In systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model.Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF.To link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of E. coli and B. subtilis.The ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of  In systems biology, a mathematical approach is required to integrate heterogeneous data, such as transcriptome, proteome, metabolome, and fluxome, to build comprehensive metabolic models. Mathematical models are consistently improved or modified by accommodating new experimental data with the current models, thereby enhancing the validation of metabolic networks or the prediction of their dynamic behaviors,2.Network-based pathway analysis becomes a core method for constructing a mathematical model that predicts the flux distribution for large-scale metabolic networks. Network-based analysis generally employs a constraint-based modelling approach , e.g., FRecent network-based metabolic pathway analysis has focused on two approaches, elementary modes (EMs)  and extrSince some genetic or environmental perturbations often cause a significant change in gene expression, a network-based flux analysis requires considering not only pathway architectures but also the proteins or mRNA transcripts to predict a flux distribution. However, only a few methods have been developed that connect such heterogeneous data to network-based analyses,8, e.g.,To elucidate theoretical relationships between transcriptome or enzyme activities and flux, the control-effective flux (CEF) method has previously been proposed to predict gene expression (enzyme) levels through elementary mode analysis, but CEFE. coli and B. subtilis wild-type to simulate the metabolic flux distributions of the mutants.To link metabolic enzyme data to flux distributions, we propose Enzyme Control Flux (ECF), a non-mechanistic and static method that integrates enzyme activities into EMA. The ECF model presents a novel mathematical formula describing how the change in enzyme activities between wild-type and a mutant are related to that in the EM coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of the central metabolism of E. coli and B. subtilis mutants by integrating the change in an enzyme activity profile into the EMCs of their wild-type.The algorithm of ECF that links an enzyme activity profile to a metabolic flux distribution is presented as shown in Figure S). The rows of S correspond to metabolites in a reaction network. The columns of S correspond to the reactions in a network, with elements corresponding to stoichiometric coefficients of the associated reactions. At steady-state, mass balance provides the flux-balance equations:Biological networks can be represented by a stoichiometric matrix t is the vector whose elements correspond to fluxes through the associated reactions in S. The set of all possible solutions can be described by a set of basis vectors. The elementary mode (EM) is the minimal set of enzymes that can operate at steady-state with all the irreversible reactions operating properly and  and pykFF Figure . One hunpykF(-) knockout mutant, we integrated the enzyme activity data  by Eqs. . The EMC vectors for the mutant were employed to simulate the flux distribution for the mutant vjmutant = t . Figure In this estimation, the factor of β is set to 1, which will be validated in the later section . Notice that a β factor of 1 does not reflect any linear relationship between flux and each enzyme activity, as shown in Eqs. . In order for ECF to simulate the flux distribution in the central metabolic pathways of the ta Table  into theFirst, to explore a representative value for such broad spectrums, we plotted the relative standard deviation of the estimated flux with respect to the mean as shown in Figure pykF(-) mutant. The relative model error for each flux (Eq. (15)) is plotted with respect to the experimental flux for the mutant as shown in Figure Second, to characterize the precision of ECF we compared the mean estimated fluxes with the experimental ones for the pykF(-) knockout mutant, we calculated the part of Eqs. , ι is 1 for wild-type. The ει values of the EMs that contain the reactions of 11 (ppc) and 12 (mez) are greater than 1, whereas the ει values of the EMs that do not have them are less than 1. These show the fluxes that contain ppc and mez are enhanced, which are consistent with the fact that the pykF-knockout-mediated blockage of the PEP to PYR pathway greatly activates both ppc and mez enzymes to supply PYR through alternative pathwaysE. coli pd[pgi[zwf,16 knockpgi, gnd, and zwf knockout mutants was not significant due to the small number of incorporated enzymes (n ≤ 4) (data not shown). However, for a relatively large number (n ≥ 6) of integrated enzymes, the mean model error of the ppc, cra, and fnr knockout mutants and the als-overexpressing and pta (-) knockout mutant decreased greatly. For these mutants a β factor of 1 is a best choice, which minimizes the mean model error with a relatively small value of the relative standard deviation. Furthermore, the mean model error for a β factor of 1 clearly decreases with an increase in the number of integrated enzymes, indicating that an enzyme activity profile is effectively used by ECF. The comparison between the estimated and experimental fluxes for each reaction is shown in Figure pykF knockout mutant . A β factor of 1 is not the exact optimal value but an approximate or representative one. The important thing would be to notice that the ECF model decreases the model error at an appropriate value of β rather than to insist biological basis of a β factor of 1.In the metabolic engineering field, powerful analyses had been proposed to simulate the change in flux distributions in response to environmental or genetic changes. For example, network rigidity had extensively been investigated to reveal how the rigidity of some principal nodes at branch reactions is generated by complex enzyme regulations including allosteric regulation. Such a Finally we show two limitations of ECF. One is that metabolic control analysis (MCA) is not applied to analysis for the ECF model. The other is the limitation of network size. Generally MCA is used to quantify flux control of each reaction by estimating the logarithmic gain of a flux with respect to changes in an enzyme activity -21. In tE. coli has served as a testing ground for network analysis methods. The studies of this metabolic network have been facilitated by the extensive availability of E. coli biochemical data. In the present analysis, we simplified the central metabolic networks of glycolysis, the tricarboxylic acid cycle (TCA), and the pentose phosphate pathway  and the knockout mutants , which were constructed by deletion of the corresponding gene from E. coli derivative BW25113[13C-labeled glucose experiments, and the key enzymes involving the branching reactions  were measured in the main pathway of the TCA cycle and glycolysis [For more than a decade, the metabolism of y Figure , which te BW25113. In thesycolysis ., is employed to draw the E. coli metabolic network map and describe its corresponding reactions in XML format (SANAC)[CADLIVE, which is freely available from t (SANAC),26. The t (SANAC). FluxAnaHK and KS designed the research. HK, ZQ, and RO performed the simulations and analyzed the results. All authors have read and approved the final manuscript.A plain instruction for ECF. Supplementary text.Click here for fileSupplementary figure 1. Estimation of 73 EMCs in central metabolic pathways of wild type.Click here for filepykF(-) knockout mutant.Supplementary figure 2. Frequency distributions for the estimated flux in the Click here for filepykF(-) knockout mutant.Supplementary figure 3. Frequency distributions for the model error in the Click here for fileB. subtilis metabolic network.Supplementary figure 4 and tables 1 and 2. Data for the Click here for file


Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae metabolism in 30,000 diverse simulated environments. We identify a set of metabolic reactions forming a connected metabolic core that carry non-zero fluxes under all growth conditions, and whose flux variations are highly correlated. Furthermore, we find that the enzymes catalyzing the core reactions display a considerably higher fraction of phenotypic essentiality and evolutionary conservation than those catalyzing noncore reactions. Cellular metabolism is characterized by a large number of species-specific conditionally active reactions organized around an evolutionary conserved, but always active, metabolic core. Finally, we find that most current antibiotics interfering with bacterial metabolism target the core enzymes, indicating that our findings may have important implications for antimicrobial drug-target discovery.Understanding the system-level adaptive changes taking place in an organism in response to variations in the environment is a key issue of contemporary biology. Current modeling approaches, such as constraint-based flux-balance analysis, have proved highly successful in analyzing the capabilities of cellular metabolism, including its capacity to predict deletion phenotypes, the ability to calculate the relative flux values of metabolic reactions, and the capability to identify properties of optimal growth states. Here, we use flux-balance analysis to thoroughly assess the activity of  Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae and the computational method of flux-balance analysis to investigate the functional plasticity of these three metabolic networks for a large number of simulated growth conditions.Although cellular metabolism is among the most investigated cellular functions, it is not well understood how it changes and adapts on a systems level in response to environmental variations. In this study, the authors take advantage of the reconstructed metabolic networks of Within this approach, the authors identify the metabolic cores of these organisms. These metabolic cores represent connected sets of reactions that are used in all tested environments. When cross-correlating the predicted cores with experimental data, the authors find that the cores display a significantly higher fraction, both of essential and evolutionary conserved enzymes, than their noncore counterparts. Additionally, the extended mRNA half-lives of the core enzymes give further support to the notion that core reactions represent main integrators of metabolic activity.Since most of the metabolic core reactions are indispensable for the growth of the microorganism, and several existing antibiotics target select bacterial enzymes in the core, the authors suggest that the metabolic core may have important applications for antimicrobial drug-target discovery. Constraint-based modeling approaches, such as flux-balance analysis (FBA) ,2, have Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae [E. coli reactions by more than 20%. Less commonly, environmental changes can also induce structural plasticity, resulting in changes in the metabolism's active wiring diagram, turning on previously zero-flux reactions and inhibiting previously active pathways. For example, when shifting E. coli cells from glucose- to succinate-rich media, 11 previously active reactions are turned off completely, while nine previously inactive reactions are turned on.To thoroughly examine the utilization and relative flux rates of each metabolic reaction in a wide range of simulated environmental conditions, we sampled 30,000 randomly and uniformly chosen optimal growth conditions, as well as all single-carbon-source minimal medium conditions sufficient for growth, using FBA ,2 on therevisiae –9 , 90 of 758 E. coli (11.9%), and 33 of 1,172 S. cerevisiae (2.8%), metabolic reactions are always active , while the second type of reaction is required only to assure optimal metabolic performance , the size and identity of the E. coli metabolic core remains largely unchanged, retaining 83 of the original 90 reactions, of which two are nonessential. This indicates that the concept of the metabolic core is valid under both optimal and suboptimal growth, although with some difference in the identity of individual reactions. Of note, the metabolic core represents a subset of the minimal reaction sets [E. coli for all 136 aerobic and anaerobic single-carbon-source minimal environments capable of sustaining optimal growth. The latter finding is in excellent agreement with The metabolic core contains two types of reactions: The first type consists of those that are essential for biomass formation under all environmental conditions  are essential, compared with a 19.6% lethality fraction characterizing the noncore enzymes. The gap between the core and noncore enzymes is also significant for S. cerevisiae, for which essential enzymes catalyze 84% of the core reactions, whereas the average essentiality of the conditionally active enzymes is only 15.6% . In contrast, the conditionally active noncore enzymes have an evolutionary retention of only 47.7% [Given the important functional role played by the metabolic core in a given organism, one would expect significant parts of the core to be conserved in different organisms. Indeed, the ores see A. Also, ly 47.7% . Given tly 47.7% , this diE. coli metabolic enzymes when cells were grown in Luria-Bertani medium [p = 0.016). Furthermore, only 2% of the core enzymes have corresponding mRNA half-life times shorter than 5 min, compared to 23% for the ones catalyzing conditionally active noncore reactions . Notably, this finding is different from the average correlation of mRNA expression found in 66 “correlated reaction sets” (groups of between two and nine reactions that are turned on and off together), where the frequency with which a correlated reaction set is used does not affect its mRNA correlation [The same synchrony is observed when we inspect experimental mRNA data among the core reactions during changes in environmental conditions. Calculating the correlations between core and noncore enzymes using their corresponding mRNA copy number data see  for 41 erelation .H. pylori, 51 for E. coli, and 44 for S. cerevisiae), the number of metabolic reactions contributing to them differs. The larger size of a metabolic network significantly increases its capacity and redundancy, decreasing the size of the metabolic core. Therefore, the metabolic core contains reactions that are necessary for optimal cellular performance regardless of the environmental conditions, while the conditionally active metabolic reactions represent the different ways in which the cell is capable of optimally utilizing substrates from its environment.Previous studies have firmly established that environmental changes induce flux plasticity, altering the flux levels of individual reactions (reviewed in ). SimilaS. cerevisiae genes that are inactive in nutrient-rich conditions are in fact not only active, but are also essential in some other growth conditions [H. pylori as well as the E. coli core  in reaction ijj, S < 0 (ijS > 0). Any vector of fluxes {νj} which satisfies equation 1 corresponds to a state of the metabolic network, and hence, a potential state of operation of the cell. Using linear programming, we choose the solution that maximizes biomass production of the respective organisms. Reactions that are never active, likely reflecting annotation errors or incomplete data, are ignored in our flux analysis.where E. coli, H. pylori, and S. cerevisiae, plus unlimited ergosterol and zymosterol for S. cerevisiae. For each reaction, we assign a value, i,q of between zero and unity, representing the relative number of conditions for which reaction i is active. The preliminary metabolic core is defined as the set of reactions that are active in all sampled conditions (i =q 1). We determine the final metabolic core by removing from the preliminary core those reactions that, when their flux rate is constrained to zero over the total set of initial conditions, leave the growth rate unchanged.We model all possible cellular environments in the investigated FBA models, as described before . BrieflyWe evaluate the FBA flux correlations by calculating the Pearson coefficient for all possible reaction pair combinations in the 30,000 different simulated growth conditions. We subsequently group the reactions by employing the correlation values as the metric in a standard hierarchical average-linkage clustering algorithm .k,E for all the catalyzing gene products, asThe annotated metabolic FBA models specify the enzymes and genes catalyzing the various metabolic reactions. We represent the mRNA activity level or half-life time of a metabolic enzyme by averaging the available experimental values, iN is the number of genes. We assess the essentiality of a reaction by considering it essential if at least one of its catalyzing enzymes (or gene products) is essential for the survival of the organism  as given in Table S2 of Gerdes et al. [The deletion phenotype of s et al. . For nons et al. , resultis et al. .Protocol S1(628 KB DOC)Click here for additional data file.Table S1(57 KB XLS)Click here for additional data file.


M. tuberculosis, was constructed and validated using experimental data.GSMN-TB, a genome-scale metabolic model of  Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis.An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of M. tuberculosis, was constructed, consisting of 849 unique reactions and 739 metabolites, and involving 726 genes. The model was calibrated by growing Mycobacterium bovis bacille Calmette Guérin in continuous culture and steady-state growth parameters were measured. Flux balance analysis was used to calculate substrate consumption rates, which were shown to correspond closely to experimentally determined values. Predictions of gene essentiality were also made by flux balance analysis simulation and were compared with global mutagenesis data for M. tuberculosis grown in vitro. A prediction accuracy of 78% was achieved. Known drug targets were predicted to be essential by the model. The model demonstrated a potential role for the enzyme isocitrate lyase during the slow growth of mycobacteria, and this hypothesis was experimentally verified. An interactive web-based version of the model is available.GSMN-TB, a genome-scale metabolic model of M. tuberculosis. The model provides a means to examine the metabolic flexibility of bacteria and predict the phenotype of mutants, and it highlights previously unexplored features of M. tuberculosis metabolism.The GSMN-TB model successfully simulated many of the growth properties of  Mycobacterium tuberculosis, is one of the most important diseases in the world today, being responsible for more than 8 million cases of disease each year and approximately 3 million deaths [M. tuberculosis and Mycobacterium bovis) in many parts of the world [Tuberculosis (TB), caused by n deaths ,2. Contrhe world . Very feM. tuberculosis, particularly during infection. One reason for this lack of knowledge is difficulty in applying biochemical techniques to the bacterium in vivo. In spite of this, several features of in vivo bacterial metabolism have been established. First, the essentiality of the glyoxylate shunt during intracellular growth indicates that M. tuberculosis survives by scavenging host lipids [M. tuberculosis metabolism remains a major goal of TB drug research.An impediment to the rational development of novel drugs against TB is a general paucity of knowledge concerning the metabolism of t lipids -7. Secont lipids -10. Thest lipids , but a mHaemophilus influenzae [Escherichia coli [Helicobacter pylori [Saccharomyces cerevisiae [Availability of full genome sequences allows reconstruction of genome-scale metabolic reaction networks in micro-organisms. Metabolic capabilities of reconstructed networks consistent with stoichiometry of enzymatic conversions, their physiologic direction, and maximal allowable throughput can be studied by constraint-based computer simulation methods. These simulations provide a very useful framework in which to study metabolism in a systemic manner; they are also a novel approach to rational design of biochemical processes and drug discovery. Whole-genome metabolic network models of sequenced micro-organisms such as fluenzae , Escherihia coli , Helicobr pylori , and Sacrevisiae  have prorevisiae , elementrevisiae , or extrrevisiae , providerevisiae .M. tuberculosis. The model is calibrated by comparison with our experimental data on M. bovis bacille Calmette Guérin (BCG) growth in continuous culture. The model correctly predicted the growth phenotype of 78% of mutant strains in a published global mutagenesis dataset. Software allowing constraint-based simulations of M. tuberculosis metabolism via a web-based interface was developed in order to make our model available to the research community. This is the first reconstruction of a genome-scale metabolic reaction network published as a web resource, providing both data and interactive access to constraint-based simulation methods. We also demonstrate here that this model can be used to generate new hypotheses and thereby guide future research in the development of novel chemotherapeutics against TB.FBA has already been conducted in a network of reactions involved in mycolic acid synthesis  to identM. tuberculosis (GSMN-TB) was constructed as described in the Materials and methods. The GSMN of Streptomyces coelicolor [S. coelicolor is an actinomycete that shares significant portions of genome synteny with M. tuberculosis [S. coelicolor model following KEGG gene orthology mapping. This preliminary model has been further supplemented by data from KEGG and BioCyc databases.The genome-scale metabolic network of elicolor  was usedrculosis . The KyoM. tuberculosis metabolic pathways, including those relevant to the synthesis of the cell envelope of M. tuberculosis, which contains a diverse array of complex lipids and carbohydrates that are important for growth and pathogenesis, and are important drug targets. Because fatty acid metabolism is thought to be a crucial factor in TB pathogenesis [M. tuberculosis consumes host-derived lipids in vivo [M. tuberculosis [A significant proportion of the model could not be constructed using semi-automatic methods and was therefore generated by analysis of original research articles. Table ogenesis , standar in vivo . Iron merculosis . We simuM. tuberculosis is a facultative intracellular parasite that is capable of growth within host cells, in the extracellular milieu, and in vitro. Biomass composition data are available only for in vitro grown M. bovis BCG, and so this was used to model the M. tuberculosis cell for the in silico model. However, it is well established that many of the outer cell wall components of M. tuberculosis (such as phenolic glycolipid), although produced in vitro, are not essential for in vitro growth but are required for pathogenesis. In order to make the model applicable to M. tuberculosis grown both in vitro and in vivo, we therefore defined two biomass components based on published experimentally derived values for macromolecular composition of M. tuberculosis.  The first (BIOMASS1) reflects the actual macromolecular composition of M. tuberculosis. The second (BIOMASSe) is a minimal macromolecular composition of M. tuberculosis and includes only those components  that are thought to be essential for in vitro growth. It is this second biomass that was used to make predictions regarding gene essentiality in vitro. To simulate the requirement of co-factors for nonessential reactions, we introduce the concept of a 'replenishing flux', in which the co-factors are included in reactions but with a low (0.001), unbalanced stoichiometric coefficient toward consumption, forcing co-factor synthesis only when the co-factor utilizing reaction is active.The final model contains 849 reactions and 739 metabolites, and involves 726 genes ; the cost of polymerization of the building blocks into biologic polymers ; and ATP costs for growth-associated maintenance . These parameters must either be measured or calibrated by comparison of the model predictions with experimental data. For well established model systems such as s report  and the M. bovis BCG in glycerol-limited continuous culture at three growth rates were compared with the quantitative predictions of the GSMN. BCG and M. tuberculosis have a high degree of homology, sharing 99.9% of DNA, and possess identical metabolic pathways for utilization of glycerol [Experimental data obtained for growth of glycerol . FBA minglycerol ,27, becaA possible explanation of the discrepancy between the predicted and experimental data is that BCG cells consumed carbon from an additional source. Although glycerol is the main carbon source in Roisin's minimal medium, Tween 80 is also present in the culture medium to reduce cell clumping. Tween 80 is an oleate ester of sorbitol, with an oleate content above 75%, and minor amounts of other unsaturated and saturated fatty acids. The tubercle bacillus is known to be able to hydrolyze Tween 80 and can also utilize the fatty acids released as a sole carbon source . The FBAin silico predictions of gene essentiality with the findings of a previously reported global mutagenesis study of gene essentiality in M. tuberculosis by transposon site hybridization (TraSH) [To evaluate the predictive power of the model we compared  (TraSH) . The TraM. tuberculosis, although essential for virulence, are not required for in vitro growth. For in vitro gene essentiality predictions, we therefore used BIOMASSe as the objective function of the GSMN-TB; BIOMASSe is a minimal biomass composition that reflects current knowledge of the biomass components of M. tuberculosis that are essential for growth in vitro. To model the composition of the minimal media Middlebrook 7H10 used in the TraSH experiment of Sassetti and coworkers [It is well established that many of the macromolecular components of oworkers  we simulin silico mutation) and calculating the resulting maximum growth rate for each in silico mutant. We emphasize, however, that this predicted maximum growth rate should be viewed solely as a qualitative prediction. Our aim was to identify genes that prevented or severely compromised the capacity to synthesize biomass, which would lead to zero or greatly reduced growth rates in the GSMN-TB. Most mutations had little or no effect on growth rate, but some in silico mutations were lethal (in the sense that the resulting maximum growth rate was zero) or depressed growth rate to values between zero and the maximum predicted growth rate for the 'wild type'. To identify essential genes we set an arbitrary growth rate threshold  such that mutants with a maximum predicted growth rate below that threshold were considered to be essential for growth. Theoretical predictions were generated by removing single genes from the GSMN-TB  on the sensitivity and specificity of the GSMN-TB predictions, receiver operating characteristic (ROC) curves were plotted Figure . The ROCs Figure . The ROCaSH data , using aM. tuberculosis genes in the model are essential for growth in minimal Middlebrook 7H10 media, which is very close to the estimated value of 35% essential genes in M. tuberculosis [P < 2.2 10-16). The overall fraction of correct predictions is 78%, with sensitivity and specificity of 71% and 80%, respectively, if a TraSH ratio threshold of 0.1 is applied. Predictions are robust with respect to the quantitative parameters of the FBA model. When energetic parameters were set to 1 (P/O ratio), 5.0 mmol/g DW per hour (ATP dissipation), and 60 mmol/g DW per hour ATP molecules (growth-associated maintenance), the result changed for only one gene . Therefore, the prediction accuracy was not affected by substantial change in energetic parameters.The GSMN-TB model predicts that approximately 34% of rculosis . The numP < 2.2 10-16). The genes predicted to be essential have significantly lower TraSH hybridization ratios than genes predicted to be nonessential. This is in accordance with the experimental data. This demonstrates the predictive power of the model using an approach that is independent of the TraSH signal ratio threshold.To validate further the predictive power of the model, the distributions of TraSH hybridization signal (TraSH probe/genomic probe) were plotted for both essential and nonessential genes as predicted by the model  with gene essentiality prediction by both TraSH result and GSMN-TB.  As can be seen in Table mshB and mshC are both involved in mycothiol synthesis, which is nonessential in the GSMN-TB because mycothiol is currently not a biomass component and neither is it required for the synthesis of any biomass component. The essentiality of mshC and poor growth of mshB indicate that mycothiol should be included as either a biomass component or an essential co-factor for synthesis of a biomass component, or both.As a further check of the accuracy of the GSMN-TB, we compared Table  the pheninhA (isoniazid and ethionamide), fasI (pyrazinamide), embAB (ethambutol), ddlA (cycloserine), and alr (cycloserine). All of these genes were correctly predicted as essential for growth on 7H10. This demonstrates the utility of the GSMN-TB in identifying potential drug targets in metabolic reactionsThe GSMN-TB contains five genes that encode enzymes that are drug targets: M. tuberculosis, particularly in situations that are difficult to approach experimentally, such as during infection. M. tuberculosis is a versatile chemoheterotroph that can utilize a wide range of sources of carbon and nitrogen. Similarly, the in silico model is able to generate feasible solutions to optimize biomass or 'grow' on a range of carbon and nitrogen sources. Feasible flux distributions include expected biochemical pathways; for instance, most of the flux from glucose is directed through glycolysis and the tricarboxylic acid (TCA) cycle, whereas the glyoxylate shunt is utilized for growth on acetate (or fatty acids). The GSMN-TB also indicated that M. tuberculosis has much more metabolic flexibility than is generally accepted. For example, TraSH data [in silico using the GSMN-TB, the resulting carbon flux was predicted to be shunted through the anaplerotic reactions catalyzed by malic enzyme, pyruvate phosphate dikinase, and phosphoenolpyruvate carboxykinase.An important application of the GSMN-TB is to model the metabolic state of aSH data  demonstrin silico metabolic response of M. tuberculosis to slow growth, because this is a key component of persistence/dormancy in M. tuberculosis. We compared the predicted flux ratios for two different growth rates that could be experimentally verified in a chemostat. A doubling time of 23 hours (dilution rate 0.03) was compared with a doubling time of 69 hours (dilution rate 0.01). Flux ratios for central metabolism (0.01/0.03) were calculated by flux variability analysis (FVA) as the ratios of midpoints of flux ranges obtained for slow and fast growth rates  in the slow growing cells . In contrast to the flux distribution computed in a single FBA simulation, the FVA flux ranges are unique. Our server also allows gene and reaction essentiality predictions.All calculations described above can be performed for a variety of experimental conditions. The user is able to specify media conditions by changing the bounds of the GSMN transport reactions (most of the transport reactions in the GSMN-TB are currently constrained to zero). Both model file and results are displayed in tabular format, with the gene annotation linked to the TubercuList database .in vivo growth of M. tuberculosis using the web-based software. The use of FBA to model in vivo growth is more problematic because it is not clear what to use as an objective function for optimization. We have tackled this problem by including two objective functions that can be optimized: one utilizes a minimal biomass composition, which includes only those components that are thought to be essential for in vitro growth; and the other uses a 'complete' biomass composition, which includes synthesis of macromolecular components (virulence factors), such as dimycocerosate esters and sulfolipid, that are thought to be essential for infection. This allows the user to model both in vitro and in vivo growth and, for instance, to predict genes that are only essential for growth in vivo.We have also implemented methods to investigate the Constraint-based computer simulations methods available in our software are computationally fast enough to allow interactive online work. Results of FBA and single gene essentiality predictions appear instantaneously in the user's browser, and FVA results are computed in less than 10 min.The web interface to our interactive resource is now available . FiguresM. tuberculosis cell, including all levels of biochemical network organization.We have built the first genome-scale metabolic network (GSMN) model of the tubercle bacillus, which is the agent responsible for approximately 5% of all deaths worldwide and 9.6% of all adult deaths. The model incorporates nearly all known biochemical reactions of the micro-organism and describes the biosynthetic pathways that lead to the synthesis of all of the major macromolecular components, including known virulence factors. The model provides new insights into the biology of the pathogen and provides a framework for integrating metabolic, proteomic, and transcriptomic data. Thereby, it can serve as a platform on which to build extended models of the M. bovis BCG. FBA simulations predicted a consistently higher rate of glycerol consumption than was observed. The most likely explanation for this is that the cells are simultaneously utilizing both glycerol and oleic acid (derived from hydrolysis of Tween 80) as a carbon source. This pattern of mixed substrate utilization is in contrast to the more extensively studied diauxic growth that is typical of batch-grown micro-organisms, in which the substrate supporting the greatest growth rate is utilized first and the second substrate is only consumed after exhaustion of the preferred substrate. However, mixed substrate utilization has been shown to operate in carbon-limited chemostat cultures of organisms such as E. coli that demonstrate diauxic growth in batch culture [To be representative, systems level models must be constrained with experimental data. The model was therefore calibrated using our data from chemostat cultivations of  culture . It is l culture .M. tuberculosis was tested by comparison of model predictions of gene essentiality with global mutagenesis (TraSH) experimental data. The model was shown to have a high degree of accuracy, correctly predicting the phenotype for more than 75% of single gene mutants. Discrepancies between the model and TraSH mutagenesis data were also informative. In some cases the model prediction matched the phenotype of individual gene knockout studies more closely than the TraSH mutagenesis data. This was true for the inhA gene, whose product is a target for the key antituberculous isoniazid. This result verifies the use of the model as a tool for drug discovery. In addition to known drug targets, the model predicts 220 essential genes in M. tuberculosis, any one of which is a potential target for new antituberculous drugs. The remaining 24% discordant predictions (174 genes) clearly must be investigated further in a reiterative cycle of hypothesis generation, experiment, model improvement, and further experimentation. Identification of discrepancies between model predictions and experimental data are informative in that they indicate errors in the model, errors in gene annotation, or incomplete knowledge of M. tuberculosis metabolism, such as the presence of an unknown isoenzyme.The accuracy of the GSMN-TB model of The model is also an excellent tool for mining existing datasets, for instance those resulting from TraSH mutagenesis studies examining gene essentiality in different environments. Interrogation and integration of datasets such as global mutagenesis data can thereby be used to refine further the model in an iterative process. The genome-scale model has considerable advantages over traditional genome annotation and pathway databases, including its internal stoichiometric consistency, systems level integration, and its ability to predict gene essentiality for different media conditions automatically. The model inputs data such as growth characteristics of particular genotypes to auto-generate hypotheses in the form of predicted flux maps of internal metabolism. In addition, the model provides a platform that could be used to integrate and manage 'omics data in a manner that is consistent with the underlying biochemistry and genetics of the organism ,15,36,37M. tuberculosis, our results indicate that - under the carbon-limited conditions we tested - the organism uses carbon source efficiently with yields close to the maximal theoretical values predicted by the model. However, the conclusions presented in our work are qualitative and do not depend on whether the objective function flux actually reaches its maximal/minimal possible value. We demonstrated that gene essentiality predictions were not sensitive to large changes in the growth rate thresholds used for viability predictions in mutants. Similarly, the findings of the FVA of M. tuberculosis growing at two different growth rates should be considered qualitative rather than quantitative predictions.The constraint-based simulation methodology, used in this work and implemented in the GSMN-TB server, is currently the most practical solution for studying metabolic flux distribution in the genome-scale metabolic reaction networks. This method involves optimization of the objective function represented by one of the fluxes in the network, usually the flux to biomass that determines growth rate. Although it could be argued that optimization of growth rate is not appropriate to the very slow growing M. tuberculosis to carbon-limited slow growth. Persistence is a central feature of the biology of M. tuberculosis, being responsible both for latency and the necessity for long treatment regimens [M. tuberculosis during persistence, but slow or zero growth is generally thought to be a key property. In vitro models of persistence invariably involve slowing the growth rate through, for instance, oxygen limitation [in silico response of the tubercle bacillus to slow growth in our model by FVA. The most significant alteration was a predicted increased flux through the glyoxylate shunt, particularly the isocitrate lyase reaction, at slow growth rate. This hypothesis was supported experimentally by the demonstration that isocitrate lyase activity was higher in slow (doubling time 69 hours) growing BCG cells than in faster (doubling time 23 hours) growing cells. This finding is consistent with the model flux prediction and thereby supports the hypothesis that isocitrate lyase plays a specific role in slow growing mycobacteria. Several pathogens, including M. tuberculosis, require isocitrate lyase for long-term persistence in the host [in vitro model of M. tuberculosis persistence [M. tuberculosis led to attenuation for survival and multiplication in mice and macrophages, but this finding has generally been interpreted as indicating a role for fat catabolism in survival of this pathogen in the host. Our results indicate that isocitrate lyase may play a more general role in the slow growth of M. tuberculosis, irrespective of the means of growth limitation. This finding could have implications for drug development, because isocitrate lyase has been intensively investigated as a potential antitububerculous drug target [In addition to validating the model, we also demonstrated its potential to generate experimentally testable hypothesis by predicting the metabolic response of regimens . Little mitation  or nutrimitation . We therthe host -45. Incrsistence . In addig target ,11,47.in vivo situation is of course more challenging than simulation of in vitro growth. Because the pathogen probably does not maximize growth rate during infection, an objective function based exclusively of maximization of growth rate is unlikely to be entirely successful at simulating the pathogen's in vivo metabolic state. In order to investigate metabolic requirements for in vivo growth, the web-based version of the M. tuberculosis GSMN is able to optimize for two different objective functions: the first specifying a minimal biomass with components essential for in vitro growth, and the second specifying a complete biomass composition that includes synthesis of virulence factors such as sulfolipid and dimycocerosate esters. Even if the growth rate of the pathogen does not reach its maximal value, simulation using the complete biomass composition in the GSMN-TB will still produce valuable qualitative predictions concerning essentiality of genes and reactions required for in vivo growth. These qualitative results will be useful in predicting drug targets, even in the situation in which the actual growth rate of the pathogen is lower than that predicted by the model.The application of constraint-based modeling to the M. tuberculosis. This resource will be developed by continuous curation of the metabolic model, leading to improved gene annotation, incorporation of high throughput datasets, and direct experimental testing of hypotheses generated by the model.Our web-based software makes, for the first time, genome-scale metabolic simulations available to the nonspecialist. It is therefore a valuable resource for biologists investigating the physiology and pathogenicity of M. bovis BCG strain (ATCC 35748) was cultured in a 2-l bioreactor  under aerobic conditions and at pH 6.6, as previously described [escribed . Chemost ATCC 357 was cult2, 5 mmol/l L-cysteine, 1 mmol/l EDTA) supplemented with protease inhibitors . The cells were disrupted using a Ribolyser ; speed setting 6.5 for 30 s) with careful cooling between each cycle. A lactose dehydrogenase coupled continuous method was used to assay isocitrate lyase activity [Crude enzyme extracts were prepared as described by Honer zu Bentrup and coworkers  with minactivity .Streptomyces coelicolor [Mycobacterium tuberculosis. The gene orthology clusters computed by the KEGG database [M. tuberculosis H37Rv gene numbers to the genes present in the S. coelicolor model. The KEGG and MtbRvCyc (part of BioCyc) databases were used to incorporate additional M. tuberculosis specific reactions that do not have S. coelicolor counterparts. In situations in which a reaction was essential to produce a viable in silico model but the gene was not annotated in the genome, the reaction formulae was included in the model without genomic evidence. A significant proportion of the model was manually generated from journal publications describing dedicated experimental work  is the vector of concentration changes of m metabolites (ci being the concentration of the ith metabolite); v is the vector of metabolic fluxes carried out by n reactions in the network; and S is the m × n matrix of the stoichiometric coefficients. Fluxes are further subjected to the capacity constraints: αj ≤ vj ≤ βj (where αj and βj are the lower and upper bounds of the flux carried by the jth reaction). The bounds of irreversible reactions were set to αj = 0 and βj = +∞; the bounds of reversible reactions were set to αj = -∞ and βj = +∞, allowing these reactions to carry either positive or negative flux. Following FBA methodology, we have studied metabolism under steady-state conditions in which there is no accumulation of intracellular  metabolites, and concentrations of these metabolites do not change:Where b =  metabolites transported from or secreted to the environment were not required to obey the balance equation (Equation 2) and were considered to have unlimited sources and sinks. Transport reactions for nutrient sources, which were rate limiting in chemostat experiments, were constrained to the experimentally determined flux values. In addition, a pseudo-reaction has been added to the system to model growth (biomass synthesis). The growth reaction has only one product, representing biomass, where the substrates correspond to biomass components and real-valued stoichiometric coefficients represent biomass composition. The flux through this reaction is equal to the growth rate of the bulk cell culture.Linear programming was used to determine maximal theoretical flux toward a selected  metabolite Z in the network, consistent with constraints imposed by stoichiometric matrix, balance conditions, and capacity bounds. The following linear programming problem was solved.Maximize Z such that:If the coefficients of optimization function Z have been set to the row of the stoichiometric matrix corresponding to biomass metabolite, the result of linear programming optimization represented the maximal theoretical growth rate.The optimal value of the objective function calculated by FBA is unique, but associated metabolic flux distribution is not. There may be many flux distributions resulting in the same, optimal value of objective function. Therefore, flux distributions computed by single FBA simulation cannot be used to study internal metabolic state of the cell. The FVA method finds the full numerical range of each flux in all alternate flux distributions resulting in the same optimal value of objective function. During FVA simulation the objective function is constrained to its optimal value. Subsequently, the flux through each reaction in the model is subjected to minimization and maximization, resulting in minimal and maximal value defining the flux range. These values are unique and can be used to investigate metabolic state of the cell.M. tuberculosis growth in vitro and in vivo, we defined two biomass synthesis formulae with different sets of components required for growth. BIOMASS1 reflects the actual macromolecular composition of M. tuberculosis. BIOMASSe is a minimal macromolecular composition of M. tuberculosis and includes only those components thought to be essential for growth in vitro.Biomass composition was estimated from reported data from a variety of sources . To account for the different growth requirements of de novo synthesis. To force a flux toward synthesis, FBA models often include the co-factor in biomass composition but this has the effect of making synthesis of the co-factor constitutive in all conditions, which may be appropriate for essential co-factors such as NAD but is less so for co-factors such as molybdenum, which may be required only under certain conditions. To simulate the nonconstitutive requirement for these co-factors, we introduce the concept of a replenishing flux in which the nonconstitutive cofactors are included in reactions but with an arbitrary very low (0.001) stoichiometric coefficient toward consumption. Reversible reactions are written twice with co-factor consumption in both directions. This has the effect of forcing co-factor synthesis only in conditions where the co-factor utilizing reaction is active. The small replenishing flux toward co-factor synthesis has little influence on the magnitude of flux carried by the reaction and on the energy and mass balance of the metabolism, but it makes co-factor synthesis essential for the reaction to proceed.Numerous enzymes require nonpeptide co-factors that are regenerated either within the reaction or in a coupled reaction. Co-factor utilization can provide useful clues to metabolic activity, and co-factor synthesis pathways may provide potential drug targets. Stoichiometric models of metabolism published thus far either do not include co-factors in reaction formulae or balance co-factor consumption reactions with reactions that regenerate the cofactor (as for NAD/NADH in our model). However, in FBA analysis this strategy has the effect of eliminating the need for co-factor synthesis. Although enzyme co-factors are not consumed by reactions, they have finite chemical stability and must be replenished by ATP). To account for growth-dissociated cost, ATP dissipating reaction was included in the system and its flux was constrained to YxATP. The final parameter is the cost of growth-associated maintenance (YxATP), modeled by including ATP hydrolysis as a part of the biomass synthesis formula.Calibration of the model involved determination of three energetic parameters. The first of these is the ratio of the number of ATP molecules formed to the number of O atoms reduced by electron transport (P/O ratio). The P/O ratio is set by the stoichiometric coefficients of the reactions involved in electron transport and ATP synthesis. The second is the growth-dissociated cost of polymerization of the building blocks into biologic polymers (DNA replication, transcription, translation, and so on; mCorynebacterium glutamicum [xATP and the maintenance flux mATP were systematically changed to achieve good agreement with chemostat data, following previously reported approaches [To compare quantitative model predictions with our chemostat data, we constrained the growth rate to a chemostat dilution rate and computed the minimal glycerol uptake rate. The stoichiometry of the electron transport chain was considered to be similar to that of utamicum . The ATPproaches .For each gene we pre-computed the list of all reactions in the model that require the product of this gene, according to Boolean rules describing gene-protein relationships. Subsequently, the effect of gene inactivation on growth was predicted by constraining the fluxes through all reactions requiring this gene to 0 and running linear programming to determine the resulting maximal theoretical growth rate. If the resulting growth rate was less than 0.001, then the gene was considered to be essential for growth.in vitro was compared with the list of in vitro essential genes identified by TraSH mutagenesis [Computational predictions were compared with the findings of TraSH mutagenesis experiments ,57. Tranagenesis . To testLinear programming calculations and evaluation of Boolean expressions representing gene-protein associations were implemented in C programming language using GNU Linear Programming Kit library . The webM. tuberculosis. Additional data file The following additional data are available with the online version of this paper. Additional data file M. tuberculosis.Illustrated is the estimated macromolecular composition for Click here for fileShown are the calculations used to estimate the composition shown in Additional data file 1.Click here for fileShown is the conversion between stoichiometric formulae and mmol/l per gram of biomass.Click here for fileShown are the reaction formulae, limits, EC numbers, genes, and pathway classifications.Click here for fileProvided are references for the reactions shown in Additional data file 4.Click here for fileProvided are metabolite names.Click here for fileProvided are instructions on how to use the GSMN-TB server, illustrated by screenshots.Click here for file


C-flux analysis in Saccharomyces cerevisiae revealed that the apparent dispensability of knockout mutants with metabolic function can be explained by gene inactivity under a particular condition, by network redundancy through duplicated genes or by alternative pathways.Genome-scale 13 13C-tracer experiments is maturing into a routine higher-throughput analysis. The question now arises as to which mutants should be analyzed. Here we identify key experiments in a systems biology approach with a genome-scale model of Saccharomyces cerevisiae metabolism, thereby reducing the workload for experimental network analyses and functional genomics.Quantification of intracellular metabolite fluxes by 13C flux analysis revealed that about half of the 745 biochemical reactions were active during growth on glucose, but that alternative pathways exist for only 51 gene-encoded reactions with significant flux. These flexible reactions identified in silico are key targets for experimental flux analysis, and we present the first large-scale metabolic flux data for yeast, covering half of these mutants during growth on glucose. The metabolic lesions were often counteracted by flux rerouting, but knockout of cofactor-dependent reactions, as in the adh1, ald6, cox5A, fum1, mdh1, pda1, and zwf1 mutations, caused flux responses in more distant parts of the network. By integrating computational analyses, flux data, and physiological phenotypes of all mutants in active reactions, we quantified the relative importance of 'genetic buffering' through alternative pathways and network redundancy through duplicate genes for genetic robustness of the network.Genome-scale S. cerevisiae.The apparent dispensability of knockout mutants with metabolic function is explained by gene inactivity under a particular condition in about half of the cases. For the remaining 207 viable mutants of active reactions, network redundancy through duplicate genes was the major (75%) and alternative pathways the minor (25%) molecular mechanism of genetic network robustness in  This flux solution was then mapped to the genome scale by using minimization of the Euclidean norm of fluxes as the objective function. In total, 339 of the 745 biochemical reactions were active during growth on glucose alone . To assess the quantitative contribution of each gene to the organism's fitness, we determined maximum specific growth rates in minimal and complex medium using a well-aerated microtiter plate system glucose  or 100% [1-13C]glucose . Cells from overnight cultures were harvested by centrifugation and washed using sugar-free MM to remove residual unlabeled carbon sources. Cultures were routinely inoculated to an maximum OD600 of 0.03 and harvested by centrifugation at an OD600 ≤ 1. Residual medium was removed by washing the pellet with water. Cell protein was hydrolyzed for 24 h at 105°C in 6 M HCL and dried in a heating block at 85°C for 6 h. The free amino acids were derivatized at 85°C for 1 h using 15 μl dimethylformamide and 15 μl N-(tert-butyldimethylsilyl)-N-methyl-trifluoroacetamide glucose experiments by tracking the positional 13C-labeling  glucose experiment and a compartmentalized yeast model [δ of ± 10% when mapping the determined central metabolic fluxes to the genome-scale reference flux solution. To overcome mathematical artifacts such as futile cycling , the original linear programming problem was modified. A minimization of the Euclidean norm of fluxes was chosen as the objective function such that (s.t.) the mass balance equations hold:We used the experimentally determined eriments . In partanalysis  for GC-Mst model . These ej as the set of experimentally determined fluxes. Reactions were categorized as flexible when fulfilling the following criteria: the reactions carried a non-zero flux; and the reaction was not essential for growth.with Mi (1 × n) that have to be fulfilled, when multiplied with the overall flux vector ν (n × 1):Phenotype predictions of deletion mutants were analyzed computationally with FBA ,59. AssuMi·ν = 0.     (12)m metabolite mass balances yields the stoichiometric matrix S (m × n), where:The entity of all ν = 0.     (13)S·μ as the global cellular goal [ν results in a linear programming (LP) problem:To pick one solution out of the overall solution space formed by the stoichiometric constraints, FBA generally assumes maximization of biomass growth lar goal ,59. Thusi = 1,..,M and νlb,i and νub,i correspond to upper and lower bounds of a specific reaction i. Gene knockout mutants can be simulated easily in silico by setting the deleted reactions to zero. All LPproblems were solved using the open-source GNU linear programming kit [where ming kit .The following additional data are available with the online version of this paper. The classification of reactions according to Figure Classification of reactions according to Figure 1Click here for fileThe flux analysis model. The flux analysis modelClick here for filePhysiological data, flux ratios and the calculated flux distributions. Physiological data, flux ratios and the calculated flux distributionsClick here for file


To plan, implement, and evaluate programs designed to improve health conditions among racial and ethnic minority populations in the United States, public health officials and researchers require valid and reliable health surveillance data. Monitoring chronic disease and behavioral risk factors among such populations, however, is challenging. This study assesses the effects of race, ethnicity, and linguistic isolation on rates of participation in the Behavioral Risk Factor Surveillance System (BRFSS).County-level data from the 2003 BRFSS survey and 2000 U.S. census were used to examine the effects of race, ethnicity, and linguistic isolation on six measures of survey participation .Participation rates were significantly lower in counties with higher percentages of black people and people who did not speak English. Response rates decreased by 4.6% in counties with the highest concentration of black residents compared with counties with few black residents. Likewise, response rates decreased by approximately 7% in counties in which a larger percentage of the population spoke only Spanish or another Indo-European language compared with counties in which all residents spoke English.The negative relationship between the percentage of Spanish-only–speaking households and participation rates is troubling given that the BRFSS is conducted in both Spanish and English. The findings also indicate that more needs to be done to improve participation among other minorities. Researchers are investigating several ways of addressing disparities in participation rates, such as using postsurvey adjustments, developing more culturally appropriate data-collection procedures, and offering surveys in multiple languages. Healthy People 2010 , high mobility patterns, reluctance to reveal personal information, and data-collection procedures  -26. BecaThe potential for such problems has increased over the past several decades as the U.S. population has grown more diverse. From 1980 to 2002, according to the U.S. Census Bureau, the proportion of minorities among the civilian, noninstitutionalized population grew from 6.4% to 13.3% among Hispanics, 11.7% to 13.0% among blacks, and 1.5% to 4.4% among Asians -30. AddiLinguistic isolation is defined by the U.S. Census Bureau as living in a household in which all members aged 14 years and older speak a non-English language and also speak English less than "very well"   of the 262.4 million people aged 5 years and older spoke a language other than English at home . This peEnglish) . In 2000English) . Table 1 Percentage of linguistically isolated Asian-language or Pacific Island-language households, United States. Source: U.S. Census 2000  has been instrumental in tracking health disparities across populations in the United States . Yet oveThe BRFSS gathers data through computer-assisted telephone interview (CATI) surveys designed to collect uniform, state-specific data on preventive health practices and risk behaviors that are linked to the leading causes of morbidity and mortality among adults. The survey is conducted by all 50 states and the District of Columbia, as well as by Puerto Rico, Guam, and the Virgin Islands with assistance from the Centers for Disease Control and Prevention (CDC). However, the three territories are not included in the analysis presented here. Further details on the BRFSS design, methodology, and questionnaire are presented elsewhere .To examine aspects of survey participation, we calculated the following six dependent measures of survey participation at the county level based on final case disposition for telephone numbers called between January 1, 2003, and December 31, 2003:1. Resolution rate: the percentage of all sampled telephone numbers for which household status with a working telephone number has been determined2. Screening rate: the percentage of all known households in which the presence or absence of an eligible respondent has been determined3. Cooperation rate: the percentage of known, eligible households in which a completed or partially completed interview has been obtained4. Response rate: the percentage of all confirmed and potentially eligible sample members for whom an interview has been completed, which we calculated using Response Rate 4 recommended by the American Association for Public Opinion Research language barrier. Interviewers could not communicate with household members because of the language spoken in the home 5. Language-barrier rate: the percentage of all sampled households given a final disposition of refusal, indicating either the selected sample member's refusal to complete the interview or the interviewer's inability to recontact the household because of a hang-up or other refusal by someone in the household6. Refusal rate: the percentage of all sampled households given a final disposition of We conducted the analysis at the county level because of a lack of available information about survey nonrespondents at the individual or household level. Counties were included in the analysis if they had 30 or more observations in the denominator of each of the six participation measures. Our use of these criteria ensured greater stability in the measures calculated and helped us compare the impact of independent variables across the six models estimated; however, it also limited the analysis to 1894 of the 3141 counties in the United States.County-level predictor variables for race, ethnicity, and linguistic isolation were derived from 2000 U.S. census counts . We calcWe also developed several county-level control variables to account for some of the other factors that are thought to affect participation rates within certain geographic areas. Socioeconomic status, often measured through a combination of income and education levels, is an important mediator of racial and ethnic health disparities and an important predictor of survey participation ,38. LikeP < .001) and between Hispanic ethnicity and Spanish-language isolation . We used separate models to determine which variables  were better predictors, but we found the differences between them to be marginal. Although linguistic isolation is a definite barrier to survey participation, race and ethnicity may or may not be factors; thus, the Asian-language–only and Spanish-language–only variables were retained in the final models, but the variables Asian race and Hispanic ethnicity were not retained. Because of the strong correlation between race, ethnicity, and language-isolation variables, however, we had difficulty determining the proportional impact of each.Because all variables in the analysis were expressed as percentages, we used ordinary least squares (OLS) regression modeling to assess the impact of race, ethnicity, and linguistic isolation on survey participation. In preliminary analyses, we found a high degree of correlation between Asian race and Asian-language isolation , we removed urbanicity from the final models.We also examined the possible effects of multicollinearity in our analysis. Because we found that regressing the other predictor and control variables on urbanicity explained more than 50% of the variance in the percentage-urban variable . The dependent variables used were the county-level estimates for percentage of black adults, percentage of Spanish-language–only households, percentage of other Indo-European-language–only households, and percentage of Asian-language–only households. The control variables used were the percentage of households with incomes of $50,000 or more, the percentage of adults aged 25 and older with less than a high school education, the percentage of heads of household with a one-way work commute of 30 minutes or more per day, and the percentage of BRFSS calls that reached an answering machine or call-screening device. Model selection was based on forced entry of all variables into the models rather than stepwise selection. The models were estimated using SPSS 13.0  with the Complex Samples module. Finally, we used the OLS coefficients from the final models and the maximum county-level population parameters to calculate the maximum impact of race, ethnicity, and linguistic isolation on the six measures of survey participation.P = .001), screening rates , cooperation rates , and response rates , although a significant positive relationship was seen with refusal rates . The percentage of black residents in a county did not have a significant effect on the rate of nonparticipation attributed to a language barrier.In general, minority race and ethnicity and linguistic isolation had significant negative correlations with survey participation rates . The regP = .04), screening rates , cooperation rates , and response rates . The impact of Spanish-language isolation on response rates was more than four times the impact of the percentage of black adults in a county. Counties with higher percentages of Spanish-language–only households also had higher percentages of nonparticipation attributed to language barriers . Rates of Spanish-language isolation did not, however, significantly affect the percentage of nonparticipation attributed to refusals.Linguistic isolation also had a negative effect on participation rates, although the magnitude of this effect differed across the three language types. Higher rates of Spanish-language isolation led to lower resolution rates , cooperation rates , and response rates . Counties with higher rates of Indo-European-language–only households also had higher language-barrier and refusal rates.The percentage of households in which only Indo-European languages were spoken did not significantly affect resolution rates, but it did have a significant negative effect on screening rates  and screening rates , but they did not have significantly lower cooperation or response rates. Similarly, although higher percentages of Asian-language isolation led to a significant increase in the language-barrier rate , there was a significant decrease in the refusal rate in these counties .In contrast, Asian-language–isolated households had less effect on survey participation rates. Counties with higher percentages of Asian-language–only households did have significantly lower resolution rates , a state-based telephone survey similar in content to the BRFSS survey. The 2001 CHIS was translated into Spanish, Mandarin Chinese, Cantonese, Vietnamese, Korean, and Cambodian (Khmer). Approximately 10% of the completed interviews in the state were conducted in Spanish, and 5% were conducted in one of the Asian languages .The second approach is to rely upon third-party interpreters to administer the survey. Some language-service providers can provide interpreters in more than 150 languages .Both of these translation approaches have advantages and disadvantages. The use of native-language–speaking interviewers helps ensure that the survey questionnaire is administered in a standardized manner but reduces the number of languages and interviewers available. In contrast, third-party interpretation allows questionnaires to be administered in many languages, but administration of the questionnaire may be less consistent. Third-party interpretation also does not allow for assessment of cultural equivalence, thereby potentially leading to measurement error. Both approaches are also relatively costly. Additionally, neither approach provides a complete solution to the problem of increasing survey participation among people isolated by language.There are several limitations to the current study. First, sample sizes in some counties limited the analysis to 1894 of 3141 counties. Second, because information on key variables such as race, ethnicity, and language spoken in the household was not available at the individual level of nonresponding households, the analysis was conducted at an aggregate (county) level. Although aggregate-level approaches to studying racial and ethnic disparities have been encouraged by the U.S. Department of Health and Human Services when individual-level data are not available, future studies of survey participation could be strengthened by surveys that are designed to collect data on key variables from nonrespondents (Adequately identifying racial and ethnic disparities in health care and developing effective strategies to eliminate these disparities depends on the availability of valid and reliable data. Considerations of race, ethnicity, acculturation, and language are critical to the success of such health surveillance efforts. Researchers need to infuse these elements into their study designs, data-collection protocols, and data-processing routines. Indeed, several pilot studies are now being conducted in conjunction with the BRFSS to try to address these issues. These studies are using alternative sampling frames to reach individuals who are inaccessible by landline telephones, multiple modes of survey data collection, prenotification techniques tailored to minority racial and ethnic populations, surveys in languages other than English and Spanish, and case-management techniques for preassigning likely non-English–speaking households to bilingual interviewers. Such efforts are essential for meeting the challenges to health surveillance posed by the growing diversity of the U.S. population.


S. Metabolite coupling is derived from the matrix ŜŜT, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where Ŝ is the binary form of S.Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein "metabolite coupling." These metabolite pairs can be directly computed from the stoichiometric matrix, Metabolite coupling in the studied networks was found to be dominated by a relatively small group of highly interacting pairs of metabolites. As would be expected, metabolites with high individual metabolite connectivity also tended to be those with the highest metabolite coupling, as the most connected metabolites couple more often. For metabolite pairs that are not highly coupled, we show that the number of reactions a pair of metabolites shares across a metabolic network closely approximates a line on a log-log scale. We also show that the preferential coupling of two metabolites with each other is spread across the spectrum of metabolites and is not unique to the most connected metabolites. We provide a measure for determining which metabolite pairs couple more often than would be expected based on their individual connectivity in the network and show that these metabolites often derive their principal biological functions from existing in pairs. Thus, analysis of metabolite coupling provides information beyond that which is found from studying the individual connectivity of individual metabolites.The coupling of metabolites is an important topological property of metabolic networks. By computing coupling quantitatively for the first time in genome-scale metabolic networks, we provide insight into the basic structure of these networks. Cellular metabolism is an extensively studied process that is essential to the survival of any free-living organism. The metabolic process can be characterized as a set of biochemical transformations, each of which involves the consumption of one or more metabolites and the production of one or more metabolites. Subject to the law of mass conservation, the net sum of elements and electrical charge is conserved in each reaction and thus in the network as a whole. Each transformation by definition must involve more than one metabolite. Herein, we define "metabolite coupling" as the appearance of a pair of metabolites in the same biochemical transformation.+) and water in metabolic reactions. The ubiquity of the proton in metabolic networks is interesting in light of the fact that at standard pH a typical bacterial cell contains approximately 60 free protons. Assuming pH = 7 . [2 3 1]. The integers 1 and 2 appear in both of these vectors, so the random value for m12 is 2. When this process is repeated many times , the random value converges to (mii)(mjj)/(number of reactions). Importantly, actually performing the randomizations produces a distribution of all possible values for each mij. By sorting the distribution of 10,000 random coupling values and noting how many random values are greater (less) than mij, we estimate the probability of all pairs of two metabolites coupling as often (rarely) as they do by random chance.Monte Carlo sampling was used to find the distribution of possible off-diagonal elements of SAB carried out the computations described in the manuscript, analyzed the results, and drafted the manuscript. NDP helped perform computations, analyze the results and draft the manuscript. BOP conceived of the study, helped analyze the results, and helped draft the manuscript. All authors read and approved the final manuscript.


The aim of this work was to trace the evolutionary pathway leading to the appearance of the extant biosynthetic routes and to try to disclose the interrelationships existing between them and other pathways in the early stages of cellular evolution.It is generally assumed that primordial cells had small genomes with simple genes coding for enzymes able to react with a wide range of chemically related substrates, interconnecting different metabolic routes. New genes coding for enzymes with a narrowed substrate specificity arose by paralogous duplication(s) of ancestral ones and evolutionary divergence. In this way new metabolic pathways were built up by primordial cells. Useful hints to disclose the origin and evolution of ancestral metabolic routes and their interconnections can be obtained by comparing sequences of enzymes involved in the same or different metabolic routes. From this viewpoint, the lysine, arginine, and leucine biosynthetic routes represent very interesting study-models. Some of the The comparative analysis of the genes involved in the biosynthesis of lysine, leucine, and arginine, their phylogenetic distribution and analysis revealed that the extant metabolic "grids" and their interrelationships might be the outcome of a cascade of duplication of ancestral genes that, according to the patchwork hypothesis, coded for unspecific enzymes able to react with a wide range of substrates. These genes belonged to a single common pathway in which the three biosynthetic routes were highly interconnected between them and also to methionine, threonine, and cell wall biosynthesis. A possible evolutionary model leading to the extant metabolic scenarios was also depicted.The whole body of data obtained in this work suggests that primordial cells synthesized leucine, lysine, and arginine through a single common metabolic pathway, whose genes underwent a set of duplication events, most of which can have predated the appearance of the last common universal ancestor of the three cell domains . The model proposes a relative timing for the appearance of the three routes and also suggests a possible evolutionary pathway for the assembly of bacterial cell-wall. In. InE. coArabidopsis thaliana is known to synthesize lysine through the DAP pathway [At4g33680) that specifically catalyzes the conversion of tetrahydrodipicolinate to LL-diaminopimelate, a reaction requiring three enzymes in the DAP-pathway variant found in E. coli [ pathway ,31. A DA E. coli .P. horikoshii represents the main exception. In fact, in this organism just one pathway for the biosynthesis of all these three amino acids has been identified so far  extant scenario.P. horikoshii , dapF, and lysA. On the basis of the available data, it is not still possible to discern between the possibility that the product of the reaction catalyzed by Asd directly interacts with DapC, or if other enzymes  were required to complete the DAP route. However, it is possible that once the assembly of the lysine-branch of the DAP pathway was completed the primordial cells possessed two alternative ways to synthesize lysine. If the model we propose here is correct, it is possible that, in this context, the entire AAA pathway became superfluous. Hence, the fourth step .In our opinion, the duplication events from Figure lysZ and lysY, argB and argC, and ask and asd?According to the model proposed, the metabolic pathway leading to methionine and threonine diverged from the ancestral pathway before lysine, leucine, and arginine biosynthetic ones. However, (at least) another plausible scenario can be depicted, involving the previous appearance of the leucine pathway and the further assembly of the others biosynthetic routes. In other words, is it possible to establish the relative timing of the separation of the extant three pairs of genes involved in lysine, arginine, threonine, and methionine biosynthesis, that is LysZ, ArgB, Ask, and LysY, ArgC, and Asd). To this purpose a dataset of all the retrieved LysZ, ArgB, Ask and, LysY, ArgC, and Asd amino acid sequences was aligned using the program ClustalW [Useful hints concerning this issue can be obtained by a phylogenetic analysis of the two paralogous triads  biosynthesis, respectively Figure .2. Asd sequences form a unique cluster separated from ArgC and LysY sequences, involved in arginine and lysine biosynthesis, respectively Figure .ask and asd genes; hence, argB and argC might be the outcome of a later duplication event of their corresponding ancestral sequences.Hence, the topology of the tree in Figure Thus, in our opinion, the model proposed in the previous section appeared to be in agreement with both phylogenetic analyses and the metabolic schemes shown in Figures In this work a likely model for the evolution of genes involved in the biosynthesis of lysine, leucine, and arginine is depicted. The model proposed is based on the analysis of the structure of these biosynthetic routes and the phylogenetic distribution of their genes. The phylogenetic analysis performed allowed us also to determine a possible relative timing of the appearance of genes that are involved in the extant lysine (DAP) and arginine biosynthetic routes. This analysis gave a strong support to the hypothesis that extensive gene duplication events played a key role in shaping the extant biosynthetic routes of lysine, leucine and arginine. According to the model proposed in this work a common metabolic pathway for the biosynthesis of these three amino acids predated the appearance of the last universal common ancestor. This ancestral metabolic route was probably composed of a set of unspecific enzymes able to react with chemically related substrates interconnecting different biosynthetic routes Figure . The occHence, starting from a metabolic network whose (highly interconnected) nodes represent unspecific enzymes Figure , novel mdapC and argD genes  and L-arginine [dapC gene and ddh in C. glutamicum does not generate auxotrophy to lysine, suggesting that the product of argD may substitute the DapC function [dapC, ddh and argD genes from C. glutamicum does not affect its growth, the existence of another aminotransferase able to substitute ArgD and DapC, has been invoked [One of the main consequences of this evolutionary pathway, i.e. gene duplication followed by evolutionary divergence, is the separation of metabolic routes that were originally fused in a single, common one. The biological significance of this cascade of duplication events might rely on both (i) the appearance and refinement of regulatory mechanisms specific to each novel metabolic pathway and on (ii) the increased robustness acquired by the whole metabolic network, i.e. its ability to respond to environmental changes or to "knock-out" mutations falling within biosynthetic genes (while maintaining unchanged biosynthetic activity). In fact, the removal of one of the multifunctional enzymes belonging to the ancestral common pathway of lysine, leucine and arginine Figure  would les Figure 4b, 4c retivities  whereas eactions . Althougarginine . On the function . Since t invoked . This, i invoked , that maet al. [T. thermophilus and D. radiodurans (TD group), lack the AAA pathway, we suggest that, during the separation of Archaea from Bacteria, the latter may have lost the AAA pathway, maintaining the DAP pathway for the biosynthesis of lysine, LL-diaminopimelate, and meso-diaminopimelate organisms belonging to the TD group as suggested by Nishida [T. thermophilus and D. radiodurans have maintained the AAA pathway. The model proposed fits with the metabolic schemes shown in Figures On the basis of gene distribution analysis, Velasco et al.  proposede Figure . The pre Nishida . AlternaThe distribution of the AAA and the DAP pathway in Archaea defies a simple explanation since all currently known pathways for lysine biosynthesis in Bacteria and Eucarya exist also in the domain of Archaea . Hence, Lastly, on the basis of the scenario depicted in this work, the evolutionary history of lysine, leucine, and arginine biosynthetic routes might supply useful hints to disclose the origin and the assembly of bacterial cell wall. In the extant bacteria, the rigidity of the cell wall is due to a huge macromolecule containing acylated amino sugars and three to six different amino acids. This heteropolymer, the peptidoglycan, is built out of glycan strands cross linked through short peptides. In contrast to the uniform structure of the glycan, the peptide moiety reveals considerable variations. The greatest variation occurs at position 3 of the peptide moiety, where usually a diamino acid is found. The most widely distributed diamino acid is meso-diaminopimelate. It is present in gram-negative bacteria and in many other (micro)organisms, such as some species of bacilli, clostridia, lactobacilli, corynebacteria, propionibacteria, actinomycetales, myxobacteriales, rickettsiae, and cyanobacteria. Some studies have shown that the L-asymmetric carbon of meso-diaminopimelate is bound to the peptide subunit  do not have diaminopimelic acid within their cell wall [As expected by the lack of DAP pathway, all those bacterial (micro)organisms biosynthesizing lysine through the AAA pathway (ell wall ,42. In tell wall , whereasell wall . Hence well wall  whose biThis evolutionary step corresponds to the last one that is depicted in the evolutionary model that we have previously proposed Figure . This vix in genome A and gene y in genome B is called best-best hit when x is the best hit of query y against all genes in A and vice versa, and it is often used as an operational definition of ortholog [Amino acid sequences were retrieved from GenBank and KEGG databases and were used to build a local database for BLASTp  probing ortholog ,47.The ClustalW  program Phylogenetic trees were obtained with Mega 3 software  using thThe authors declare that they have no competing interests.All authors equally contributed to the preparation of the final version of the manuscript; MF performed the analyses during its PhD thesis under the supervision of Prof. RF.