Mycobacterium tuberculosis (MTB) is the causative bacterium of tuberculosis, a disease responsible for over a million deaths worldwide annually with a growing number of strains resistant to antibiotics. The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations. Therefore, we expanded a genome-scale regulatory-metabolic model for MTB using the Probabilistic Regulation of Metabolism (PROM) framework. Our model, MTBPROM2.0, represents a substantial knowledge base update and extension of simulation capability. We incorporated a recent ChIP-seq based binding network of 2555 interactions linking to 104 transcription factors (TFs) (representing a 3.5-fold expansion of TF coverage). We integrated this expanded regulatory network with a refined genome-scale metabolic model that can correctly predict growth viability over 69 source metabolite conditions and predict metabolic gene essentiality more accurately than the original model. We used MTBPROM2.0 to simulate the metabolic consequences of knocking out and overexpressing each of the 104 TFs in the model. MTBPROM2.0 improves performance of knockout growth defect predictions compared to the original PROM MTB model, and it can successfully predict growth defects associated with TF overexpression. Moreover, condition-specific models of MTBPROM2.0 successfully predicted synergistic growth consequences of overexpressing the TF whiB4 in the presence of two standard anti-TB drugs. MTBPROM2.0 can screen in silico condition-specific transcription factor perturbations to generate putative targets of interest that can help prioritize future experiments for therapeutic development efforts. Mycobacterium tuberculosis (MTB), and how it adapts to survive the broad range of conditions within hosts. To help this effort, we extended a computational model that uses our understanding of how the MTB transcriptional regulatory network (genes that interact to control the abundance of target genes) influences the metabolic network . Using this model, MTBPROM2.0, we were able to successfully predict whether disrupting or boosting the action of regulatory genes would cause a growth defect in MTB. By applying these predictions, across many environmental conditions, this tool can help find potential new drug targets for more effective MTB treatments.Tuberculosis remains a major global health challenge with a need for enhanced drug development efforts. Drug development would be aided by understanding more about the bacteria that causes the disease,  Tuberculosis (TB) remains a major global health challenge with a need for enhanced drug development efforts. The current standard treatment for drug-susceptible tuberculosis involves treating for 6 months with a cocktail of first-line drugs that have serious side-effects . AntibioMycobacterium tuberculosis (MTB) has the ability to tune its physiology to adapt and survive in the broad range of conditions within the host, altering its drug susceptibility in the process. A promising approach for discovering novel drug targets has been to develop computational models that recapitulate the organismal phenotypes of interest. For example, to identify candidate metabolic drug targets, genome-scale mechanistic models of metabolism have been used to simulate growth and metabolic phenotypes under gene deletion conditions for multiple pathogens [athogens . Anotherathogens , 5. TranThus far, the effects of gene regulation have been integrated into constraint-based metabolic modeling with three major strategies: (1) modifying the objective function of a metabolic model to impose an implicit regulatory goal , 7, (2) In the absence of such condition-specific gene expression data, efforts at integrating information about regulation and metabolism have reconstructed known regulatory interactions between transcription factors, metabolic target genes, and environmental conditions. These approaches do not require the generation of transcriptional profiles directly probing transcription factor perturbations, and thus are applicable to new conditions. Most of these approaches (e.g. rFBA and SR-FBA) involve manually defining a set of Boolean regulatory rules based on literature information –15. A siing PROM .MTBPROM2.0 encapsulates a substantially expanded knowledge base of underlying metabolic and regulatory mechanisms, featuring a regulatory network for 104 TFs based on ChIP-seq interactions that interface with a refined genome-scale metabolic model with 810 genes. We also extended the PROM framework to enable the prediction of metabolic consequences of TF overexpression. Using this expanded model, we can generate both knockout and overexpression phenotype predictions for 3.5 times as many TFs while exposed to a greater range of environmental and genetic perturbations. MTBPROM2.0 has improved agreement between predictions and experimental datasets assessing gene essentiality and overexpression growth defects compared to alternative methods, and it has successfully predicted synergy between TF perturbations and anti-TB drugs.In this work, we present a significantly improved regulatory-metabolic model for MTB based on the PROM framework. MTBPROM2.0, incorporates a knowledge expansion in both the metabolic and regulatory components. We have reconstructed an updated regulatory-metabolic model for MTB using the PROM framework. The updated model, MTBPROM2.0, we updated the metabolic component by integrating gene-associated reactions with literature evidence from the multiple existing genome-scale metabolic models and supplementing with additional reaction information from literature . This improved markedly upon the overall performance of the original model, MTBPROM1.0, which had a MCC = 0  on this new kind of data for comparison. MTBPROM2.0 performance remained higher than MTBPROM1.0 when other essentiality thresholds were tested as well  as an experimental cutoff threshold to delineate growth defect vs. no defect, we evaluated the ability of MTBPROM2.0 to correctly distinguish between these groups. The overall MCC was 0.2, with precision = 0.23, and recall = 0.69.To extend the predictive scope of the PROM framework, we modified the simulation to enable prediction of transcription factor overexpression growth phenotypes, using the same input gene expression dataset to train conditional probabilities   or based on condition-specific metabolic models generated by overlaying overexpression microarray data with iMAT [est) see . MTBPROMith iMAT , 10, 28 MTBPROM2.0 can be integrated with condition-specific metabolic models to predict effects of combinatorial perturbations on the growth of MTB. To assess our ability to predict synergies with drugs, we exploited test compounds with demonstrated anti-TB activity [MTBPROM2.0 and simulated the growth outcome of each TF knockout and overexpression event. The simulations predicted multiple TF knockouts and overexpression conditions that would have a growth defect in the presence of the drug but not under standard culturing conditions , which MTBPROM2.0 predicted to have a growth defect when exposed to each of four agents: ethionamide (ETH), isoniazid (INH), a coumarin analog (IMTB009), and a guanosine analog (IMTB0044). We compared the growth (by OD600) and metabolic activity . While no appreciable growth difference was detected between the two strains in the absence of ETH, dosing the strains with 3μM of ETH (approximately 0.5x the MIC) resulted in significantly more growth inhibition  resulted in significantly more growth inhibition , and observed no differential growth upon exposure to these compounds (data not shown).To experimentally validate, we tested the overexpression of the TF ue Assay ) of wildMycobacterium tuberculosis that incorporates expanded and improved knowledge of the metabolic and regulatory systems. The refined model, MTBPROM2.0, can predict metabolic consequences of TF knockout or overexpression under different environmental conditions, and suggest hypotheses of underlying molecular mechanisms that contribute to consequent phenotypes. This work has shown that we can predict growth phenotypes resulting from overexpressing or disrupting transcription factors with a mechanistic regulatory-metabolic model of MTB, and that integrating more knowledge of underlying metabolism and regulation into mechanistic models improves predictive ability. Moreover, we have demonstrated the utility of MTBPROM2.0 in predicting TF perturbations that synergize with the activity of drugs. Our model minimizes complicating assumptions, and with this simplified rule set, a significant proportion of predictions generated can be successfully validated. Therefore, using mechanistic models to screen and generate hypotheses for interesting targets can help to prioritize experiments that further extend knowledge of the underlying biology.Models that leverage knowledge of biological circuitry to generate accurate predictions of phenotype can synergize with experimental efforts to improve understanding of biology and identify novel intervention strategies. In this work, we have generated an updated mechanistic regulatory-metabolic model for in silico screening that can be followed up with experiments, and to the best of our knowledge, MTBPROM2.0 is the first explicitly reconstructed regulatory-metabolic model to incorporate overexpression simulations. Given that the experimental tools are already in place to control and monitor the effects of TF overexpression in MTB [MTBPROM2.0 to simulate a broader set of genetic perturbations under a greater range of conditions to guide experiments, we can begin to gain a better understanding of the condition-specific genetic sensitivities of MTB.Expanding the PROM framework to enable prediction of TF overexpression phenotypes opens a new dimension of n in MTB , introduMTBPROM1.0. Moreover, the improvement in gene essentiality predictive performance of observed with MTBPROM2.0 compared to MTBPROM1.0 suggests that additional representation of biochemical and regulatory knowledge captured by the integrated model further drives improvements to predictive ability. Continuing efforts to improve the representation of biochemistry and regulation in the metabolic and regulatory model components—as well as inclusion of other important processes—will likely generate more comprehensive predictions that are more reflective of MTB behavior.Incorporating additional regulatory and metabolic reaction information expanded the scope of the predictions, enabling predictions on a broader range of transcription factors than MTBPROM2.0 with drug-specific metabolic models informed by transcriptional responses in MTB, we successfully predicted synergistic interactions between overexpression of the transcription factor whiB4 and the drugs ethionamide and isoniazid. This synergy suggests that the regulatory targets of whiB4 include potential drug targets that can enhance the activity of these agents. While two other predicted whiB4-drug interactions were not validated, the ability of the model to identify synergistic TF-drug interactions in 50% of cases and to identify correctly drugs that do not synergize with the TF in four of four instances that we tested argues that the model is capturing a significant portion of the mechanism(s) that underlie the synergistic phenotypes. Applying this approach to other anti-TB agents could contribute to rationally informing identification of novel candidates for synergistic targets.Finding effective combinatorial therapies is an important challenge in TB drug development. While drug combinations can be more effective and less likely to promote MTB resistance, identifying viable combinations by experimental efforts alone is hindered by the large search space. Integrating MTBPROM2.0 predictions and those that are not based on the network properties. This approach gave a network-based criterion with which to rank predictions that were most promising to follow-up in experiments. Although in silico predictions are easy to make in high throughput, it is often difficult to follow-up on more than a handful of predictions with in-depth experimental characterization. Therefore, having an accurate means to prioritize the predictions that are most likely to yield meaningful follow-up results will streamline the iterative process of investigation. Moreover, the ability to identify properties that make a TF hard to predict by the current regulatory-metabolic model also can direct method development towards improvements that can address the current limitations. In the case of using MTBPROM2.0 to predict TF overexpression growth phenotypes, the factors that could stratify the TFs suggest that being able to account for more complex regulatory mechanisms would improve predictive ability. Future method development to address combinatorial regulation and the effects of both activation and repression may help to improve overall predictive accuracy for regulatory-metabolic models.There remain substantial gaps in the knowledge and representation of regulation and metabolism that lie beyond the scope of the current PROM framework. Determining which phenotypes can be modeled with by rough representations of mechanism and which phenotypes require more detailed, in-depth models can shed light into the complexity of the phenotypes themselves and inform future modeling efforts. Our logistic regression model enabled us to distinguish between TFs that are amenable to Growth phenotype simulations were performed on the genome-scale metabolic models using the flux balance analysis tools in the COBRA Toolbox , 32. GroTo evaluate the ability of the metabolic and regulatory-metabolic models to predict gene knockout growth defects, single gene deletion growth simulations for each of the models were compared to an experimental essentiality screening dataset generated by Griffin et al. . The GriTP represented the ‘true positives’, TN represented the ‘true negatives,’ FP represented the ‘false positives,’ and FN represented the ‘false negatives.’For all gene perturbation predictions, performance was evaluated based on how well the prediction method could correctly separate the effects of the TF perturbations, where a true positive is a TF that is correctly predicted to cause a defect, and a true negative is a TF that is correctly predicted to cause no growth defect. Performance was summarized by precision *(TN+FN) . In eachTo evaluate the ability for the metabolic models to predict growth under different environmental conditions, we simulated growth under different carbon and nitrogen sources using the COBRA Toolbox by adjusting the exchange reaction fluxes to allow input of the desired metabolite. Growth predictions on diverse carbon and nitrogen sources were compared to experimental growth condition data reported in . We addiThe original PROM approach to estimating the influence of a regulatory interaction in the event of a TF knockout was to calculate from a gene expression dataset the conditional probability that a target gene is ‘ON’ in the absence of the transcription factor:The expression threshold that delineated between the ‘ON’ and ‘OFF’ states could be either set externally or calculated to be a desired quantile from the input expression data. These conditional probabilities were then used to constrain the maximal fluxes of the reactions  catalyzed by the gene products in the metabolic model.To predict the regulatory effects of TF overexpression, we adapted the conditional probability calculation to estimate the probability that a target gene is ‘ON’ when the expression of the transcription factor is above a threshold value denoted ‘OVEREXPRESS’:This formulation required setting two expression thresholds: one that delineated between ‘ON’ and ‘OFF’ states, and another that delineated between ‘ON’ and ‘OVEREXPRESS,’ which transformed the numerical expression values into the three expression states. This formulation is compatible with any gene expression datasets, not just profiles of TF overexpression. It is important to note also that this model only takes into account the effect of genes that are repressed by transcription factors. The effects of target genes that were activated by the overexpression of a transcription factor fell outside the scope of this model. The code for simulating TF knockouts and overexpression is available in Given that the calculation of the conditional probabilities is dependent on the gene expression dataset being used, we used a sampling approach to estimate the uncertainty of the conditional probabilities. We used microarray data profiling individual TF overexpression perturbations  to estimMTBPROM2.0 to predict transcription factor overexpression phenotypes using condition-specific metabolic models generated using the iMAT algorithm [We compared the ability of lgorithm , 10. We lgorithm . For eaclgorithm , 10, 28.2 fold change < -1 upon exposure to drug were designated as ‘OFF.’ Drug-specific iMAT models were generated and used to simulate TF perturbations by integrating with the MTBPROM2.0 regulatory framework.To simulate drug-specific metabolic models, we applied iMAT to constrain iSM810 based on the transcriptional profiles of MTB response to different drugs measured by arrays provided by TIGR under the NIAID contract N01-AI-15447. To binarize the expression data, genes with logMTBPROM2.0 prediction of a particular TF was likely to be correct, we trained a logistic regression model with the R package “stats” [Y belongs to a particular category given the state of input predictor features  based on the following relation:To estimate a confidence score for how likely the  “stats”  using di “stats”  designedMTBPROM2.0 prediction for a TF overexpression growth defect would be ‘TRUE’ or ‘FALSE,’ where ‘TRUE’ indicates either a true positive or a true negative prediction, and ‘FALSE’ indicates either a false positive or a false negative prediction. The predictor features we used to train the initial model were the number of metabolic target genes, the fraction of target genes that were highly confident essential based on Griffin data, and the average number of additional regulators that the target genes had. To refine the logistic regression model, we retrained the model using only the features that were evaluated to be significant by the z-statistic [In this case, the category we predicted was whether the tatistic . To testtatistic , and calwhiB4, were cultured as described previously [whiB4 was induced using an ATc concentration of 100 ng/mL culture. Growth was monitored via OD600 for a period of 14 days post drug supplementation and induction of whiB4 overexpression. MTB metabolism was measured by the extent of conversion of the oxidation-reduction dye, Alamar Blue [The MTB strain H37Rv and the strain containing an ATc-inducible expression vector for the TF gene of interest, eviously , 30. Brimar Blue . Ten perS1 Text(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLS)Click here for additional data file.S1 FileThe.xml version has exchange fluxes set to match Middlebrook 7H9 growing conditions. MATLAB files contains a model version with exchange fluxes set to match Middlebrook 7H9 growing conditions and a model version with all exchange fluxes set to 0. File also contains an array detailing which reactions are exchange reactions. Can be imported into the COBRA Toolbox.(ZIP)Click here for additional data file.S2 FileFile contains codes to run knockout and overexpression simulations, where the conditional probability is calculated from the entire input expression dataset , as well as code for simulating where the conditional probability is calculated with sampling (promSampling.m). Code for simulating TF-metabolic gene double knockouts is also provided (PROMdoubleKO.m). Additionally, code describing how to generate the carbon and nitrogen source models is included (Metabolite-condition-specific-ModelCode.m).(ZIP)Click here for additional data file.S3 FileCode in (ZIP)Click here for additional data file.


S. cerevisiae has attracted considerable interest in recent years as a model for ecology and evolutionary biology, revealing a substantial genetic and phenotypic diversity. However, there is a lack of knowledge on the diversity of metabolic networks within this species.S. cerevisiae, we used a dedicated constraint-based model to predict the central carbon metabolism flux distribution of 43 strains from different ecological origins, grown in wine fermentation conditions. In analyzing these distributions, we observed a highly contrasted situation in flux variability, with quasi-constancy of the glycolysis and ethanol synthesis yield yet high flexibility of other fluxes, such as the pentose phosphate pathway and acetaldehyde production. Furthermore, these fluxes with large variability showed multimodal distributions that could be linked to strain origin, indicating a convergence between genetic origin and flux phenotype.To identify the metabolic and evolutionary constraints that shape metabolic fluxes in Flux variability is pathway-dependent and, for some flux, a strain origin effect can be found. These data highlight the constraints shaping the yeast operative central carbon network and provide clues for the design of strategies for strain improvement.The online version of this article (doi:10.1186/s12934-016-0456-0) contains supplementary material, which is available to authorized users. Cellular metabolism entails a large number of reactions that are involved in the conversion of various resources into precursors and energy for biosynthesis and cellular compounds. The rates of these reactions, i.e. fluxes, reflect metabolic activity through the operative network. Fluxes are the combined result of regulation at many different biological levels, such as transcription, translation, post-translational protein modification and protein–protein interactions. Therefore, metabolic fluxes are a global representation of the cellular phenotype expressed under specific conditions; thus, analyzing flux distribution is a valuable approach to study cell metabolism .13C-MFA [13C-MFA approach, cells are fed 13C-labeled glucose, and the analysis of the subsequent 13C enrichment in different amino-acids generates experimental data that can be used to constrain internal fluxes and therefore estimate intracellular fluxes [While intracellular fluxes are difficult to measure experimentally, they can be predicted by different methods that rely on constraint-based models (CBM) that formalize the metabolic network as a stoichiometry matrix. These CBM range from small networks focused on a specific aspect of cellular metabolism to genome-scale models that include all reactions of a given organism. The first step to solve these systems and predict fluxes from these networks is to add constraints on the input and output fluxes. Depending on the number of constraints and the size of the network, it is possible to estimate the fluxes in some cases; this approach is referred to as metabolic flux analysis (MFA). However, in most cases, adding constraints only on input and output data is not sufficient; therefore, there are two possibilities: the 13C-MFA  and the r fluxes . By contr fluxes –6. Objecr fluxes , minimizr fluxes , 9 or, mr fluxes , 11. The13C-MFA and FBA approaches have been used to predict intracellular fluxes of central carbon metabolism of S. cerevisiae in conditions where the intracellular redox balance is modified [In a previous study, modified . Comparamodified .S. cerevisiae wine yeast to environmental conditions, including various glucose concentrations, temperature or acetoin levels [13C flux approach in S. cerevisiae, Blank et al. [Understanding how metabolic fluxes are modulated by environmental and/or genetic perturbations is a central question to understanding cellular physiology. For example, the FBA approach has been used to study the flux distribution sensitivity of n levels , 13. In n levels –16. For k et al.  have shok et al.  studied Flux prediction has also been used to guide metabolic engineering and strain improvement strategies , 20. For13C flux analysis has been used to compare flux distributions in central carbon metabolism for pairs of species, including S. cerevisiae and Phaffia rhodozyma [S. cerevisiae and Pichia stipitis [13C flux analysis, Blank et al. [The estimation of metabolic fluxes was also used in a few studies to investigate the divergence of flux distribution among species. hodozyma  or S. cestipitis , highligk et al.  and Chrik et al.  studied S. cerevisiae [S. cerevisiae, and they have identified two main life-history strategies, the “ants” and “grasshoppers,” which are characterized by divergence in cell size, reproductive rate and carrying capacity. A wider phenotypic analysis, performed with 72 S. cerevisiae strains from different origins and studying seven life-history traits and eleven metabolic traits, showed that strain origin has a wide impact on phenotypes [In recent years, tremendous knowledge has been gained regarding the genetic and phenotypic diversity of revisiae –34. The revisiae  have stuenotypes . Other senotypes  or bio-eenotypes .Thus, the intra-species diversity of flux distribution remains unexplored. Studying the diversity of metabolism, particularly of metabolic fluxes, is fundamental to understanding the constraints and regulations that shape strain phenotypes. The functional and regulatory properties of yeast central carbon metabolism (CCM) determine most of the phenotypic traits relevant for various industrial processes, including food and beverage production , bioethanol or the use of yeast as a cell factory. For example, the fermentation rate, ethanol yield or production of acetate, and even aroma production are all dependent on carbon metabolism.S. cerevisiae strains from different genetic and ecological origins. To this end, we used a FBA approach to predict flux distribution for 43 strains of S. cerevisiae from six different ecological origins: bread, rum, wine, flor, Mediterranean and American oak. The analysis of flux distribution dataset enabled us to identify the most flexible/robust fluxes and several correlations or trade-offs between metabolic pathways. In addition, we analyzed the flux structuration to strain origin in order to observe a possible convergence.Thus, understanding how metabolic constraints structure metabolic pathways may enable a better exploitation of this diversity for industrial biotechnology. The objective of this study was to characterize the diversity of metabolic fluxes in a large set of In this work, we used DynamoYeast, a previously developed constraint-based model of central carbon metabolism , to stud2 released), which can be considered as steady state (a prerequisite to CBM). These experimental data (±2.5 %) were used to constrain the model as upper and lower bound to then perform a flux balance analysis (FBA).Fermentation was performed for all strains in a synthetic medium simulating grape must, containing high sugar and low nitrogen concentrations. Typical wine fermentation comprises a lag phase, a growth phase of approximately 24–36 h followed by a stationary phase, during which most of the sugar is fermented (reviewed in Marsit and Dequin ). We meaThe FBA consists of choosing the best solution for the objective function in the space of possible fluxes. Instead of using an optimization that maximizes biomass flux, which is frequently used in FBA studies, we chose to minimize the glucose input, allowing us to use the experimental biomass as a constraint for the model. By making this optimization choice, we considered that the yeasts were optimal, in that they used the least amount of resources (here the glucose input) to produce biomass and fermentation byproducts. This strategy also has the advantage of optimizing the modeling approach by maximizing the use of available experimental data. Using this approach, we obtained a flux distribution for 68 fluxes of the central carbon metabolism for each strain, expressed as relative fluxes normalized to the specific glucose uptake in the corresponding strain.In this type of optimization, the given solution is often not the only one that meets the optimization criterion; i.e., different possible pathways are perfectly equivalent for the optimization criteria. We thus decided to characterize all equivalent solutions to determine the fluxes that varied most between alternative solutions, which would therefore correspond to poorly predicted fluxes. To achieve this, we first fixed the input and output fluxes to the exact values predicted by the FBA, and we then used the “enumerateOptimalSolution” algorithm from the cobra toolbox  to identThen, we considered the biological variance between strains to identify the more robust and variable fluxes of the central carbon metabolism by studying the individual flux distributions Fig.  and by cThe acetaldehyde node displayed a particular pattern as it includes individual fluxes with very different variabilities Fig. : besidesThen, we searched for potential links between fluxes by studying all correlations between the model’s fluxes Fig. . This apWe also found correlations between blocks that had two main origins. In first case, these correlations were compulsory due to the model structure. For example, there was an expected negative correlation between the glycerol fluxes and the lower part of glycolysis because these two pathways diverged from the upper part of glycolysis. For the same reason, the flux through the PPP was negatively correlated with upper glycolysis. Positive correlations were also found between the PPP Fig. a, the TCBecause the fluxes were mostly organized in blocks Fig. , we deciInterestingly, these two particular clusters were overwhelmingly composed of strains having one ecological origin. The cluster characterized by a high production and output of acetate was composed of “Flor” strains, and the cluster with high acetaldehyde production was only composed of “Bread” strains. To better understand the effect of strain origin on flux distribution, we considered the mean fluxes by origin Fig. b–i. The −1 of acetaldehyde on average, while the strains from other origins produced 59.88 ± 35.51 mg L−1  at the fermentation time point of 11 g L−1 of CO2 produced.Thus, this analysis indicated interesting physiological differences between strains, some of which were related to the ecological origin. To experimentally confirm the higher production of acetaldehyde by the bread strains, we a posteriori measured the production of acetaldehyde for seventeen strains from various origins and compared the relative variations of production with flux prediction Fig. . These eMoreover, a correlation was also found within groups of strains with similar ecological origins Fig.  as well Finally, to obtain an integrated vision of flux structuration, we performed a principal component analysis (PCA). For this, we selected the same subset of 19 fluxes, among which we excluded the fluxes of glycolysis and ethanol synthesis on the basis that they were stronger but also less variable fluxes, which would therefore give them too much importance in the PCA. A final subset of 14 fluxes was used to perform the PCA Fig. . The firIn this work, we used a constraint-based model of yeast fermentative central carbon metabolism to study the diversity of flux distribution among 43 strains of different origins. We used a whole set of experimental data  to constrain the model and a FBA approach with minimization of the glucose input to predict the distribution of metabolic fluxes. This method allowed us to optimize the modeling process by using all the available biological information. We first considered the variability of the predictions to determine the confidence of the estimates. Considering alternate optimal solutions led us to conclude that the DynamoYeast model was very well determined, with only small variations in the reductive branch of the TCA due to free mitochondrial transport of the involved metabolites . This very low level of variability between alternate optimal solutions for a given set of constraints was the main advantage of using a reduced model. Indeed, the same constraints used with a genome-scale model  led to S. cerevisiae strains from different origins. We found that this variability was strongly pathway-dependent. The glycolysis and ethanol synthesis pathways, despite being the stronger fluxes, showed almost no variability between strains. In contrast, flux through the PPP was the most variable, with a coefficient of variation more than two times higher than that of other pathways. This high variability of the PPP is in accordance with a previous study stressing high variability of the specific activity of the first enzyme of the PPP, glucose-6-phosphate dehydrogenase, in eleven S. cerevisiae strains [The main objective of this study was to characterize the variability of flux distributions between  strains . This, i strains , suggestS. cerevisiae during respiro-fermentative growth in different conditions of pH and NaCl concentration, Heyland et al. [S. cerevisiae and its ancestor showed a similar trade-off: an increased flux thought the PPP and a decreased production of acetate in the evolved strain [Our study also highlighted several correlations between metabolic pathways. The PPP produces around 2/3 of the NAPDH demand and displays a strong trade-off with the cytoplasmic synthesis of acetate from acetaldehyde , the other main reaction generating NAPDH. An indication of a link between these two pathways was found in previous studies. For example, in a study comparing the flux distributions of d et al.  found and strain .13C-flux analysis of seven yeast species [S. cerevisiae and 90 % for P. angusta [S. cerevisiae in the Blank study compared to this work (10 versus 2 %); this discrepancy between fluxes predicted by 13C-MFA or FBA is common [Interestingly, among the intra-species correlations that we identified in this study, some have also previously been found when different yeast species were compared. The positive correlation between PPP and biomass fluxes (which we linked to biomass precursor synthesis) was also found in a comparative  species  and of f species . Between angusta . Similars common . Anothers common .Another issue addressed in this study is the contribution of strain origin to intra-species metabolic diversity. For the variable fluxes, the flux distribution was divergent in broadness and could also be mono-, bi- or multimodal, indicating dichotomous behavior between strains. We could explain these different patterns of distribution by strain origin peculiarities. For example, the long tail of the acetaldehyde output distribution can be explained by the four “Bread” strains that produce twice as much acetaldehyde Fig. g and theSuch knowledge on the most flexible fluxes and strain-dependent flux variability could be very useful for metabolic engineering strategies aimed at rerouting metabolic fluxes. Numerous studies –54 have Overall, this work highlights the potential of flux analysis to identify the most variable and robust nodes of central carbon metabolism within a species and to provide information on the metabolic or evolutionary constraints that shape flux distribution. This knowledge will help to identify relevant targets and yeast strains for metabolic engineering. In addition, the availability of whole genome sequences for the strains used in this study offers a framework to decipher the links between flux distribution and strain genotypes. In particular, the finding of a strain origin effect on the distribution of various fluxes opens the way for flux quantitative trait loci (QTL) detection (fQTL) to elucidate the genetic basis of flux distribution.S. cerevisiae strains of six different ecological origins  used in this study are listed in Additional file 2 release was followed by automatic measurements of fermentor weight loss every 20 min. The amount of CO2 released allowed us to monitor the progress of the fermentation. Samples were harvested for further analysis when the released CO2 reached approximately 11 g. The dry weight of the yeast was measured by filtering 50 mL of culture through a 0.45-mm-pore Millipore nitrocellulose filter, which was washed twice with 50 mL distilled water and dried for 24 h at 105 °C. Metabolites in the supernatant  were analyzed by high-pressure liquid chromatography [The 43 tography . Acetaldtography .2. We then used a linear mixed model  to correct measures for “block” effects, and the average values between the two replicates were calculated. From these normalized and corrected data, we recalculated the biomass and metabolite concentrations corresponding to 11 g/L of CO2.Fermentation was carried out in duplicate spread over various fermentation blocks. Data  were first normalized by the released CO−1) and dry weight (g L−1) were used to constrain DynamoYeast, a previously developed dedicated constraint-based model of yeast fermentative central carbon metabolism [Metabolite concentrations , the mitochondrial glutamate dehydrogenase reaction  and the futile cycle around glycerol [We considered that all sugars were glucose (instead of glucose and fructose) for the modeling approach, as this assumption did not impact the flux predictions. For all strains, we used the biomass composition previously determined for the EC1118 strain  and set glycerol  to 0.All predictions were performed with Matlab R2010b. The flux balance analysis (FBA) was performed with the “optimizeCbModel” function from the cobra toolbox  and the For each strain, we obtained a prediction of the flux distribution through the metabolic network. However, the predicted glucose uptake was different for each strain. To compare flux distributions between strains, we normalized each flux to the specific glucose uptake in the corresponding strain and expressed it as a percentage. We calculated the coefficient of variation for each flux: the standard deviation divided by the mean flux of all strains.On a subset of 19 fluxes, we calculated the relative deviation from the average http://www.inkscape.org) for visual ameliorations.Principal component analysis of flux values was performed with fourteen fluxes that were representative of the entire model’s network, with the exception of the glycolysis and ethanol synthesis fluxes. All analysis and graphical representations were performed with RStudio  and with


Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on  Without affecting the operation of the internal flux distribution, OptKnock identifies a set of gene (reaction) deletions to maximize the flux of a desired metabolite so that the growth or another objective function is optimized In the recent years, the study on computational algorithms to identify optimal gene knockout strategies for obtaining improved phenotypes is growing rapidly. The first rational modeling framework (known as OptKnock) is introduced by Burgard in silico design problem by using Genetic Algorithm (GA) OptKnock uses mixed integer linear programming (MILP) to formulate a bi-level linear optimization that is promising to find the global optimal solution. OptGene is extended from OptKnock which formulates the et al.et al.E.coli (Escherichia Coli) iJR904 model, B.subtilis (Bacillus subtilis) model, and C. thermocellum (Clostridium thermocellum) model are the target microorganisms A hybrid of BA and FBA (BAFBA) was proposed by Choon This paper is organized as follows: Firstly, problem formulation is introduced, and the details of the BAFBA and the proposed DBFBA are described. The validation analyses by using OptKnock are also explained. Then, the datasets and experimental setup are described. Next, experimental results are presented. Then, the discussion on the obtained results is addressed, which deliberates the contributions of this work. Lastly, this paper is summarized by providing main conclusion and future developments.S provides the linear relationship of the model between the flux rates of the reactions (v) and the derivatives of the reactant concentrations (x). The matrix is a constant, while the flux vector is a variable. Assume that there are m reactants and n reactions between them.The problem to identify optimal gene knockout strategies from the biological models can be formulated as follows: Suppose a model which contains the stoichiometric matrix Flux vector:Concentration vector:T represents the time.Dynamic mass balance equation:v represents the vector of fluxes, S is the stoichiometric matrix. The expression (Txc) to be maximized or minimized is known as the objective function, where c is a vector of weights, indicating contributions of each reaction to the objective function. The inequalities of lower bound and upper bound define the maximal rates of flux for every reaction corresponding to the columns of the stoichiometric matrix.The chemical elements, ionic charge, and biochemical moieties must be balanced in the stoichiometric matrix. The objective is to find the optimal gene knockout strategy which can improve the product yields of industrially important chemicals, while sustaining the growth rate of the microorganism. This is commonly performed by using the linear programming, defined as follows:×n. The amount of selected bees was limited within the range  to prevent unnecessary selection of sites for neighborhood search. Each bee was required to go through this repetitive local search neighborhood procedure until the best possible answer obtained. Meanwhile, the remaining bees were assigned randomly to search for new potential solutions.Before attempting to propose DBFBA, it is crucial to find the limitations of BAFBA In this paper, DBFBA with OptKnock for identifying optimal gene knockout strategy was proposed to overcome the limitations of BAFBA and previous works One or more genes can be discovered in each reaction in a metabolic model. In this paper, each of those genes is represented by a binary variable, where 0 represents the absence of the gene and 1 represents the presence of the gene in the reaction. These variables form a ‘bee’ representing a specific mutant that lacks some metabolic reactions when compared with the wild type .n scout bees. Each bee is initialized as by assuming a reaction with n genes. Bees in the population are initialized by setting present or absent status to each gene randomly. Initialization of the population is done randomly so that all bees in the population have an equal chance to be selected. Specific rules like co-regulated genes tending to be clustered did not affect the result of selecting the genes. The result might not truly reflect the population if it is done with bias setting.The algorithm starts with an initial population of et al.Each site is given a fitness score that determines whether to recruit more bees or should be abandoned. In this work, FBA has been used to calculate the fitness score for each site refer to ). In theet al. (2013), metabolic networks can function in living cells under various biological objectives, depending on the relevant organism and its genetic and environmental context. However, biological objectives have been clarified as only applicable for analyzing a number of organisms in terms of the microbial metabolic engineering. It is desirable to couple the formation of the desired product to growth We used Biomass-product coupled yield (BPCY) as the fitness score in this work. According to Soons The flow of the fitness calculation is showed in m) by using DE algorithm. DE algorithm operates by maintaining a population of candidate solutions and creating new candidate solutions through the mutation and crossover operation of DE, and keeps the fitness candidate solution. In this paper, the candidate solutions are the m favored sites from the population initialized by using BA. The algorithm starts with the solution, then goes through the mutation and crossover operation to create new candidate solutions. Since the candidate solutions are initialized by BA randomly, hence the specific rules on the distribution of gene on chromosome did not affect the result for neighbourhood gene selection too. The mutation factor F is a constant from  and elements of the candidate solutions enter the new candidate solutions with probability CR. In this paper, m is equal to 15, F is equal to 1, and CR is equal to 0.5. The values are obtained by conducting a small number of trials with the range of 10 to 25, 0 to 2, and 0.1–0.9, respectively. This step is important as there might be better solutions than the original solution in the neighborhood area.This algorithm carries out neighbourhood searches in the favored sites  has no impact in our works since our works are not dealing with the expression patterns of the genes. All the models are pre-processed through several steps based on biology assumptions and computational approaches before it was applied to DBFBA. Lethal reactions such as genes that are found to be lethal in vivo, but not in silico, should be removed to improve the quality of the results. The results are invalid if a lethal reaction is deleted. In addition, dead-end reactions that cannot be activated within a feasible flux distribution when considering the entire universal reactions set are removed. The first model is E. coli model that contains 904 genes, 931 unique biochemical reactions, and 761 metabolites http://bigg.ucsd.edu/). E. coli iJR904 has been used in this work to test the reliability of DBFBA as this model was also used in previous works B. subtilis iBsu1103 model http://genomebiology.com/content/supplementary/gb-2009-10-6-r69-s4.xml) that includes 1437 reactions associated with 1103 genes. This model has been preprocessed and the size is reduced to 763 reactions. The last model is C.thermocellum (ATCC 27405) iSR432 model http://www.biomedcentral.com/content/supplementary/1752-0509-4-31-s3.xml) that contains 577 reactions representing the function of 432 genes. This model underwent the pre-processing step and the size has reduced to 351 reactions. This work has generated three results, which are the list of knockout genes, growth rate, and BPCY (Biomass Product Coupled Yield). Unit for growth rate is hour−1 while unit for BPCY is in milli-gram (gram-glucose.hour)−1. The experiments were conducted using a 2.3 GHz Intel Core i7 processor with 8 GB RAM workstation.There are three models used to test the operation of DBFBA in this work, which are The results were compared with the previous works as reported in the literature studies In this paper, an improved method, DBFBA, was proposed to test the performance of DBFBA. For evaluation, a benchmarking analysis was conducted. However, benchmark functions could only be tested on DB and BA, as FBA is an objective function. Hence, the benchmark functions on DB and BA in this study were tested. As BA is used to attain the maximum, the functions are inverted before the algorithm is applied. The De Jong, Martin & Gaddy, Branin, and Griewangk functions have been used in this study. A total of 100 individual runs have been carried out to test on DB and BA.E.coli. As shown from the results, this method produces better results than the previous works in terms of growth rate and BPCY, and can also identify potential genes that can be removed DBFBA is then applied to identify the knockout strategy for producing lactic acid. B. subtilis. Ethanol is a volatile, flammable, colorless liquid, and it is a promising biofuel. Ethanol is currently used as an alternative fuel for gasoline worldwide. Therefore, ethanol was found to be the most suitable case study for this research.et al. (2013), glycerol is one of the main by-products in ethanol fermentation hence a substantial reduction of glycerol yield may lead to a significant increase in ethanol yield. In Pagliardini et al. (2013) experiment, a drastic reduction of the glycerol yield improved the yield of ethanol DBFBA obtained a better growth rate and BPCY which are 122.9657 and 1.1573e+05 respectively. Glycerol kinase (GK) and sn-Glycero-3-phosphoethanolamine glycerophosphohydrolase participate in glycerophospholipid metabolism, which involves the formation of glycerol. As stated by Pagliardini C.thermocellum. DBFBA shows a better result for C.thermocellum model with the growth rate of 11.4826 and the BPCY 1.07e+004. et al. (2013), a drastic reduction of the glycerol yield improved the yield of ethanol due to which glycerol becomes one of the main by-products in ethanol fermentation et al. (2012), lactate dehydrogenase (LDH_L) plays a key role in the fermentative metabolism in the metabolic engineering for ethanol production. The deletion of LDH_L inhibited the conversion from pyruvate to lactate, therefore more pyruvate was decarboxylated to acetaldehyde and further converted to ethanol B.subtilis and C.thermocellum.In addition, The results showed that both DBFBA and DB performed better than other algorithms. It can be concluded that the capability of DE algorithm in finding local optimum improved the performance of the original BA. The original BA had the problem of repetitive iterations of the algorithm in local search, where each bee keeps searching until the best possible answer was obtained. The proposed DBFBA solved the problem by implementing DE algorithm into the local search part. DE algorithm operates by maintaining a population of candidate solutions and creating new candidate solutions through the mutation and crossover operation of DE, and keeping the fitness candidate solution. Furthermore, DE algorithm is a heuristic approach mainly having three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. In addition, the OptKnock validation, which is integrated into BAFBA in this work, improves the reliability of BAFBA as the identified lists of potential genes have proven crucial in improving the desired phenotypes through literature E. coli model, B. subtilis model, and C.thermocellum model showed a hybrid approach, where DBFBA works as an effective tool in generating optimal solutions for the gene knockout identification, and therefore could be a useful tool in the field of metabolic engineering. The ability of DBFBA in predicting a set of genes that might affect the production of desired phenotypes is helpful to the biologists. Instead of testing single gene knockout effect on the production, the biologists can test on the effect of multiple genes knockout. Lastly, since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful models, simulated methods, or predictors The development of accurate and efficient modeling methods as well as optimization methods in the field of metabolic engineering is crucial, as they will contribute significantly to the field of biotechnology. Consequently, it will lead to substantial economic gains in the production of pharmaceuticals, fuels and food ingredients. In this work, DBFBA has been proposed to be able to predict optimal sets of gene knockout strategies to maximize the production of desired phenotype and in the meantime, sustains the cellular growth. DBFBA improves the performance of BAFBA by implementing DE algorithm, which is indeed a promising algorithm in finding local optimum. Experimental results on


Both inherently involve a supplementary phosphoenolpyruvate carboxykinase cycle, which affords increased metabolic flexibility and robustness under diverse environments.Only two distinct C 4 photosynthesis has higher light-use, nitrogen-use, and water-use efficiencies than C3 photosynthesis. Historically, most of C4 plants were classified into three subtypes , NAD-ME, or phosphoenolpyruvate carboxykinase (PEPCK) subtypes) according to their major decarboxylation enzyme. However, a wealth of historic and recent data indicates that flexibility exists between different decarboxylation pathways in many C4 species, and this flexibility might be controlled by developmental and environmental cues. This work used systems modelling to theoretically explore the significance of flexibility in decarboxylation mechanisms and transfer acids utilization. The results indicate that employing mixed C4 pathways, either the NADP-ME type with the PEPCK type or the NAD-ME type with the PEPCK type, effectively decreases the need to maintain high concentrations and concentration gradients of transport metabolites. Further, maintaining a mixture of C4 pathways robustly affords high photosynthetic efficiency under a broad range of light regimes. A pure PEPCK-type C4 photosynthesis is not beneficial because the energy requirements in bundle sheath cells cannot be fulfilled due to them being shaded by mesophyll cells. Therefore, only two C4 subtypes should be considered as distinct subtypes, the NADP-ME type and NAD-ME types, which both inherently involve a supplementary PEPCK cycle.C Then, the C4 acid is transported to the BSCs where it is decarboxylated to release CO2 around Rubisco. Decarboxylation in BSCs, together with the CO2 diffusion barrier between BSCs and MCs, elevates the CO2 concentration around Rubisco, thereby minimizing photorespiration (Plants using Cpiration . In mostls (MCs) . In C4 ppiration .4 photosynthesis is classified into three biochemical subtypes, according to their different decarboxylation mechanisms. The first enzyme of the C4 cycle, PEPC, is common to all three subtypes. The product of PEPC, oxaloacetate (OAA), can be either reduced to malate by malate dehydrogenase (MDH) or converted to aspartate (Asp) by aspartate aminotransferase  type, most of the OAA is converted to PEP and CO2 in BSC cytosol by PEPCK. NAD-ME is used to provide NADH for generation of ATP, which can be used to fuel PEPCK  by the differences between rates of fluxes generating and consuming this metabolite:vin and vout represent the rate of fluxes generating and consuming metabolite (M), respectively. This work developed one differential equation as equation 1 for each metabolite used in the model. The equations and parameters used in the model were listed in Supplementary Data (available at JXB online). The C4 NADP-ME type model .where 4 subtypes can coexist with each other , which shows that Z. mays uses both as transfer acids  is used to calculate the rates of ATP and NADPH synthesis in the models. The maximum rate of ATP and NADPH synthesis reactions were described as:The electron transport rate  which is calculated from the input of photosynthetic photon flux density (PPFD), Jmax is the maximal electron transport rate, and θ is an empirical curvature factor (where e factor .J of MC (Jm) and BSC (Jb) were calculated separately (Supplementary Data available at JXB online), In this model, the proportion of linear electron transport (LET) in BSCs, as reflected in the PSII content in BSCs, was assumed to be variable:l_bI and maxl_bJ represent incident photosynthetic photon flux density and Jmax, which participate in liner electron transport in BSCs. u and v are variables representing the proportion of light and Jmax of LET in BSCs; this model assumes that they are equal, i.e. u=v=LETBSC. If u and v are 0, there is no LET in BSCs; if u and v are 1, all light and Jmax take part in LET in BSCs. Ib represents light absorbed by BSCs, and max_bJ is the maximum electron transport capacity of BSCs. ATP used by PEPCK in BSCs cytosol is assumed to be supported by light reactions in BSCs.where 2 generation from LET was considered in both MCs and BSCs. The total equation is:OvLET) was considered as:The reaction rate of equation 8 (Jl is the LET rate. The LET rate of BSCs (l_bJ) is calculated by equation 7. In MCs, the model assumed that all electrons were transported through the LET chain.where 2 assimilation rate (A) was calculated as  and MCs (Rm) being equal as 0.5 μmol m–2 s–1.where 2 assimilation rate (A) to the photosynthetic photon flux density (PPFD)  showed the highest assimilation rate. Compared to the standard NADP-ME model, all the mixed models assuming PEPCK activity had lower assimilation rates at photon flux densities below 2000 μmol m–2 s–1. Apart from the PEPCK-only model, which has no photosynthesis rate, photosynthetic responses predicted by all other models were comparable to experimental data from maize  . The staom maize .Flaveria, two typical NADP-ME subtype C4 plants. The predicted photorespiratory rate was highest in the standard NADP-ME model and lower in all mixed models . Therefore, the simulations showed that the photorespiratory rate was suppressed in all mixed models; furthermore, compared to models including PEPCK activity, the standard NADP-ME model had a higher assimilation rate and lower leakiness while the Asp+MAL+ME model had a lower photorespiratory rate.The predicted leakiness was increased in all mixed-pathway models compared to the standard NADP-ME model . The modd models . The phoThe mixed model presented for maize  and 3. H3 but only marginally so for the C4 acids . Leakiness was also lowest for the classical NADP-ME model. Having a mixed pathway increased the overall C4 pathway activity since it reduced limitations on acid generation and transport in the C4 cycle, while the Calvin–Benson cycle activity stayed the same. The faster C4 cycle released more CO2 in BSCs (enhanced overcycling), which increased the CO2 concentration in BSCs . As a result, leakiness increased in all mixed models  modulates the NADPH balance between MCs and BSCs . If PEPC activity was decreased by 15%, leakiness was reduced by about 7% in high-light conditions. Coincidently, enzyme activity data of a leaf developmental gradient in Z. mays show that in the tip of the leaf PEPC activity decreased when PEPCK activity was significantly elevated . A higher LET rate in BSCs is needed for a high photosynthesis rate (2 in BSCs cytosol and uses ATP in BSCs. Thus a PEPCK-only type prefers more energy allocated to BSCs. The current theoretical analysis suggests that the optimal energy allocation was 20% in MCs and 80% in BSCs. This, however, is next to impossible to realize because of the structure of the Kranz anatomy, where BSCs are surrounded by MCs and correspondingly light will be inevitably preferentially absorbed by MCs before reaching the BSCs. This unbalanced energy supply and usage in BSCs and MCs might have prevented evolution of C4 plants with PEPCK as the only decarboxylase and aspartate as the only transfer acid.Can the PEPCK pathway exists in isolation: i.e. is there a C to BSCs . This issis rate  in such Another possibility that may limit the existence of the PEPCK-only pathway is the inability to maintain a proper amino group balance between BSCs and MCs. In the PEPCK pathway, aspartate is transported from MCs to BSCs, and PEP was transported from BSCs back to MCs, which creates an imbalance of amino groups between two cell types . This pr4 photosynthesis provides metabolic robustness through reduced metabolite gradients and metabolite concentrations. Operation of a mixed mode of C4 photosynthesis can confer higher ecological robustness due to increased tolerance to fluctuating light conditions. A pure PEPCK-type C4 photosynthesis is not beneficial because the energy requirements in BSCs cannot be fulfilled due to them being shaded by MCs. Hence, only the NAD-ME and NADP-ME subtypes should be considered as distinct subtypes, with the PEPCK pathway as a supplement to these.In summary, this modelling analysis and literature survey shows that a mixed mode of CJXB online.Supplementary data are available at Supplementary Fig. S1. The structure of the mixed-pathway model of C4 photosynthesis.Supplementary Fig. S2. Decreased PEPC reduced leakiness level of the Asp+MAL and PEPCK+ME model.Supplementary Fig. S3. Simulated CO2 concentration of BSC cytosol and chloroplast.


E. coli growth rates under different media and gene essentiality, amongst other things. In order to improve predictions, additional constraints are coupled with optimization of the biomass function. Studies have suggested, however, that unicellular organisms - like multicellular organisms - do not grow at optimal rates. To further improve FBA predictions, particularly of internal cell fluxes, new techniques to explore the sub-optimal solution space need to be developed.Flux Balance Analysis (FBA) is a widely used tool to model metabolic behavior and cellular function. Applications of FBA span a breadth of research from synthetic engineering of biofuels to understanding evolutionary adaptations. FBA predicts metabolic reaction fluxes that optimize a given objective. This objective is generally defined for unicellular organisms by a theoretical reaction which simulates biomass production. FBA has been extremely successful at predicting in corsoFBA based on the optimization of protein cost at sub-optimal objective levels. Our method shows good agreement with experimental data of E. coli grown at different dilution rates. Maintaining the objective function close to its maximum value predicts metabolic states that closely resemble low dilution rates; while higher dilution rates can be mirrored by lowering the biomass production value. By using a modified version of Extreme Pathways, we are also able to quantify the energy production and overall protein cost for all possible pathways in the central carbon metabolism.We present an innovative FBA method called E. coli central carbon metabolism can be better predicted by exploring the sub-optimal FBA solution space. The corsoFBA method presented here is able to predict the behavior of PEP Carboxylase, the glyoxylate shunt and the Entner-Doudoroff pathway at different glucose levels, a behavior not predicted by the minimization of metabolic steps and FBA alone. This technique can be used to better predict internal cell fluxes under different conditions, and corsoFBA will be of great help for the study of cells from multicellular organisms using Flux Balance Analysis.Metabolic flux distributions at the optimal objective can be substantially different from the near-optimal distributions. Importantly, the behavior of The online version of this article (doi:10.1186/s12918-015-0153-3) contains supplementary material, which is available to authorized users. Genome-wide metabolic reconstructions provide a powerful platform for the analysis of metabolic pathways. Reconstructions for at least sixty-five different species spanning fifty-one different genera, including humans ,2, are aA vast array of computational methods for the analysis of Constraint Based Models is available, and the number continues to grow ,6. PerhaE. coli the uptake and release rates of certain metabolites [The FBA solution is not unique. There are several fluxes within the model that are not well defined, but can exist within certain bounds while the objective function is being optimized [ptimized . These fOrganisms might not be operating at maximum capacity [capacity -18. In tThe observed metabolic state is also not unique. That is, a single flux distribution cannot explain all the flux states observed in the experiments, and variation occurs within the bacterial population. Wintermute et al. [cloud theory for metabolic regulation, where bacteria are allowed to fluctuate within a near-optimal solution space. The study also demonstrates that the variability of fluxes within this region matches the observed variability within the data.e et al.  proposesThermodynamically infeasible loops can appear in the FBA output. These are sets of pathways termed “type III pathways” [athways” , which aathways” .This technique is most commonly applied to metabolic reconstructions of unicellular organisms with the optimization of a theoretical Biomass reaction. This reaction consumes resources such as amino acids, nucleotides and ATP at the rate the given organism would need in order to grow and multiply ,9. FBA aabolites -12, cellabolites ,11 and gabolites  with greabolites , mainly To better predict internal fluxes, some studies have relied on additional thermodynamic constraints . SeveralSignificant efforts have also been directed toward integrating omics data and FBA -38, but E. coli chooses its pathways based on the minimization of protein cost [More recent studies have relied on the theory that ein cost ,40 and nein cost . Severalein cost , minimizein cost ,42,43 anein cost -49. One ein cost .These methods have been very successful at improving predictions of growth rate, substrate usage and internal cell fluxes in unicellular organisms. Several studies have suggested, however, that unicellular organisms in reality grow at rates lower than those predicted by FBA alone -17. AlthE. coli can vary freely within a near-optimal space [Additionally, these one-step optimization methods with additional constraints return a single flux distribution, and they are unable to explore the near-optimal solution space. This limitation is significant given a recent study has suggested the flux distribution of al space . TherefocorsoFBA), which is suitable for sub-optimal objectives. Rather than imposing additional constraints when optimizing the objective function, we fix this objective at a predefined value. An estimated protein cost throughout the metabolism is then minimized in order to predict the internal cell fluxes. By varying the biomass production value, we are able to predict how changes in pathway usage depend on changes in other conditions, particularly glucose availability. Although this method is not well suited for predicting growth rates, we are able to predict metabolic flux distributions within a near-optimal solution space. Furthermore, by decomposing the model using an adapted version of the Extreme Pathway analysis, we are able to break down the energy production pathways and further understand the model behavior as glucose concentrations change. We validate our methods using E. coli as the model organism.To address this need, we propose a two-step optimization FBA method for predicting internal fluxes, termed COst Reduced Sub-Optimal FBA , the enzyme molecular weight (MW), and a thermodynamic penalty for reversible reactions. The cost term used in corsoFBA is defined as: Some of the methods most successful at predicting internal cell fluxes have been based on the theory that nstraint -49. FiveΔrG′o is the standard Gibbs free energy of the given reaction, R is the ideal gas constant and T is the temperature. The molecular weight term is included to represent the amount of resources needed in order to produce a sufficient amount of enzymes to maintain the associated reaction flux. The thermodynamic penalty, applied only to reversible reactions, represents the cost associated with the change in the concentration of metabolites required for the reaction to flow in the desired direction. That is, a reaction with positive ΔrG′o would require a large disparity in the concentration of the associated metabolites, so the cost term is multiplied by a positive number larger than one. On the other hand, if ΔrG′o is negative, and the reaction is therefore favorable, the cost term is multiplied by a positive number between zero and one, given that little or no metabolic adjustment is necessary. It is worth noting that this thermodynamic cost does not directly represent a change in enzyme concentration, but rather an increase or decrease in the overall cost associated with the reaction based on its standard Gibbs free energy. Furthermore, this term is applied separately to forward and backward directions of reversible reactions, favoring the direction with negative standard Gibbs free energy. A similar thermodynamic cost, used without molecular weight considerations, has been applied by Holzhütter [Where lzhütter , weighinα is chosen to be Finally, the parameter ΔrG′o) or zero , and only the thermodynamic cost would essentially be considered. In that case, reaction directionality would essentially be predetermined based solely on the standard Gibbs free energy, which could be problematic.This value is chosen in order to balance the contribution of molecular weight and thermodynamic penalty, yielding a prediction considering both costs  molecular weights alone, (2) thermodynamic penalties alone, (3) the previously mentioned combination of molecular weights and thermodynamic penalties and (4) uniform costs. The use of uniform costs gives a simple minimization of the overall flux through the enzyme associated reactions. This minimization strategy is a modified version of a widely regarded two-step optimization method called pFBA . pFBA haSimulations comparing these four costs have been performed for growth rates ranging from 50 to 100% of the predicted optima. Flux distributions were normalized by the flux of glucose to glucose-6-phosphate, and results for several central carbon metabolism reactions can be found in the supplemental information , and the highest correlation values for the higher dilution rates in the Yao et al. data .The same set of simulations was also compared to several data points from two Metabolic Flux Analysis (MFA) experiments: Ishii et al.  and Yao In order to visualize the trend of how different fluxes change in the sub-optimal space, simulations utilizing molecular weight costs, thermodynamic costs and a combination of both, normalized again by the flux of glucose to glucose-6-phosphate, were plotted alongside the experimental values from the two MFA experiments. Fluxes were plotted starting at 100% down to 75% of the predicted optima, and the results for several central carbon metabolism reactions are presented in Figure corsoFBA flux distributions show close agreement with MFA data for higher growth rates  to relieve enzymatic cost, and that considering these costs during FBA may increase internal flux predictions when comparing to the minimization of metabolic steps alone.Predicted fluxes using molecular weights only and a combined cost also found excellent agreement with the experimental values in the reactions PPC (PEP Carboxylase) and ICL (Isocitrate Lyase),62. Bothn et al. , that focorsoFBA approach can better predict fluxes through certain reactions, such as PPC and ICL. While previous studies have shown that the FBA solution space increases drastically when considering the objective function at near-optimal to optimal values [Simulation results also suggest that the optimization of the biomass function may yield fluxes that are fundamentally different from those at near-optimal conditions. At optimal growth, there is no predicted flux through the glyoxylate shunt, considerably lower fluxes through the TCA cycle and PPP, and a flux through the PPC reaction not present at near-optimal conditions. Although comparisons between experimental data and simulated flux distributions show that the highest correlation and lowest error are found at optimal growth, our simulations also indicate that the implementation of these costs yield comparable results in the sub-optimal space  pathway. Genes  E. coli , but the E. coli . Due to  E. coli ,62, whice et al. , where fE. coli iJR904 reconstruction were decomposed using the Fundamental Pathways analysis described in the Methods section. Details on how these reactions were calculated can be found in the supplemental information , according to ATP production by both mmol of glucose and protein cost. The most energy productive pathway is the full oxidation of glucose through the TCA cycle (OP2). The most cost efficient pathway was found to be the production of acetate through glycolysis (OP1). Although this pathway potentially produces less than half the amount of ATP as OP2, the total enzymatic cost per mmol of ATP is lower. Also in agreement with general knowledge, this analysis predicted the release of acetate to be more efficient than the release of both lactate and ethanol.In accordance with general OP2 to OP1 is observed as the objective function value decreases. This trend also supports the idea that overflow metabolism takes place in order to alleviate enzymatic cost. Under low glucose conditions, E. coli would need to extract from glucose the largest possible amount of energy in order to sustain growth. As more glucose becomes available, however, this bacteria can afford to consume more substrate and produce energy through cheaper pathways.In the simulations presented in previous sections, a gradual transition from NOP2 and NOP3 both produce more ATP per mmol of glucose than OP1 but less than OP2, while remaining less cost efficient than OP1 but slightly more so than OP2. NOP2 and NOP3 reduce the flux through upper glycolysis by partially re-routing the flux through the PPP, and they exhibit no flux through Phosphoglucose isomerase (pgi). A third Near Optimal Pathway, termed NOP1, skips the upper glycolysis through the ED pathway. This pathway is both less cost and energy effective than OP1.One interesting observation from this analysis is the existence of Near Optimal Pathways (NOP), which combine the previously described optimal pathways with the PPP or ED pathways. −1 (Figure ΔpfkA deficient E. coli strains reduce flux through Phosphofructokinase 1 by diverting fluxes through the PPP [NOP2 and NOP3 instead of OP1 or OP2 when the cost through upper glycolysis is increased.The use of these near optimal pathways could explain the inconsistent PPP fluxes reported by the MFA experiments considered here. A short recovery in the simulated PPP flux near a growth rate of 0.65 h 1 Figure  demonstr the PPP , hence uOP2 to OP1 as we move away from optimal growth. Although this is in fact the general trend we observe, anaplerotic needs also need to be considered. These needs are addressed most efficiently by reactions not in OP1 or OP2, such as PEP carboxylase and the glyoxylate shunt. Interestingly, the model also predicts the use of the ED pathway with anaplerotic purposes at high growth rates. While this pathway has been studied mostly for its catabolic activity in Z. mobilis and several Pseudomonas species, simulations here predict this pathway to be the cheapest way to get glucose shuttled to the TCA cycle for the production of building blocks, while giving up little efficiency in energy pathways through NOP1.This Fundamental Pathway analysis elucidates how we would expect the model to behave in terms of energy generation as we optimize the protein cost at sub-optimal states. Based on this analysis, in order to fulfill its catabolic needs, the model transitions from corsoFBA, a method for analyzing and predicting metabolic flux distributions at sub-optimal states based on protein and thermodynamic cost minimization. CorsoFBA demonstrates that the optimization of protein cost at near-optimal states can produce significantly different results from those at the optimal objective. Furthermore, although correlation and error calculations indicate better predictions at the optimal solution, protein-optimal results at sub-optimal objectives show better agreement with MFA experiments and gene expression profiles for anaplerotic reactions. These results suggest it is important to explore the sub-optimal FBA space in order to better predict internal cell fluxes. Although the method described here is not suited for predicting growth rates, it provides a platform for analyzing internal cell fluxes in the sub-optimal space.Here we present corsoFBA will be particularly useful given recent studies have suggested E. coli can exist freely within a near-optimal space [We believe al space . We alsoal space  and FBAwal space , would a


By mapping translated metagenomic reads to a microbial metabolic network, we show that ruminal ecosystems that are rather dissimilar in their taxonomy can be considerably more similar at the metabolic network level. Using a new network bi-partition approach for linking the microbial network to a bovine metabolic network, we observe that these ruminal metabolic networks exhibit properties consistent with distinct metabolic communities producing similar outputs from common inputs. For instance, the closer in network space that a microbial reaction is to a reaction found in the host, the lower will be the variability of its enzyme copy number across hosts. Similarly, these microbial enzymes that are nearby to host nodes are also higher in copy number than are more distant enzymes. Collectively, these results demonstrate a widely expected pattern that, to our knowledge, has not been explicitly demonstrated in microbial communities: namely that there can exist different community metabolic networks that have the same metabolic inputs and outputs but differ in their internal structure. Vertebrates play host to an enormous diversity of microbes, and while the numbers alone are impressive, exceeding our own cell count by at least an order of magnitude , it is tOur poor understanding of these organisms is largely due to the fact that they are quite difficult to work with: many or most will not grow in laboratories and they interact not only with the vertebrate host but also with each other in a variety of complex ways. Fortunately, applications of inexpensive DNA sequencing technologies  are transforming our understanding of the microbial world more rapidly than perhaps any technology since van Leeuwenhoek first looked through his microscope. To date, most surveys of microbiomes both in vertebrate hosts and in other environments have used taxonomic approaches, attempting to identify the different species that are present. The most effective technique for such assays has been using the 16S rDNA gene as a phylogenetic marker ,7,11–13.etal.  and to a catalog of enzymes, we show that some of the observed taxonomic dissimilarities are not necessarily mirrored at the metabolic level. Second, we hypothesized that, while the metabolic inputs and outputs of our samples would be similar (the inputs because of the common diet and the outputs because of the needs of the host species), there would be differences in the internal structure of metabolism between the samples. We tested this hypothesis in several ways, including asking if the variation in copy number for microbial enzymes that can interact with the host is smaller than the variation observed in other enzymes and by showing that the variation in enzyme abundance between the two animals is too large to be explained by sampling variation in the metagenomic sequences. Since the networks are also distinctly non-random in both their reaction and metabolite distributions, we argue that there are metabolically similar but not identical microbial assemblages that are capable of performing the same processes from the host's perspective, even if they appear distinct at the taxonomic level.n = 35; initial body weight = 333.77 ± 8.54 kg; age = 10.14 ± 0.12 mo). These animals were weighed, administered an anthelmintic  and randomly assigned to four pens  each equipped with two GrowSafe®  feed intake bunks  where the model fitted was,i = average actual daily feed intake for animal i, β0 = regression intercept, β1 = partial regression coefficient of actual daily feed intake on daily body weight gain for animal i, and β2 = partial regression coefficient of actual daily feed intake on metabolic mid-test body weight for animal i. The two steers chosen for the metagenomic experiment had similar body weight gains with different feed efficiencies . To obtain the microbial DNA for sequencing, we followed the procedure of Guan etal.  were constructed following the manufacturer's recommended protocol with reagents supplied in Illumina's DNA sample preparation kit. Genomic DNA was sheared using a BioRuptor  to generate fragments. The resulting 3′ and 5′ overhangs were removed by an end repair reaction using a 3′ to 5′ exonuclease activity and polymerase activity to blunt the fragment ends. A single adenosine was added to the 3′ ends of the blunt fragment followed by the ligation of Illumina adapters. The adapter-ligated fragments were then size selected using an agarose gel. Fragments of average length 436  or 477  bp were recovered from the gel slice by elution and ethanol precipitation. Each purified library was quantified with a Qubit assay and fragment size was confirmed by Agilent BioAnalyzer High Sensitivity DNA assay.Libraries were diluted and sequenced according to Illumina's standard sequencing protocol on a GenomeAnalyzer II. The two libraries  were each sequenced on two lanes of the instrument, resulting in 150 bp, paired-end sequences. The average insert size was 303 bp for animal 1 and 345 for animal 2. Raw sequence reads are available from NCBI's short read archive (Project accession number PRJNA291523).Paired-end reads were quality filtered by truncating each read after the first run of three bases with a phred quality score <15 . From thTo explore the taxonomic structure of these two animals’ ruminal metagenomes, we searched the filtered reads for fragments of 16S rDNA genes. We did so by comparing them to a reference database made up of two pieces: 16S rDNA genes from the Ribosomal Database collection of sequences  and 16S Database sequences were clustered into Operational Taxonomic Units (OTUs) by first computing all possible pairwise Needleman-Wunsch sequence alignments  using oubL, the likelihood obtained from fitting both animals to a common distribution, and 1L and 2L, those obtained when allowing distinct distributions for each animal. We then compared the value ΔlnL = (ln(1L) + ln(2L)) − ln(bL) from the real samples to the same value computed on 1000 datasets where the OTUs were randomly repartitioned between the two animals .As shown in Figure  animals . As the We obtained the complete catalog of microbial metabolic networks known to the MetaCyc project . This caWe next constructed two metabolic networks for our analysis, one for the bovine host and one for the microbes. In our framework, nodes in the metabolic network  to 174 (for 100N).For all networks, we defined edges in the metabolic networks to connect pairs of nodes that share a metabolite. Only annotated metabolites present in at least two taxa were considered for these purposes. Because a handful of metabolites, such as water and hydrogen ions, occur in an enormous number of reactions, it is necessary to remove these ‘currency metabolites’ from the metabolic networks. The alternative would be to have nearly every reaction connected to every other reaction. Unfortunately, there is no universal definition of a currency metabolite . InsteadWe defined three potential sets of metabolites exchanged between the microbes and the host animal, sets that we refer to as ‘interface metabolites’. The most minimal set (set VFA) consisted of (the anions of) three VFAs: acetate, propionate, and butyrate. VFAs are important nutrients for ruminants  but unfoOur second set (VFA + AA) expands the VFA set to include the 20 amino acids, which are also important ruminant nutrients. Finally for completeness, we created a greatly expanded interface metabolite set (VFA + AA + HUM). In addition to the metabolites of VFA + AA, this set incorporates a large number of metabolites known to be transferred into human cells from the extracellular matrix. To obtain this set, we started with the list of 404 exchangeable human metabolites from the human metabolic network of Duarte et al. . We matcGiven the interface metabolite list and these added reactions, we defined edges between the host and microbial metabolic networks as any case where a reaction in the host network and one in the microbe network shared an interface metabolite. The result was a single merged metabolic network for the host and microbes  longer than 45 amino acids. We then used these metagenomic ORFs as queries in searching an enzyme database (see next section).We queried 1000 reads whose translations produced strong hits to the MetaCyc protein database  against the nucleotide version of that database. The results were discouraging; only 30% of those reads produced any BLASTN  hit.Programs such as BLAST, because they are optimized to not miss distant matches, are very slow for searching metagenomic ORFs against protein databases. To speed our searches, we developed a tool based on the SeqAn library and OpenMP . For eacWe next assigned reads to metabolic network nodes if they had hits to annotated sequences for that node meeting the above criteria. Enzymes in the sequence database are often annotated as catalyzing more than one reaction. In such cases, if an ORF pair has hits to multiple database sequences, one of three scenarios can be observed. First, all of the database hits may map to the same set of reactions. Second, some of the database hits may only map to a subset of the reactions of other hits. Finally, different database hits may map to non-overlapping sets of reactions. In the first two cases, we compute the reactions catalyzed by an ORF pair as the union of the reactions catalyzed by each enzyme with hits to that ORF pair. In the last case, the read pair is discarded because we cannot be certain of the identity of the reactions in question. Cases where read pairs did not both map to the same enzymes were likewise omitted. Note that while this mapping approach is applicable to many metagenomic datasets, our analysis of differences between animals from similar environments precludes comparing these data to other samples from different species  or envir25, N50N and 100N with no mapped reads. No major omissions are apparent from these data.We sought to assess if there were highly connected enzymes from MetaCyc that might be expected to be in the rumen that were not identified with our read mapping approach. Supplemental Table S6 lists the 10 most connected reactions  from networks p) and the average number of reads mapped to those nodes with at least one mapped read (r). We then calculated Pearson's correlation coefficient between these two values and the layer's distance to the host network. To assess whether the observed trends were stronger than expected by chance we randomized the read assignments among the microbial nodes 1000 times and recalculated these two correlations. For p, we randomized read counts across all microbial nodes, occupied or not. For r (the average number of mapped reads), we only randomized the read numbers within the set of nodes with at least one mapped node.For each layer of the microbial network in Figure 2 ratio of abundances for the animals, we normalized all reads counts by the total number of reads mapped for each animal . We then pooled the mapped reads, randomly reassigned 1n of them to animal 1 and 2n of them to animal 2 and recomputed the number of nodes with every abundance difference .P < 0.001, ‘Materials and Methods’ section). We also normalized the read counts for each animal and compared the percentage of reads from the two that were mapped to the same OTU: only 44% of reads from the two animals mapped to the same OTUs.To define microbial OTUs, we computed those OTUs  from a database of known 16S rDNA sequences by all-against-all pairwise alignment and sequence clustering . Using t25N) to moderate . While these networks disregard the compartmentalization of the ruminal ecosystem into distinct cells, any compartmentalized metabolic network for this environment will be a subset of the community network.It is conceptually obvious that one could infer the gene contents of a metagenome from a reference database. As we were interested in metabolism, we chose the collection of metabolic network reconstructions in MetaCyc  as our dMore than 1.2 million read pairs from the two animals mapped uniquely to protein sequences from MetaCyc. Those protein sequences in turn catalyze one or several reactions each . Of the 6140 nodes in the microbial metabolic network, 2022 nodes had at least one mapped read pair and 1734 had read pairs mapped from both animals. A reaction from the electron transport chain interconverting ubiquinones and ubiquinols had the largest number of mapped reads: 16 443 reads from a single animal.r = 0.31, Spearman's ρ = 0.57, P < 10−10 in both cases). However, given that one strong predictor of a reaction having many sequences in the MetaCyc database is that the reaction is widely distributed phylogenetically, we argue that the magnitude of these correlations do not suggest database coverage is the primary signal being detected here. Similarly, the number of reads mapped to a node is correlated to that node's metabolic centrality (node degree), but this effect is rather weak . Central nodes also have a very slight tendency to be more variable between animals after accounting for the number of mapped reads for networks 50N and 100N , but no such effect was seen for 25N.We were concerned that the number of reads that mapped to a reaction might simply be a function of the number of known enzyme sequences for that reaction. For those nodes with at least one read mapped, there is a statistically significant correlation between the number of reads that mapped to a reaction and the number of known enzyme sequences for that reaction . Moreover, there are 2022 reactions with mapped sequence reads compared with 122 OTUs detected, meaning that the metabolic network in fact has more potential points of variation.The two animals are much more similar in their enzyme node profiles than in their OTUs: 74% of the reads between the two animals matched at the node level. A similar situation was observed when considering the between-animal correlation in OTU counts or in nodes with mapped reads: the OTU mapping was more variable between individuals than was the node mapping, especially when considering non-parametric correlation statistics Figure  versus Cinterface metabolites: three VFAs and the twenty amino acids . These interface metabolites allowed us to infer the layered networks of Figures We next sought to assess how the microbiome might interact metabolically with the bovine host by inferring a host metabolic network. To do so, we first used our previously described orthology inference tool  to map tWe used these merged networks to measure how closely a given microbial reaction interacts with the host's metabolism. A microbial reaction that involves one of the interface metabolites has distance 0 from the host (and vice versa for host reactions). If a reaction involves a metabolite that is also involved in one of the distance 0 reactions (but does not itself possess an interface metabolite), that reaction has distance 1, and so forth. Note that interface metabolites were considered whether or not they were also defined as currency metabolites (see above). All of our observations also hold for a larger set of interface metabolites including the VFAs, the amino acids and human extracellular metabolites . Supplemental Figure S2 illustrates how the network structure changes using this larger interface set.p) and the average number of reads that mapped to a node (r) for all three networks .This layered network structure has several features that suggest that it reflects an underlying biological reality. There is a strong correlation between the distance to the host network (layer number) and the proportion of nodes with at least one mapped read pair .Figure We can also make limited inferences regarding the set of metabolites that are present in the microbiome by assessing the proteins that are found there and the reactions that they catalyze. We first ranked the interface metabolites by the number of read pairs that mapped to each microbial reaction that involved such an interface metabolite. As can be seen in Figure P < 0.001 for all three networks). Moreover, the two animals were more similar in their metabolite profiles than would be expected by chance: for all reactions using each metabolite, we computed the net difference in normalized read count between animals. The sum of this statistic across all metabolites was much smaller than that found when the read counts were randomized between the two animals . Table The total set of metabolites inferred to be present in the microbiome (based on the enzymes present) is also non-random. The number of metabolites associated to nodes with mapped read pairs is smaller than would be expected if the same number of nodes were selected at random (Considering that much of ruminal nutrition is thought to be provided by VFAs , we nextWe have presented an example where two bovine ruminal microbiomes that appeared rather different at the taxonomic level were much more similar in their metabolisms. While we do not, on the basis of two animals, claim this observation is universally true, it is in keeping with the known complexity of microbial ecosystems, where it is both possible to create taxonomically distinct assemblages with similar metabolic properties and taxonomically similar ones with differing metabolic properties ,55. It aWhile previous work has compared taxonomy and gene complements ,17,21–24It would be very desirable to understand this difference in how feed is used for growth using predictive stoichiometric metabolic models ,57–58. UThe conclusion that these two steers are functionally similar in their metagenomic metabolic networks is hardly unexpected given the shared environment and diet. However, the fact that detectable differences are evident even under these rather controlled conditions, suggests another point: there is more than one way to build an ecosystem . This poThis last result has an interesting linkage to one of the more surprising discoveries of systems biology: the highly redundant structure of metabolic networks ,63. ThisUnfiltered metagenomic sequences are available from NCBI's short read archive under project accession number PRJNA291523.