 2 in defining lipid metabolism, where in the interests of simplicity, multiple reactions involving fatty acids of differing chain lengths were condensed into a single reaction. The R-groups that remain are those representing conserved moieties such as acyl-carrier protein (ACP), which cannot be represented as a defined chemical formula but whose presence do not affect the mass and charge balancing of reactions.Recon 2 included reactions with incomplete mass and charge balancing Table . DespiteReversible reactions that thermodynamics suggest should be unidirectional under typical physiological conditions can also impact the accuracy of model predictions. Many of these have been discovered through multiple rounds of manual curation, driven by the requirement to predict realistic ATP yields. This iterative process involved performing a flux-balance analysis (FBA) test, inspecting the resulting flux pattern for anomalous reactions, and correcting their directionality based on literature searches and definitions in pathway databases.Previous work has highlighted the advantages of augmenting metabolic reconstructions with unambiguous, publicly available identifiers mapping elements to entries in persistent, external data resources . Regarding simplicity, updates are supplied in simple text files that are parsed and interpreted by custom software. By providing the facility to update models in such an automated manner, the potential user base for the model building process expands beyond those that have an intimate understanding of software, and of formats such as SBML and the COBRA Toolbox. The benefits in terms of promoting reproducible science are also clear: all changes made to a given model to produce a new version are catalogued in text files, which essentially act as a Supplementary material 1 (XML 23979 kb)Supplementary material 2 (PDF 18 kb)Below is the link to the electronic supplementary material.


To that end, several detection and quantification methods have been developed to study NO, as well as its redox relatives, nitrite and S-nitrosothiols. While no single technique can offer a complete picture of the nitrogen cycle in a given system in vivo, familiarity with the benefits and limitations of several common tools for NOx determination can assist in the development of new diagnostics and therapeutics.The detection and quantification of nitric oxide and related reactive nitrogen species  S-nitrosothiols, in vivo (summarized in Nitric oxide (NO) is an important bioactive molecule with numerous roles in physiological systems and potential therapeutic applications, many of which are discussed throughout this issue. Understanding the role of NO, particularly in cases where NO homeostasis may be disrupted or NO regulation is intrinsically tied to pathology, relies on the accurate detection and quantification of this molecule. Many techniques exist for both the direct and indirect quantification of NO and related nitrogen species  to oxidize NO to NO+, which results in a small redox current (in the range of picoAmps to nanoAmps). By measuring this redox current in the system of interest and comparing it to the redox current between the electrodes when calibrated using NO standards, nearly real-time (requiring 30–60 s to equilibrate) NO concentrations can be monitored in vivo.Real-time, +. Early electrochemical probes used working electrodes that were either gold-plated or made of pure platinum, though more recently, an alloy of platinum (90%) and iridium (10%) has been suggested due to its improved durability over pure Pt and the electrocatalytic properties of Ir in redox reactions [Working electrodes at a potential of 0.8–0.9 V relative to a silver/silver chloride reference electrode oxidize NO to NO iridium 0% has beeactions .o-phenylenediamine to reduce interaction with cations [Care must be taken with working electrodes to prevent interaction with anions such as nitrite, nitrate, ascorbate or urates; pretreating the electrode with Nafion repels anions. Moreover, oxidation of nonnegative small molecules, such as dopamine and 5-HT, can also result in overestimation of NO levels; this can be partially compensated for by pretreating electrodes in  cations .in vivo, despite extensive calibration against standards. Recent work suggests that this is due in part to electrode size, with larger electrodes underestimating NO due to rapid NO consumption from an unstirred layer; this work suggests that smaller electrodes are preferable not only for decreased invasiveness, but also for increased accuracy [Unfortunately, the extreme variability between different electrodes casts doubt on the utility of such devices for NO quantification. Different working electrodes can vary by as much as six orders of magnitude when attempting to quantify NO accuracy .Electron paramagnetic resonance (EPR) spectroscopy is a technique that can, in theory, detect the paramagnetic species in a sample . However, in practice, several factors may limit the applicability of EPR to any given system. NO is difficult or impossible to detect directly via EPR in most biological systems due to its broad, indistinct EPR signal in aqueous solution. However, it is possible to take advantage of the unique chemical properties of NO in order to create a reaction product, adduct or intermediate with a resolvable EPR spectrum.2, have led to a decrease in the use of NNOs to quantify NO generation in vivo via EPR, except under specific conditions [Conventional diamagnetic spin trapping agents , which are widely used to detect transient free radicals, are often not effective traps for NO. However, nitronyl nitroxides (NNOs), most notably carboxy-PTIO (c-PTIO), have been shown to be extremely effective at scavenging and trapping NO, resulting in a chemical shift that allows real-time EPR observation of the scavenging of NO. The efficacy of this assay is strongly dependent on the use of a proper amount of NNO for the experimental conditions. Moreover, both NNO reactants and products are easily reduced in many biological systems to EPR silent species, limiting their utility in quantification of NO production over time. NNOs can also directly interfere with NO generating pathways, or provide new reactive pathways for the generation of NO. These issues, coupled with the fact that NNOs  have recently been shown to react with other radicals, such as NOnditions .N-methyl-d-glucamine dithiocarbamate (MGD) [S-nitrosothiols.Other families of spin traps exist for the purpose of isolating NO, many of which utilize iron to bind NO and create an adduct that can be detected by EPR. The most common family of synthetic iron-based spin traps are iron-dithiocarbomate-based traps, including diethyldithiocarbamate  and N-mete (MGD) . These s7 M-1 s-1), quantification of metHb formed from oxyHb during a process known to generate NO is often a reliable means of quantifying NO production [-3–10-4 s-1), and has a distinct EPR signal that can be used to quantify hemoglobin-bound NO at concentrations of approximately 0.5 µM and above [in vivo. In addition, EPR can only detect these reaction products at concentrations of a few hundred nanomolar or more.Among the many biological targets for reaction with NO, hemoglobin (Hb) and myoglobin (Mb) are some of the most relevant, and can also be used for the detection and quantification of NO. NO reacts or coordinates with ferrous heme depending on the presence or absence of oxygen. If the ferrous heme is oxygenated , NO reacts to form nitrate and ferric methemoglobin (metHb). MetHb at concentrations aboveapproximately 0.5 µM is easily resolved and quantified by EPR, even in a background with high concentrations of other Hb species, though the NO is consumed in its formation. Because oxyHb reacts very quickly with NO . DAFCs  react quantitatively with NO under aerobic physiological conditions to create fluorescent products with absorption and emission spectra in the visible or near-IR range, allowing for detection and imaging of NO concentrations above 5 nM. DAFCs do not react with nitrite and nitrate, allowing for NO imaging amid a background of nitrogen oxides at much higher concentrations than NO [2O3) and other nitrogen-containing radicals and reducing agents [The first successful fluorescent imaging of NO  than NO , nor do  than NO . Howeverg agents .in vivo. Thus, use of these fluorescent probes has been largely limited to cell cultures, with benzimidazole derivatives having been used in frozen tissue samples.To overcome many limitations of the DAFCs, a new family of copper (II) based fluorescent probes with sensitivity similar to DAFCs (5 nM) was developed  and exteDAFCs and copper-fluoresceins react with NO irreversibly, and may lead to the buildup of a high level of background fluorescence. To create a reversible NO detector, a fluorescent protein with the NO-specific heme domain of soluble guanylate cyclase was created. While both NO-specific and reversible, the threshold of detection for this molecule was only 10 µM. A related protein system with a much better detection limit (0.1 nM) has been developed for use with FRET imaging, but this requires genetic encoding of the detector into the target organism. Future work in this area may lead to a more ubiquitous fluorescent NO detector, but current sGC- and FRET-related detectors, while they have a few unique advantages, are generally less useful than current DAFCs and copper-fluorescein fluorescent probes .in vivo, ranging anywhere from tens of seconds in some tissues to milliseconds or microseconds in the presence of heme-containing proteins such as hemoglobin. With the correspondingly low steady-state concentration of NO, real-time quantification of NO is difficult and at often times, the concentration of the nitrite and nitrate metabolites of NO will serve just as well as (or better than) the concentrations of NO itself [Nitric oxide generally has a relatively short half-life O itself . MoreoveO itself . Due to in vivo quantification of nitrite presents unique challenges. For instance, the typical Griess assay has a detection limit of 1–2 µM, a full order of magnitude above basal nitrite levels in many biological fluids such as plasma. Moreover, the reaction cannot be readily utilized in whole blood due to interference from blood constituents such as hemoglobin and plasma proteins. Thus, various techniques have been developed to improve sensitivity and facilitate blood, plasma and serum sample analysis using techniques based on the Griess reaction, each with advantages and disadvantages. The presence of even trace contaminants, anticoagulants and proteins can all reduce the accuracy of Griess-based assays [For over a century, the Griess reaction and its derivatives have been used to quantify nitrite in a variety of situations. However, application of the Griess reaction for d assays ,15.4 to both prevent oxidation of nitrite and precipitate proteins from the solution that could potentially interfere with nitrite detection via nitrosation of cysteine or nitration/nitrosation of carbon or nitrogen under the acidic conditions necessary to conduct the Griess reaction [Numerous sample preparation and storage methods have been used in the analysis of nitrite and nitrate concentrations by the Griess reaction, and some of the procedures used are not clear. Thus, even seemingly similar protocols can differ by an order of magnitude or more in their reported concentrations. Out of the commercially available storage monovettes, serum has been shown to have the lowest levels of nitrite contamination. EDTA and citrate, and to a lesser extent heparin, have been found to have significant levels of nitrite contamination, often greater than the nitrite concentration in the sample itself. Beyond selection of storage media, samples must be properly preserved and prepared for analysis by techniques based on Griess reactions. Nitrite can oxidize forming nitrate, a process that can be inhibited by alkali. Alkali is often combined with ZnSOreaction . Dilutioreaction . Due to reaction .Although the Griess reaction is the most widely cited technique for the quantification of nitrite, it is by no means the only method available, nor does it exclude the concurrent use of other techniques to improve the quality of data collection, detection limits and range of conditions under which data can be gathered. Numerous chromatography-based assays exist for the detection of nitrite (and often nitrate), frequently employing pre- or postcolumn treatments (sometimes based on the Griess reaction) to quantify these anions without interference from contaminants in the initial solution .HPLC techniques can be used to quantify nitrite in conjunction with the Griess reaction described above or other methods of nitrite detection, such as the DAN fluorochrome  described elsewhere . HPLC syx) employ both HPLC and spectrophotometric analysis of the diazo product of the Griess reaction. These specialized systems have the advantage of highly reproducible results that can be obtained with less time-consuming sample preparation procedures. However, as with any commercial assay system, care must be taken to validate the assay in each new situation and to ensure consistent quality of results [Specialized systems designed and marketed for the detection of nitrogen oxides  used for the simultaneous derivatization and quantization of nitrite ,17. Recein vivo NO metabolites such as nitrite and nitrate, and can be used in situations where traditional Griess reaction assays and chromatography fail. This typically requires a specialized chemiluminescent detector, and such detectors designed for the quantification of NO are commercially available and well validated [Ozone-based chemiluminescence is a technique for the detection of NO gas that has been used with great success in a variety of applications. It is particularly useful in the determination of alidated .S-nitrosothiols, as discussed later in this article). Notably, chemiluminescence does not require deproteination prior to analysis, and can be conducted in the presence of hemoglobin and other contaminants, although oxidizing hemoglobin before analysis  has been shown to be useful in the prevention of oxidation of nitrite to nitrate [While the potential applications of chemiluminescence are numerous, it is a particularly valuable tool for the determination of nitrite and nitrate  acidified with acetic acid can be used to reduce the majority of relevant NOx species and is preferred for analysis of nitrite concentration , whereas copper(I)/cysteine is often used for the determination of S-nitrosothiols (discussed later in this issue).Chemiluminescent determination of nitrite or nitrate requires the reduction of the relevant NOChemiluminescence is highly sensitive, with a detection threshold on the order of 1 pmol of total nitrite or 1 nM of nitrite in solution, depending on the quantity of sample used. However, it also has limitations and potential pitfalls. Foaming /cysteine as a reductant) can cause an overflow from the reaction vessel when biological samples (particularly those with high protein levels) are injected into the reaction chamber. There is also a great deal of variability between instruments, and even on the same instrument between uses; thus, care must be taken to calibrate the instruments against a set of known standards before each use .in vivo as a protein modification factor. NO in the presence of oxygen can form S-nitrosothiols (R-S-N=O) (RSNOs). These RSNOs may transport NO activity, and also likely affect cellular NO signaling pathways. Thus, determination of the in vivo concentration of these RSNOs is important for understanding NO transport and signaling within the body. Quantification of small-molecule RSNOs at relatively high concentration in vitro can often be accomplished using UV/Vis spectroscopy, but the relatively low concentrations of these compounds in vivo, coupled with the fact that S-nitrosothiols of interest are often associated with large macromolecules, render spectroscopic techniques untenable. Thus, the most commonly used techniques for quantification of RSNOs generated in vivo rely on detection of liberated NO or the labeling of thiols vacated following the liberation of NO  method, potassium iodide (KI) and iodine (I2) create tri-iodide in the presence of acetic acid. This serves as the reducing agent, simultaneously liberating NO from RSNOs, nitrosamines, iron–nitrosyl complexes and nitrite. Because of the less-specific nature of this assay, and the frequent contamination of materials by nitrite, care must be taken to maintain accurate controls [In the tri-iodide . In this technique, Cu(I) reduces the nitrosothiol, while cysteine both reduces Cu(II) and forms CSNO. CO gas flow prevents any heme present from capturing NO before it can be detected. This method has the significant advantage of being nitrite silent, and thus ignores many otherwise-troublesome sources of contamination . The 3C 3Fe(CN)6, NEM and DTPA. K3Fe(CN)6 (ferricyanide) oxidizes hemoglobin to the non-NO capturing metHb state, while NEM blocks free thiols from becoming S-nitrosated during the treatment, and the metal chelator DTPA protects SNO bonds from free metals. The modified 2C method eliminates the complications of CO flow during the experiment, while preserving many of the 3C method's advantages (particularly nitrite independence). However, this method requires pretreatment of samples and additional care when dealing with some hemoglobin-based samples [To overcome some of the difficulties with the 3C assay, a modified copper/cysteine procedure has been developed, frequently referred to as the modified 2C method. This uses pretreatment with K samples .S-nitrosothiol reduction, it does not provide any insight into the specific location of that NO. For this, other techniques must be used, and can often be used in tandem with chemiluminescence for improved results.Each of these methods has advantages and disadvantages, but all three share certain key features. Perhaps most notably, while chemiluminescence can allow for accurate quantification of NO liberated from S-nitrosothiols in proteins is the biotin switch technique (BST). This technique involves three basic steps. First, the free cysteine thiols must be blocked ; next, RSNOs are reduced ; finally, free thiols are tagged with a biotin label or other compound, such as a fluorescent marker, that facilitates separation of labeled proteins by techniques like western blotting or biotin pull-down. However, there are several concerns surrounding this technique, notably, the possibility that some sites of S-nitrosation are destroyed during the procedure, and the potential lack of specificity and completeness of ascorbate in reducing RSNO, with indications that ascorbate may not reduce RSNOs at all sites, and may also reduce sulfenic acids and some disulfides. Despite these concerns, the BST has been widely used to identify SNO nitrosation sites, with many recent variations offering improved sensitivity and specificity. These various modified versions of the BST are collectively a topic of some length, and have been widely reviewed [The most widely used method for the indirect detection and labeling of reviewed .S-nitrosation by various NO releasing compounds, including those that may be developed for therapeutic applications. Another method, termed the d-Switch technique, has recently been developed for analysis of cell lysates. This technique uses NEM to block and tag all free thiols, followed by a procedure similar to the BST to reduce S-nitrosothiols and label them with isotope labeled d5-NEM. This technique allows quantification of both total cysteine and S-nitrosated cysteine by LC–MS. Such systems allow for identification of S-nitrosated proteins directly from cell lysate. While this is not an in vivo assay, the ability to identify proteins as candidates for S-nitrosation can provide valuable insights into physiological systems, and contribute to models of biological activity [Examples of notable recent advances in BST-related assays include the development of a microarray-based assay, which uses an antibiotin antibody to facilitate high-throughput analysis with great sensitivity. Although this technique does not cover the entire proteome, and can return a relatively few false positives, it can provide rapid insight into target proteins of activity .in vivo analysis or imaging, promising initial steps in this direction have been taken. For instance, a coumarin-based phosphine was recently developed that fluoresces upon reaction of the phosphine with RSNO [in vitro. However, the potential of such molecules, including the family of triarylphosphines [in vivo may be attainable [S-nitrosothiols based on this phosphine chemistry [RSNOs can also be detected by fluorescent methods. In general, reactive species such as RSNOs may be targets of less-specific fluorescent probes . While fith RSNO . This moosphines , to servtainable . A recenhemistry .S-nitrosothiols in vivo present numerous challenges, which depend largely on the conditions of study and the inherent difficulty in tracking transient species with short lifespans and relatively low physiological concentrations. While by no means an exhaustive list, many of the most useful and most widely used techniques, as well as some promising candidates for future investigation, have been summarized here. The interested researcher is encouraged to seek more details on any methods of interest in the articles referenced here. In the future improvements in fluorescence-based or mass-spectrometry-based techniques may improve in vivo detection. For now, there are many available techniques all of which can provide useful information provided proper controls are incorporated.The accurate detection and monitoring of NO, nitrite and S-nitrosothiols in vivo, but none of them are universally applicable in all situations.There are many ways to investigate nitric oxide, nitrite and in vivo use of electrochemical probes (though these are subject to many limitations), electron paramagnetic resonance spectroscopy and fluorescence imaging.NO can be determined by direct Nitrite can be determined by use of the Griess reaction and its derivatives, chromatography and chemiluminescence.S-nitrosothiols can also be quantified by chemiluminescence; determination of their location on proteins requires tagging the sites of S-nitrosation, such as by the Biotin switch technique or a derivative thereof.


E.coli GFP-tagged enzymes for cells growing exponentially.Although much is understood about the enzymatic cascades that underlie cellular biosynthesis, comparatively little is known about the rules that determine their cellular organization. We performed a detailed analysis of the localization of E.coli’s metabolic network shows that localized enzymes, in contrast to non-localized ones, form a tree-like hierarchical structure, have a higher within-group connectivity, and are traversed by a higher number of feed-forward and feedback loops than their non-localized counterparts. A Gene Ontology analysis of these enzymes reveals an enrichment of terms related to essential metabolic functions in growing cells. Given that these findings suggest a distinct metabolic role for localization, we studied the dynamics of cellular localization of the cell wall synthesizing enzymes in B. subtilis and found that enzymes localize during exponential growth but not during stationary growth.We found that out of 857 globular enzymes, at least 219 have a discrete punctuate localization in the cytoplasm and catalyze the first or the last reaction in 60% of biosynthetic pathways. A graph-theoretic analysis of We conclude that active biochemical pathways inside the cytoplasm are organized spatially following a rule where their first or their last enzymes localize to effectively connect the different active pathways and thus could reflect the activity state of the cell’s metabolic network.The online version of this article (doi:10.1186/s12918-014-0131-1) contains supplementary material, which is available to authorized users. The imain vitro ,6; neverin vitro -9, littlIt is possible that the existence of large multi-enzyme complexes, as opposed to freely diffusing enzymes, could either be determined by constraints limited to highly specialized reactions, or a general mechanism used throughout the cell to achieve a generic metabolic function. Supporting the latter option, the hypothesis of metabolic channeling proposes that reaction products in a metabolic pathway move from one active site to another within tightly associated multi-enzyme complexes. Such organization may possess several kinetic advantages for the cell ,11; for E.coli cells growing exponentially. We found that 25% of known cytoplasmic enzymes show a discrete punctuate localization in the cytoplasm. When the E.coli biosynthetic pathways were organized into Elementary Flux Patterns (EFPs), we observed a significant preference for the first or last enzyme in such EFP-defined pathways to be localized and that localized enzymes form a tree-like hierarchical structure with higher within-group connectivity.In order to determine if enzyme localization is a generic and functionally relevant property of enzymatic reactions in cells, we studied the distribution of genome-wide GFP-tagged enzymes in A complete Set of E.coli K-12 ORF Archive (ASKA) library of GFP-tagged ORF clones [E.coli biosynthetic enzymes. We used the images displayed on GenoBase representing E.coli cultures growing exponentially and expressing each a protein whose C-terminus was fused to a GFP (see Sup. Mat for a complete definition of the list). As the KEGG collection, a set of manually drawn pathway maps indicating molecular interactions and reactions networks, indicates that E.coli K-12 MG1655 has 857 enzymes involved in known metabolic reactions, at least 25% of the enzymes in E.coli cells growing exponentially show a discrete punctuate localization at diverse locations of the cytoplasm .The complex organization of the bacterial cytoplasm suggests that enzymes could, not only freely diffuse, but also show spatial ordering in the cytoplasm. To explore the rules of putative enzymatic organization, we took advantage of the images of F clones  displayeE. coli [Due to the high connectivity of metabolic reactions, it is difficult to uniquely assign pathway positions to enzymes. We decided to analyze the position of localized enzymes organizing biochemical pathways from a genome-scale metabolic network of E. coli  into EleE. coli . EFPs arE. coli . EFPs deE.coli EFPs composed of 3 to 12 reactions per EFP, from 27 different biochemical pathways including nucleotide, amino acid, nitrogen, central and alternate carbon metabolism, as defined in [or the last enzyme localized to the total number of same-length EFPs having at least one enzyme localized  have either the first or the last enzyme localized and 33 EFPs have both enzymes localized. This indicates an important role for the start or end position of pathways that might underlie enzymatic regulatory strategies.E.coli metabolic network . The metabolic network is described by an asymmetric Boolean square matrix Mij of 1214 unique reactions where each reaction is associated to an enzyme and a non-zero matrix value mij = 1 indicates that the reaction i feeds into reaction j by producing a compound used by reaction j  enrichment analysis of the list of localized enzymes. Significantly enriched GO terms related to enzymatic functions that are essential for growth, the top ones being nucleotide, ion, ATP and amino acid binding as well as ubiquinone synthesis Table . UbiquinE.coli cells growing exponentially, we decided to test whether enzyme localization would vary in different growth conditions. We studied the dynamics of cellular organization in exponential and stationary growth in the bacterium B. subtilis. Due to its larger size, B. subtilis has emerged as an excellent model organism for bacterial cytology and in particular to study the very well conserved set of cell wall synthesizing enzymes [E.coli as shown in Additional file B.subtilis and in both organisms only MurA and MurG show specific localization.As the significant list of GO terms reflects enzymatic activities necessary for  enzymes . We fluoB.subtilis cell cultures strain JDB2840 expressing MurA-CFP . Genobase Images were generated using the ASKA library and obtained from E.coli cultures growing exponentially and expressing each an enzyme fused C-terminally to a GFP (NO addition of 0.1 mM IPTG). E.coli K-12 W3110 strain has 4364 ORFs, 4351 were tagged with a C-terminal GFP fusion and established as plasmid clones as ASKA library. As indicated in the ASKA website, 516 fusions showed GFP focal distribution, 2874 were shown to be around the whole cell, 390 showed membrane distribution, 103 showed both membrane and focal pattern and 355 showed no GFP signal.Expression of the cloned ORF is under the control of an IPTG-inducible promoter, which is strictly repressed by lacIB.subtilis cultures growing exponentially in DSM medium at 37°C, 100 μl of cells were taken at designated times. To stain membranes, 0.5 μl of FM4-64 (100 mg/ml) was added to samples just before the cells were collected by centrifugation. The pellet was resuspended in 10 μl PBS, and added to a poly-L-lysine pre-treated coverslip. All microscopy was performed on a Nikon Eclipse 90i with a 100X objective using phase contrast and captured by a Hamamatsu Orca-ER camera using Nikon Elements BR software. Exposure for GFP, CFP and TRITC was 400 ms for all pictures taken.For E.coli defined in KEGG and extracted 1214 unique reactions ordered by their R00000 number  and used the list of localized genes from Genobase defined by gene numbers b0000 , given that there are k localized enzymes in that EFP is given by:q the probability that there are k enzymes localized in any position in an EFP of length L, then the probability that the first enzyme is localized isIf we call The same expression holds for the probability that the last reaction is catalyzed by a localized enzyme:The probability that the first and last are localized, given that there are k localized enzymes in an EFP of length L isTherefore the probability that the first and last are localized given that the EFP is of length L isFinally, the probability that the first or the last reaction is localized is equal to:


The Anaerobic Digestion (AD) processes involve numerous complex biological and chemical reactions occurring simultaneously. Appropriate and efficient models are to be developed for simulation of anaerobic digestion systems. Although several models have been developed, mostly they suffer from lack of knowledge on constants, complexity and weak generalization. The basis of the deterministic approach for modelling the physico and bio-chemical reactions occurring in the AD system is the law of mass action, which gives the simple relationship between the reaction rates and the species concentrations. The assumptions made in the deterministic models are not hold true for the reactions involving chemical species of low concentration. The stochastic behaviour of the physicochemical processes can be modeled at mesoscopic level by application of the stochastic algorithms.In this paper a stochastic algorithm (Gillespie Tau Leap Method) developed in MATLAB was applied to predict the concentration of glucose, acids and methane formation at different time intervals. By this the performance of the digester system can be controlled. The processes given by ADM1 (Anaerobic Digestion Model 1) were taken for verification of the model.The proposed model was verified by comparing the results of Gillespie’s algorithms with the deterministic solution for conversion of glucose into methane through degraders. At higher value of ‘τ‘ (timestep), the computational time required for reaching the steady state is more since the number of chosen reactions is less. When the simulation time step is reduced, the results are similar to ODE solver.It was concluded that the stochastic algorithm is a suitable approach for the simulation of complex anaerobic digestion processes. The accuracy of the results depends on the optimum selection of tau value. Anaerobic Digestion (AD) is the process by which the complex form of organic matter such as carbohydrates, fats and proteins are converted into simpler form by the cells of microorganisms in the absence of oxygen. Energy production, high organic loading and low sludge production are major advantages of AD process. The energy produced can replace fossil fuel use, and also has positive effect on reduction of global warming. Modeling is a powerful tool which can be applied to simulate various processes occurring in the digester. Models are applied for parameter estimation also. Using the simulation results it is easy to predict and avoid digester failure. The modeling results give useful guidelines for the design of the digester also. The reaction system in an anaerobic digester is complex with many sequential and parallel steps. The reactions can be Biochemical or Physico – chemical in nature which involves species of higher and lower concentrations. A stochastic approach can be applied to simulate these reactions in exact manner. The complex organic matter which is called substrate is converted into simpler form through various steps by living cells called biomass. These cells grow at suitable environmental conditions of pH, temperature etc. They interact with the environment and substrates in a complicated way. Generally the biochemical processes include acidogenesis, acteogenesis, and anaerobic oxidation of Volatile Fatty acids, methanogenesis and extracellular hydrolysis step. The reaction kinetics of growth and decay of biomass and conversion of substrates from one form to another are detailed in the following sections.Anaerobic digestion modeling was started in the early 1970’s when efficient operation of anaerobic systems was needed. The first developed models were simple with limited number of reaction equations . Importa2 as regulator for VFA production; (c) ammonia inhibition. The growth of methanogenic population is greatly affected by un-dissociated acetic acid, un-ionised VFA and total VFA which cause a drop in pH. Several models have been developed taking the substrate (un-dissociated acetate and VFA) as inhibition factor [2 partial pressure. Due to sudden increase in organic loading, the accumulation of VFA takes place, since acetogens grow at a slower rate than the acidogens. This will increase pH which in turn the H2 partial pressure is increased. This will cause further accumulation of acids and thus methane generation is reduced. Few models have been developed based on the H2 partial pressure as inhibition factor [Various models have been developed based on five major categories: models considering: (a) non-ionised Volatile Fatty Acids and total VFA inhibition; (b) Hn factor ,8. The fn factor –12 and mn factor –15. AmmoIn all the above models, many species are involved in more than one reaction. The reaction kinetics is solved by formulated Ordinary Differential Equations (ODE). To determine the rate of concentration of a particular species in time, all the reactions in which the species is involved are to be included during the formulation of ODEs. The complexity increases with increase in number of reactions and species. To avoid this, Stochastic Algorithm has been applied where the state of the system is updated based on the current state of the system and the transition probability.The complex organic matter which is called substrate is converted into simpler form through various steps by living cells called biomass. These cells grow at suitable environmental conditions of pH, temperature etc. They interact with the environment and substrates in a complicated way. The reaction kinetics of growth and decay of biomass and conversion of substrates from one form to another are detailed in the following sections.max).Once the inoculum is introduced into anaerobic digester, the cells pass through lag phase where they adjust to the new environment. Once they get accustomed to the new environment, they start growing in exponential phase called log phase. During this phase, the specific growth rate remains constant. The specific growth rate is given by dx/dt = µx. When the growth limiting substrate is exhausted, the growth remains stationary and reaches the maintenance mode in the stationary phase. When there is no substrate, the cell population slowly starts decreasing in the death phase. In each growing culture, there is a maximum rate of growth per unit biomass with unlimited substrate in the given environment . This is an empirical formula which can be applied in batch studies. Generally, Monod kinetic model is applied in most of the biological wastewater treatment processes. In this model, when one of the substrate concentrations (S) is Limiting, the biomass growth is represented by µ = µmaxS/(Ks + S). Ks is the value of the limiting nutrient concentration at which the specific growth rate is half its maximum value.A kinetic model represents the cell population kinetics. The models can be unstructured, structured unsegregated and segregated. In the unstructured model a single substrate is considered as growth limiting one. Multiple substrates are considered in structured model. In unsegregated model, the average properties of the cell population are considered. In segregated model the discrete and heterogeneity of cell populations are considered. Also various kinetic models such as Maltha’s Law, Slater model and Monod model are used in modeling AD processes. In Maltha’s law the rate of increase of biomass is a function of microbial population only (F(X) = µx). In this model, lag or death phase is not considered. It is assumed that there is unrestricted growth of biomass. In Slater model, final population of biomass is also included. This is represented by logistic equation of biomass growth which relates the specific growth rate μ, biomass concentration X, maximum specific growth rate μmax ,Ks) which describe the microbial processes are able to predict the conditions of maximum growth and when the activity will cease. The main disadvantage of this model is that since the kinetic parameters  vary with substrate, one set of parameters cannot describe biological process at short and longer retention time. To overcome this limitation, first – order models are used [In this model, the kinetic parameters  glucose fermenting acidogens; (ii) propionic acid degrading acetogens; (iii) butyric acid degrading actetogens, acetoclatic methanogens and (iv) hydrogenotrohic methanogens.+/NADH in the cells of biomass depend on the concentration of hydrogen.After hydrolysis of complex substrate into simpler organic compounds such as glucose, short chain fatty organic matter takes place, acidogens degrade glucose into acetic, propionic and butyric acids. Hydrogen is considered as an inhibitor for acidogens according to Embden- Myerhoof pathway since the correlation of NADIn this slowly growing and pH sensitive acteogens oxidize propionic and butyric acids to acetate. At high partial pressure of hydrogen, it acts as an inhibitor in acetogenesis phase. Also acetate inhibition of the propionic and butyric acid degradation step has been considered in numerous studies. So this can be represented by non-competitive type inhibition model.In this step, pH –sensitive and slowly growing acetocalstic methanogens reduce acetate to methane. Here, free ammonia is the inhibitor for the growth of methanogens. The stoichiometric equation and specific growth kinetic reactions are given below:2 in the reaction system due to digestion of propionic and butyric acids by acetogens. So dual substrate form of monod equation can be applied to represent the specific growth rate of Hydrogenotrohic methanogens.There may be growth limitations due to deficiency of COBiochemical reactions and their kinetics in the anaerobic digestion system were assumed to follow first order reactions (hydrolysis), monod type kinetic reactions and inhibition reaction. The complex particulate waste from industries or household is first disintegrated into carbohydrate, protein and lip . During hydrolysis by extra cellular enzymes (hydrolases), monossaccharides, amino acids and Long Chain Fatty Acids (LCFA) are formed. All these bio-chemical extracellular steps were assumed as first order . The firIn the deterministic approach for reaction simulation, the time evolution of the system is considered as continuous which is governed by a set of coupled ODEs. But the stochastic approach regards the time evolution similar to a random walk process which is governed by a single differential equation which is called the master equation. In the deterministic approach, given the initial concentration of various species in a free homogeneous macroscopic environment, the concentration at all future time intervals is determined by averaging random fluctuations to produce a predictable deterministic behaviour . All thekj ,t) , that the reaction event Rμ occurs in time interval [t,t + τ ) is given by a Poisson random variable, P(aj (X (t)),τ ), where j = 1,.. M. The flowchart of Gillespie’s Tau Leap Method is outlined in Figure a0(X (t)) when the number of reactions or the concentration of the reactant species is low. In that case it will be equivalent to Gillespie’s Direct Method where only one reaction is chosen in a time step. In these cases, the algorithm becomes inefficient.Master equation describes the transition of a system from one state to another state using probabilistic formulations. The state of system is determined by incoming and outgoing transitions. Since the state change of a chemical reaction system occurs in a discrete number, the probability to find the current state of the system can be described according to the Master Equation approach. In the Master Equation for reactions, all states are represented by a discrete number of molecules and transition intensities are given by reactions per second. Various Stochastic Simulation Algorithms (SSA) such as Gillespie Direct Method, Tau leap methods, slow reaction method and adaptive tau leap method to generate the trajectories by evolving the reaction type and the time of occurrence of the reaction through the probability distribution –22. It wBased on stoichiometric values, each species is apportioned with theoretical numbers of particles based on the assumed mass. The fluctuations which occur due to stochastic nature of simulation can become less pronounced either by repeating the simulation with less number of particles and computing the mean or by assuming more number of particles. According to the theoretical statistical physics, the fluctuations in the system are inversely proportional to the square root of the number of particles involved in the simulation. So in this work, more number of particles is assigned for a particular species in which the substrate Glucose is assigned with theoretical number of particles proportional to the concentration in the ratio of 1:5. Figure max,glu = 1.25, μmax,bu = 0.833, μmax,pro = 0.542, μmax,ace = 0.333, μmax,hyd = 0.35, Ks = 500, Kb = 200, Kp = 100, Ka = 150, Kh = 150, where μmaxS are maximum uptake rate of various species, K’s are the half saturation value [μ is determined. Then based on the total reaction rate of the system, a reaction is chosen by Poisson random variable. The time step is assumed in such a way that more than one reaction is chosen in a single simulation step. If higher value is chosen, more reactions are chosen where there will be great variation in the propensity function of the chosen reactions. Based on the chosen reactions in a given time step and the stoichiometric constants of products and substrates of the chosen reactions, the number of particles assigned for the reactant and product species involved in the chosen reactions will be modified. So the accuracy of the algorithm depends on the selection of the time step. The simulation results obtained from SBTOOLBOX in Matlab and stochastic algorithm s are shown in Figures The kinetic parameters in this model μon value . The yieWhen simulation time step is more than 1, the number of selections of the chosen reaction is insignificant and the formation of methane is less. Update of sugar takes longer time to reach the steady state. The simulation of formation of acids and formation of methane are rapid. When the tau value is reduced, the simulation results approaches the results of simulation using ODE solver. This is due to the selection of reactions where the propensity function remains constant. At the time step of 0.4, the simulation results get closer to deterministic values. Thus the accuracy of simulation depends on the selection of the optimal value of tau.When more number of reactions are involved in a biochemical processes, the rate of change of concentration of species is determined by evolving a rate equation for each species. As the number of species increases, the number of rate of reactions also increases to evolve the change in the concentration of species. But it is a complex process. The difficulty in formulation of Ordinary Differential Equation (ODE) for reaction simulation can be overcome using Gillespie’s algorithms where the reactions can be represented in the form of matrix which involves the stoichiometric constants and the reaction rate constants. Stochastic Algorithm (SA) has been identified as a better solution where multiscale concentration species are involved in reactions such as sorption, precipitation, degradation, sequential and parallel decay reactions etc. In this paper, the feasibility of applying SA (Gillespie Tauleap Method) in simulation of various bio chemical processes occurring during anaerobic degradation of complex organic matter has been studied.