Pseudomonas putida KT2440 is an outstanding biocatalyst given its versatile metabolism, its ability to generate sufficient energy and turnover of NADH and NAD. To apply this strain optimally in industrial production, a previously developed genome-scale metabolic model (iJP815) was experimentally assessed and streamlined to enable accurate predictions of the outcome of metabolic engineering approaches.Genome scale metabolic reconstructions are developed to efficiently engineer biocatalysts and bioprocesses based on a rational approach. However, in most reconstructions, due to the lack of appropriate measurements, experimentally determined growth parameters are simply taken from literature including other organisms, which reduces the usefulness and suitability of these models. DCW · gglc-1, the maintenance coefficient of 0.037 gglc · gDCW-1 · h-1 and the maximum specific growth rate of 0.59 h-1 were determined. Only the DNA fraction increased with the specific growth rate. This resulted in reliable estimation for the Growth-Associated Maintenance (GAM) of 85 mmolATP · gDCW-1 and the Non Growth-Associated Maintenance (NGAM) of 3.96 mmolATP · gDCW-1 · h-1. Both values were found significantly different from previous assignment as a consequence of a lower yield and higher maintenance coefficient than originally assumed. Contrasting already published 13C flux measurements and the improved model allowed for constraining the solution space, by eliminating futile cycles. Furthermore, the model predictions were compared with transcriptomic data at overall good consistency, which helped to identify missing links.To substantially improve the accuracy of the genome scale model (iJP815), continuous bioreactor cultures on a mineral medium with glucose as a sole carbon source were carried out at different dilution rates, which covered pulling analysis of the macromolecular composition of the biomass. Besides, the maximum biomass yield (on substrate) of 0.397 gPseudomonas putida, providing a solid basis for its use in designing superior strains for biocatalysis. By consideration of substrate specific variation in stoichiometry and kinetics, it can be extended to other substrates and new mutants.By careful interpretation of growth stoichiometry and kinetics when grown in the presence of glucose, this work reports on an accurate genome scale metabolic model of  Pseudomonas putida is a non-pathogenic, soil bacterium that developed a notably versatile metabolism and thus can inhabit several environmental niches. Numerous strains, some of them being solvent tolerant  and from 0.34 to 0.32 [gDCW·gglc-1], respectively, which thus narrows down the predictive range.The original etyl-CoA . This coetyl-CoA ,25. Mutaion flux . In ordeTo further validate the model, the consistency between model predictions and the transcriptomics data, generated at steady-state, was assessed. The flux balance analysis (FBA)-predicted fluxes and the levels of respective mRNA transcripts showed overall good agreement with each other. It should be emphasized, however, that measured abundances of mRNA transcripts cannot be directly translated into fluxes. The consistency was evaluated by analyzing the expression levels of: i) genes essential for growth, ii) non-essential genes with low expression that are involved in reactions with non-zero flux, and iii) genes with high expression values that are only involved in the catalysis of the reactions with predicted zero flux.in silico that are actually not expressed. From genes identified by the model as essential those were selected and termed as potentially not expressed that complied with at least one of the following criteria: i) the number of “absent” calls  was higher than 2 (out of 4 arrays), or ii) the number of “present” calls was lower than 3, (out of 4 arrays) or iii) the mean log2 absolute gene expression value was lower than 7.5 . Out of 147 essential genes 33 were selected that were potentially not expressed. They are listed  that was originally annotated to be involved in sulfate transport was consistently identified as absent. After close inspection, these genes appeared to be miss-assigned, since there was a group of other genes that is responsible for sulfate transport (PP5168-PP5171). The model was updated accordingly.Expression of the genes essential for growth: The first assessment aimed at identifying genes essential for growth Inspecting the genes that complied with at least two of the criteria revealed that there were a number of those involved in biosynthesis of cofactors (red rows). The corresponding reactions usually have a low flux so it may be that low protein levels suffice for the required enzymatic activity. There are also six genes involved in the synthesis of LPS (green). The majority of these genes has relatively high expression levels and were selected due to the presence/absence criteria. LPS is needed in rather limited amounts so a low expression may be sufficient for the required enzymatic activity. Furthermore these six genes constitute only around 50% of the genes involved in LPS synthesis, so this also suggests that the expression levels are sufficient. PP2458, PP2460, and PP4266 (yellow) are genes that were labeled as essential due incompleteness of the metabolic model. The reason these genes are essential is, that S-adenosyl homocysteine is produced during the synthesis of cyclo-fatty acids. This compound needs to be converted further. Apparently the only way is to convert it into ribose and adenine which are then phosphorylated to ribose-6-phosphate and AMP, respectively. This route seems, however, not to be the proper one.2 expression value below 7.5 were selected. There were only five such reactions identified. They were all involved in fatty acid biosynthesis and all catalyzed by the same pair of isozymes (PP3553 and PP2795-acyl-CoA synthetases) whose mean expression values did both not exceed 7.5.Expression of non-essential genes required for optimal growth: The second assessment was an extension to the previous one. Here reactions with non-zero minimal fluxes in FVA-simulations were identified. For these reactions the “expression value” was computed and reactions with a logDCW-1∙h-1, when the μ was 0.2 h-1). These genes are listed  are also highly expressed. These are used for synthesis of rhamnolipids that are a constituent of the bacterial envelope and are not part of the biomass in the current version of the model. This assay suggests that they should be included. This requires however the determination of the exact composition of these metabolites and their share in the biomass. The reason of higher expression of remaining genes could not be elucidated.Highly expressed genes not required for growth: Finally, genes with high expression levels were identified that only catalyze reactions with a low flux . These measurements showed that the macromolecular biomass composition does not change significantly when the D and thus the μ varies. The only macromolecule that did change significantly upon increasing D was the DNA. This might have been a consequence of the appearance of a higher number of replication forks. At a μ of 0.4 h-1 the cell size increases rapidly, which might be related to the increase of genome replication -1-propansulfonat , and 0.48 g tris base . After the re-suspension 2 μL lysozyme (100 mg · mL-1) was added and the cells were incubated at 37°C for 30 min while shaking at 400 rpm. During the incubation the samples were frozen three times with liquid nitrogen. Next, the incubation was continued at 37°C for 10 min while shaking at 1400 rpm. 50 μL of the supernatant was taken and centrifuged at 16,873 · g for 15 min at 4°C. The supernatant was diluted 50 times with re-swelling solution. Standards of bovine serum albumin of 0.5, 0.25, and 0.125 mg · mL-1 were prepared in triplicate. Three times 10 μL aliquots of the diluted supernatants as well as three separate samples of the three standards were mixed with 200 μL of fivefold diluted (with distilled water) Biorad protein assay solution . The absorbance of samples and standards was measured at 595 nm with a microplate reader . The absorbance values of the samples were recomputed to the concentrations using a regression line estimated from the absorbance of the standards. Additionally, the added lysozyme concentration was distracted from the total protein concentration.For the analyses of the protein and DNA concentration in the biomass, one sample of 15 mL culture was taken and centrifuged at 20,201 · -1 RNAse A  was added, and the mixture was incubated at 37°C for 10 min under regular mixing by vortexing. To ensure the complete RNA degradation, the samples were analyzed with a formaldehyde agarose gel electrophoresis. The concentration of 20-fold diluted sample of DNA was measured at OD260 with an Eppendorf Biophotometer . A total of 7 measurements were carried out of three dilutions, two of which were measured twice and one was measured three times.The second batch of suspended cells was used to determine the concentrations of genomic DNA using the genomic DNA purification kit #K0512 . The procedure described by the manufacturer was followed until the step in which the DNA was dissolved in 100 μL of 1.2 M NaCl by gently vortexing. Then, 0.2 μL of 100 mg · mLP. putida KT2440 at a D of 0.2 h-1 the amino acid composition of 1.5 mL medium was analyzed by an amino acid analyzer equipped with a ninhydrin detection system  at Ansynth Service B.V. (The Netherlands). Two samples were centrifuged at 16,873 · g at room temperature for 2 min and the supernatant was discarded before sending the samples off.From the continuous bioreactor culture with ms) in gglc · gDCW-1 · h-1 and the maximum biomass yield (Yx/smax) in gDCW · gglc-1 of P. putida KT2440 the following formula of Pirt using the specific rate of substrate utilization (qs) in gglc · gDCW-1 · h-1 was applied: qs = D · Yx/smax-1 + ms waE. coli[DCW-1 · h-1.The new biomass equation for the iJP815 model was created based on a number of sources. First, the fraction of each type of macromolecule  was determined as described in Analytical methods. Since our protein determination method accounts only for water-soluble proteins, the fraction of proteins was increased by 12.2 percentage points – the amount of water-insoluble proteins. This value was assessed based on the fraction of 20% to 30% of proteins that diffuse into the membrane of E. coli,44. EquiE. coli biomass [The amino acid composition was based on the experimental values obtained in continuous culture analyzed by Ansynth Service B.V. The amino acids that were not identified in this assay  were assumed to have identical molar fraction as in the  biomass .The fraction of each of four deoxyribonucleotide moieties was determined using the genomic sequence of the bacterium. Although replication forks of the genome are formed during growth it is assumed that the DNA sequence is equal for all cells.E. coli at a D of 0.6 h-1[The RNA composition was assumed to be identical to f 0.6 h-1.E. coli[in silico LPS molecule in the constraint-based model consists only of lipid A and core oligosaccharide. The polysaccharide chains were not included, since their molecular composition and length are not well-defined, and, furthermore, they are accounted for in the carbohydrate biomass constituents. In addition, the cytidine diphosphate ethanolamine moiety was removed from the in silico LPS molecule as any evidence of the ability of P. putida KT2440 to synthesize this compound is missing. The composition of phospholipids was defined with regard to both head-groups and fatty-acid compositions. The composition of the former was identified from the measurements of the very close relative Pseudomonas putida S12 , while the in silico fatty acid composition was based on experimental measurements of Stead [The lipids were assumed to consist of two subclasses – lipopolysaccharide (LPS) and phospholipids. As the information regarding LPS was missing it was assumed that its fraction and molecular composition is the same as in E. coli. The in of Stead .The carbohydrate pool was assumed to be present entirely in the form of glycogen as a proxy for all sugar polymers.-1). The biomass was augmented with vitamins and co-factors, albeit with several differences – the spermidine and UDP-Glucose were removed as there was no evidence that these two compounds can be produced by P. putida KT2440. As the small molecules are represented in minimal amounts in the biomass, the influence of their coefficients in the biomass equation on model predictions such as biomass yield (Yx/s) or flux-distribution is negligible. Important is only whether they are considered to be present or not as this influences, for example, the essentiality predictions for genes and reactions.In addition to these macromolecules peptidoglycan was assumed to be present  at a conIn order to evaluate the dependency between the fraction of every macromolecule and the growth rate, a regression analysis was performed. In addition, with a statistical F test the significance was determined if the slope differs from zero.DCW is synthesized – was adjusted so that FBA-predicted (see below) Yx/smax is equal to the experimentally measured value. NGAM – the amount of ATP that is dissipated for maintenance purposes by an amount of living cells (corresponding to 1gDCW) in a unit time (1 h) – was computed from the experimentally measured ms by multiplying it by the maximal theoretical ATP yield (YATP) from glucose of 19.25 molATP molglc-1. The latter value was computed using the stoichiometry of the reactions and thus depends on the P:O ratio (1.33) assumed within the model.The GAM and NGAM are two coefficients that need to be fit in the model in order to make accurate flux predictions . GAM – tRx or substrate uptake rate) and calculates value of this objective given the constraints included into the model. This is achieved by means of linear optimization (maximization of a linear objective in constraint-based stoichiometric models poses a linear problem). In flux predictions reported here the objective was to minimize the glucose uptake rate of P. putida KT2440 while maintaining the specific μ at 0.2 h-1 (one of the D used in the continuous culture experiments). For more details on FBA, see Puchałka et al., and Kauffman et al.[Flux Variability Analysis (FVA) was used to predict flux values using the model. FVA is a variant of Flux Balance Analysis (FBA). FBA finds a flux distribution  that maximizes or minimizes a previously defined objective  (♦), glucose concentration (Cglc) (■) (g∙L-1) and CO2 (▲) (g · L-1 · h-1) of P. putida KT2440 at various dilution rates (D) (h-1) on MM with 10 mM glucose.The dry cell weight concentration (Click here for file-1) (▲) at various specific growth rates (μ) (h-1).A/B Median of the forward scatter (FSC) (♦) and side scatter (SSC) (■) as well as the cell counting (mLClick here for fileGenes predicted essential by the iJP815 model for which transcriptomics data suggest a lack of expression. Colors mark distinctive groups discussed in the text.Click here for fileGenes predicted to be involved in the catalysis of reactions with predicted zero flux, for which the transcriptomics data suggest high expression. Colors mark distinctive groups discussed in the text.Click here for fileModifications to the original iJP815 model.Click here for file


Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate “leak” must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis,  Trypanosoma brucei, a tropical parasite that causes African sleeping sickness, by extending it with an additional pathway in two compartments. During this modeling process we highlighted uncertainties in parameter values and network structure and used these to formulate new hypotheses which were subsequently tested experimentally. The models were improved with the experimentally derived data, but uncertainty remained concerning the exact topology of the system. These models allowed us to investigate the effects of the loss of one enzyme, 6-phosphogluconate dehydrogenase. By taking uncertainty into account, the models demonstrated that the loss of this enzyme is lethal to the parasite by a mechanism different than that in other organisms. Our methodology shows how formally introducing uncertainty into model building provides robust model behavior that is independent of the network structure or parameter values.Mathematical models have been valuable tools for investigating the complex behaviors of metabolism. Due to incomplete knowledge of biological systems, these models contain inevitable uncertainty. This uncertainty is present in the measured or estimated parameter values, but also in the structure of the metabolic network. In this paper we increase the coverage of a particularly well studied model of glucose metabolism in  Trypanosoma bruceiT. brucei by inhibitors of glycolysis was demonstrated to be sufficient to kill the parasites Human African trypanosomiasis, or sleeping sickness, is a lethal disease caused by the protozoan parasite T. brucei relies on the PPP as the primary source of NADPH, in contrast to procyclic T. brucei which can also produce NADPH via malic enzyme In this paper, we extended this model of glycolysis with a description of the pentose phosphate pathway (PPP), another essential T. brucei glycolysis, include kinetic parameters that are uncertain for a variety of reasons, such as measurement errors or lack of data. We have previously shown the importance of taking uncertainty of these parameter values into account T. brucei metabolism. We illustrate the consequences of this uncertainty, and the robust predictions that are still possible, by exploring model behavior under various physiological conditions  and characterizing the effects of the inhibition of 6-phosphogluconate dehydrogenase (6PGDH), an essential enzyme of the pentose phosphate pathway, proposed as another potential drug target Kinetic models of metabolism, such as those constructed of T. bruceiT. brucei, the PPP is localized to both the cytosol and glycosomes, based on subcellular localization studies The existing mathematical model of glycolysis in To provide glucose 6-phosphate (Glc-6-P) to the cytosolic glucose 6-phosphate dehydrogenase (G6PDH), a trace amount of cytosolic hexokinase was added in models B–D. An alternative solution, where transport of Glc-6-P over the glycosomal membrane is allowed, gave similar results. Fractionation studies that suggest an exclusively glycosomal localization of hexokinase activity The model of glycolysis , model Aa priori knowledge exists on balancing of bound phosphates in the glycosome that would convincingly support either solution over the other.There is uncertainty as to how this glycosomal phosphate leak is prevented in the parasite. Two types of solution can be considered: (i) maintaining the conserved moiety of phosphorylated metabolites by including additional reactions in the glycosome, or (ii) transferring phosphorylated metabolites over the glycosomal membrane. No For the first type of solution, a stoichiometric analysis of all known and predicted glycosomal enzymes T. brucei ribokinase were characterized and used to update the model  was generated to knockdown the levels of ribokinase transcript by RNA interference. However, no growth phenotype was observed  represents a healthy proliferating trypanosome and results in a low PPP flux, supported by the experimental observation that almost all consumed glucose is excreted as either pyruvate or glycerol Dynamic simulations using both models, containing either of the two theoretical solutions to the phosphate-leak problem, showed that oxidative stress regulates the flux through the PPP , in lineT. bruceiIt has previously been proposed that inhibition of the third enzyme of the PPP, 6-phosphogluconate dehydrogenase (6PGDH), should be lethal to trypanosomes T. brucei.6PGDH inhibition in both models resulted in the accumulation of 6-PG as anticipated . Both moThe second hypothesis initially proposed posits that 6PGDH inhibition is lethal due to the reduced flux through the PPP and the associated increased sensitivity for oxidative stress. In both models, the simulation of 6PGDH inhibition demonstrates how the cell's ability to cope with oxidative stress is strongly affected, as the flux through trypanothione reductase is inhibited when the activity of 6PGDH is reduced by more than 50% . A thirdRNAi) yielded reduced levels and activities of 6PGDH  predict that growth of parasites is supported on fructose, although the lethal effect of 6PGDH inhibition could not be averted by using this substrate . Various genetic mutants of ribokinase, however, subsequently indicated that it is unlikely that only ribokinase is involved in restoring the phosphate balance. The gene appears to be essential, but the ribokinase enzyme is apparently present in vast excess since no phenotype could be observed in cells in which the transcript had been knocked down by RNA interference. Also, in cells where an inducible copy of the gene is present in place of both endogenous alleles, growth occurs even when the gene is switched “off”, presumably due to leaky expression. The essential role for small amounts of ribokinase is unknown, but it seems unlikely to be the sole enzyme capable of sustaining the bound-phosphate balance in these cells. Given that the glycosome contains multiple pathways beyond glycolysis and the PPP, such as purine salvage, pyrimidine biosynthesis, and parts of the ether lipid, fatty acid and isoprenoid metabolic pathways  by the PPP intermediate 6-phosphogluconate (6-PG) was included as described in c and ADPg and the products P1 and P2 are ATPg and ADPc.Most of the reversible reactions of the pentose phosphate pathway were modeled according to k is the rate of spontaneous hydrolysis and c/gV is the volume of the relevant compartment, which are 5.4549 and 0.2451 µL per mg protein for the cytosol and glycosome, respectively.For the 6-phosphogluconolactonase (PGL) reaction, the spontaneous hydrolysis as well as the enzyme-catalyzed rate were included The Glc-6-P utilization reaction was modeled as loss of Glc-6-P to glucose through a hypothetical phosphatase reaction, using xk is the mass-action constant of reaction x.Glycosomal and cytosolic oxidation of NADPH and trypanothione are modeled with mass action kinetics:T. brucei enzyme were available, parameters from related species were used. If necessary, eqK values were corrected to pH 7. Kinetic parameters used in the fixed parameter model are listed in The kinetic parameters in the rate equations mentioned above were predominantly derived from literature. If no parameters of the The extension of the model with the glycosomal pentose phosphate pathway led to an extension of the conserved moiety of phosphorylated glycosomal metabolites as compared to earlier model versions maxV scans of The model was initially constructed and simulations were performed in PySCeS 0.7.8 T. brucei ribokinase gene (GenBank ID:3664382) was amplified from Lister 427 strain T. brucei genomic DNA and cloned into plasmid pET28a(+) (Novagen) to add an N-terminal His-tag. The protein was overexpressed in E. coli BL21(DE3), purified by Ni2+ chelate chromatography and dialyzed in 50 mM Tris-HCl pH 7.0, 100 mM NaCl, 20% glycerol. Purity was assessed by SDS-PAGE and Coomassie blue staining.The 2PO4, 15 mM NaCl, 85 mM KCl and 10 mM MgCl2 at pH 7. Ribokinase activity in the direction of ribose 5-phosphate production was assayed spectrophotometrically according to Activity of ribokinase was assayed at 25°C in buffer containing 10 mM KHT. brucei parasites were routinely cultured in HMI-9 2. For growth of 6PGDHRNAi on an alternative hexose source, glucose in HMI-9 was substituted with fructose at the same concentration. Antibiotic concentrations for genetic mutants were: 0.5 µg mL−1 phleomycin and 2.5 µg mL−1 hygromycin for RKRNAi; 0.5 µg mL−1 phleomycin, 5 µg mL−1 G418 and 10 µg mL−1 hygromycin for 6PGDHRNAi; 0.5 µg mL−1 puromycin for ΔRK::PAC/RK; 2.5 µg mL−1 hygromycin for ΔRK::HYG/RK and 0.5 µg mL−1 puromycin, 2.5 µg mL−1 hygromycin, 0.5 µg mL−1 phleomycin and 2 µg mL−1 blasticidin for ΔRK::HYG/PAC::RKtet. Sensitivity of T. brucei to ribose was assayed with alamarBlue as described previously Bloodstream form Lister 427 strain RNAi was generated using the pRPaiSL vector/2T1 cell line system RNAi was generated using the p2T7 vector/1313 cell line system N-acetyl-transferase gene as antibiotic resistance markers. The tetracycline-inducible expression of ribokinase in the conditional knockout ΔRK::HYG/PAC::RKtet was generated by cloning the ribokinase coding sequence in pHD1336 and using cell line 449 . Parasites were transfected by Amaxa nucleofection (Lonza) The ribokinase knockdown cell line RKT. brucei ribokinase were raised in rabbits by injection of purified recombinant T. brucei ribokinase, purified by protein A-chromatography, and an antibody titer of >1∶192,000 was measured by ELISA . The anti-serum was further purified by immunoaffinity chromatography. Recombinant T. brucei ribokinase was coupled to a mixture of Affi-Gel 10 and 15 (Bio-Rad). Antibodies were eluted from the column with 3.5 M MgCl2. Polyclonal antibodies against the T. brucei 6PGDH were raised in rats by infection of purified recombinant T. brucei 6PGDH.Polyclonal antibodies against 6 cells were harvested by centrifugation and washed with PBS. Samples were separated on NuPAGE 4–12% Bis-Tris precast gels (Life Technologies) for 45 min at 200 V and transferred to a nitrocellulose membrane  at 100 V for 2 h. Membranes were blocked overnight at 4°C with 5% milk in PBS-T (PBS with 0.05% Tween-20), washed three times with PBS-T, and probed with the purified polyclonal rabbit anti-T. brucei ribokinase antibody, diluted 1∶2000 in 1% milk in PBS-T, for 4 h at room temperature. After three additional washes with PBS-T, the membrane was incubated with the secondary antibody  1∶2000 in 1% milk in PBS-T for 2 h at room temperature. After five washes with PBS-T, horseradish peroxidase activity was measured with SuperSignal HRP substrate (Novagen).For protein samples, 5·107 procyclic cells were washed with STEN buffer  and resuspended in 300 µL STEN supplemented with protease inhibitors (Roche). The cells were differentially permeabilized with 40 µg digitonin per 100 µg cellular protein (2.5·107 cells) for 2 hours on ice and centrifuged at 16,000 g for two minutes. After TCA precipitation, equal amounts  of the supernatant (S) and pellet (P) were analyzed by SDS-PAGE and blotted with rat anti-Tb6PGDH. A goat anti-rabbit IgG peroxidase was used for visualization as described for ribokinase. Enolase and aldolase were used as cytosolic and glycosomal markers, respectively, and antibodies against these proteins were kindly provided by Prof. Paul Michels .Subcellular localization of 6PGDH in trypanosomes was performed by modification of the digitonin fractionation method described in Dataset S1SBML versions of models. SBML versions of the models presented in (ZIP)Click here for additional data file.Dataset S2Parameter sets used for simulations of models A–D. Sets of parameter values as sampled from the distributions described in (CSV)Click here for additional data file.Dataset S3fru and Dfru.Parameter sets used for simulations of models C Sets of parameter values as sampled from the distributions described in fru and Dfru.(CSV)Click here for additional data file.Figure S1Enzymes of purine salvage pathway localized in glycosome. The glycosomal PPP is shown with those enzymes from the purine salvage pathway that have a predicted glycosomal localization . Additional ADK and ANase reactions does not improve this situation, with a resulting overall reaction of glucose+ADP→2 ribose+adenine+2 PPi (+CO2). In contrast, ribokinase is capable of resolving the phosphate leak with a resulting overall reaction of glucose→ribose (+CO2).lization . Reactio(TIF)Click here for additional data file.Figure S2Steady-state fluxes through various models. Steady state fluxes of at standard conditions  and if cytosolic PPP is maximized . Error bars indicate interquartile ranges. NA denotes ‘Not Applicable’ for branches that are absent from certain model version, Glce is 5 mM in all models. The glucose consumption flux is distributed over the production of glycerol and pyruvate and the two branches of the PPP. Note that ALD generates two trioses from every hexose, such that the fluxes through the trioses glycerol and pyruvate are double the hexose flux. The errors bars indicated interquartile ranges as a result of the uncertainty modelling. The large error bars for glycerol production are a result of its low flux and the smaller uncertainties assigned to the fluxes through the other pathways. No information is given for model B, as this model is incomplete and cannot reach a steady state (outlined in the main text).(TIF)Click here for additional data file.Figure S3Genetic investigations in T. brucei ribokinase. (A) Northern blot of RKRNAi induced and non-induced T. brucei, samples taken 24, 48 and 72 hours post induction by tetracycline. Tubulin was used as loading control. (B) Western blot of RKRNAi and Δrk::HYG/PAC::RKtet, samples taken at t = 0, 1, 2 and 3 days post induction by tetracycline. (C) Growth curve of ribokinase knockdown mutant by RNA interference. Induction of RNAi was started at t = 0 by the addition of 1 µg/mL tetracycline. Cell densities of induced (dotted line) and control (solid line) cultures were determined by cell counts, and cultures were diluted down to 2·104 cells ml−1 at t = 48 and 96 h. No difference in growth effect could be observed. (D) Successful transfection of T. brucei with knockout constructs was confirmed by PCR.(TIF)Click here for additional data file.Figure S4Ribose sensitivity. Steady state concentrations (A) and fluxes (B) of model C at various concentrations of ribose. Solid lines indicate medians, shaded areas show interquartile ranges. Increasing ribose concentration results in a depletion of ATP and accumulation of ribose 5-phosphate in the glycosome. While the glycosomal PPP flux (JG6PDHg) remains mostly unaffected, the glycolytic flux (JPGI) is strongly reduced.(TIF)Click here for additional data file.Figure S5Simulations of oxidative stress in model D. Near identical to A) The steady state flux through the cytosolic pentose phosphate pathway in model D as a function of the oxidative stress by varying the kinetic constant TOXk. (B) Fluxes through the cytosolic PPP enzymes as a function of time upon sudden oxidative stress. During the whole time-course, TOXk = 2 µl·min−1·mg protein−1. The system is removed from steady state at t = 0, by setting 99% of the NAD(P)H and trypanothione pools to the oxidized form. Shown is the relaxation of the cytosolic PPP fluxes. Solid lines indicate medians, shaded areas show interquartile ranges.(TIF)Click here for additional data file.Figure S6Simulations of 6PGDH inhibition and 6-PG accumulation. (A–B) The effects of inhibition of 6PGDH on 6-PG concentrations and metabolic fluxes were simulated by reducing Vmax,6PGDH in model C and D at low oxidative stress (TOXk = 2 µl·min−1·mg protein−1). Results for simulations at low oxidative stress (TOXk = 200 µl·min−1·mg protein−1) are shown in (TIF)Click here for additional data file.Figure S7Simulations of 6PGDH inhibition during growth on fructose. (A–B) The effects of inhibition of 6PGDH on 6-PG concentrations and metabolic fluxes were simulated by reducing Vmax,6PGDH in model C and D at high oxidative stress (TOXk = 200 µl·min−1·mg protein−1), similar to (TIF)Click here for additional data file.Figure S8A) and low (B) oxidative stress; and in model D at high (C) and low (D) oxidative stress. Panel A and C correspond to B and D correspond to Figures S6A–B.Percentage of models reaching steady-state within 10 million simulation minutes during 6PGDH inhibitions. As explained in detail in (TIF)Click here for additional data file.Figure S9A) and D (B). Results from the simulations are shown in Percentage of models reaching steady-state within 10 million simulation minutes during 6PGDH inhibitions with growth on fructose. As explained in detail in (TIF)Click here for additional data file.Table S1c/g indicates that cytosolic and glycosomal activities are identical. Other parameters are assumed to be identical for glycosomal and cytosolic enzyme fractions. Parameter values given here are used in the fixed parameter models. Distributions of parameter values used in uncertainty modelling are given in Kinetic parameters of the enzymes related to the PPP. Activities, which depend on the expression level of the enzymes, are specified separately for the fraction of the enzyme that is localized in the glycosome and, if applicable, for the fraction which is located in the cytosol. (DOCX)Click here for additional data file.Table S2Elementary flux modes in models of PPP. The elementary modes of different model versions including the glycosomal PPP are listed as the overall reactions plus in brackets the individual enzyme-catalyzed reactions with their relative flux weight. For simplicity the cytosolic PPP and the cytosolic NADPH utilization were left out of this analysis. The modules included in each model version refer to (DOCX)Click here for additional data file.Table S3Predicted glycosomal proteome. All reactions present in the bloodstream form T. brucei glycosome, according to comprehensible glycosomal proteomics (DOCX)Click here for additional data file.Table S4Elementary modes in glycosomal proteome. A model of glycosomal metabolism was constructed for use in METATOOL version 4.9.3 2O, CO2 and O2 were not included in the reactions. Glucose, 3-phosphoglyceric acid, ribose and glycerol were set as external metabolites. The resulting model has 9 elementary modes, where the first four modes are also occurring in the models described in this paper. Elementary mode 5 is a futile cycle without external metabolites involved. Elementary modes 6–9 are unlikely to occur in dividing bloodstream trypanosomes, as a high glucose to pyruvate and glycerol flux is maintained. Additionally, the activity of fructose bisphosphatase could not be measured (DOCX)Click here for additional data file.Text S1Uncertainty modeling and distribution of parameter values. Detailed description of the methodology for uncertainty modeling and distributions defined for sampling of random parameter values.(DOCX)Click here for additional data file.


The obtained profiles and turnover rates are sensitive indicators of energy and metabolic imbalances like the ones created by genetic deficiencies, myocardial ischemia, heart failure, neurodegenerative disorders, etc. Here we describe and discuss briefly the potential use of dynamic phosphometabolomic platform for disease diagnostics currently under development at Mayo Clinic.Technological innovations and translation of basic discoveries to clinical practice drive advances in medicine. Today's innovative technologies enable comprehensive screening of the genome, transcriptome, proteome, and metabolome. The detailed knowledge, converged in the integrated “omics” , holds an immense potential for understanding mechanism of diseases, facilitating their early diagnostics, selecting personalized therapeutic strategies, and assessing their effectiveness. Metabolomics is the newest “omics” approach aimed to analyze large metabolite pools. The next generation of metabolomic screening requires technologies for high throughput and robust monitoring of metabolite levels and their fluxes. In this regard, stable isotope  Living cells represent an integrated and interacting network of genes, transcripts, proteins, small signaling molecules, and metabolites that define cellular phenotype and function. Traditionally the focus of biomedical research was on individual genes, single protein targets, single metabolites, and metabolic or signaling pathways. This “molecular reductionist” paradigm was based on the assumption that identifying genetic variations and molecular components would lead to discovery of cures for human diseases. However, most of diseases are complex and multi-factorial and the disease phenotype is determined by the alterations of multiple genes, pathways, proteins and metabolites . Therefore, an integrated “omics” approach is more viable direction for uncovering alterations in metabolic networks, disease mechanisms, and mechanisms of drug effects.Recent advent of large-scale metabolomics and fluxomic  completed the “omics revolution” , where g18O-assisted 31P NMR and mass spectrometry provide means for simultaneous measurements of phosphorous-containing metabolite (phosphometabolite) levels and their respective turnover rates in tissue and blood samples. The 18O labeling of metabolites is based on the incorporation of the 18O nuclei (from H218O), into phosphate group with each act of adenosine triphosphate (ATP) hydrolysis, and subsequent distribution of 18O-labeled phosphoryls among phosphate-carrying molecules. 18O is a natural and stable isotope of oxygen. When tissue or cells are exposed to media containing water with a known percentage of 18O, H218O rapidly equilibrates with the cellular water, and 18O from water is incorporated into cellular phosphate metabolites proportionally to the rate of the involved enzymatic reactions  and metabolite fluxes (fluxomics). Analytically, this could be perceived as a determination of concentrations and turnover rates of a large number of small molecules (metabolites) from tissue or body fluids. Thus, metabolomics and fluxomics methods heavily rely on the information-rich analytical techniques, most notably nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry. Stable isotope Dynamic phosphmetabolomic platform under development at Mayo Clinic  is shown in 13C labeling is widely used to track turnover of the carbon backbone of metabolites and label propagation through metabolic networks  will soon become practical enough to be routinely used in translational research, metabolic phenotyping, biomarker development, and ultimately in diagnostics and treatment of human diseases.The analysis of metabolic fingerprints left by disease processes and metabolic monitoring of disease progression or treatment efficacy plays a crucial role in personalized and predictive medicine. Metabolomic profiling may not be enough to predict the phenotype as it gives only an instant static picture of the physiology of a living organism. To entirely understand metabolic phenotypes and network dynamics, quantitative knowledge of metabolite turnover rates is required -13. Thisnetworks -19. Howeolecules ,16,20-24er rates ,26. We b18O-assisted gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS), and 31P NMR technologies fill a critical gap in metabolomics technologies by providing a currently unavailable tool for analysis of perturbations in cellular energetic and metabolic signaling networks induced by diseases or genetic and acquired metabolic deficiencies   and 1D 1H NMR and HPLC complement the analysis, identification, and fractionation of phosphometabolites.Phosphometabolomics is a new emerging area in metabolomic analyses targeting over 400 phosphometabolites critical in energetic and signaling processes. Currently, no single analytical tool fulfills all requirements for an ideal phosphometabolomic profiling, due to the physicochemical diversity of phosphometabolites, from hydrophobic phospholipids to hydrophilic phosphocarbohydrates, and phosphoamino- and non-phosphoamino-organic acids. Thus, different analytical techniques must be used to generate a comprehensive metabolomics profile ,32,33. Wy (HPLC) . 18O-ass18O isotopologues in ATP can be directly translated into activity of adenylate kinase, ATP synthase, ATPase, pyrophosphokinase, nucleotidyl transferases, and other energetic enzymes  on global changes in energetics and metabolic signaling networks  will bring to clinical practice advanced stable isotope-based metabolomic profiling for risk stratification, prediction of disease course, and personalized treatment of diseases by targeting drugs and procedures for each individual metabolomic profile.A large scale


Current studies on metabolic models mostly consider reaction fluxes in isolation. Based on a recently proposed metabolite-centric approach, we here describe a set of methods that enable the analysis and interpretation of flux distributions in an integrated metabolite-centric view. We demonstrate how this framework can be used for the refinement of genome-scale metabolic models.Genome-scale metabolic models are important tools in systems biology. They permit the Escherichia coli. By compiling the balance sheets of a small number of currency metabolites, we were able to fully characterise the energy metabolism as predicted by the model and to identify a possibility for model refinement in NADPH metabolism. Selected branch points were examined in detail in order to demonstrate how a metabolite-centric view allows identifying functional roles of metabolites. Fructose 6-phosphate aldolase and the sedoheptulose bisphosphate bypass were identified as enzymatic reactions that can carry high fluxes in the model but are unlikely to exhibit significant activity in vivo. Performing a metabolite essentiality analysis, unconstrained import and export of iron ions could be identified as potentially problematic for the quality of model predictions.We applied the metabolite-centric view developed here to the most recent metabolic reconstruction of The system-wide analysis of split ratios and branch points allows a much deeper insight into the metabolic network than reaction-centric analyses. Extending an earlier metabolite-centric approach, the methods introduced here establish an integrated metabolite-centric framework for the interpretation of flux distributions in genome-scale metabolic networks that can complement the classical reaction-centric framework. Analysing fluxes and their metabolic context simultaneously opens the door to systems biological interpretations that are not apparent from isolated reaction fluxes. Particularly powerful demonstrations of this are the analyses of the complete metabolic contexts of energy metabolism and the folate-dependent one-carbon pool presented in this work. Finally, a metabolite-centric view on flux distributions can guide the refinement of metabolic reconstructions for specific growth scenarios. The advent of rapid sequencing technologies, the collection and sharing of omics data in electronic databases, and the development of a wealth of computational tools in systems biology have allowed the reconstruction of metabolic networks on a genome-wide scale. Since the first whole-genome metabolic model was published in 1999 .in-silico prediction of the effects of genetic or regulatory modifications or growth in different environments on the phenotype [Whole-genome metabolic models play a dual role in systems biology: Firstly, they are structured knowledge bases that integrate data about the metabolism of the modelled organism from a wide variety of sources, including databases, primary literature, and expert knowledge . Secondlhenotype . Thus, thenotype .Escherichia coli is by far the best-studied prokaryote and an important model organism in biology. Its metabolism is particularly well understood, and many enzymes and biochemical pathways were first described in E. coli . The . The Esc iJO1366 , is one  iJO1366  and iJR9−1 h−1 or as percentage of the substrate uptake rate. Such values are, however, very hard to interpret. For instance, what does it mean if the flux from 2-oxoglutarate to L-glutamate via glutamate dehydrogenase is 6.3 mmol gDW−1 h−1? Or that the rate of aspartate formation via aspartate transaminase is 22% of the glucose uptake rate?Flux balance analysis and related methods for the analysis of whole-genome metabolic models produce a vector of reaction fluxes. These are usually either given as absolute values in mmol gDWAll genome-scale metabolic models published to date are stoichiometric models, which incorporate reaction stoichiometry but do not require any knowledge of kinetic parameters. While they do not allow the analysis of non-equilibrium dynamics, stoichiometric models have been found very useful for predicting the metabolic capabilities in steady state -15.Flux balance analysis (FBA) was introduced in 1986 , more thOberhardt et al.  list theContextualisation of high-throughput data is achieved by adding constraints to the model or modifying existing constraints . Usually13C-labeling experiments [While the performance of models may initially be analysed in terms of growth rate and efficiency, the most specific tool for the validation of metabolic models is the comparison of predicted flux distributions to experimentally determined fluxes, usually obtained via eriments ,24. Due eriments -27.Model-based metabolic engineering also employs a reaction-centric view. The most commonly used approaches try to modify individual reaction fluxes to redirect metabolic flux or to find a combination of gene knockouts leading to overproduction of the desired metabolite -31.A first metabolite-centric approach for the analysis of genome-scale metabolic models was introduced in 2007  and expaThe concept of branch points and flux split ratios has been used in wet-lab metabolic flux analysis using small-scale metabolic models -37, partin vivo or known for very specific scenarios only, i.e. reactions or enzymes whose presence in the model may lower the quality of the model’s predictions in some, possibly most, scenarios.Here we show that the system-wide analysis of split ratios and branch points, leading to a metabolite-centric view on flux distributions, allows a much deeper insight into the metabolic network based on the results of flux balance analysis. For this purpose, we introduce a set of methods that generate a metabolite-centric description based on a given flux distribution. Moreover, we demonstrate how a system-wide analysis of metabolite essentiality, which is only possible within a metabolite-centric framework, can identify potential problems in a network reconstruction. We further show how, e.g., energy metabolism can be characterised by examining the flux split ratios for a very small number of metabolite nodes. We also demonstrate how a metabolite-centric view can be used for the refinement of models against experimental data, in particular by identifying enzymatic reactions not known to occur −1 h−1, and a lower bound of 6.4 mmol gDW−1 h−1 was imposed on the acetate export flux. For this scenario, FBA predicted a growth rate of 0.85 h−1, an oxygen uptake rate of 16.5 mmol gDW−1 h−1, and a CO2 emission rate of 18.2 mmol gDW−1 h−1. In the FBA solution, only 552 of the 2583 reactions (21%) involving 557 of the 1805 metabolites (31%) are active (at a cutoff of 10−12 mmol gDW−1 h−1). Of the 557 active metabolites, 401 are part of unbranched paths, i.e. they are each produced via a single reaction and consumed via another single reaction. These metabolites are purely transitory in the scenario under consideration. Consequently, only the remaining 156 metabolites  are branch points, where fluxes split or join.FBA was performed for the chosen scenario, carbon-limited aerobic growth in glucose minimal medium with forced acetate secretion (see Methods section), with the following constraints: Glucose uptake was limited to a rate of at most 11.0 mmol gDWSplit-ratio analysis was used to explore the fate of currency metabolites, most importantly energy-rich phosphates ) and reducing equivalents (chiefly NADH and NADPH).−1 h−1, which has to be corrected for salvage reactions, such as for diphosphate, which regenerate some “spent” ATP. This correction yields an ATP production rate of 66.9 mmol gDW−1 h−1, i.e. the net yield is 6.1 molecules of ATP per glucose molecule. The largest part  is produced via oxidative phosphorylation. 25.5% is produced in glycolysis, while 8.5% is produced in the conversion of acetyl-CoA to acetate, which is excreted. While 4.0% of the total producing ATP flux stems from the reaction of ADP with diphosphate, only 0.7% is produced in the TCA cycle.Table The ratios of ATP-consuming fluxes are shown in Table −1 h−1), and their contributions to energy metabolism are not substantial compared to those of ATP and PEP.The fluxes through the other nucleoside triphosphates are low  and lipopolysaccharides (0.13%).The split ratios for PEP are shown in Table E. coli except as a usable carbon source [−1 h−1, FVA: 4–19 μmol gDW−1 h−1). In our FBA solution, DHA is produced at a large rate (2.1 mmol gDW−1 h−1) via fructose-6-phosphate (F6P) aldolase, which cleaves F6P into glyceraldehyde 3-phosphate and DHA. FVA was used to verify that activity in this reaction is not required for optimum growth. If the flux through F6P aldolase is constrained to the condensation direction , all of the glycolytic flux must go through either the fructose bisphosphate aldolase reaction or the sedoheptulose bisphosphate bypass (first described in [The large flux through dihydroxyacetone (DHA) kinase is unexpected, as DHA is not known as an intracellular metabolite in n source  or in thn source ,45, whicribed in ) in ordeBy combining the balance sheets for ATP and PEP, it is possible to compute the net balance of glycolysis: It generates 1.6 molecules of ATP and 1.5 PEP per glucose molecule, while consuming 0.6 ATP and 1.2 PEP, i.e. the balance is positive with a net production of 1.1 ATP and 0.3 PEP per molecule of glucose. This corresponds to 67.4% of the theoretical maximum of 2 molecules of ATP or PEP , i.e. on−1 h−1. 52.7% is produced in glycolysis, with another 32.1% generated in the formation of acetyl-CoA from pyruvate, while only 8.1% is produced in the TCA cycle. 6.2% of the NADH is generated as a byproduct of amino acid biosynthesis pathways, while the rest is produced as a byproduct of purine and folate metabolism, respectively. Almost all of the NADH is used for energy metabolism: 94.5% enters the electron transport chain via NADH dehydrogenase. Most of the remaining NADH (5.1%) is consumed in fatty acid biosynthesis, while 0.4% is used in the biosynthesis of L-methionine. This is consistent with the general observation that in bacteria grown aerobically on sugars, NADH is produced primarily in the catabolic pathways of glycolysis and TCA cycle and used almost exclusively for ATP generation [Equally important to the energy balance of the cell are the reducing equivalents NADH and NADPH. The corresponding balance sheets are shown in Tables neration .−1 h−1, the flux through NADPH is 59% lower than that through NADH. The largest part (80.7%) stems from the oxidative pentose phosphate pathway (PPP). Another 13.4% is produced by the enzyme isocitrate dehydrogenase (ICD) in the TCA cycle, while 5.9% is generated as a byproduct in purine metabolism. It has been shown in 13C tracer experiments that in E. coli grown aerobically on glucose, 35–45% of the NADPH is produced from NADH and NADP+ by the membrane-bound proton-translocating transhydrogenase PntAB, while another 35–45% is produced in the PPP, and 20–25% is produced by ICD [At 13.9 mmol gDWd by ICD . In contd by ICD .−1 h−1 yielded a flux distribution in agreement with the results of [−1 h−1; experimental value: 18.2 ± 0.8 mmol gDW−1 h−1[2 emission rate.An exploratory FBA in an alternative scenario with a forced transhydrogenase flux of 5 mmol gDWsults of , predictgDW−1 h−1) than th−1 h−1. 77.8% is used for the biosynthesis of various amino acids, of which two-thirds  is accounted for by L-glutamate alone, followed by L-threonine (6.5%) and L-lysine (4.1%). The biosyntheses of all three branched-chain amino acids together consume 7.0% of the NADPH. Other major processes consuming NADPH are fatty acid biosynthesis (13.4%) and sulfur assimilation (4.6%). 2.1% is used for restoring thioredoxin to the reduced state, which in turn is used for sulfur assimilation (71%) and biosynthesis of deoxyribonucleotides (29%). 1.0% of the NADPH is used in phospholipid biosynthesis, while the remainder is consumed in the biosynthesis of other biomass components, including polyamines, peptidoglycans, lipopolysaccharides, and various cofactors. 0.16% is consumed in folate metabolism. Both the lower flux through NADPH compared to NADH and the distribution of the NADPH-consuming flux over a large number of reactions in various biosynthetic pathways are in good agreement with the paradigm that while NADH is used for energy metabolism, NADPH acts, among other functions, as a reducing agent in anabolic pathways [Apart from being a biomass component itself, NADPH is consumed in 33 reactions with a flux above 0.1 μmol gDWpathways .A detailed analysis of the split ratios in selected metabolite nodes was performed with the two goals of elucidating the metabolic roles of metabolite species and identifying the relative contributions of biochemical pathways to biomass formation and energy generation, respectively, in the scenario under consideration.2-oxoglutarate. The total flux through the metabolite is 8.1 mmol gDW−1 h−1, of which 77.0% is generated as a byproduct of various transaminases, which transfer the amino group of L-glutamate to different acceptors, producing L-amino acids. In contrast, only 23.0% is produced de novo from isocitrate in the TCA cycle. The consuming side also reflects the presence of a transamination cycle: 88.5% of the 2-oxoglutarate is converted to L-glutamate, while only 11.5% is metabolised in the TCA cycle.Table −1 h−1 going through the transamination cycle yields a net balance, in which 100% of the 2-oxoglutarate (1.9 mmol gDW−1 h−1) is produced in the TCA cycle. Of this non-cyclic flux, 50.1% continues in the TCA cycle, while 49.8% is converted to L-glutamate as starting molecule of various biosynthetic pathways.Subtracting the flux of 6.3 mmol gDWL-glutamate. The balance sheet for L-glutamate , L-glutamine (23.5%), and L-proline (19.8%), while another 23.5% enters the biomass directly. The rest is consumed in the biosynthesis of polyamines (3.7%), peptidoglycans (2.5%), and various cofactors.The net balance of L-glutamate with all fluxes in the two transamination cycles removed is shown in Table Glucose 6-phosphate (G6P) is the first branch point in glycolysis. It is shown in metabolic context (two levels of depth) with split ratios in Figure −1 h−1 flux of carbon atoms through G6P, 48.9 mmol gDW−1 h−1 (74.1%) reach PEP via glycolysis. The fluxes in the whole PPP and its connections with glycolysis are shown in Additional file acetate in its metabolic context. Acetate is produced and secreted at the forced rate of 6.4 mmol gDW−1 h−1. While some acetate is produced as a byproduct of arginine metabolism (4.4%), L-cysteine biosynthesis (3.3%), and the biosynthesis of lipopolysaccharides (0.2%), the largest part (92.1%) is produced from acetyl-CoA via phosphotransacetylase and acetate kinase. At this rate of acetate secretion, more than 50% of all acetyl-CoA takes the acetate-exporting route instead of the TCA cycle. FVA was used to verify that indeed the forced acetate secretion is the sole reason for activity in this route: If this constraint is disabled, acetate kinase and phosphotransacetylase work in the opposite direction to salvage the acetate produced as a byproduct of the other pathways. FVA also confirmed that this route is the cheapest way with regard to biomass yield to produce acetate at the required rate.Figure Branch point analysis permits the visualisation of whole pathways with actual fluxes. This is particularly useful for metabolic cycles and metabolite pools that are depleted and replenished via many different reactions. Figure As mentioned above, only about one-sixth of the acetyl-CoA enters the TCA cycle, while the majority is converted to acetate for secretion. All the flux entering citrate synthase also goes through isocitrate dehydrogenase, i.e. all the citrate is converted to 2-oxoglutarate, which is the first of two major branch points towards anabolic pathways. As described above, only 50.1% of the 2-oxoglutarate continues in the TCA cycle, forming succinyl-CoA. All of the succinyl-CoA is eventually converted to succinate, 52.7% within the TCA cycle, 33.0% in L-lysine biosynthesis, and 14.3% in the biosynthesis of L-methionine. All of the succinate is then oxidized to fumarate, but succinate dehydrogenase is not the sole source of fumarate. Virtually all of the fumarate is converted to oxaloacetate via fumarase and malate dehydrogenase, but the largest part of the oxaloacetate (58.1%) results from the anaplerotic reaction catalysed by PEP carboxylase.−1 h−1, L-aspartate formed from oxaloacetate transfers its amino group to different acceptors in purine and arginine metabolism, respectively, yielding fumarate, which is converted back to oxaloacetate in the TCA cycle. Correcting for this cycle yields a ratio of 54.3% of oxaloacetate remaining in the cycle and 45.7% entering anabolic pathways. The fraction of anaplerotic oxaloacetate (produced by PEP carboxylase) increases to 72.8% with this correction.Oxaloacetate is the second major branch point of the TCA cycle. 56.6% of the flux through this node is redirected towards anabolism, forming L-aspartate as the first step. However, branch point analysis revealed the presence of a cycle between oxaloacetate and fumarate  is close to the value determined by Fischer et al.  (55–59%)−1 h−1, the flux through the TCA cycle is considerably larger (isocitrate dehydrogenase: 2.9 vs. 1.9 mmol gDW−1 h−1), which is much closer to experimental observations – 2.9 mmol gDW−1 h−1 amounts to 26% of the glucose uptake rate of 11 mmol gDW−1 h−1. As a result, the fraction of NADH produced via the TCA cycle increases from 8.1% to 12.9%.In the alternative scenario with a forced transhydrogenase flux of 5 mmol gDW5,6,7,8-Tetrahydrofolate (THFA), via its activated forms 5,10-methylene-THFA, 5-methyl-THFA, and 10-formyl-THFA, is one of the cell’s most important donors of one-carbon groups. In addition, it can act as a reducing agent. Figure −1 h−1, but only 0.08% of this stems from de-novo synthesis, while most of the THFA circulates. Almost all of the THFA is converted to 5,10-methylene-THFA, where the transferred methylene group stems from cleavage of either L-serine (94.8%) or glycine (4.9%). 0.23% is converted to 10-formyl-THFA instead, reassimilating formate at the cost of ATP hydrolysis. The largest part of the 5,10-methylene-THFA (84.0%) is also subsequently converted to 10-formyl-THFA, which transfers a formyl group in two reactions in the biosynthesis of purine nucleotides. 13.8% of the 5,10-methylene-THFA is converted to 5-methyl-THFA, which donates a methyl group in the biosynthesis of L-methionine. Another 2.2% is consumed in the biosynthesis of dTMP, where it donates both a methylene group and two electrons, resulting in dihydrofolate as the depleted form, which is subsequently reduced back to THFA. Finally, 5,10-methylene-THFA transfers a methylene group in the first step of coenzyme A biosynthesis, which accounts for 0.05% of the total flux.The total flux through THFA is 0.98 mmol gDW548 of the 557 metabolite nodes carrying a flux 98%) were identified as essential for optimal growth by metabolite flux minimisation. The remaining nine metabolites are shown in Table 8% were i2+ ions. As the only Ca2+ importer available in the model is a Ca2+/Na+ antiporter, a second transporter must be active to pump Na+ ions to the cytosol. A large number of Na+ symporters  are present in the model, and most of these have the same energy cost, as most of the used organic compounds can be exported to the periplasm via 1:1 H+ symport. Excess intracellular protons are exported to the extracellular space in our FBA solution. There exists a stoichiometric alternative via a periplasmic ferroxidase, which produces water from protons, oxygen, and electrons donated by Fe2+, so that no protons are exported to the medium. The additional import flux of Fe2+ required for the conversion of all excess protons in this way is 14.0 mmol gDW−1 h−1, which is equal to the required Fe3+ export flux. Hydroxypyruvate occurs as an intermediate in the reaction pair hydroxypyruvate isomerase + hydroxypyruvate reductase, which is stoichiometrically equivalent to the single reaction catalysed by tartronate semialdehyde reductase. Thioredoxin can be replaced by other reducing agents, as the model contains alternative reactions to those catalysed by thioredoxin-dependent enzymes.Adenosine and α-D-ribose 1-phosphate occur in a salvage pathway for adenine, which can alternatively be converted to AMP in one step via adenine phosphoribosyltransferase. Fructose 1,6-bisphosphate can be completely bypassed in the model by the sedoheptulose bisphosphate bypass, where phosphofructokinase and fructose bisphosphate aldolase act on sedoheptulose 7-phosphate from the pentose phosphate pathway and sedoheptulose 1,7-bisphosphate instead of fructose 6-phosphate and fructose 1,6-bisphosphate, respectively. Since these pathways are stoichiometrically equivalent, the use of either pathway allows optimal biomass production. Periplasmic glycine is an intermediate in a transport cascade that has the net effect of importing CaThe result that most active metabolite nodes are essential is complemented by the observation that according to FVA, only 76 of the 552 active reactions (14%) are nonessential, i.e. most of the active reactions need to carry a flux for optimal biomass production.+, H2O, and phosphate, or circulating currency metabolites or cofactors, such as ATP/ADP, NADH/NAD+, and coenzyme A. The list also contains a large number of glycolytic metabolites, which is due to the high flux through glycolysis, as all the carbon flux initially passes through glycolysis and branches off to other pathways only gradually. Metabolite flux minimisation also predicts large required fluxes through O2 and CO2, which is expected for the aerobic condition. Due to the transamination cycle described above, L-glutamate and 2-oxoglutarate have high minimum fluxes. Acetate and acetyl phosphate appear in the list due to the high forced acetate export flux. In addition, metabolites from the pentose phosphate pathway have to carry a high flux, as that constitutes the cheapest way for the model to produce NADPH.Table 2, and acetate. The example of glucose 6-phosphate illustrates the usefulness of this alternative flux measure: It is only ranked eighth among the carbon metabolites in Table For carbon-containing metabolites, a more useful ranking is not by total flux, but by carbon flux, i.e. the product of metabolite flux and number of carbon atoms in the compound. The twenty intracellular non-cofactor metabolites with the highest required carbon fluxes are shown in Table We have demonstrated how the energy metabolism of the cell, as predicted by the model, can be characterised by compiling the balance sheets of a handful of currency metabolites. The split ratios of currency metabolites can be used to check a metabolic model for biological plausibility even in the absence of specific experimental data for the studied organism, as energy metabolism tends to follow certain well-studied paradigms solely based on ecology . Thus, split-ratio analysis is a powerful tool for plausibility checking and refinement of metabolic models.By inspecting selected metabolites in detail, we have demonstrated how branch point analysis can be used to elucidate the metabolic roles of compounds occurring in the network in the scenario under consideration. For instance, branch point analysis can be used to determine whether a metabolite carrying a flux acts as a mere pathway intermediate, is produced solely as a biomass component, acts as energy or redox carrier, acts as a group donor, or occurs as a mere byproduct, which is recycled to central metabolism.Split ratio analysis can also be used in an exploratory approach, as the relevant context for interpreting the flux through any reaction is provided by its split ratios with respect to the flux sums in each of the substrates and products involved. Branch point analysis presents just this context at a glance. To revisit the questions posed in the Background section: The flux through glutamate dehydrogenase is easily interpretable when expressed as a fraction of the total producing glutamate flux, the total consuming 2-oxoglutarate flux, the total consuming NADPH flux, and possibly the total ammonia-consuming flux. Likewise, the flux through aspartate transaminase is best expressed as a fraction of the total fluxes through oxaloacetate, aspartate, glutamate, and possibly 2-oxoglutarate.Key split ratios, such as those of 2-oxoglutarate and oxaloacetate, can be used to identify the relative contributions of a pathway to energy generation and biomass production, respectively. Hence, a metabolite-centric view on flux distributions can guide metabolic engineering, where the goal is often to optimise split ratios between pathways leading to product formation, biomass production, and energy generation in order to achieve maximum product yield ,52. In fIt should be noted that only a small fraction of the metabolites in the network are branch points in any given scenario, so that the global network behaviour can be characterised via a small number of detailed analyses.By reducing the metabolic network to only those nodes that are branch points, branch point analysis allows the visualisation of all fluxes within a metabolic cycle, a metabolite pool, or even larger subsystems, as we have demonstrated using the examples of the TCA cycle, the folate-dependent one-carbon pool, and the superpathway consisting of glycolysis and the pentose phosphate pathway. Using advanced graph layouting algorithms , it may even be possible to produce a clear and informative visualisation of the steady-state fluxes in the whole network. In addition, branch point analysis allows the graphical identification of metabolic cycles, which can subsequently be analysed for their biological significance.E. coli model involving menaquinone-8 or the ten reactions involving thioredoxin cannot reveal these metabolites to be nonessential for optimal growth. In the case of thioredoxin, seven of the ten fluxes can actually carry a flux under the constraint of optimal biomass production.The system-wide analysis of branch points and split ratios is complemented by metabolite essentiality analysis, implemented using either flux-sum analysis or metabolite flux minimisation. It should be noted that this type of analysis is only possible within a metabolite-centric view. For instance, merely examining the fluxes or flux variabilities of the twenty-three reactions in the In metabolite flux minimisation, a nonessential metabolite is identified as a metabolite with a minimum flux sum of zero under the constraint of optimal biomass flux. In flux-sum analysis, in contrast, a nonessential metabolite is one that can be removed from the network (modelled by restricting the fluxes consuming the metabolite to zero) without a decrease in biomass flux . The twoE. coli, we discovered a number of hitherto not discussed peculiarities in network behaviour:Applying the metabolite-centric view introduced here to the published model iJO1366 of E. coli in 2000 [in vivo remains obscure. Moreover, nothing is known about in-vivo expression of this enzyme. Schürmann and Sprenger [−1 more endergonic than the fructose-1,6-bisphosphate cleavage reaction, making it unlikely for the reaction to proceed in the cleavage direction rather than the reverse direction. The central role, tight regulation, and ubiquitous presence of fructose-bisphosphate aldolase in the glycolytic pathways of all organisms capable of classical (Embden-Meyerhof-Parnas) glycolysis sheds further doubt on a notable alternative being active in E. coli. While the two reaction pairs F6P kinase + fructose-bisphosphate aldolase and F6P aldolase + dihydroxyacetone kinase are stoichiometrically equivalent except for the phosphorylating agent involved (ATP vs. PEP), the model would likely reflect the metabolic behaviour in vivo more accurately if the flux through F6P aldolase were constrained so as to disallow the cleavage direction. Possibly, the reaction should even be omitted altogether until more is known about in-vivo expression and regulation of the enzyme. The inclusion of F6P aldolase in the model (which is not discussed by the authors of iJO1366 or either of its predecessors iAF1260 and iJR904) may represent a case of a spurious enzyme function, possibly resulting from blanket inclusion of all enzymes found in an automated database search. If added with unconstrained flux, such cryptic enzymes can dramatically lower the quality of the predictions made by the model.A systematic analysis of the balance sheets of all energy metabolites revealed unexpectedly strong activity of dihydroxyacetone kinase, which further led to the discovery of the high activity of fructose-6-phosphate (F6P) aldolase in the FBA solution. Usually, F6P is phosphorylated once more to fructose 1,6-bisphosphate, and only that is cleaved . F6P aldolase was discovered in  in 2000 , but itsSprenger  note thaE. coli[in vivo, the bypass reactions would not be expected to display significant activity unless there is an accumulation of sedoheptulose 7-phosphate for some reason. Therefore, it would be advisable to constrain the fluxes through the bypass reactions to zero when specifically studying the wild-type network.A systematic survey of metabolite essentiality resulted in the equally surprising observation that in the model, fructose 1,6-bisphosphate is nonessential for optimal growth. The reason was found in the sedoheptulose bisphosphate bypass, which is equivalent to the standard reactions of phosphofructokinase and fructose bisphosphate aldolase with regard to biomass flux. Notable activity in this pathway has, however, only been observed in transaldolase-deficient mutants, but never in wild-type strains of E. coli. It is lin vivo, a more costly reaction (proton-translocating transhydrogenase) accounts for a substantial fraction of the NADPH produced. Flux through this reaction reduces the number of protons in the periplasm available to ATP synthase, which means that more carbon has to be respired to CO2, lowering the biomass yield. Therefore, this reaction is not used for the generation of NADPH in an FBA flux distribution optimised for biomass production. To improve the capability of the model to predict fluxes in ‘aerobic growth on sugars’ scenarios, a flux through this reaction needs to be enforced, as we could show in preliminary tests with altered simulation parameters. Ideally, this should be done by postulating a stoichiometric coupling to one or more other reactions instead of fixing the flux at an absolute value. For instance, a constant ratio of periplasmic protons might be forced to take this route, resulting in a stoichiometric coupling to ATP synthase. Or, if that should reproduce experimental results more faithfully, the flux through this reaction could be coupled to the fluxes through other NADPH-producing reactions. In the absence of new experimental data, a black-box approach could also be taken, where the transhydrogenase flux would be set to a fixed fraction of the carbon flux entering the system. Of course, the ratio between NADPH-producing reactions may change dramatically depending on carbon source and availability of oxygen, so that any such constraints are only valid for the particular scenario of carbon-limited aerobic growth on sugars.We found a strong discrepancy between the predicted NADPH balance and that measured in . This di−1 h−1) is very low in comparison to those through acetyl-CoA (10.8 mmol gDW−1 h−1) and PEP (16.3 mmol gDW−1 h−1). In addition, the anabolic fluxes branching from the TCA cycle are very large . Taken together, these results indicate that in the original scenario, the primary purpose of the TCA cycle, as predicted by the model, is not energy generation but production of C5 and C4 compounds for the biosynthesis of biomass components. This can also be seen in the low contribution of the TCA cycle to NADH and NADPH production. When adding a flux through proton-translocating transhydrogenase of 5 mmol gDW−1 h−1, the role of the TCA cycle shifts somewhat from the production of biomass precursors towards energy metabolism, as the fraction of NADH produced via the TCA cycle increases from 8.1% to 12.9%. This is also reflected in the lowered fractions of 2-oxoglutarate (31% vs. 50%) and oxaloacetate (35% vs. 46%) branching from the TCA cycle towards anabolic pathways.In the original scenario, the predicted flux going through the TCA cycle .Metabolite flux minimisation revealed a potential problem in the model by predicting that flux through extracellular protons is not essential for optimal growth: Excess intracellular protons, which are transported to the periplasm via the complexes of the electron transfer chain, can be converted to water by ferroxidase in the periplasm instead of being exported through the outer membrane. The electrons required for the involved reduction of molecular oxygen are donated by Feutral pH . The onlWe demonstrated the ability of split-ratio analysis and branch point analysis to assign metabolic roles. These roles are often in agreement with long-established knowledge of metabolic pathways, but in other cases it becomes obvious that the metabolic roles are strongly modified in different contexts and that the metabolism is highly flexible in making use of different capabilities many metabolites provide.trxA trxC double mutants of E. coli are phenotypically inconspicuous [In the small number of discussed examples, the role of L-glutamine, L-glutamate, and L-aspartate as amino-group donors became obvious. Furthermore, the forced acetate secretion flux was shown to be responsible for more than 90% of the total acetate-producing flux. Thioredoxin was predicted to be redundant in steady-state metabolism despite being involved in ten reactions in the network. This prediction is in agreement with the observation that spicuous . Howeverspicuous , which i13C tracer analysis. The only requirement is a flux distribution at the resolution of individual reactions rather than summed “pathway fluxes”.It should be noted that the transformation of reaction fluxes to metabolite fluxes and split ratios and the computational methods introduced here are applicable to any flux distribution, whether computationally predicted or experimentally determined, e.g. via This paper introduces a new, metabolite-centric view on flux distributions in genome-scale metabolic networks. We have shown how a metabolite-centric view on flux distributions opens the door to a more detailed analysis than is possible within the traditional reaction-centric framework. Split-ratio analysis and its extension branch point analysis facilitate the interpretation of flux distributions by providing metabolic context.Within a metabolite-centric view, it is very easy to simultaneously analyse all fluxes in a metabolic context, as demonstrated here for the full metabolic contexts of energy metabolism, the TCA cycle, the interconnected transamination cycles, the superpathway of glycolysis and the pentose phosphate pathway, and the folate-dependent one-carbon pool. This contextualisation leads to new biological interpretations not apparent from isolated reaction fluxes, which can be used for validating and refining the model’s predictive capabilities for specific growth scenarios.In summary, switching from a reaction-centric to a metabolite-centric view on flux distributions allows a wealth of inferences to be drawn that are not apparent from the reaction fluxes alone. The computational methods we have introduced here allow the analysis of reaction fluxes, whether predicted or determined experimentally, in context rather than in isolation.Escherichia coli K-12 MG1655. The reconstructed network comprises a total of 2583 reaction nodes  and 1805 metabolite nodes . The model was downloaded from the supplementary material of the corresponding publication [The genome-scale metabolic model iJO1366 covers 1366 open reading frames of S and a set of lower and upper flux bounds lb ≤ v ≤ ub constraining the flux variables v[S is an m × n matrix, where m is the number of metabolites (1805), n is the number of reactions in the network (2583), and v is an n × 1 vector. The entry sij of S is the stoichiometric coefficient of metabolite i in reaction j. It is positive if metabolite i occurs on the right-hand side of reaction j, negative if it occurs on the left-hand side, and zero if it is not involved in the reaction. The flux bounds {lb, ub} represent, inter alia, thermodynamic constraints (e.g. irreversibility of reactions) and medium properties (e.g. constraining the uptake of a compound not present in the medium to zero).iJO1366 is a stoichiometric model, which is mathematically represented by a stoichiometric matrix riables v. S is angrowth-associated and non-growth-associated maintenance requirements . The GAM is part of the biomass reaction and models the ATP cost of growth-associated processes, most importantly DNA, RNA, and protein polymerisation, per gram dry weight, while the NGAM is modelled as an ATP hydrolysis reaction with a fixed flux and represents maintenance processes that consume ATP but are not associated with growth [Important model parameters are the h growth . These pin-vivo kinetic parameters. Its underlying assumptions are steady state and optimality. The first assumption, mathematically expressed as Sv = 0, is that the system is in a flux equilibrium with all metabolite concentrations constant [The fluxes through the network in steady state were computed using flux balance analysis ,61,62. Fconstant . The secconstant , usuallymetano, the open-source software toolkit developed in our group . The software is written in the Python programming language and is available online (http://metano.tu-bs.de). We verified our implementation by comparing our solutions for the earlier model iAF1260 to the corresponding published FBA solutions [−1 h−1, carbon-limited; scenario 2: glucose uptake rate 11 mmol gDW−1 h−1, oxygen uptake rate 18.2 mmol gDW−1 h−1, carbon- and oxygen-limited). Apart from alternate  pathway usage, both FBA solutions could be reproduced (data not shown).We used the implementation of FBA in olutions , using i−1 h−1, and acetate secretion was added at a rate of 6.4 mmol gDW−1 h−1. These values were reported in [metano input files for this scenario and the resulting output files are provided in Additional files For the analyses on the model iJO1366, the glucose uptake rate was set to 11.0 mmol gDWorted in  for expo−1 h−1 through proton-translocating transhydrogenase, was employed in an attempt to resolve discrepancies found initially between model predictions and experimental data.A second scenario, which was derived from the one described above by adding a forced flux of 5 mmol gDWmetano in the ‘fast FVA’ variety of the algorithm [The solutions returned by FBA are not necessarily unique, as for whole-genome models, the objective function usually assumes the optimum at more than one point in the solution space . Flux valgorithm .We developed the following algorithm to compute metabolite fluxes and split ratios from reaction fluxes:S and a flux vector v,Given a stoichiometric matrix i:Φi is the total flux through metabolite i  and expressed as percentages of Φi. More precisely, i produced (if positive) or consumed (if negative) via reaction j.For each metabolite fined in ), and thρij = sij · vj for all reactions j and group into positive (producing) and negative (consuming):1. Compute partial fluxes 2. Compute:and divide partial fluxes by this flux sum:Branch point analysis was developed to generalise the approach of split-ratio analysis to the whole metabolic network and visualise the extended metabolic context of a selected metabolite or reaction. The analysis starts with the complete bipartite reaction graph, which consists of all reactions and metabolites that are active in the given FBA solution (i.e. carry a flux above some threshold). As the complete graph is much too complex for a meaningful interpretation, the graph is reduced according to user-supplied parameters.2O or H+). The second list can be used to prevent crowding of the graph by promiscuous compounds such as currency metabolites (e.g. ATP or NADH). Finally, the user can specify a maximum distance to the selected hub node. Nodes beyond this limit will not be shown in the final graph.The user selects a node (i.e. reaction or metabolite) as the hub of the analysis. Optionally, the user can provide a list of nodes to be excluded and a list of nodes to be disconnected. Excluded nodes are completely removed from the graph, while disconnected nodes are split into separate instances for each occurrence of the metabolite or reaction in the network. Typically, the first list would be used to exclude metabolites that are ubiquitous, but in most contexts uninteresting to the user , is distributed under the GNU General Public License version 3 and can be downloaded from http://metano.tu-bs.de/ameba. Our implementation uses NetworkX [http://code.google.com/p/jrfonseca/wiki/XDot).We implemented branch point analysis in the Python programming language based on NetworkX  for grapNetworkX  for grapThe following algorithm was used to determine the minimum flux that each metabolite node has to carry under the constraint of (sub)optimal biomass production:1. Split flux variables into positive and negative components:S. The result is a vector of non-negative flux variables. Lower and upper bounds have to be adjusted accordingly. In practice, flux variables constrained to zero can be removed along with the corresponding columns of S.and duplicate and negate the corresponding columns of the stoichiometric matrix 2. Using the modified flux vector and stoichiometric matrix from step 1:i, solve the following convex linear optimisation problem:For each metabolite Sv = 0in steady state: lb ≤ v ≤ ubwith inequality constraints and the additional constraint:Φi. The optimisation problems were solved using the GNU Linear Programming Kit . As metabolite flux minimisation is very similar to FVA, the ‘fast FVA’ strategy is applicable to this algorithm as well, and it has been implemented in metano with this performance optimisation. In this study, metabolite flux minimisation was used for identifying nonessential metabolites and for determining minimal metabolite turnover rates Φi.In effect, this algorithm successively minimises each of the producing metabolite flux sums CoA: Coenzyme A; DHA: Dihydroxyacetone; F6P: Fructose 6-phosphate; FBA: Flux balance analysis; FVA: Flux variability analysis; G6P: Glucose 6-phosphate; GAM: Growth-associated maintenance; ICD: Isocitrate dehydrogenase; NGAM: Non-growth-associated maintenance; PEP: Phosphoenolpyruvate; TCA cycle: Tricarboxylic acid cycle; THFA: Tetrahydrofolate.The authors declare that they have no competing interests.in-silico study on the E. coli model, and drafted the manuscript. SAR and DS conceived of the study. DS participated in the design and coordination of the study and helped to draft the manuscript. All authors read and approved the final manuscript.SAR and RR developed and implemented the new methods described here , carried out the −1 h−1. Edge labels are split ratios as fractions of the flux through the adjacent metabolite node.Metabolite fluxes and split ratios within the superpathway of glycolysis and the pentose phosphate pathway.Green: Metabolite nodes, yellow: enzyme nodes. Numbers in metabolite nodes are total flux in mmol gDWClick here for fileMetano input files generated from model iJO1366. Model and scenario file – see http://metano.tu-bs.de/quickref.html for description of file formats.Click here for fileMetano output files . See http://metano.tu-bs.de/quickref.html for description of file formats.Click here for file