E. coli reference commensal strain Nissle 1917 was investigated. Analysis of the growth capabilities of mutants altered for various components of the Csr system  showed that only the protein CsrA - the key component of the system - exerts a marked role in carbon nutrition. Attenuation of CsrA activity in the csrA51 mutant affects the growth efficiency on a broad range of physiologically relevant carbon sources, including compounds utilized by the Entner-Doudoroff (ED) pathway. Detailed investigations of the metabolomes and fluxomes of mutants and wild-type cells grown on carbon sources representative of glycolysis and of the ED pathway , revealed significant re-adjusting of central carbon metabolism for both compounds in the csrA51 mutant. However, the metabolic re-adjusting observed on gluconate was strikingly different from that observed on glucose, indicating a nutrient-specific control of metabolism by the Csr system.The role of the post-transcriptional carbon storage regulator (Csr) system in nutrient utilization and in the control of the central metabolism in  Escherichia coli is a normal inhabitant of the intestine and the predominant facultative anaerobe in the gastrointestinal tract of mammals E. coli experiences constantly shifting growth conditions. Recent findings indicate that the ability to compete for carbon nutrition is a critical factor for gut colonization, and is part of the arsenal of strategies employed by pathogenic E. coli strains to outcompete the gut microbiotia E. coli in the gut is supported by less favorable substrates, including both sugars and non-sugar compounds such as the small organic acids resulting from the degradation of mucus by anaerobes of the microflora E. coli has developed a variety of adaptation mechanisms. Highly sophisticated global regulatory networks coordinate physiological and metabolic responses by controlling the functional expression of relevant sets of genes. The carbon storage regulator (Csr) system To cope with the changing environment in the intestine, E. coli to changes in nutrient availability. Because it regulates glycolysis and gluconeogenesis in opposite ways, the Csr system is assumed to be a master switch between glycolytic and gluconeogenic metabolisms, and to increase competitiveness of E coli for the utilization of glycolytic carbon sources E. coli metabolism – i.e. the actual metabolic phenotype - has not been fully investigated. The impact of Csr on its target metabolic pathways, in terms of metabolic pools and carbon fluxes, remains unknown. Indeed, the final metabolic phenotype may substantially differ from the direct action of Csr on its target enzymes. Metabolism is submitted to a highly complex regulation network which contains regulators that have effects opposite to that of Csr. The actual result of all the regulatory interactions is not fully predictable. In addition, the flux in a metabolic pathway is not correlated linearly with the activity of its enzymes, so that the effect of Csr on its target enzymes does not necessarily result in significant changes in pathway fluxes E. coli cells grown on glucose, a carbon source which is not found in the colon, the main ecological niche of this bacterium. In contrast, very little is known regarding the role of Csr in the utilization and metabolism of physiologically-relevant carbon sources, which are often not metabolized by the same pathways as glucose and are not submitted to the same regulation networks. To fully understand the role of Csr in the adaptation to the gut environment, it is therefore necessary to investigate its role in the metabolism of physiologically relevant carbon sources.The global role of Csr in the control of metabolism suggests that this system could play a role in the adaptation of E. coli metabolism. First, we analyzed the capability of mutants altered for the various components of the Csr system to grow on a range of carbon sources that are found in the colon. Next, we studied the impact of Csr on the entire central carbon metabolism by analyzing the metabolomes and fluxomes of selected Csr mutants on two carbon sources, glucose and gluconate, which are metabolized via the EMP and ED pathways respectively. The identification of CsrA target enzymes and of the mechanisms by which their activity is controlled is out of the scope of this work. Because the deletion of csrA is lethal for growth on glucose and hence prevents investigation of the metabolic effects of CsrA on this carbon source - and potentially on other glycolytic compounds – the previously described attenuated CsrA protein CsrA1-50 was used in this study. The protein CsrA1–50 has a reduced RNA affinity and retains partial activity csrA51 mutant in which the native CsrA protein was replaced by the attenuated CsrA1–50 protein is viable on glucose E. coli such as K-12. This was done to ensure that all the physiological and metabolic mechanisms necessary for gut colonization and survival were in place and had not been affected by the prolonged lab use of K-12-like strains E. coli strains persisting in the microbiota of humans from Europe, Australia, Japan and the USA In this work, we investigated the role of the Csr system in carbon nutrition and in the control of E. coli Nissle 1917 mutants were constructed using the allele replacement method described in csrB::CmR locus was amplified by PCR using primers EH 12– EH 13 and strain RG1-B csrB allelic replacement. For csrC mutagenesis, the csrC::TcR locus was amplified from TWMG1655 csrA gene was amplified from chromosomal DNA and cloned into pGEM-T Easy (pCsrA). The resulting plasmid was transformed into E. coli Nissle ΔcsrA51 by electroporation. E. coli Nissle 1917 and its mutant derivatives are listed in Bacteria were grown at 37°C either in Lysogeny Broth (LB) containing no glucose, or in synthetic minimal medium supplemented with 15 mM of carbon source, as described elsewhere Detailed growth phenotyping of the strains was performed using real-time multiplex measurement of growth parameters based on fluorescent dyes sensitive to pH or oxygen and optical density, according to the method developed by 13C-labelling experiments, the unlabeled substrate (glucose or gluconate) was replaced by a mixture of 80% [1-13C]- and 20% [U-13C]- substrate . Inoculation was performed with cells pre-grown with the same labeled carbon sources and collected at mid-exponential growth phase, after centrifugation and washing with the same medium deprived of carbon source. All experiments were performed in three biological replicates.For in-depth metabolic characterization, cells were cultivated in 500 mL baffled flasks  with 40 mL of medium. For To compare their glycogen content, the different strains were grown overnight at 37°C on LB plates containing 2% glucose and stained with iodine vapor A colony from a fresh LB plate was streaked on semi-solid LB plates (0.3% agar) and incubated upright at 37°C overnight. To determine the extent of bacterial motility, the diameter of the diffusion halo was measured after 24 h. Motility assays were determined in three independent experiments.Cultures were grown statically overnight in 500 µl of LB, in a 48-well polystyrene microtiter plate. Biofilm formation was measured by discarding the medium, rinsing the wells (three times) with water, and staining bound cells with 20% crystal violet in methanol 600), which was detected with a spectrophotometer  with appropriate dilutions when needed. Extracellular fluxes were determined from the rates of disappearance (or appearance) of substrates and products, in the culture supernatants. The different compounds were quantified by HPLC  as previously described CDW/OD600) were established for each strain and are given in The growth rate (μ) was determined from log-linear regression of time-dependent changes in optical density at 600 nm  in 5 mL of an ethanol/water (75/25) solution at 95°C, incubated for 2 minutes, cooled on ice and stored at −80°C. Three samples of broth and filtrate were taken and analyzed for each biological replicate.Samples were taken at the mid-exponential phase using the differential method Sampling was performed at the mid-exponential phase in two steps; i) rapid quenching of metabolism followed by ii) metabolite extraction. For quenching, 120 µL of broth were rapidly sprayed into precooled centrifuge tubes maintained at −80°C and containing 500 µL of cold ethanol, homogenized using a vortex and centrifuged  with a Sigma 3–18K centrifuge . Metabolites were extracted by pouring 5 mL of an ethanol/water (75/25) solution at 95°C onto the cell pellets. After incubation for 2 min in closed tubes, the cellular extracts were cooled on ice and stored at −80°C. For each biological replicate, three metabolite samples were collected and analyzed.13C-labeling patterns of central metabolites, including organic acids  and phosphorylated compounds  were determined as described in Cellular extracts were evaporated for 4 h . The remaining aqueous extracts were freeze-dried, resuspended in 200 µL of milliQ water and stored at −80°C. Intracellular metabolites were analyzed as previously described 1H 1D-NMR at 298 K, using a 30° pulse and a relaxation delay of 20 s, with an Avance 500 MHz spectrometer . The four acetate isotopomers and the eight pyruvate isotopomers were fully resolved and quantified.The labeling patterns of the main metabolic products – i.e. acetate and pyruvate - which accumulated during growth in the extracellular medium were measured by NMR spectroscopy E. coli central metabolism: glycolysis (EMP), pentose phosphate pathway (PPP), Entner Doudoroff (ED) pathway, the tricarboxylic acid cycle (TCA), and anaplerotic reactions. The precursors required for biomass synthesis were estimated according to biomass composition 13C-labelling patterns of metabolites using appropriate mathematical models of glucose or gluconate metabolisms in E. coli1H NMR, as detailed above. The fluxes were normalized to the rate of substrate uptake, which was arbitrarily set at 100. To determine the confidence intervals of the calculated fluxes, we used a Monte-Carlo approach where 100 independent optimization runs were performed from datasets in which noise was added according to the standard deviations of the measurements. Isotopic data and metabolic fluxes for each independent biological replicate are given in Dataset S1.Fluxes were calculated using influx_s software The adenylate energy charge (AEC) was calculated from the intracellular pools of adenosine mono-, di- and tri-phosphate according to 2: for each reduced cofactor, total production was calculated by summing the fluxes of all reactions which produced the reduced cofactor. The NADH that was synthesized in biosynthetic pathways, as well as NADPH requirements for anabolism, were estimated from biomass composition NADH, NADPH and FADH2 via oxidative phosphorylation was estimated assuming a P:O ratio of 1 for both redox cofactors ATP: production of ATP via substrate-level phosphorylation was calculated by adding all the fluxes measured for all ATP-producing reactions in carbon metabolism, and subtracting the fluxes of all ATP-consuming reactions. The ATP produced from NADH and FADHt-tests with two tailed distributions.Standard deviations of metabolic fluxes and intracellular pool sizes were determined from three biological replicates. Statistical differences in metabolite concentrations between the wild-type and mutant strains were calculated using Student’s paired csrB, csrC, csrBC (double mutant) and csrD genes of the E. coli strain Nissle 1917 by allelic exchange  was successfully generated in strain Nissle 1917 to analyze the effects of attenuated CsrA activity. Mutants of the Csr system are expected to be altered for glycogen storage, biofilm formation and motility csrB, ΔcsrC, ΔcsrBC and ΔcsrD mutants accumulated amounts of glycogen similar or slightly lower than the wild-type strain. In contrast, the ΔcsrA51 mutant accumulated substantially more glycogen than all other strains , although no significant difference was observed for fructose and fucose. The latter two carbon sources enter glycolysis after the phosphofructokinase step , so that the activation of the latter enzyme by CsrA is not needed to utilize these compounds. In addition, the inhibition of fructose-1,6-biphosphatase by CsrA csrA51 mutant grew poorly on acetate.The growth phenotypes of the Δe strain . In conts tested . The gros tested . As expecsrA51 mutant. This was unexpected as the Csr system is not assumed to control the PP pathway – at least in the K-12 strain MG1655 csrA51 mutant on compounds degraded via this pathway was impaired. The growth rate was reduced by 10% and 20% on galacturonate and glucuronate, respectively, and by 30% on gluconate, compared to the wild-type strain. This indicates that the Csr system is involved in the utilization of the three compounds degraded by the ED pathway.Furthermore, the gastrointestinal tract contains compounds that are used by the Entner-Doudoroff (ED) – i.e. gluconate, glucuronate - and Pentose-Phosphate (PP) – i.e. xylose, ribose - pathways. Although all strains grew similarly on xylose, growth on ribose was reduced by 50% in the ΔcsrBC and ΔcsrA51 mutants was performed upon growth on glucose and gluconate, as valuable representatives of nutrients utilized via the EMP and ED pathways (csrA51 mutant was complemented with a plasmid carrying the intact csrA gene (pCsrA). The complemented mutant displayed physiological parameters similar to those of the wild-type strain , indicating less efficient use of the sugar.The wild-type strain Nissle 1917 grew on glucose at a rate of 0.79 hcsrA51 mutant was impaired on gluconate (E. coli Nissle 1917 and its csr mutants upon growth on gluconate. The wild-type strain grew on gluconate at a rate similar to that observed on glucose (0.74 h−1 vs. 0.79 h−1), although much more gluconate than glucose (20.0 vs. 12.5 mmol.g−1.h−1) was needed to achieve the same growth rate, indicating different metabolic efficiencies. Gluconate was converted into two by-products, pyruvate and acetate, with molar yields of 0.16 and 0.52, respectively. Like on glucose, the growth of the ΔcsrBC mutant did not significantly differ from that of the wild-type strain, whereas the mutant ΔcsrA51 showed marked differences  the lower growth rate was correlated with a 40% reduction in gluconate consumption, (iii) acetate production rate was reduced but acetate yield remained unchanged, and (iv) the production of pyruvate was drastically reduced and was almost negligible in the ΔcsrA51 mutant.Because the growth of the Δluconate , and dueferences : (i) thecsrA51 and ΔcsrBC mutants were compared to those of the wild-type strain. Absolute quantification of central and energetic metabolites was obtained from cells growing exponentially in minimal medium with glucose as the sole source of carbon. The results of the most representative metabolites are listed in csrBC mutant generally did not significantly differ from that of the wild-type strain. In contrast, the mutant with a truncated CsrA protein underwent a dramatic increase in the levels of metabolites from the upper part of the EMP and PP pathways. The levels of F6P, 6PG and S7P, were two to three times higher than in the wild-type. Interestingly, a significant drop in the intracellular levels of FBP was observed in the ΔcsrA51 strain, indicating a reduction in phosphofructokinase activity, a known target of CsrA. The level of M6P in the ΔcsrA51 mutant was twice as high as in the wild-type. No changes were detected in metabolites of the lower part of glycolysis, such as 2- and 3-PG and PEP. Surprisingly, the pools of metabolites related to the TCA cycle  were lower than in the wild-type.To increase our understanding of the role of the Csr system in glucose metabolism, the metabolomes of the ΔThe adenylate energy charge (AEC) was measured from the ATP, ADP and AMP contents listed in csrBC and ΔcsrA51 mutants. For this purpose, steady-state 13C-labeling experiments were carried out in which the strains were fed with a mixture of 80% 1-13C and 20% U-13C-glucose as sole carbon source.To better understand the effect of the Csr system on central metabolism, the distribution of metabolic fluxes was analyzed in the wild-type strains and in ΔcsrA51 partial deletion mutant csrA51 mutant were in the range of µmoles.g−1.h−1 (−1.h−1) or the fluxes observed in the main metabolic pathways. Thus, the flux of carbon diverted towards glycogen formation in the ΔcsrA51 mutant is much too low to significantly alter the overall carbon flux, and is likely not the main reason for the flux redistribution observed in exponentially growing cells for this mutant.We first wondered about the flux toward glycogen production as this is assumed to be responsible for the growth deficiency of a Δ.g−1.h−1 . This iscsrBC mutant  by the sensitivity analysis applied to each flux data set, and reproducible in three biological replicates.The flux data obtained for the wild-type Nissle 1917 stain  showed tC mutant  did not E. coli Nissle 1917 metabolism, the production of high-energy molecules NADPH and ATP was calculated from the 13C flux data. In the wild-type strain and the ΔcsrBC mutant, the PP pathway and the TCA cycle contributed almost equally to the production of NADPH. The contribution of the TCA cycle was significantly (around 30%) lower in the ΔcsrA51 mutant and was compensated for by the PP pathway. In all strains, the production of NADPH was sufficient to fulfill the anabolic needs  were observed in the ΔcsrA51 mutant in the upper part of carbohydrate metabolism . A significant proportion was also used via the PP pathway, and the PP/ED flux ratio was 20∶80. A small proportion of hexose 6-phosphate was also recycled in the ED and PP pathways. The conversion of GAP into pyruvate in the lower part of glycolysis was much lower than observed on glucose . However, on gluconate, pyruvate is also generated via the ED pathway. The total flux of pyruvate formation by the two EMP & ED glycolytic pathways was higher on gluconate than on glucose . Flux data indicated that 10% of the pyruvate produced was released into the medium as a fermentation by-product of gluconate, and that 86% was converted into acetyl-CoA via pyruvate dehydrogenase. The TCA cycle used 51% of acetyl-CoA, while 38% was released as acetate. From flux data and the data listed in 2 (via the combined action of the glycolytic pathways and the TCA cycle), while 34% were converted into by-products (pyruvate & acetate). This is significantly different from what happened on glucose, where 25% of the molecules were fully oxidized by the combined action of glycolytic pathways+the TCA cycle, and 25% were released as a by-product (acetate).The metabolic flux distribution in cells grown on gluconate was determined from steady-state eriments  with 13CcsrBC mutant was no different from that in the wild-type strain. Not surprisingly, significant differences were observed in the ΔcsrA51 mutant. In this strain, the PP flux (i.e. flux through 6-phosphogluconate dehydrogenase) fell from 20% to 7% of the substrate uptake rate. While the ED flux increased from 81% in the wild-type to 94% in ΔcsrA51, indicating almost pure ED metabolism. The formation of hexose 6-phosphate via the PP pathway stopped. Instead, hexose phosphates were obtained by recycling of the triose phosphate generated by the ED pathway, as shown by the negative flux in the series of reactions from F6P to GAP.Here again, the relative flux distribution in the ΔcsrA51 mutant was reduced compared to that in the wild-type strain. The reduced formation of pyruvate by lower glycolysis was compensated for by an increase in direct production of the latter compound via the ED pathway. Indeed, the total pyruvate flux was almost the same in the two strains . However, pyruvate utilization differed significantly in the two strains, since the excretion of pyruvate was almost abolished in the ΔcsrA51 mutant. Unlike growth on glucose, the relative fluxes through the TCA cycle and acetate production were not altered in the ΔcsrA51 mutant for growth on gluconate. These data showed that the reduced activity of the CsrA protein in this mutant did not affect the fraction of gluconate molecules that were fully oxidized to CO2 (34% vs. 35% in the wild-type strain).The flux in the lower part of glycolysis in the ΔcsrA51 mutant, in which 90% of NADPH was produced in the TCA cycle, compared with 76% in the wild-type strain. In that respect, the effect of the csrA51 mutation was the reverse of the situation observed for growth on glucose, when the contribution of the TCA cycle decreased.The NADPH and ATP balances in the different strains were calculated for growth on gluconate. Compared to glucose metabolism, whatever the strain, the production of NADPH for growth on gluconate relied more on the TCA cycle than on the PP pathway. This phenomenon was amplified in the ΔcsrA51 and ΔcsrBC strains (−1). This is consistent with the fact that much more gluconate than glucose is consumed to obtain similar growth rates. As expected, there were no significant differences between the ΔcsrBC mutant and the wild-type strain. In the ΔcsrA51 mutant, ATP production was drastically reduced (77 mmol.g−1). Nevertheless, the respective contributions of substrate-level phosphorylation and oxidative phosphorylation to ATP production were constant in all three strains, as observed on glucose.The ATP balance for growth on gluconate was determined in the wild-type, Δ strains . As obseE. coli Nissle 1917, the flux in the PP pathway is similar to that in the K-12 strains, but a striking difference between the two strains is the significant contribution of the ED pathway (17%) to glucose catabolism in the B2 strain. The ED pathway plays a critical role in the implantation of E. coli in the colon because it allows the utilization of compounds – such as gluconate and other sugar acids utilized by this pathway - that are abundant therein E. coli to switch rapidly from the utilisation of glucose to that of the ED-compounds. In addition, E. coli possesses a periplasmic glucose dehydrogenase (GDH) that converts glucose into gluconate, which is further used via the ED pathway. This oxidative glucose utilization process is not active in vitro because the cells do not produce pyrroloquinoline-quinone (PQQ), the cofactor of the periplasmic GDH. But PQQ is available in the gut, meaning that a part of glucose could be utilized by the process in situ + at the expense of the transmembrane proton gradient, which means that part of the energy is consumed to produce sufficient NADPH in K-12 strains. Hence, significant differences in NADPH-producing mechanisms exist between the two types of strains, which might – at least partly – explain their differences in growth rates.The Nissle 1917 strain belongs to the phylogenetic B2 group, which contains efficient gut colonizers. Consistently, this strain shows growth rates higher than the laboratory strains (K-12 strains) upon similar growth conditions. The higher rates of substrate uptake provide advantage in the gut environment since it decreases the availability of carbon nutrients for competing strains or organisms. The metabolic differences between the two types of strains can be detailed for cells grown on glucose, for which metabolome and fluxome data are available for both of them. In K-12 strains, glucose is predominantly catabolized via the EMP pathway, with 20% contribution of the PP pathway, while the ED pathway displays negligible activity E. coli. The data reported here for the wild-type Nissle strain are generally consistent with current knowledge of gluconate metabolism in E. coli, i.e. gluconate is majorly metabolized via the ED pathway. As observed upon growth on glucose, all biosynthetic requirements in NADPH are fulfilled from the operation of carbon pathways, without the need for additional transhydrogenase activity. This means that such metabolic capability of the Nissle strain is likely independent of the carbon source, and might be a specific trait of the strain. A striking feature of growth on gluconate is the significant production of pyruvate, which represents 15% of gluconate uptake, while the production of acetate is comparable to that observed on glucose (50% of gluconate uptake). Because the rate of gluconate uptake was very high and no oxygen limitation was observed, we hypothesized that pyruvate excretion on gluconate is due to overflow metabolism. Consistently, pyruvate production is abolished in the ΔcsrA51 mutant which shows decreased gluconate uptake rates, and is not observed upon growth on glucose where the substrate uptake – hence, the intracellular rate of pyruvate synthesis – is significantly lower.The above observations indicate significant differences in the operation of central carbon metabolism between the Nissle 1917 strain and K-12 strains for two major carbon sources. Further investigations are needed to determine whether such differences are due to particular traits of the Nissle strain, or if they represent characteristics features of strains belonging to the phylogenetic B2 group.The present work provides also detailed insights into the metabolism of gluconate, which plays a major role in the colonization of the gut by E. coli in the use of carbon sources metabolized through the upper part of the EMP such as glucose The Csr system is thought to reinforce the competitiveness of csrA51 mutant shows that CsrA indeed plays a significant role in the control of cellular metabolism. The metabolic effects depend on the nature of the carbon source, and striking differences were observed between the two carbon sources investigated, i.e. glucose and gluconate. The effects observed on glucose metabolism are generally consistent with the known role of the Csr system. Less efficient use of glucose in the ΔcsrA51 mutant, with a reduced glycolytic flux, oxidative metabolism (reduced TCA cycle activity) and increased acetate production were all observed. These effects can be explained by the known targets of CsrA, since the positive control of glycolysis and the negative control of acetate metabolism triggers the carbon flow towards the TCA cycle. However, a direct control of the latter by CsrA, though not yet described, cannot be excluded. All these data suggest that CsrA plays a significant global role in the control of central metabolism on glucose by triggering carbon flow towards complete substrate oxidation (glycolysis+oxidative metabolism). These effects are likely to explain how the Csr system contributes to a higher metabolic efficiency on glucose.The significant re-adjusting of central carbon metabolism, resulting in both metabolome and fluxome changes - occurring in the ΔE. coli strain MG1655 was reported to be lethal for growth on glucose due to a drastic diversion of the glycolytic flow towards glycogen synthesis csrA51 mutant studied here accumulated significant amounts of glycogen during the stationary phase of growth, but the carbon flux towards glycogen synthesis measured during the exponential growth phase was much less than the glycolytic flux. Hence, the increased glycogen synthesis in this mutant is responsible for neither the decreased growth efficiency nor for the observed metabolic rearrangements. Because CsrA activity is only reduced but not suppressed in the ΔcsrA51 mutant, our results cannot exclude that in a CsrA-deletion mutant the effect on glycogen synthesis is much higher and can be responsible for the lethal phenotype on glucose. However, glycogen synthesis and its regulation are strain dependent The deletion of the gene encoding for CsrA in the laboratory csrA51 mutant. However, the opposite effects were observed. Both the metabolome and fluxome data showed that gluconate was metabolized almost exclusively via the ED pathway, while the contribution of the PP pathway was strongly reduced. Contrary to the observations made on glucose, the relative fluxes in the TCA cycle and acetate metabolism in the mutant did not differ from that in the wild-type strain. Moreover, the wild-type strain released significant amounts of pyruvate as a by-product, but this process was almost absent in the ΔcsrA51 mutant. The complete metabolisation of gluconate by the ED pathway is also accompanied by the concomitant inversion of the carbon flux in upper glycolysis, which operates in a gluconeogenic direction, resulting in the recycling of 6PG. Such recycling of triose phosphate has been already observed in ED-using species csrA51 mutant upon growth on gluconate differs significantly from that observed on glucose. The regulatory mechanisms by which the Csr system exerts global control over central metabolism upon growth on gluconate remain unclear. Trioses-phosphate recycling in the ΔcsrA51 mutant is consistent with the known antagonistic effects of CsrA on glycolysis and gluconeogenesis, which is in favor of the latter process in the with ΔcsrA51 mutant. It was recently reported that CsrA interacts with mRNAs transcribed from genes of the PP (zwf) and ED (edd) pathways csrA51 mutant . However, the effect of CsrA on PP and ED enzymes could not be clearly established E. coli metabolism is likely triggered through indirect mechanisms resulting from the interplay between the various regulation systems. To identify these regulatory mechanisms, further molecular investigations combining proteomics and transcriptomics should be performed, in light of the metabolome and fluxome data obtained here. Thus, a more detailed study should be done to identify CsrA enzyme targets, as well as, the molecular mechanism behind such regulation.The detailed investigations of gluconate metabolism revealed surprising features. There is currently no biochemical evidence that Csr targets enzymes of the PP pathway, and only an indirect – as yet unidentified – action of CsrA on one of the ED enzyme  has been proposed E. coli in the changing environment of the intestine. Because it influences the operation of E. coli metabolic network, the Csr system is also a potential target for biotechnologically-driven manipulations of the metabolism of this bacterium Whatever the regulatory mechanisms involved, this work shows that Csr is involved in the utilization of ED compounds and other physiologically-relevant carbon sources. Hence, CsrA appears as a key regulator for establishment and persistence of Table S1List of primers used in this study.(DOC)Click here for additional data file.Table S2600) for the wild-type E. coli Nissle 1917, ΔcsrBC and ΔcsrA51 mutants.Conversion factors between cell dry weight and optical density (OD(DOC)Click here for additional data file.Table S3E. coli Nissle 1917, ΔcsrBC and ΔcsrA51 mutants. Statistical differences between wild-type and ΔcsrA51 mutant are given.Metabolite concentrations in the wild-type (DOC)Click here for additional data file.Table S4Adenylate energy charge.(DOC)Click here for additional data file.Dataset S1E. coli Nissle 1917, ΔcsrBC and ΔcsrA51 mutants.Metabolic fluxes and isotopic data in the wild-type (XLSX)Click here for additional data file.


Inferring decreased or increased metabolic functions from transcript proﬁles is at ﬁrst sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction ﬂuxes. However, the growing interest in this ﬁeld can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA proﬁles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classiﬁed. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the ﬁeld is given.  A general description of the ﬂow of information from the genome to metabolism is as follows: the process is initiated by transcription factors, RNA polymerase transcribes genes into RNA, RNA is transported to the ribosome and translated into proteins, and after folding, post-transcriptional modiﬁcation, and transport to the site of action, proteins act as enzymes and transporters catalyzing biochemical reactions ﬂuxes of the molecules in the cell . The mechanisms of this control have already been recognized as being complex and multi-level—setting up predictive quantitative models is difﬁcult [Genetic regulation is a major control mechanism of the activity of the cell’s metabolic functions, especially in the frame of longer times where the metabolic function is described as a speciﬁc metabolic input/output behavior of the cell. Its activity is deﬁned as the metabolic ﬂux, difﬁcult . Transcrdifﬁcult  and trandifﬁcult  are diredifﬁcult . Finallydifﬁcult . The metdifﬁcult . For manIt might seem presumptuous to propose that transcript data can be used to predict metabolic functions. However, these predictions have received much interest because: the layer of RNA transcripts  is the only layer where a complete quantitative snapshot of all molecular species is currently feasible. Reaction ﬂux estimations currently cover only a tiny share of all reactions. Metabolite concentrations are currently measured for some 100 s metabolites but speciﬁc classes of metabolites such as lipids still present large challenges. Protein amount estimations at the genome-scale are now being done but the effort necessary is huge. The layer of DNA, whose information is a precondition for any transcription-based analysis, is not mentioned as qualitative information.transcript arrays are moderately priced in relation to the amount of data gathered,the experimental effort for the researcher is moderate due to an highly automatized process,13C marked substrates, andthe technology provides a low ambiguity and accurate estimates of the RNA amount changes . The higwell-curated genome-scale reconstructions of the metabolic networks are available ,8,9,10.If all enzyme activities and metabolite concentrations were available, a much more accurate prediction would be possible, but that is not the case on the large scale. Thus, to judge the results of the reviewed studies squarely it must be stressed that the expectations must be lowered accordingly. The systems biologist faces the trade-off between coverage versus accuracy versus the data being closer to the enzymatic activity, i.e., that quantitative proteomics data would provide a better indication of enzymatic activity, but the technique does not have the coverage provided by transcriptome data.The measurement of reaction ﬂuxes, metabolite concentrations, enzyme activities, and protein amounts are currently undertaken for a subset of all molecular species. The measurement of protein amounts is just becoming feasible with the advent of techniques such as single-shot ultra HPLC . If all Here, studies with the primary focus on metabolism are reviewed. Other major areas of application of transcript data  are not To demonstrate the difﬁculty of the task bridging several layers of cellular interaction, selected studies of the relation from one layer to the next will be sketched. et al. [DNA microarray read-outs depend on the RNA concentrations but also on the varying afﬁnity of the RNA to the probes, which is unknown on the large scale, thus special care is needed when analyzing the data . Neverthet al. . Often, et al. . Advanceet al.  and SAGEet al.  allow a et al. , but effet al. . et al. [et al. advocates the combined consideration of both mRNA and protein levels to understand the regulation of central metabolic functions in yeast [et al. predicted protein abundances from mRNA expression levels by taking into account additional information on the genes [et al. [In a pioneering study by Gygi et al. , the corin yeast ,24. Tullhe genes . The res [et al. . In part [et al.  should b [et al. . in vivo has been assessed on a large scale in yeast [Mechanistically, the relation between RNA and protein concentrations can be seen as the interplay of three aspects: (i) the life span of RNA and (ii) proteins as well as (iii) the translation efﬁciency at the ribosome . In a grin yeast  and for in yeast . vs. maximal turnover numbers in [The enzyme activity  depends on the enzyme concentration. Mostly, the relation is approximately linear in a predeﬁned environment—the ratio is called turnover number. The turnover numbers of enzymes (together with other kinetic parameters) have been estimated for many enzymes, comprehensively reviewed and made available in public databases ,32. Withmbers in ). ConsidThe prediction of metabolic ﬂuxes from enzyme activity information  has been extensively studied in the ﬁeld of kinetic modeling and its results are available in public databases ,34. A maet al. analyzed the relation of RNA abundance to metabolite concentrations in combination with the topological structure of the network. Based on clustering of correlated genes, their approach allows the identiﬁcation of hub reactions depending on a speciﬁc change of condition, which subsequently leads to a minimal set of commonly controlled metabolites. Their results support the hypothesis that the gene expression response (on different forms of stress on E. coli in this case) targets a small number of metabolites which consequently entails a large-scale change in the metabolism [Hancock tabolism . et al. found that the highest co-expression of metabolic genes is arranged in simple motifs in the metabolic network, in other words, “regulation of metabolic genes is local” [et al. studied the transcriptional adaption of yeast on growth media. They calculate optimal transcript ratios on the basis of elementary ﬂux modes [Kharchenko s local” . Cakir eux modes  and the ux modes . This reet al. compared the proteome of different oilseed to reveal differences in the intermediary metabolism, and their analysis showed a diverging use of malate as a precursor for lipids [et al. reviewed studies using transcript and metabolite co-occurrence for various applications in plant biology [et al. performed a large-scale proteomic screening of E. coli and found that, among the enzymes, only proteins involved in energy metabolism are highly abundant [Hajduch r lipids . Saito e biology . Ishihamabundant . Of particular interest are studies which measured RNA, protein, ﬂuxes, and metabolite concentrations in parallel in the same experiment ,46. The As a summary of an early attempt to relate transcript values to metabolic ﬂuxes, ter Kuile expressed “strong doubts on whether transcriptome and proteome analysis sufﬁces to assess biological function” . The conet al. [essential to draw a predictive relation from transcript abundances to ﬂuxes. Moxley et al.  correlatet al. studied gene expression in Synechocystis in combination with 13C isotope-based ﬂux measurements and emphasizes the importance of integrating transcript and ﬂux data for the understanding of regulatory mechanisms [Yang chanisms . et al. studied the role of “hierarchical” ﬂux regulation  versus metabolic regulation (change of ﬂux due to changed metabolite concentrations) for glycolytic enzymes in yeast [roles to the regulated enzymes in glycolysis in yeast: regulation of some is predominately hierarchical, for others it is metabolically. For some, the regulation is cooperative between both, and for others it is antagonistic. In an earlier study they compared other central metabolism pathways and found strong qualitative correspondence between transcript and ﬂux changes for the maltose metabolism, partial correspondence for triose-phosphate cycle and pentose-phosphate pathway, and little correspondence for glycolysis [in vivo, thus, it is likely that the structure of the metabolic system is not optimized to this situation. So their ﬁndings regarding glycolysis do not seem to be sufﬁcient to discard the idea of observing metabolic changes from transcript data for the entirety of the metabolism. Daran-Lapujade in yeast . Factor in yeast  led to tycolysis . Their ret al. analyzed transcript proﬁles of E. coli and found that pathways  associated with high protein cost are “controlled by ﬁne-tuned transcriptional programs” and those with low protein cost are only regulated in key reactions [Which metabolic genes are regulated at all? Wessely eactions . E. coli, a hierarchy of general and speciﬁc transcription factors has been found, and each metabolic function is controlled by a distinct combination of them. Enzymes catalyzing sequential reactions are co-regulated by the same transcription factors, while the regulation at junctions in the metabolic network is more complex [et al. which provides an argument to analyze a metabolic network with respect to metabolic functions and not the graph structure alone: “The co-regulation of metabolic genes is better explained by ﬂux coupling than by network distance” in E. coli [And how are the genes (resp. transcription factors) controlled? In the transcription factor network of  complex . An inte E. coli . et al. studied genetic interactions for the metabolic genes using the ﬂux-balance framework for yeast [In the studies reviewed so far, the focus was the correlation between an individual RNA and the protein, ﬂux, or concentrations. There is also another form of interactions called epistasis, which has also been modeled in the context of metabolic networks. An epistatic interaction occurs if the phenotypic impact of the knockout of one gene depends on the knockout of another gene . Such anor yeast . They foTo systematically assess the multitude of studies relating RNA proﬁles to the metabolism, criteria will be given to distinguish how the proﬁles are used. absolute, e.g., to decide whether a gene is active [relative, e.g., to quantify changed metabolic activities [co-expression, e.g., to assert which metabolic paths are controlled concertedly [Expression proﬁles can either be used in several ways. (i) Expression proﬁles can be directly used to assess a single state, which is called s active . (ii) Ditivities . (iii) Acertedly . Absolute expression proﬁles are widely used to predict the active regions in metabolic networks ,67,68,69Relative expression proﬁles are often simply analyzed by counting the number of up-or down-regulated genes using a threshold on the ratio  or the signiﬁcance level  with respect to classiﬁcations such as gene ontology  or KEGG et al. analyzed the co-expression of genes coding enzymes and found higher correlations along linear reaction paths between branch points and a hierarchical modularity of the regulation [Expression correlations are used to determine which genes are commonly regulated, for instance to predict transcription factors. Metabolic pathways with a high correlation of genes coding the necessary enzymes can be considered as a functional mode of operation in a particular cell type ,76,77. Igulation . Lorainegulation . absolute expression proﬁles. The distinction between active and inactive genes is crucial for all methods using et al. gave clear evidence that in reality there is a clear distinction between genes which are expressed and those which are not expressed (in the sense that the gene product is present in sufﬁcient abundance to take effect in the cell) [threshold is the method of choice. As there is an overlap of RNA abundance levels of inactive and active genes [Hebenstreit he cell) . The obsve genes , methodsclearly active genes receive a bonus, the clearly inactive genes a penalty. More sophisticated is the MADE approach [The negative effect of uncertainty of the optimal threshold value is reduced by its “soft” application. For instance, in the GIMME algorithm  the threapproach  that avoIn other approaches, the setting of a threshold is completely circumvented and the expression values are used in a continuous way ,82,83. The way the metabolic system is represented is another important aspect of the methods. Mostly, the system is represented by the metabolic network which consists of the metabolites and the biochemical reactions which convert the metabolites in ﬁxed quantities, the stoichiometric factors—thus, it is called stoichiometric model. Often a stoichiometric model is used to compute ﬂux distributions in the ﬂux-balance framework ,65,69,74et al. use such a graph representation where the nodes are the metabolites, and for every biochemical reaction an edge is drawn from each substrate to each product [In a simpliﬁcation of the stoichiometric model, the stoichiometric factors are ignored . Hancock product . inactive genes and/or bonusing nonzero ﬂuxes belonging to active genes  [rank reaction paths [A ﬂux distribution or an active subnetwork can be computed by penalizing ﬂuxes belonging to  proﬁle) ,67,81,89 proﬁle) . Express proﬁle) . Another proﬁle) . A simil proﬁle) , can in  proﬁle) . Its bason paths  or similon paths . Based oon paths ), expreson paths ,92,93. Ton paths . Also, gon paths . The topon paths . Gene set analysis  can be aMethods applying transcript data to the metabolism can have many different aims. As a distinguishing characteristic, some studies are directed to lay theoretical foundations, while others are directly targeted to answer speciﬁc biological question. For the ﬁrst category, the reconstruction of a metabolic network for a speciﬁc cell with the help of transcript data can be mentioned . Once a There are a number of studies that try to understand the regulation patterns by analyzing the co-expression of metabolic genes in a large number of transcript proﬁles ,54,55,78i.e., concerted regulation of genes to change the concentration of a certain metabolite) was the focus of another study [E. coli, then this method would open a path to identify biomarkers in biotechnology and medicine. Some applications of transcript data are directly related to clinical questions such as the prediction of biomarkers ,94, the er study . If the Often, studies have an explicit biotechnological focus, for instance plant strain optimization , bacteriThe threshold-based activity prediction GIMME  . The most likely purpose is to enhance a function for which the coded protein is required. Inferring metabolic activity changes from transcript proﬁles is justiﬁed in two ways: mechanistically and by the assumption of evolutionary optimality. The former is based on the fact that RNA is translated into proteins then working as enzymes or transporters, thus modifying the metabolic ﬂux related to the function. The latter is based on the argument: if the cell undertakes the effort to increase the mRNA production rate, it does so only with a purpose (related to the philosophical concept of As the direct correlation of transcript proﬁles to metabolic reaction ﬂuxes is not high, there is a wide range of applied methods having different strengths and weaknesses. The critical question is whether a particular method is suited to a particular application. There is a clearly recognizable trend that the applied methods are increasingly enriched with available knowledge as the only way to increase the predictivity. For the outlook of the ﬁeld, it is foreseeable that large-scale metabolomics, proteomics, ﬂuxomics, and enzyme characterization will become more manageable and affordable and the need to cover the wide distance from transcript to metabolism will vanish. The methods can then be improved with mechanistic descriptions of the underlying processes as soon as they are discovered. The methods crossing several layers will have to include more components as it will be possible to parametrize them using experimental data. Genome-scale quantitative proteomics is on the brink of being widely available and feasible . Quantit


In this review, we describe the reconstruction of the human metabolic network, introduce the constraint based modeling approach to analyze metabolic networks, and discuss systems biology applications to study human physiology and pathology. We highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system. Metabolism is crucial to cell growth and proliferation. Deficiency or alterations in metabolic functions are known to be involved in many human diseases. Therefore, understanding the human metabolic system is important for the study and treatment of complex diseases. Current reconstructions of the global human metabolic network provide a computational platform to integrate genome-scale information on metabolism. The platform enables a systematic study of the regulation and is applicable to a wide variety of cases, wherein one could rely on  Metabolism is crucial to cell growth and proliferation. Deficiencies or alterations in metabolic functions are known to be involved in many human diseases. For example, the pathogenesis of diabetes results from malfunction in the regulation of metabolic pathways, leading to alterations in insulin signaling, oxidative metabolism, and lipid/fatty acid metabolism . Dysreguin silico the global effect of perturbations on the network to generate hypotheses and help understand the mechanisms underlying the genotype-phenotype relationship.As a highly-connected complex reaction network wherein the functionality and the connectivity is strongly associated , changesIn this review, we first describe the reconstructions of global human metabolic network, and then introduce the constraint based modeling approach to analyze metabolic networks. We further discuss systems biology applications of the metabolic networks to study human physiology and pathology. Finally we highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system.The global human metabolic network has been manually curated based on an extensive collection and evaluation of the genomic and bibliomic data. The first two installation of the network were released in 2007: The Edinburgh Human Metabolic Network  and the The two networks (Edinburgh Human Metabolic Network and the human Recon 1), developed independently by different research groups, consist of many different genes and reactions. The Edinburgh Human Metabolic Network contains more genes and metabolites, but was not compartmented in its initial release. Compartmentalization requires assignments of metabolic reactions into different cellular organelles  and accounts for the transportation and exchange of metabolites between organelles. Human Recon 1 is a compartmented network which could be used in reconstructing predictive models for systems biology studies, therefore, most of the recent applications have been based on Recon 1. An overview of the publications thus far that used Recon 1 is reviewed by Bordbar and Palsson . NotablyThe reconstruction of the global human metabolic network uses a bottom-up approach. Researchers begin by compiling reactions of cellular metabolism to build a network through the collection of gene annotations, enzymes and pathway information from genome  and pathway  databases. Researchers then refine the network by manually collating literature evidences, including journal articles, reviews and textbooks on metabolic functions, biomass composition, growth conditions and gene-reaction associations. The constructed draft network is converted to biochemical models to evaluate the basic functionality, and simulations are performed to check for consistency with the current knowledge. The whole process runs iteratively to incorporate as much information and minimize gaps and inconsistencies. The protocol for the reconstruction process is available in .The major difference in a metabolic network as compared with other biological network, e.g., Protein-Protein Interaction, Protein-DNA network, is that the metabolic network represents a biochemical system that is charge-balanced, mass-balanced and compartmentalized. This not only provides information about whether there is an interaction, but also how it happens and what it produces as a biochemical reaction, and thus can be directly converted into mathematical equations based on the biochemical reactions, for model predictions. A reconstructed human metabolic network can be represented by a system of stoichiometric reactions. This system of reactions can be modeled as ordinary differential equations, however the reaction rate constants and metabolite concentrations are typically difficult to obtain, thereby limiting their applicability to small well-studied networks. However, since the stoichiometry of metabolic reactions are not organism or context-dependent but is fixed by mass balance, one could apply Constraint Based Modeling  to simuMathematical representation of reaction network and Constraint Based Modeling:S (Stoichiometric Matrix), with m compounds (rows) and n reactions (columns). The stoichiometric coefficients are negative for the substrates of each reaction, and positive for the products.Reactions: v (n by 1 vector) on all reactionsFlows: X (m by 1 vector) of all compoundsConcentrations: Assuming pseudo-steady state, the time derivative is zero, therefore:v that satisfies this equation is in the null space of S.So the flux distribution n > m results in an under-determined system that does not have a unique solution. Adding constraints permit a “feasible” solution to the system of equations, for example, a “flux capacity” constraint determines the upper and lower bounds of the flux through a reaction. Imposing mass balance and capacity constraints will define the space of feasible steady-state flux distributions of the network. Geometrically the space looks like a “flux cone” in the null space of S. A visualization of the “flux cone” is shown in [Z, which is a linear function of fluxes. An objective function could beIn the human metabolic network, shown in   to showc is a column vector which assigns weights to each reaction, Tc is the transpose of the vector c, and V is the flux vector through all the reactions. Optimization of the objective function Z identifies a unique (or multiple) set of flux configurations within the flux cone. The constrained linear optimization problem can be solved by linear programming [where gramming ,20.The form of the objective functions, constraints and the optimization problems can vary depending on the biological applications, which are different variants of the Constraint Based Modeling. For example, the Flux Sensitivity Analysis (FSA) estimates the objective flux change in response to perturbations in some reactions of interest . The Flui.e., condition/cell-type/tissue/organ specific) human metabolic systems in order to generate biological hypotheses and study the physiology or pathology of cellular processes. Since the global human metabolic networks (the human Recon 1 and Edinburgh Human Metabolic Network) are generic metabolic networks that collate information from all types of human cells, the reconstruction of a context-dependent network is required prior to in silico analysis of the particular system under investigation. Once the context-dependent reconstruction is obtained, one can simulate the metabolic phenotypes under different perturbations to identify essential gene targets or pathways, or predict cellular responses to different treatments. A summary of the pipeline for the systems biology applications of human metabolic network is shown in A central goal in the application of systems biology on the human metabolic network is to reconstruct and simulate context-dependent  or “absent” (low expression level) for the condition being investigated, then selects the active reactions according to their corresponding gene/enzymes’ expression level. For example, the Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm, developed by Becker et al. , uses ge [et al. . The app [et al.  developein silico analysis can be used to generate hypotheses on the cell growth, ATP production, or the states of specific metabolic functions upon perturbation [Constraint based modeling and simulation based on a condition-specific human metabolic network can be used to predict the flux distribution in the network for that specific metabolic phenotype. Such urbation .et al. [Modeling and simulations of a metabolic model of human kidney reconstructed with the GIMME algorithm were used to evaluate the metabolic phenotypes associated with the side-effects of a drug treatment . The sidet al.  further in silico perturbations to predict novel targets, interpret systemic effects, and identify alterations in the metabolic states to better understand the genotype-phenotype relationships. In summary, the reconstruction and simulation of human metabolic networks enable a systematic study of the regulatory system and is applicable to a wide variety of cases. One could rely then on Current reconstructions of the global human metabolic network are incomplete due to limitations in our knowledge of the complexity of the human genome. Both the human Recon 1 and Edinburgh Human Metabolic Network have gaps and missing parts. For example, there are many reactions in the networks that do not have corresponding regulatory genes/enzymes annotated. For the reactions with known genes/enzymes in either network, there are only around one thousand genes that are common to both, with some collated in only one of the networks. It would be better to combine the two reconstructions to achieve a more comprehensive network. Nevertheless, the integration would be difficult since the standards are different between the two networks—Different compound names, different reaction names, and different IDs are used for the network components.The community needs to drive the effort for better reconstruction and integration of these network models. A community approach of “reconstruction jamborees” ,38 was set al. [i.e., the lower a gene is expressed, the lower the flux that could be conducted through its corresponding reactions. Although it could be more sensitive to changes (noises) in the gene expression by directly associating the gene expression level with flux bounds, the approach is less dependent on the discretization of the gene expression and the present/absent call of the reactions, which could provide a more realistic model. It has been applied to bacteria to predict metabolic modulators and responses to different drugs [Applications of the metabolic model require reconstruction of context-dependent networks, thus computational approaches to automate the generation of condition-specific models are of great interest. Current approaches rely on present and absent (high/low expression) of the genes to determine potentially active reactions, which could be problematic since a small change in the threshold in the expression analysis could result in very different lists of present and absent genes and affect the reconstruction. Most genes’ expressions are not naturally bimodal but have many levels. A discretization process separates such expression levels into ON and OFF states with some arbitrary thresholds , thereby the analysis based on discretization will be affected by the threshold that is chosen. A change in the threshold will change many of the genes’ ON/OFF categorization, which could affect the network reconstruction, since the reconstruction process is also bimodal with reactions being absent or present according to the ON and OFF state of the genes. For example, a gene expressed a little lower than the median may be classified as absent/OFF, then the reaction it is associated with may be removed. However a gene is not necessary in an entirely OFF state when expressed at a low level, and a reaction may not necessary be completely abandoned just because there are fewer enzymes than normal. Current approaches rely on the stoichiometric constraints to mitigate the possible inconsistencies. Future reconstruction approaches could impose more realistic models of the functional relationship between genes and reactions. For example, a novel approach developed by Colijn et al.  associatnt drugs .With the rapid development of metabolomics, metabolic flux data becomes more readily available and novel approaches are being developed to more efficiently monitor flux distribution in mammalian cells with Metabolic Flux Analysis, by employing isotope-labeling  techniques . Researcet al. [M. tuberculosis to study host-pathogen interactions. Simulation of this metabolic system under different infection states helped identify differentially active pathways and potential gene targets. Another pioneering work by Lewis et al. [in silico model was used to analyze the gene expression data to identify changes in the metabolic states as well as important genes in Alzheimer’s disease. In both studies, the incorporation of the metabolic networks was based on manual curation, since there are no computational approaches for reconstructing a multi-cellular metabolic system to date. In the future we should expect novel approaches and applications to higher level systems modeling that involve different cells and tissues.Context-dependent metabolic models of different cells could be incorporated to achieve higher level systems to study cell-cell interactions. A recent study by Bordbar et al.  incorpors et al.  reconstrThe gene-reaction associations in current reconstructions of the human metabolic networks account for enzymes, isozymes, transcript variants and protein complexes, but however are limited to metabolic-related genes and enzymes. There are other layers of regulation of gene expression and enzyme activity, including transcriptional regulation, post-transcriptional and post-translational processes. Many genes involved in transcriptional or post-transcriptional regulation of the metabolic genes or enzymes could also affect the metabolic states, which are excluded from current applications. Although it is difficult to have a comprehensive model due to limitations of our knowledge of biological regulation, we could incorporate more information into the metabolic network to provide a better model of the regulation of the human metabolic system. Given the increasing amount of transcriptome data, protein interaction data and other high-throughput data, they could be capitalized upon to enhance the regulatory model. Future studies should consider incorporating multiple layers of regulation to better understand the molecular mechanisms behind the regulation of human metabolism.


The ability to perform quantitative studies using isotope tracers and metabolic flux analysis (MFA) is critical for detecting pathway bottlenecks and elucidating network regulation in biological systems, especially those that have been engineered to alter their native metabolic capacities. Mathematically, MFA models are traditionally formulated using separate state variables for reaction fluxes and isotopomer abundances. Analysis of isotope labeling experiments using this set of variables results in a non-convex optimization problem that suffers from both implementation complexity and convergence problems.13C MFA models using a new set of variables referred to as fluxomers. These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization. A powerful fluxomer iterative algorithm (FIA) is developed and applied to solve the MFA optimization problem. For moderate-sized networks, the algorithm is shown to outperform the commonly used 13CFLUX cumomer-based algorithm and the more recently introduced OpenFLUX software that relies upon an elementary metabolite unit (EMU) network decomposition, both in terms of convergence time and output variability.This article addresses the mathematical and computational formulation of Substantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models. We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments. Metabolism is the complete set of chemical reactions taking place in living cells. These chemical processes form the basis of all life, allowing cells to grow, reproduce, maintain their structure and respond to environmental changes. Metabolic reactions are divided into groups called metabolic pathways, which are typically constructed heuristically according to their connectivity and presumed function . Each meInitially, the structure of metabolic pathways was examined by identifying their intermediate compounds. Subsequently, the various biochemical reactions connecting these compounds were mapped. Due to the success of this research, the topological structure of many metabolic pathways is nowadays fully documented . The nex13C at specific atom positions). By measuring the resulting labeling patterns of intracellular metabolites after steady state has been achieved, additional flux information is obtained.Various experimental techniques have been developed to enable measurement of intracellular metabolic fluxes, either directly or indirectly. One of these approaches makes use of isotope labeling experiments. In this method, the metabolic system is administered a known amount of an isotopically labeled substrate  required to interpret these labeling measurements. In their series of articles, Wiechert et al. -9 constrPresence of unstable local minima: due to the non-convex nature of the objective function.• Complex mathematical representation and computational implementation. This results in the need for ad-hoc algorithms and mathematical analysis, and long running times are required for reliable convergence.• scaling factors for each isotopomer measurement, because of the fact that the available experimental techniques are only capable of measuring isotopomer fractions up to a proportional scaling factor  and initial conditions• Eliminates the need for measurement scaling factor estimation• Poses the problem using simple mathematical expressions, allowing the use of generic optimization algorithmsThe rest of the article is constructed as follows. The Results and Discussion section illustrates the advantage of our approach via simulation results comparing fluxomer variables to the commonly used cumomer approach and the more recently introduced EMU approach. The Methods section presents the detailed formulation of the fluxomers optimization problem and the fluxomers iterative algorithm (FIA) that provides a reliable and efficient method for solving it. All source code and executables for our algorithms are freely available at the author's website .Embden-Meyerhof and Pentose Phosphate metabolic pathways of Escherichia coli CorynebacWhen comparing the running times of FIA with OpenFLUX, the different algorithmic approaches of the two must be kept in mind. While OpenFLUX requires the user to supply it with sets of free fluxes, FIA requires no free fluxes nor initial values. Open-FLUX rapidly evaluates dozens of different optimization cycles with random initial values and seeks the best fitting result among them, while FIA uses only one single (longer) run. As such, the convergence probability of OpenFLUX depends on the number of attempts and random values generated during its operation, while the FIA results do not depend on any random value. Furthermore, in its analysis, EMU based algorithms evaluate only the fluxes necessary for measurement comparison, and thus their running time depends both on the metabolic network structure and the amount and location of the given measurements. FIA, on the other hand, can supply the entire set of metabolic fluxes at any given time, with no additional computation requirement (which depends mainly on the network structure).First, we ran the exact same simulation as Quek et al. performed in their article. They supply very accurate (mean error in the order of 0.15 mol%) values for the label measurements, and used the given measured fluxes as if they were noiseless measurements (thus as constants). We start by comparing the simulation time for this simple case. According to .yi - yi - 1||2 >εN4. If ||(a) Solve i y= i y- r according to Newton's method.(b) Set yi - yi - 1||2 e > εgo to 3.5. If ||x = [g(u) ○ (H1yiL) ○ (H2yiL)].6. Calculate 7. Solve The supplied software uses a variant of the "sequential least-squares" algorithm ,25 for sOS developed and implemented the theory, algorithm, simulations and software. YCE and JDY initiated and supervisied the study, and contributed to writing the manuscript. All authors reviewed, edited and approved the final manuscript.


Mycobacterium tuberculosis requires the enzyme isocitrate lyase (ICL) for growth and virulence in vivo. The demonstration that M. tuberculosis also requires ICL for survival during nutrient starvation and has a role during steady state growth in a glycerol limited chemostat indicates a function for this enzyme which extends beyond fat metabolism. As isocitrate lyase is a potential drug target elucidating the role of this enzyme is of importance; however, the role of isocitrate lyase has never been investigated at the level of in vivo fluxes. Here we show that deletion of one of the two icl genes impairs the replication of Mycobacterium bovis BCG at slow growth rate in a carbon limited chemostat. In order to further understand the role of isocitrate lyase in the central metabolism of mycobacteria the effect of growth rate on the in vivo fluxes was studied for the first time using 13C-metabolic flux analysis (MFA). Tracer experiments were performed with steady state chemostat cultures of BCG or M. tuberculosis supplied with 13C labeled glycerol or sodium bicarbonate. Through measurements of the 13C isotopomer labeling patterns in protein-derived amino acids and enzymatic activity assays we have identified the activity of a novel pathway for pyruvate dissimilation. We named this the GAS pathway because it utilizes the Glyoxylate shunt and Anapleurotic reactions for oxidation of pyruvate, and Succinyl CoA synthetase for the generation of succinyl CoA combined with a very low flux through the succinate – oxaloacetate segment of the tricarboxylic acid cycle. We confirm that M. tuberculosis can fix carbon from CO2 into biomass. As the human host is abundant in CO2 this finding requires further investigation in vivo as CO2 fixation may provide a point of vulnerability that could be targeted with novel drugs. This study also provides a platform for further studies into the metabolism of M. tuberculosis using 13C-MFA. Mycobacterium tuberculosis has been intensively studied since the demonstration that both of the isocitrate lyase genes encoded by this pathogen, icl1 and icl2  are essential for the survival of this pathogen in the host. This finding has been generally interpreted as being due to a shift to the consumption of fats in the host. We previously demonstrated increased ICL activity when the vaccine strain of M. tuberculosis (BCG) was growing slowly on carbohydrates, suggesting a more extended role for ICL than just fat metabolism. Here we show that the gene icl1 is actually essential for slow growth on glycerol. By feeding BCG and M. tuberculosis cells isotopically labeled carbon sources and using a combination of experimental and computational analysis we identified a novel metabolic route for carbohydrate metabolism in which ICL is a key enzyme. We also demonstrated that M. tuberculosis is able to acquire biomass carbon from CO2. As CO2 is abundant in the human host, inhibition of CO2 fixation could be exploited in the development of novel drug treatments against tuberculosis.The role of the enzyme isocitrate lyase (ICL) in the metabolism of  Mycobacterium tuberculosis is either asymptomatic or leads to the development of acute disease (primary tuberculosis). In both cases, infection (including recovery in the absence of treatment) is generally followed by a period of latency which may last for months, years, decades or a lifetime. Reactivated tuberculosis (post-primary) can occur at any time and is a major source of transmission. Unlike many pathogens M. tuberculosis does not rely on the production of specific toxins to cause disease. Rather the ability to adapt and survive within the changing and adverse environment provided by the human host during the course of an infection seems to be the secret to the success of M. tuberculosis. It is becoming apparent that key to this adaptation is the metabolic reprogramming of M. tuberculosis during both the acute and chronic phase of tuberculosis (TB) disease and therefore a more complete understanding of mycobacterial metabolism remains a major goal of TB research.Initial infection with M. tuberculosisin vitro has many similarities to the transcriptional response characteristic of the adaptation of M. tuberculosis to the macrophage environment M. tuberculosis to the host environment. Yet, the shift to slow growth is much more than a general slowing down of cellular processes. Global mutagenesis analysis demonstrated that growth rate control in M. tuberculosis is in fact a carefully orchestrated process which requires a distinct set of genes encoding several virulence determinants, gene regulators, and metabolic enzymes in vitro and in vivo remain unclear.Our previous studies have investigated the influence of growth rate on metabolism in M. tuberculosis in silico using flux balance analysis (FBA) of our genome-scale reconstruction of TB metabolism We previously investigated the metabolic response of slow growing glycerol-limited M. tuberculosis in both macrophages and mice Isocitrate lyase (ICL) has been shown to have an essential role in the survival and persistence of M. tuberculosis has therefore been generally interpreted to be due to this enzyme's role in the glyoxylate shunt and reflect a metabolic shift in the principal carbon source from carbohydrates to fat in the host. However, the ICL's of M. tuberculosis also function as methylisocitrate lyases in the methyl citrate cycle where they are involved in the metabolism of propionyl-CoA in vitro enzyme activity data demonstrates a role for ICL during slow growth rate on glycerol M. tuberculosis operates under more general conditions.The glyoxylate shunt is an anaplerotic pathway important for the metabolism of fatty acids which is mediated by the enzyme ICL along with malate synthase. The essentiality of ICL in the survival and persistence of M. tuberculosis during slow growth. A more direct means of establishing the intracellular fluxes is through 13C metabolic flux analysis (13C-MFA). This powerful technique has been successfully applied to identify functional flux states in various microbes M. tuberculosis. The method utilizes the fact that amino acids are constructed from metabolic precursors in central metabolism. Growth of a cell with 13C-labeled substrate therefore leads to a pattern of 13C incorporation in the proteinogenic amino acids that is determined by the intracellular fluxes to these compounds. Computational tools may then be used to find the values of the intracellular fluxes that are consistent with the pattern of amino acid labeling, as determined by NMR or mass spectrometry.However, FBA cannot provide unique solutions for intracellular fluxes because of the existence of multiple parallel pathways; so the method could not identify the metabolic pathways utilized by M. tuberculosis at slow growth rates using mutational, enzymatic and macromolecular analysis. We demonstrate that ICL is actually essential in slowly growing Mycobacterium bovis BCG in a glycerol-limited chemostat. In addition we use 13C-MFA to determine the metabolic phenotype of M. bovis BCG at fast and slow growth rates and M. tuberculosis H37Rv at slow growth rate, in order to provide a system-level understanding of the metabolism of slowly growing mycobacteria and particularly the role of ICL. This approach provides a novel insight into the M. tuberculosis metabolism that may have implications for how the tubercle bacillus survives in vivo.In this study we further examine the hypothesis that ICL plays a role in dissimilation of carbohydrates in icl1 gene was deleted from BCG to generate BCGΔicl1 and this was confirmed by Southern blot (−1 mg protein−1) was less than one tenth that of the wild type value (21.20 nmol min−1 mg protein−1). The residual activity demonstrates that another gene encodes an active ICL in M. bovis BCG. Two ICL genes have been identified in M. tuberculosis H37Rv (icl1 and icl2) but only one (icl1) encodes a functional enzyme in this strain M. tuberculosis CSU93 M. bovisM. avium both genes encode active enzymes. The arrangement of the isocitrate lyase genes in the M. bovis BCG genome sequence suggests that both enzymes are also functional in this strain and the data presented here confirms this.To further explore the role of ICL during slow growth, the ern blot . Enzyme icl1 mutant strain was characterized at two specific growth rates in the chemostat: Sµ = 0.03 h−1 (td = 23 h) and Fµ = 0.01 h−1 (td = 69 h). The physiological icl1 gene did not impair the ability of the mutant to adapt to growth in the bioreactor, as growth of the mutant strain was indistinguishable from the wild type during the batch phase (−1 (corresponding to a doubling time of 69 h), wild type BCG reached a steady state after approximately six volume changes (day 40) after which little variation was observed in the CO2 and biomass production rates, indicating that steady state (continuous culture) conditions had been attained    in that of 23 h) . A signi strains . Macromo strains  the carbin vitro activities of ICL were two-fold higher in slow (td = 69 h) growing chemostat cultures than in the more rapidly (td = 23 h) growing cultures in vitro activities of the competing enzyme, isocitrate dehydrogenase (IDH), and also enzymes involved in downstream anaplerotic reactions  was however below the detection level of the assay at both growth rates indicating that this enzyme is either inactive in the growth conditions tested or that the assay conditions were not optimum. Very low levels of malic enzyme (MEZ) were also detected at both growth rates. This is in accordance with previous observations We have previously demonstrated that the eactions  in the wwth rate . The pat13C3]glycerol. The labeling experiments with M. bovis BCG were carried out at both fast (td = 23 h) and slow (td = 69 h) growth rates. The physiological characteristics of cells at these growth rates are summarized in In order to complement the enzyme activity data and further explore the metabolic phenotype of slowly growing mycobacterial cells we performed metabolic flux analysis (MFA) using [13C3]glycerol and samples were removed for analysis every volume change for a minimum of five volume changes. Labeling of proteinogenic amino acids was measured using gas chromatography coupled to mass spectrometry (GC-MS). The labeling pattern of most of the fragments changed very little between the third and fourth volume change indicating that the culture had reached an isotopic steady state  normally associated with the reductive TCA cycle and this is thought to complete the oxidative TCA cycle M. tuberculosis13C-MFA as the carbon flow from α-ketoglutarate to succinate is the same irrespective of which route is taken. Consequently both routes are combined and lumped into one flux model. Simplifications were also introduced where the accuracy of flux estimates was predicted to be poor. For example based on available measurements it was also not possible to discriminate between the MEZ and PCA reactions with high confidence so the summed flux is shown between pyruvate and malate/oxaloacetate. The refined network is illustrated in For flux calculations we constructed an isotopomer model composed of reactions extracted from our GSMN-TB model −1, td = 23 h) and slow  growth rates and also to incorporate information from the enzymatic data the final isotopomer model consists of two sub-models, representing fast and slow growth rates. Both sub-models are structurally identical and are coupled via inequality constraints to reflect the tendencies of the measured in vitro enzyme activities  ICL flux was higher at the slow growth rate (8.76–9.04%) than at the fast growth rate (5.49%) for both slow growth rate solutions. In addition, at either the slow or fast growth rate use of the glyoxylate shunt was coupled with an unexpectedly low flux (0–1.55% at slow growth rate and 0.02% at fast growth rate) through the later part of the TCA  with the malate/oxaloacetate pool being replenished primarily using carbon fixing anaplerotic reactions. Another interesting feature of the BCG flux solutions obtained for both fast and slow growth rates was a negative flux (going in the reductive direction) through the α-ketoglutarate ferredoxin oxidoreductase (KOR) reaction. Approximately 11% of the oxalocetate originated from the anaplerotic PCK at slow growth rate, whereas at fast growth rate there was only a 1.2% anaplerotic flux through PCK with an additional 3.47% flux to malate/oxaloacetate through the reactions catalysed by either PCA or MEZ. Both slow growth rate solutions show a negative flux from succinate to succinyl CoA via succinyl-CoA synthetase (SCS) whereas at the fast growth rate there is negligible flux to succinyl CoA. Succinyl CoA is a precursor of methyl branched lipids, which are a major component of biomass, as well as being used for synthesis of diaminopepimelate, sulpholipids and heme. The increased flux to succinyl CoA may therefore reflect an increased requirement for some or all of these metabolites at slow growth rate. In contrast to the increased succinate/succinyl CoA output flux at slow growth rate, there was a dramatic increase in the output flux of acetyl CoA at the fast growth rate and this was also present in one of the two solutions for slow BCG cells.M. tuberculosis we performed steady state chemostat cultivations of the tubercule bacillus at a dilution rate of 0.01 h−1 (td = 69.3 h). The steady state glycerol and Tween 80 consumption rates were very similar to those of BCG (M. tuberculosis exhibited a lower CO2 production rate (0.23 mmol g biomass−1 h−1) compared to BCG (0.57 mmol g biomass−1 h−1).In order to confirm that the observed metabolic pathway for pyruvate dissimilation in slowly growing BCG was also occurring in e of BCG . M. tubeM. tuberculosis were calculated from GC-MS data obtained from steady state [13C]glycerol labeling experiments by applying methodology similar to that used for BCG , it was not possible to perform full 13C-MFA analysis of these experiments. However, GC-MS analysis of the proteinogenic amino acids clearly demonstrated incorporation of the CO2-derived 13C into several amino acids including those derived from oxaloacetate, such as lysine , disruption of the icl1 gene resulted in a loss of viability at the slower growth rate (corresponding to a doubling time of 69 h). This finding echoes the previous demonstration that persistence of the icl1 mutant was attenuated in mice in vivo growth. It also demonstrates the value of the chemostat for identifying growth-rate dependent phenotypes.The demonstration that ICL was essential for the multiplication of icl1 for adaptation of BCG to slow growth under glycerol limiting conditions was not due to an increased consumption of the hydrolysis products of the detergent Tween 80 which is added to mycobacterial cultures to minimize clumping. Although some Tween 80 was metabolized by BCG during carbon limited growth in a chemostat, the Tween 80 consumption rate was significantly lower at slow growth rate than at the faster growth rate. In addition, the Tween 80 consumption rate did not differ significantly between the wild type and BCGΔicl1. Whilst deletion of icl1did not reduce viability of M. bovis BCG at the faster growth rate in the chemostat, our findings indicate that the icl1 mutant had altered biomass yields and macromolecular composition, indicating that the icl1 gene is also playing a non-essential role in the physiology of BCG at the higher growth rate. Although it is unclear why the removal of icl1 results in an increased yield on glycerol it is consistent with similar findings for Escherichia coli where it was demonstrated that a functional icl gene was associated with a growth disadvantage in competition experiments in the chemostat The essential requirement of 13C-MFA we have identified a new route for pyruvate dissimilation which occurs in both BCG and M. tuberculosis, in which ICL is a key enzyme. The GAS pathway is characterized by flux through the glyoxylate shunt and also through the carbon fixing anaplerotic reactions at the PEP-pyruvate-oxaloacetate node  in BCG and M. tuberculosis, as has been found for Bacillus subtilisM. tuberculosis in macrophages and mice M. tuberculosis is metabolizing glucose M. tuberculosis the metabolic flexibility to fine tune the balance of catabolic and replenishing reactions. This could be particularly important in the nutrient restricted human host.The ‘anaplerotic reactions’, PCK/PCA/MEZ, play a key role in the novel pathway we have described. These reactions may be used for gluconeogenesis but are also utilized to convert C13C-MFA was unable to distinguish which enzyme was responsible but we were unable to detect any activity of PCA in vitro. Although activity of this enzyme has been detected in Mycobacterium smegmatisM. tuberculosis. However, the gene encoding this enzyme was identified as essential for growth of M. tuberculosis on media containing glycerol and glucose Corynebacterium glutamicum has been shown to be very unstable 13C data has provided evidence for malic enzyme activity in M. tuberculosisAt the fast growth rate, the flux solutions indicated that some carboxylating anaplerosis was catalysed by either PCA or MEZ. 13C]bicarbonate confirmed that, in accordance with the flux solutions, an appreciable proportion (up to 22% incorporation in some amino acids after the addition of one volume of media containing sodium [13C]bicarbonate) of carbon in M. tuberculosis growing in a glycerol limited chemostat arises from CO2 fixation through carboxylation reactions. The estimated flux through carboxylating anaplerotic reactions is much more than the relatively modest requirement for anaplerotic replenishment of the TCA cycle intermediates due to their removal for amino acid synthesis  and suggests that fixed CO2 is being incorporated into other components of biomass, as well as amino acids. These data alongside a number of different studies provide evidence that CO2 fixation maybe important for the growth and virulence of M. tuberculosis. The stimulatory effect of CO2 on the growth of mycobacterium has been known for decades 14C tracer experiments performed in the 1950's demonstrated that M. tuberculosis could use CO2 as a source of carbon 2 prevent the growth arrest of M. bovis BCG in a hypoxic model of persistence M. tuberculosis encodes three carbonic anhydrases, Rv1284, Rv3273 and Rv3588c which have a role in the growth and virulence of M. tuberculosis: Rv3588c is essential for growth in vivoin vitro growth 2 to bicarbonate and are associated with metabolic enzymes such as PCK, PCA and MEZ which produce or consume CO2 or bicarbonate.Isotopic tracer experiments using sodium glycerol and 4 ml of natural glycerol l−1. For the carbon fixation chemostat experiments a modified Roisin's medium was used which contained 0.15% sodium bicarbonate 13C]bicarbonate. Since CO2 is rapidly lost to air from a stirred solution of sodium bicarbonate, the chemostat air supply was made recyclable by connecting the effluent gas outlet to the air inlet. Oxygen concentrations were monitored carefully to ensure that oxygen levels remained above 70%. Biomass samples for GC-MS analysis were taken for analysis every volume change.The Biomass and supernatant samples were collected and harvested The macromolecular composition of the cellular biomass was determined from freeze-dried cell pellets as previously described E. coli strain DH5α was grown in solid or liquid Luria-Bertani (LB) medium as described by Sambrook et al. M. bovis BCG cells were propagated in Middlebrook 7H9 broth or 7H11 agar containing 5% (v/v) OADC enrichment media supplement (Becton Dickenson), 0.5% glycerol plus 0.05% Tween 80 for liquid cultures. For mycobacteria, when selection was required, kanamycin at 20 µg ml−1, X-gal at 50 µg ml−1 and sucrose at 2% (w/v) were added to the culture media.For the construction of an isocitrate lyase mutant, M. bovis BCG was constructed using the strategy described by Parish and Stoker M. bovis BCG icl1 gene. These fragments were cloned into p2NIL to generate p2NIL-icl1. The final delivery vector was generated by cloning the Pac I cassette from pGOAL19 into the p2NIL-icl1 construct to give pUSicl1. Single crossovers were selected for on 7H11 agar supplemented with kanamycin and X-gal and then grown in media lacking antibiotics to allow the second recombination to occur. Following growth the cells were sub-cultured onto sucrose plates to select for sucrose-resistant white colonies, which had lost the integrated plasmid through a second crossover. PCR and Southern analysis were performed to verify the expected genotypes.An unmarked isocitrate lyase mutant of et al (2005) with minor modifications M. bovis BCG lysates.Crude enzyme extracts were prepared according to Tian ICDH was assayed as described et al (2001) was used for assaying PCK activity in the direction of oxaloacetate formation The procedure described by Mukhopadhyay 2, 40 mM sodium l-malate, 0.6 mM NADP+, 20 mM KCl. Reactions were monitored spectrophotometrically by following the production of NADPH at 340 nm (ε = 6.223 M−1 cm−1) at 30°C.A modification of previously described procedures was used to assay for MEZ activity PCA was assayed exactly as described GDH was monitored by the method described by Wayne (1996) M. tuberculosis and M. bovis BCG was constructed in the framework of 13CFluxhttp://www.13cflux.net/), a software toolbox for flux distribution estimation.An isotopomer model of central metabolism  in M. tuM. tuberculosis and includes reactions of glycolysis (EMP), the pentose phosphate pathway (PPP), the tricarboxylic acid cycle (TCA) and anaplerosis (ANA). Moreover, all biosynthesis pathways relevant for M. tuberculosis are formulated in simplified form in that linear reaction sequences are combined into one reaction . 0.1 ml was dried in-vacuo and this mixture was derivatised by adding 140 µl acetonitrile:N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA):1% tert-butyldimethylchlorosilane (TBDMCSI), 1:1., sonicating  and then heating  to complete the derivatisation. Samples were analysed by Gas Chromatography-Mass Spectrometry (GC-MS) within 72 h after derivitisation. The concentration of protein in each hydrolysate was determined using the Lowry method Biomass samples from the −1. This temperature was held for 1.5 min. The flow was held constant at 1.4 ml He min−1. Injection volume, 5 µl. Inlet temperature, 250°C. Interface temperature, 310°C. Source temperature, 245°C. The system was operated with a mass range of 40–800 m/z at an EM voltage of 70 V with a spectral acquisition rate of 20 spectra s−1.The analysis was performed with a 6890N Network GC system (Agilent) fitted with a DB-5ms capillary column  and deactivated quartz wool packed FocusLiner (SGE) coupled to a Pegasus III time-of-flight (TOF) mass spectrometer (Leco) equipped with a splitless injector. The injector temperature was initially held at 70°C for 2 min followed by heating to 350°C at a rate of 17°C min13C isotopologue abundances  for each amino acid were determined for fragments containing the intact carbon skeleton for each amino acid; generally using the [M-57]+ ion. To obtain further information relating to the 13C incorporation at individual positions within individual amino acids, isotopologue abundances were also determined for fragments formed by cleavage of the C1–C2 bond in some samples. Within a subset of these, isotopologue abundances for the fragment formed by cleavage of the C2–C3 bond in serine were also determined allowing comprehensive characterization of the isotopic incorporation into serine. Mass spectra of the derivatized amino acids were corrected for the natural abundance of all stable isotopes.Non-linear weighted least squares fitting is applied in order to determine the flux values which are the most likely description of the labeling data and biomass constraints Based on normally distributed measurement errors, flux errors were calculated using a linearized statistical approach Figure S1Southern analysis of the icl1 mutant. Chromosomal DNA isolated from WT M. bovis BCG (lane 2) and Δicl (lane 3) were digested with BglII and transferred to a nylon membrane. The membrane was probed with the digoxigenin labeled probe HR containing the icl1 gene and its flanking regions. The WT fragments are the expected 3.99 kb and 1.9 kb fragments while the genomic DNA from the Δicl had the expected 2.62 kb fragment. The sizes of the bands were determined from the migration distance of the DNA molecular mass marker EcoRI SPP1 (Roche ladder number VII) which is present in lane 1.(TIF)Click here for additional data file.Table S113C isotopomer abundance of amino acids from protein hydrolysis.Non stationary and stationary  Incorporation of 13C label into the protein amino acids of M. bovis BCG cells grown in a chemostat at a dilution rate of 0.01 h−1 with 20% [13C3]glycerol.(XLS)Click here for additional data file.Table S2Network model of the central metabolism of Mycobacterium bovis BCG and Mycobacterium tuberculosis. Stoichiometry and carbon transformations for reactions in the network of M. bovis BCG and M. tuberculosis. This network consists of 75 reactions.(DOC)Click here for additional data file.Table S3Experimentally derived requirements and estimated effluxes from intermediate and amino acid pools for biomass synthesis of Mycobacterium bovis BCG (BCG) and Mycobacterium tuberculosis (MTB) growing in a chemostat. The precursor requirements for amino acid pools were derived from measurements of the macromolecular composition of biomass hydrolysates.(XLS)Click here for additional data file.Table S4Experimental (GC-MS) and calculated mass distributions in the amino acid derivatives of Mycobacterium bovis BCG (BCG) and Mycobacterium tuberculosis (MTB) growing in a steady state chemostat. GC-MS measured values for the 13C incorporation into proteogenic amino acids as compared with the values calculated using 13CFlux.(XLS)Click here for additional data file.Table S5Estimated metabolic fluxes of Mycobacterium bovis BCG (BCG) and Mycobacterium tuberculosis (MTB).In vivo fluxes for glycerol limited M. bovis BCG and M. tuberculosis growing at either fast (td = 23 h) or a slow (td = 69 h) growth rate in a chemostat.(XLS)Click here for additional data file.Table S6Abbreviations of metabolite names. Explicit names of enzymes and metabolites used in this work.(DOC)Click here for additional data file.