Synechococcus UTEX 2973, a strain closely related to Synechococcus PCC 7942, was recently identified as having the fastest measured growth rate among cyanobacteria. To facilitate the development of 2973 as a model organism we developed in this study the genome-scale metabolic model iSyu683. Experimental data were used to define CO2 uptake rates as well as the biomass compositions for each strain. The inclusion of constraints based on experimental measurements of CO2 uptake resulted in a ratio of the growth rates of Synechococcus 2973 to Synechococcus 7942 of 2.03, which nearly recapitulates the in vivo growth rate ratio of 2.13. This identified the difference in carbon uptake rate as the main factor contributing to the divergent growth rates. Additionally four SNPs were identified as possible contributors to modified kinetic parameters of metabolic enzymes and candidates for further study. Comparisons against more established cyanobacterial strains identified a number of differences between the strains along with a correlation between the number of cytochrome c oxidase operons and heterotrophic or diazotrophic capabilities.The photosynthetic capabilities of cyanobacteria make them interesting candidates for industrial bioproduction. One obstacle to large-scale implementation of cyanobacteria is their limited growth rates as compared to industrial mainstays.  Cyanobacteria are a morphologically and geographically diverse group of photosynthetic prokaryotes that have adapted to a wide variety of environmental niches including fresh, sea, and wastewater125791115E. coli and Saccharomyces cerevisiae. The model cyanobacterium Synechocystis PCC 6803 has a maximum doubling time of 7–10 hours under optimal conditionsSynechococcus UTEX 2973 has been shown to have the lowest doubling time among cyanobacteria at 1.9 hoursSynechococcus PCC 7942, with a total of 55 single nucleotide polymorphisms and insertion-deletions, including a 188.6 kb inversion and a deletion of six open reading frames that are present in 7942Synechococcus 2973 an attractive candidate for bioproduction. Developing Synechococcus 2973 as a platform requires a more thorough understanding of its metabolic network and capabilities.Despite the inherent advantage of not requiring a reduced carbon substrate, cyanobacteria are still limited by growth rates lower than industrial mainstays such as de novo model development with the risk of losing details specific to certain organisms associated with completely automated developmentet al. uses an available curated model and reviewed gene annotations to expedite the development of a GSM modelSynechocystis PCC 680324Cyanothece ATCC 51142Synechococcus PCC 7002Genome-scale metabolic (GSM) models provide a blueprint for tracing all possible routes for utilizing carbon substrates in the context of the overall metabolism. They contain the set of metabolic reactions catalyzed within the organism, the gene-protein-reaction (GPR) relationships, and reaction directionalities21Once a GSM model is created, flux balance analysis (FBA) can be used to estimate the maximum theoretical biomass yield by maximizing the flux through the biomass equationSynechococcus 7942 and Synechococcus 2973 using a previously defined workflowiSyn731 GSM model of Synechocystis PCC 6803 was chosen as the reference model following the identification of similar percent identity between previously modeled cyanobacteria24Synechococcus strains . These two strains are represented via a composite model given that the minimal genomic differences between the two strains do not result in any changes to the set of reactions in each strain. The model is combined with additional experimental measurements; specifically carbon dioxide uptake rates and biomass composition, to further interrogate what factors contribute to these differences in growth rates. When the two strains were evaluated, the same reaction network was used with carbon dioxide uptake rates and a biomass reaction specific to each strain. Additional model refinement was performed by comparing the model against available gene essentiality data2 uptake as constraints on the model, the in silico ratio of the growth rate of Synechococcus 2973 to Synechococcus 7942 of 2.03 is close to the in vivo ratio of 2.13, identifying differing carbon uptake rates as the main contributing factor to the fast-growth phenotype. Specific SNPs and their possible effects on metabolism and the fast-growth growth phenotype are discussed. Genomic comparisons between the Synechococcus strains and several subsets of organisms identify genes and functionalities exclusive to the two strains.In this paper we describe the development of a composite GSM model for both Synechococcus PCC 7942 and Synechococcus UTEX 2973 (hereafter referred to as Synechococcus 7942 and Synechococcus 2973). The composite model contains 1,178 reactions and 1,028 metabolites. GPRs were developed for both strains, with the Synechococcus 2973 GPRs containing 683 genes and the Synechococcus 7942 GPRs containing 687 genes. This difference in gene number is due to four pairs of Synechococcus 7942 genes that each map to a single Synechococcus 2973 gene. The composite model contains 150 reactions not present in iSyn731Synechocystis model used in the model generation workflow. The energetic parameters from iSyn731 (i.e. non-growth and growth associated maintenance energy costs) were also used in the Synechococcus model. 145 reactions were not transferred from iSyn731 to the composite model. 93 of these reactions have a GPR and did not have the necessary homologs to transfer.This work resulted in the creation of a composite GSM model for two closely related cyanobacteria strains, iSyu683 have in vivo essential/nonessential verdicts from the transposon library by Rubin et al.In silico predictions of these gene knockouts correctly predicted 232 of 268 non-essential genes and 234 of 395 essential genes .607 of the 687 genes in al genes . All refet al., in review) and used in the formulation of strain specific biomass equations . The most pronounced difference between the strains is in the glycogen levels, with Synechococcus 7942 generating 8.3 times the amount produced by Synechococcus 2973 during the exponential growth phase . This supports the observations made by Yu et al. on the number of electron-dense bodies between the two strainsSynechococcus 2973 biomass by weight composed of amino acids, as compared to 40.9% for Synechococcus 7942. Both strains have similar weight percentages of lipids, although measurements for C18:0 and C18:1 fatty acid chains are an order of magnitude higher in Synechococcus 7942. The weight percentage of chlorophyll a in the two strains is also quite similar, with 2973 having only 1.05 times the amount of the pigment found in 7942. These measured components sum to 755.46 and 743.89 mg/gDW for Synechococcus 7942 and Synechococcus 2973, respectively. Therefore, the unmeasured biomass components (e.g. nucleic acids) account for similar fraction of biomass.Measurements for each strain were obtained for amino acids, lipids, glycogen, and chlorophyll  present in Synechococcus 7942 that were not found in Synechococcus 2973. The six ORFs not found in Synechococcus 2973 are five hypothetical proteins and a chromophore lyase, none of which are present within the Synechococcus 7942 GPR. Of the 21 genes with SNPs, 11 are found within the Synechococcus 7942 GPR  categoriescus 7942 . AnotherSynechococcus 2973: Synechococcus 7942 is 2.132 taken up. The required moles of carbon for one theoretical mole of biomass were calculated by multiplying biomass stoichiometric coefficients by the number of carbon atoms in each precursor. Synechococcus 2973 requires 0.502 g carbon per g biomass as compared to 0.497 for Synechococcus 7942. When supplied equal amounts of CO2 the ratio of biomass yields per mol of carbon of Synechococcus 2973 to Synechococcus 7942 is 0.98 implying that Synechococcus 2973 requires slightly more carbon per gDW of biomass. Experimental measurements have shown that under their optimal conditions, Synechococcus 2973 takes up CO2 at a rate 2.06 times that of 7942  was used to calculate the flux ranges for both strains at max biomass with minimum required photons. When the flux ranges are standardized by the amount of CO2 taken up the reactions with non-overlapping ranges are dictated by the biomass composition, with 2973 having a higher flux through the amino acid biosynthetic pathways and 7942 having a higher flux through the biosynthesis pathways for glycogen as well as C18:0 and 18:1 lipids , competes with the carboxylation of RuBP and generates 2-phosphoglycolate (2PG). Eisenhut et al. previously showed that 2PG metabolism is essential for organisms such as cyanobacteria that perform oxygenic photosynthesisin silico modelsSynechococcus 2973 could be due to the production of glyoxylate, a precursor to glycine, serine, and cysteine, from the photorespiration pathway and the increased requirements for amino acids in Synechococcus 2973.Under their optimal illumination conditions at 38 C, the growth rate ratio of  of 7942 . Using t1 lipids . Other n2 flux, both models can achieve growth rates higher than experimentally reported, with Synechococcus 2973 achieving a growth rate of 0.4569 hr−1 (doubling time: 1.5 hours) as compared to its experimentally reported optimal rate of 0.3014 hr−1 (doubling time: 2.3 hours), and Synechococcus 7942 with a growth rate of 0.2256 hr−1 (doubling time: 3.1 hours) as compared to the experimental value of 0.1415 hr−1 (doubling time: 4.9 hours). When the flux through the biomass reaction was fixed to the experimentally reported values, the model identified the main destinations of excess carbon as TCA cycle intermediates or metabolites that are several reactions removed from the TCA cycle . In total 33.4% of the carbon taken up is exported for 2973 and 37.2% is exported for 7942. This increased fraction of carbon allocated to biomass in 2973 could in part contribute to the divergent growth rates.With the supplied COSynechococcus 2973’s potential as a production strain, it is important to compare its capabilities with that of model cyanobacteria, with pairwise genome-wide BLAST comparisons performed between Synechococcus 7942 and Synechocystis PCC 6803, Synechococcus PCC 7002, Cyanothece ATCC 51142, Anabaena PCC 7120, and Anabaena variabilis. A set of 696 genes in 7942 does not have satisfactory hits in the other five genomes . 47 of these genes are in the 7942 GPR relationships and are essential for 21 reactions that can carry flux at max biomass production. These reactions are spread across a variety of pathways including amino acid and deoxyribonucleotide biosynthesis. As these genes comprise only 6.8% of the genes within the Synechococcus 7942 GPR relationships, a majority of the metabolic genes have homologs in other cyanobacterial strains. Therefore insights gained into the metabolism of these strains can more readily inform work in the metabolism of the fast-growing strain. Expanding past metabolism, gene ontology (GO) terms provide a means to contextualize the remaining genes. Of these 696 genes, 304 have annotations including GO terms in the Uniprot database. This fraction of genes with GO terms (43.7%) is much lower than that of the entire 7942 genome (70.7%), following a trend seen in the percent of reviewed annotations  and percent of genes annotated as uncharacterized . Two GO terms have at least ten genes in the subset and greater than 50% of the genes in the organism with that GO term within the subset. There are 14 genes within this subset annotated with the term GO:0000155 – phosphorelay sensor kinase activity, and 10 genes annotated with the term GO:0043565 – sequence specific DNA binding. This suggests that most of the differences between these strains are related to the response to external stimuli and transcription regulation. These differences are consistent with the comparison of a number of thioredoxins involved in light dependent protein regulationSynechococcus 7942 in contrast to four and eight thioredoxin genes in Synechocystis 6803 and Anabaena 7120Given et al. showed that the deletion of a three gene cytochrome c oxidase (coxBAC) operon from Anabaena variabilis ATCC 29413 resulted in a strain capable of unhindered photoautotrophic growth but which was no longer capable of chemoheterotrophic growthA. variabilis 29413’s five operons and seven other cyanobacteria with information on their ability to grow heterotrophically. An independent annotation search of each organism was also performed, which identified the same operon sets for each organism as were identified through the BLAST search. coxBAC operons including an operon required for growth on sugarAnother key difference between cyanobacterial strains is the classification of some organisms as obligate photoautotrophs. Schmetterer Synechococcus 7942 and Synechococcus 2973 with GPR relationships that were tested and refined using gene essentiality data for Synechococcus 7942. This model was used to compare the two strains and identify possible reasons for the diverging fast-growth phenotype. The GSM model allowed for the identification of the pathways where flux was allocated to differing levels between the two strains along with the identification of four SNPs as possible contributors to modified kinetic parameters of metabolic enzymes and candidates for further study. Several differences between the two strains were identified including the fraction of carbon allocated to biomass and the weight fractions of different biomass precursors, with the most prominent being the difference in carbon uptake rates, which contributed to the different in silico growth rates. Finally, a genomic comparison between Synechoccocus 7942 and model organisms highlighted a number of differences including regulatory genes without identified homologs and a variation in the number of copies of the cytochrome c oxidase operon as related to heterotrophic and diazotrophic capabilities.In this paper we presented a GSM model for both et al.iSyn731 model of Synechocystis PCC 6803Synechococcus 7002 and Synechocystis 680324Synechococcus 7942, resulting in the selection of our previously developed iSyn731 model of Synechocystis 6803Synechocystis and the Synechococcus strains to identify which reactions should be transferred to the new GSM model. To identify homolog pairs a bidirectional BLAST search between each organism pair was performed using an e value cutoff of 10−2. A result was determined to be a hit if the alignment length was at least 75% of the length of both the query and target genes, and if the raw score was at least 25% of the self-hit score of the query gene. A homolog pair was identified if both genes had only each other as a hitSynechococcus 7942 and 2973. Given that the organisms are closely related, the score cutoff was increased to 75% of the self-hit score. This reduced the number of occurrences of multiple hits for a gene and identified only the best hit, increasing the number of homolog pairs from 2274 to 2538. These homolog pairs were used to map reviewed annotations from Synechococcus 7942 to Synechococcus 2973. The composite model is available in both table . These measurements were used to determine biomass equation stoichiometric coefficients for lipids, glycogen, amino acids, and chlorophyll. The coefficients for the unmeasured precursors were updated by scaling the coefficients from the iSyn731 biomass equation so that all biomass precursors added up to 1 g per gDW of biomass. This standardization facilitates comparison between the two strains.The biomass equation previously developed for the Synechococcus strains were grown in a MC-1000 multicultivator (Photon Systems Instruments) in standard BG11 medium at 38 C, and supplemented with 3% CO2 to an OD720 of 1.0. Synechococcus 2973 was grown at 500 μE·m−2·s−1 light and 7942 was grown at 300 μE·m−2·s−1 light, the light intensities at which each strain achieved its maximal growth rate2 for 5 minutes. One tube for each strain was placed on its side on a shaker under their respective white LED illumination at 38 C for 1 hour after which time total CO2 was measured. The second tube was measured immediately. Total CO2 was calculated as the sum of the dissolved + gaseous CO2 within a sealed system. Total CO2 of the system was determined after injecting 100uL 10 N HCl into the tube to gas out dissolved CO2, followed by quantification of the headspace CO2 content on a HP 5980 gas chromatograph under the following conditions: porapak N column; temperature, 100 °C; carrier gas, helium at a flow rate of 40 ml min−1; and TCD detector. These uptake rates were given as umol CO2 mg−1 chl hr−1. Using the molecular weight and the biomass stoichiometry (mmol chl gDW−1) of chlorophyll A these rates were converted to the units of CO2 uptake flux within the model (mmol CO2 gDW−1 hr−1). The original measurements and their conversion to fluxes are in Experiemental measurements of the rate of carbon dioxide uptake were taken for both strains. Both et al.FBA was performed as described by Orth in silico and in vivo essentiality, the GPR for each reaction was reviewed upon the removal of each individual gene. If the GPR logic statement was no longer true  then the reaction was not allowed to carry flux for the in silico mutant. If the model could not produce at least 10% of the wild-type biomass the mutation was classified as lethal. Gene knockouts were performed in silico for every gene in the model. The in vivo results were taken from those genes that were identified as essential or non-essential from the transposon library of Synechococcus 7942 published by Rubin et al.in silico and in vivo results for growth or no growth match (i.e. GG and NGNG) or conflict (i.e. GNG and NGG)In order to compare How to cite this article: Mueller, T. J. et al. Identifying the Metabolic Differences of a Fast-Growth Phenotype in Synechococcus UTEX 2973. Sci. Rep.7, 41569; doi: 10.1038/srep41569 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The method is compared with the method based on a binary distinction between reversible and irreversible reactions. When all reactions are reversible, adding the constraints based on equilibrium constants reduces the number of elementary flux modes (EFMs) by a factor of two. Declaring in advance some reactions as irreversible, based on reliable biochemical expertise, can in general reduce the number of EFMs by a greater factor. But, even in this case, computing tEFMs can rule out some EFMs which are biochemically irrelevant. We applied our method to two published models described with binary distinction: the monosaccharide metabolism and the central carbon metabolism of Chinese hamster ovary cells. The results show that the binary distinction is in good agreement with biochemical observations. Moreover, the suppression of the EFMs that are not consistent with the equilibrium constants appears to be biologically relevant.We present a method for computing thermodynamically feasible elementary flux modes ( EFM) is a key concept in the analysis of metabolic networks from a pathway-oriented perspective. An EFM is defined as a smallest (with respect to reactions set inclusion) sub-network that enables the metabolic system to operate at steady state with all irreversible reactions proceeding in the appropriate direction  . It is known that lactate comes from cytosolic pyruvate while alanine comes from mitochondrial pyruvate (which arises from glutamine). Moreover, after T = 48h the concentration of glutamine decreases and is not enough to be assimilated. For the same reason, the EFM 13 and EFM 16 cannot be thermodynamically feasible. At T = 0, the cells are entering the exponential growth phase. Therefore, the EFM 16 makes no biological sense with ATP consumption for the transformation of extracellular glutamine into extracellular alanine, while cells massively need supporting anabolic activity using intermediate metabolites as precursors as well as the ATP regenerated. EFM 18 cannot be feasible as well as there is no lactate in the medium but it can be feasible thereafter because the concentration of lactate increases. EFM 23 comes down to CO2 production and ATP consumption by ATPase, such as a futile cycle. The thermodynamical infeasibility of this EFM seems pertinent because a major problem of this EFM is that the flux associated to proton leak is amplified compared to biological expected levels, and this flux may thus serve as a fitting parameter reaching improbable level. At time 48h, the cells are in the middle of the exponential growth phase. Therefore, the EFM 15 and EFM 16 are barely possible while cells require precursors and ATP as stated above looking at the EFMs at T = 0. More importantly, these pathways can barely be active since glutamine is decreasing from 72h in the culture medium to low level that does not allow the EFM thermodynamical feasibility. It is worth noting that if we suppress the reaction leak because this reaction normally accounts for less than 20 (percent) of O2 consumption in reality, all the EFMs are thermodynamically feasible at T = 0h.We then verified if the shown in . EFM 8 ilutamine . In the EFMs and so 7,529 tEFMs since they have all to verify tEFMs which consume glucose to produce biomass increases from 2,610 at T = 0h to 2,852 at T = 96h. The number of tEFMs which consume glucose to produce lactate is on average the same from 1,003 at T = 0h to 1,013 at T = 96h. Moreover, for each period of time T, we have tested if the thermodynamics imposes some reactions to be irreversible. We verified if there exists reactions that always operate in the same direction in each set of tEFMs. Actually, this is not the case, i.e. all the reactions remain reversible, which means that the knowledge of the equilibrium constants is not enough to replace  the binary distinction of the expert. And the example of the six non-thermodynamical EFMs above, depending on the context, show that, for biochemical reasons, they are not feasible. And these reasons are not reducible to thermodynamics .Let us now consider all the reactions of the CHO network as reversible. The new network contains 7,529 reversible Xi and Mi. The stoichiometric coefficients for metabolites i in reactions j are denoted by sij and combined into the m × r real stoichiometry matrix S, where m is the number of internal metabolites and r the number of reactions. It is known from thermodynamics that the Gibbs free energy difference must be negative in order that a reaction j can proceed in the forward direction [Gj = 0). One gets:Kj is the equilibrium constant of reaction j.We denote the concentrations of internal and external metabolites to the system by irection :ΔGj=ΔGjj proceeds in the forward direction (ΔGj < 0) we can, using For non-equilibrium states where the reaction This equation is intuitively understandable: if the concentrations of substrates are high, the reaction proceeds in the forward direction, while it would run backwards if the concentrations of products were sufficiently high.j that involve external metabolites, we move their concentrations to the right-hand side of For those reactions Under the plausible assumption that all concentrations are positive, it is allowed to use the logarithm of concentrations (concentrations are assumed to be dimensionless quantities after division by the unit concentration):To do so, we can fix a common unit (e.g. mM) for all concentrations and equilibrium constants and neglect that unit when taking the logarithm. In the equations and inequalities, the unit would cancel anyway. Now we can write vj ≥ 0, i.e. j such that vj > 0, that is, for all reactions in the support supp(v) of the flux distribution v.In this way, we have obtained a linear inequality system. We now deal with the question whether a certain flux distribution n monomolecular reactions. The chain starts with an external metabolite For example, consider a  linear chain of yi for some given Ki and Thus, each internal metabolite is confined to a certain interval in order that the flux is positive. In a large, complex metabolic network, the inequality system EFMs analysis , the syvj to be non-negative (see above) is formally equivalent to assuming all reactions j to be irreversible. Actually, we will assume this is the case, by splitting each reversible reaction into two irreversible reactions of opposite directions. Under this condition, the set of flux distributions is a pointed convex polyhedral cone [EFMs are the extremal rays of Considering all fluxes ral cone :K={v∈IRv, we can use the above argument of adding inequalities. More precisely, if When we check the feasibility of linear inequality system j) of the inequalities Let EFMs with support included in supp(v). This leads to the general result stated as Lemma 1 in [From the right-hand side we obtain:mma 1 in :Lemma 1The linear inequality systemfor j ∈ supp(v), has a solution for y if and only if the vectorfulfills the inequality systemwhere ek).v to be thermodynamically feasible. In particular, if the flux distribution considered is an elementary mode e, then the set of EFMs with support included in supp(e) is reduced to e itself  and the necessary condition for thermodynamic feasibility becomes:Thus the linear inequality system EFM, checking its thermodynamic feasibility by inequality e with its coefficients and not only its support as in system v, the complexity of using kmax of EFMs with support included in supp(v) and supposes these EFMs previously computed.We see that, for such an S verifies the condition, so it is for any flux distribution with support included in S. This is obvious either on the form EFMs have to be considered for a smaller support) of the condition. Thus, if a flux distribution is not thermodynamically feasible because it violates the condition, so it is for any flux distribution with a larger support.The thermodynamic condition verifies the nice property to be monotonic with regard to set inclusion of the supports; i.e., if a given flux distribution of support EFMs. In general, the nullspace approach is adopted at the initialization step, which guarantees the satisfiability of all the m stoichiometric equalities and of r − m irreversibility inequalities among the r ones defining m iteration steps, each one integrating a new irreversibility constraint vj ≥ 0 among the remaining ones. At step l the following cone EFMs computed:This property is key for integrating the filtering by the condition inside the iterative process of the Motzkin Double Description (DD) method , 49, on m steps, as well at its EFMs. Let suppl(v) be the support of a vector r − m + l components (those from which we are sure they are nonnegative). At each step l, 1 ≤ l ≤ m, the new cone vj ≥ 0 (say j = r − m + l for a convenient ordering) and its extremal rays are computed from those of el at step l could contribute to build would have its support larger than suppl(el). And thus, would violate the necessary thermodynamical feasibility condition if suppl(el) does so. So, in this case, el can be definitely discarded at step l.Starting from the cone el. This can obviously be done for the condition expressed as consistency of the linear inequality system j ∈ suppl(el), and achieved by a call to an LP program. This is actually what is done in  implemented as an extension of efmtool [m) EFMs with support included in suppl(el), which are not yet known at current step l. This means we would need at step l the EFMs of suppl(el) but we only have the extremal rays of To make the filtering effective along the iteration process, it remains to check if the thermodynamic feasibility condition can be checked at each step on the restricted support of each newly created extremal ray done in , 29) wit witel. T efmtool  by callsthermoEFM, for performing the thermodynamic calculations explained above and a program, EFMdraw, allowing one to visualize all the EFMs. The implementation of both programs is performed with MATLAB 2015a, and they are freely available on our website https://www.lri.fr/~speres/EFM/ and figshare https://figshare.com/. They are both connected to METATOOL [EFMs. Celldesigner [EFMs generated with EFMdraw.We developed a software, METATOOL  to compudesigner  was usedEFM analysis [EFMs, we have here used a Lemma described in a previous work [thermoEFM) in a MATLAB function connected to Metatool [EFMs and then selects those that are consistent with the formula EFMdraw) using Metatool and allowing the graphical representation of all the EFMs of a metabolic network designed in cellDesigner [In this work, we have compared the traditional approach in metabolic pathway analysis in which a binary distinction is made between reversible and irreversible reactions, with an approach in which more detailed thermodynamic information is used. In particular, the values of equilibrium constants and external metabolite concentrations are considered. The latter approach had been suggested even before the advent of “traditional” analysis . To decious work . In the ous work , 28. We Metatool . The metDesigner .EFMs on the basis of the binary declaration is often much lower than that based on the thermodynamic method. We show that in the case of a network composed of an arbitrary number of linear chains sharing one internal “hub” metabolite, the steady-state assumption allows a number of possible EFMs that is quadratic in the number of chains. Using thermodynamics in addition to the steady-state assumption divides this number by two, but it is worth to notice that knowing the direction of a number of reactions linear in the number of chains would produce the same result and even completely fix the set of EFMs. So, as the steady-state assumption already strongly constrains the directionality of reactions, only little additional gain can be expected from using thermodynamics without knowledge of the concentrations of internal metabolites, as demonstrated in Section 2.1. In particular the number of EFMs is only divided by two. Indeed, a biochemical knowledge of the irreversible directions of some reactions is enough to achieve similar or even better results than thermodynamics.The number of EFMs which seem to be biologically irrelevant. Moreover, for the CHO model, we have changed over time the concentrations of external metabolites as reported in [tEFMs. In addition, we found three EFMs that never operate at each period of time; their infeasibility is coherent for each phase of the cell.However, the advantage of our reformulation of the thermodynamic method is that formula orted in . The restEFMs are biochemically irrelevant, although they are thermodynamically feasible. This might be because they would require inhibition of some enzymes and activation of some others, i.e. regulatory mechanisms that are not taken into account. This hypothesis has been verified by several examples, e.g. monosaccharide metabolism (glycolysis and pentose phosphate pathway) [EFMs).The question arises whether authors have sometimes declared too many reactions as irreversible. Or perhaps they were right and included, intuitively, kinetic information in addition to thermodynamics. We here suppose that some of the pathway)  and a mopathway) . Taken aEFMs.The present approach allows the testing of hypotheses on metabolic network structure as well as on the direction of fluxes. Therefore, our work contributes to expanding our knowledge on feasible metabolic networks. For reduced, incomplete networks, such as those covered in this work, results may be useful in indicating where to focus for better describing sub-networks in which only lumped reactions can be described because of lack of knowledge. In conclusion, thermodynamics is key, in the form of knowledge on reaction irreversibility or on reaction equilibrium constants, and finally experimental data on a cell behavior, enabling the reliable determination of feasible S1 File(PDF)Click here for additional data file.


Oryza sativa) is one of the most important grain crops, which serves as food source for nearly half of the world population. The study of rice development process as well as related strategies for production has made significant progress. However, the comprehensive study on development of different rice tissues at both transcriptomic and metabolic flux level across different stages was lacked.Rice  contains supplementary material, which is available to authorized users. Oryza sativa) is one of the most important grain crops in the world, which serves as food source for nearly half of the world population. Rice is also a model organism for research in plant growth and development. The process of rice growth and development starts from seed germination and ends when the formation of the grain begins. This complex process can be divided into three periods: vegetative growth, reproductive growth, grain filling and maturity. These three main phases correspond to the development of different tissues and organs in rice. Since it is important for the agricultural production of rice, the study of rice development process as well as related strategies for production has made significant progress , where negative number means reverse reaction flux. We directly used the flux value to measure the pattern similarity and made hierarchical clustering as shown in Fig. By evaluating the total variation (see Methods) of every reaction across the 8 different stages and tissues, we found some pathways exhibit significant changes, including glycolysis/gluconeogenesis, fatty acid metabolism, nitrogen metabolism, TCA cycle, glyoxylate cycle, cysteine and methionine metabolism, photosynthesis (light phosphorylation), folate metabolism, and fermentation process.The flux distribution of primary metabolism in 8 tissue-specific models were shown in Additional file We determined the reactions achieving upper bound of flux as rate-limiting steps, and then selected the pathways enriched in the rate-limiting reactions. Glycolysis/Gluconeogensis pathway is found to be rate limited in all tissue-specific models, Fatty acid, Porphyrin and Chlorophyll Metabolism are also rate limited in most of the tissues. These observations are in good agreement with the point that the basal metabolism maintains the most basic energy expenditure of the organism’s life activities. In comparison, folate metabolism and TCA cycle are rate-limiting steps in only two or three tissues. Nitrogen metabolism is rate-limiting only in leaves at booting stage, and glyoxylate cycle is rate-limiting step only in endosperm at mature stage. It is consistent with previous studies that ynthesis . The glyynthesis . MoreoveNext, we focused on the metabolic features of some fundamental processes, the flux distribution of Fatty acid metabolism and Amino acid metabolism are shown in Fig. The flux distribution of Nitrogen metabolism and Starch and sucrose metabolism are shown in Fig. Oryza sativa (monocot plant) [The secondary metabolism of plants has very important effect on orchestrating the cellular phenotype in response to abiotic stresses , 28. In t plant) . Abscisit plant) .Fig. 9MeFlux distributions in quinone and folates metabolism are similar in all tissues. Folates (vitamin B9) and phylloquinone (vitamin K1) have potential antioxidant activity within the plant cell . Stamen There is a significant difference between the gene expression level and metabolic flux distribution, which indicate the importance of post-transcriptional control in organisms. And the integration of transcriptome data with metabolic model is important to analyze the metabolic changes. Here we compared the pairs of samples of the same tissues at different stages or the different tissues at same stage to uncover the difference between transcriptomic level and metabolic level. The significantly up- and down-regulated genes were determined by the fold change of fpkm larger than 2 or smaller than 0.5. While for the significantly up- and down-regulated reaction fluxes, we calculated the difference of absolute of two flux values and divided by the range of flux variability (See Methods). If the ratio is larger than 0.5, the reaction is upregulated; If the ratio is smaller than − 0.5, the reaction is downregulated. Then we conducted functional enrichment analysis to show the significantly up- and down-regulated pathways between three compared pairs at the two different levels in Fig. The second pair is the sheath and leaves both at booting stage. We found the process of photosynthesis and fatty acid biosynthesis are much stronger in leaves than in sheath, as shown in Fig. The third pair is embryo and endosperm both at mature grain stage. Rice is a representative of a monocotyledonous plant, which possesses a tiny embryo and large endosperm used for starch storage. During germination, sucrose has the potential to transport carbohydrate from endosperm to embryo. The endosperm of rice is a major storage tissue for starch and proteins, which is essential for embryo growth and postembryonic development prior to the start of seedling photosynthesis . PreviouTranscriptome can reveal the gene expression level at different development stage, but in reality, the flow of information deriving from genes is networked by many loops with their downstream products, resulting in a complex and dynamic system of transcripts, proteins and metabolites. Therefore metabolic flux analysis can be applied to reveal the mechanism of rice development. By integration of gene expression data with the genome-scale rice metabolic model, we constructed tissue-specific models, and simulated the flux distribution, which can reflect the changes of metabolic features without further experimental measurements. There are different numbers of reactions in the 8 tissue-specific models, majority of them are common, but each has specific reactions. More reactions are specifically required in palea and lemma in both booting and mature grain stage for cell growth and development. This might probably because compared with other organs, the palea and lemma is more resistant to drought stress. Meanwhile, the palea and lemma may be the major sources of carbon for grain-filling during drought, which requires more reactions to support cell growth and development . In addi2 assimilation and photosynthesis as well as other electron transport chains [Oryza sativa (monocot plant) [Primary and secondary metabolism played different roles in rice development, and we revealed their different features at both transcriptomic and metabolic level. The transcriptome data indicated the gene expression of the primary processes could be categorized into four clusters: sheath and leaves, pistil and stamen, endosperm and embryo, lemma and palea of booting and mature grain stages Fig. , which dt chains . And glyt chains . The patt plant) . Abscisit plant) . DiffereWe compared the pairs of samples of the same tissues at different stages or the different tissues at same stage to uncover the difference between transcriptomic level and metabolic level. Three compared pairs showed there are more altered pathways at gene expression level than metabolic level, which indicate the metabolism is more robust to reflect the phenotype. By comparing palea and lemma at maturity stage with booting stage, we found although gene expression of calvin cycle, photosynthesis, terpenoid metabolism are upregulated in mature palea and lemma, these processes have no significant changes in metabolic flux. While genes in photorespiration, phenylalanine, tyrosine and tryptophan metabolism are upregulated, the fluxes are downregulated. Thiamine and phytohormones pathways are enriched in downregulated genes, but no significant changes in flux. Since more reactions including starch metabolism, fermentation, photorespiration, fatty acid biosynthesis, are downregulated in mature grain stage, we concluded that palea and lemma mainly develop in booting stage. During the booting stage, the palea and lemma interlock with each other to enclose the developing floral organs , 38.For leaves and sheath both at booting stage, the process of photosynthesis, photorespiration, and fatty acid biosynthesis are much stronger in leaves than in sheath, which is consistent with the actual situation that leaves are responsible for photosynthesis. For comparison of endosperm with embryo, the starch and sucrose metabolism were upregulated in endosperm. The endosperm of rice is a major storage tissue for starch and proteins, which is essential for embryo growth and postembryonic development prior to the start of seedling photosynthesis . The genOryza sativa tissues in booting and mature grain stage. By further integrating transcriptome data with the rice metabolic model, we uncovered metabolic pattern shift among different tissues and stages, which can be exploited to uncover the mechanism of the complex metabolic behavior of rice and further improve production and quality of rice.This study revealed the systematic transcriptomic profile of eight Oryza sativa L. ssp. japonica., cv. Zhonghua 11, were grown under natural growth conditions at an experimental field. The tissue samples of Zhonghua 11 were collected from leaves, sheath, stamen, pistil, lemma and palea of the booting stage, and from embryo, endosperm, and lemma and palea of the mature grain stage. Tissue samples were separated from the connecting tissues with a scalpel under a dissecting microscope before they were collected for RNA extraction.The rice line of O. sativa tissue samples were extracted following the manufacturer’s instructions. RNA degradation and contamination was monitored on 1% agarose gels. RNA purity was checked using the NanoPhotomete® spectrophotometer . RNA concentration was measured using Qubit® RNA Assay Kit in Qubit® 2.0 Flurometer . The RIN number was checked to determine RNA integrity by RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system .Total RNA from the eight A total amount of 3 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® Ultra™ RNA Library Prep Kit for Illumina®  following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3′ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 150~ 200 bp in length, the library fragments were purified with AMPure XP system . Then 3 μl USER Enzyme  was used with size-selected, adaptor-ligated cDNA at 37 °C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq SR Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq 2500 platform and 125 bp paired-end reads were generated.http://rapdb.dna.affrc.go.jp/download/irgsp1.html). Clean reads were aligned onto the Os-Nipponbare-Reference-IRGSP-1.0 reference genome using TopHat (version 2.0.12) with qdefault parameters [Sequencing reads were filtered according to fastq_quality_filter of fastx_toolkit-0.0.14, a command line tools for Short-Reads FASTA/FASTQ files preprocessing (−q 30). The genome and gene models of Oryza. sativa were downloaded from RAPDB .KEGG and GO annotations were grabbed from reference gene GFF file , including 2165 genes, 2444 reactions and 1999 metabolites, which is classified into 59 major metabolic sub-systems . Model iThe Integrative Metabolic Analysis Tool (iMAT) method was used to integrate transcriptome data with the rice metabolic model to reconstruct tissue-specific models. We defined “highly expressed” and “lowly expressed” genes according to their expression level, corresponding to the highest 15% genes and the lowest 15% genes respectively . Next, aTo analyze the fluctuation of each reaction flux across different tissues in different stages, we used the concept of total variation in statistics Eq. .1\documei is the flux value in the i-th tissue-specific model. The greater the total variation of a reaction is, the fluctuation of this reaction is more remarkable.Where, fFor the comparison of the same tissue in different stages or the different tissues in same stage, we need to determine the significantly up- and down-regulated genes and reaction fluxes. The genes with fold change of fpkm between two tissues larger than 2 or smaller than 0.5 are regarded as significantly up- and down-regulated respectively. While for metabolic level, the fluxes carried by reactions may be negative because of reversibility, therefore the simple metric like fold change is not suitable for up- and down-regulation definition. Here we calculated the difference of absolute of two flux values and divided by the range of flux variability to get the ratio of flux change, as shown in Eq. Where the maxflux and minflux represent the maximal and minimal value of flux variability, derived by the function ‘fluxVariabilityAnalysis’ in COBRA toolbox in MATLAB. The reactions with the ratio larger than 0.5 are determined as upregulated, while those with the ratio smaller than − 0.5 are determined as downregulated. Then we conducted functional enrichment analysis using hypergeometric test in MATLAB, to find the significantly up- and down-regulated pathways between three compared pairs at the two different levels.Additional file 1:The list of primary and secondary metabolic pathways in rice. (DOCX 13 kb)Additional file 2:Input data for iMAT to construct tissue-specific models, including rice model, expression data, and transformation of reaction activity. (TIFF 3788 kb)Additional file 3:Metabolic flux distribution of primary metabolism in different tissues at booting and mature grain stages. (ZIP 3206 kb)


Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce  Meneco, a tool dedicated to the parsimonious gap-filling of metabolic networks. Unlike other tools, Meneco allows using sparse data (missing stoichiometries) and draft metabolic networks to suggest reactions to fill gaps in the networks. Subsequently, we apply it to two biological case studies and show that the flexibility of Meneco enables it to be adapted to a variety of research questions and types of available data. We show that Meneco performs better than reference tools with respect to large-scale heterogeneous reference database and with respect to the recovery of important reactions in highly degraded networks. Specifically, it allowed the analysis of two interacting metabolic networks and the reconstruction of the first metabolic network of Euglena mutabilis.In the era of fast and massive genome sequencing, one challenge is to transform sequence information into biological knowledge. Reconstructing metabolic networks that include all biochemical reactions of a cell is a way to infer reactions from genomic data. Unfortunately, those data are usually incomplete, poorly annotated, and missing reactions create gaps in the metabolic networks. Here we introduce  In. InE. muStarting point for any metabolic gap-filling problem  is a draa priori criteria to select one rather than the other. Then, a cautious strategy is to compute the set of all reactions contained in at least one solution reported by the algorithm and to manually curate them.Obviously, the solution to a gap-filling problem may not be unique. The sizes of both of the search space and the solution space for the gap-filling problem grow exponentially with the size of the reference database from which the reactions are taken. Any gap-filling method thus needs to find a suitable trade-off between the number of solutions reported by the method, that should not be too large to enable a manual curation, and their biological significance, in order to capture the main properties of the GEM being reconstructed. Gap-filling methods may report one, a few, or many sets of reactions to complete the draft GEM. When several solutions are produced by a gap-filling algorithm, there are no T1, T2, T3.Three main approaches can be distinguished according to the number of solutions they report and the characteristics of these solutions  number of solutions to the problem, although there is no a priori estimation of the number of iterations needed to solve the problem.rategies , 10, whifastGapFill [GapFill approach combined with the Fastcore algorithm, which eliminates the focus on the biomass production by identifying a single set of reactions which unblocks all reactions within the draft metabolic network. The price to pay is to allow import and export fluxes, especially to resolve issues related to the activation of isolated reactions in the GEM. When applied to our toy example, fastGapFill reported seven reactions from the reference database and one import/export reaction of the reactant of the reaction producing T3 that was not present in the database to be added to the model. The reactions R1 and R2 are introduced to produce T2 in the absence of R3. Since no accumulation of the reactant of R3 is possible, fastGapFill adds an import/export reaction to enable the production of T3 without using the previous reactions of this pathway. The reactions R4, R6, and R9 are equivalent to produce T1 and were all chosen in order to unblock all fluxes going threw all reactions present in the draft model . Finally, R7 is added to enable the production of T1.tGapFill  is an exMIRAGE [MIRAGE samples the space of solutions by randomly iterating the search algorithm. In our toy example, the algorithm was iterated 100 times without applying a priori scores to reactions of the database. Four different sets of reactions were obtained {R3, R7}, {R3, R6, R7}, {R3, R4, R7} and {R3, R7, R9}. The reaction R3 is mandatory to produce T3 and it also produces T2. Therefore, no other reaction producing T2 is necessary, so that R1, R2, R5 and R8 were never used. Given the cycle, the reaction R7 would be the minimal completion to produce T1 from a stoichiometric point of view, but the subset-minimality criterion also enables the MIRAGE algorithm to find the reactions R4, R6 and R7.In contrast to parsimonious approaches, top-down approaches start from all available information and remove reactions without added-value to the solution of the problem , 23, 24.MIRAGE  aims at R5 is identified by none of the stoichiometry-based methods.Together, we noticed that the criteria used to complete the network highly impact on the reconstruction procedures which all report different solutions. In particular, the reaction Stoichiometry-based tools fail to address the fact that species-specificity of cofactors in the reactions is often not precisely described in databases neither is stoichiometry of reactions. This may lead to predictions errors for degraded metabolic networks while producing biomass. Topology-based tools on the other hand do not take into account constraints yielded by the stoichiometry that may lead to non-producibility of biomass in some cases.Meneco tool, which uses qualitative constraints to express the producibility of metabolites purely based on the topology of the metabolic network. Following a qualitative approach to elaborate the bio-synthetic capacities of metabolic networks according to [Meneco defines the synthetic capability of the system, with respect to a set of input compounds, as the set of metabolites that can be produced following a route in the network. As described in the third part of Meneco requires the independent production of all inputs in the cycle to “initiate” the reactions [We designed the rding to –27, Meneeactions .S using a topological approach. In contrast, using a FBA-based approach, the stoichiometric coefficient n will have a great influence on the producibility. The first criterion to produce T1 is to have an equal quantity of d and c available. One a will be used to produce one d and a second a is necessary to produce one c. Consequently, the only way to produce T1 using the stoichiometric approach is to have the coefficient n equal to twice the value of the stoichiometric coefficient of b, here 2. It is also interesting to note that, if the objective function was only formed by the reaction producing metabolite d, the only way to enable it from a stoichiometric point of view would be to have n = 1. Using a topologic based approach, d would be always producible from S.In the first part of Meneco uses the ASP paradigm [Meneco attempts to complete the network by importing minimal sets of reactions from a metabolic reference database such that the resulting network provides the required functionality with respect to topological metabolite producibility. The ASP technology is not only able to enumerate all minimal network completions, but also to compute the union and intersection of all minimal network completions without enumerating them, leading to strong gains in efficiency while still being exhaustive. Applied to the example depicted in T1, T2 and T3 are: {R3, R4, R7}, {R3, R6, R7}, {R4, R5, R7}, {R6, R7, R8}, {R4, R7, R8}. Meneco missed the reactions R1 and R2, so did GapFill, because of the parsimonious criteria they used. On the contrary, Meneco reported the reactions R5 and R8 because it checked that T2 could be simply produced by adding R8 as soon as T1 was produced. Finally due to the cycle, Meneco missed the reaction R9. This could have been circumvented by adding one of the metabolites of this cycle to the seeds, like explained in the section dedicated to the application of Meneco to E. mutabilis. In particular, as Meneco is a graph-based approach, it may fail to capture the intrinsic non-linearity of metabolic behaviours [Based on this topological producibility criterion, paradigm  to effichaviours  as well haviours . The othMeneco is the reconstruction of GEMs for non-classical organisms. The automatically reconstructed bacterial GEMs in the BioCyc repository [iJR904 E. coli GEM [iJR904 E. coli GEM. Degradation occurred through all types of pathways, including the central ones, such as the Tri-Carboxylic Acid cycle (TCA)  GEMs. None was capable of producing the corresponding biomass.As described in the introduction, the main application area of pository  containecoli GEM  were ranE. siliculosus), requiring to explore all possible metabolic reactions to fill the network. The identifiers of iJR904 were manually mapped to identifiers of the MetaCyc database and both files were merged to create a complete reference database which contains all the reactions removed from the original GEM.In order to complete these degraded metabolic networks, we selected the MetaCyc reference database (version 18.5) , motivatMeneco, coupled with the fact that stoichoimetry information is not taken into account, can be seen in the benchmark in E. coli networks with the MetaCyc database. The completion was performed using either the fastGapFill, MIRAGE or Meneco tools. Even if this comparison is not entirely representative due to the different backgrounds and aims of the methods, it is worth noting that Meneco in general selected smaller sets of reactions to complete the networks compared to the other methods. Indeed, on average, Meneco returned answers 8 times smaller than fastGapFill and 125 times smaller than MIRAGE. This could enable an easier and faster manual curation of the results. Nevertheless this greatly reduced number of reactions proposed comes with a cost: Meneco restores less functionality, especially when it comes to highly degraded networks. The results of this benchmark are summarized in GapFill could not be used to perform an exhaustive gap-filling of the GEMs in practice. Moreover the analysis suggests that MIRAGE is not suited to be used with minimal data  for gap-filling and that the algorithm needs all the recommended data (phylogenetic and/or transcriptomic scores) to perform correctly and gain robustness.The effect of the parsimonious criterion of Meneco and fastGapFill with regard to the proposition of reactions to produce targets, all reactions added to a functional GEM for gap-filling were classified into essential, blocked and alternative reactions with respect to the production of biomass. The analyses are depicted in fastGapFill were blocked in the completed GEM towards the production of the biomass, i.e. the production of the target compounds. This high rate of blocked reactions was independent of the initial rate of degradation of the considered GEM. This is consistent with the main purpose of fastGapFill which aims at unblocking all reactions in the GEM core set without any focus on a set of targeted reactions. Conversely, only 12% of reactions added by Meneco were blocked with respect to the biomass reaction in the completed GEMs, and even less for 10% and 20% degraded GEMs. This confirms that fastGapFill does not solve the exact same problem as Meneco does, that is proposing sets of reactions to produce unproducible compounds. Since the problem fastGapFill solves is a larger one, it is more difficult to filter and manually curate the proposed solution as it is often required after gap-filling. Meneco is a relevant tool for the preliminary completion of a new draft metabolic network when limited phenotypic information are available. Its parsimonious feature allows suggesting reactions directly related to the production of the targets and manual curation is facilitated through a study of the whole space of solutions.In order to estimate the accuracy of Meneco provides a good trade-off in terms of scalability with respect to the size of the reference database used for completion: the output of Meneco remains reasonable in terms of size , with a reasonable loss of impact on the restoration of biomass production compared to parsimonious approaches.Our results suggest that the topological parsimony criterion used in Meneco, we completed the 3,600 degraded GEMs of our benchmark derived from the iJ904 E. coli metabolic network by 7,200  additional degraded GEMs of the iAF1260 [iJO1366 [E. coli networks. The Meneco tool was applied to complete each of the 10,800 degraded GEMs by using the networks prior to degradation as a reference database. The main motivation for changing the reference database was to be able to analyze how the classification of reactions into essential, alternative and blocked with respect to biomass production evolved with the completion process.To further evaluate the quality of the output of  iAF1260  and iJO1[iJO1366  E. coli Meneco output, even if the size of the solution set increased linearly with the degradation rate of the network. Nevertheless, the quality of the reference E. coli network has a strong impact on the quality of the results as shown in E. Coli network multiplied by 6 the percentage of functional networks after completion  were recovered in the networks filled by the Meneco procedure. Our analysis shows that Meneco was able to recover 97.5% of the essential reactions on average among the 10,800 experiments, and few blocked reactions were included in the networks filled by this tool.The reactions of the 40 degraded iMeneco recovered all essential reactions of the reference network. When failing to restore network functionality, it still recovered all essential reactions in 6,041/7,312 completions (82.3%). In the cases when Meneco did not recover all essential reaction, it generally missed a single essential reaction, and at most 3 .To gain better insights into the importance of essential and alternative reactions in the gap-filling procedure, we classified each gap-filling experiment according to four categories: (i) the network is functional after gap-filling (ii) the network has recovered all essential reactions after gap-filling but it is not functional (iii) the gap-filling procedure missed one essential reaction and (iv) the gap-filling procedure missed more than one essential reaction. Results are depicted in Meneco and capable of producing biomass: among the reactions that were essential in the reconstructed network, on average 40% were classified as alternative in the reference network  can only be applied if biomass can be quantitatively produced, we studied the 3,488 networks  capable of biomass production after their gap-filling. To do so, we compared the classification of reactions before degradation to the classification obtained after degradation and gap-filling. In all cases, reactions that were initially blocked with respect to biomass production remained blocked after the completion. In the same way, 98.6% of the reactions initially essential remained essential after the completion, the others becoming alternative  over the parsimonious gap-filling procedure. The GapFill benchmark was run as single target completion since it was impossible to be run on a global biomass function while being exhaustive. This is due to the combinatorial characteristics of the problem: the number of solutions when filling the networks based on a complete biomass was too high (mandatory bounding of the number of solutions with GapFill) and so was the computation time. Hence, we used as output for GapFill the union of the completion sets  for each individual target. We noticed that only 68 networks reached the limit of 30 completion sets for individual targets among the 10,800 degraded networks.Previous experiments show the added-value of parsimony towards the selection of adequate gap-filling solutions. As a final study, all the analyses were run with the Meneco and GapFill solutions are comparable in sizes, they only share 45.3% of the reactions (see Section 4 in GapFill returned 1.6% more reactions (i.e. 4) than Meneco. Ability to restore biomass production (FBA) was lower for GapFill than for Meneco: 21.6% and 32.3% respectively on 10,800 networks, and particularly for 10% degraded networks: 49.4% and 81.9% respectively. Recovery of essential reactions was less efficient for GapFill: it missed more than one (and up to 21) essential reactions in 13.9% of the cases, a major contrast with Meneco (0.8% and no more than 3 missed reactions). Analyses and results are available in Section 6 in Interestingly, although Altogether these features confirm that the topological over-approximation, when compared to stoichiometric criteria, is relevant for the parsimonious gap-filling of a new draft metabolic network not only in terms of performances but also in terms of functional accuracy.Meneco tool was applied to the analysis of candidate algal-bacterial interactions in the brown algal model E. siliculosus. Interactions between algae and their associated bacteria are of increasing interest because of the potential key roles bacteria may have on the biology of the algae, in particular through metabolic connections between both types of organisms [Ectocarpus genome paper [Meneco we then identified which of these targets were producible in two situations. Firstly, we used as a draft network the algal network itself, more precisely a version of EctoGEM 1.0 [Candidatus Phaeomarinobacter ectocarpi, a bacterium suggested to live in association with E. siliculosus [The rganisms . For thirganisms . In parame paper . For eacoGEM 1.0  for whiciculosus .Ca. P. ectocarpi network to the algal network. These targets and the metabolic pathways enabling their production were then manually examined. In total 83 previously non-producible algal targets became producible according to the Meneco semantics when combining the metabolic capacities of the bacterium with those of the alga. For each of these 83 targets, the essential reactions allowing its producibility by Ca. P. ectocarpi were studied, as well as the target and its related EST in E. siliculosus. The purpose was to determine whether the production of the target by the alga could depend on an interaction with the bacterium. These individual examinations revealed some errors or missing annotations in E. siliculosus as well as some cases of weak sequence homology leading us to no longer consider 21 (25%) of the 83 compounds as targets. Improvements in genome annotations also allowed recovering alternative reactions or pathways in E. siliculosus related to the production of 37 (45%) of the compounds. Nineteen compounds (23%) however are likely to be part of an exchange between the alga and the bacterium (i.e. confirmation of the target), ii) the target was not producible by the algal itself through the presence of other ESTs or genes and iii) the bacterial reaction(s) necessary for the target production was (were) correctly annotated. Details are available in A comparison of both cases allowed the identification of targets that became producible when adding the metabolic capacities of the acterium . The proMeneco were the result of potentially interesting interactions.Several of the reactions identified by E. siliculosus on its own is probably incapable of producing histidine or histidinol because a histidinol-phosphatase (EC 3.1.3.15) is missing from its genome. Meneco has previously identified this mandatory reaction to complete the histidine biosynthetic pathway [E. siliculosus , as well as the recently published genome of Saccharina japonica [Ca. P. ectocarpi, which encodes all enzymes of the histidine biosynthetic pathway.For example,  pathway . Recent japonica , confirmE. siliculosus is capable of producing vitamin B5 from β-alanine via the activity of a pantoate-β-alanine ligase (Esi0070_0043), but it lacks a biosynthetic pathway to produce β-alanine , which is an important vitamin for the formation of coenzyme-A. -alanine . Our anaEctocarpus still remains unknown, the E. siliculosus metabolic network possesses two metabolic reactions  for the degradation of agmatine and polyamine synthesis. These reactions in Ectocarpus are well-supported, despite the fact that it lacks the metabolic capacity to produce agmatine, as no arginine decarboxylase (E.C. 4.1.1.19) is present in its genome. As in the case of histidine synthesis described above, this latter observation also holds true for other sequenced Ectocarpus strains and the S. japonica genome. A plausible explanation is that E. siliculosus  may use external sources of agmatine to produce polyamines. Ca. P. ectocarpi is capable of producing agmatine, which constitutes a potential interaction point between both organisms. Our current working hypothesis is that when external or bacterial agmatine is available, agmatine-derived polyamines may complement or replace ornithine-based polyamine synthesis in E. siliculosus.A third example is agmatine, an aminoguanidine, which is well-studied in human where it modulates for instance receptors of neurotransmitters or ion channels. While the role of agmatine in These three examples provided interesting working hypotheses that will now need to be tested experimentally. Corresponding experiments are ongoing but time-consuming as they first require separate cultivation of algae and bacteria.i.e. candidate algal-bacterial interactions either due to missing gene annotations or false/overly precise annotations. One example for each of these cases is detailed in The aforementioned three examples highlight promising candidate pathways for algal-bacterial interactions. For other identified metabolites and reactions, however, sequence information was insufficient to determine the exact specificity of the enzymes and reactions, and thus to draw conclusions without additional experimentation. Furthermore, in E. mutabilis, a photosynthetic protist and primary producer in acidic aquatic environments. Despite important roles of E. mutabilis in those ecosystems and despite the fact that it has often been considered as an indicator species for acid mine drainages, this organism has been poorly described so far.As a second case study, we focused on the reconstruction of a metabolic network for Euglena. Therefore, we used assembled transcript sequences as described in [Meneco. This ensured that the reconstruction of the E. mutabilis metabolic network would be highly conservative, avoiding the inclusion of reactions based on false annotations. The major drawback of this approach was the resulting high rate of missing reactions in the initial draft network, which did not allow Meneco to restore the production of all targets at once. The 72 targets are listed in Supplementary files S6. Of those targets, 54 were defined according to [E. mutabilis cultured in a minimal mineral medium. The 17 other targets were myristic acid, shown to be present in the E. mutabilis membrane [Due to their complexity and size, no genome sequences are available so far within the genus ribed in  and annording to  who showmembrane , and theMeneco with the 8 mineral seeds and |Light| (Supplementary files S6) could propose a completion solution only for UREA and OXYGEN-MOLECULE among the 72 targets. These two metabolites could be produced only because they were the products of a single reaction involving only mineral reactants, a situation which was not biologically satisfying.Running E. mutabilis metabolic network must be produced from the mineral seeds only, including cofactors that are essential to the vast majority of reactions. These cofactors themselves depend for their biosynthesis on metabolites that they contribute to produce. For instance, NAD is biosynthesized from aspartic acid or tryptophan, two amino-acids whose production requires NAD. In other words, most of the E. mutabilis initial draft network consisted of a complex cycle with long range circular dependencies that no external mineral nutrient could initiate. Thus, this network could not produce the targets with Meneco without adding the appropriate extra seeds to solve the circular dependencies. The addition of NAD to the seeds permitted to initiate the draft network, enabling Meneco to restore the production of 69 targets out of 72. The addition of |ACP| to the seeds was required to restore the production of two remaining targets—PALMITATE, CPD-7836 (myristate) –, and adding |Red-Thioredoxin| to the seeds was necessary to restore the production of DCTP (deoxycytidine-triphosphate).The principal reason for the near impossibility to produce the targets lies in the fact that all targets and internal metabolites of the Meneco because of local cycles and circular dependencies. For instance, in oxygenic photosynthesis, oxygen is produced by photosystem II: light provides the energy for transferring 4 electrons to 2 molecules of plastoquinone from 2 molecules of water which are split into 4 protons and one molecule of oxygen. Since the |PLASTOQUINONE| compound was not available in the network, Meneco could not return the correct reaction PSII-RXN as a solution for the production of OXYGEN-MOLECULE, but proposed instead to reverse the reaction of water-forming NAD oxidation RXN-14692.Although the production of all the targets was enabled by the addition of the 3 seed metabolites NAD, |ACP| and |Red-Thioredoxin|, some pathways could still be non-functional with E. mutabilis is a primary producer, the photosynthesis reactions are at the origin of biomass production. The photosynthesis pathway can be divided into two subpathways: the light reactions collecting the energy from light and the Calvin-Benson-Bassham cycle, also known as the dark reactions, building the precursor components of biomass from carbon dioxide.Since de novo biosynthesis pathway described in MetaCyc, and therefore, the production of the corresponding donor and acceptor are mutually dependent. In order to unblock the whole chain, we added the electron acceptors  to the seeds, with the exception of NADP which could be produced from NAD as shown in Before addressing the capacity of the Calvin cycle to produce biomass precursors, we manually completed the light pathway by adding the missing reactions  and checked whether they could provide a viable route in the draft network . This paMeneco using the same seed metabolites as for the whole network. A single solution was returned with three reactions, NADPYROPHOSPHAT-RXN, APYRASE-RXN, NAD-KIN-RXN, producing respectively AMP from NAD, ATP from AMP, and eventually NADP from NAD and ATP. This confirmed that the four reactions of the photosynthesis light pathway were able to produce NADPH provided that NADP was available.To verify that the chain of photosynthetic light reactions was functional, we isolated the individual reactions  in a subnetwork and checked for the production of NADPH with Meneco on the whole draft network to check its capacity to produce NADPH returned two solutions of one reaction each, RXN-10862 and APYRASE-RXN, both allowing the production of ATP from AMP and unblocking the reaction NAD-KIN-RXN. At this stage, ATP was provisionally added to the seeds until more information could be gathered about which reactions should be included in the network for its production, and to enable the photosynthesis pathway to provide the Calvin-Benson-Bassham cycle with the necessary NADPH.The same dependence on ATP was observed with the whole draft network which could already produce AMP but not NADP although the reaction NAD-KIN-RXN of NAD phosphorylation into NADP was present. Running The Calvin-Benson-Bassham cycle is the pathway of the photosynthesis process that reduces carbon dioxide to produce glucides. The net equation of this cycle can be written as:Meneco with one reaction each: AMP-DEAMINASE-RXN, AMP-NUCLEOSID-RXN and NMNNUCLEOSID-RXN. The latter two reactions directly produce |RIBOSE-5P| whereas the first one yields IMP which can be used to produce |RIBOSE-5P| as well, suggesting that the Calvin-Benson-Bassham cycle requires this metabolite to be functional. We thus added |RIBOSE-5P| to the seeds and all 5 targets previously tested with the whole network were now producible by a subnetwork composed of only the 13 reactions of the Calvin cycle, confirming that this pathway was now functional.The net production of 3-phospho-glyceric acid (G3P) is the main source of organic carbons for further biosynthetic processes, but other metabolites may exit the cycle towards glycogenesis, amino-acid-, nucleotide-, or lipid biosynthesis. In order to assess whether the Calvin-Benson-Bassham cycle was functional we therefore tested the draft network for the production of G3P, FRUCTOSE-6P, RIBULOSE-5P, GAP and DIHYDROXY-ACETONE-PHOSPHATE. Three solutions were proposed by E. mutabilis to initiate the Calvin-Benson-Bassham cycle.It is worth noting that, at this stage, the metabolite seed |RIBOSE-5P| could be replaced by D-RIBULOSE-15-P2. This solution was provisionally preferred as this compound is a reactant of the carbon fixation reaction in photosynthesis. Thus, adding it to the seeds, along with ATP, may simulate the initial batch of metabolites that allows 3), in order to trigger the metabolism of amino-acids and nucleotides. Since the Tri-Carboxylic Acid cycle (TCA) is at the intersection between the metabolisms of glucides, lipids and amino-acids, this pathway was completed manually to ensure that it could use the G3P coming from photosynthesis. The following 5 reactions were thus added to the draft network: MALATE-DEHYDROGENASE-ACCEPTOR-RXN, ACONITATEDEHYDR-RXN, 2OXOGLUTARATEDEH-RXN, ISOCIT-CLEAV-RXN, PYRUVDEH-RXN. Furthermore, CO-A was provisionally added to the seeds as the TCA depends on ACETYL-COA. Assessing with Meneco whether the TCA was able to produce MAL , CIT (citrate) or 2-KETOGLUTARATE suggested that either GLYOX (glyoxylate) or OXALACETIC_ACID should be added to the seeds in order to unblock the TCA cycle. Both compounds are involved in many reactions in MetaCyc, however, as the addition of OXALACETIC_ACID to the seeds would have artificially enabled the production of ATP, GLYOX was used as a seed instead.The photosynthesis pathway being functional, a first route was now open between the mineral nutrients (seed metabolites) and the internal and excreted metabolites. Carbon-based biomass precursors such as G3P or FRUCTOSE-6P were producible and could enter the central metabolism. The next step was therefore to address the incorporation of nitrogen, in the form of ammonium . Since glutamic acid (GLT) is the principal component resulting from the incorporation of ammonium and a key metabolite in nitrogen metabolism, we had thus achieved the completion of the metabolic backbone leading from mineral nutrients through photosynthesis to carbon and nitrogen metabolism.Checking the current draft with The same process was repeated successively for the targets listed in Supplementary files S6 until all were producible. After the addition of new reactions, the provisional seeds were removed to test if they were still needed or if a biologically satisfying completion could replace them.Meneco criteria, we proceeded to verify whether it was functional in FBA. We created 10 import reactions , a biomass reaction, and 54 output reactions for the 54 metabolites that were experimentally shown to be secreted by E. mutabilis. Forty dead-end metabolites, i.e. non-consumed products that may prevent the network from satisfying the stoichiometric constraints for the production of target metabolites (secreted metabolites and biomass) were identified manually and defined as boundaries (boundaryCondition=“true”). Additionally, three reactions  were added to the network in order to regenerate reduced flavodoxins, oxidized ferredoxins, and thioredoxin since the corresponding transcripts could be identified in the transcriptome. We then checked which output reactions (biomass and secretion reactions) had a zero flux and determined the corresponding dead-end metabolites using FVA. Only the secretion of SPERMIDINE was thus blocked for stoichiometric reasons. We therefore added a dummy reaction of 5-METHYLTHIOADENOSINE degradation to satisfy the stoichiometric constraints on this metabolite for SPERMIDINE biosynthesis. The final draft thus contained a total of 1,068 reactions, including 10 entries, 54 secretion reactions, 1 biomass reaction and 1 additional degradation reaction. FBA showed that a positive optimum could be attained when defining the biomass reaction as an objective, and allowing only mineral nutrients and light  and targets (compounds whose producibility has to be restored). This flexibility appears to be crucial to investigate draft GEMs produced from NGS technologies, as illustrated in two applications.The main characteristic of Meneco checks the same topological criteria for producibility as the subset-minimality top-down method [GapFill family. This allows an exhaustive computation of the family of solutions.Most gap-filling methods solve optimization problems over a search space whose size grows exponentially with the size of the reference database from which the reactions are taken. Two different strategies can then be used to explore the search space and compute solution sets to a gap-filling problem: either a parsimonious bottom-up strategy which enriches the draft metabolic network until the targeted properties are satisfied , 10, 27,n method , but it From a biological point of view, having a method that enables the completion of a metabolic network without the need to know the real concentration of metabolites is a real advantage. It enables a completion of the metabolic graph not only for quantified or estimated compounds, but also for those identified by qualitative measurements. Moreover, having access to an exhaustive enumeration of the possible solutions allows researchers to choose the best one among them, instead of having only a subset of solutions without knowing if this subset is representative of the entire solution space or not.et al. introduced refined semantics for producibility taking into account the impact of cycles on the production of metabolites. This alternative definition can be viewed as an over-approximation of FBA-consistent networks, and appears to be very useful for the efficient computation of precursor sets for metabolic targets. However, experiments consisting in encoding this alternative definition in the Meneco framework and performing gap-filling of the same 10,800 degraded networks evidenced that this alternative definition of producibility is not constrained enough to capture essential reactions. Indeed, running our benchmark on the iJR904 network with that definition of producibility returns a set of solutions containing on average only 59.5% of the essential reactions  fail to recover enough alternative reactions to restore the biomass producibility of the network. This bottleneck can be explained by the parsimony criterion used by both tools since they identify sets with a minimal number of reactions allowing either to simultaneously restore the topological producibility of all targets (Meneco) or to enable the production of individual targets (GapFill). However, the identification of alternative routes to produce a targeted set of compounds is crucial to have a global understanding of species metabolic capability, as soon as essential reactions have been properly identified and validated. In order to improve the Meneco tool, we plan to study the impact of additional topological criteria that may take into account larger pathways. The difficulty will be to select relevant metrics in order to extend the search space while sufficiently constraining the search to be able to track the complete solution space. Indeed, with most of the methods based on extended scores and criteria for stoichiometry-based formalisms [Our experiments using the rmalisms , 15, theGapFill approaches with genomic information about alternative functions for genes. In the future, it will be interesting to adapt such scores to the Meneco tool and measure their impact on the functional classification of reactions in the reconstructed network.Alternatively, other metrics can be developed to improve the choice of reactions by the gap-filling methods. Scores based on likelihood value computations have been introduced in  to improet al. in [“clearly, the role of a gap-filling method is to simply pinpoint a number of hypotheses which need to subsequently be tested”. In this line of research, the Meneco tool constitutes a flexible framework to pinpoint hypotheses in the context of large-scale datasets applied to newly investigated organisms.As stated by Satish Kumar t al. in , “clearlMeneco is a versatile tool to complete draft GEMs and to suggest relevant reactions with respect to the response of the system to environmental perturbations. Importantly, it does not aim at providing a complete functional network, but rather at pointing out essential reactions and some of the alternative ones, which are crucial to explain the system response. It can be then combined with refined stoichiometry-based analyses and gap-filling methods to produce functional networks. In this sense, we promote Meneco as a tool to be used as an intermediary step within a workflow consisting of (i) producing a draft GEM [Meneco, (iii) refinement of the model with stoichiometric-based approaches relying on additional data , a), aMenecMeneco cannot replace manual curation and network analysis, but it may provide a flexible tool to aid this process. Analyses are fast, easy to implement, and invaluable because they enable biologists to focus their attention on a few highly interesting compounds and reactions without making a priori assumptions.From a biological point of view, the examples show that R metabolic reactions and M metabolites is represented by a bipartite directed graph G =  where edges in E connect either a metabolic reaction in R to metabolites in M or a metabolite in M to a metabolic reaction in R. Predecessors of a reaction node r are reactants: reac(r) = {m ∈ M| ∈ E}. Successors of a reaction node are products: prod(r) = {m ∈ M| ∈ E}. The biological concept of the producibility of metabolites can be formalized in terms of reachability. Given a metabolic network  and a set S ⊆ M of seed metabolites, a reaction r ∈ R is reachable from the seeds S if all reactants in reac(r) are reachable from S. Pushing forward this definition, a metabolite m ∈ M is reachable from the set of seeds S if m ∈ S or if m ∈ prod(r) is the product of a reaction r ∈ R that is itself reachable from S. Following [S according to a reaction set R contains all metabolites which can be iteratively produced from the seed metabolites. It is the set of metabolites reachable from S through the reactions in R and can be defined by iterating the set relation Mi+1 = Mi ∪ prod({r ∈ R|m ∈ reac(Mi), ∀m,  ∈ E}), with MO = S until it reaches a fixed point. The scope can be computed in polynomial time.A topological metabolic network comprised of ollowing –27, the Rdraft; (ii) a set Mtarget ⊂ M of target metabolites that are produced by the organism of interest; (iii) a set Mseed ⊂ M of seed nutrients found in the growth medium; and (iv) a database of metabolic reactions available to fill the network Rdatabase. The topological gap-filling problem is stated as follows.We assume that we are given (i) a draft network i.e. search space exploration strategies, preserving correctness and completeness of the search procedure. Clasp allows the computation of the brave and cautious consequences  by means of a linear number of calls to a solver  rather than enumerating the entirety of all solutions. This is accomplished by consecutive refinements of an internal constraint by relying on the incremental solving techniques introduced in [This combinatorial problem, expressed as a logic encoding, is solved by the ASP solver clasp 3.0.5  with domduced in . Rather Meneco [http://bioasp.github.io/meneco. Experiments related to the E. coli benchmark were run on a node of 24 cores part of a computational cluster. Case studies were run on personal laptops: the computation time required to get the union of solutions was less than 30 seconds.The ASP encoding, and the solver are encapsulated in a python package named Meneco , 41 whicS. Under steady state conditions, metabolite balances yield ∑j∈RSijvj = bj for all i ∈ M where bi is a parameter standing for uptake or secretion. In this setting, the stoichiometric gap-filling problem consists in minimizing the number of added reactions from the database to restore fluxes through a metabolite i* which is not producible from the draft metabolic network.A stoichiometric metabolic network can be viewed as an enrichment of a topological metabolic network with a stoichiometric matrix GapFill tool, encoded in a GAMS  program [This problem is solved by the so-called  program .To solve this optimization problem, solutions to the stoichiometric gap-filling problems for each individual target were computed by running the GAMS encoding from  on the NMIRAGE algorithm was run to perform a subset-minimal gap-filling using the implementation provided in [a priori information on the importance of the reactions in the draft networks, the biomass reaction was the only one added in the core reactions. The weights for all reactions of the databases were assigned to 1.The vided in . WithoutTo be able to run the software on a computer cluster, the code was compiled with the mcc function of MATLAB (R2014a). The MATLAB runtime software (version 8.3) was used to run the compiled code on a computational cluster consisted of 138 computing nodes .fastGapFill algorithm. Cplex (version 12.6.2) was used as MILP solver to solve the optimisation problem. A weight of 100 was set for the addition of transport and import/export reactions to penalize any modifications of the models. The draft models were used as core sets.The Portable System for the Analysis of Metabolic Models (PSAMM)  was usedE. coli: iJR904 [iAF1260 [iJO1366 [USCD repository. To test the impact of the MetaCyc database on the gapfilling problem, cross-references between the SBML identifiers of metabolites (SBML species) in the networks and MetaCyc identifiers were created. To that end, we followed an iterative strategy based first on metabolite names and formulas, then on similar reactions.We analyzed the following GEMs of : iJR904 , iAF1260[iAF1260  and iJO1[iJO1366 . The corE. coli models iJR904, iAF1260 and iJO1366, we generated 90 random biomass functions obtained by setting a defined percentage of non-zero coefficients of the true biomass to zero. The choice of replaced non-zero coefficients relied on Bernoulli-distributed random binary numbers. For each model consisting of an E. coli metabolic network and a random biomass, we classified the metabolic reactions according to their functionality: blocked , alternative  and essential . We implemented the classification of metabolic reactions within TOMLAB environment [For the three ironment , using GE. coli models iJR904, iAF1260 and iJO1366, we randomly removed 10%, 20%, 30% or 40% of metabolic reactions, with 10 different replicates by percentage. The same percentage of the three types of reactions was removed in each degradation process. The degraded networks were chosen such that none of the 90 random biomass functions could be produced by any of the 40 degraded networks.Then, for the three All together, we obtained a set of 10,800 metabolic networks which were not functional with respect to the production of their associated biomass.http://bioinformatics.psb.ugent.be/orcae/overview/Ectsi) [Ca. P. ectocarpi genome used for network reconstruction is available from the European Nucleotide Archive (accession HG966617). Both networks were mapped against MetaCyc 19.0 as a reference database. The construction of the set of targets for Meneco relied on the dataset of 90,637 ESTs published in the Ectocarpus genome paper ([EctoGEM 1.0 is based on genome data downloaded from the ORCAE website (w/Ectsi)  on June e paper  were discarded. The Meneco completion of the resulting draft metabolic network was performed with the modified version of MetaCyc 17.5 as the repair database and the 71 target metabolites listed in Generic reactions in MetaCyc 17.5 involving generic carbohydrates, amino-acids, nucleotides or NAD-P-OR-NOP (standing for NAD or NADP) were replaced with the appropriately balanced instances involving the corresponding metabolites present in the Meneco. At each step, seed metabolites included the basic mineral compounds and the metabolites required to unblock the cycles. Some metabolites such as ATP were added provisionally in order to solve long range dependencies until more information could be gathered about which reactions should be added to the network to enable their production. The production of the target subsets was addressed in the following order: photosynthesis, TCA cycle and amino-acids, glycolysis and glycogenesis, polyamines, nucleotides, and then all the remaining targets. Respiration was added as a single reaction.In order to avoid mutual dependencies and to solve cycles, the 72 targets were finally arranged into subsets corresponding to subnetworks (pathways and super-pathways) of the whole draft network. Completion was then performed with each subset as the list of targets for E. mutabilis transcriptome by tblastn. Transcripts with an expect value lower than 10−3 were extracted and used to search the protein nr database with blastx on the NCBI web server. The presence of the reaction was validated if the search yielded significant hits  in the Conserved Domain Database (CDD) and with sequences of enzymes that were consistent with the tested reaction.When a reference enzyme sequence corresponding to an added reaction was available in the MetaCyc database, this sequence was used as a query to identify potential homologs in the After each step, if the added reactions produced new metabolites previously absent from the network, the spontaneous reactions described in MetaCyc consuming those metabolites were also added to the network. The appropriate instances were manually added for the generic reactions corresponding classes of the newly added metabolites.The Flux Balance Analyses studies were performed using the python-based toolbox COBRApy (version 0.3.2)  and the Flux Variability Analysis was performed using both COBRApy in association with GLPK, and homemade scripts within the TOMLAB environment using the Gurobi solver.E. gracilis as determined by [E. mutabilis membrane adapted to arsenic contamination was obtained from [The biomass reaction was defined according to  and basemined by –55. The ned from . The proMeneco, the network was tested with COBRA findBlockedReactions command in Matlab R2015b. The dead-end metabolites blocking the production of biomass or of the experimentally determined targets [After completion with  targets  were manThe resulting network was eventually tested in Flux Balance Analysis with COBRA in Matlab R2015b, using the BIOMASS-RXN reaction as the objective function, and the mineral seed metabolites listed in S1 Table(PDF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S1 FilesAll files needed to run gap-filling experiments on the benchmark are provided in an archive as well as a Readme.txt file detailing the commands used to produce the results shown in this study. This archive also contains the global results for FVA and a detailed analysis of the network 256 and the biomass 89 as an example.(ZIP)Click here for additional data file.S2 FilesE. siliculosus and Ca. P. ectocarpi are provided together with the list of seeds and targets used to run the Meneco tool. The exhaustive analysis of essential reactions which allow the production of 83 target metabolites thanks to the combination of E. siliculosus and Ca. P. ectocarpi networks is provided in a separate file. Finally, detailled examples of false positive predicted interactions and their explanation are provided in a separate pdf file.The two draft networks used to study potential cross-feeding relations between (ZIP)Click here for additional data file.S3 FilesE. mutabilis are provided here. The folder also provides the SBML file describing the repair database  required to run the Meneco tool. Files corresponding to targets and seeds are also provided. The file seed_minimum_medium_Emutabilis.sbml describes light and the 8 mineral nutrients of the minimal growth medium for E. mutabilis. As described in the main text, the network is not functional with only these seeds as reactions in cycles are blocked. The file “seeds_min_Emutabilis_final.sbml” provides the minimal set of seeds necessary to unblock the cycles and produce all the targets in “targets_Emutabilis_72.sbml” while the list of seeds and targets is also provided as tables in the file “seedsAndTargets_euglena.pdf”.Both draft and functional reconstructed metabolic networks for (ZIP)Click here for additional data file.