ATCC 51142 and Synechocystis sp. PCC 6803. All reactions are elementally and charge balanced and localized into four different intracellular compartments  and biomass descriptions are derived based on experimental measurements. Newly added reactions absent in earlier models  span most metabolic pathways with an emphasis on lipid biosynthesis. All thermodynamically infeasible loops are identified and eliminated from both models. Comparisons of model predictions against gene essentiality data reveal a specificity of 0.94 (94/100) and a sensitivity of 1 (19/19) for the Synechocystis iSyn731 model. The diurnal rhythm of Cyanothece 51142 metabolism is modeled by constructing separate (light/dark) biomass equations and introducing regulatory restrictions over light and dark phases. Specific metabolic pathway differences between the two cyanobacteria alluding to different bio-production potentials are reflected in both models.Cyanobacteria are an important group of photoautotrophic organisms that can synthesize valuable bio-products by harnessing solar energy. They are endowed with high photosynthetic efficiencies and diverse metabolic capabilities that confer the ability to convert solar energy into a variety of biofuels and their precursors. However, less well studied are the similarities and differences in metabolism of different species of cyanobacteria as they pertain to their suitability as microbial production chassis. Here we assemble, update and compare genome-scale models ( Cyanothece sp. ATCC 51142 (hereafter Cyanothece 51142) is one of the most potent diazotrophs characterized and the first to be completely sequenced Synechocystis sp. PCC 6803 (hereafter Synechocystis 6803), the first photosynthetic organism with a completely sequenced genome Cyanothece 51142 and shares many characteristics with all CyanotheceCyanothece 51142 is about 35% larger than that of Synechocystis 6803 mostly due to the presence of nitrogen fixation and temporal regulation related genes in Cyanothece 51142 Synechocystis 6803 has been the subject of many targeted genetic manipulations  as a photo-biological platform for the production of valuable chemicals such as poly-beta-hydroxybutyrate, isoprene, hydrogen and biofuels Cyanothece 51142 are still lacking thus hampering its wide use as a bio-production strain even though it has many attractive native pathways. For example, Cyanothece 51142 can produce  pentadecane and other hydrocarbons while containing a novel (though incomplete) non-fermentative pathway for producing butanol The genus 2 production Cyanothece 51142. Therefore, a promising path forward may be to use Synechocystis 6803 as a “proxy” (for which a comprehensive genetic toolkit is available) and subsequently transfer knowledge gained during experimentation with Synechocystis 6803 to Cyanothece 51142. This requires high quality metabolic models for both organisms. Comprehensive genome-wide metabolic reconstructions include the complete inventory of metabolic transformations of a given cyanobacterial system. Comparison of the metabolic capabilities of Cyanothece 51142 and Synechocystis 6803 derived from their corresponding genome-scale models will provide valuable insights into their niche biological functions and also open up new avenues for economical biofuel production.A breakthrough in solar biofuel production will require following one of two strategies: 1) obtaining photosynthetic strains that naturally have high-throughput pathways analogous to those in known biofuel producers, or 2) creating cellular environments conducive for heterologous enzyme function. Despite its attractive capabilities including nitrogen fixation and HCyanothece 51142 was recently published Synechocystis 6803 has been the target for metabolic model reconstruction for quite some time Synechocystis 6803 focused on only central metabolism et al.et al.Synechocystis 6803, analyzed growth under different conditions, identified gene knock-out candidates for enhanced succinate production and performed flux coupling analysis to detect potential bottlenecks in ethanol and hydrogen production. A more recent model describes in detail the photosynthetic apparatus, identifies alternate electron flow pathways and highlights the high photosynthetic robustness of Synechocystis 6803 during photoautotrophic metabolism Synechocystis 6803 and cyanobacterial systems in general.Genome-scale models (GSM) contain gene to protein to reaction associations (GPRs) along with a stoichiometric representation of all possible biotransformations known to occur in an organism combined with a set of appropriate regulatory constraints on each reaction flux Cyanothece 51142 iCyt773 and Synechocystis 6803 iSyn731  but also the allowable model metabolic phenotypes are contrasted against MFA flux data Cyanothece metabolism is modeled for the first time via developing separate (light/dark) biomass equations and regulating metabolic fluxes based on available protein expression data over light and dark phases This paper introduces high-quality genome-scale models for Synechocystis 6803 and Cyanothece 51142 were grown for several days from an initial OD730 of ∼0.05 to ∼0.4. Synechocystis 6803 was grown in BG-11 medium Cyanothece 51142 in ASP2 medium 2/sec provided from cool white fluorescent tubes. Synechocystis was maintained at 30°C and Cyanothece at 25°C. For Synechocystis, the illumination was constant and doubling time was ∼24 hours. Cyanothece alternated between 12 hours of light and 12 hours of darkness, with a doubling time of ∼48 hours.Wild-type Synechocystis 6803 and Cyanothece 51142 (from light and dark phases) was pelleted and extracted twice with 5 mL 80% aqueous acetone and the extracts pooled. Spectra of this extract and of a sample of whole cells were taken on a DW2000 spectrophotometer  against 80% acetone or BG-11 media as a reference. Chlorophyll a contents were calculated as reported 1 mL of cells of both Cyanothece 51142 and Synechocystis 6803 across a range of conditions http://genome.kazusa.or.jp/cyanobase) and thus the ‘proteome’ was taken to include all of the proteins whose peptides were observed in our data set, in proportion according to how often their peptides were observed. Amino acid frequencies were averaged across the proteome by a weighting factor of number of observations divided by the number of amino acids in the protein, similar to RPKM normalization for next-gen sequencing Total protein contents were measured using a Pierce BCA Assay kit. Amino acid proportions were determined according to published shotgun proteomics data for both Synechocystis 6803 and Cyanothece 51142 were estimated based on values in the literature. DNA and RNA contents for Synechocystis 6803 were reported by Shastri and Morgan Synechocystis 6803  were extracted from the measurements carried out by Nogales et al. Cyanothece 51142, biochemical compositions of macromolecules such as lipids, RNA, DNA and soluble pool were extracted from the measurements reported by Vu et al. The compositions of other cellular components of Cyanothece iCyt773 and Synechocystis iSyn731 models were evaluated in terms of biomass production under several scenarios: light and dark phases, heterotrophic and mixotrophic conditions. Flux distributions for each one of these states were inferred using FBA:Flux balance analysis (FBA) Maximize Subject toijS is the stoichiometric coefficient of metabolite i in reaction j and jv is the flux value of reaction j. Parameters j,minv and j,maxv denote the minimum and maximum allowable fluxes for reaction j, respectively. Light and dark phases in Cyanothece 51142 are represented via modifying the minimum or maximum allowable fluxes with the following constraints, respectively:Here, Biomassv is the flux of biomass reaction and Glytrv, Glyctrv and CO2trv are the fluxes of glycerol, glycogen and carbon dioxide transport reactions and lightv and cfv are the fluxes of light reactions and carbon fixation reactions. For light phase, constraint (3) was included in the linear model, whereas for dark phase constraint (4) was included.Here, Synechocystis iSyn731 model was validated, it was further tested for in silico gene essentiality. The following constraint(s) was included individually in the linear model to represent any mutant:Once the mutantv represents flux of reaction(s) associated with any genetic mutation.Here, 13C MFA measurements Flux variability analysis Maximize/Minimize Subject toSynechocystis iSyn731 model.Here, CPLEX solver  was used in the GAMS  environment for implementing GapFind and GapFill Synechocystis 6803 contain approximate biomass equations completely or partially adopted from other species without direct measurements. This can adversely affect the accuracy of maximum biomass yield calculations, gene essentiality predictions and knockouts for overproduction.The biomass equation approximates the dry biomass composition by draining all building blocks or precursor molecules in their physiologically relevant ratios. Most of the earlier genome-scale modeling efforts Synechocystis iSyn731 and Cyanothece iCyt773 models were generated by defining all essential cellular biomass content values by experimental measurement or collection from existing literature . Macromolecules present in both cyanobacteria such as protein, carbohydrates, lipids, DNA, RNA, pigments, soluble pool and inorganic ions were assigned to their corresponding metabolic precursors  (see File S3 for the complete list of biomass components). Based on the experimental measurements of precursor molecules needed to form a gram of the biomass, stoichiometric coefficients were assigned. For Synechocystis 6803 we measured compositions of proteins and pigments and extracted compositions of the remaining biomass macromolecules from the model by Nogales et al.Cyanothece 51142 between light and dark conditions and nitrogen supplementation. Since pigments such as chlorophyll, carotenoids and phycocyanobilin play important roles in photosynthetic processes their quantities are consequently higher under light conditions. In the presence of light Cyanothece 51142 uses photosynthesis to store solar energy in the form of carbohydrates , while in dark it expends that energy to fix nitrogen. Surprisingly, no significant change was measured in the carbohydrate pool between light and dark phases due to infinitesimal contribution of photosynthetically stored carbohydrates to total carbohydrate content in the biomass of Cyanothece 51142. Aggregate quantities of the remaining biomass macromolecules for Cyanothece 51142 such as lipids, RNA, DNA and soluble pool were extracted from the most recent Cyanothece 51142 model by Vu et al.Biomass composition for Cyanothece 51142 revealed that 113 proteins are expressed in higher abundance in the light phase while 137 are expressed in higher abundance in dark conditions Cyanothece 51142 (File S4). In contrast, diurnal differences observed in Synechocystis 6803 ‘unassigned’ functions). When compared to existing biomass equations of Synechocystis 6803 Synechocystis 6803 and 53% for Cyanothece 51142 vs. 84% Cyanothece 51142 An earlier characterization study for Synechocystis iSyn731 model, GapFind iJN678 model iSyn731, while metabolites such as ubiquinone, which was proposed as an alternate substrate for succinate dehydrogenase iSyn731.Upon ensuring biomass formation, GapFind Cyanothece iCyt773 model, 74 blocked metabolites were found after applying GapFind iCce806 iCce806 (during the draft model creation phase) were linked to five reactions with spurious gene associations and thus both metabolites and reactions were removed from iCyt773. GapFill iCyt773 as no information to corroborate the GapFill suggested changes was found in the published literature and databases. For example, biotin is produced in Cyanothece 51142; however, there is no literature evidence to support the presence of the initial step of the primary production pathway  and the intermediate step . This indicates that Cyanothece 51142 may utilize a currently unknown pathway for producing biotin. The six other blocked metabolites are involved in the nonfermentative alcohol production pathway (as explained in model comparison section) known to be incomplete in Cyanothece 51142. Synechocystis iSyn731 and Cyanothece iCyt773 models.For the Synechocystis 6803 do not provide in detail complex GPR associations, rather list only gene(s) and enzyme(s) involved in a specific reaction iCyt773 and iSyn731 models, we included comprehensive GPR associations (see GPR associations connect genotype to phenotype by linking gene(s) that code for the protein(s) that catalyze a particular reaction. They are important to trace correctly as they provide the means to target at the gene level any change in the network desired at the reaction level. This is critical because genes may catalyze multiple reactions in multiple pathways. Many earlier models for Cyanothece iCyt773 model produces 0.026 mole biomass/mole carbon fixed whereas Synechocystis iSyn731 yields 0.021 mole biomass/mole carbon fixed. These yields are almost identical to the ones calculated using the most recent models of Cyanothece 51142 Synechocystis 6803 Under high light intensity in photoautotrophic conditions, Synechocystis 6803 onto iSyn731 model to assess if the measurements are consistent with the model and whether the biomass maximization assumption correctly apportions fluxes to the metabolic network. For each reaction that was assigned a flux we calculated the flux-range under the maximum biomass assumption. 2 plus H2CO3 uptake We superimposed photoautotrophic flux measurements 2 fixing RuBisCO (RBC) reaction, which has a measured flux range of (123.00 to 132.00) vs. the model-calculated range of (102.49 to 106.33). In both cases the increased RBC flux (in comparison to the basis of 100 millimole of CO2 plus H2CO3 uptake) is needed to counteract the carbon loss due to the CO2 releasing reactions such as isocitrate dehydrogenase (ICD) and pyruvate dehydrogenase (PDH). We find that flux ranges, under the maximum biomass production assumption, of reactions such as glucose 6-phosphate dehydrogenase (G6PD), 6-phosphogluconolactonase (6PGL) and phosphogluconate dehydrogenase (6PGD) in oxidative pentose phosphate (OPP) pathway are negligible (0.00 to 0.03). In contrast, the experimentally derived range for OPP is (12 to 21). This is approximately equal to the difference between the model-predicted vs. experimentally deduced RBC reaction range implying the persistence of OPP flux even under the photoautotrophic condition Perhaps the most informative discrepancy is for the COiSyn731 model. Furthermore, in contrast with experimental observations, under the maximum biomass assumption, the model predicts no flux through the malic enzyme (ME) reactions presumably because it is a less energy-efficient route  for pyruvate generation than the pyruvate kinase (PYK) reaction Model predicted lower flux ranges for RBC are propagated to seven other reactions in the Calvin cycle , glyceraldehyde-3-phosphate dehydrogenase (13PDG), triose-phosphate isomerase (TPI), transketolase (TKT1), ribose-5-phosphate isomerase (RPI), ribulose 5-phosphate 3-epimerase (RPE) and phosphoribulokinase (PRK). The remaining six reaction fluxes with lower model predicted fluxes compared to measurements in silico non-essential. As depicted in in vivo. Note that the presence of experimentally measured fluxes is important to test the model and the adopted maximization principle. We were fortunate in this case to have access to such data as for most organisms they are absent.There are nine reactions with experimentally derived ranges completely subsumed within the ones derived under the maximum biomass assumption. Five of them are in the Calvin cycle , fructose-bisphosphatase (FBP), Sedoheptulose 1,7-bisphosphate D-glyceraldehyde-3-phosphate-lyase (SBGPL), sedoheptulose-bisphosphatase (SBP) and bidirectional transaldolase (TAL)). The first four reactions are essential with experimentally deduced flux ranges of (53.00 to 66.00) for FBA and FBP and (29.00 to 43.00) for SBGPL and SBP. In contrast, the calculated flux ranges (−0.08 to 73.17) for FBA and SBGPL and (0.00 to 73.17) for FBP and SBP imply that they are iSyn731 model possesses by having alternate enzymes with different cofactor specificities. The max biomass flux range of fumarase (FUM) is found to be (−7.26 to 1.49), compared to the experimentally measured (1.70 to 2.00). Therefore, it appears that under the photoautotrophic condition, the forward direction is kinetically favorable. By restricting the reaction to be irreversible the model predicted a FUM flux range of (0.00 to 1.49) which is close to the experimentally derived one (see in silico biomass production.Phosphoglycerate mutase (PGM) and enolase (ENO) reactions have very similar model derived and experimentally obtained flux ranges. Model-predicted flux values of the remaining two reactions, pyruvate kinase (PYK) and pyruvate dehydrogenase (PDH), could reach as low as zero due to the metabolic flexibility that the  one see . HoweveriSyn731 for Synechocystis 6803 was tested using experimental data on the viability (or lack thereof) of single gene knockouts. We used the CyanoMutants database in vivo gene essentiality data for 119 genes  with metabolic functions in iSyn731 model. Cases that were flagged with incomplete segregation in the database were omitted in iSyn731 model comparisons. We examined the feasibility of biomass production for the model iSyn731 by comparing the maximum biomass formation upon imposing the gene knockout with the maximum theoretical yield of the wild-type organism. A threshold of 10% of the maximum theoretical yield was used as a cutoff in vivo and in silico results led to four possible outcomes, as previously delineated by Kumar et al., GG, GNG, NGG and NGNG The quality of model IchlA exhibiting no growth under aerobic conditions I system is a Mg-protoporphyrin IX monomethylester (MPE) cyclase system that is responsible for forming the isocyclic ring (E-ring) in chlorophylls under aerobic conditions II systems  to complement for the loss of the ChlAI system leading to an in silico viable mutant. However, the same literature source II systems are unlikely to be active under aerobic conditions and thus rescue mutant ΔIchlA. This prompted the introduction of a regulatory restriction in iSyn731 model where only ChlAI reactions were active under aerobic conditions as MPE while ChlAII and BchE system reactions were deactivated. Using these regulatory restrictions resolves the single GNG inconsistency.The single GNG case corresponds to mutant Δin silico and in vivo growth. Genes deemed homologous  were linked with “OR” GPR relations implying that the loss of one gene can be complemented by the other. Seven out of twenty NGG cases  were resolved by modifying the corresponding GPR using an OR relation psaE, psbD2, psbO, psbU, psbV, psb28, psbX, psb27, petE, cpcA, cpcB, apcE, apcF) unknown gene’, similar to what was previously performed in the refinement of the iMM904 model Twenty (out of 27) NGG cases were associated with Photosystem I (PSI), Photosystem II (PSII) and other photosynthesis reactions. While reconstructing the model, we assumed that all genes involved in photosynthetic reaction system were essential to the functioning of the overall system. Published literature modBC mutant corresponding to the sole ABC molybdate transporter in the model. Literature evidence Anabaena variabilis ATCC 29413, could continue to grow despite the loss of its molybdate ABC transporter due to the presence of another low affinity molybdate transporter or an inducible sulfate transport system that can serve as a low affinity molybdate transporter when required. We found the same gene coding for the sulfate transporter in A. variabilis (cysA) in the iSyn731 model allowing the resolution of the discrepancy by adding a cysA-linked alternate molybdate transporter. Another NGG case is mutant ΔcrtO that cannot produce echinenone (a biomass component) in iSyn731 with no effect on observed growth. Therefore, it appears that iSyn731 cannot capture the flexibility of Synechocystis 6803 metabolism ctaA mutant eliminates the copper ABC transporter without affecting growth, which alludes to the existence of another unknown mode of copper uptake not present in iSyn731 menG mutant eliminates a reaction for the production of phylloquinone while mutant Δppd affects the production of homogentisate, a precursor for both tocopherols and plastoquinone. Finally, the Δvte3 mutant affects the production of both plastoquinone and α-tocopherol ccmA mutant restricts the production of chorismate (a precursor to aromatic amino acids) and also restricts carboxysome formation modBC mutant, we have found no plausible mechanism for the six remaining mutants.The remaining seven NGG cases are associated with a variety of metabolic functions. One such case is the ΔiSyn731 correctly predicted all 19 essential genes  and 94 (out of 100) non-essential genes . Synechocystis 6803 models by Knoop et al. et al. iJN678 model by Nogales et al. iJN678 model correctly predicted 18 essential genes  and 69 non-essential genes . The model by Knoop et al.et al.’s After resolving the discrepancies, as described above, iJN678 were also present in the iSyn731 model. 26 NGG and one GNG cases present in iJN678 model correspond to NGG and GNG cases that were either fixed or still present in iSyn731 as discussed before. Lethal mutant Δppa is correctly predicted as NGNG in iSyn731 but deemed GNG in Knoop et al.ppa in iSyn731 codes for the degradation of both triphosphate into diphosphate and diphosphate to phosphate. Only the latter activity is linked to ppa in the Knoop et al’s model. Out of 4 NGG cases in cmpA and ΔcmpB mutants. Both these genes are involved in the ABC transporter system for bicarbonate from periplasm to cytosol. iSyn731 avoids this inconsistency as it contains an alternate sodium and bicarbonate co-transport system.All 114 genes tested for iSyn731 model integrates the description in the photosystems of the model presented by Nogales et al. iSyn731 uses a separate photon for each reaction center  as they are optimized for different ranges of wavelength iJN678 iSyn731 distributed across many pathways. Most of the additions are in the lipid and fatty acid metabolism to support the synthesis of measured fatty acids and lipids present in the biomass equation. This list includes myristic acid (14-carbon saturated fatty acid) and lauric acid (12-carbon saturated fatty acid). iJN678 iSyn731 by eliminating redundant functions. In addition, the glycine cleavage system was recast in detail by abstracting the separate action of the four enzymes  that ultimately catalyze the demethylamination of glycine.The iSyn731 improves upon iJN678 iSyn731 and iJN678 2 fixation  enzyme). Literature iSyn731 adds carboxysome as a cellular compartment and also all necessary transport reactions iSyn731 allows for a complete TCA cycle using the bypassing step. In addition, iSyn731 contains an intact heptadecane biosynthesis pathway as recently described Synechocystis 6803 models iSyn731).iCyt773 model for Cyanothece 51142 improves upon the iCce806 model iCyt773 segregates reactions into the periplasm, thylakoid lumen, carboxysome, and cytoplasm compartments thus introducing an additional 60 transport reactions compared to iCce806 iCce806 iCyt773 does not track macromolecule synthesis for DNA, RNA, and proteins to maintain consistency with the Synechocystis 6803 model. This difference accounts for 69 genes present in iCce806 iCyt773. iCce806 The iCyt773 contains 43 unique genes and 266 unique reactions  as shown in iCyt773 model captures in detail the lipid biosynthesis pathway composed of 73 reactions and links as many as 28 biomass precursor lipids  directly to the biomass equation. The porphyrin and chlorophyll metabolism and carotenoid biosynthesis pathways were updated to include 24 reactions for the production of accessory pigments such as echinenone, an accessory pigment, and (3Z)-phycocyanobilin, a phycobilin. Accessory pigments donate electrons to chlorophyll rather than directly to photosynthesis. Phycobilins are adapted for many wavelengths not absorbed by chlorophyll thus broadening the spectrum useful for photosynthesis. The variety of pigments in cyanobacteria is well documented iCyt773 is L-Aspartate Oxidase. L-Aspartate Oxidase allows the deamination of aspartate, forming oxaloacetate a key TCA-cycle metabolite and ammonia. The impact of this addition to iCyt773 is not evident under the photoautotrophic condition but becomes relevant for growth in a medium containing aspartate. iCyt773 also uniquely supports the synthesis of pentadecane as documented by Schirmer et al. iCce806 iCyt773 were corroborated using genome annotations A number of lumped reactions in iCyt773. In addition, we used data from Stockel et al. Cyanothece 51142 to identify regulatory reaction shutdowns in our metabolic model. File S4 lists the reactions that were inactivated under light and dark conditions, respectively. As expected, the nitrogenase genes cce_0559 and cce_0560, known to be active in the absence of light, exhibited low spectral counts under light conditions. In contrast, photosystem II gene cce_1526, showed no spectral count under dark conditions. Unexpectedly, the data suggested that the Mehler reactions associated gene (cce_2580), known to be active in Synechocystis 6803 Cyanothece 51142, exhibited lower expression in light than in dark conditions.A shift in biomass composition was observed under light, dark, and nitrate supplemented (light and dark) conditions. These differences were captured in four separate biomass descriptions present in iSyn731 and iCyt773 models. The Cyanothece 51142 genome Synechocystis 6803 iCyt773 is smaller than iSyn731 due to differences in the level of detail of annotation and biochemical characterization. As many as 670 reactions and 596 metabolites are shared by both models corresponding to 47% and 63% of the total reactome and metabolome, respectively . The much larger set of unique iSyn731-specific reactions compared to iCyt773 reflect more complete genome annotation and biochemical characterization rather than augmented metabolic versatility.vely see . The higiCyt773 does not have the enzyme threonine ammonia-lyase, which catalyzes the conversion of threonine to 2-ketobutyrate and as a consequence lacks the traditional route for isoleucine synthesis. Instead it employs part of the alternative citramalate pathway for isoleucine synthesis with pyruvate and acetyl-CoA as precursors. Follow up literature queries revealed the existence of this alternative pathway in Cyanothece 51142 iCyt773 model, we determined that only 2-ketoacid decarboxylase is missing from these three-step processes. In contrast, iSyn731 was found to have only the traditional route for isoleucine production with the citramalate pathway completely absent   see .iCyt773, in contrast to iSyn731, does not have a complete urea cycle as it lacks the enzyme L-arginine aminohydrolase catalyzing the production of urea from L-arginine. Literature sources Cyanothece 51142 Cyanothece 51142 can fix nitrogen directly from the atmosphere and produce ammonium via the enzyme nitrogenase, genes corresponding to the activity of L-arginine aminohydrolase and urease (for breaking down urea) become redundant, explaining why they are not present in its genome iCyt773 and iSyn731 models reveal marked differences in anaerobic metabolic capabilities. Unlike iSyn731, iCyt773 includes a L-lactate dehydrogenase activity that enables the complete fermentative lactate production pathway. On the other hand, iSyn731 contains the anaerobic chlorophyll biosynthetic pathway using enzyme protoporphyrin IX cyclase (BchE) that is absent in iCyt773. Other differences in metabolism include lipid and fatty acid synthesis, fructose-6-phosphate shunt and nitrogen fixation. Model iSyn731 traces the location of the double bond for unsaturated fatty acid synthesis pathways, as two separate isomers of unsaturated C18 fatty acids are part of the biomass description. iCyt773 allows for the shunting of fructose-6-phosphate into erythrose-4-phosphate along with acetate and ATP using the fructose-6-phosphate phosphoketolase activity. Finally, both iSyn731 and iCyt773 contain multiple hydrogenases allowing both to produce hydrogen. However, only the latter has a nitrogenase activity that can fix nitrogen while simultaneously producing hydrogen.Model iSyn731 and iCyt773 by comparing the predicted maximum theoretical product yields with experimentally measured values for two very different metabolic products: isoprene and hydrogen. Isoprene, a volatile hydrocarbon and potential feedstock for biofuel, is mostly produced in plants under heat stress Synechocystis 6803 has all but one gene (encoding isoprene synthase) in the methyl-erythritol-4-phosphate (MEP) pathway for isoprene synthesis from dimethylallyl phosphate (DMAPP). Upon cloning the isoprene synthase from kudzu vine (Pueraria montana) into Synechocystis 6803 isoprene production was demonstrated using sunlight and atmospheric CO2 of 4.3×10−4 mole isoprene/mole carbon fixed iSyn731 to be 3.63×10−5 mole isoprene/mole carbon fixed upon adding the isoprene synthase activity to the model and simulating the conditions described in iJN678 Synechocystis 6803 We tested the recently developed models Cyanothece 51142 and Synechocystis 6803 produce hydrogen by utilizing nitrogenase and hydrogenase activities, respectively Cyanothece 51142 was measured at 49.67 mole/mole glycogen consumed. Simulating the same conditions in iCyt773 and iCce806 iCyt773 is due to the nitrogenase activity. In contrast, the predicted doubling of the maximum hydrogen yield in iCce806 is due to the utilization of the reverse direction of two hydrogen dehydrogenase reactions without any nitrogenase activity. Utilization of the nitrogenase reaction requires the use and recycling of more ATP than simply running the dehydrogenase reactions in reverse. However, it has been reported that hydrogen production in Cyanothece 51142 is primarily mediated by the nitrogenase enzyme iCyt773 model. Experimental results for Synechocystis 6803 support up to 4.24 mole/mole glycogen consumed iSyn731 predicts a maximum hydrogen theoretical yield of 2.28 mole/mole glycogen consumed while iJN678 iJN678 iSyn731 describes this biotransformation in two steps  Both Synechocystis iSyn731 and Cyanothece iCyt773) for cyanobacterial metabolism by integrating all available knowledge available from public databases and published literature. All metabolite and reaction naming conventions are consistent between the two models allowing for direct comparisons. Systematic gap filling analyses led to the bridging of a number of network gaps in the two models and the elimination of orphan metabolites. Two separate biomass equations as well as two different versions of Cyanothece iCyt773 models were developed for light and dark phases to represent diurnal regulation. The development of two separate models for Cyanothece 51142  provides the two “end-points” for the future development of dynamic metabolic models capturing the temporal evolution 13C MFA measurements for Synechocystis 6803 iSyn731 model upon biomass maximization yields flux ranges that are generally consistent with experimental data. Discrepancies between the two identify metabolic nodes where regulatory constraints are needed in addition to biomass maximization to recapitulate physiological behavior. The ability of iSyn731 to predict the fate of single gene knock-outs was further improved (specificity of 0.94 and sensitivity of 1.00) by reconciling in silico growth predictions with in vivo gene essentiality data Cyanothece iCyt773 model once such flux measurements and in vivo gene essentiality data become available.In this paper, we expanded upon existing models to develop two genome-scale metabolic models (Cyanothece iCyt773 model can be used to explore in silico the effect of genetic modifications aimed at increased production of useful biofuel molecules. By taking full inventory of Cyanothece 51142 metabolism (as abstracted in iCyt773), and applying available strain optimization techniques Cyanothece 51142 at certain times during the diurnal cycle can be exploited for the expression of novel pathways that are not usually found in oxygenic cyanobacterial strains that largely maintain an aerobic environment. However, the use of Cyanothece 51142 as a bio-production platform is currently hampered by the inability to efficiently carry out genetic modifications.It is becoming widely accepted that focusing on a single pathway at a time without quantitatively assessing the system-wide implications of genetic manipulations may be responsible for suboptimal production levels. By accounting for both primary and some secondary metabolism pathways, the iSyn731 and iCyt773, successful genetic interventions assessed experimentally for Synechocystis 6803 can be “translated” to Cyanothece 51142. For example, it has been reported Synechocystis 6803 upon cloning a fatty acyl-CoA reductase (far) from Jojoba (Simmondsia chinensis) and the over-expression of gene slr1609 coding for an acyl-ACP synthetase. By using models iSyn731 and iCyt773 we can infer that in addition to cloning far from Jojoba, over-expression of gene cce_1133 coding for a native acyl-ACP synthetase would be needed to bring about the same overproduction in Cyanothece 51142.By systematically cataloguing the shared (and unique) metabolic content in File S1Synechocystis iSyn731 model along with established GPR, metabolite, gene and protein information.(XLSX)Click here for additional data file.File S2Cyanothece iCyt773 model along with established GPR, metabolite, gene and protein information.(XLSX)Click here for additional data file.File S3Biomass component measurements and stoichiometry of biomass equation.(XLSX)Click here for additional data file.File S4Reactions with diurnal activation/inactivation.(XLSX)Click here for additional data file.File S5in silico vs. in vivo gene essentiality results for iSyn731 and modifications made in GPR associations.Comparison of (XLSX)Click here for additional data file.File S6Synechocystis iSyn731 and iCyt773 models in terms of genes, proteins, reactions and metabolites.Comparison between (XLSX)Click here for additional data file.File S7Synechocystis iSyn731 model.SBML file of (XML)Click here for additional data file.File S8Cyanothece 51142 iCyt773 model.SBML file of (XML)Click here for additional data file.


E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT) method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of  In the 21st century, metabolic engineering has been pinned outstanding hopes on many aspects, such as energy, environment pollution, climate improvement, food sources and human health There were a series of published algorithms to predict the target reactions for deletion to improve the productivity of chemicals. OptKnock Text S1 to point out the problem.The motivation of this study is: Although it is a good idea to use bi-level optimization strategy to coordinate cellular growth and bioengineering objective, where the MIBLP contains integer control variables of the upper problem appearing in the inner problem, the solution of large scale MIBLP is intractable and there are only a few methods The solving approaches for the MIBLP problems depend only on the structure of inner problem y vector was used to indicate some reactions being deleted or not. The mathematical expression of ReacKnock was:v is the distribution of flux; The mathematical model of ReacKnock was similar to OptKnock and it was also a bi-level optimization structure. The first level (the upper problem) was to maximize bioengineering objective , KKT method can be used to transform bi-level problem to a single level problem The Lagrangian for the inner is So the above MIBLP (II) can be reformulated to a single level one, a MILP (IV) through (III).The above MILP (IV) may probably have multiple integer solutions, i.e. for different deletion strategies but the industrial objective was the same. As we knew, up to date, there was no optimization tool which can directly provide multi integer solutions for a MILP. Here we utilized an approach named Combinatorial Bender's cut When we obtain a deletion strategy from a prediction algorithm, we get the values of cell growth and industrial objective at the same time. It is best to substitute the deletion strategy to the metabolic network model, delete those target reactions (or enzymes) predicted, do the FBA, and see whether cell growth and industrial objective are the same with the values we predict by our algorithm. But for the reason that FBA usually has multi solutions, so when we do FBA testification and if a strategy does not get to the predicted value of industrial objective, we can't decide the strategy is valid or not. But FBA can be used to testify growth.To multiple solutions of FBA, Flux Variability Analysis (FVA) can provide an estimation of the flux scope of every reaction in the FBA model. We think it is a good way to testify deletion strategies predicted. After substituting the deletion strategy to the metabolic network model, delete those target reactions (or enzymes) predicted, we do the FVA now and compare the maximum value of industrial objective with what we predict.E. coli metabolism, named iAF1260 E. coli. We can't obtain the original algorithm program of OptKnock and OptKnock algorithm in this study was from the corresponding function of COBRAToolbox E. coli may be produced probably both under anaerobic condition (such as Ethanol) and aerobic condition (such as Threonine). Here, it was for the reason of computational aspect that we used unified aerobic condition. Of course, it is easy to get the results under anaerobic condition. In order to show the change of flux distribution after removing those target reaction, intracellular flux distributions for each chemical production using ReacKnock and OptKnock were respectively provided in S2, and flux distribution of wild strain was also provided in the tables for comparison. At the same time, detailed maps of metabolic flux distributions were respectively provided in Figure S1 where those reactions with relatively large flux were shown. As for the whole names of Knockout enzymes (reactions), please refer to the supplementary materials of Ref To evaluate the performance of ReacKnock in comparison with previous algorithms (OptKnock), we applied ReacKnock on a genome-scale metabolic network model of E. coli_iAF1260. All these ten solutions were consistent with the results of FVA and FBA. Intracellular flux distributions for each solution were provided in Table S3. 2) In most cases, the objective value for a given chemical predicted by ReacKnock is higher than the value predicted by OptKnock and is much more near the theoretical conversion ratio (Max_yield). As we demonstrated in Appendix, the solving method of OptKnock for MIBLP was not precise in mathematics, and we think this may be the first reason why OptKnock is unable to find the optimal solution. When running OptKnock for these chemical targets, we have set the maximum computation time to be 3600 s. The second reason may lie in that it will take a very long time to get the optimal solution while the permitted time is not enough. We have also tried not to set the maximum computation time  and the yields of the targeted chemical products seemed not improved obviously. We provided new solutions and computation time of OptKnock in Table S4. Although there was a distance between the maximum production predicted by ReacKnock and the theoretical conversion ratio, this is due to the constraint of deletion number. As an example, we computed 15-reaction deletion for succinate production which could get the maximum production to 13.97. The 15 reactions were “3OAS120, AKGt2rpp, CO2tpp, FUMtex, GLCNt2rpp, GLUDy, GLYCL, GND, PDH, PFL, PGI, PPKr, PSP_L, PYRt2rpp, TDECOAI”. 3) ReacKnock is stable and all the rates of chemical objective predicted by ReacKnock were consistent with the results of FVA and FBA. OptKnock is instable in the cases of Hydrogen production, and it can't provide effective deletion strategies for the chemical productions. 4) Our algorithm does not confine to a given number of gene deletions and it searches in a given scope defined by K1 and K2, such as between 5 to 20 genes. 5) Our algorithm will need shorter time than previous approaches. Our computation environment is a server, sugon A840-G with 4 AMD Opterons and 48 cores, and CPU speed is 2.2 GHz. When using OptKnock to do 5 gene deletion study, it will need tens of minutes in a run, and of course, will need much more in actual gene deletion calculations (such as >10 genes). In general, our algorithm will need no more than several minutes in a run and especially do not restrict to the scale of gene deletion. All the computational results of ReacKnock were obtained with setting computation time as 10 minutes. Actually, OptKnock will need more time for we have set the terminal time to be 3600 s when running it, and for comparison and in Table S4, we also provided the result of OptKnock with setting computation time as 10 minutes.There are several merits of our algorithm over previous methods. 1) First and especially, ReacKnock will return all the alternative deletion strategies in the same search scope with the near industrial objective. This will be very useful in strain design and can provide alternative gene operation strategies. All the previous algorithms just give only one deletion strategy for a given deletion number. in silicon what the response of cell to the gene deletion operation is. Therefore, bi-level optimization is a pertinent strategy to consider cell growth and industrial objective together. But when we use integer control variables to indicate the decision of deletion or not for some reactions, this type of bi-level optimization, known as MIBLP, become very difficult to solve. In fact, the inner problem of MIBLP on which we focus here is continuous and the solving approaches for MIBLP depend only on the structure of inner problem. In this article, we utilize KKT method to solve the MIBLP model which is the core of previous predictive algorithms of gene deletion study.For the scarcity of dynamic data, genome scale metabolic network models based on constraint-based modeling (CBM) provide a possible way to describe the metabolism of cells. FBA has been used successfully to simulate the phenotype of cells. But in metabolic engineering, we hope to know There are several merits of ReacKnock: 1) Our algorithm can provide all the alternative deletion strategies in given deletion number. 2) ReacKnock may give better predictions than previous methods in term of production rate or conversion ratio. 3) ReacKnock is more stable and reliable than previous tools. ReacKnock may obey the original FBA model, i.e. applying FVA to predict the possible production rate under knockout strategies of ReacKnock reveals possible rates that are very near to the results of ReacKnock. 4) The computation time of ReacKnock is greatly shorter than that of previous algorithms. 5) ReacKnock does not confine to single, double or triple knockouts, and it searches a scope which we can define at first, such as 5–20 reactions (enzymes). ReacKnock will return the best set which should be deleted.It should be noticed that, sometimes, the optimal knockout strategy found by ReacKnock and OptKnock is not SUFFICIENT condition for optimal production. Due to the inherency of multiple solutions of FBA approach, ReacKnock and similar can only ensure that there is at least a distribution of metabolic flux leading to maximal production yield. This means with the given set of gene deletions, there are multiple flux distribution modes which all have the same maximal growth rate but different production rate. Only a few modes lead to the maximal production rate. Therefore, the experimental practice according to the prediction of ReacKnock or OptKnock may show less optimal or even bad production yield. It is of strong need to develop new algorithms searching for sufficient solutions with which the cell is obligated to produce the target product at the maximum yield.Alternative deletion strategies are actually interrogation of multiple solutions of the MILP (mixed integer linear programming) transformed from MIBLP. COBRA toolbox provides a method named random sampling Figure S1Maps to show the intracellular flux distributions for each chemical production after deleting those target reactions predicted by ReacKnock and OptKnock. The intracellular flux distribution of wild strain was also provided for comparison.(RAR)Click here for additional data file.Text S1Appendix to point out where the problem of OptKnock is and to give the derivation of our algorithm.(DOCX)Click here for additional data file.Table S1Intracellular flux distributions for each chemical production after deleting those target reactions predicted by ReacKnock.(XLSX)Click here for additional data file.Table S2Intracellular flux distributions for each chemical production after deleting those target reactions predicted by OptKnock.(XLSX)Click here for additional data file.Table S3Intracellular flux distributions of multiple strategies for succinate production after deleting those target reactions by using ReacKnock.(XLSX)Click here for additional data file.Table S4Comparison between new predictions of OptKnock with default setting of max computation time in COBRA toolbox and old predictions (first time computation) of OptKnock with setting computation time to be 3600 s.(DOCX)Click here for additional data file.


Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions.We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner.https://sourceforge.net/projects/f2c2/files/.We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at  In thflux cone:S is the m×nstoichiometric matrix of the network, with m internal metabolites (rows) and n reactions (columns), and the vector flux distribution. Furthermore, Irr⊆{1,…,n} denotes the set of irreversible reactions in the network, and Rev={1,…,n}∖Irrdenotes the set of reversible reactions.where The flux cone contains the full range of achievable behaviors of the metabolic network at steady state. Various approaches have been proposed either to search for single optimal behaviors using optimization-based methods -16 or toFlux coupling analysis (FCA) is concerned with describing dependencies between reactions . The stoAfter introducing the main existing algorithms for flux coupling analysis, we propose in this paper a new algorithm which significantly speeds up the calculation of flux coupling. Our algorithm is based on two main principles. First, we reduce the stoichiometric model as much as possible when parsing the stoichiometric matrix. Second, we use inference rules to minimize the number of linear programming problems that have to be solved. We prove the efficiency of our algorithm by successfully competing with the most recent approach . We showSeveral algorithms were developed to calculate flux coupling between reactions. For a comparison among the existing approaches, the reader may refer to ,32. In tWe give a short overview of the important concepts we will use throughout this paper. First, we formally define blocked reactions in a metabolic network.C, let i∈{1,…,n}be a reaction. If vi=0, for all v∈C, reaction i is called blocked, otherwise i is unblocked.Given the steady-state flux cone In the following, we assume that the flux cone is not trivial, i.e., not all reactions are blocked. Next, we define the (un)coupling relationships between reactions.i,j be two unblocked reactions. The (un)coupling relationships Let v∈C, vi=0implies vj=0.•v∈C, vi=0is equivalent to vj=0.•i∽jif there exists λ≠0such that for all v∈C,vj=λvi.•i  ↦  jif there exists v∈Csuch that vi=0and vj≠0.•i and j are fully  coupled if the relation i∽j algorithm [Rev|(resp. 2|Rev|). Since the FCF algorithm solves four LP problems to identify the maximum and minimum flux ratios for every pair of reactions, the total number of LP problems that have to be solved is very large. Furthermore, the FCF algorithm does not compute directly coupling relationships between reactions. A post-processing step is needed to deduce couplings between reactions  from those between reaction directions (in the reconfigured network). More importantly, the FCF algorithm explores exhaustively all possible reaction pairs. This leads to a very big number of LP problems that have to be solved. This strategy may not scale well for genome-scale models of complex microorganisms which involve a large number of reactions.The lgorithm  determini and j, we have exactly three cases where a flux coupling is possible between i and j:To cope with the drawbacks of the FCF algorithm, we used the reversibility type of reactions to develop an improved version (WRP-FCF) of the original FCF method ,34. Wheni,j∈Prev or i,j∈Frev, i∽jare equivalent. More importantly, only the stoichiometric constraints determine whether If Assume that blocked reactions have been identified beforehand and their respective columns in the stoichiometric matrix have been removed. Consider the following LP problemv∗ be an optimal solution. According to Proposition 6.13 in [and let  6.13 in , i→=0j ii∈Irev and j∈Prev, we only have to check whether v1resp. v2 be optimal solutions of the two LP problemsIf Then i,j∈ Irev, in analogy with the FCF algorithm, we determine upper and lower bounds Uij and Lij such that 0≤Lijvj≤vi≤Uijvj for all v∈C by solving the LP problemsIf Lijand Uijallows us to determine whether reactions i and j are coupled using the following rules:Comparison of Lij≠0(resp. Uij≠ + ∞),j∽iif and only if Lij≠0, Uij≠0and Lij=Uij.The improved version WRP-FCF does not make an exhaustive computation for all pairs of reactions and does not require a reconfiguration of the metabolic network. Accordingly, not only is the number of LP problems used by WRP-FCF smaller than the number solved by FCF, but also the LP problems used by WRP-FCF are simpler than those employed by FCF. For mathematical proofs, the reader may refer to ,34.Linear programming can be solved by enumerating a set of adjacent feasible solutions such that at each solution the objective function improves or is unchanged. Accordingly, searching for a feasible solution can be faster than computing an optimal solution . Based oBefore using linear programming to calculate flux coupling between reactions, we preprocess the metabolic network in order to i) reduce the number of variables and constraints of the subsequent LP problems and ii) classify reactions according to their reversibility type. The network reduction is mainly based on the removal of trivially blocked reactions and the merging of the stoichiometric columns corresponding to trivially coupled reactions -39. For dead-end metabolites[H is produced by reaction 13 without being consumed by any of the remaining reactions.Certain metabolites, called tabolites, are proAs stated below, reactions which are consuming or producing dead-end metabolites are blocked.k∈{1,…,m}be a metabolite. Then, the following hold:Let i such that Ski≠0and Skj=0for all j≠i, then reaction i is blocked.If there exists a reaction I⊆Irrsuch that Ski>0(resp. Ski<0) for all i∈Iand Skj=0for all j∉I, then all reactions i∈Iare blocked.If there exists a set of reactions k is called a dead-end metabolite.In each of these cases, E is produced/consumed only by reactions 8 and 9. The following observation states that the fluxes through reactions involving such metabolites are proportional to each other.Certain metabolites are involved in exactly two reactions. For instance, in the network MetNet depicted in Figure i and j be two reactions such that, for some metabolite k∈{1,…,m}, Ski≠0, Skj≠0and Skl=0for all l≠i,j. Then, reactions i and j are either blocked or fully coupled.Let The identification of dead-end metabolites and their corresponding blocked reactions allows us to reduce the number of metabolites and reactions that matter for identifying coupled reactions. As stated in the following observation, the removal of the rows (resp. columns) in the stoichiometric matrix corresponding to dead-end metabolites (resp. blocked reactions) does not influence the flux coupling between reactions.D be a set of dead-end metabolites and let B be a set of blocked reactions. For convenience, suppose B={s + 1,…,n}. Let S′be the submatrix of S formed by the rows Sk∗with k∉Dand the columns Sl∗with l∉B. Let Irr′=Irr∖B. Then, for all pairs of reactions i∉Band j∉B,Let S′v′=0and •i∽jif and only if there exists λ′≠0such that S′v′=0and •The next observation shows that two fully coupled reactions can be represented by only one column in the stoichiometric matrix, without altering the flux coupling between reactions.k,lbe two reactions such that for all v∈C,vl=λvkfor some λ≠0. For convenience, suppose l=nand λ>0. Let S′be the m×(n−1)matrix defined by p≠k,land Irr′=(Irr∪{k})∖{l}if l∈Irr, and Irr′=Irrotherwise. Then, for all pairs of reactions i≠land j≠l,Let S′v′=0and i∽jif and only if there exists λ′≠0such that S′v′=0and H and reaction 13 are removed, the pairs of reactions  and  are merged and metabolites A and E are removed. In the second iteration, the equivalent reactions  are merged and metabolite G is removed. The preprocessed network depicted in Figure Note that when applying the reduction rules of Observations 3 and 4, further metabolites and reactions may fulfill the conditions of Observations 1 and 2. Accordingly, we apply these reduction rules in an iterative way. As an illustration, the reduction of the network MetNet depicted in Figure Certain fully coupled reactions could not be identified using Observation 2. The following lemma proves that all fully coupled reaction pairs can be deduced from the kernel S,Irr)be a metabolic network with n unblocked reactions. For a pair of reactions the following are equivalent:Let .there exists ⇐" Immediate.i∽j, there exists λ≠0such that vi=λvjfor all v∈C. Assume by contradiction that there is v∈kern(S)such that vi≠λvjand let L={l∈Irr∣vl<0}. Since every reaction is unblocked, for every l∈Lthere exists gl)(∈Cwith w∈kern(S)and wl>0for all l∈Irr, thus w∈C. However, wi≠λwj, contradicting i∽j. The required statement follows. □"⇒" Since In analogy with the WRP-FCF and FFCA approaches, we identify the reversibility type of reactions in order to apply linear programming only in those cases where coupling relationships can occur . Here, wBased on the results above, we propose to apply the preprocessing procedure given in Table C into D in the same way. This holds also for reaction 7 and the merged equivalent reactions  in Figure In certain metabolic networks, the conversion of a set of substrates into a set of products can be made by different reactions having the same stoichiometry. A simple example of such reactions are isoenzymes which make the same conversion of substrates into products. This concept is illustrated in Figure i,j∈Irevand k,l∈Prev∪Frevbe four reactions. Then, the following holds:Let Si∗=αSj∗for some α>0, then i↦pand j↦pfor all p∉Blk.• If Si∗=αSj∗for some α<0, then p↦i(resp. p↦j) for all p∉Blk∪{j}(resp. p∉Blk∪{i}).• If Si∗=αSk∗for some α≠0, then i↦pand p↦ifor all p∉Blkand q↦kfor all q∉Blk∪{i}.• If Sk∗=αSl∗for some α≠0, then k↦pand p↦kfor all p∉Blk∪{l}and l↦qand q↦lfor all q∉Blk∪{k}.• If Si∗=αSj∗for some α>0and let us prove i↦p. Let p∉Blk. There exists v∈Csuch that vp≠0. Let wi=0,wj=αvi + vjand wq=vqfor all q≠i,j. We have w∈C,wi=0,wp≠0and so the claim follows. □The proofs of the four statements are similar. We only consider the first one. Suppose The next observation states that metabolites which are involved only in irreversible reactions and consumed or produced by exactly one reaction define trivial directional couplings between these reactions.k be some metabolite such that Skl=0for all l∈Frev∪Prev. Let P={i∣Ski>0}and N={j∣Skj<0}. If card(P)=1(resp. card(N)=1), then i,j)∈P×N.Let Since directional flux coupling is a transitive relation, the flux (un)coupling between many reactions can simply be deduced from dependencies between reactions whose flux (un)coupling has been determined beforehand. This allows us to significantly reduce the total number of LP problems to be solved. Examples of such inferred flux (un)couplings are given in Figure Table For some pairs of reactions, we need to solve at least one LP problem. The optimal solution not only determines the flux coupling between the considered pair of reactions, but also allows one to determine many other uncoupled reactions.v∈Cbe a steady state flux vector and let I={i∣vi=0}and J={j∣vj≠0}. Then i↦jfor all ∈I×J.Let i,j∈Irev are coupled, we propose to solve the bounded LP problemsIn general, most irreversible reactions are uncoupled to each other. Accordingly, the LP problems (4) used to determine coupled irreversible reactions are often unbounded. This limits the use of the feasibility rule, which requires the calculation of a feasible flux vector. In order to optimally use the feasibility rule, instead of solving the LP problems (4) to decide whether two irreversible reactions and to use the following scheme:Lij≠0(resp. Lji≠0),j∽iif and only if Lij≠0, Lji≠0and Lij=1/Lji.The following observation states that removing a fully reversible reaction does not alter the flux coupling between (pseudo-)irreversible reactions.k∈Frevbe a fully reversible reaction. For convenience, suppose k=n. Let S′be the m×(n−1)matrix defined by p≠kand let Irr′=Irr. Then, for all pairs of reactions i∉Frevand j∉Frev,Let S′v′=0and i∽jif and only if there exists λ′≠0such that S′v′=0and SRev∗ be the submatrix defined by the columns in S corresponding to the reversible reactions and let t be the dimension of kern(SRev∗). Based on Observation 7, we can remove up to t independent fully reversible reactions without altering the flux coupling between (pseudo-)irreversible reactions. Since certain fully reversible reactions may change their reversibility type after the deletion of a fully reversible reaction, we first remove a randomly chosen reaction i∈Frevtogether with the coupled reactions with i. We calculate the impact of this deletion on the dimension of kern(SRev∗). If this dimension decreases by 1, the deletion is maintained; otherwise the removed reactions are restored to the network. This is repeated until t independent fully reversible reactions and their coupled reactions are removed. We assume that the flux coupling between fully reversible reactions is determined beforehand.Let Based on the above results, we propose the Fast Flux Coupling Calculator (F2C2) to determine coupled reactions. The main steps of the F2C2 algorithm are given in Table MATLAB environment, using CLP  is summarized in Table E. coli, iJO1366. Four different sets of improvements were cumulatively switched on, and the linear programs solved were plotted for each case  and  pairs need not be solved anymore. The use of the algorithmic improvements  and  blocks, greatly reducing the total number of LP problems solved.Figure https://sourceforge.net/projects/f2c2/files/.We have presented the new flux coupling algorithm F2C2, which outperforms previous methods by orders of magnitude. Flux coupling analysis of genome-scale metabolic networks can now be performed in a routine manner. A software tool F2C2 is freely available for non-commercial use at F2C2: Fast Flux Coupling Calculator; FCA: Flux Coupling Analysis; FFCA: Feasibility-based Flux Coupling Analysis; FCF: Flux Coupling Finder; LP: Linear Program; TDC: Trivial Directional Coupling; TFC: Trivial Full Coupling.The authors declare that they have no competing interests.The F2C2 algorithm was designed by all authors, based on ideas from AL and LD. The implementation and computational experiments were done by LD. The first draft of the manuscript was written by AL, and revised by LD and AB. The final document was approved by all authors.


Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem.Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches.Here, we describe a community-driven effort, in which more than 20 experts in Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation. Salmonella is a major human pathogen and a model organism for bacterial pathogenesis research [S. enterica subspecies I serovar Typhimurium (S. Typhimurium) is the principle subspecies employed in molecular biology and its variants are causative agents in gastroenteritis in humans. The publication of the annotated genome for S. Typhimurium LT2 provided a foundation for numerous applications, such as drug discovery [S. Typhimurium's metabolic network [S. Typhimurium's metabolic network imposes combinatorial challenges for in vitro and in vivo approaches to identify synthetic lethal genes sets . Employing a systems biology network perspective could facilitate their identification.The evolution of antibiotic resistance by a variety of human pathogens is a looming public health threat ,2. Salmoresearch . S. enteiscovery . Previou network . Since nGEnome scale Network REconstructions (GENRE)  represenSalmonella reconstruction jamboree was held at the University of Iceland, Reykjavik, from September 5th to 6th, 2008. The jamboree team consisted of over 20 experts in microbiology, proteomics, Salmonella physiology, and computational modeling. Based on the experience with the yeast reconstruction jamboree [Salmonella reconstruction jamboree.In June 2008, it became apparent that two MRs were being assembled by two different research groups [jamboree , a methojamboree  and applS. Typhimurium with a focus on virulence. Furthermore, we aimed to include standard identifiers for reconstruction metabolites, reactions, and genes to facilitate subsequent mapping of 'omics' data. The starting MRs were AJRecon  and BRe, iJR904  and iAF1, iJR904 , respectWe developed an automatic approach to initiate the reconciliation of the two MRs by converting their metabolites and reactions into a common language Figure . The MR the E. coli MR when it was converted from its earlier version, iJR904 [Initial comparison revealed that there were 760 reactions common to the starting MRs while 521 and 1684 reactions were unique to AJRecon and BRecon, respectively  accounting for the periplasm as a compartment between the extracellular space and cytoplasm; ii) Salmonella-specific virulence characteristics, such as iron chelation by salmochelin and serovar Typhimurium LT2 O-antigen production; iii) the possibility to employ the consensus MR as mathematical, predictive model; and iv) comprehensive support data for reactions and associated genes . Gaps in iMA945 were detected and filled with GapFind and GapFill, respectively [After finishing the consensus reconstruction, a third metabolic reconstruction (iMA945) was published . Similarectively ; and iMAectively  to fit ee.g., different reactants, products, stoichiometry, or directionality: reversible, forward only, backward only) and GPR associations. There were a total of 629 distinct reaction ids between STM_v1.0 and iMA945: 446 were unique to STM_v1.0 and 183 to iMA945. Of the 183 reactions flagged as unique to iMA945, the majority represents reactions that were intentionally excluded from the consensus reaction . Some of the distinct reactions, such as adenosylcobalamin phosphate synthase, were due to different metabolite and reaction identifiers. No bibliomic data were included in iMA945, so it was not possible to assess whether the reactions were inserted by the automated gap-filling methods or supported by additional evidence. The 446 reactions unique to STM_v1.0 include Salmonella-specific chelators, O-antigens, and lipid modifications that were not present in the starting network derived from the E. coli MR (iAF1260). Overall, the core metabolic network is similar between STM_v1.0 and iMA945, which is expected as the draft scaffolds for both MRs were derived from E. coli MRs and S. Typhimurium has a notable metabolic homology with E. coli; however, STM_v1.0 includes over 300 more genes than iMA945 and includes a variety of Salmonella-specific reactions that are essential for virulence and could serve as coupling points for constructing a host-pathogen model.We performed a preliminary comparison between STM_v1.0 and iMA945. However, we did not reconcile iMA945 with the consensus reconstruction, as this will require detailed evaluation of the discrepancies in a subsequent jamboree meeting. Overall, 2,057 reactions were present in both the consensus reconstruction and iMA945, of which 1,706 reactions have identical gene-protein-reaction (GPR) associations . An in silico approach using an MR, such as STM_v1.0, could be implemented to prioritize the experiments by indicating which multi-target therapies would adversely affect the pathogen's metabolic capabilities.Our observed, very small number of synthetic lethal pairs in STM_v1.0 indicates that antimicrobial regimens may need to target more than two elements to be effective. Unfortunately, it will take less time for a pathogen to evolve a solution to a conjoint two-target antimicrobial strategy compared to a single-target strategy. To reduce the probability of a pathogen evolving resistance to a conjoint two-target strategy, it may be plausible to employ a combination of two-target strategies. Although a combination approach may be suitable for dealing with antibiotic resistance, there are potential shortcomings associated with clearance and toxicity because all the components of a regimen must reach a target at a specific time with the requisite concentrations. Despite these difficulties, multi-component, multi-target drugs are becoming standard therapeutics for complex diseases, including cancer, diabetes, and infectious diseases . ExperimS. Typhimurium in silico. We grouped these gene pairs based on different criteria to assess their potential value as multi-drug targets [Additional gene products shown to play a central role in virulence yet are not essential for growth in laboratory conditions or do not have an unequivocal functional annotation represent additional therapeutic targets. These gene products could serve as potential points for manipulating host metabolism , could bent poor), and wilent poor),9. Recenent poor); as the S. Typhimurium was published after the jamboree [E. coli MR[S. Typhimurium emphasizes the importance and the value of the effort presented in this report as well as the need for additional outreach when assembling jamboree committees.Taken together, the community-developed consensus MR is a curated reconstruction with the combined properties of the starting MRs and new information that was added during and after the reconstruction jamboree. The expanded metabolic versatility with a focus on virulence, updated annotation, including corrections, and curation of hundreds of additional reactions, genes, and metabolites by a community of experts present in STM_v1.0 highlights the value of a community-based approach. Another MR for jamboree , which w. coli MR. The recE. coli MRS. AJRecon is a pre-publication version of iRR1083 [E. coli MRs that were used as comparative genomics scaffolds for initializing the Salmonella MRs .The starting reconstructions, AJRecon and BRecon, were built on scaffolds derived from published  iRR1083 , and was iRR1083 . For its iRR1083 - a direcThere are three essential phases for community-based MR development: 1) preparation, (2) jamboree, and (3) reconstruction finalization . The pre preparatReconciling multiple MRs requires that the MRs' contents employ a common nomenclature so that the contents may be compared. For this work, we employed the KEGG database  as the sThermodynamic directionality for each reaction was calculated as described in . BrieflyDW/hr.The conversion of a reconstruction into a mathematical model has been described in detail elsewhere . The unihttp://www.biolog.com. False positive predictions (G/NG) and false negative predictions (NG/G) were then compared with the data from Gutnick et al. [et al. [et al. [et al. [Flux balance analysis  was emplk et al.  and refe [et al. . For lim [et al.  or cited [et al. .min, i = vmax, i = 0 mmol.gDW-1.hr-1) and the ability of the model to produce biomass was assessed, i.e., the biomass reaction was chosen as the objective function and maximized.The gene deletion studies were performed by converting STM_v1.0 into a stoichiometric model and performing flux balance analysis . For eacAll simulations were performed using the COBRA Toolbox v2.0  using MaIT, DRH, BOP, JNA, and DB conceived the study. BS and DB compiled the BRecon. IT and DRH compiled the consensus MR. IT, DRH, BOP, and DB wrote the manuscript. GF and IT designed and performed initial MR comparisons. RMTF and DRH performed thermodynamic directionality analysis. DHR and IT carried out the computational analysis of the consensus MR. IT, BOP, DB, BS, DKA, SB, PC, FCC, RMTF, CAH, SCJK, YCL, KM, MLM, EÖ, AR, JLR, SIS, SS, JS, SS, NS, IMT, KZ, BOP, JNA, DB actively participated during and/or after the metabolic reconstruction jamboree to generate content for the consensus MR. All authors read and approved the final manuscript.Consensus MR. This xlsx file contains the consensus reconstruction and simulation setup/results. - Table S1. Statistics for automated reconciliation of starting reconstructions. - Table S2. Consensus Reconstruction in SBML format. - Table S3a. M9/glc. - Table S3b. LB. - Table S3c. Biomass. - Table S4. Growth benchmark errors. - Table S5a. All Lethal deletion predictions. - Table S5b. Single Deletion/Virulence. - Table S6a. LB Synthetic Lethal. - Table S6b. M9 Synthetic LethalClick here for fileConsensus MR in SBML format. Consensus MR as a computational model in SBML format.Click here for file


E. coli and C. acetobutylicum across various growth media. Our approach, based strictly on first thermodynamic principles, is the first method that successfully predicts high-throughput metabolite concentration data in bacteria across conditions.Steady-state metabolite concentrations in a microorganism typically span several orders of magnitude. The underlying principles governing these concentrations remain poorly understood. Here, we hypothesize that observed variation can be explained in terms of a compromise between factors that favor minimizing metabolite pool sizes (e.g. limited solvent capacity) and the need to effectively utilize existing enzymes. The latter requires adequate thermodynamic driving force in metabolic reactions so that forward flux substantially exceeds reverse flux. To test this hypothesis, we developed a method, metabolic tug-of-war (mTOW), which computes steady-state metabolite concentrations in microorganisms on a genome-scale. mTOW is shown to explain up to 55% of the observed variation in measured metabolite concentrations in  Cellular metabolism involves the joint activity of hundreds of enzyme-catalyzed biochemical reaction A system-wide understanding of cellular metabolism requires the quantification of both fluxes through these reactions and the concentrations of the corresponding metabolites. In recent years, mass spectrometry has become a popular tool for high-throughput measurements of metabolite concentrations. In combination with isotope tracing, mass spectrometry can also measure metabolic fluxes -3. The aFor steady-state systems, substantial insight into metabolic reaction rates can be achieved through constraints imposed by the law of mass balance under a steady-state assumption: for each internal metabolite, total influx must equal total efflux. Systems level application of this constraint, typically referred to as Flux Balance Analysis (FBA) , requireIn contrast to the success of FBA in predicting fluxes, there is no comparable tool for explaining and predicting metabolite concentrations in cell-wide setups. Explicitly modeling metabolite concentrations would require a systematic understanding of in vivo enzyme kinetics, which is currently lacking, despite ongoing progress on modeling metabolic systems using simplified rate equations and parameter estimation techniques -18.ΔG', which is calculated according to the equation:An alternative approach involves extending FBA to genome-scale modeling of metabolite concentrations by accounting for thermodynamic considerations -24 . Throughout this work we assume the cytoplasm is a dilute aqueous solution  is insufficient for effectively predicting metabolite concentrations, as for most metabolites, a wide range of possible concentrations satisfies all network thermodynamic constraints [Where trations ). Extendtrations ,20. Howestraints .E. coli [Schuster and Heinrich  suggesteE. coli ,31,32  and maintaining sufficient thermodynamic driving force such that enzymes are utilized efficiently, with most flux in the forward direction. By maintaining adequate forward driving force for all reactions, the enzyme concentrations required to support the necessary net metabolic flux is reduced. Thus, steady-state metabolite concentrations reflect a balance between minimizing metabolite levels and maximizing enzyme efficiency . MinimizE. coli [C. acetobutylicum [We formalize the trade-off between metabolite and enzyme levels in a computational framework, which we term metabolic Tug-of-War (mTOW), in analogy to the concept of genetic tug-of-war for balancing other evolutionary constraints . To faciE. coli  and of Cutylicum , and is utylicum ,6.To identify the most likely metabolite concentrations for a given metabolic system, we present a novel computational approach, metabolic tug-of-war (mTOW), that searches for fluxes and metabolite concentrations, that (i) obey the second law of thermodynamics, (ii) whose total metabolite concentration  is limited, and (iii) whose enzyme levels are small, by ensuring that all active reactions are thermodynamically favorable with a reasonable driving force. The input data required for this method are the stoichiometry of the reactions in a given metabolic network (readily available for numerous microbes -43) and ΔG'0 of a major fraction of the reactions in a typical cell [ΔG'0 data [E. coli’s metabolism  have been directly measured and hence there is no need to reevaluate their energy based on group decomposition. CCM’s estimation of reactions Gibbs energy in E. coli shows a marked improvement in accuracy over GCM, with a RMSE of 2.9 kJ/mol .The group contribution method (GCM) for estimating reaction Gibbs energies is a practical method for providing genome-wide coverage of metabolic reactions. The assumption that chemical groups are independent enables a state-of-the-art GCM approach to estimate the cal cell -46, but G'0 data ). Here, G'0 data , which acat values, which are rarely available. However, there is a fundamental relationship between reaction thermodynamic driving force and the enzyme level required to maintain a given amount of net flux. For reactions far from equilibrium, changes to thermodynamic driving force have a negligible effect on the net flux. In contrast, for reactions close to equilibrium, the required enzyme level increases dramatically as the driving force approaches zero  [E) required for maintaining a given net reaction rate (v) and the associated thermodynamic driving force (−ΔG'):Absolute enzyme levels cannot be computed explicitly without extensive kinetic knowledge, including in vivo ket flux) . Equatiocat and km values; see Supp. Material) and (iii) the effect of the thermodynamic driving force. Note that enzyme level rises and eventually becomes infinite as the thermodynamic driving force approaches zero  (i) enzE. coli [C. acetobutylicum [ΔG for all forward reactions is negative (i.e. a positive thermodynamic force) [ΔG' approximating The complete formulation of mTOW involves a non-convex optimization, searching for a flux distribution and metabolite concentrations ,49, miniE. coli , and forutylicum  (Supp. Mc force) . To formc force) ). Hence,E. coli and C. acetobutylicum, we assume that metabolite concentrations are bounded between 10 nM (a concentration reflecting at least a single molecule per cell in these bacteria) and 100 mM , were further constrained based on experimental measurements in each growth medium .Analyzing metabolite concentrations in both bacteria ). In botE. coli grown on glucose, acetate and glycerol media, for which high-throughput measurements are available for validation [E. coli by Feist et al. [E. coli grown on acetate or glycerol, while a solution that is close to optimal in both objectives is achievable in glucose media. To choose a single biologically plausible mTOW solution per media , we chose solutions achieving high values for both objectives . Using this strategy, mTOW predicts the concentration of 507, 412 and 412 metabolites in glucose, acetate and glycerol media, respectively , with the dual function of producing energy (ATP and NADH) and biomass precursors . Our measurements confirm mTOW’s prediction, showing a marked decrease of ~33% in flux through the oxidative branch of PP pathway in anaerobic conditions  pathway . SpecifiNAD/NADH ratio) under anaerobic vs. aerobic growth. This contributes to an even larger predicted concentration gradient across metabolites under anaerobic conditions with a decrease of two orders of magnitude in the concentration of the downstream metabolite in this system, glycerate-1,3P.Observing glycolysis, the predicted metabolite concentrations are mainly affected by the higher glycolytic flux ,53 and tE. coli, we examined the biofuel producer C. acetobutylicum. We analyzed metabolite concentrations in this organism during its acidogenic growth phase  and its solventogenic growth phase (stationary phase with high secretion rates of solvents such as acetone and butanol), using a metabolic network model by Lee et al [To evaluate mTOW’s ability to predict metabolite concentrations in another microorganism whose overall metabolism is less well characterized than that of ee et al .Applying mTOW to predict metabolite concentrations under both growth phases resulted in the prediction of 206 and 217 concentrations for acidogenesis and solventogenesis, respectively. Comparing the predicted concentrations with 40 measured concentrations collected by Amador-Noguez et al [We demonstrated that under some growth media metabolite concentrations reflect a compromise between cellular adaptations towards minimizing the total metabolite vs. enzyme levels. Since metabolite and enzyme levels cannot be explicitly computed based on metabolic fluxes without extensive kinetic knowledge, we approximated enzyme levels for reactions based on their thermodynamic forces, as low thermodynamic forces result in substantial backward fluxes, and thus higher required enzyme levels. mTOW does not take into account how enzyme kinetic considerations influence metabolite concentrations, for example by changing an enzyme rate via increasing both its substrate and product concentrations proportionally (i.e. without changing its thermodynamic driving force). Furthermore, it does not account for allosteric regulation or for the effect of specific enzyme kinetic parameters on the enzyme levels required to catalyze a unit of flux. mTOW’s predictions also implicitly assume that enzymes are fully saturated, an assumption which is partially supported by experimental evidence . While mE. coli. Second, although the coverage of group contribution-based methods for estimating thermodynamic parameters (such as CCM) is much higher than what can be derived from experimental data, it is still not complete and covers an average of 90% of the active (i.e. flux carrying) reactions across the various growth media (and 75% of the entire reactions set in E. coli). Third, the exact intracellular conditions  affect the energetics of reactions [E. coli model lacks information on the thermodynamics of aminoacyl tRNA synthetase that utilizes amino-acids, which is likely to limit mTOW’s ability to correctly predict amino-acid concentrations . Fifth, as mTOW is formulated as a non-convex optimization problem, whose exact solution is computationally intractable for large-scale networks, we obtain only approximated solutions based on a combination of mixed-integer linear programing and quadratic programming optimizations .To enable the prediction of metabolite concentrations via a genome-scale flux and thermodynamic analysis, mTOW is bound to make additional simplifying assumptions that may bias its predictions: First, it relies on estimated Gibbs energies for reactions via the CCM method rather than solely on experimental data which is lacking for most of the reactions in eactions , and curIdentifying optimization criteria that would explain complex metabolic behaviors remains a major open challenge in systems biology . The facGiven a set of metabolic reactions, CCM assigns them to the following categories:i. Reactions that can be completely determined using a linear combination of measured reactions (from the TECRDB ).ΔG'0 (same as in standard GCM).ii. Reactions for which group contributions must be used for all reactants in order to estimate the iii. Reactions that can be decomposed to two half-reactions, one which is in category I and the other in category II . The resulting set of Gibbs energy estimations for the reactions in all three categories combined is guaranteed to be consistent with the first law of thermodynamics .E. coli), shows a marked advantage to CCM . Moreover, there are about 400 other reactions that fall in category I, and their CCM estimations are expected to be more accurate than the GCM-based ones, since the estimation does not use the simplifying assumption needed for the group contribution method. To support this claim, we ran a cross-validation test based on the leave-one-out method and found that, when using CCM instead of standard GCM, there is a 42% decrease in the median error for reactions in category I, and a 20% decrease on average in all categories.mTOW aims to find a flux distribution (v) and metabolite levels (c) with low enzyme cost and metabolite load and can be formulated as:S'j⋅ln(c) representing the inner product of the jth column in the stoichiometric matrix with the ln of metabolite concentrations. For reactions for which standard Gibbs energy data is available (whose set is denoted by RG), the thermodynamic driving force is computed based on the metabolite concentrations, with R and T denoting the gas constant (kJ/mol*K and temperature (K), respectively, and β represent a minimal value from which a reaction can operate (see below). The first term of the optimization function represents the minimization of total metabolite concentrations, while the second term represents the minimization of total enzyme concentrations. Hence, ideally, M(c), should be equal to c, and E should be equal toδ represent the weighting between the two optimization criteria, while varying it between 0 and inf enables to obtain a set of Pareto optimal solutions.where m BRENDA  , followed by a QP problem to identify metabolite concentrations (c) with minimal metabolite and enzyme concentrations . In the QP problem, the total sum of metabolite concentrations is approximated by:where ci is the predicted concentration for metabolite i, and cL is the minimal concentration value (set to 10 nM).ΔG'j, as follows:Similarly, the enzyme level terms, α=4 (corresponding to a ΔG' of -10 kJ/mol) and β=0.02 (corresponding to a ΔG' of -0.05 kJ/mol) while as discussed in the Supp. Material, the results are robust for a wide range of parameter choices  of 0.03 into liquid minimal medium. This liquid culture was grown to OD650 of ~ 0.1, and transferred to a filter culture as follows: for each filter culture, 1.6 mL of liquid culture was passed through a 47 mm diameter round nylon filter (Millipore) and the filter placed cell-side up onto a medium-loaded agarose plate. The filter cultures were allowed to grow to an OD650 of 0.35 at which point metabolism was quenched and cells extracted by dropping the filters directly into 1.6 mL of −20°C 40:40:20 acetonitrile: methanol: water. For the growth of anaerobic cultures all procedures were carried out inside an anaerobic chamber (Bactron IV SHEL LAB) with an atmosphere of 90% nitrogen, 5% hydrogen, and 5% carbon dioxide.Wild-type K-12 strain NCM3722 of Steady state labeling experiments using 1,2-13C-glucose were performed in liquid cultures under aerobic and anaerobic conditions. To prepare liquid cultures, a single colony was picked from a Luria Broth plate, and grown to saturation overnight in minimal media containing 1,2-13C-glucose (4g/L). The saturated overnight culture was then diluted to OD650 of 0.03 into liquid minimal medium containing 1,2-13C-glucose (4g/L). This liquid culture was grown to OD650 of ~ 0.3. 5ml of this liquid culture was passed through a 47 mm diameter round nylon filter (Millipore) and the filter was dropped immediately into 1.6 mL of −20°C 40:40:20 acetonitrile: methanol: water.E. coli filter cultures grown under identical conditions [Cell extracts were analyzed by reversed-phase, ion-pairing liquid chromatography coupled by electrospray ionization  to a high-resolution, high-mass-accuracy mass spectrometer  operated in full scan mode for the detection of targeted compounds based on their accurate masses. This analysis was complemented with liquid chromatography coupled by ESI (positive and negative mode) to Thermo TSQ Quantum triple quadrupole mass spectrometers operating in selected reaction monitoring mode. Measurements from aerobic and anaerobic cell extracts were performed in parallel during the same day. Concentrations were determined based on the ratio of metabolite peak heights from aerobic and anaerobic cultures and using previously published metabolite concentrations from aerobic nditions . The steFile S1mTOW supplementary.Supplementary file also containing all supplementary tables and figures.(DOCX)Click here for additional data file.File S2mTOW predictions.(XLSX)Click here for additional data file.File S3Gibbs energy estimations made by CCM.(XLSX)Click here for additional data file.