A flux balance analysis method for gene essentiality prediction, which takes into account variation in biomass composition under different growth conditions. Flux balance analysis is a common method for predicting steady-state flux distributions within metabolic networks, accounting for the growth demand for the synthesis of a predefined set of essential biomass precursors. Ignoring the growth demand for the synthesis of intermediate metabolites required for balancing their dilution leads flux balance analysis to false predictions in some cases. Here, we present metabolite dilution flux balance analysis, which addresses this problem, resulting in improved metabolic phenotype predictions. A practical approach to gaining biological understanding of complex metabolic networks requires the development of mathematical modeling, simulation, and analysis techniques. Traditional modeling techniques are based on mathematical approaches that require detailed and accurate information regarding reaction kinetics as well as enzyme and metabolite concentrations ,2. The lμ denoting the growth rate) ..E. coli,j that is produced by a certain reaction at a rate greater than zero (referred to as an 'active metabolite') has a non-zero concentration and should hence be diluted with a rate greater than zero (denoted by dj). To compute a feasible flux distribution, To formulate a mass-balance constraint while accounting for metabolite growth dilution, we assume that each metabolite subject toε) for active metabolites, produced in some non-zero rate in the flux distribution E. coli we set ε = 10-4 μmol/mg, which represents a common concentration of intermediate metabolites [ε values (data not shown). Enzyme directionality and capacity constraints are formulated in Equation 5 by imposing where a mass-balance constraint, accounting for the dilution of all active metabolites, is formulated in Equation 3. Equation 4 assigns a positive dilution rate above a pre-defined threshold , via the following linear constraints:The above optimization problem is solved by formulating it as a MILP problem, replacing the Equation 4 constraint with the linear equations specified below: for each metabolite Rj denotes the set of reactions in which metabolite j participates. Equation 6 is a linear formulation of the statement 'if νi ≥ ε then yj = 1' and Equation 7 is the symmetric for negative fluxes . Given the where εμ < 1) as:which can be represented in linear form  gave qualitatively similar results to the above linear formulation (data not shown). The commercial solver CPLEX running on 64-bit Linux machines was used for solving LP and MILP problems within a few dozens of seconds per problem.A simplified formulation assuming a constant growth rate of AUC: area under curve; CBM: constraint-based modeling; FBA: flux balance analysis; LP: linear programming; MD-FBA: metabolite dilution flux balance analysis; MILP: mixed integer linear programming; OD: optical density; q8: ubiquinone-8-oxidized; q8h2: ubiquinone-8-reduced; ROC: receiver operating characteristic.TB developed the method, implemented the method, performed analyses and wrote the manuscript. OF performed analyses. ER conceived the study. TS conceived the study, developed the method and wrote the manuscript. All authors discussed the results, and read, commented and approved the final manuscript.ASAP growth conditions. Excel file including the growth conditions used to model the ASAP experiments used by both FBA and MD-FBA.Click here for fileASAP experimental optical density values. Excel file including the experimentally measured OD values taken from the ASAP database.Click here for fileReaction activity difference across subsystems. Figure showing the mean reaction activity difference between FBA and MD-FBA across the different metabolic subsystems available in the model.Click here for fileGene essentiality analysis results. Excel file containing the gene essentiality predictions by FBA and MD-FBA as well as the experimental gene essentiality data.Click here for fileCofactor dilution-related demand activity predictions. Excel file containing MD-FBA's predictions for the dilution related growth-demand synthesis of the three cofactors: 10-formyltetrahydrofolate (10fthf), 2-octaprenyl-6-hydroxyphenol (2ohph) and FAD across different gene knockouts.Click here for file


Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are “genome-scale” and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available. Escherichia coli and will be essential for understanding human cellular metabolism.Systems biology aims to characterize cells and organisms as systems through the careful curation of all components. Large models that account for all known metabolism in microorganisms have been created by our group and by others around the world. Furthermore, models are available for human cells. These models represent all possible biochemical reactions in a cell, but cells choose which subset of reactions to use to suit their immediate purposes. We have developed a method to combine widely available gene expression data with presupposed cellular functions to predict the subset of reactions that a cell uses under particular conditions. We quantify the consistency of subsets of reactions with existing biological knowledge to demonstrate that the method produces biologically realistic subsets of reactions. This method is useful for determining the activity of metabolic reactions in  Commonly, genome-scale metabolic networks are reconstructed to contain all known metabolic genes and reactions in a particular organism Knowledge of transcriptional regulation of metabolism comes from different sources. At a low level, from the bottom up, some of the regulatory proteins that control the transcription of sets of metabolic genes are known Escherichia coli, it has been estimated from dual perturbation experiments that only about one-fourth or one-third of its TRN is currently known There are three ways to study how regulation tailors gene-expression under a specific condition. First, if a transcriptional regulatory network network (TRN) is available, then the transcription state of the cells can be computed for a given input The third approach to study regulation relies on the available of expression profiling data. If such data is available for the conditions being examined we can directly examine the expression of the ORFs accounted for in a genome-scale reconstruction. Metabolic network reconstructions can be combined with gene expression data from different states to identify regulatory principles in organisms The results of these methods are dependent on the quality of the expression data that is used as input. Expression data is known to be noisy, and the variety of methods for converting the fluorescence intensity of thousands of spots on a chip to semi-quantitative readings of mRNA molecule counts do not produce equivalent results Here we use gene expression data in combination with objective functions to create functional models despite potentially noisy data. We describe the use of genome-scale transcriptomic data to constrain reactions in both bacteria and human cells, enabling context-specific metabolic networks to be reconstructed and compared. We quantitatively define the consistency of gene expression data with assumed functional states of a cell, demonstrating agreement with physiological data. Context-specific metabolic networks will be virtually essential to accurately model human metabolism due to the variety of cell types and their corresponding metabolic processes.The approach to the construction of context-specific metabolic networks is termed Gene Inactivity Moderated by Metabolism and Expression (GIMME) and is illustrated in Simply speaking, reactions that correspond to mRNA transcript levels below a specified threshold are tentatively declared inactive. If the cell cannot achieve the desired functionality without at least one of these reactions, linear optimization is used to find the most consistent set of reactions to reactivate. Inconsistency scores are calculated based on the product of distance from threshold and necessary flux for each reaction required to be reactivated, as illustrated in Find the maximum possible flux through each RMF .Constrain the RMF's to operate at or above some minimum level  and identify the set of available reactions that best fit a quantitative data set.i is the normalized gene expression data mapped to each reaction. xcutoff is a cutoff value set by the user above which a reaction is definitely present; there is no contribution to the inconsistency score from reactions that are above this threshold. S is the stoichiometric matrix with reactions as columns, metabolites as rows, and stoichiometric coefficients as elements. v is the flux vector, quantitatively describing the flow through each reaction. ai and bi are the lower and upper bounds, respectively, for each reaction and define the minimum and maximum allowable flux. These bounds are set according to the maximal value of the RMF(s) found in step (1), in general by setting the lower bound corresponding to each RMF to some fraction of its maximal value. The great majority of the lower and upper bounds do not correspond to an RMF and are set to the same value as in a standard FBA problem, usually to arbitrarily high and low values, but sometimes to finite values as for input constraints  and irreversible reactions.Part A is achieved through flux balance analysis (FBA) The above optimization problem would generally be difficult to solve due to the presence of an absolute value operator, but in this case, a trivial simplification converts the above problem to a standard LP problem. Each reaction defined as possibly reversible (containing a negative lower bound) is converted to two irreversible reactions, thus restricting all fluxes to be positive, and removing the need for the absolute value.E. coli.In general, some reactions will not have available data. The algorithm takes a conservative approach and designates these reactions as active; hence the term “gene inactivation” is part of the method name. The algorithm treats these reactions as if they had data that surpassed the cutoff; this is a conservative approach to avoid any penalty for absent data. The lack of data does have implications for the interpretation of results. It is entirely possible that given better data, these reactions would be determined to be absent, perhaps necessitating the activation of other reactions. Clearly, with limited data, the results must be considered with caution. In general, this is far more of a concern for human metabolic networks than for E. coli for several different conditions and to compare inconsistency scores for different strains of the bacterium. We show that the inconsistency scores agree with experimental data in nearly all cases. Gene expression data from different conditions of E. coli growth are the input data, and the independent validation data is phenotypic data describing the relative growth and product secretion.We have used the GIMME algorithm to produce context-specific metabolic networks for E. coli on culture media to which it is unaccustomed E. coli predict better growth than it initially achieves in the laboratory when it is given a substrate other than glucose to use as a carbon source for growth. However, after selective pressure is applied to maximize growth, the actual growth has been shown to improve to match the prediction, typically after a month or two of serial passage of cells Adaptive evolution has been used in the laboratory to improve the growth rate of cutoff) was set at 12, meaning that reactions assigned a normalized value greater than 12 are assumed to be present; similar results were noted at other thresholds. The RMF was growth on a given carbon source, and the context-specific metabolic networks were forced to grow no less than 90% of optimal growth. Because the evolved strains nearly always grow better than wild-type strains on a variety of carbon sources The gene expression data used to construct the models consists of CEL files containing the data described in E. coli can be used to optimize such strains Metabolic engineering seeks to optimize bacterial strains to produce a valuable product from a less expensive set of molecules. Rational design of strains for metabolic engineering is possible with genome-scale metabolic models We used this data set to verify the robustness of the algorithm to two different factors. First, we tested the effect of altering the cutoff by recalculating the results for cutoffs ranging from eight to 14, in increments of 0.1. We found that the consistency scores were significantly different for all cutoffs. For some cutoffs, the p value was not as good as for others, but p<0.01 for all choices of cutoff within the range tested. Second, we verified the robustness of the algorithm with a jackknife test. We randomly removed 5% of the expression values mapped to reactions 100 times and recomputed the context-specific networks and consistency scores. We found that for all repetitions the same conclusion was reached, although in some cases the p values were not quite as low as when using all of the data. In all cases, the conclusion was reached with p<0.02, which demonstrates slightly lower performance with all of the data available. This suggests that the algorithm should be expected to have greater statistical power when as many reactions as possible are assigned data.E. coli varies depending on the availability of terminal electron acceptors, usually oxygen or nitrate. Gene expression data from a total of 21 different strain/electron acceptor conditions was analyzed to construct the most consistent models for growth with oxygen, without oxygen, and with nitrate. The expectation is that the strain data taken from a given condition  should be more consistent with growth on that condition  than strain data from a different condition. Pairwise comparisons were made between all consistency scores, and the results are shown in The growth of The wide variety of human cell types in the body do not share a simple objective such as cellular growth, but rather have a multiplicity of functions necessary for multi-cellular life. Accordingly, understanding the metabolism of any particular cell type requires a model that contains only the reactions present in that cell type, without potentially thousands of extraneous reactions. Human Recon 1 We used three publicly available sets of gene expression data for skeletal muscle cells, as depicted in E. coli datasets described earlier were. Nevertheless, they can be interpreted in the context of a genome-scale metabolic network towards this end. All three datasets were collected using Affymetrix  gene expression arrays. The GB dataset used U133+ 2.0 arrays, while the GI and FO datasets used U133A arrays. While the arrays are similar, the U133+ 2.0 array is able to provide reliable trancriptomic data for 179 reactions beyond what the U133A array can provide. The coverage of these arrays in terms of model reactions is shown in These three datasets were originally gathered for purposes completely distinct from creating context-specific metabolic networks, just as the Each of the 42 (6+12+24) gene expression datasets was used with the GIMME algorithm to create a model that produces ATP at no less than half the optimal efficiency and matches the data as closely as possible. These models were compared on a pairwise basis by finding the number of reactions that are different in the two models under comparison. On average, two models differ by 340 reactions, which is approximately 10% of the reactions in the global model. The pairwise distances are shown graphically in In spite of the difficulties of comparing models derived from different sources, two statistically significant differences emerge from the analysis. First, a given patient is more similar to himself before and after either gastric bypass or glucose/insulin infusion than he is to other patients. We took the similarity scores for the GB and GI patients and created two separate groups: (A) all matched patients before and after and (B) all unmatched patients from the same dataset. Permutation testing demonstrated that group A has a smaller mean distance than group B (p<0.01). Secondly, we looked at consistency scores, asking if any group was more consistent with high ATP production than any other. Only one statistically significant (p<0.01) result emerged, that the after-GI patients are more consistent with high ATP production than the before-GI patients. Again, this result is exactly as expected; muscle cells that have been given a substantial dose of glucose and insulin in the bloodstream should be more consistent with high ATP production.E. coli and human skeletal muscle cells. We have shown that (1) the computed consistency between gene expression data for different conditions and RMF agrees with physiological data, (2) the most consistent networks depend on the metabolic objective and media conditions, and (3) the most consistent networks for human skeletal muscle cells contain significantly fewer reactions than the global human model.The work reported herein details the first available method to both produce a guaranteed functional metabolic model specific to a set of gene expression data and quantify the agreement between gene expression data and one or more metabolic objectives. We have demonstrated the functionality of this GIMME method with gene expression data from E. coli because nearly all of the gene-associated reactions have data available, replicates are available, and controls are present. We also found that a substantial number of reactions in E. coli do vary in activity when different input conditions are provided. In the end, we conclude that a tool originally conceived to plug a key gap in the analysis of human cellular metabolism actually provides more immediate use in the analysis of microbial metabolism.Initially, we expected that the results for human models would be more interesting than those for any other organism reconstructed to date, principally because we expected that human cells would show the most variability across conditions. However, the lack of available data for a substantial number of human metabolic reactions confuses attempts at comparison. We showed that reducing the number of reactions considered by 5% can change the apparent differences between different datasets. In addition, the lack of replicates in human gene expression data sets and the difficulty in obtaining high quality biological controls complicates matters and reduces the statistical power of comparisons. We have higher confidence in the results presented for With metabolic reconstructions growing in size and becoming available for more and more organisms, tools to filter global reaction lists into context-specific reaction lists will be highly useful. Meaningful analysis of the human metabolic network will require procedures such as GIMME in order to accurately predict phenotypes.E. coli and human cells were imported into Matlab with the COBRA Toolbox E. coli has been obtained by our lab and is previously published as cited above. The gene expression data for human skeletal muscle cells was downloaded from NCBI GEO The metabolic networks for E. coli was processed using GCRMA as implemented within Bioconductor The gene expression data was obtained as CEL files and processed using Bioconductor The GIMME algorithm is implemented in Matlab, using functions in the COBRA Toolbox. In general, any robust linear programming solver should work; we used Tomlab .The output from the GIMME algorithm is an inconsistency score, and a higher score means that the gene expression data is less consistent with the model achieving the desired objective. For visualization purposes only, these scores are converted into normalized consistency scores, with a higher score indicating greater consistency between the data and the modal achieving the objective. For a given set of scores, each inconsistency score is subtracted from 1.02 * (maximum inconsistency score) to produce a set of consistency scores. Each consistency score is divided by the maximum consistency score to produce a set of normalized consistency scores. The 1.02 factor assures that the smallest consistency score is slightly greater than zero and easy to visualize on a graph.Permutation testing with 10,000 randomizations was used to determine the statistical significance of all results with regard to consistency scores. This testing was implemented in Matlab.Heat-map type representations were produced in Matlab. Other graphs were produced in Excel .


Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs. The dysregulation of the Human metabolism is closely related to human diseases. Many human diseases are metabolic diseases, which are directly caused by a deficiency of metabolic enzymes and the subsequent accumulation of toxic substances or the lack of essential metabolites. Many common diseases such as cardiovascular disease, cancer and diabetes are related to a malfunction of the human metabolism. The statistics from OMIM show that around 23% of the metabolic genes are disease-related genes and more than 48% of the metabolic reactions are affected by disease-related genes . The emeRecently, significant work has been gradually done to reconstruct human genome-scale metabolic network, which provides a unified platform to integrate the experimental data for genes, proteins, drugs, drug targets, metabolites and metabolic reactions in the same system for the study of human metabolism and the identification of novel drug targets. Currently there are two high quality human metabolic networks developed independently: EHMN reconstructed by Ma et al.  and HumaOne traditional approach to analyze a metabolic network is constraint-based flux distribution prediction, which employs stoichiometric, thermodynamic, flux capability and other constraints to determine the space of flux distributions. One commonly used constraint-based approach is Flux Balance Analysis (FBA), which utilizes stoichiometries to predict a network's ability to produce pseudo-steady state conditions. RecentlA promising approach in drug target discovery involves the integration of available biomedical data through mathematical modeling and data mining. Significant work -11 has bhttp://bigg.ucsd.edu/. The COBRA toolbox T. Each reaction is thus profiled using its 59 flux values in NCI-60 cell lines, and can be considered as a data point in 59-dimensional flux feature space. We use cosine similarity /PE(t) are increasing with t, thus the similarity metric sRF is likely to be an ideal metric for drug-reaction interaction prediction.We now define a similarity metric based on reaction flux profiles and then check whether it's a possible ideal metric. The flux of reaction i by Mi. Target sequence (TS) similarity for protein s and t is denoted by sTS  . Then the reaction similarity for reaction i and j based on target sequence is defined as the maximum, average or minimum of all the possible enzyme target sequence similarities RS since it is defined based on enzyme targets' structure.Denote the set of target enzymes for reaction RF measures the functional similarities among reactions, while sRS measures their similarity based on enzyme targets' structural information. The integration of sRF and sRS may provide an optimal measurement. Thus we define a new reaction similarity sR as the weighted sum of reaction flux similarity and reaction structure similarity: sR =λsRF  + (1 - λ) sRS for any reaction i and j.sm reactions from known drug-target interactions. We aim to predict whether a new metabolic reaction interacts with the drug or not. Here we propose a Kernel KNN (K-Nearest Neighborhood) method for this binary classification problem using the similarities between reactions. We denote xi = 1 if the drug has interaction with the reaction i, xi = 0 otherwise, for i = 1,...,m. Then, for a new metabolic reaction, we predict its interaction with all drugs as s is a reaction similarity metric, s represents the similarity between the new reaction and reaction i and N{k} is the set of k reactions nearest to the new reaction under the meaning of the metric s. s can be sRF, sRS or their integration sR. Here xnew is called the association score of the new reaction and the specific drug. Higher association score indicates a higher probability of the reaction interacting with the drug. Kernel KNN is a kernel version of KNN classification method. Note we still call this model as Kernel KNN method even if s may not be positive semi-definite. Both KNN and Kernel KNN classify a new point simply through its nearest neighborhood. However, Kernel KNN considers the weight of voting from different individuals in the neighborhood.For a specific drug, we can obtain its true interactions with The flow chart of the whole task of drug reaction/target prediction using reaction flux similarity is shown in Figure Although relationships between drugs and targets have been depicted in a global view , the relWe constructed a Drug-Reaction Network (DRN) to depict the interactions between the approved drugs and metabolic reactions. We used Cytoscape  to show We also colored all the drug nodes in Figure From Figure Reaction profiling we propose here is a method to quantify metabolic reactions. Using this method, the profile for each metabolic reaction is identified by its flux states in different cellular environments. Compared with the traditional equation representation of metabolic reaction, this new representation makes quantitative analysis of reactions obviously easier. In each of NCI-60 cell lines, LP model was used to predict metabolic flux distribution for all the metabolic reactions based on gene expression data in the cell line. Thus, each metabolic reaction was only designated one profile as its signature, which was used to quantitatively measure the similarity among reactions and thereby for prediction of novel drug-reaction interactions. We only focused on enzymatic reactions since only enzymatic reactions have direct interaction with drugs through enzymes. Around 2299 of total 3742 metabolic reactions in the human metabolic network can be catalyzed by enzymes. Some of the enzymes in metabolic network have been identified as drug targets. Among the 811 enzymatic reactions in DRN, the flux analysis shows that 400 reactions are inactive (-1 < flux < 1) in NCI-60 cancer cell lines. We only consider 353 reactions that are active in at least one cancer cell line. We report, in Figure R. The analysis shows that 3744 pairs of reactions ΓRDR share the same drugs while 670 pairs (ΓRER) share the same enzymes. β = 670/3744 = 0.18 indicates that around 18% reaction pairs interact with the same drugs because they are catalyzed by common enzyme targets, while the remaining 72% pairs interact with the same drugs through different enzyme targets. We also found that if reaction flux similarity is larger than 0.9, more than 50% of reactions interact with same drugs through the same enzymes. Figure β increases as the cutoff value of reaction flux similarity increases. This means that if the reactions are more similar, they are more likely to interact with same drugs through same enzymes rather than through different enzyme targets.To understand more about the relationship between reactions, enzyme targets, and drugs, we listed a table  target similarity and 2) known drug-target interactions. Machine learning algorithms were first used to predict drug-reaction interactions, which then lead to novel drug-target interactions. The basic assumption is that if a drug is predicted to interact with a reaction, the enzyme catalyzing the reaction is likely to be the target of the drug. For the metabolic reactions, the constraint-based flux analysis in NCI-60 cell lines were first applied to obtain reaction profiles. Reaction flux similarity is then defined as the cosine similarity among reaction flux vectors. All the known drug-reaction interactions are obtained from the DrugBank database. Since we represent each metabolic reaction based on NCI-60 cancer cell lines, we focused on 32 cancer drugs in DRN to perform cross validation of the prediction method. We eliminated the reactions that have inactive flux states (-1 < flux < 1) in all the 59 cell lines. Training set includes 260 metabolic reactions. We denote this dataset as SET-I, which contains 32 cancer drugs, 260 metabolic reactions in DRN and their ground truth interactions. We also generated the dataset, SET-II, which includes 32 cancer drugs, available expression in NCI-60 for genes encoding 629 targets and their ground truth interactions. SET-I was used to predict drug targets through target reaction classification based on target reaction similarity, while SET-II was through target gene classification based on target gene similarity. In the process of classification, both proteins and reactions are considered as drug targets.Different classification methods were performed on both SET-I and SET-II, such as Nearest Profile, Bipartite graph model , SupportTable R = λsRF + (1 - λ)sRS, where k = 1 ~ 100 and λ = 0,0.1,0.2,...,1, where k is the parameter in Kernel KNN method and λ is the weighting of reaction flux similarity mentioned in the section of Methods. Figure k and λ. It shows that the maximum prediction accuracy is obtained around k = 20 and λ =0.1 ~ 0.3. In fact, the best AUC can reach 0.92 and the worst AUC is around 0.66. Figure λ-paths of the heat map. When λ = 0, only enzyme target sequence is used for classification, while when λ = 1, only reaction flux profile is used. From the figure of λ-paths we can see that λ-paths for λ = 0.1 ~ 0.6 are almost always higher than λ-path for λ = 0, this means the integration of reaction flux with target sequence makes the classification performance better, for almost all k = 10 ~ 100. We also performed the t-test on the respective 50 AUCs of λ = 0 and λ = 0.2 when k = 20 is chosen, the resulting p-value of 1.17 e-028 shows the difference is statistically significant. The synergism of reaction flux profiles and enzyme target sequences further supports that reaction flux similarity obtained by reaction profiling does help for exploring the associations between drugs and reactions or targets. Figure λ = 0.1 ~ 0.3 are best choice for the classification model. From the figure of λ-paths of the prediction performance, we can also see that the changes of the performance by k at around λ = 0.1 ~ 0.3 is much less than λ = 0 and λ = 1, this means the prediction becomes more stable after the integration of two data sources.In Figure λ = 0 and λ = 0.2 by removing metabolic reactions one after another from the training data and then predicting its associations with all the drugs. This is the Leave-One-Out method. We chose k = 20 for the validation. The prediction AUC by the methods using λ = 0 and λ = 0.2 is 0.83 and 0.85, respectively. This shows that the integration of flux information can improve the prediction accuracy. For each reaction, we further reported in Additional file λ = 0 and λ = 0.2, respectively. Although the two methods have 13 common predicted drug-reaction pairs, with 11 true positives and 2 false positives, their results have some differences. By the method with λ = 0.2, we can get 12 other true positives and only 2 false positives, while with λ = 0 we can get 3 other true positives and 8 false positives. Specifically, the method with reaction structure similarity cannot identify the targets AMPD1, AMPD2, AMPD3, GMPR, GMPR2, GMPS, HPRT1, IMPH1 and IMPH2 of azathioprine and mercaptopurine, and the target ABCC1 of daunorubicin, while the integration of the reaction flux similarity can identify the relationships by leveling up the their association scores. For example, ABCC1, the multidrug resistance protein 1, is one of the approved targets [We also further compare the prediction results of the methods using  targets -23 of dak = 20 and λ = 0.2. The results show that for four cancer drugs Daunorubicin, Cladribine, Mercaptopurine and Azathioprine, some new related reactions are identified with high association scores. We report all the predicted reactions and enzymes for these 4 drugs in Additional file We then predict new reactions and enzyme targets for the cancer drugs by Kernel KNN of Daunorubicin is an aminoglycoside of the anthracycline class that is mostly used in the treatment of leukemia. The cytotoxic activity of daunorubicin is through intercalation between DNA base pairs, which results in DNA strand breakage. The drug also inhibits topoisomerase II and polymerase. Daunorubicin is the substrate of ATP-binding cassette proteins (sub-family C member 1 and sub-family B member 1). Studies indicate that daunorubicin is effluxed through these transporters together with glutathione. The stoin vivo phosphorylated derivative of cladribine is incorporated into DNA and leads to DNA strand breakage. Cladribine and some of its analogs have also been shown to be substrates and inhibitors of E. coli. purine-nucleoside phosphorylase, although they do not react with mammalian purine nucleoside phosphrylase [Cladribine is a synthesized analog of the nucleoside adenosine. It interferes with synthesis and repair of DNA by inhibiting adenosine deaminase, and therefore has a strong inhibitory effect on the rapidly-replicating leukemia cells. The phrylase . Some anphrylase . The GPXMercaptopurine is a purine analogue which interferes with nucleic acid biosynthesis by inhibiting purine metabolism and has been found active against human leukemias. It is usually used in the treatment of or in remission maintenance programs for leukemia. Our prediction suggests that the mechanism of action of mercaptopurine may be related to 5'-nucleotidase (GMP). This reaction preferentially hydrolyzes inosine 5-prime-monophosphate (IMP) and other purine nucleotides, and is allosterically activated by various compounds, including ATP. The enzyme GMP, also known as NT5C2, is exclusively located in the cytoplasmic matrix of cells and may have a critical role in the maintenance of a constant composition of intracellular purine/pyrimidine nucleotides in cooperation with other nucleotidases. Besides NT5C2, GGH and FPGS are also suggested to be targets of mercaptopurine. These two enzymes are both in the pathway of folate biosynthesis. Folic acid is especially important during periods of rapid cell division and cell growth. Mercaptopurine may inhibit the tumour cell growth by interfering folate biosynthesis. SLC25A15 and SLC25A2, two orinithine transporters for the urea cycle in liver, are also predicted as targets of mercaptopurine. The two transporters are responsible for removing ammonia from blood. Besides the predicted reactions and targets listed in Table Like mercaptopurine, azathioprine is also an immunosuppressive agent which works by suppressing the body's immune response. Azathioprine is a prodrug which is converted to 6-methyl-mercaptopurine by thiopurine methyl transferase (TPMT) and then metabolised to the active metabolite 6-thioguanine. Thus it's reasonable to predict the same reactions/targets for mercaptiopurine and azathioprine.in vivo activation , enzyme activity is best measured after administration of the drugs both in vitro and in vivo . If there are known mutations that affect enzyme structure or activity, these may be taken advantage of to see if the mutations also alter effect of the predicted drugs on the enzyme.The activity of these four cancer drugs against the predicted enzymes can be tested by direct enzyme activity assays. Considering some drugs may need In this study, we first construct a drug reaction network to show their relationships in a unified context, and then present a reaction profiling method to depict systematically the metabolic reactions in a quantitative way. Reaction flux profiling makes it possible to measure the similarity of metabolic reactions functionally, which is a key step in many kinds of data analysis techniques such as clustering and classification. The comparison of target gene classification with target reaction classification shows the effectiveness of the indirect method to predict drug targets, and implies the important contribution of reaction flux similarity in drug target prediction. We also integrate the reaction structure similarity with this reaction flux similarity, and the results show the synergism of the structural similarity and functional similarity. We also notice that the integration of two similarity metrics makes the prediction more stable. This further implies the effectiveness of reaction profiling for drug target prediction. We thus predict some related reactions and possible targets for four cancer drugs for further biological research.Note that one limitation of reaction profiling lies in that proteomics and phorsphoproteomics are not involved. It may decrease to some extent the impact in the understanding of the multitude of interactions between drugs and entire metabolic network. In the future work we will address it when proteomics and phorsphoproteomics data become available. Metabolic reaction profiling has more applications than what we have discussed in this work. Besides NCI-60 cancer cell lines, other disease cell lines can be used to discuss the related metabolic reactions. The statistics of the drugs in DRN show that drugs for metabolic diseases, cardiovascular diseases or antineoplastic drugs are the most related to metabolic networks. This gives us a hint that these three diseases may also be discussed using metabolic network. We also note that our flux analysis is based on the mRNA content in the context of transcription, thus further flux analysis using metabolomic data may be developed to explore the underlying mechanism of interactions between drugs and metabolic reactions. Another possible application is metabolic biomarker discovery, which attempts to explore significant metabolites, metabolic reactions and enzymes. A biomarker discovery method involving gene expression and protein-protein interaction has been developed by Ideker et al. . It compLL and XZ conceived the idea and designed the research. LL acquired and processed the data. LL performed the research and analyzed the results. LL, XZ and WC wrote the paper. All authors read and approved the final manuscript.Different reaction measure of similarity. The file gives the comparison of performance using different reaction measure of similarity in Kernel KNNClick here for fileDrug-Reaction Network. This file lists the detailed information in Drug-Reaction NetworkClick here for fileReaction flux similarity score This file lists the reaction flux similarity and the common drugs between each pair of metabolic reactions.Click here for fileThe performance for the integration of reaction flux similarity and reaction structure similarity. This file lists the performance for the integration of reaction flux similarity and reaction structure similarity.Click here for fileThe predicted reactions and targets for cancer drugs. This file lists the predicted reactions and targets for cancer drugs.Click here for file


Bacillus subtilis are potential riboflavin production hosts with low maintenance metabolism.Selection of an appropriate host organism is crucial for the economic success of biotechnological processes. A generally important selection criterion is a low maintenance energy metabolism to reduce non-productive consumption of substrate. We here investigated, whether various bacilli that are closely related to B. subtilis exhibited indeed the highest maintenance energy coefficient, B. licheniformis and B. amyloliquefaciens exhibited only statistically insignificantly reduced maintenance metabolism. Both B. pumilus and B. subtilis (natto) exhibited irregular growth patterns under glucose limitation such that the maintenance metabolism could not be determined. The sole exception with significantly reduced maintenance energy requirements was the B. licheniformis strain T380B. The frequently used spo0A mutation significantly increased the maintenance metabolism of B. subtilis.While 13C-detected intracellular fluxes, all investigated bacilli exhibited a significant flux through the pentose phosphate pathway, a prerequisite for efficient riboflavin production. Different from all other species, B. subtilis featured high respiratory tricarboxylic acid cycle fluxes in batch and chemostat cultures. In particular under glucose-limited conditions, this led to significant excess formation of NADPH of B. subtilis, while anabolic consumption was rather balanced with catabolic NADPH formation in the other bacilli.At the level of B. subtilis does not seem to be the optimal cell factory from a bioenergetic point of view. The best choice of the investigated strains is the sporulation-deficient B. licheniformis T380B strain. Beside a low maintenance energy coefficient, this strain grows robustly under different conditions and exhibits only moderate acetate overflow, hence making it a promising production host for biochemicals and riboflavin in particular.Despite its successful commercial production of riboflavin,  Optimal choice of the host organism for cell factory engineering is pivotal to the economic success of biotechnological processes. Typical host selection criteria are available genetic tools and methods, safety status, genome annotation, well-characterized process characteristics and scale-up behavior. For many products, in particular for bulk chemicals, cellular energetics is another key criterion whose impact becomes most relevant in near optimized processes . In partB. subtilis, which is used extensively for enzyme and biochemicals production [2, riboflavin [B. subtilis fulfills most of the industrially desirable host criteria, it does not have a particularly low maintenance energy coefficient  when co g-1 h-1 ) or Kleb g-1 h-1 ). Anothemitation . To prevsubtilis . Here, wEscherichia coli, B. subtilis [B. clausii [B. megaterium [B. subtilis relatives as alternative riboflavin cell factories, we quantified their distribution of fluxes as well as NADPH and maintenance metabolism. Specifically we focus on the growth rate-dependent flux distribution in glucose-limited chemostat cultures because of their similarity to the conditions during riboflavin production in fed-batch with continuously decreasing growth rates.Another important host characteristic is the distribution of intracellular fluxes as the starting point for metabolic engineering. For the production of riboflavin – synthesized from three pentose units, glycerin and two C1 units – significant fluxes through the oxidative pentose phosphate (PP) pathway deem intuitively advantageous . While ssubtilis , B. clau clausii  and B. mgaterium , many otgaterium ,17. Partgaterium . To asseA recently described mini-scale chemostat setup was used for parallel operation of 12 chemostats . BrieflyB. amyloliquefaciens was the only species that was cultivated in 500 ml bioreactors . The cultivation volume of 250 ml was kept constant by level control. Throughout the cultivation, active mixing was achieved with a magnetic stirrer, and aeration was kept constant at two volume of air per volume of culture and minute.Due to sedimentation in the mini-scale reactors, Species used in this study are listed in Table 2HPO4, 3 g KH2PO4, 0.5 g NaCl, 1 g NH4Cl, 0.246 g MgSO4·7H2O, 0.014 g CaCl2, at pH 7.4 and 10 ml trace element solution containing (per liter) 1.35 g FeCl26H2O, 0.1 g MnCl2·H2O, 0.17 g ZnCl2, 0.043 g CuCl2·2H2O, 0.06 g CoCl2·6H2O, 0.06 g Na2MoO4·2H2O [13C-labeling experiments, glucose was added either as a mixture of 20% (wt/wt) U-13C-labeled isotope isomer  and 80% (wt/wt) natural glucose or as 100% 1-13C-labeled isotope isomer . Labeled glucose was used throughout the experiment, including the batch phase.The M9 minimal medium was composed of the following components : 5.64 g NaoO4·2H2O . Filter-B. subtilis natto and B. pumilus, biotin and adenine were added from sterile stock solutions to final concentrations of 0.1 μg/ml and 20 μg/ml, respectively.Batch experiments were done at 250 rpm in 500 ml baffled shake flask with a culture volume of 50 ml and 5 g/L glucose for non-labeled experiments and 30 ml volume and 3 g/L glucose for labeling experiments. The medium composition and preculturing were identical to the chemostat experiments. For 600). Glucose and acetate concentrations in culture supernatants were determined by using refractive index (RI) and UV detectors, respectively, on a HPLC system (Agilent/Hewlett Packard Series 1100) with an Aminex HPX-87H column .Cell growth was monitored by determining optical density at 600 nm (ODS(or P) = ΔS (or P) (D/X) we calculated specific production and consumption rates, where X is the biomass concentration. For determination of maintenance energy coefficients glucose consumption rates (qglc) were plotted against dilution rates. The maintenance energy coefficients were determined as the y-axis intercept of the weighted least square (WLS) regression line using the SPSS statistical software package .For continuous cultures, all physiological parameters were determined during steady state between 5 to 7 volume changes after inoculation. Since dilution and thus growth rate are constant in chemostat cultures, consumption and production rates were determined from the differences between substrate (S) and product (P) concentrations in the feed medium and culture supernatant. The relationship q600 versus time. The biomass yield on the substrate (YX/S) was determined as the coefficient of a linear regression of biomass concentration versus substrate concentration during the exponential phase. The specific substrate consumption rate was determined as the coefficient of a linear regression of substrate concentrations versus biomass concentrations multiplied by μ. The same relationship holds for the specific rate of formation of (by-)products.In batch culture, the growth rate (μ) was determined as the coefficient of the log-linear regression of OD600 was used. To determine the cellular dry weight, 10 ml culture broth was transferred into preweighted 15 ml glass tubes and centrifuged for 10 min at 3000 g at 4°C. The pellets were washed twice with 0.9% NaCl and dried at 105°C for 24 h to constant weight. Except for the spo0A mutant, a single correlation factor (gCDW/OD) was used for all dilution rates (D) of a given species: i.e., B. subtilis wild type 0.48, B. subtilis sigE 0.41, B. licheniformis T218a 0.55, B. licheniformis T380B 0.48, B. subtilis natto 0.54, B. pumilus 0.61 and B. amyloliquefaciens 0.36. For the B. subtilis spo0A mutant the CDW-to-OD correlation varied with the growth rate (data not shown), hence we determined the correlation factors for this mutant at dilution rates of 0.05, 0.1, 0.2 and 0.4 h-1 from mini-scale cultivations. These values were confirmed in a 1 l working volume stirred tank reactor (data not shown). The resulting correlation factors for the spo0A mutant were 0.39 gCDW/OD for D of 0.05 h-1, 0.51 gCDW/OD for D of 0.2 to 0.3 h-1 and 0.44 gCDW/OD for D of 0.4 to 0.5 h-1.To calculate specific biomass yields, maintenance coefficients, consumption and production rates, a correlation factor for cellular dry weight (CDW) to OD13C-labeled chemostat cultures was harvested after stable OD600 for at least 2 volume changes . For shake flask experiments, cells were harvested during mid-exponential growth at an OD600 of 1–1.5. Cell pellets were hydrolyzed in 6 M HCl at 105°C in sealed microtubes overnight. Hydrolyzates were then dried under a constant air stream at 60°C. Derivatization was carried out at 85°C in 30 μl dimethylformamide  and 30 μl N-(tert-butyldimethylsilyl)-N-methyl-trifluoroacetamide with 1% (v/v) tert-butyldimethylchlorosilane  for 60 min. Derivatized amino acids were analyzed on a series 8000 GC, combined with an MD 800 mass spectrometer . The GC-MS-derived mass isotope distributions were then analyzed using the software FiatFlux [13C-labeling patterns of their related precursor molecules in central metabolism were inferred. A set of probabilistic equations and the mass distributions of selected amino acid fragments were then combined to calculate the relative contribution of converging fluxes to a given metabolite pool [Samples for gas chromatography-mass spectrometry (GC-MS) analysis were prepared as described previously ,21. BrieFiatFlux . Briefly2, O2, and the cofactors NADH and NADPH. Precursor requirements for biomass formation were taken from [Intracellular fluxes were estimated by fitting a flux distribution to the above flux ratios and quantitative physiological data within a stoichiometric model described by  using thken from . To solvken from . The comB. subtilis 168 and four closely related bacilli as cell factories for biochemicals production, we determined their physiological parameters and fluxes during exponential growth in glucose batch cultures. The subspecies B. subtilis natto is phylogenetically the closest relative to B. subtilis, followed by B. amyloliquefaciens and B. licheniformis, while B. pumilus is somewhat more distal [A desirable physiological characteristic of cell factories is rapid and fully respiratory growth. For riboflavin production, in particular, an active PP pathway is expected to be a second relevant criterion because the product is primarily synthesized from pentose units. To investigate the potential suitability of e distal .13C-flux data obtained for B. subtilis 168 compare favorably with previously reported data from other B. subtilis strains [B. subtilis network, with the exception of an absent 6-phosphogluconate dehydrogenase in B. pumilus . In addition to the B. subtilis network, the B. licheniformis genome contains the two glyoxylate shunt-encoding genes, which enables B. licheniformis to grow on two-carbon units such as acetate or 2,3-butanediol [2 [None of the investigated bacilli contain genes encoding enzymes of the Entner-Doudoroff pathway and absence of this pathway was also confirmed from the obtained tanediol . For theediol [2 . This reediol [2 .B. subtilis natto was similar to B. subtilis, with the obvious difference of elevated acetate secretion and a concomitantly lower TCA cycle flux. B. amyloliquefaciens and B. pumilus both grew rather slowly and were the most distinct from the others. B. amyloliquefaciens has the lowest biomass yield and low relative flux through the PP pathway. Moreover, its overflow metabolism is entirely different, secreting massive amounts of pyruvate instead of acetate. B. pumilus exhibited mostly respiratory metabolism with high relative TCA cycle fluxes. As a peculiarity, it has unusually high fluxes through the pyruvate shunt from malate to pyruvate and on to oxaloacetate that are indirectly inferred from the 13C data.During unlimited growth on glucose, intracellular fluxes Figure  and physB. licheniformis strains are derivatives of the parent strain T5, a producer of thermostable alpha-amylase [B. licheniformis strains have about half of the TCA cycle flux of B. subtilis.The two -amylase ,32. SpecB. subtilis wild type 168, we analyzed also the otherwise isogenic B. subtilis 168 mutants spo0A and sigE. In particular, we were interested whether the frequently used spo0A mutation has unfavorable effects on metabolism through one of its many pleiotrophic effects rather than through the intended block of sporulation. As control for block of sporulation we used the sigE mutant that cannot enter stage III of sporulation [B. subtilis wild type and spo0A compared to earlier work [sigE mutation is phenotypically silent under this condition, the spo0A mutant has a significantly increased glucose consumption and acetate formation rate  no overflow metabolism, ii) about doubled relative TCA cycle flux that remained stable over different dilution rates, and iii) significant gluconeogenic flux through the PEP carboxykinase at the lowest dilution rate -1 h-1 of the related B. subtilis wild type 1012 [spo0A mutant, but not the sigE mutant, exhibited an increased maintenance coefficient of 0.49 mmol g(cdw)-1 h-1, which is qualitatively consistent with the previously reported high maintenance energy coefficient of riboflavin-producing spo0A mutants of B. subtilis [spo0A mutation has a stimulating and biotechnologically undesirable effect on maintenance metabolism.For these bacilli with linear dependencies of the specific glucose consumption rate with dilution rate, the maintenance energy coefficients were determined as the intercept of the weighted least square regression line with the y-axis Table . The detype 1012 . The sposubtilis . TogetheB. licheniformis strains and B. amyloliquefaciens exhibited lower maintenance energy coefficients than B. subtilis wild type  general growth physiology, ii) maintenance metabolism, and iii) a favorable distribution of intracellular fluxes for production of the vitamin riboflavin, including catabolic overproduction of NADPH.B. subtilis is not a preferred cell factory for riboflavin production because it had the highest maintenance energy requirements of all bacilli investigated here. The maintenance advantage of most other bacilli, however, was rather small and was counterbalanced by several other disadvantages such as slow growth and/or metabolism that render them even less favorable to production than B. subtilis. The by far best choice would be B. licheniformis T380B, which had only about half of the maintenance energy coefficient of B. subtilis wild type. Since this strain grows also robustly in different cultivation devices and shows only moderate acetate overflow, it is a promising cell factory host for biochemicals production in general, and riboflavin production in particular.From a bioenergetic point of view, -1 h-1). Reduction of the maintenance coefficient by 50% through a host switch, for example to B. licheniformis T380B, seems possible as demonstrated here. Provided a similar riboflavin formation is engineered, this could reduce the glucose consumption in the overall riboflavin fed-batch process by 15–20%. Even at otherwise unaltered riboflavin titers, this constitutes a major yield improvement with high commercial relevance for a low-cost, feed chemical like riboflavin because substrate costs are the key driver in this process.Just how important are these differences in maintenance metabolism on a process scale? In fermentation for penicillin production, for example, up to 70% of the carbon source is utilized for maintenance . SimilarB. subtilis wild-type (0.44 mmol g(cdw)-1 h-1), its riboflavin producing counterpart exhibited a 50% higher coefficient of 0.66 mmol g(cdw)-1 h-1 [i.e. it contained only a single copy of the modified rib operon, the deregulating ribC mutation, and the spo0A mutation. A key finding in the present work is that the frequently used spo0A mutation [rib operon in the genome as well as further uncharacterized mutations had the virtually identical maintenance coefficient of 0.67 mmol g(cdw)-1 h-1 as the above subtly engineered strain [sigE mutation, in contrast, also prevents sporulation but had no detectable physiological phenotype and a wild-type-like maintenance coefficient. It thus appears to be the preferred choice for B. subtilis, and this seemingly simple change in sporulation mutations has the potential to significantly decrease glucose consumption in the above mentioned riboflavin fed-batch process [spo0A mutation alone increased the maintenance coefficient of the wild-type by 20%.Obviously, changing production hosts is a major undertaking that must be justified by the potential gains. Even within one strain line, however, maintenance metabolism can become a key process factor. Firstly, maintenance metabolism is not a constant but can be affected by various genetic manipulations. While a similar maintenance energy coefficient as reported here was described in a different w)-1 h-1 . This isd strain ,36. FurtB. subtilis are consistent with previous reports on particular growth conditions in disparate studies [B. subtilis exhibited rather high respiratory TCA cycle fluxes under all conditions. At a dilution rate of D 0.2 h-1, the absolute TCA flux of B. subtilis was 2.9 ± 0.45 mmol g-1 h-1 while the maximum values of other bacilli were in the range from 1.3 ± 0.26 to 1.9 ± 0.29 mmol g-1 h-1 and thus significantly lower. As a result of their comparatively low TCA cycle fluxes, the other bacilli produced much less NADPH in the isocitrate dehydrogenase reaction. Consequently catabolic NADPH formation was either balanced or even insufficient to match the anabolic demand in all non-B. subtilis species. Although not a rigorous criterion like maintenance metabolism, the tendency towards catabolic overproduction of NADPH renders B. subtilis more attractive for riboflavin production than the other species.The intracellular fluxes reported here for  studies ,35,38-40B. subtilis thus requires a transhydrogenase-like biochemical mechanism to counteract catabolic NADPH overproduction, at least in chemostat culture. Most of the other bacilli, in contrast, require the opposite mechanism, i.e. an additional supply of NADPH. How this may be achieved remains elusive at this point because these species contain no homologue of the NADPH-producing transhydrogenase PntAB of E. coli [To balance NADPH metabolism,  E. coli .SD carried out most of the experimental work, ST participated in experimental work, carried out the data analysis and drafted the manuscript, US designed and coordinated the study and contributed to the writing of the manuscript. All authors read and approved the final manuscript.B. subtilis, B. licheniformis, B. amyloliquefaciens and B. pumilus based on the KEGG databaseNetwork comparison of Click here for fileflux ratios of glucose batch experimentsClick here for fileflux ratios from chemostat experimentsClick here for fileB. subtilis wild type, spo0A and sigE mutants during batch cultivationAbsolute net and relative fluxes of Click here for fileB. subtilis at different dilution rates.Absolute net and relative fluxes of Click here for fileAbsolute net and relative fluxes of different bacilli during batch cultivationClick here for file-1Absolute net and relative fluxes of different bacilli at dilution rate D 0.2 hClick here for file


Geobacter sulfurreducens is a member of the Geobacter species, which are capable of oxidation of organic waste coupled to the reduction of heavy metals and electrode with applications in bioremediation and bioenergy generation. While the metabolism of this organism has been studied through the development of a stoichiometry based genome-scale metabolic model, the associated regulatory network has not yet been well studied. In this manuscript, we report on the implementation of a thermodynamics based metabolic flux model for Geobacter sulfurreducens. We use this updated model to identify reactions that are subject to regulatory control in the metabolic network of G. sulfurreducens using thermodynamic variability analysis.E. coli by evaluating the expression ranges of the corresponding genes. We then identified ten reactions in the metabolic network of G. sulfurreducens that are predicted to be candidates for regulation. We then compared the free energy ranges for these reactions with the corresponding gene expression fold changes under conditions of different environmental and genetic perturbations and show that the model predictions of regulation are consistent with data. In addition, we also identify reactions that operate close to equilibrium and show that the experimentally determined exchange coefficient (a measure of reversibility) is significant for these reactions.As a first step, we have validated the regulatory sites and bottleneck reactions predicted by the thermodynamic flux analysis in rG' of every reaction in the model leading to the prediction of regulatory sites in the metabolic network, thereby characterizing the regulatory network in both a model organism such as E. coli as well as a non model organism such as G. sulfurreducens.Application of the thermodynamic constraints resulted in identification of potential bottleneck reactions not only from the central metabolism but also from the nucleotide and amino acid subsystems, thereby showing the highly coupled nature of the thermodynamic constraints. In addition, thermodynamic variability analysis serves as a valuable tool in estimating the ranges of Δ Geobacter sulfurreducens is a well studied representative of the Geobacteraceae family of microorganisms. The Geobacter species are of interest due to their role in carbon and mineral recycling and their capacity to harvest electricity from organic wastes. Furthermore, previous studies in a uranium contaminated aquifer in Rifle, CO have shown a significant decrease in the levels of hexavalent U(VI) when acetate was injected in the subsurface. The injection of acetate was followed by a bloom in the population of Geobacter species in the subsurface. G. sulfurreducens is the most commonly studied species as its complete genome has been sequenced and protocols for whole genome microarrays and proteomics are available. Furthermore, recent sequencing efforts have provided us with a wealth of information regarding the genetic content of various organisms [G. sulfurreducens, a genome scale in silico model was constructed utilizing the wealth of omics data [E. coli to both environmental and genetic perturbations. Hence, characterizing the reactions that are subject to regulation in the metabolic network of G. sulfurreducens is important for optimizing the practical applications of G. sulfurreducens. In this report, we first validate the utility of a thermodynamics based metabolic flux analysis model (TMFA) to predict regulatory sites by comparing model predictions with the corresponding expression data for E. coli. Subsequently, we present the implementation of a TMFA model for the genome-scale metabolic network of G. sulfurreducens in order to eliminate internal flux cycles and to generate thermodynamically feasible flux distributions. This approach takes into account both standard Gibbs free energy change as well as the concentration/activity dependent free energy change associated with the mixing term, thereby effectively coupling the metabolite concentrations and the Gibbs free energy change. Based on the standard free energy change of reactions and the defined molar concentration ranges, the ranges in free energy change of every reaction in the model was estimated by thermodynamic variability analysis (TVA) which involved maximizing and minimizing the standard free energy change of each reaction in the metabolic network of G. sulfurreducens at optimal growth conditions.rganisms . In ordeics data . This moics data . Furtherics data . PreviouThe mass balance constraint described by the following equation enforced by Flux balance analysis (FBA) forms the basis of the TMFA algorithm.G. sulfurreducens, this matrix has the dimension of 542 × 611. FBA has been used in the past to address the effect of gene knockouts, predicting the ability of a cell to produce a biochemical or to predict the maximum possible growth yield of a cell. Hence, coupling of thermodynamic constraints based on the second law of thermodynamics with the mass balance constraint of the FBA limits fluxes through futile cycles while still allowing fluxes through feasible pathways.In the above equation, S is the Stoichiometric matrix with dimensions (m × n) where m and n refer to the total number of metabolites and fluxes, respectively, in the metabolic network of an organism and c represents a biological objective function. In the case of E. coli metabolic network. Briefly, the TMFA algorithm complements the mass balance constraints of FBA with mixed integer type thermodynamic constraints and is reproduced below for the sake of continuity:We used the TMFA algorithm previously developed by Henry and co-workers  to predii is a binary decision variable that can either assume values 0 or 1 depending on the Gibbs free energy change of a particular reaction. In some cases, the free energy of formation of certain metabolites cannot be estimated by the group contribution method due to the unique molecular substructures associated with them. Such reactions  are typically lumped together so as to eliminate those metabolites whose standard free energy change is unknown. Here, yi represents whether the lumped reaction is active (0) or not (1) and αi, j represents whether the jth reaction participates in the ith lumped reaction and equals 1 when the jth reaction is a part of the lumped reaction. Consequently, the final two constraints in the equation indicate the thermodynamic feasibility constraint as applied to these lumped reactions.In the above formulation, ZImplementing the TMFA algorithm requires the knowledge of the standard free energy change of every reaction in the metabolic network to be either determined or estimated. Since experimental values are available for only a small number of reactions in the literature, the improved group contribution method  was usedG. sulfurreducens metabolism, simulations were done for growth on acetate as the donor and fumarate as the electron acceptor. For growth on acetate as the energy source and fumarate as the sole electron acceptor, the uptake rate for acetate was set to 5 mmol/gdw hr and the uptake rate of fumarate was set to 25 mmol/gdw hr, so that these uptake rates were consistent with experimentally determined values. For growth conditions involving acetate as the energy source and fumarate as the sole acceptor, the optimal growth rate was forced to be 0.055 1/hr [The free energy ranges of reactions were estimated using thermodynamic variability analysis , which iG. sulfurreducens, we investigated twenty one different microarray experiments - E. coli community gene expression database (GenExpDB), in which gene expression data for over 800 experiments were reported.The thermodynamic ranges calculated by TVA were used to identify reactions that were 1) bottleneck reactions which have free energy changes close to zero, and 2) candidates for regulation which have free energy change ranges that are either positive or negative and do not span zero (equilibrium condition). The thermodynamic bottleneck reactions were identified by utilizing the free energy change values reported previously for near equilibrium systems as the basis . In orderG' is strictly negative were classified as candidates for regulation as these reactions cannot reach equilibrium under the activity ranges studied. Thus, based on the above criteria, we used the TVA analysis to predict both bottleneck reactions and candidates for regulation based on the observation that reactions that are irreversible under most conditions are likely to be regulated [The magnitude of free energy change of a biochemical reaction dictates if the reaction is subject to regulation. Typically, biochemical reactions that operate close to equilibrium are not transcriptionally regulated, while reactions that operate far from equilibrium are insensitive to perturbations in the metabolite concentrations and can therefore be subject to regulation as the flux through such reactions can be manipulated by enzyme regulation alone. In contrast, thermodynamic bottlenecks are reactions that operate close to equilibrium. Consequently, even minor perturbations in the concentration of the reactants or products can force the net flux through these reactions to zero, thereby blocking them or changing their directions. Reactions identified using TVA for which Δegulated ,7,8.E. coli [et al. further refined this method to quantitatively analyze metabolomic data sets in E. coli and S. cerevisiae [E. coli. However, we note that, in previous studies, the identified bottleneck reactions and regulatory sites were not compared against gene expression data and were not thus comprehensively validated. It is in this aspect that our present work differs significantly as we have analyzed microarray data from a central repository with over 800 experimental datasets.Beard, Qian and co-workers were pioneers in conducting energy balance analysis using non linear optimization to eliminate flux cycles generated by FBA in E. coli -11. Kummrevisiae , while Hrevisiae  extendedE. coli, we identified the range of gene expression for those reactions that were previously predicted [E. coli  with these reactions. Cficients . Also, tG. sulfurreducens compared to E. coli, we implemented the TMFA for the genome-scale metabolic network of G. sulfurreducens. We subsequently used the TVA to calculate the range of Gibbs free energy change for each reaction in the network resulting in the identification of thermodynamic bottlenecks and putative regulatory sites  which was identified to be thermodynamically constrained in E. coli is similarly identified as a constrained reaction in the G. sulfurreducens. In addition, Cystathionine b-lyase (CYSTL), 4-aminobenzoate synthase (ADCL), acetolactate synthase (ACLS), dihydrodipicolinate synthase (DHDPS), citrate synthase (CS), malic enzyme (ME1x) were predicted to be regulated in E. coli and these reactions were identified by TVA to be subject to regulation in G. sulfurreducens as well. Of these reactions, CYSTL, DHDPS and ACLS form a linear pathway in both G. sulfurreducens and E. coli.There were several common reactions among the predicted regulatory and bottleneck reactions for both G. sulfurreducens . The rate of transfer of electrons to the electrode is directly linked to the metabolic rate. However, the rate of electron transfer is very slow in existing MFCs and it is possible that some of the limitations may be attributed to thermodynamic bottlenecks existing in the metabolic network of G. sulfurreducens. Thus, thermodynamics based modeling to determine potential bottlenecks will be valuable to prioritize candidates for metabolic engineering in order to increase flux through these target pathways. Hence, we anticipate that the modeling results will be ultimately beneficial to the improved characterization of the metabolism of G. sulfurreducens and optimization of power production in a MFC. Finally, in order to further constrain the metabolite activities and the ΔrG' based on thermodynamic feasibility constraints in the TMFA model, future efforts should focus on including experimentally measured large-scale metabolite concentrations coupled with kinetic constraints [This study shows the importance of applying thermodynamic constraints to a genome-scale metabolic model for a rigorous study of the entire metabolic network of straints ,16 to imThe authors declare that they have no competing interests.SG and RM conceived and designed the experiments. SG performed the experiments and generated the results. LY provided valuable insights and suggestions during execution of the experiments and helped in coding a part of the TMFA algorithm. SG and RM analyzed the data and wrote the manuscript. All authors read and approved the final manuscript.G. sulfurreducens and their associated standard free energy change Supplemental table S1: List of reactions, metabolites in the metabolic network of . The list of standard free energy change associated with the metabolic reactions in G. sulfurreducens as determined by the group contribution method.Click here for fileSupplemental table S2: List of previously published microarray experiments utilized in this study. A list of microarray experiments used in this study to investigate the change in the level of gene expression under varying environmental and genetic conditions for the model predicted reactions subject to regulatory control and thermodynamic bottlenecks. Supplemental table S3. Predicted regulatory sites and their corresponding ranges of gene expression in E. coli. A list of model predicted (gene-associated and a non gene associated) reactions that are subject to regulatory control in the metabolic network of E. coli and the expression ranges of the corresponding genes. Supplemental table S4. Bottleneck reactions and range of expression fold changes for the corresponding genes in E. coli. A list of model predicted (gene-associated and a non gene associated) bottleneck reactions in the metabolic network of E. coli and the expression ranges of the corresponding genes. Supplemental table S5. List of predicted strong candidate reactions subject to regulation, their associated genes and the minimum and maximum model predicted free energy changes  in G. sulfurreducens. A list of model predicted (gene-associated and a non gene associated) reactions that are subject to regulatory control in the metabolic network of G. sulfurreducens and the lower and upper bounds of their Gibbs free energy change. Supplemental table S6. List of reactions at the threshold of being subject to regulatory control and their associated free energy change values  in G. sulfurreducens. List of reactions in the G. sulfurreducens metabolic network whose upper bound on the Gibbs free energy change is in the vicinity of equilibrium and whose lower bound is far from equilibrium. Supplemental table S7. List of model predicted bottleneck reactions, their associated genes and the ranges in free energy under optimal growth using acetate as the electron donor and fumarate as the electron acceptor, in G. sulfurreducens. Analysis assuming no uncertainty in standard free energy change. Model predicted thermodynamically constrained (thermodynamic bottleneck) reactions that operate in the vicinity of equilibrium in the metabolic network of G. sulfurreducens and the associated lower and upper bounds of the Gibbs free energy change.Click here for fileG. sulfurreducens and their corresponding gene expression rangesSupplemental table S8: List of model predicted reactions in . Gene expression ranges under various environmental and genetic perturbations filtered below a co-variance threshold for reactions subject to regulatory control and bottleneck reactions in G. sulfurreducens.Click here for file