E. coli across different single-carbon-source conditions.Metabolic interactions involve the exchange of metabolic products among microbial species. Most microbes live in communities and usually rely on metabolic interactions to increase their supply for nutrients and better exploit a given environment. Constraint-based models have successfully analyzed cellular metabolism and described genotype-phenotype relations. However, there are only a few studies of genome-scale multi-species interactions. Based on genome-scale approaches, we present a graph-theoretic approach together with a metabolic model in order to explore the metabolic variability among bacterial strains and identify and describe metabolically interacting strain communities in a batch culture consisting of two or more strains. We demonstrate the applicability of our approach to the bacterium A different diversity graph is constructed for each growth condition. The graph-theoretic properties of the constructed graphs reflect the inherent high metabolic redundancy of the cell to single-gene knockouts, reveal mutant-hubs of unique metabolic capabilities regarding by-production, demonstrate consistent metabolic behaviors across conditions and show an evolutionary difficulty towards the establishment of polymorphism, while suggesting that communities consisting of strains specifically adapted to a given condition are more likely to evolve. We reveal several strain communities of improved growth relative to corresponding monocultures, even though strain communities are not modeled to operate towards a collective goal, such as the community growth and we identify the range of metabolites that are exchanged in these batch co-cultures.E. coli strains under different conditions and shows how metabolic differences can be used to identify metabolically interacting strain communities. This work also extends the existing stoichiometric models in order to describe batch co-cultures and provides the extent of metabolic interactions in a strain community revealing their importance for growth.This study provides a genome-scale description of the metabolic variability regarding by-production among  In metabolic interactions also known as cross-feeding, microbial species exchange usable metabolic products arising from the metabolism of a primal nutritional resource. These metabolic products can serve as alternative, secondary resources to microbial species for obtaining their energy or composing the building blocks for biosynthesis. In nature, microbes live in communities and develop cross-feeding interactions, which provide an overall increase of nutrients and a more efficient exploitation of a given environment ,2. In thSeveral studies have worked on the characterization of microbial interactions in either synthetic or natural communities  and havein-silico genome-scale metabolic models [Desulfovibrio vulgaris and Methanococcus maripaludis in methanogenic laboratory co-cultures. These two microbes develop a specific hierarchical association with each other, known as syntrophy. Although the model focuses on the central metabolism rather than the full genome, it predicts several features of co-culture growth, including the ratio of the two microbe populations in culture and the dominant electron carrier during growth. An alternative stoichiometric network analysis approach was developed by Taffs et al. [E. coli mutants, Wintermute and Silver [Based on existing genome-scale metabolic models, this study aims to investigate the detailed metabolic interactions, which can develop between bacterial strains as they grow in simple, single-source, batch cultures. Genome-scale metabolic models account for the inter-connectivity of metabolic pathways that utilize the environmental resources and produce energy and biomass precursors required for cellular growth. These models describe genotype-phenotype relations revealing the full extent of metabolic capabilities of genotypes under various environmental conditions and have been broadly used -25 with c models ,26-28, tc models  is the fs et al.  to descrd Silver  studied E. coli across different single-carbon-source conditions.In this study, single, metabolic gene knockouts define the pool of bacterial strains among which potential cross-feeding interactions are examined. Based on the hypothesis that metabolic products are exchanged in a bacterial population, we previously developed a graph-theoretic approach (diversity graph) in order to map pair-wise genetic to metabolic alterations with respect to by-production and identify communities of metabolically different mutants in a given environment . DiffereS, reaches a steady state  that satisfies the physiochemical constraints under a given environmental condition (Equation 2). Flux Balance Analysis (FBA) further assumes that a cell follows an optimization strategy in order to accomplish cellular tasks. The most commonly applied objective is the maximization of growth rate reflected in biomass production (Equation 1), which has proved to successfully describe unicellular organisms [Utilizing a genome-scale metabolic network reconstruction of an organism, constraint-based metabolic approaches model the relation between the genomic information and metabolic activity at flux level and reveal properties that cannot be predicted by descriptions of individual components ,25. The rganisms . ThermodVarma and Palsson  extend texC0) are given as well as the initial biomass concentration (b0) of the bacterial population. The whole time regime is divided into time intervals δ t where intracellular steady-state is assumed. At each time interval the flux distribution, v, which optimizes the growth rate μ is calculated by solving the optimization problem described in Equations 1, 2, 3 and 6. The biomass and substrate concentrations are updated at each time interval as shown in Equations 4 and 5, respectively. The flux j is positive if the substrate corresponding to reaction j is produced and negative when it is consumed. Equation 6 corresponds to the uptake bounds of the exchange fluxes, which are determined by the availability of substrates at each time point.The initial concentrations of the substrates (E. coli strains are performed using the in silico metabolic network of E. coli (iJR904) by Reed et al. [δt) is 0.1 h. Maximization of the growth rate is used as the objective function of the optimization problem. A second optimization problem is also applied to minimize the enzymatic cost expressed by the absolute flux values under the constraint that the cell continuously operates at the maximum growth rate [In this work, the batch monoculture simulations of the single-gene knockout d et al. , which id et al. , 58 carbd et al. . The timwth rate . The glpwth rate  is used wth rate .Building upon the existing dynamic FBA model, which describes batch (and fed-batch) monocultures , we haveexC0) in the growth medium are given. At each time interval the flux distribution (vi), which optimizes the growth rate μi is calculated for each strain i independently solving the optimization problem described by the Equations 1, 2, 3 and 9. Equations 7 and 8 show how the biomass concentration of each strain (bi) and the substrate concentrations (exC ) of the medium are updated at each time interval. The current availability of the substrates and the bacterial population determine the uptake bounds of the exchange fluxes as shown in Equation 9. The simulation terminates when none of the strain-competitors can grow further in the shaped medium, which usually corresponds to the phase of nutrient depletion. If the population consists of identical cells, the multi-competitor metabolic model is reduced to the existing dynamic FBA model [The initial biomass concentration  of the common growth medium. The single-carbon-source conditions that are examined in this study for the identification of efficient strain communities include glycolate, acetate, glycine, glucose, pyruvate and melibiose.In this work, communities consisting of two or more Metabolic interactions that involve the exchange of intermediate metabolic products are assumed to occur only if the metabolic capabilities of the members of the bacterial population differ with respect to by-production. Based on this assumption, a graph representation is constructed in order to reflect the metabolic variability with respect to by-production within a pool of genetically different cells .E. coli metabolic network [E. coli strain in a single-carbon-resource batch culture is simulated using the dynamic FBA method described previously for monocultures. The nodes of the metabolic diversity graph correspond to viable strains in a given growth condition. For each viable strain i, we construct a feature vector, which consists of the maximum concentration values (s ) that is produced during its growth on a specific single-carbon environment. This vector is used as a metabolic blueprint for the potential interactions of the strain. As shown in Equation 10, for each pair of strains the relative difference of their feature vectors is calculated and the maximal difference over all byproducts is used as a weight for the interaction between them. According to the above definition, the edge weights in diversity graphs take values between 0 and 1, where weights equal to 0 imply that the two strains are identical regarding by-production and weights equal to 1 imply that (at least) one of the two strains provides a novel byproduct to the other strain. As the metabolic products of a strain depend on the growth environment, a different diversity graph is constructed for each single-carbon environment.Specifically, single-gene knockouts are applied on the  network  to generThe by-production efficiency is defined as the ratio of by-production intake rate to primal carbon resource uptake rate. As shown in Additional file E. coli strains under different carbon-source conditions and reveal biologically relevant properties such as the association between metabolic difference and its evolutionary trait. In this study, the graph-theoretic analysis is performed on the  weighted representations of the diversity graphs.Several graph-theoretic measures are applied in the diversity graphs to allow the direct comparison of the overall metabolic variability of The definitions of the graph-theoretic measures used in this study, which include strength centrality, assortativity and clustering coefficient are given in Additional file The composition of a metabolically interacting strain community can be assumed to consist of individuals with the potential to differently shape the given environment and provide each other with products of their metabolism. To ensure compositions of strains with different metabolic capabilities, each strain must be different from all the other strains. Therefore, compositions of strains with different metabolic capabilities correspond to cliques  in a diversity graph Figure . FurtherBecause of the genetic robustness and redundancy that is inherent in metabolic networks under single gene knockouts -46, therIn this work, growth efficiency concerns the ability of a cell population to maximize its growth performance in a batch culture given an initial limited amount of resources. The growth performance of a cell population is measured with respect to the maximum (endpoint) biomass concentration. If the cellular population consists of different cells, then the growth performance of the heterogeneous population corresponds to the total biomass concentration of the group, which is determined by the summation of the biomass concentration of each of the members of the group.absolute and the relative benefit. If the group performance of an heterogeneous cell population, g, is superior to the performance of any (wild type or mutant) monoculture, mi, then the heterogeneous community under study is beneficial. We term this benefit 'absolute'. However, the condition of 'any' can be relaxed, so that the growth performance of a heterogeneous community is compared to the homogeneous performances of its corresponding community members. In this case we call the benefit 'relative'. The relative benefit indicates whether there is group benefit, that is, whether the group is more efficient as a whole than the efficiency of any of its members when functioning as individuals.In cases of homogeneous, monoclonal cell populations, individual growth performances are compared with each other under the same initial conditions to determine the population with superior performance. To quantitatively describe superior performance in heterogeneous populations, we define the i elements correspond to all strains or only the members of the specific group. If the co-culture does not show improved growth relative to corresponding monocultures then the benefit is negative.To measure benefit we use Equation 11. Absolute and relative benefits differ with respect to whether the E. coli strains are henceforth named after the name of the gene that has been deleted.For simplicity reasons, E. coli strains (see Methods). Each single-carbon-source growth condition corresponds to a different diversity graph. The node centrality [Diversity graphs are constructed in order to represent pair-wise differences in by-production between ntrality ,48 is a The strength distribution of each diversity graph  after structural compression that maps all metabolically redundant nodes of same connectivity onto a super node (see Methods) are shown in Figure The binary graph representations of two examples . The maximum clique size of a diversity graph reflects the actual number of the different metabolic patterns regarding by-production and determines the upper bound of the metabolic diversity that emerge in a given growth condition under single-gene knockouts. As shown in Figure N is the number of by-products and if we consider a metabolite as only produced or non-produced by a strain, ignoring its concentration, then the total number of different subsets is 2N. However, as shown in Additional file N. This is due to the fact that the metabolic pathways are coupled so that activating a pathway that leads to the production of a metabolite affects the production of other metabolites as well.Since the metabolic variability is defined with respect to by-production, the total number of the metabolites that are by-produced bounds the maximum metabolic variability that can emerge in a given growth condition. Theoretically, if Cliques are found using the binary representation of the diversity graphs, which is produced using a threshold of 0.6 in the corresponding weighted graph (see Methods). However, as shown by the edge weight distributions  to express gene conservation as introduced in the study of Gerdes et al. [Two strains are heavily connected in the diversity graph if they are highly different with respect to their by-products. The assortativity coefficient -53 is uss et al. . A graphAs shown in Additional file It was previously shown that the diversity graphs are highly centralized consisting of many nodes of low centrality and a few nodes of high centrality. The frequency of appearance of the central and the non-central nodes across the carbon conditions is shown in the left panel of Figure -10).We define the gene conservation density as follows. We use a resolution step of 10 mutants and measure the percentage of mutants, in which the corresponding deleted genes have a conservation value (ERI) above 0.7. As shown in the right panel of Figure To conclude, most environment-invariant central mutants correspond to deletions of highly conserved genes. This observation seems controversial to the evolution of polymorphism, which necessitates the presence of unique phenotypes, because if a gene is evolutionary conserved, the mutant derived from its deletion is expected to be evolutionary unstable. However, it suggests that among all potential communities only those consisting mainly of mutants that are environment-specific are likely to evolve.glycolate, acetate, glycine, glucose, pyruvate and melibiose. As defined in the Methods section, the term relative benefit is used when the growth performance of the group is compared with the growth performance of the monocultures of each individual member mutant, whereas the term absolute benefit is used when the co-culture is compared with each monoculture in the diversity graph.Previous results based on an exhaustive, computational evaluation of all pairs consisting of the wild-type and a single-gene knockout mutant have revealed several pairs of improved growth relative to the corresponding monocultures . An updaglucose. Both these mutants consistently appear as central nodes in the diversity graphs  compared to the growth performance of the wild-type population. As illustrated in Figure acetate and formic acid. These products are consumed simultaneously with the metabolism of glycolate  on limited % Figure . The mut% Figure . When thpyruvate. The nutrients that are exchanged between them include glycine, glycolate, acetate and formic acid. The corresponding flux profiles of each strain in the community are shown in Additional file glycolate and glycine are consumed respectively by the strains b0721 and b3403 during the metabolism of pyruvate. This parallel consumption of the by-products with the main resource increases the maximum growth rates of both mutants  as well as the interactions between them. Metabolic interactions can alter the biochemical phenotypes of the participating strains allowing novel, unexpected phenotypes to emerge.pyruvate. Among these strains, b0721 and b4015 do not interact, while the strain b0728 both exploits the metabolites produced by these strains and provides by-products that these two strains can metabolize.A strain community is considered unexploited regarding by-production if cross-feeding interactions either do not occur or do not fully exploit the metabolites produced in co-culture. An unexploited community gives the opportunity to a new strain to utilize the available metabolites. One such example was discussed previously and concerns the community consisting of the strain b0721, b4015 and b0728 when growing on limited Interestingly, the growth performance of a strain community of any size linearly depends on the mean of the growth performances of its constituent strain pairs  with the potential to exchange products of their metabolism in a given growth condition. A different diversity graph has been constructed for each single-carbon environment. The properties of the diversity graphs reflect the inherent high metabolic redundancy of the cell to single-gene knockouts, reveal mutants of unique metabolic capabilities regarding by-production and show an evolutionary difficulty towards the establishment of polymorphism. Furthermore, findings of this work suggest that polymorphic communities consisting of strains specifically adapted to a given condition are more likely to evolve.Using a graph representation, this work has first explored the metabolic variability regarding by-production among single-gene knockout In addition to the diversity graph and its structural analysis, a developed genome-scale metabolic model has been used to describe the co-growth of different cells in a batch culture. The proposed multi-competitor metabolic model was based on the existing dynamic FBA model , which sE. coli strain communities were built upon the genome-scale metabolic network reconstruction of E. coli [diauxic growth, or simultaneously depending on the medium [acetate in glucose batch cultures. In our simulations, both sequential and simultaneous substrate consumption examples are predicted, the accuracy of which remains to be experimentally validated. Efforts to model mixed-substrate growth based on the FBA framework have been proposed such as the work of Beg et al. [The construction of diversity graphs as well as the evaluation of the  E. coli  as well  E. coli . The pre E. coli -60. In te medium ,62. The e medium  for monog et al. , which pg et al. ,64,65 toOther sources of genetic to metabolic diversity such as multiple gene deletions or differential expression of certain genes can also contribute as nodes to the graph reconstruction and as potential strain-competitors in communities, beyond the single-gene knockout strains. Metabolic interactions across different species can also be analyzed under the framework proposed here, as more genome-scale metabolic network reconstructions are available. The results presented in our work are not primarily focused on evolutionary stable communities that may arise from cross-feeding. The method aims to identify and describe metabolic interactions between strains that co-exist in a batch culture. Further analysis regarding the evolutionary stability of the strain communities and their survival from 'cheats', who only gain the benefit from others ,19,66-69The authors declare that they have no competing interests.ET and MR conceived the study, IGT conceived the graph theoretic analysis, PP and MR participated in the coordination of the study and contributed to the interpretation of data and to the writing of the paper. ET conducted all experiments and wrote the paper. All authors read and approved the paper.Supplement Diversity Graph construction. Supplemental material showing analytically whether the construction of the diversity graph depends on the initial amount of the main source in a batch culture.Click here for fileSupplement Structural Analysis. Supplemental material providing further information regarding the definition and application of graph-theoretic measures on the diversity graphs.Click here for fileSupplement Functional Analysis. Supplemental material including additional information on the simulations of strain communities in batch cultures under several growth conditions.Click here for file


Burkholderia cenocepacia is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF) or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain B. cenocepacia J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of B. cenocepacia J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets.B. cenocepacia J2315. An iterative reconstruction process led to the establishment of a robust model, iKF1028, which accounts for 1,028 genes, 859 internal reactions, and 834 metabolites. The model iKF1028 captures important metabolic capabilities of B. cenocepacia J2315 with a particular focus on the biosyntheses of key metabolic virulence factors to assist in understanding the mechanism of disease infection and identifying potential drug targets. The model was tested through BIOLOG assays. Based on the model, the genome annotation of B. cenocepacia J2315 was refined and 24 genes were properly re-annotated. Gene and enzyme essentiality were analyzed to provide further insights into the genome function and architecture. A total of 45 essential enzymes were identified as potential therapeutic targets.We reconstructed the genome-scale metabolic network of B. cenocepacia J2315, iKF1028 allows a systematic study of the metabolic properties of B. cenocepacia and its key metabolic virulence factors affecting the CF community. The model can be used as a discovery tool to design novel drugs against diseases caused by this notorious pathogen.As the first genome-scale metabolic network of  Burkholderia cenocepacia is a Gram-negative opportunistic pathogen and formerly Genomovar III of Burkholderia cepacia complex (Bcc). The Bcc comprises at least 17 taxonomically related species [B. cenocepacia is the dominant Bcc species in patients with CF, accounting for between 50% and 80% of the infection cases [B. cenocepacia J2315 belongs to a clonal lineage known as ET12, which is of increased transmissibility and dominates fatal infections among CF patients in the United Kingdom and Canada [B. cenocepacia J2315 is notorious for its high resistance to the majority of clinically useful antimicrobial agents [ species , which h species  and huma species . B. cenoon cases ,7. As a l agents ,13, incll agents ,15. Yet B. cenocepacia J2315 has been sequenced and recently annotated [The genome of nnotated . It is oB. cenocepacia J2315, named as iKF1028, which accounts for the major metabolic pathways for the synthesis of each component of biomass and for the degradation of common biologically important carbon sources. Syntheses pathways for key virulence factors highly associated with metabolism were particularly emphasized and reconstructed. The in silico model was validated by performing BIOLOG substrate utilization assays, which can test the ability of a microorganism to oxidize various substrates simultaneously [iKF1028 provides researchers a framework to explore and understand the global metabolism of B. cenocepacia J2315 and its key metabolic virulence factors affecting CF patients upon infection. It allows a broad spectrum of basic and practical applications, especially the application for drug design which may open new doors for anti-infection strategies.In this study, we presented the manually curated genome-scale metabolic network of aneously . Model-dB. cenocepacia J2315, referred by the conventional naming rules [iKF1028, consists of 859 internal reactions (including transport) and 834 metabolites. The reconstruction accounts for 1,028 genes, covering 14.4% of the 7,116 protein coding genes identified from whole genome sequencing (http://www.ncbi.nlm.nih.gov/genome?term=burkholderia%20cenocepacia%20J2315). The model iKF1028 includes all major pathways required for cell growth and the degradation of common biologically important carbon sources of B. cenocepacia. Apart from these central metabolic pathways, model iKF1028 also includes pathways associated with key metabolic virulence factors, which provides insights into how the system-level metabolic properties affect pathogenicity. For an overview, the properties of the J2315 genome and the reconstructed model iKF1028 were summarized in Table Staphylococcus aureus [Acinetobacter baumannii [Mycobacterium tuberculosis [Salmonella typhimurium [Pseudomonas aeruginosa [iKF1028 and the above five recently published metabolic reconstructions is also illustrated in Table B. cenocepacia J2315 with key metabolic virulence factors is shown in Figure iKF1028 and, for each pathway, the number of reactions with assigned and non-assigned genes. The high ratio of gene-associated reactions shows that the reconstructed metabolic model of B. cenocepacia J2315 is reliable. [Additional file iKF1028 and Additional file The genome-scale reconstructed metabolic model of ng rules  as iKF10s aureus -20, Acinaumannii , Mycobacrculosis , Salmoneruginosa . A basicB. cenocepacia as a pathogen originates from the ability of its large genome to encode numerous virulence mechanisms [iKF1028. Table The success of chanisms , includichanisms -30, sidechanisms -33, cablchanisms -36, motichanisms ,38, hemochanisms , ZmpA anchanisms -42, phoschanisms , secretichanisms -46, lipochanisms ,47-49, achanisms . SynthesB. cenocepacia J2315 has an important role in both disease aetiology and antibiotic resistance [B. cenocepacia, all these studies focused on a certain part/component of LPS. So far, there is no systematic elucidation of the LPS structure and composition specifically for B. cenocepacia strain J2315, nor any global analysis on its biosynthesis process of the LPS. In this study, we depicted the detailed features of the complete LPS structure in B. cenocepacia J2315 by integrating all available reports on LPS. We also reconstructed the LPS-synthesis pathways supplemented with all necessary proteins involved and major metabolic precursors, as illustrated in Figure The LPS produced by sistance ,52. LPS B. cenocepacia strains possess multiple quorum sensing systems, which regulate the expression of versatile virulence determinants, such as biofilm formation and motility. Strain J2315 owns the ability to synthesize and recognize three types of chemical signals used for cell-to-cell communication: N-acylhomoserine lactones (AHLs), 4-quinolones (4Qs), and the DSF-like molecule cis-2-dodecenoic acid (BDSF). Two AHLs-based QS systems have been found in J2315, namely CciIR and CepIR [N-hexanoyl-L-homoserine lactone (C6-HSL) and N-octanoyl-L-homoserine lactone (C8-HSL) signals using acyl side chain  and S-adenosyl-methionine (SAM) as precursors [B. cenocepacia island (cci), which was the first time that cell-signalling genes were found on a genomic island [B. cenocepacia strains [B. cenocepacia can be recognized by Pseudomonas aeruginosa within which HHQ is converted into PQS which is one of the QS signals for P. aeruginosa [P. aeruginosa and B. cenocepacia. BDSF is a newly discovered signal molecule produced by B. cenocepacia [nd CepIR ,57, whicecursors . The Cepc island . The 4Qs strains . HHQ is  strains  and its ruginosa , highligocepacia . The synocepacia .iKF1028. Although there has not been any report demonstrating that B. cenocepacia can produce rhamnolipid, Dubeau et al demonstrated that Burkholderia thailandensis has the orthologs of rhlA, rhlB, and rhlC, which are responsible for the biosynthesis of rhamnolipids in P. aeruginosa [B. cenocepacia J2315 were identified as highly similar in sequence to rhlA, rhlB, and rhlC in both B. thailandensis  and P. aeruginosa . This facilitates us to hypothesize that B. cenocepacia can potentially generate rhamnolipids. Further experimental investigations are needed.The synthesis pathway of rhamnolipids was also reconstructed in ruginosa . By protB. cenocepacia J2315 were performed in triplicates in order to validate and refine the model. In silico growth on various substrates was simulated by setting each of them as sole carbon source and its uptake rate to 10 mmol/gcell/h under aerobic conditions based on M9 minimal medium. The simulation was performed on the ToBiN platform by flux balance analysis, as described in Methods. Of the 95 carbon sources tested, 40 could be directly compared with the in silico model of B. cenocepacia J2315, iKF1028. Preliminary disagreement between BIOLOG assays and in silico predictions were probably due to metabolic gaps, improper gene annotations and unacquainted transporters. These discrepancies were checked through gap analysis and literature mining. After continuous gap-filling and network refinement, the overall prediction accuracy was improved to 87.5%, a value that supported iKF1028 as being a proper reconstruction of the B. cenocepacia J2315 core metabolism (comparison results are showed in Table BIOLOG substrates utilization assays for in silico and in the BIOLOG assays. Then we made the assumption that each of these carbon sources could be transported into the cell (i.e. to function as intracellular compounds) and re-tested whether the in silico model can grow on each of them. The results showed that 11 of the 14 carbon sources enabled iKF1028 to grow after applying the above assumption. This supports the hypothesis that J2315 lacks of transporters for all those 11 carbon sources, even though their catabolic pathways are complete. For the rest 3 carbon sources, the agreement between the in silico results and BIOLOG assays remained.Of the remaining 55 carbon sources tested, 14 were indirectly compared with the model due to the missing knowledge of whether the transport mechanisms of these compounds exist in J2315 or not. Initially, all those 14 carbon sources showed a no-growth phenotype both As the catabolism of the remaining 41 carbon sources out of 55 has not been well studied and information regarding their role in the cell could not be found in public resources, these 41 carbon sources cannot be analyzed in our model.  could be identified in the Burkholderia Genome Database and KEGG. By querying GeneDB, we found that the genomic location from 872938 to 873820, named BCAL0802, was not assigned any function in the above two databases. A BLAST search of BCAL0802 against the UNIPROT database revealed a perfect match with IspE from other B. cenocepacia strains. Consequently, BCAL0802 is annotated as a 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase and this missing reaction was supplemented into the model iKF1028. This example exemplifies how the reconstruction process can drive the reconciliation of isolated data from different biological databases.Another type of annotation refinement was based on gap analyses, which pinpointed reactions for which the gene products involved were missing. For instance, we identified a missing reaction that should be catalyzed by IspE and that takes part in the biosynthesis of polyprenyl-PP, a necessary precursor of the ubiquinone biosynthesis. The IspE encoding genes for other strains of BIOLOG substrate utilization assays have already been successfully used on the refinement of metabolic reconstructions . It is ai.e. no biomass yield) under certain conditions  [iKF1028 was used as a framework to predict computationally identified essential genes in B. cenocepacia J2315 on both M9 minimal medium and synthetic CF medium (SCFM). About 19% (192) and 15% (154) of the 1,028 metabolic genes included in iKF1028 were predicted to be essential on M9 and SCFM media, respectively. There are more genes predicted as essentials on M9 than on SCFM, which indicates the influence of the living environment on the bacterium. 147 overlapping predictions were on both media. These essential genes are unequally located on the three chromosomes and most of the essential genes are located on chromosome 1  . Identif1 Figure . This rein silico essential genes predicted on SCFM with experimental essentiality data for P. aeruginosa PAO1 and P. aeruginosa PA14 [B. cenocepacia. As both P. aeruginosa and B. cenocepacia are CF pathogens, and B. cenocepacia was historically classified under the genus Pseudomonas [P. aeruginosa strains was chosen for comparison in order to reduce the effect of strain-dependent variation. Totally, there are 294 in silico essential metabolic and non-metabolic genes of B. cenocepacia J2315 with high similarity to the common set of P. aeruginosa by BLAST searches, of which 91 in vivo essential genes are present in iKF1028. Genes in the in vivo essential set but not in the iKF1028 were assumed to be involved either in non-metabolic functions or in accessory functions of metabolism. A total of 55% (50) of the in silico predicted essential genes agreed with the in vivo essential genes based on gene homology with P. aeruginosa  relationship of "BCAL0660 or BCAL3421". Through in silico gene deletion study, BCAL3421 is identified as non-essential, which is inconsistent with the in vivo essential gene results. Such discrepancy was subject to further analysis. AccA, AccB, AccC, and AccD were four subunits of Acetyl-CoA Carboxylase (ACC) catalyzing the first step of fatty acid biosynthesis [iKF1028. Further studies are necessary to validate the function of BCAL0660.Based on gene essentiality analysis, the genome-scale metabolic model of ynthesis . The genynthesis ,74. Furtynthesis . Taken ti.e. imposing the fluxes through these reactions to zero) is lethal under certain conditions. Through FBA using iKF1028,we could obtain a collection of essential enzymes (protein), based on which 45 essential enzymes were identified as potential drug targets and supported by experimental evidences from literatures. There are 39 of them which were also predicted as drug targets for P. aeruginosa PAO1 [B. cenocepacia J2315 and P. aeruginosa PAO1. Among 39 targets, there are 9 targets, namely AccA, AccB, AccC, AccD, MurA, FolP, PhoA, RibE, and RibH, which have approved drugs in the DrugBank database [Essential enzyme/protein refers to a gene product  for which individual deletion  [B. cenocepacia and its resistance to polymyxin B [B. cenocepacia J2315. Further studies are necessary to test their applicability. An overview of the 45 proposed targets is given in Table The other 6 potential targets, namely ArnT, ArnB, ArnC, ArnA1, ArnA2, and Ugd are unique in s (AMPs) . AMPs has (AMPs) ,79. It hs (AMPs) . The UDPymyxin B . These tB. cenocepacia J2315, a Gram-negative pathogen for CF patients. An iterative reconstruction process led to the establishment of the model, termed iKF1028, which captures the important metabolic capabilities and biosynthesis of key metabolic virulence factors. The model iKF1028 shows its predictive potential when compared with BIOLOG assays. Model-driven analyses on gene annotation refinement and identification of gene and enzyme essentiality analyses are helpful to understand the genome and discover promising novel drug targets. Through careful investigation, we proposed 45 enzymes that catalyze reactions predicted to be essential for growth with priority to be considered as drug targets. The model will keep being further validated and improved with experimentally determined biomass composition, large-scale gene deletion experimental data, proteome, and metabolome data, as they become available for B. cenocepacia. The model herein developed provides a valuable tool to explore the metabolic space of B. cenocepacia, to describe its metabolic wiring under a range of conditions, to pinpoint possible targets and to generate testable hypotheses. Taken together, our study underlined the value of the model iKF1028 as a framework to systematically study the metabolic capabilities of B. cenocepacia and its metabolic virulence factors affecting the CF community.In this study, we reconstructed the first manually curated genome-scale metabolic network of B. cenocepacia J2315 is illustrated in Figure http://www.lifewizz.com), which was first mentioned in the paper [http://www.coin-or.org). The platform works in a similar way as the COBRA toolbox with the main difference that, by being web-based, it permits users to adopt a more efficient and collaborative workflow.The reconstruction process for he paper . ToBiN iBurkholderia cenocepacia J2315 available at the Burkholderia Genome Database (http://www.burkholderia.com). To link annotated genes to proteins and proteins to reactions, biological databases such as KEGG, GeneDB, UniProt, BRENDA, Transport Classification Database (TCDB), and TransportDB were used [An initial draft reconstruction was derived from the annotated genome of ere used -88. ManuAfter the initial reconstruction was generated, gaps in metabolic pathways necessary to produce biomass components and key virulence factors were filled by cautious literature mining, BIOLOG substrates utilization assays, and BLAST searches on homology and protein sequence similarity analyses ,90. The iKF1028 under various environments. In addition to minimal medium, synthetic cystic fibrosis medium (SCFM) representing the physical living environment during CF infection was simulated in silico to investigate the metabolic flux distribution in a CF-like condition.Flux Balance Analysis (FBA) was carried out throughout this study to explore the metabolic capabilities of B. cenocepacia J2315 was adapted by selecting the well-studied biomass composition of E. coli as a template [B. cenocepacia. However, the amount of metabolic precursors to formulate the cellular component was specific to B. cenocepacia according to previous study [B. cenocepacia [The biomass composition in the genome-scale metabolic model of template , since tus study . Moreoveocepacia ,64,92,93Flux balance analysis (FBA) is an algorithm based on linear programming (LP) and on the assumption that the represented metabolic network is in steady-state . Being a LP problem, FBA also requires the selection of an objective function and of whether the value for that same function should be maximized or minimized. FBA is usually used to compute the optimal growth yield (the maximized objective function) based on the assumption that the evolutionary fitness of the organism depends on growth alone and, consequently, the implicit regulatory mechanism are organized to permit the theoretical maximal growth. If the system of equations (stoichiometric matrix which represents the metabolic network) is feasible, the algorithm generates an optimal flux distribution for that same network, taking into account the imposed thermodynamic constraints  and limits on substrate uptake rates. The mathematical description is as follows:S is a stoichiometric matrix containing i rows representing metabolites and j columns representing reactions, v is a vector of all reaction fluxes, vmin and vmax are imposed lower and upper bounds on flux vj respectively, and cT is a vector of coefficients for each reaction that is to be maximized.Where in silico for strain J2315: M9 minimal medium [43-, SO42-, NH4+, H+, Fe2+, K+, Mg2+, Na+, H2O, and thiamine, with glucose or other BIOLOG substrates as sole carbon source; and synthetic CF sputum medium (SCFM) [Two different living environments were simulated l medium , which cm (SCFM)  represenm (SCFM) .in silico predictions is provided in the supplemental material. [Additional file To validate the model and estimate the metabolic capabilities of strain J2315, BIOLOG assay was performed by using various carbon sources for strain cultivation . The BIOin silico predictions on gene and enzyme essentiality [FBA can be used to interpret genetic modification, such as gene deletion and enzyme inhibition, and subsequently make comprehensive ntiality . To asseBurkholderia cenocepacia J2315. KCF designed the study and performed the analysis. KCF and CMCL arranged the BIOLOG substrate utilization analysis. JW and VMDS supervised the research. KCF, CMCL, VMDS, and JW drafted the manuscript. GP provided technical support for the reconstruction of B. cenocepacia J2315 in ToBiN. MG was involved in the early stage of the development of iKF1028 in ToBiN. All authors read and approved the final manuscript.KCF, HSZ, SCY, SHC, and KLZ carried out the reconstruction of iKF1028Reconstructed metabolic network model Click here for fileiKF1028 in SBML formatModel Click here for fileBIOLOG assaysClick here for fileGene essentiality comparison resultsClick here for fileBiomass detailed informationClick here for filein silicoSynthetic CF medium simulated Click here for file


The creation and modification of genome-scale metabolic models is a task that requires specialized software tools. While these are available, subsequently running or visualizing a model often relies on disjoint code, which adds additional actions to the analysis routine and, in our experience, renders these applications suboptimal for routine use by (systems) biologists.http://f-a-m-e.org/.The Flux Analysis and Modeling Environment (FAME) is the first web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program. Analysis results can be automatically superimposed on familiar KEGG-like maps. FAME is written in PHP and uses the Python-based PySCeS-CBM for its linear solving capabilities. It comes with a comprehensive manual and a quick-start tutorial, and can be accessed online at With FAME, we present the community with an open source, user-friendly, web-based "one stop shop" for stoichiometric modeling. We expect the application will be of substantial use to investigators and educators alike. In the post-genome era, genome-scale stoichiometric models have gained popularity, as the absence of the need to experimentally determine model parameters one enzyme at a time made it possible to build bigger metabolic models than ever before . HoweverAlthough tools that facilitate common tasks are available  software environment for most routine questions a (systems) biologist would want to ask a genome-scale metabolic model. Based on our own extensive experience in developing and using such models, we have developed FAME: the Flux Analysis and Modeling Environment, a "one stop shop" that addresses these issues.In an analysis of available applications, the programs that approach FAME's functionality the closest are the aforementioned COBRA Toolbox, OptFlux, and CellNetAnalyzer. We will discuss these tools here, but also refer to Table The COBRA Toolbox  is one oOptFlux  and CellBesides the externally visible HTML and CSS that convey its markup, the parts of the FAME web application that do the work are implemented in PHP5 and JavaScript. PHP was chosen because it is a fast, browser independent language that integrates well with other programs. FAME uses the Python-based PySCeS-CBM (Python Simulator of Cellular Systems-Constraint Based Modelling toolkit)  as an inmodel creation, result generation, and interpretation   Figure . Traditi) Figure .de facto standard for representing such models. A proposed SBML Level 3 package "Flux Balance Constraints" allows the definition of constraint based models [FAME allows users to either upload their own preexisting model Figure , or to bd models . Models Once a model is loaded, FAME offers all tools a seasoned constraint-based modeler would need to study physiology. This includes easy editing of the flux bounds on all internal and exchange reactions, editing existing reactions, adding/deleting objectives, recognizing dead-end metabolites (orphans), recognizing synonymous reactions, and assigning reactions to different or new compartments Figure . Adding S. cerevisiae model [Analysis commands are forwarded to PySCeS-CBM, which handles the mathematical operations and returns the results to FAME. Operations supported by PySCeS include Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA). Given the more compute-intensive nature of the latter, if a subset of pathways is selected, only reactions in those pathways will be included in the variability analysis. For instance, whereas an FBA of the ae model  from BiGThe included Gene Association Workbench allows users to intuitively take advantage of gene association information present in the model metadata, e.g. by simulating (multiple) knockout mutants. Results are presented in the same interactive manner as the other analyses. If on any occasion the model system is over-determined, FAME will relay the solver's message that the solution status is 'infeasible' and additionally issues a warning to the user. Under-determined systems will run and produce a result; upon interpreting run results, users may run further analyses such as FVA to assess the properties of the solution space.The visualization module generates images in SVG format, based on the analysis results returned by PySCeS. The advantages of using SVG are manifold, some of the more notable being image scalability and ease of editing using third party software. Depending on the web browser used, users may need to download a (free) plug-in to view the images.For each selected pathway an interactive KEGG-like image is drawn Figure , on whicMany elements in the results images are clickable (another advantage of the SVG format), to make more information available more conveniently. For instance, clicking a metabolite will display an overview of reactions producing or consuming it, along with the KEGG information page for the metabolite, while clicking a reaction will display that reaction's KEGG information page.With FAME, we present the community with an easy to use web-based "one stop shop" for the manipulation and execution of stoichiometric models. It enables biologists to create or import models, edit them, run them at the click of a button, and visualize the results from the browser window. We expect that its install-free integration of execution and visualization will appeal to investigators and educators alike. Future releases of FAME will feature integration with web-based annotation services and further analysis options. Finally, the novel SOAP interface to PySCeS-CBM will facilitate the creation of user-friendly interfaces based on PySCeS that will uncover powerful modeling functions that may otherwise remain hidden behind the ever-enigmatic command line cursor.http://f-a-m-e.org/, where a full user manual and guided tutorial are available. PySCeS-CBM and Mariner are also open source, and can be downloaded from http://pysces.sourceforge.net/cbm. FAME and PySCeS/Mariner are covered by their own respective BSD-style licenses, which can be found on the respective web pages and, in short, entail that they are open-source and free to use for both academic and non-academic users.FAME is intended and offered as a web service, but can also be installed locally, as source code will be made available upon request. FAME can be accessed online at JB created FAME. BGO created Mariner and PySCeS. JB, BGO and BT wrote the paper. All authors read and approved the final manuscript.


Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution.We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD), a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart.The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery of the network. This suggests that natural selection and biochemical correlations can act both to diversify and to narrow down metabolic evolution. E. coli and its closest neighbours in the γ-proteobacteria class, and more thorough studies are required to draw more accurate conclusions. In this paper we take a broader view by incorporating 134 bacteria from 16 phyla, and utilising a network perspective to gain quantitative insight into structural correlations between metabolic networks of different species. Comparative studies of microbial metabolic networks have shown that the overlap in metabolic capability between species correlates well with evolutionary kinship, and that phylogenetic trees based on this metric are similar to those which are based on more traditional measures of similarity in 16 S rRNA [Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions. From a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms, ranging from extremophiles thriving in extreme conditions and feeding on inorganic compounds such as ironsulfide, to endosymbiontic parasites who are dependent on their hosts and cannot themselves synthesise essential compounds like aminoacids. In spite of this diversity there are still reactions and pathways which are found in a large number of organisms, such as the glycolytic pathway, which probably emerged early in the history of life on earth . Changes6 S rRNA ,11. It h6 S rRNA . The pro6 S rRNA  showed t6 S rRNA -17 and s6 S rRNA  correlatWe have focused on the reaction-representation of the metabolic networks of 134 bacterial species. The metabolism of an organism can conveniently be represented by a network, which consists of two types of nodes: reactions and metabolites. In the network a directed link connects a metabolite and a reaction if that metabolite is a substrate of the reaction, and conversely a link connects a reaction node and a metabolite if the metabolite is produced in that reaction. However, for simplicity one often considers a projected network where only reaction nodes are present. This means that the nodes in the network represent metabolic reactions, and there is a directed link between two nodes if any of the products of one reaction is the substrate of the other. We have studied both the metabolic networks of a union of 134 bacterial species, as well as those of a subset of two phyla, Chlamydiae and Proteobacteria. One of the key concepts in this work is the organism degree (OD) of a metabolic reaction which denotes the number of different species it is present in. This is of course a crude approximation and ignores species specific information, which could be used for inferring the evolution of metabolic networks. For example, one could from known phylogenies and metabolic networks infer the gain and loss of reaction along the phylogenetic tree, as was done by Pal and colleagues . Furthersuper node if there exists a direct link between them (see Methods for more details). The smallest super nodes have been omitted in the figure for the sake of clarity, resulting in a highly disconnected network for the largest union. Figure s > 1 and drops below at s = 1 is because there are many more single nodes, i.e. reactions that are not connected to others with the same OD, and fewer large super nodes in the randomised version. This suggests that reactions with the same OD tend to be found close to each other in the real data . To quantify the difference between the two distributions in figure μrand and σrand denote mean and standard deviation of the distribution when the OD is randomised, respectively. The z-score estimates how likely it is that the observed value μrand = 1.45 and σrand = 0.01 give Z ≈ 70 which corresponds to a p-value of < 2 × 10-16 . In summary, based on the course grained picture of the metabolic networks we infer that (i) metabolic reactions with high OD tend to be located close to the core and (ii) that reactions with the same OD are likely to be found close to each other. These conjectures are investigated in more detail below.In order to visualise structural correlations in the metabolic network graphically we made a coarse-grained representation of the networks where reactions of the same OD were collapsed into one In order to quantify to what extent reaction nodes with high OD are enriched close to the centre of the metabolism we calculated the average OD as a function of distance (number of nodes) from the most central reaction node in the undirected reaction network. The most central reaction was chosen as the one with the highest betweenness centrality conditioned that it was present in all organisms. For the smaller families the reaction with highest betweenness centrality coincided with the maximum OD , while In order to get an understanding of the interplay between evolution and biochemical constraints on the growth and development of metabolisms we made use of a network growth model which is completely neutral. By this we mean that reactions are removed or added to the metabolism based only on their biochemical compatibility, as defined by the BioCyc database, with the existing metabolism, and no consideration is taken as to the usefulness or change in fitness the addition or removal gives rise to. Starting from a single reaction, we incrementally add reactions which can be linked directly to at least one of the reactions already present in the metabolism (more details are given in the Methods section). The initial, or seed reaction, can be chosen in several ways and we explored two different variants: (i) the initial seed is fixed and chosen as a reaction which is present in all organisms, or (ii) the initial seed is a different random reaction for each generated network. Adding and removing reactions or metabolites based on known metabolisms to construct synthetic metabolic networks is not a new concept. The model presented here is inspired by the work of Maslov et al. , where rscore) compared to the total number of reactions (Ntot) in the network. This measure reflects the size of the metabolic core when compared to the size of the entire network. Second, the size of the largest super node with maximum OD relative to the total number of reactions with maximum OD To put numbers on the question of the existence of a commonly shared metabolic core, we analysed the coarse-grained network figure  and calcp < 1), add/remove chunks of reactions, or that all substrates must be available before a new reaction can be incorporated. This is all very true, however the model is not meant to give a detailed description of the evolutionary path of metabolic networks. Its purpose is to capture how the diversity of possible networks is influenced by the underlying biochemical constraints. In this setting it effectively means that it is the fluctuations of the "surface" of the networks (reactions not included in the common core at the periphery of the network) that is the interesting property, which is independent of the details of the growth model. The model used here is then just the simplest, most straight forward, way of obtaining an ensemble of networks of a given size, grown from a given seed reaction. The growth model has an obvious flavour of the retrograde model of metabolic evolution, where new reactions are added at the periphery of the network. It is therefore not surprising to find that nodes with high OD are found close to the centre of the network and that the average OD decreases as we move out from the centre, but the similar results for the real metabolic networks suggest that an analogous growth process has shaped their evolution. Our results thus point to the retrograde model as being more likely, however this should be looked upon in the light of HGT, as has been suggested in [The structural correlations between metabolic networks from different species bear a clear mark of their evolutionary history. Species which are closely related usually have more similar metabolisms compared to distant relatives. This feature can clearly be seen in figure ested in .We have presented a study of the structural correlations in the metabolic networks of 134 bacterial species. By considering the union-network formed by these networks we were able to show that the universal reactions lie at the centre of the network and more specific reactions are placed closer to the periphery. The analysis of two smaller groups of species taken from the Chlamydiae and Proteobacteria phyla showed that these union-networks exhibited metabolic cores onto which species-specific subnetworks were attached. These features could be reproduced by a simple network growth model confirming that horizontal gene transfer is a prominent mechanism in the evolution of metabolic networks. The model does however overestimate the number of species specific reactions, and also overestimates the size of the common metabolic core, highlighting the effect of environmental selection and biochemical correlations.is, of the super node, i, equals the number of reactions that were condensed together. For the small subsets of the phyla Chlamydiae and Proteobacteria two neighbouring reaction nodes are placed in the same super node if their OD is exactly equal. For the much larger set of 134 organisms the sparseness in OD naturally increases, which we compensate by letting each super node represent a range of 10 OD values . Two super nodes are then joined if there exists at least a reaction in each super node which are linked together. In figure The coarse-graining of the union of the metabolic networks was done by clustering all nodes that are connected to each other, and have the same OD, into one super node, using a depth-first algorithm. The size, Bordetella parapertusis (including itself), and the subset of the Chlamydiae was chosen as all the species of that phyla which were included in the phylogenetic study, which amounted to 7 species. A full list and the two subsets of species are given in additional file In our analysis we have done a small-scale analysis on a subset of two bacterial phyla (Proteobacteria and Chlamydiae) as well as a large-scale analysis based on a union of bacterial species taken from the MetaCyc database . In ordeThe data describing the metabolism of the 134 species was downloaded from MetaCyc (22/10/2009) . We tranR0. In each time step t of the model we do one of the following: (i) with probability p we add a reaction node to Rt-1 by at random picking a node which is not in Rt-1, but is neighbour of at least one of the current nodes (as defined in the underlying BioCyc-network). With complementary probability 1- p we pick a node in Rt-1, which is a leaf, i.e. has only one link, and remove it. The following is repeated either until the network defined by t Rreaches a given size or after a given number of time steps. In our version of the model the seed set is only one reaction; either the Methenyltetrahydrofolate cyclohydrolase-reaction  or a randomly chosen reaction, and the parameter p was set to unity, meaning that we only add reactions to the network. If we would have used p < 1 the end result would have been roughly the same  but would have required more iteration steps. Since we wished to produce a large collection of metabolisms, and not study their exact evolution, we used p = 1. The model was run 134 times to produce an ensemble of organisms having the same size as the union of bacteria. Also, the size of each individual network was set to match exactly that of each considered species. In order to make sure that the choice of seed set, in the fixed reaction scheme, does not bias the dynamics of the model we also ran the model with a starting node with OD = 1. The result of this simulation can be found in additional file The network growth model used in this study was inspired by a previously published model by Maslov et. al , which wSB conceived the idea behind the paper, analysed the super-network and co-wrote to the manuscript. PG analysed the MetaCyc-data, implemented the network growth model and co-wrote the manuscript. LL analysed the MetaCyc and model data, and co-wrote the manuscript. All authors have read and approved the final manuscript.Additional table and figures. The file contains additional Table S1 and additional figures S1-S3.Click here for file


Mitochondria are a vital component of eukaryotic cells and their dysfunction is implicated in a large number of metabolic, degenerative and age-related human diseases. The mechanism or these disorders can be difficult to elucidate due to the inherent complexity of mitochondrial metabolism. To understand how mitochondrial metabolic dysfunction contributes to these diseases, a metabolic model of a human heart mitochondrion was created.iAS253, containing 253 reactions, 245 metabolites and 89 transport steps across the inner mitochondrial membrane. To demonstrate the predictive abilities of the model, flux balance analysis was used to calculate metabolite fluxes under normal conditions and to simulate three metabolic disorders that affect the TCA cycle: fumarase deficiency, succinate dehydrogenase deficiency and α-ketoglutarate dehydrogenase deficiency.A new model of mitochondrial metabolism was built on the principle of metabolite availability using MitoMiner, a mitochondrial proteomics database, to evaluate the subcellular localisation of reactions that have evidence for mitochondrial localisation. Extensive curation and manual refinement was used to create a model called The results of simulations using the new model corresponded closely with phenotypic data under normal conditions and provided insight into the complicated and unintuitive phenotypes of the three disorders, including the effect of interventions that may be of therapeutic benefit, such as low glucose diets or amino acid supplements. The model offers the ability to investigate other mitochondrial disorders and can provide the framework for the integration of experimental data in future studies. Due to the involvement of the mitochondrion in many essential metabolic processes it is implicated in a wide and growing variety of disorders and pathologies . HoweveriAS253 by using the MitoMiner database and other public resources. We demonstrate that by using flux balance analysis (FBA) normal mitochondrial metabolism can be successfully simulated. The FBA method has been described in detail elsewhere [min ≤ ν ≤ νmax) of individual reactions. Directionality is incorporated by setting either the upper or lower bound of a reaction to zero. Although these constraints limit the number of possible metabolic flux distributions that satisfy the steady state, a very large number of possible solutions still exist, so a unique solution representing a flux distribution for the model is found by using an objective function. The objective function is a set of criteria chosen to match the biological objective of the system in vivo and an optimal flux distribution is found that maximises (or minimises) this function by using linear programming. If the objective function of the model matches the biological objective of the system, the optimal flux distribution should correspond with the flux distribution in vivo. For example a common objective function used for mitochondrion metabolism is maximum ATP production, so to reflect the primary role of the mitochondrion in cellular energy production.Here we describe the construction of a model of mitochondrial metabolism in human heart cells called lsewhere ,8, but bThe benefits of the FBA approach are that detailed kinetic information such as concentrations are not required, allowing the simulation of large models that would be infeasible using alternative methods. This makes the technique particularly attractive in biomedicine, where a phenotype can be the result of many interacting reactions and detailed kinetic information for each enzyme involved under disease conditions is unavailable and difficult to obtain experimentally. Furthermore, the models can be used to discover and assess the effect of interventions on a disease state that may restore the system to a normal state and thus be of therapeutic value.In our new model we account for the most recent stoichiometric information of the ATP synthase , thermodThe first disorder, fumarase deficiency (OMIM: 606812), is caused by impairment of the fumarate hydratase enzyme, which converts fumarate into malate in the TCA cycle. The condition is exceptionally rare with around 40 cases reported globally to date . The effThe second disorder, succinate dehydrogenase deficiency OMIM: 252011) affects mitochondrial complex II, which links the TCA cycle with the electron transport chain by coupling the conversion of succinate to fumarate to the quinone pool. In cases of isolated succinate dehydrogenase deficiency the phenotype is highly variable and can include Leigh syndrome -17, leuk11 affectThe third disorder, α-ketoglutarate dehydrogenase deficiency (OMIM: 203740) affects the conversion of oxoglutarate to succinyl-CoA in the TCA cycle. The condition is extremely rare with only 23 cases recorded in the literature ,25 and iin vivo in the blood, urine and cerebrospinal fluid of patients. Furthermore, the simulations suggest that treatments, such as a low glucose diet or amino acid supplements, may reduce the risk of lactic acidosis and increase ATP production and thus be of therapeutic value.We show that simulations using our model can replicate the disease state phenotypes of these disorders by correctly predicting the effects of these perturbations on normal mitochondrial metabolism. The simulations show how alternative metabolic pathways can maintain production of ATP, but lead to the over-production of other metabolites that must be excreted, as observed iAS253 , was coin vivo capabilities of the mitochondrion and to remove all thermodynamically disallowed flux loops from the model, 110 of the 258 reactions and 50 of the 89 transport steps in the model were assigned a directionality constraint. Reaction directionality was set using thermodynamic information, general rules of irreversibility, and information from several public resources and the primary literature.To more accurately reflect the iAS253 .The final model contained no orphan metabolites and all internal flux loops were eliminated. 247 reactions were capable of having a flux under the current boundary conditions between the cytosol and the matrix, with the remaining 6 reactions requiring the import of acetaldehyde or propanoate to have a flux, which is currently disallowed in the model. Details of the reactions, metabolites and constraints included in the model can be found in Additional Files Six separate pseudo reactions were devised to denote the metabolite requirements of the mitochondrion. This included the production of 20 amino acids required for protein synthesis, RNA and DNA, haem, ATP and lipids in a ratio found in the inner mitochondrial membrane of rat liver mitochondria . SeparaOne of the most noticeable effects of fumarase deficiency is the presence of fumarate in the urine. However, in the default model there was no mechanism for the efflux of fumarate from the system. Therefore, to model fumarase deficiency it was necessary to add an unconstrained transport step and boundary condition to allow fumarate in the mitochondrial matrix to leave the system. Separate simulations were run under each of the six objective functions with this adapted model and no flux was observed through the transport step or boundary condition under any simulation under normal conditions, showing that these additions did not affect the model.To model fumarase deficiency the reaction that represents the fumarase enzyme in the model R01082MM) was constrained to different flux values representing reduced enzyme activity and an objective function of maximum ATP production used. Compared to simulations under normal conditions, the most notable effect of these constraints was a dramatic drop in maximum ATP production of up to 96%  was constrained and an objective function of maximum ATP production used, succinate was effluxed from the system rather than fumarate. A large drop in ATP production was also observed that was slightly more pronounced than fumarase deficiency  . An efflux of oxoglutarate from the system was observed when the flux through the bypass was constrained by any level. An efflux of lactate was not observed under any of these conditions. To simulate the effect of a defect in the E3 subunit of α-ketoglutarate dehydrogenase that is shared with pyruvate dehydrogenase and ketoacid dehydrogenase enzyme complexes, the dihydrolipoamide dehydrogenase reaction was also constrained (R07618MM). This resulted in the production of lactate, as pyruvate from glycolysis could no longer enter the TCA cycle, but maximum ATP production remained stable, as fatty acid synthesis increased proportionally  to maintain acetyl-CoA production levels.iAS253 , the inclusion of the other pseudo reactions ensures the model can produce metabolites known to be required by the mitochondrion. However the flux distributions found when using these other pseudo reactions individually as objective functions are unlikely to be realistic, as no one objective function in isolation will match the biological objective of the system in vivo. The pseudo reactions that are used for these objective functions have been included in the model for convenience and to allow other researchers to easily replicate our results.A common objective function used with models of microorganisms is the production of biomass. Biomass represents all the metabolites that are required for growth of that particular organism, with the assumption that the organism is biologically optimised for maximum growth. The relative proportions of each metabolite in the biomass are derived from experimental figures measured from cellular biomass. However, a single biomass objective function cannot currently be devised for the mitochondrion, as no experimental figures exist for its composition. It is also unlikely that the biological objective iAS253 model.To demonstrate the predictive abilities of the model we simulated and analysed the complicated phenotypes of three disorders of the TCA cycle using the Patients with the most severe symptoms of the disorder often have residual fumarase activities near zero . This woet al. [iAS253 model predicts that if aspartate is available, this bypass is used. A relatively small uptake resulted in a large difference in maximum ATP production, which suggests it could be biologically relevant.The effect of aspartate on the system supports the hypothesis of Bourgeron et al. , which sin vivo may explain the oxoglutarate observed in urine of patients with fumarase deficiency.Oxaloacetate and malate also had a large effect by using the reactions of the malate-aspartate shuttle. In the simulations oxaloacetate was converted to malate before being transported to the matrix using the oxoglutarate transporter. Malate was then converted back into oxaloacetate to complete the TCA cycle. As a consequence of the transport step oxoglutarate was counter-exchanged from the matrix into the cytosol and then effluxed from the system. Such a mechanism The import of proline, arginine, glutamate and valine caused a large increase in ATP production due to the degradation of these metabolites into oxoglutarate, increasing the flux through part of the TCA cycle, and leading to an increase in fumarate efflux. The model predicts that a corresponding increase in the efflux of glutamine and alanine should also be observed. The production of glutamine was required to remove the ammonia produced during the deamination of these metabolites, whereas alanine efflux was involved in the glucose-alanine cycle, where pyruvate is converted into oxoglutarate Figure . Howeverin vivo.The uptake of bicarbonate had a large impact on ATP production by allowing the direct conversion of pyruvate to oxaloacetate. However, due to the production of lactate, and bicarbonate acting as a cellular buffer, it may not be available at sufficient levels to have a significant affect Serine had an effect on increasing ATP production, but only from its degradation to glycine, producing NADH and NADPH in the process. It did not directly contribute to the TCA cycle, which explains why even at high uptakes its effect on maximum ATP production is lower than the other metabolites except glucose.Although increased glucose uptake did increase ATP production the effect was small, while dramatically increasing lactate efflux. Therefore, although increasing glucose levels may be easily achieved physiologically, a low glucose diet, while increasing the levels of the other metabolites may be more beneficial to maintain ATP production, while minimising lactic acidosis.in vivo the uptakes of any of these metabolites are increased beyond normal limits and have a significant impact on ATP production. Although it may be unlikely that any one metabolite could sustain a high uptake flux under physiological conditions, it may be feasible that all of these metabolites contribute collectively to increasing ATP production. Further experimental data would be required to test these predictions and to further refine the model.It is not apparent if in vivo, these excess fatty acids may be stored in adipocytes or alternatively be expressed as an increase in the production of ketone bodies , a known cellular method to use excess acetyl-CoA, rather than a reduction in fatty acid metabolism. This would be supported by the elevated levels of acetoacetate in the blood plasma of two patients with the disorder (0.07 and 0.08 mmol/l) in comparison to controls (0.016 - 0.04 mmol/l), although levels of hydroxybutanoate appeared to be within the normal range [A common occurrence throughout these simulations was fatty acid metabolism was restricted, as acetyl-CoA could not enter the TCA cycle. If this occurs al range . AdditioThe simulations of succinate dehydrogenase deficiency were very similar to fumarase deficiency in both the reduction in ATP production Figure  and the in vivo is unable to sustain the large flux required for the TCA cycle to function at full capacity, this could explain the phenotype of oxoglutarate excretion. Gene expression data for patients with the disorder may give some indication whether the enzymes that constitute the GABA shunt are up-regulated in heart to allow the bypass to function. GABA also acts as an inhibitory neurotransmitter and its involvement in the bypass may begin to explain the encephalopathy reported in patients.The effect of α-ketoglutarate dehydrogenase deficiency on maximum ATP production was minor even when no flux was allowed through the reaction representing the enzyme. This was the result of a bypass known as the GABA shunt , which cAn efflux of lactate was never observed when only α-ketoglutarate dehydrogenase was constrained. Therefore to determine whether a defect in subunit E3 of the enzyme complex, which is also shared with pyruvate dehydrogenase and ketoacid dehydrogenase complexes, was responsible, the reactions representing the latter were also restricted. Only when pyruvate dehydrogenase was constrained was lactate produced, as not all pyruvate from glycolysis could enter the TCA cycle. This suggests that a lactic acidosis phenotype is the result of a defect in the E3 subunit, and isolated α-ketoglutarate dehydrogenase deficiency can be discounted.We have successfully simulated metabolism of heart mitochondria under normal conditions as demonstrated by close similarity of flux values with experimentally measured figures. We show that the model also has the ability to simulate the metabolic phenotype of three rare single gene disorders and illustrate that the model can provide an insight into the disease mechanism. As the mitochondrion is involved in many more complicated disorders including Parkinson's, diabetes and heart failure, the next step is to use this model with further development to simulate and understand these conditions. We believe that our model can be used as framework for integrating genomics, proteomics, metabolomics and clinical data and so contribute to the development of therapies for mitochondrial related diseases.The mitochondrion consists of four distinct components: the outer mitochondrial membrane, the intermembrane space, the inner mitochondrial membrane, and the mitochondrial matrix. For modelling metabolites, no distinction is required between the intermembrane space, outer mitochondrial membrane and the cytosol, as porins in the outer mitochondrial membrane allow any molecule below 3-6 kDa to exchange freely between the cytosol and the intermembrane space. In contrast the inner mitochondrial membrane is impermeable to most metabolites  and the Initial metabolite availability within the mitochondrial matrix was determined with the addition of inner mitochondrial membrane transporters to the model and defining the metabolite requirements of the mitochondrion. The inner mitochondrial membrane transporters represent the members of the mitochondrial transporter family, many of which are characterised and their substrates and transport mechanism  are known ,47. SomeThe next step in defining metabolite availability was the addition of reactions known to take place in the mitochondrial matrix . Networks were visualised using Cytoscape [The model was encoded in SBML format and validated using the SBML validator tool . KEGG idytoscape . All graReversibility of all the reactions was manually evaluated and a direction assigned using general rules of irreversibility  and estiAdditional constraints on the directionality of the reactions and transporters in the model were applied to prevent thermodynamically disallowed flux loops occurring, known as type II and III extreme pathways . Internain vivo. If core metabolism was disrupted by the import of a metabolite, the unconstrained boundary condition was identified and constrained to an experimentally determined value or directionality.To prevent unrealistic flux distributions from the unlimited uptake or efflux of metabolites, the fluxes through the boundary conditions were constrained. To determine which boundary conditions had a critical effect on the model and so required constraining, all boundary conditions were set to an arbitrarily small upper bound value of 0.01 μmol/min/gDW. Each boundary condition was unconstrained one by one and simulations were run for each objective function. Any large influx or efflux from an unconstrained boundary condition was investigated and constrained to experimentally determined values obtained from the primary literature with conversions to the correct units (μmol/min/gDW) where appropriate. A wet to dry weight conversion of five times was used where required, based upon the study of isolated mouse hearts . In someFumarase deficiency was modelled by constraining the fumarase reaction (R01082MM) to various percentages of its flux under normal conditions. Due to the non-linearity between enzyme activity and reaction flux in networks , it is nTo model succinate dehydrogenase deficiency the reaction that represents the enzyme (R02164MM) was constrained to a maximum of 33% and then 0% of its flux when the objective function was set to maximum ATP production. The 33% figure was chosen to represent that there is some residual enzyme activity in many patients, but as this does not directly correlate with flux, an arbitrary reduction in flux was chosen. No other alterations to the model were made. Simulations were carried out using each of the six objective functions. The effect of un-constraining each boundary condition on the system was then determined in separate simulations.The disorder of α-ketoglutarate dehydrogenase deficiency was modelled by constraining the reaction flux of the initial oxidation reaction of 2-oxoglutarate (R01700MM) to 33% of its level under normal conditions to represent reduced enzyme activity. Separate simulations was performed where the reaction shared with pyruvate dehydrogenase and ketoacid dehydrogenase complexes (R07618MM) were also constrained to various levels of the normal reaction flux to simulate the effects of a defect in the E3 subunit, which are shared between the complexes. In both cases simulations were carried out using each of the six objective functions.The authors declare that they have no competing interests.ACS conducted the reconstruction, and analyses. AJR conceived the study and participated in its design. Both authors contributed to, read and approved the final manuscript.Reaction details. Excel file detailing the reactions included in the model including the applied directionality, evidence for mitochondrial localisation and corresponding KEGG identifier, where applicable.Click here for fileMetabolite details. Excel file detailing the metabolites included in the model, the assigned compartment and corresponding KEGG identifier.Click here for fileApplied constraints. Excel file detailing the constraints placed on the boundary conditions and the mitochondrial transport steps.Click here for fileiAS253 metabolic model in SBML format. XML file of the iAS253 model, in SBML format. Boundary constraints and biomass pseudo reactions are included.Click here for fileSupplementary Figures. Supplementary Figure S1. Graph of the effect of varying succinate dehydrogenase flux on maximum ATP production. Supplementary Figure S2. Effects of metabolites on maximum ATP production while succinate dehydrogenase deficiency flux is at 33% of its flux under normal conditions. Supplementary Figure S3. Effects of metabolites on maximum ATP production while succinate dehydrogenase deficiency flux is at 0% of its flux under normal conditions. Supplementary Figure S4. The active pathways (red) that contribute to ATP production during succinate dehydrogenase deficiency. Black dotted lines show pathways that are inactive.Click here for file