Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa,” and their animal host, the whitefly Bemisia tabaci, we apply metabolic modeling to investigate host-symbiont nutrient exchange. Our in silico analysis revealed that >60% of the essential amino acids and related metabolites synthesized by “Candidatus Portiera aleyrodidarum” are utilized by the host, including a substantial contribution of nitrogen recycled from host nitrogenous waste, and that these interactions are required for host growth. In contrast, “Candidatus Hamiltonella defensa” retains most or all of the essential amino acids and B vitamins that it is capable of synthesizing. Furthermore, “Candidatus Hamiltonella defensa” suppresses host growth in silico by competition with “Candidatus Portiera aleyrodidarum” for multiple host nutrients, by suppressing “Candidatus Portiera aleyrodidarum” growth and metabolic function, and also by consumption of host nutrients that would otherwise be allocated to host growth. The interpretation from these modeling outputs that “Candidatus Hamiltonella defensa” is a nutritional parasite could not be inferred reliably from gene content alone but requires consideration of constraints imposed by the structure of the metabolic network. Furthermore, these quantitative models offer precise predictions for future experimental study and the opportunity to compare the functional organization of metabolic networks in different symbioses.An important factor determining the impact of microbial symbionts on their animal hosts is the balance between the cost of nutrients consumed by the symbionts and the benefit of nutrients released back to the host, but the quantitative significance of nutrient exchange in symbioses involving multiple microbial partners has rarely been addressed. In this study on the association between two intracellular bacterial symbionts, “IMPORTANCE The metabolic functions of unculturable intracellular bacteria with much reduced genomes are traditionally inferred from gene content without consideration of how the structure of the metabolic network may influence flux through metabolic reactions. The three-compartment model of metabolic flux between two bacterial symbionts and their insect host constructed in this study revealed that one symbiont is structured to overproduce essential amino acids for the benefit of the host, but the essential amino acid production in the second symbiont is quantitatively constrained by the structure of its network, rendering it “selfish” with respect to these nutrients. This study demonstrates the importance of quantitative flux data for elucidation of the metabolic function of symbionts. The in silico methodology can be applied to other symbioses with intracellular bacteria. The trait of nutrient overproduction has evolved in diverse bacteria that have entered into intimate associations with animals , 2. This1–5–Buchnera  bacteria produce complementary sets of EAAs . For insBuchnera , while iBuchnera –12. In suchnera –, 5, 8.The metabolic contribution of the second symbiont in multipartner symbioses is unambiguous where its metabolic gene content perfectly complements that of the primary symbiont , 13, 14.Bemisia tabaci. These plant sap-feeding insects harbor a primary (obligate) symbiont, “Candidatus Portiera aleyrodidarum”   and, as such, have fewer metabolic genes and reactions than would be expected in a free-living bacterium, such as Escherichia coli  that comprises 76 intracellular reactions and 148 metabolites  with 462 reactions and 469 metabolites (iNA332) comprises 332 genes, 236 intracellular reactions, and 253 metabolites . Additionally, ∼49% of the metabolites in the “Candidatus Portiera aleyrodidarum” iNA94 metabolic network are exchanged with the host. This is in sharp contrast to the 8% of the metabolite pool “Candidatus Hamiltonella defensa” iNA348 exchanges with the host  of each individual organism was determined. A gene was considered essential if its removal from the model resulted in a >99% decrease in growth relative to the wild type. Our simulations showed that 67%, 59%, and 9% of the genes in “Candidatus Portiera aleyrodidarum” iNA94, “Candidatus Hamiltonella defensa” iNA348, and BemisiaiNA332, respectively, were essential for sustaining growth in silico ; (ii) “Candidatus Portiera aleyrodidarum” overproduces tryptophan, threonine, and methionine, which are released to the host bacteriocyte, and tryptophan and methionine are consumed by “Candidatus Hamiltonella defensa”; and (iii) “Candidatus Portiera aleyrodidarum” releases precursors of the remaining 7 EAAs , which are metabolized by the host bacteriocyte to EAAs, with all but lysine required by “Candidatus Hamiltonella defensa” defensa” .Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” require 48 and 23 different inputs from the host, respectively  from the host than “Candidatus Hamiltonella defensa” (0.5 mmol g DW−1 h−1) . Approximately 64% of the material “Candidatus Portiera aleyrodidarum” utilizes from the host is central carbon metabolism intermediates while ∼54% of “Candidatus Hamiltonella defensa” inputs are amino acids . Ten inputs are shared between “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa”: the carbon source glucose, three nonessential amino acids , and six EAAs   .Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” may compete for the 10 shared input metabolites . The exceptional case of glucose arises because “Candidatus Portiera aleyrodidarum” does not use it for either energy production or growth; it is metabolized to 6-phosphogluconolactone (a pentose phosphate pathway intermediate), which is exported back to the host. In summary, these data point to multiple candidate instances of competition between the two bacteria for host resources, but the significance of these competitive interactions in vivo varies with the supply of these compounds from the host.We hypothesized that “abolites , resultiCandidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” export 24 and 14 unique metabolites to the host, respectively .We probed our three-compartment metabolic model for partitioning of metabolic functions between the bacterial partners by examining the metabolic outputs exported to the host from each bacterium. The metabolic networks of “Candidatus Portiera aleyrodidarum” (∼2.4 mmol g DW−1 h−1) to the host is ∼40-fold greater than from “Candidatus Hamiltonella defensa” (∼0.06 mmol g DW−1 h−1) . The two bacteria also differ in the compositions of the outputs, with amino acids and their derivatives accounting for ∼40% of the total quantity of “Candidatus Portiera aleyrodidarum” exports, while ∼93% of “Candidatus Hamiltonella defensa” outputs are central carbon metabolism intermediates . “Candidatus Hamiltonella defensa” has the genetic capacity to synthesize B vitamins, but these micronutrients are retained within the “Candidatus Hamiltonella defensa” cells under the conditions simulated in our model .Our simulations also predict that the total quantity of metabolites exported from “Candidatus Portiera aleyrodidarum” alone supplies threonine and 2,6-diaminoheptanedioate (a lysine precursor) to the host. The amounts of these compounds released by “Candidatus Portiera aleyrodidarum” are proportional to the aspartate supply from the host under physiologically meaningful conditions  for threonine and lysine biosynthesis, our simulations indicate “nditions . , we tracked the fates of ammonia (a nitrogenous waste product of the host), nonessential amino acids, and amino acid derivatives generated in the host bacteriocyte. Our simulations indicate that ∼40% the total flux of N into “Candidatus Portiera aleyrodidarum” is sequestered. Approximately 0.8 mmol g DW−1 h−1 of N-containing metabolites is received by “Candidatus Portiera aleyrodidarum” from the host and 0.5 mmol g DW−1 h−1 of N-rich metabolites, mostly in a form the host cannot synthesize, is released back to the host ; glutamine (22%); other nonessential amino acids (15%); ornithine, carbamoyl phosphate, and homocysteine (15%); and essential amino acids (13%). Aspartate is used by “Candidatus Portiera aleyrodidarum” to synthesize threonine and the lysine precursors 2,3-dihydrodipicolinate and 2,6-diaminoheptanedioate. These three metabolites constitute 58% of the total N released by “Candidatus Portiera aleyrodidarum” to the host. Glutamine is used in three EAA-biosynthetic pathways to make tryptophan, histidinol phosphate (a histidine precursor), and arginosuccinate (an arginine precursor), together accounting for 34% of the total N output. Ornithine, carbamoyl phosphate, and homocysteine are used to make arginosuccinate and methionine, representing 7% of the total N output , glutamine (29%), serine (12%), and essential amino acids (23%)  .Candidatus Hamiltonella defensa”-host model was incapable of supporting host growth. In contrast, removal of “Candidatus Hamiltonella defensa” resulted in an ∼12% increase in the host growth rate , together with 11, 6, and 15% increases in the uptake rates of the shared metabolites glucose, glutamine, and serine, respectively. The uptake rates of the EAAs with host-mediated terminal reactions  remained unchanged, but the export of tryptophan, threonine, and most EAA precursors from “Candidatus Portiera aleyrodidarum” increased by 0.5 to 22%. Exceptionally, the efflux of methionine and the C skeletons of the essential amino acids isoleucine, leucine, and valine were reduced by 2 to 18% relative to the three-compartment model including “Candidatus Hamiltonella defensa” . Altogether, these data highlight the impact “Candidatus Hamiltonella defensa” has on nutrient exchange in this three-partner symbiosis and provide data on the nutritional costs associated with harboring “Candidatus Hamiltonella defensa” by the host.To investigate the metabolic significance of the two bacterial partners to the host, host growth was compared between the three-compartment model and two-compartment models generated by omitting all reactions associated with one bacterium. In the two-compartment models, uptake fluxes for glucose, fructose, ammonium, and phosphate, the main sources of C, N, and P were capped at the observed uptake rates in the three-compartment model. Our simulations indicated that a two-compartment “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” with the whitefly host highlights the distinctive traits of bacterial symbionts with small genomes. Notably, “Candidatus Portiera aleyrodidarum” is highly cooperative, with >60% of essential amino acids synthesized in the system predicted to be allocated to the host   , as welliJO1366) . The fraIn this discussion, we consider these interactions first from the perspective of metabolic inputs to the bacteria and then from the perspective of metabolic outputs from the bacteria to the host.Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” share 10 host-supplied metabolites and that competition for these shared resources decreases bacterial growth rates and may impact the capacity of each symbiont to provide enough nutrients for the host, thereby negatively impacting host fitness. Competition can incur additional costs by driving the evolution or expression of antagonistic traits in the bacteria . This level of recycling is high compared to the aphid-Buchnera symbiosis, where the predicted nitrogen input is 11-fold greater than EAA release  or apparently uncommon (lysine).Our interpretation that “yptophan . The EAAin silico study fast tracks discovery. As metabolic models come to be applied to other associations, it will become feasible to conduct detailed comparisons, with the opportunity to investigate the roles of host and symbiont traits that define the convergent functions of EAA overproduction and release, as well as important differences that may be related to the evolutionary histories of the partners and the ecological circumstance of the different symbioses.This first metabolic model of a multipartner symbiosis in an insect host offers quantitative predictions about the functions of two bacterial symbionts sharing a single host cell. The key experimental advantage is that the models create precise hypotheses for empirical testing. For systems such as whiteflies, where experimental studies are technically demanding and time-consuming because of the very small size of the insects, this Candidatus Portiera aleyrodidarum” (iNA94)  and “Candidatus Hamiltonella defensa” (iNA348)  associated with B. tabaci, reciprocal BLAST searches of symbiont genomes against E. coli strain K-12 substrain MG1655 were used to identify gene orthologs. The criteria for the bidirectional matches included in the model were as follows: an E value of <1e−5, 35% amino acid sequence identity, and match lengths of >70% of the lengths of both query and subject. The list of orthologous genes generated from reciprocal BLAST searches was compared to that of the E. coli strain K-12 substrain MG1655 metabolic model, iJO1366  , reactions capable of generating or consuming dead-end metabolites in each bacterial-symbiont model were identified and incorporated into a draft reconstruction of the host bacteriocyte metabolic network. Additional host reactions completing metabolic pathways for each added reaction and host gene products enriched in host bacteriocytes were incorporated into a final reconstruction of the metabolic network. With the exception of the objective function, exchange, and demand reactions, all reactions included in each model were mass and charge balanced. All the metabolic networks were visualized using Cytoscape_v3.4.0  into a single multicompartment model, reactions and metabolites assigned to each partner were renamed, with reactions in POR , HAM (“Candidatus Hamiltonella defensa”), and BTA (Bemisia) and metabolites in [p] , [h] (“Candidatus Hamiltonella defensa”), and [b] (Bemisia). The stoichiometric matrices of the three models were combined to generate an integrated model, iNA774 . Transport reactions were added to connect the compartments of “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” to the Bemisia compartment, which was adopted as the site for the exchange of metabolites with the external environment.To integrate the three individual models  dead-end metabolites in each bacterial compartment were assigned transport reactions to or from the host bacteriocyte to allow enzymatic reactions mediating their synthesis or consumption to carry flux and (ii) metabolites produced in one species compartment for which another species compartment was auxotrophic were assigned transport reactions to and from the host bacteriocyte. The COBRA toolbox , run in −1 h−1. In the absence of empirical data on the compositions and concentrations of metabolites in the external medium bathing the host bacteriocyte (insect hemolymph), we adopted the most parsimonious strategy of a minimal medium comprising glucose and fructose as carbon sources, ammonia and homocysteine as sources of nitrogen, and hydrogen sulfide as a source of sulfur. Cofactors added to the model medium were thiamine monophosphate, thiamine diphosphate, riboflavin, pyridoxine 5-phosphate, and nicotinate d-ribonucleotide. Water, protons, carbon dioxide, iron(II), iron(III), and phosphate were allowed to freely diffuse across the bacteriocyte cell membrane. The maximum uptake fluxes of all medium components were capped at 100 mmol g DW−1 h−1. The upper bounds of all whitefly intracellular reactions were constrained by the transcript abundances of their respective associated genes, with the exception of asparagine synthase, diaminopimelate decarboxylase, argininosuccinate lyase, histidinol dehydrogenase, phenylalanine hydroxylase, phosphoserine phosphatase, and valine transaminase, which were constrained using the absolute values of amino acid concentrations measured in vivo. Empirically determined concentrations of amino acids were also used to constrain two “Candidatus Portiera aleyrodidarum” reactions, methionine synthase (METS) and threonine synthase (THRS) . The transcriptome (RNA-Seq) data used to constrain the upper bounds of all B. tabaci reactions in the metabolic model were taken from a previous study .All model simulations were performed under aerobic conditions with a maximum oxygen uptake rate of 20 mmol g DWus study . The lowiNA774) model simulations. We selected this community objective function because the primary function of this bacterial-host symbiosis is the provisioning of EAAs that supplement the EAA-deficient plant sap diet of the insect host (see the introduction). Additionally, biomass reactions for “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” were included in each bacterial compartment to represent bacterial growth. The coefficients for the components in the biomass reaction of the bacterial models   were derived from the biomass equation of the metabolic model for another bacteriocyte symbiont, Buchnera iSM199, generated using the experimental data from reference Candidatus Portiera aleyrodidarum,” “Candidatus Hamiltonella defensa,” and Buchnera are all gammaproteobacteria with highly reduced genomes and are endosymbionts in insects of the same suborder, Sternorryncha. The following modifications were made to the Buchnera biomass equation to generate biomass equations for “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa”: (i) the biomass coefficients for each amino acid were recalculated to reflect a “selfish” model where EAAs are produced solely to support bacterial protein synthesis and biomass increase, (ii) cofactors and soluble pool metabolites absent from the Buchnera model but present in the “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa” models were given small coefficients (0.00005) to ensure their production by each model, and (iii) H2O was included as a substrate and protons as products in the ATP maintenance equations . First, every transcript in the whole-body transcriptome of an adult whitefly was translated in silico; then, the number of residues of each amino acid in each protein was summed and multiplied by the estimated abundance of the transcript in the transcriptome. The estimated abundance of each transcript was determined by considering the lowest-abundance transcript as present once and normalizing the count for the more abundant transcripts accordingly  . Transcripts with zero abundance were considered to be present once to account for low-abundance transcripts omitted from the transcriptome during sample preparation and processing. Assuming a cellular protein content of 70.6% , as described in reference For of 70.6% , the fraB. tabaci MEAM1   was frozen for subsequent protein quantification. The remaining supernatant was filtered through a 0.45-μm filter plate (Millipore) by centrifugation at 1,500 × g for 10 min. Then, 2.5 μl filtrate was derivatized with AccQ Tag (Waters), following the manufacturer's protocol, and injected into a Waters Acquity ultraperformance liquid chromatograph (UPLC) with a photodiode array (PDA) detector and an AccQ-Tag Ultra 2.1- by 100-mm column. The gradient used was as follows: 0 to 0.54 min, 99.9% A and 0.1% B; 0.54 to 5.74 min, 90.9% A and 9.1% B; 5.74 to 7.74 min, 78.8% A and 21.2% B; 7.74 to 8.04 min, 40.4% A and 59.6% B; 8.04 to 8.64 min, 10% A and 90% B; 8.05 to 8.64 min, 10% A and 90% B; 8.64 to 8.73 min, 99.9% A and 0.1% B; 8.73 to 9.50 min, 99.9% A and 0.1% B (linear between time points), where A is 90% AccQ-Taq Ultra Eluent A in water and B is Accq-Taq Ultra Eluent B. Amino acids were determined by comparing their retention times with standards at 1, 5, 10, 50, and 100 pmol ammonia and protein-amino acids μl−1  and quantified with standard curves. The amino acids were normalized to the protein content determined by DC protein assay (Bio-Rad) with 0 to 10 mg bovine serum albumin ml−1 as a standard.The free amino acid content of bacteriocytes from M507785)  was quanM507785)  to gener


Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene‐reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome‐scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation‐reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development.Genome‐scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as  In order to achieve this, a comprehensive understanding of the metabolic network of the “chassis” organism is required in order to identify the most promising targets to be modified. The availability of complete genome sequences and advances in systems biology allow the analysis of large datasets in an integrated manner to increase our understanding of metabolism and enable model‐driven strain design strategies. Genome‐scale metabolic models (GEMs) can be used to predict the metabolic capabilities of a “chassis” organism, to investigate the changes in the flux distributions under different conditions, to identify metabolic bottlenecks, and to evaluate the outcomes of the possible genetic modifications  is a well‐established host organism used for recombinant protein production  comprising the standard metabolite and reaction IDs and gene‐reaction associations that permit the in silico simulations and analyses. We next investigated the effect of biomass representation on the predictive capability of Kp.1.0 by using published biomass composition datasets for this organism. We then focused on how the changing biomass representations affected the models predictions on targets to be engineered to increase this yeast's productivity. In all, our study has investigated the potential of a new GEM in identifying targets for improving the productivity of K. phaffii cells.In this study, we first reconstructed the metabolic network of 22.1K. phaffii; PpaMBEL1254 , and grown aerobically in chemostat cultures fed with 80:20, 60:40, and 40:60 (w:w) glycerol/methanol mixtures at two dilutions rates (0.05 and 0.16 hr−1) were obtained from Jordà et al.  grown under different oxygenation levels  were adopted from  was prepared as a COBRA‐compliant SBML version 2, level 4  and fold change (FC > 2) analyses. For each case, 100 random values for each flux in the distribution were generated such that each value remained within bounds of the allowable range for that flux, which was determined through flux variability analysis. The Mann–Whitney U test was applied to those values for each individual flux separately, in order to identify those changes, which have appreciable biological impact, the average of the 100 values for each individual flux were employed in all possible pairwise comparisons in fold change analyses. A similar analysis was also conducted for sample sizes of 50, 1,000, and 10,000 to test the effect of the sample size on the significance analysis. Flux values lower than 10−5 were taken as zero. The significance of between‐biomass comparisons under different conditions was calculated using Mann–Whitney U test for each macromolecule.The significance of the effect of biomass composition on the flux distributions was assessed by using both Mann–Whitney K. phaffii annotation file (34378.P_pastoris_GS115.obo) were downloaded from EMBL‐EBI (http://www.ebi.ac.uk) on March 29, 2016. Orthologous genes were obtained from InParanoid 8 database, considering only data that have 100% bootstrap confidence value   in the iPP668 and PpaMBEL1254 models, which precluded the assignment of standard reaction IDs. Following this manual cross‐checking exercise, our initial core network comprised 915 reactions and 965 metabolites. Further analysis revealed gaps and a high proportion (∼45 %) of dead‐end metabolites, and so required further manual curation.An initial core network was constructed considering only reactions found in at least two of the three models. Even some of these were omitted due to the lack of any evidence for their existence in 3.2K. phaffii metabolism in KEGG  was not well defined in the initial core network, was the principal contributor to gaps in the network. The level of detail in the representation of the fatty acid biosynthesis, elongation, and degradation pathways varied between the three extant models. This required the de novo reconstruction of these pathways, using the information for K. phaffii, where they updated both sphingolipid metabolism and the sphingolipid content of the biomass equation based on the literature information. Their representation of sphingolipid metabolism, as reported in iMT1026, was incorporated into the Kp.1.0 model.The inconsistencies in the assignment of reactions involved in glycerophospholipid metabolism to compartments also resulted in the formation of dead‐end metabolites. The localization assignments were performed using the GO  and 1,221 metabolites. Gene‐reaction associations were built on this network. Comparison of the gene‐reaction associations given in the existing models revealed crucial differences in these associations and also in the logical rules applied when multiple genes were associated with a single reaction. There were instances where the reactions in the glycolysis, amino‐acid biosynthesis, purine metabolism, pyrimidine metabolism, and fatty acid elongation pathways were associated with a single enzyme in iPP668 but with more than one enzyme in iLC915 (File S2). For Kp.1.0, we used the reaction‐enzyme associations in KEGG database   were viable. Since there is, as yet, no comprehensive collection ly Table , File S3S. cerevisiae genome. Of the 608 genes in Kp.1.0 with a S. cerevisiae ortholog, 76 genes were identified to have a second, paralogous, copy that arose from the whole‐genome duplication (File S4). For 16 of those genes, model simulations using Kp.1.0 predicted an inviable phenotype, whereas their yeast orthologs were reported to be viable. This is indicative of the lower level of genetic redundancy in the K. phaffii genome, and is entirely congruent with the fact 11 of those 16 genes were reported to have a synthetic lethal interaction with their paralogs in S. cerevisiae  were predicted employing condition‐specific biomass compositions, in the case of Carnicer et al., and using the biomass composition of wild‐type cells under normoxic conditions for the other dataset. The error rates were within a 1–15 % range with one exception, where the CO2 exchange rate (CER) for wild‐type, glucose grown‐cells was predicted to be ∼25% higher than the reported experimental value  in carbon‐limited chemostat culture at a dilution rate of 0.1 hr−1. Glycerol, methanol and oxygen uptake rates were constrained according to the data of  correlations between the flux distributions obtained when the biomass compositions measured experimentally under different conditions were used Figure a, and 22b. We fop‐value < 0.01). We found that the reactions that show high variability did not change when different biomass representations were used.Flux variability analysis was conducted to identify the reactions that display the greatest variability, and the impact of the biomass composition on these changes was investigated. It was observed that the highly variable reactions were catalyzed by enzymes involved in phosphorus, carbohydrate, and ribonucleoside diphosphate metabolism; these associations were significant   and those that showed no significant difference in biomass content.The significance of the change in flux values was evaluated, for each reaction, by comparing each condition with every other. For a sample size of 100, we found that 433 out of the 1,474 reactions in Kp.1.0 showed a significant change in at least one of the comparisons . When the same analysis was conducted with a sample size of 100 using iLC915, incorporating the same biomass equations, it was observed that 693 reactions, which were associated with 452 genes were significantly changed. These genes were identified to be enriched only with the process terms cellular lipid metabolic process and cell cycle, and cellular catabolism  (File S6). The same analysis was repeated for sets of flux distributions of various sample sizes of 50, 1,000, and 10,000, in order to identify any possible biases for sample size, and the results of the analysis indicated that the number of reactions whose fluxes were significantly different and the number of genes associated with those reactions, remained within ±1.25% and ±2.50% of one another, respectively; thus no size‐dependent trends were observed. More than 92% of both the reactions and the genes associated with these reactions were common in all sets and consequently the GO Process Term enrichment analysis highlighted the same processes for all of them, demonstrating the robustness of this analysis .Both the correlation and significance analyses indicated that the effect of biomass representation on flux distribution could not be predicted. There were 215 genes associated with the reactions whose flux was significantly changed by altering biomass composition, independent of the sample size. GO analysis showed that this gene set was significantly enriched for the following biological process terms: steroid biosynthesis, lipid biosynthetic, cellular alcohol metabolic process, and oxidation–reduction .Observation of the impact of biomass representation on flux distributions prompted us to investigate the effect of the biomass content on target identification. Overexpression targets to improve production of human copper/zinc superoxide dismutase (hSOD) were predicted using 12 different biomass representations and, in all, 59 genes were identified as candidates. Among these 59 genes, 37 were classified as targets in all cases, whereas 22 were identified only using some of the biomass representations. The genes encoding the enzymes catalyzing those reactions identified to have an increased flux in all analyses were enriched with histidine and lysine metabolic processes; whereas the genes catalyzing the reactions that showed condition‐dependent increase were enriched with glutamine family amino‐acid biosynthetic process .The effect of the biomass composition on predicting knockout targets to improve hSOD production was evaluated using RobOKoD  in K. phaffii.Kp.1.0 was validated through evaluating its success in predicting whether single‐gene null mutants would be viable or inviable. The essentiality information on K. phaffii GEMS were omitted from Kp.1.0. Furthermore, during the construction of Kp.1.0, enzymes catalyzing reactions that led to an increase in the number of dead‐end metabolites  anchor biosynthesis pathway, and in lipoic acid metabolism) were omitted, together with their cognate genes. This was done because as the number of genes associated with dead‐end reactions increases, the number of genes predicted to be non‐essential will also increase, irrespective of the model structure. The low specificity of the iLC915 GEM in predicting gene essentiality is thought to be a consequence of this fact (Table In order to confidently and unambiguously define gene‐reaction associations, putative genes and genes with unknown function, which comprise 5–10% of the genes included in the other ct Table .K. phaffii is that, in contrast to S. cerevisiae, extensive data on biomass composition of this yeast have been collected under different experimental and genetic conditions. This provided a great opportunity to study the effect of biomass composition on the success of the Kp.1.0 model in predicting growth characteristics, flux distributions, gene essentiality, and the identification of the targets to be engineered for production of a specific r‐protein.The biomass composition of microbial cells is bound to vary between different physiological conditions (e.g., different carbon sources, nitrogen limitation, pH, aeration, temperature, mode of cultivation, genetic manipulation; Aguilar‐Uscanga & Francois, K. phaffii GEM for which gene‐reaction associations are available, also highlighted the lipid metabolic processes as being sensitive to changes in biomass composition. This suggests that the specific effects of changes in biomass composition are not peculiar to a particular network reconstruction but, rather, are intrinsic to the metabolism of this industrial yeast species.These analyses showed that growth characteristics could be predicted with an error rate changing between 2% and 22% depending on the biomass composition used in Kp.1.0. It was observed that the varying the representation of biomass composition did not affect the prediction of gene essentiality. On the other hand, when the flux distributions were investigated, it was seen that the wiring of some parts of the metabolic network changed greatly depending on the biomass composition. The analyses of these significantly altered fluxes, and the genes encoding the enzymes that catalyze the reactions involved, revealed the impact of biomass composition was not random; rather, it affected specific domains of metabolism, including lipid and secondary alcohol biosynthesis and oxidation‐reduction processes. A similar analysis conducted with iLC915, the only other The representation of biomass content also affected the predictions on potential targets to be engineered for improving r‐protein production when flux distribution based approaches such as FSEOF approach were used. Identification of the overexpression targets to improve hSOD production using Kp.1.0 model employing 12 different biomass representations showed that 37% of the targets were classified as targets conditionally. Moreover, further investigation of the effect of biomass representation on predicted beneficial genetic interventions revealed that not only different reactions might be identified as candidates but also the rankings of the candidate targets altered when the biomass content was changed in the model. These results showed that biomass content was a key player in target identification using metabolic modeling.K. phaffii It is suitable to conduct such analyses and has been made, available to the community through the BIOMODELS database (Chelliah et al., We believe that the Kp.1.0 genome‐scale metabolic model represents a useful tool with which to design more productive strains of Additional Supporting Information may be found online in the supporting information tab for this article.Table S1. Effect of Sample Size on Significance AnalysisClick here for additional data file.Supporting Information.S2Click here for additional data file.Supporting Information.S3Click here for additional data file.Supporting Information.S4Click here for additional data file.Supporting Information.S5Click here for additional data file.Supporting Information.S6Click here for additional data file.Supporting Information.S7Click here for additional data file.


Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. Many large-scale mathematical models describe metabolism to understand how microbes and other organisms (including humans) function and interact with each other and with their environment. Making these models is extremely time- and effort-intensive; it requires gathering and combining information from many sources, including the organism’s genome sequence, biological databases, scientific literature, and expert advice. The exact procedure and resources used depend on the model creators’ expertise and research interests, such that independently created models for the same organism are often very different and can hardly be compared. However, each model typically contains unique information that is ‘lost’ when working with a different model. To integrate the available knowledge, we developed a computational tool to build consensus metabolic models. Our tool—COMMGEN- combines independently generated models by matching identical parts and resolving differences between inconsistent parts. We apply our tool to four sets of models of different organisms. In all these sets, COMMGEN identified and resolved hundreds of inconsistencies. COMMGEN can be generally applied to standardize and improve models of metabolism, in particular for complex scenarios, such as those involving microbial communities and host-pathogen interactions. Genome-scale constraint-based metabolic models (GSMs) are curated organism-specific knowledge repositories . They inGenome-scale metabolic reconstructions consist of metabolites, metabolic reactions (including boundary reactions and a biomass reaction), cellular compartments, and genes ,16. The Saccharomyces cerevisiae or Bacillus subtilis, many uncertainties remain during GSM construction. These uncertainties are typically manually addressed based on expert knowledge and scientific literature, which involves a laborious iterative process that can take several years, for example, for eukaryotes [However, even for well-studied organisms such as karyotes . The maikaryotes , alternakaryotes –21, and karyotes ; (ii) sukaryotes , and thekaryotes .As a consequence, when several GSMs for the same organism are developed independently, they are complementary and only partially overlapping ,25. The To represent metabolism more comprehensively, and thereby improve our understanding of a target organism, alternative GSMs of a target organism can be integrated into a so-called consensus model of the respective organism, one per organism. Consensus models have an increased scope  and they are more consolidated . When discrepancies exist between GSMs, these must be carefully examined to select the most appropriate modeling alternative. However, while consensus models have been generated successfully for several (model) organisms such as budding yeast and human, this required extensive manual curation by communities of domain experts ,24,25,27Here, we present COMMGEN, a tool for COnsensus Metabolic Model GENeration that reconciles two or more distinct GSMs of the same organism beyond a common namespace. COMMGEN automatically identifies similarities, dissimilarities, and complements of the metabolic networks based on an extensive classification of problems that typically arise during GSM integration and on novel algorithms to resolve these problem classes. For several model organisms, we show that semi-automatically created consensus GSMs in a standardized namespace  are subsOur analyses addressed model building, testing and refinement in a stepwise fashion. We started by identifying the classes of inconsistencies that exist between models for four widely different albeit representative microbes. We subsequently set up the framework for COnsensus Metabolic Model GENeration, and tested it on the four case studies for functionality and predictability.To systematically resolve inconsistencies between two or more Initial GSMs (IGSMs) to be integrated, we defined three main (coupled) inconsistency categories: metabolites, reactions, and compartments. We explain these categories and the inconsistency classes they contain using examples from four sets of IGSMs that cover gram-positive and gram-negative bacteria as well as yeast .2+’, or ‘glucose’ and ‘alpha-D-glucose’. Common metabolites may also have different chemical sum formulas in different IGSMs, for example, depending on whether functional groups are specified or not  and unique reactions. We further categorize unique reactions according to whether they are unrelated to any inconsistency, related to a single inconsistency, or related to multiple inconsistencies . Because the identified inconsistencies ultimately depend on namespace consistency, user-defined settings, and user choices, we quantified the resolution of inconsistencies by automatic processing to remove user bias as much as possible. After creating the BCM from the IGSMs and merging the identical reactions, the fraction of consensus reactions was low (11%) and approximately half of the unique reactions were associated with at least one inconsistency . The incP. putida GSMs , and they were outperformed by the RCM (except for B. subtilis). For B. subtilis, resolving inconsistencies in the BCM decreased all scores except sensitivity. This can be explained by one IGSM (iBSu1103) being largely based on a predecessor (iYO844); in addition, iBSu1103 was optimized for correct growth predictions using GrowMatch [The performance of GSMs as mathematical models for cellular metabolism is typically evaluated by assessing their ability to correctly predict wild type and mutant growth phenotypes across different growth conditions . We perfrowMatch ,36. InfoFinally, we wanted to evaluate how automatic consensus model generation compares to its (largely) manual counterpart. We focused on the community approach to establish a yeast consensus model  based onGenome-scale constraint-based metabolic models are both integrated knowledge repositories and predictive mathematical models. In terms of knowledge representation, a consensus model should be more consolidated than individual GSMs due to shared parts, more comprehensive due to unique parts, and more accurate due to reconciliation of inconsistencies in similar parts. A consensus model, however, can propagate errors in the initial models’ unique parts, and it may be less consistent than the initial models, especially when inconsistencies in similar model parts were not identified or reconciled.Inconsistencies in GSMs are typically nested, not mutually exclusive, and therefore difficult to address, which so far prevented the development of methods for the automated generation of consensus models . Manual Regarding a GSM’s predictive mathematical model character, it is important to note that remaining inconsistencies in a consensus model can have severe effects, for example, when inconsistencies resulting from model merging are not adequately addressed. As a consequence, individual GSMs may outperform a consensus model in terms of predictive ability even though the latter is more representative of the available information. COMMGEN’s aim (and design) is to compare and reconcile IGSMs in order to obtain a high-quality representation of the IGSMs’ combined information. In contrast to model optimization methods such as GrowMatch , COMMGENP. putida, we argue that (semi-) automatically generated consensus models provide the basis for additional improvements due to their comprehensiveness and standardized naming system. Gap-filling methods [As demonstrated by our case study for  methods ,39 may b methods ,40 to re methods  can reso methods ,42 may a methods .More generally, the systematic integration of heterogeneous information is an essentially unsolved challenge in (post-)genomic biology. For metabolism, consensus GSMs are formalized means for complementing incomplete information, and for identifying and addressing errors through the comparison of independently generated GSMs for the same organism. COMMGEN automatically identifies and semi-automatically resolves widespread and highly interlinked inconsistencies between initial GSMs, thereby moving beyond existing approaches for manual and computer-aided consensus model generation. It can therefore facilitate the construction of new models by comparing and combining information from automatic model construction tools such as the modelSEED  and manuWhile we focus here on the reconciliation of multiple GSMs for the same species, we argue that COMMGEN’s methods and standardization are more widely applicable. The identification of similar, yet distinct, biochemical entities can help to compare metabolic capabilities of organisms in detail via their GSMs, or even to compare entire pathway databases. However, dealing with different species will require new, systematic preprocessing steps to map gene sets in different organisms functionally to each other , which is a topic of future research. In addition, COMMGEN’s methods for identifying redundancies and hierarchical relationships in networks can be used to further advance standardization of terms and ontologies. We therefore expect COMMGEN to be of substantial aid in future integration of knowledge for metabolic networks, to greatly accelerate model-building processes and to thereby improve subsequent high-throughput model-based network analyses. Although COMMGEN will not directly address the domain-specific problems, these capabilities will lay a solid foundation for the systematic, genome-scale comparison of metabolic spaces within and across genera and will have substantial impact for large-scale evolutionary analyses, design of microbial communities, and understanding of host-microbe  interactions.http://sysbio3.fhms.surrey.ac.uk/. iNJ661 was obtained from the supplementary files of the corresponding paper. The remaining models were taken from the model repository at www.metanetx.org. See iJN746 and iJP962 were requested from and received by email from the first authors of the corresponding papers. GSMN-TB was downloaded from −6) was used whereas a minimal relative growth rate (30%) to the wild type was used for mutant strains. See For comparison to experimental data, the models were loaded into the COBRA toolbox . The bouIn a metabolic network, reaction nodes are only connected to the metabolite and gene nodes that are involved in the corresponding reaction. Similarly, metabolite and gene nodes are only connected to reaction nodes. However, reaction nodes are not informative for the identity of metabolites as two metabolites representing the same chemical compound are non-overlapping in their connected reaction nodes. Therefore, we characterize metabolites by the other metabolite and gene nodes that are connected to the same reactions. We use this information to quantify how similar metabolites from different models are based on their network context. These similarity scores are then compared to the scores of metabolites that are known to match because they are present in both models: pairs of metabolites that score comparable to these shared metabolites may consist of functionally equivalent chemical compounds. We use a user-defined percentile of shared metabolite scores as a threshold to identify similar metabolites. The method is described in the following:m (m x m) where a 1 indicates that the two metabolites share a reaction.We create a Boolean metabolite-to-metabolite matrix Mg (g x m) where a 1 indicates that the metabolite and gene share a reaction.We create a Boolean gene-to-metabolite matrix Ma ((m + g) x m) by vertically concatenating Mm and Mg.We create an attribute matrix Ma by dividing each row by its sum such that the numbers in each row sum up to 1. Thereby, the values in Ma reflect both that a metabolite is connected to a metabolite or gene and how rare (defining) this connection is.We normalize Ma that correspond to metabolites and genes that are not included in both models for these cannot aid in the identification of common metabolites between the models.We discard rows from Ma that correspond to metabolites that are identified to be the same in both GSMs.We discard the columns from Ms (m x m) where the number at position i,j corresponds to the Pearson’s correlation coefficient between columns i and j of Ma.We create a scoring matrix Ms using a minimal score. The minimal score equals a user-defined percentile of scores for metabolites that are present in both models.We distinguish between similar and non-similar metabolites in MA lumped reaction is an artificial reaction that represents the net effect of multiple individual reactions. Therefore, if the lumped and non-lumped representations carry flux in opposite directions, steady state is maintained as they cancel each other out. We use this property to identify lumped reactions by linear programming. The method is described in the following:We determine the directionality for each reaction as forward, backward, or reversible.We transform each reaction such that it only runs in the forward direction; backward reactions are reversed and reversible reactions are split into two reactions.We update the stoichiometric matrix S (m x r) accordingly.We remove the boundary reactions from S as these reflect exchanges of metabolites between the organism and the medium.We define the linear programming (LP) problem:We initiate the variables of the LP problemc: Vector (1 x r) containing the objective coefficient for each reaction. We set each value to -1 to penalize flux through each reaction; this ensures that the total flux in the network is minimized.lb: Vector (1 x r) containing the lower bounds of each reaction. As all reactions are forward reactions, every value is set to 0.ub: Vector (1 x r) containing the upper bounds of each reaction. As all reactions are forward reactions, every value is set to 1000.b: Vector (m x 1) containing the desired accumulation or dissipation of each metabolite. Each value in this vector is set to 0 to ensure a steady-state flux distribution.LR to be considered as a lumped reaction. We set:We select a reaction LR with index ic(iLR) = -1000lb(iLR) = -1.LR is thus now allowed to carry flux in the backward direction, which results in a positive contribution to the objective value.We run the LP problem as defined under step v.x that either only contains zeros (no non-lumped representation available), or contains a flux distribution such that the flux through LR is maximized in the reverse direction while having a minimal flux through the rest of the network. In the first case, we skip steps ix and x. In the latter case, we identified a set NL1 of corresponding non-lumped reactions.The LP problem returns a flux distribution 1 for future reference.We save the set NLx may be identified.We modify the LP problem such that any alternative sets NLc(NL1) = 3 x c(NL1)1 such that it becomes more ‘rewarding’ to use other reactions.This effectively further penalizes flux through the reactions of NL1 by NL2, NL3, …) until:We repeat steps viii-x , orThe number of reactions in NLx.There is a recurring set NLx such that only sets remain that overlap to a pre-defined extent in gene associations with LR.We filter the different sets NLWe repeat steps v-xi such that we obtain sets NL for each reaction in the model.Alternative transport reactions result in the transport of a metabolite between two compartments with a different net reaction. We identify metabolites with alternative transport reactions one metabolite at a time. If a metabolite is present in two or more compartments, we identify all transport reactions for this metabolite by selecting reactions where the metabolite is on both sides of the equation. If two of these reactions transport the metabolite between the same two compartments, these reactions are alternative transport reactions.Invalid transport reactions are reactions that transport metabolites between two unconnected compartments. We identify these by forming a list of all compartments that are directly connected through transport reactions in the IGSMs and asking the user to indicate if any of these are invalid. For any of the invalid compartment connections, we identify reactions that contain metabolites from both compartments; these reactions are invalid transport reactions.cmp (m x r) for each compartment. These matrices only contain reactions of which all metabolites are in the same compartment. Columns (reactions) that are identical between these matrices represent identical reactions with an alternative compartmentalization.We create a separate stoichiometric matrix SIn the MnXRef namespace, metabolites with an unclear compartmentalization are placed in the compartment UNK_COMP. For each reaction that contains a metabolite in UNK_COMP, we identify reactions from the other IGSM(s) that involve all metabolites with known compartmentalization similarly to the identification of alternative stoichiometries. These reactions are then filtered for reactions that also involve the metabolite with the unknown compartmentalization.Boundary (exchange) reactions are artificial reactions that represent the exchange of metabolites with the medium. They only involve a single metabolite, and have no metabolites on the other side of the equation. In some models these reactions are lumped together with transport reactions that import metabolites from the extracellular compartment. After the MnXRef namespace conversion these reactions are still annotated as boundary reactions, and are thus easily identified in COMMGEN by searching for boundary reactions with non-extracellular metabolites.To combine GSMs with an alternative compartmentalization, it is sometimes most straightforward to remove a compartment ‘RC’ from a GSM and move its reactions to a different target compartment ‘TC’. We defined four categories of reactions in RC, which are treated differently when RC is removed: (i) Reactions that only involve metabolites from RC are moved to TC; (ii) Multi-compartment reactions that transport a metabolite between RC and TC are removed; (iii) Multi-compartment reactions involving RC and TC that involve a chemical conversion are kept, but all metabolites from RC are placed in TC; (iv) Multi-compartment reactions involving RC and a metabolite other than TC are kept, and all metabolites from RC are placed in TC.dbl (m x 2r) that contains the normal stoichiometric matrix S (m x r), as well as its negative -S (m x r). We then identify columns (reactions) in Sdbl that are identical.Identical net reactions are reactions that involve the same set of metabolites in the same stoichiometries, but they may be defined in opposing directions. Therefore, we create a double stoichiometric matrix Slog (m x r). We then identify columns in Slog that are identical; these correspond to reactions involving the same metabolites, but in different stoichiometries.We convert the S (m x r) matrix to a Boolean (0/1) representation Srdx (m+1 x r) by adding an artificial metabolite ‘redox pair’. Then, for each reaction that involves a redox pair, we put the stoichiometric coefficients of the redox metabolites in Srdx to ‘0’, and add a ‘1’ in the ‘redox pair’ row instead. We then use the same approach as for the identification of alternative stoichiometries to identify reactions that only differ in stoichiometries and redox pairs.GSMs often differ in their involvement of redox pairs in any particular reaction. The first step in identifying these inconsistencies is the creation of a list of redox pairs. COMMGEN comes with a list of commonly used redox pairs in the MnXRef namespace, and this list can be expanded by the user. COMMGEN can suggest expansions for this list by selecting metabolite pairs that co-occur frequently (≥ 80% of reactions). We identify reactions that are identical except for their redox pairs by expanding the stoichiometric matrix S (m x r) to Slog (m x r). For each column (reaction) we then identify other columns that contain nonzero elements on each row where the respective column has a nonzero element. These sets of columns (reactions) are potentially nested reactions. We then confirm these sets by detecting sets where two or more metabolites that are on the same side of the equation for one reaction, are on the same side of the equation for the other reaction.We convert the S (m x r) matrix to a Boolean (0/1) representation SSimilar reactions are reactions from different IGSMs that share a predefined number of genes, substrates and products. We identify similar reactions by constructing three sets of pairs of reactions: (i) reactions that originate from different IGSMs, (ii) reactions that share the required number of substrates and products, and (iii) reactions that share the required number of genes. All combinations of two reactions in each of these three sets are considered similar reactions.All computational simulations and analyses were performed using MATLAB . Gurobi COMMGEN uploads SBML files to MetaNetX.org , where thttps://gitlab.com/Rubenvanheck/COMMGEN.The COMMGEN version used for this paper is freely available as MATLAB code as S1 DatasetThis file contains the original models, the input models, the BCMs, and the RCMs for COMMGEN as well as an overview of the changes made between original and input models.(ZIP)Click here for additional data file.S1 ProtocolThis file contains the code that was used in order to obtain the data for (ZIP)Click here for additional data file.S2 ProtocolUpon the merging of reactions differing in gene rules a choice has to be made in how the final gene rule looks. This file shows how the consensus procedure as applied for this study affects the use of ‘OR’ and ‘AND’ operators.(ZIP)Click here for additional data file.S3 ProtocolThis file contains the scripts and reference data for the prediction of growth and no-growth phenotypes and subsequent creation of (ZIP)Click here for additional data file.S4 ProtocolThis file contains two example scripts of how to start with COMMGEN.(ZIP)Click here for additional data file.S5 ProtocolThis file contains the code that was used in order to obtain the ROC curve in (ZIP)Click here for additional data file.S6 Protocolhttps://gitlab.com/Rubenvanheck/COMMGEN.This file contains the code for COMMGEN and is recommended to use when running scripts from other additional files. However, we recommend obtaining the current version of COMMGEN from (ZIP)Click here for additional data file.


Chromohalobacter salexigens is a natural producer of ectoines, compatible solutes with current and potential biotechnological applications. As production of ectoines is an osmoregulated process that draws away TCA intermediates, bacterial metabolism needs to be adapted to cope with salinity changes. To explore and use C. salexigens as cell factory for ectoine(s) production, a comprehensive knowledge at the systems level of its metabolism is essential. For this purpose, the construction of a robust and high-quality genome-based metabolic model of C. salexigens was approached.The halophilic bacterium C. salexigens metabolic model (iFP764). This comprised an exhaustive reconstruction process based on experimental information, analysis of genome sequence, manual re-annotation of metabolic genes, and in-depth refinement. The model included three compartments , and two salinity-specific biomass compositions, partially based on experimental results from C. salexigens. Using previous metabolic data as constraints, the metabolic model allowed us to simulate and analyse the metabolic osmoadaptation of C. salexigens under conditions for low and high production of ectoines. The iFP764 model was able to reproduce the major metabolic features of C. salexigens. Flux Balance Analysis (FBA) and Monte Carlo Random sampling analysis showed salinity-specific essential metabolic genes and different distribution of fluxes and variation in the patterns of correlation of reaction sets belonging to central C and N metabolism, in response to salinity. Some of them were related to bioenergetics or production of reducing equivalents, and probably related to demand for ectoines. Ectoines metabolic reactions were distributed according to its correlation in four modules. Interestingly, the four modules were independent both at low and high salinity conditions, as they did not correlate to each other, and they were not correlated with other subsystems.We generated and validated a high quality genome-based C. salexigens metabolism at low and high salinity conditions, driving to low and high production of ectoines. In addition, it can be useful to optimize the metabolism of other halophilic bacteria for metabolite production.Our validated model is one of the most complete curated networks of halophilic bacteria. It is a powerful tool to simulate and explore The online version of this article (10.1186/s12934-017-0852-0) contains supplementary material, which is available to authorized users. Chromohalobacter salexigens is a moderately halophilic gamma-proteobacterium of the family Halomonadaceae, phylogenetically related to the genus Halomonas . Interestingly, all the four modules were independent both at low and high salinity conditions, as they did not correlate to each other, and they were not correlated with other subsystems , the second module corresponded to the futile cycle for ectoine synthesis and degradation  , the thiems Fig. .P-uridylyl transferase) and glucose-6P . Both products are precursors of trehalose-6-phosphate. As observed for ectoines metabolism, this correlation pattern of the trehalose metabolism module did not vary with salinity  formed a separate module including internal reactions of central C subsystem, particularly those involved in the conversion of glucose-1-P into UDP-glucose , independent from other subsystems, and with no interdependence pattern variation in response to salinity. It is plausible that reactions included in each module respond to a similar regulation, and/or that a coordinated functioning of cyclic modules is necessary to adapt solute compatible metabolism, and therefore metabolic osmoadaptation, to external conditions. Our results are only an example (“the tip of the iceberg”) of the potential of the joint rational use of computational tools, together with experimental data, in order to explore metabolism in halophilic microorganisms.The predicted modular distribution of correlated compatible solutes metabolism sets of reactions is very intriguing, and deserves experimental work to better understand iFP764 model is a powerful tool to simulate and explore C. salexigens metabolism at different salinity conditions. This halophilic bacterium has one of the broadest salinity range of growth, and is considered as a model to study osmoadaptation. Notwithstanding the existing limitations in reconstructing and validating a C. salexigens specific network, the iFP764 model is one of the most complete and curated networks of halophilic bacteria, and accounts for all existing relevant experimental information. Thus, it may become an important tool to understand the metabolism of halophilic microorganisms, helping to drive the generation of new experimental hypotheses. In addition, C. salexigens is considered as a good cell factory for ectoines, whose production is salinity-dependent. Therefore, this model could be a robust and reliable tool to be used for the design of new C. salexigens strains optimized for the production of ectoines and other biotechnologically interesting compounds, avoiding the expenditure of resources.The information resulting from this work showed that the Additional file 1: Table S1. Biomass composition at high salinity (´´BIO_H``). Table S2. Biomass composition at low salinity (´´BIO_L``); Table S3. Composition of M63 minimal medium and the exchange reactions formulated to simulate the uptake of metabolites from medium; Table S4. In silico  constraints used for simulation at high or low salinity; Description of the bottom-up building and exhaustive manual refinement of the C. salexigens metabolic reconstruction iFP764; Table S5. The total number of dead ends metabolites found in the iFP764 model: root no-production metabolites and root no-consumption metabolites; Table S6. In silico prediction of utilization of various metabolites as carbon sources in C. salexigens; Figure S1. Selected histograms of possible flux values obtained in the first scenario by Monte Carlo sampling at low and high salinity relative to salinity-specific glucose consumption rate.Additional file 2. Genome-scale metabolic reconstruction of C. salexigens. Details of iPF764 containing all genes associated-reactions and metabolites.Additional file 3: Table S7. Essential genes at low and high salinity and their associated COGs.


Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum, a leading cause of malaria, has a complex life cycle [ Anopheles genre is responsible for all the transference of malaria from one patient to another [ Plasmodium falciparum lifecycle comprises three (3) important developmental stages: the mosquito stage, the liver stage, and the blood stage [ Sporozoites injected into a host by a mosquito that is infected travel to the liver and begin the hepatic stage of the life cycle of the Plasmodium by invading hepatocytes. Here, they get to increase and segregate into schizonts, then comprising numerous hepatic merozoites. All of these merozoites are successively set loose into the blood where the erythrocytic stage is initiated and begins by invading and duplicating inside the red blood cells (RBCs) [fe cycle , 2. The  another , 2. Desp another –5. The Pod stage . Sporozos (RBCs) , 7. Plasmodium falciparum (P. f.) in cells that are infected would be quite a potential source of targets for novel drugs but it is complex and difficult to understand intuitively. In silico methods can handle and take care of this complexity. They also give room for integrative analyses of the cell metabolism [ P. f.'s resistance to most identified antimalarial drugs has given rise to the increase of antimalarial drug discovery research [ P. f. genome, the reconstruction of the metabolic network is required for a comprehensive understanding of the molecular mechanisms of the organism [ P. f. This study identifies new reactions as potential targets for drugs in the metabolic network of P. f. that contributes to its survival in the host and validates predicted drug targets. The computational model used in this paper is an enhancement of the computational model used in our preceding paper [Evidences abound to the fact that the parasite is already developing resistance to many front-line antimalaria therapies. Therefore, novel antimalaria cures are in immediate need to combat the drug-resistant malaria parasite , 8. The tabolism . In silitabolism  and facitabolism . They hatabolism , 8, 12. research , 13, 14.research . Due to organism , 17. Thoorganism . Thus, tng paper . The comThe malaria parasite metabolic pathways are quite different from those of its human host. This uniqueness can be exploited in the design of therapeutic strategies –21. MetaIdentifying essential reactions in a metabolic network allows the identification of potential drug targets in the network , 37. Essk-shortest paths passing through metabolites and its closest neighbour links is defined as the load point of a particular metabolite in a metabolic network. The usefulness or the importance of a particular metabolite in the metabolic network of an organism is determined by load points and choke points [k-shortest paths passing through them. When a choke point in an organism is absent, the organism can rarely survive [Flux balance analysis (FBA) is an in silico method used in gaining deepened understanding into the abilities and the metabolic behaviour of a cell , 42, 43.e points –60. The  survive , 60. The survive , 62. TMF survive , 62. TMF survive , 61, 62. survive . MCA is  survive . MCA stu survive . The app survive .Perturbation is an approach generally applied to study the conduct and atomic components underlying cellular systems . A pertu Plasmodium falciparum metabolic network was identified and proposed as potential drug target for Plasmodium falciparum.In this study, the essentiality of the different reactions was determined. Therefore, a list of indispensable reactions in the Plasmodium falciparum which was extracted from the BIOCYC flat file database version 19.5 [ Plasmodium falciparum from [ P. falciparum 3D7 from PlasmoDB and the version of KEGG as of July 2014. The BIOCYC identifiers were chosen for this study as the generally accepted means of identification. Plasmodium falciparum reactions gotten from Chiappino-Pepe were mapped to BIOCYC reactions via enzymes commission numbers and common name. The reconstructed metabolic network of Plasmodium falciparum is made available as SBML file with some reactions considered to be reversible and some considered to be irreversible. In this study, currency metabolites of the 24 currency metabolites outlined by [In this study two different resources were considered for the reconstruction of the metabolic network, which is the genome-scale metabolic dataset of the 3D7 strain ofion 19.5  because lined by  were rem Plasmodium falciparum metabolic network was created leading us to perform an in silico knockout experiment and analysis on the metabolic network enabling us to analyse the network for the essentiality and perturbation of the knocked-out reactions and we also moved further to determine the perturbations and essentiality of all reactions in the network. When a transition (reaction) is knocked out, all reactions that have a corresponding reactant or product of the knocked-out reaction are equally knocked out, helping to ascertain the effect of that reaction to the network. Single knockout analyses were performed and our results were outputted in  .txt formats to list the reactions that were affected after knocking out a specific reaction.Once the metabolic network was reconstructed, aIn the network a reaction is knocked out to determine the dependent reaction on the knocked-out reaction. The procedure for determining which reaction is connected to the knockout is outlined in the following steps.Start.Get the SBML file.Extract all reactions in the file.Extract all products and reactants related to the various reactions.Identify reaction to knockout initially.Get other reactions that are linked to the identified reaction which can be knocked out.Determine if the other reactions are linked to other reactions.If true, do not knock out the reaction; else knock out the reaction.Repeat Steps If there are no other reactions to be knocked out, attach the resulting reactions to the initial reaction.Assign “knocked-out reactions” as the list of resulting reactions attached to the initial knocked-out reaction.Repeat Steps Determine the essentiality of each reaction by comparing the knocked-out items to the total network.Determine the most essential reactions by comparing the results of Determine the least essential reactions by comparing the results of Extract result to a spreadsheet file.Make visualisation of the result in the spreadsheet file.Stop.All above steps are performed for all reactions in the network enabling this study to predict the essentiality of all reactions in the network and exporting them in hierarchical order. ri represents the list of reactions, E(r) represents the essentiality of a reaction in the reconstructed metabolic network, k(ri) represents the knocked-out reactions in the reconstructed metabolic network, N(ri) represents a reaction in the network, ∑k(ri) represents the summation of the knocked-out reactions in the reconstructed metabolic network, and ∑N(ri) represents the summation of all reactions in the network.In this study reconstructed network, we determined the essentiality of every reaction in the network and proposed some essential reactions and validated the essentiality of previous proposed reactions in literature. The essentiality of all reactions is saved in a  .txt file for easy access. The formula for determining the essentiality of every reaction in the network is outlined in the following steps: Plasmodium falciparum which mostly have been predicted in several literatures to be druggable was considered when testing and validating our model and these 77 essential reactions are given in the supplement  predicted most essential reactions based on our method in the network are represented in Computationally predicted essential reactions from six different literatures were compared with our method. Ten reactions that were common to over 80% of all literature considered in this study were identified and validated to be essential by our method, thereby confirming our method as valuable computational technique for validation of predicted drug target as given in The reactions that were knocked out from the network when the top seven (7) most essential reactions from our network were knocked out are given in the Supplementary  Plasmodium falciparum which includes 24 new potential candidates for drug targets of which 9 are orphans and 7 potential candidates for drug target which has been predicted computationally in literature of which 1 is an orphan. The reactions are presented in The predicted essential reactions based on the analysis of our network are given in the Supplementary  Plasmodium falciparum and identifies reactions in the network that are essential to the survival of the network according to its essentiality. These essential reactions are predicted as potential drug targets for Plasmodium falciparum; the essentiality of the reaction in the network is listed according to its effect on the network when knocked out (the amount of reactions knocked out when the parent reaction is knocked out). This study identifies that there are over 200 essential reactions in the network of which, among the top 7 predicted most essential reactions, 4 were identified to be found in the gold standard which includes superoxide dismutase, 3-phosphoshikimate 1-carboxyvinyltransferase, 5-O-(1-carboxyvinyl)-3-phosphoshikimate phosphate-lyase, and adenosylhomocysteinase and 1 was already predicted as essential computationally which is methionine adenosyltransferase. The result of our study was compared with an extensive list of 77 essential reactions with biological evidence. We finally present a polished list of 31 including 24 new potential candidates for drug targets of which 9 are orphans and 7 potential candidates for drug target which has been predicted computationally in literature of which 1 is an orphan. This model also helps to improve the understanding of the biological processes within this network and any other metabolic network. It would be quite exciting to further our research by confirming our in silico predictions experimentally and also test if our essential reactions can be successfully targeted without collateral partial or complete targeting of the corresponding human reactions. Potential candidates for drug targets for Plasmodium falciparum already biologically proven were disregarded from our list as our method validated a number of them. The method developed could handle multiple knockouts but we plan to do this when we have some amount of drug combination to validate our method. The method used in this study is capable of predicting essential reaction in any other organisms of a robust genome-scale metabolic network.This study established a novel method that performed an analysis on the genome-scale metabolic network ofThe dominance of malaria in resistance to identified antimalarial drugs in current circulation has given rise to the increase of antimalarial drug discovery research. Hence, researches on the development of novel drug targets which would serve as effective solutions for malaria treatment are urgently needed. P. f. metabolic reactions under different growth conditions and perturbations. We have been able to identify new essential reactions as possible targets for drugs in the metabolic network of P. f. that contributes to its survival in the host and validate predicted drug targets. The computational model used in this study enhances and enables computational analysis of large dataset.In this study, a novel computational model was constructed which makes the validation of predicted antimalarial drug target cheaper, easier, and faster as well as the validation of P. f. available and provide guidance to experimental studies helping to develop a better description of P. falciparum metabolism and to identify antimalarial drug targets.Generally, the results from this study make a deep understanding of the metabolism of