S. cerevisiae we developed a new computational approach where we characterize the flux solution space by determining elementary flux modes (EFMs) that are subsequently classified as thermodynamically feasible or infeasible on the basis of experimental metabolome data. This allows us to provably rule out the contribution of certain EFMs to the in vivo flux distribution. From the 71 million EFMs in a medium size metabolic network of S. cerevisiae, we classified 54% as thermodynamically feasible. By comparing the thermodynamically feasible and infeasible EFMs, we could identify reaction combinations that span the cytosol and mitochondrion and, as a system, cannot operate under the investigated glucose batch conditions. Besides conclusions on single reactions, we found that thermodynamic constraints prevent the import of redox cofactor equivalents into the mitochondrion due to limits on compartmental cofactor concentrations. Our novel approach of incorporating quantitative metabolite concentrations into the analysis of the space of all stoichiometrically feasible flux distributions allows generating new insights into the system-level operation of the intracellular fluxes without making assumptions on metabolic objectives of the cell.One of the most obvious phenotypes of a cell is its metabolic activity, which is defined by the fluxes in the metabolic network. Although experimental methods to determine intracellular fluxes are well established, only a limited number of fluxes can be resolved. Especially in eukaryotes such as yeast, compartmentalization and the existence of many parallel routes render exact flux analysis impossible using current methods. To gain more insight into the metabolic operation of  Saccharomyces cerevisiae using experimentally determined metabolite concentrations. We show that next to conclusions on single biochemical reactions in the metabolic network, we obtain system-level insights on thermodynamically infeasible flux patterns. We found that the compartmental concentrations of Fluxes in metabolic pathways are a highly informative aspect of an organism's phenotype. The experimental determination of such fluxes is well established and has proven very useful. To address some of the limitations of experimental flux analysis, such as when the cell is divided in multiple compartments, stoichiometric modeling provides a valuable addition. The approach that we take is based on stoichiometric modeling where we consider the thermodynamic feasibility of many different possible routes through the metabolic network of  Metabolic fluxes give immediate insights into the metabolism of a cell Despite successful quantification of fluxes with E. coliTo improve flux quantification, we need additional constraints on the possible flux distributions in a metabolic network. In stoichiometric network analysis, the metabolic network is modeled as a collection of biochemical reactions where all internal metabolite concentrations are assumed constant Saccharomyces cerevisiae. The method combines network embedded thermodynamic (NET) analysis Here, we develop a novel approach to integrate metabolite data into metabolic network flux analysis, to get additional insight into the compartmentalized flux physiology of S. cerevisiae using quantitative metabolite data acquired under glucose batch growth conditions allowed us to generate novel insight into the system-level causalities underlying the intercompartmental redox metabolism. Specifically we show that the Our new approach to analyze the compartmentalized central metabolic network of Our approach uses elementary flux modes (EFMs) to describe metabolic flux distributions. The concept of EFMs is well known for stoichiometric network analysis, and it provides a way to explore the flux solution space of a metabolic network that is commonly addressed with flux balance analysis (FBA). With all the EFMs of a metabolic network, any stoichiometrically possible flux distribution can be obtained by a non-negative linear combination of the EFMs The mass balanced flux solution space of a stoichiometric metabolic network can be described with a non-negative linear combination of its EFMs:As we show in the proof provided in The mathematical proof demonstrates that by eliminating infeasible EFMs we do not loose feasible flux distributions, because any feasible flux distribution can be composed of only feasible EFMs. Specifically, in the hypothetical case that an infeasible EFM is part of a feasible flux distribution it must involve a cancellation or directionality change of a specific reversible reaction. In this case, the flux distribution can be decomposed into one or more feasible EFMs, and a feasible combination of an infeasible EFM with another EFM. The combination of the infeasible EFM and another EFM must then be either a feasible EFM by itself, or it must be possible to achieve the feasible combination by other feasible EFMs. Hence, Eq. (2) allows us to exclude thermodynamically infeasible EFMs from the complete set of EFMs without excluding thermodynamically feasible flux distributions. It is important to note that the resulting flux solution space defined by the feasible EFMs can still contain infeasible flux solutions because it is possible that the combination of multiple feasible EFMs leads to an infeasible flux distribution.S. cerevisiae. Therefore, we assembled a 230 reaction stoichiometric network of its central carbon metabolism and amino acid synthesis pathways  EFMs as feasible and 32.846.753 (46%) EFMs as infeasible. Assuming that the flux solution space in the metabolic network of an organism can be approximated by the number of EFMs, our result shows that roughly at most half of the solution space is thermodynamically feasible.With the finding that 54% of all the EFMs are thermodynamically feasible we reduced the number of EFMs that can constitute a thermodynamically feasible flux distribution considerably. In a first analysis step towards generating insights into the flux distribution, we searched the feasible EFM set for reactions that only use a subset of the possible reaction directions compared to the complete set of EFMs. For each reaction in each EFM we determined whether a backward, inactive or forward reaction activity was used. Then, the possible reaction activities for each reaction were compared between the complete set of EFMs and the feasible set of EFMs.into the mitochondrion. It is important to note that the EFMs with active oxaloacetate transport, or acetaldehyde transport out of the mitochondrion, are not infeasible because of the metabolite concentration constraints on the respective single reaction only, since single reaction infeasibilities are removed in the first NET analysis step before EFM generation  see . The inaThe identified patterns can also be used as possible constraints in FBA approaches. To confirm whether using such a constraint would be of value, we checked for the presence of the patterns in FBA predictions. Interestingly, it turns out that the identified infeasible flux patterns do not occur in FBA-predicted flux distributions using the objective function “maximization of the ratio of ATP production over the sum of squared fluxes” that was identified to be suitable for glucose batch conditions Strikingly, all the patterns in To address this question, we analyzed the individual metabolite concentration constraints by releasing one concentration constraint at a time. We found that the concentrations of de novo synthesized To find out how the applied thermodynamic constraints affect redox metabolism we analyzed all reactions involved in NADH redox metabolism in more detail. Yeast has multiple ways to oxidize NADH and to pass the electrons to the electron transport chain, both in the cytosol and the mitochondrion Thus, we determined the rates of the various cytosolic NADH oxidation mechanisms in every EFM and asked whether there is a difference between feasible and infeasible EFMs. S. cerevisiae and cell-averaged metabolome data generated under glucose batch conditions, 46% of the 71.3 million EFMs were found thermodynamically infeasible, leading to direct insights into reaction directionalities, to constraints in several metabolic rates, and to the identification of reaction patterns that must be inactive due to a thermodynamic infeasibility.In this work, we demonstrated that an in vivo thermodynamically feasible metabolic flux distribution is only composed of thermodynamically feasible elementary flux modes. This EFM property allowed us to integrate EFMs and NET analysis into a novel approach to study the system-level properties of complex metabolic networks on the basis of quantitative metabolome data. As exemplified with a compartmentalized model of central metabolism in S. cerevisiae to analyze cell-averaged metabolite data. Notably, the results we obtain are directly related to compartmentation, as can be seen from the identified infeasibility patterns that involve both compartments. The identified infeasibility of the ethanol-acetaldehyde redox shuttle has been previously identified using NET analysis This work builds on earlier work that integrated FBA, thermodynamics and quantitative metabolite data With the recently developed new group contribution method to estimate thermodynamic properties on a genome-scale Saccharomyces cerevisiae obtained from four independent experiments with at least two replicates S. cerevisiae on glucose as carbon source from one of the four experiments. In We used experimental data on metabolite concentrations for S. cerevisiae in the cytosol and mitochondrion with 230 reactions and 218 metabolites  were added to properly connect additionally included alternative pathways for amino acid synthesis to the metabolic network.The biomass composition was adopted from iND750 besides that trehalose and glycogen were discarded since carbohydrate storage was not considered in our model. Lumped reactions for synthesis of the remaining biomass constituents, i.e. lipids, nucleotides, and cell wall components from the corresponding precursors were determined based on the biomass composition as provided in iND750. In the model, carbon molecules that can be exchanged with the environment are glucose, glycerol, pyruvate, acetate, ethanol, succinate, and The model is not proton balanced. The reason for this is that it is close to impossible to do the proton balancing correctly . Thus, we did not want to add any potentially wrong constraints on the model and therefore did not account for proton balancing, with one exception. We only balanced the protons around the respiratory chain by replacing the cytosolic protons produced and consumed in the reactions CYOR_u6m, CYOOm and ATPS3m by a unique species “hcyt”. As a result, ATP generated in ATPS3m can only occur through the respiratory chain.With NET analysis we can determine the feasibility of a flux distribution based on ranges for the concentrations of the involved metabolites. A flux distribution is thermodynamically infeasible when one or more reaction activities conflict with the calculated Gibbs energy of reaction range(s). Conversely, a flux distribution is feasible when no conflicts are found. It is important to note that a metabolite concentration is constrained in NET analysis by any reaction that has the metabolite as a reactant. Therefore, a flux distribution can be infeasible due to propagated constraints in a pathway. The NET analysis implementation constrains metabolite concentrations of metabolites that occur in multiple compartments as a sum of the compartment specific concentrations, corrected for their volume. Therefore, the compartmental distribution of such metabolites is left free. The compartmental volume fractions of the cytosol and mitochondrion are set to 0.35 and 0.1, respectively. The NET analysis approach is similar to other thermodynamic analysis approaches For NET analysis we used the concentration ranges defined from the experimental data. For all other metabolites in the network we assumed a default range with a minimum concentration of 0.0001 mM and a maximum of 120 mM, except for carbon dioxide (“co2tot”), phosphate (“pi”) and diphosphate (“ppi”) that were constrained to a range of 1 mM to 100 mM Computations for FVA, NET analysis and EFM generation were done using MATLAB (The Mathworks). For optimization of FVA problems we used the LINDO API library (LINDO Systems Inc.) and for NET analysis we used anNET To find the reaction activity patterns that cause infeasibility, we considered each infeasible EFM separately and performed an iterative analysis. In NET analysis of the EFM, we removed consecutively each reaction's activity constraint from the NET analysis optimization and determined the feasibility. If the activity pattern became thermodynamically feasible, the reaction activity that was removed was identified as part of the pattern. Then we continued with removing the next reaction activity, while keeping the activities that were identified as part of the pattern. We continued this process for all the reaction activities. This process led to a set of reaction activities, which is a subset of the activities in the analyzed EFM, of which the removal of one activity leads to a feasible system. The set of reaction activities is only infeasible as a whole system, and no single reaction can be marked infeasible by itself. All possible infeasible reaction patterns may not be found when multiple patterns are present in an EFM, because the order of reaction activity removal determines which pattern is found. The obtained infeasible patterns cover all the infeasible EFMs.Flux balance analysis with maximization of biomass production, maximization of ATP production and maximization of the ratio of ATP production over the sum of squared fluxes was performed according to Schütz et al. Dataset S1Overview showing for each model reaction at which stage during the analysis a directionality constraint was added.(PDF)Click here for additional data file.Dataset S2Details regarding the experimental data used in this study.(PDF)Click here for additional data file.Dataset S3The used metabolic network model in SBML format.(XML)Click here for additional data file.Text S1Mathematic proof showing that a flux distribution containing an infeasible EFM is always infeasible.(PDF)Click here for additional data file.Text S2Additional results regarding flux balance analysis showing the obtained patterns correspond with known operating principles.(PDF)Click here for additional data file.


Constraint-based models of metabolism have been used in a variety of studies on drug discovery, metabolic engineering, evolution, and multi-species interactions. These genome-scale models can be generated for any sequenced organism since their main parameters  are highly conserved. Their relatively low parameter requirement makes these models easy to develop; however, these models often result in a solution space with multiple possible flux distributions, making it difficult to determine the precise flux state in the cell. Recent research efforts in this modeling field have investigated how additional experimental data, including gene expression, protein expression, metabolite concentrations, and kinetic parameters, can be used to reduce the solution space. This mini-review provides a summary of the data-driven computational approaches that are available for reducing the solution space and thereby improve predictions of intracellular fluxes by constraint-based models. PLOS Computational Biology.This is an “Editors' Outlook” article for Genome-scale constraint-based metabolic models can be used to predict or describe cellular behaviors, such as growth rates, uptake/secretion rates, and intracellular fluxes. These models have been used for a variety of applications, involving studies on drug discovery The variables used in constraint-based models include the fluxes through transport and metabolic reactions, and model parameters include reaction stoichiometry, biomass composition, ATP requirements, and the upper and lower bounds for individual fluxes. A common misconception is that these metabolic models rely on detailed kinetic parameters; however, such kinetic parameters are not required and are generally absent from most constraint-based models. Because there are often more variables  than equations, no unique solution exists. The large number of solutions that satisfy the model's constraints define the model's solution space, which can be queried using a number of approaches Given the non-uniqueness of constraint-based model solutions, a growing number of methods have focused recently on incorporating additional constraints to reduce the solution space and thereby improve the precision and accuracy of model predictions. This editorial reviews recent methods that utilize additional biological information  to further restrict metabolic fluxes, many of which are available in a variety of software packages All constraint-based models use two types of fundamental constraints. Steady-state mass-balance constraints ensure that for each metabolite in the network the net production rate equals the net consumption rate. Additional inequality constraints are used to place restrictions or bounds on the values of individual fluxes based on measured rates  or reaction reversibility, where irreversible fluxes have a zero lower bound. Most models to date base reversibility on biochemical characterization of enzymes or consideration of network properties . In standard models, none of these constraints limit metabolic fluxes based on metabolite, mRNA, or protein concentrations; however, a variety of additional constraints can be included based on thermodynamic, molecular crowding, gene expression, and regulatory and kinetic considerations.O). If a reaction is to proceed, the change in Gibbs free energy for a reaction must be negative. A few different approaches for incorporating these types of thermodynamic-based directionality constraints have been proposed. One of the first methods, network-embedded thermodynamic (NET) analysis, uses the directionality of reactions  to calculate ΔG or metabolite concentration ranges Thermodynamic constraints are used to place restrictions on the directionality of reactions by considering metabolite concentrations and Gibbs energies of formation. From thermodynamics, the change in Gibbs free energy for a reaction (ΔG) depends on the temperature, concentrations of substrates and products, and change in Gibbs free energies in a reference state (ΔGj) depend on an enzyme's volume and activity . Molecular crowding constraints have been used to predict cellular growth rates and acetate production in Escherichia coliE. coli under glucose aerobic conditions.Recent efforts have used spatial constraints to place upper limits on a sum of fluxes, rather than individual fluxes. Molecular crowding constraints were first proposed by Beg et al. to restrict the total amount of enzyme that could be packed into a cell E. coliShewanella oneidensis but whose genes were expressed identified pathways that were reducing biomass yields Gene expression is one of the most widely accessible measurements that can provide a global snapshot of a cell's metabolic state. A number of studies have compared constraint-based model flux predictions to expression data, to find consistencies and inconsistencies e.g., . For exaMost current methods for incorporating gene expression data into the models compare gene expression levels in a single condition and disfavor fluxes through reactions that are associated with lowly expressed genes. The E-flux method uses gene expression values to set upper limits on metabolic fluxes, where reactions associated with more highly expressed genes will be allowed to take on higher flux values All of these previous methods typically use expression data from a single condition to constrain fluxes. A more recent approach (MADE) uses expression data from multiple conditions (or a time-series) to identify patterns of increased/decreased expression based on significant changes in expression across conditions The methods described above for using gene expression-based constraints require expression data under the condition(s) of interest. In other words, to predict flux in a particular condition the methods would need gene expression data from that condition. Other methods can instead use models of transcriptional regulatory networks to predict the effects of transcriptional regulation on metabolic fluxes. In this case, integrated models of metabolism and regulation can predict metabolic fluxes under conditions  for which gene expression data are not available. Transcriptional regulatory networks can be reconstructed from high-throughput data, such as gene expression, ChIP-chip, and genome sequencing datasets using a variety of approaches  models can then use bioreactor concentrations to constrain metabolic fluxes, which in turn affect the bioreactor concentrations. Feng et al. recently included rate laws for the uptake and secretion of organic acids into a genome-scale model for E. coli and Saccharomyces cerevisiae, since the kinetic properties of their enzymes have been biochemically characterized. Databases, such as BRENDA E. coli model, the authors were able to improve flux predictions in 23 gene deletion strains Traditional kinetic models already take into account the kinetic relationships between metabolic fluxes, metabolite, and protein concentrations. However, such detailed models are often available for only a few pathways in well-characterized organisms, such as As we continue to be able to measure intracellular levels of biological components with greater accuracy and precision, the need for computational approaches to integrate and analyze such large-scale datasets grows. As reviewed above, a variety of constraint-based approaches are available that use these types of datasets to reduce the solution space and improve model predictions of metabolic phenotypes. Over the coming years, more computational approaches for integrating individual and multiple types of experimental measurements will likely appear, as new biological measurement approaches are developed and more data becomes available. For example, we are likely to see integration of datasets into models of microbial communities, as multi-species models Jennifer L. Reed is an Assistant Professor in the Department of Chemical and Biological Engineering at the University of Wisconsin–Madison and a Project Lead at the Great Lakes Bioenergy Research Center. She is a recipient of an NSF CAREER award and a DOE Early Career award. She has been an Associate Editor for PLOS Computational Biology since 2010. She received her BS degree in Bioengineering: Biotechnology in 2000 and her PhD in Bioengineering in 2005 from the University of California, San Diego. She spent two years after her PhD as a Faculty Fellow at the University of California, San Diego, where she taught classes and conducted research in the Bioengineering Department. Her research interests are in the development and analysis of microbial metabolic and regulatory models for bioenergy, biotechnology, and health applications. Her research has included the development of genome-scale models for a variety of different microbes, including Escherichia coli, Salmonella, Shewanella, and Cyanobacteria species, as well as computational tools for metabolic engineering, model comparison, model refinement, and experimental design.


Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network.The E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions.A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.Numerous improvements to the  Escherichia coli there are still many genes with unknown functions  (bis-molybdopterin guanine dinucleotide), a component of the core biomass formulation. Because these gene knockout strains are experimentally viable on most conditions, it is likely that this cofactor is not essential for growth, and thus should not be included in the iJO1366 core biomass reaction.All genes with false negative predictions for at least one substrate and no computationally predicted growth on any substrate were investigated in more detail. If a constraint-based metabolic model fails to predict growth under a condition where growth was observed experimentally, it is an indication of missing metabolic reactions or pathways in the model. In the next section, use of the SMILEY algorithm to predict likely missing reactions is presented. There are several other possible explanations for false negative predictions. First, it is possible that the model biomass reaction being used as an objective is incorrect Table . SeveraliJO1366 (Table hisH (b2023) is required for the reaction IG3PS (Imidazole-glycerol-3-phosphate synthase), along with hisF (b2025). This reaction is an essential part of the histidine synthesis pathway, and is thus essential on all minimal media for the model. In the in vivo datasets, however, hisH is not essential under any aerobic conditions. It is not essential because without HisH, HisF is still able to catalyze this reaction, using NH3 instead of glutamine as an N donor  cluster synthesis. In the model, this gene is an essential component of two reactions in both the ISC and SUF [Fe-S] cluster synthesis pathways, and is essential under all conditions. This gene is still not well characterized, and since it is experimentally non-essential, it is likely not strictly required for the reactions I2FE2SSI2FE2SS2S2FE2SS, and 2FE2SS2. Other false positive cases are likely due to experimental errors , which currently in the model is associated with MCITS (2-methylcitrate synthase), has been confirmed to also be an isozyme of gltA (b0720), catalyzing CS (citrate synthase)  cluster generation system and in the model is essential due to its role in molybdenum cofactor synthesis. Each of these alternate solutions involves the import of this cofactor. The average number of optimal solutions found per SMILEY run was 2.56. Most optimal solutions included only one reaction, and none included more than five  were found that eliminate false negatives while producing no new false positives. GapFind was also run on the model with each solution added, to determine if any model gaps were eliminated. 74 solutions that fill at least one gap were found.As the molybdenum cofactor uptake reactions demonstrate, not all SMILEY solutions are realistic. A computational feasibility check was performed to identify the most realistic solutions. Each of the solutions was added to the E. coli genome (Table aspC (b0928), pabC (b1096), aldA (b1415), argD (b3359), and hemE (b3397). Another solution suggested that false negatives for ΔaspC strains could be corrected by adding the existing model reaction ASP1DC (aspartate 1-decarboxylase) in reverse. No literature evidence was found to support or refute the reversibility of this reaction. The other five solutions involve the addition of new reactions to the model. Four of these provide potential production routes for aspartate to compliment an aspC deletion. The other provides a new reaction to consume glycoaldehyde for ΔaldA strains. None of these five reactions have associated genes in the KEGG database, indicating that they are global orphan reactions. Candidate genes for these reactions could not be identified with no reference sequences available.The most feasible SMILEY solutions out of the complete set of 1176 solutions were investigated in more detail. For the most feasible solutions, BLASTp was used to try to identify candidate genes in the me Table . These sE. coli. Several other solutions added deleted reactions back into the network, and two solutions added feasible new reactions. In one, a slightly different reaction for producing dTMP was added to compliment a thyA (b2827) deletion. A currently uncharacterized E. coli gene, ybiU (b0821), was identified by BLASTp as a candidate gene for this reaction, providing a testable hypothesis for the function of this gene. The other category II feasible solution added a new reaction to convert l-glutamate to α-ketoglutarate. Two candidate genes with high sequence homology to known genes from other organisms, ydbL (b0600) and ydcR (b1439), were found.The second category of SMILEY solutions to be investigated in detail was all optimal solutions that fixed more false negatives than the number of new false positives they created. There were 70 category II solutions . The average MCC for these solutions was 0.5531. Most of these solutions involve the uptake of molybdenum cofactors or their precursors. As explained above, the most likely explanation for these false negatives is that the molybdenum cofactor is not strictly required for growth by ASPT (l-aspartase) in reverse. Experimental evidence supports the reversibility of this reaction [iJO1366. The fourth and final category of SMILEY solutions to be examined was all other optimal solutions that were not in categories I and II. There were 62 solutions in this category and they had an average MCC of 0.5304, slightly worse than the unmodified iJO1366 model. Most of these solutions were simply new uptake reactions for blocked essential biomass components, but 14 new realistic reactions were suggested, as well as three current model reactions running in their opposite directions. One of these new reversible reactions, ICL (isocitrate lyase), was confirmed in a published study [AKGDH (2-Oxoglutarate dehydrogenase), is not actually reversible according to EcoCyc [The third category to be investigated consisted of the suboptimal solutions that fixed at least one false negative while producing no new false positives. A total of 133 category III solutions were found, having an average MCC of 0.5433. Some of these solutions included unrealistic reactions, such as the oxygen consuming KEGG reaction R00357 in the reverse, oxygen producing direction. Others attempt to compensate for the loss of cofactor producing pathways by simply adding new uptake reactions for those cofactors. Still, many realistic reactions were suggested and BLASTp identified candidate genes. One solution consisted of the addition of the current model reaction reaction , which ied study , while ao EcoCyc . See AddDKGLCNR1  [DKGLCNR2y ) [All 72 gap-filling SMILEY solutions were also investigated   and DKGL(NADPH)) .iJO1366 model was used to design simple growth phenotype experiments to confirm some of these predictions. Each of the 1176 solutions was added to the model one at a time, and growth was simulated on all combinations of a set of 115 carbon sources and 62 nitrogen sources under both aerobic and anaerobic conditions. These substrates were selected for being readily available chemicals for use in the laboratory. For every substrate combination on which growth is predicted for the model with a SMILEY solution added, but not for the unmodified iJO1366 model, an in vivo experiment can be performed to determine if E. coli can actually grow with those substrates, giving supporting experimental evidence to the predicted reactions. For most solutions, no new growth conditions were identified. All realistic solutions with testable experimental conditions are listed in Additional file SMILEY and other gap-filling algorithms are useful because they can use a model and existing experimental data to generate predictions. Without performing an experiment to verify these predictions, they are only hypotheses. The d-glucuronate and water. Myo-inositol is a root-no consumption gap in the iJO1366 model, and this reaction fills this gap. With this reaction included, E. coli is predicted to grow with myo-inositol as a substrate. An in vivo experiment was performed, and wild-type E. coli was inoculated into 2 g/L myo-inositol minimal media with no other carbon sources. Three replicates were performed, and after 72 h, the cultures had reached an OD600 of 0.017 ± 0.006. This result indicates that E. coli can grow very slowly with myo-inositol as its only carbon source. Next, four candidate genes were predicted for this reaction using BLASTp. These genes were ydeN (b1498), yfdE (b2371), yphC (b2545), and yhiJ (b3488). The functions of these genes are currently unknown, and they are non-essential. The Keio Collection knockout strains for these four genes were then obtained and grown in both glucose and myo-inositol minimal media. All four strains grew to a similar OD600 as wild-type E. coli on glucose, but on myo-inositol, the knockout strains did not grow as well . The associated reactions without isozymes  were constrained to carry zero flux. All other model reactions retained their default bounds [2+, Cl-, CO2, Co2+, Cu2+, Fe2+, Fe3+, H+, H2O, K+, Mg2+, Mn2+, MoO42-, Na+, Ni2+, NH4-, O2, HPO42-, SeO42-, SeO32-, SO42-, WO42-, and Zn2+. A lower bound of −0.01 was placed on the cob(I)alamin exchange reaction. Each knockout strain was modeled by using the deleteModelGenes function to constrain the correct reactions to zero. Model growth phenotypes were determined using FBA with the core biomass reaction as the objective, one at a time on each condition. Strains with growth rates above zero were classified as non-essential, while strains with growth rates of zero were classified as essential. The Tomlab  linear programming solver was used to perform FBA.The  Toolbox , and wast bounds . MinimaliJO1366 model reactions. The minimum growth threshold required by the SMILEY algorithm was 0.05 h-1. Up to 25 alternate solutions were allowed, with a single solution time limit of 2 h.The COBRA Toolbox 2.0 implementation of the SMILEY algorithm (growthExpMatch) was used to predict sets of gap-filling reactions for each false negative model comparison. The universal database of reactions was obtained from KEGG Release 58.0 . All reaiJO1366 model. A lower bound of 0.01 mmol/gDW/h was applied to each reaction, one at a time, and SMILEY was used to predict gap-filling reactions. For gaps that have demand reactions in the model, the demand reactions were constrained to zero before running SMILEY. The Tomlab mixed-integer linear programming solver was used.When SMILEY was run on gaps instead of false negative cases, each producing or consuming reaction for each gap metabolite was identified from the iJO1366 model one at a time. The growth phenotype of each of these strains on all 13,470 experimental data conditions was then predicted using FBA, with a threshold of zero for determining growth or no growth. The number of new false positives for each solution was determined from the number of conditions that were true negatives with the wild-type model but could grow when the new reactions were added. The number of corrected false negatives for each solution was the number of false negatives that became true positives when the new reactions were added. GapFind was also run on the iJO1366 model with each set of solution reactions added to it, one at a time. The set of network gaps was compared to the set of gaps in the original model to determine if any gaps were eliminated.After predicting sets of false negative correcting and gap-filling reactions, each of these sets of solution reactions was added to the iJO1366 model with each solution added on a set of 115 carbon sources and 62 nitrogen sources under both aerobic and anaerobic conditions. The growth of the unmodified iJO1366 model was first tested on each condition using FBA. Next, the model with each solution added, one at a time, was tested on all 3896 conditions on which the unmodified model predicted no growth. Conditions on which the modified versions of the model could grow were used to design experiments.In order to determine which SMILEY solutions could be tested with simple in vivo experiments, FBA was used to test growth of the E. coli K-12 MG1655 genome. Protein sequences from organisms that are phylogenically close to E. coli were used when possible. The gene with the highest BLAST expectation value  found was reported. When multiple genes were found with E-values below 10-13, all were reported as candidate genes.Candidate genes for SMILEY predicted reaction sets were predicted using bi-directional protein BLAST (BLASTp) between a gene from another organism in KEGG and the E. coli with myo-inositol as a carbon and energy source, 2 g/L myo-inositol M9 media was made and filter sterilized. This media contained M9 salts , trace elements (0.1 g/L iron (III) chloride, 0.02 g/L zinc sulfate, 0.004 g/L copper chloride, 0.01 g/L manganese sulfate, 0.006 g/L cobalt chloride, 0.006 g/L disodium EDTA), and Wolfe’s Vitamin Solution. Wild-type E. coli along with four strains from the Keio Collection with yphC (JW5842), yfdE (JW2368), ydeN (JW5243), and yhiJ (JW3455) gene knockouts (supplied by Open Biosystems) were grown overnight in LB. The next day, 15 mL of each culture was centrifuged at 4000 rpm for 8 min, the supernatant was discarded, and the culture was resuspended in an M9 salt solution with no carbon or nitrogen sources. The culture was centrifuged and resuspended in new M9 four more times to completely wash out all LB. Next, 1 μL of washed E. coli cultures were then used to inoculate 10 mL aerobic myo-inositol M9 cultures, which were grown at 37°C. The optical density at 600 nm was measured at several points during growth.To test the growth of The authors declare that they have no competing interests.JDO and BOP designed the study. JDO performed all computational predictions, analyzed the results, performed the experimental analysis, and wrote the manuscript. JDO and BOP revised the manuscript. Both authors approved the content of the final manuscript.Experimental and computational growth phenotypes for all 13,470 conditions.Click here for fileAll false negative conditions and gaps on which SMILEY was run.Click here for fileThe KEGG based universal database of reactions and list of excluded reactions.Click here for fileAll Category I-IV SMILEY solutions.Click here for fileAll gap-filling SMILEY solutions.Click here for fileMedia conditions that can indicate the presence of predicted reactions.Click here for file


While genome scale metabolic reconstructions have been developed for many prokaryotes and model eukaryotes, the application to less well characterized eukaryotes such as algae is challenging not at least due to a lack of compartmentalization data.Microalgae have the potential to deliver biofuels without the associated competition for land resources. In order to realise the rates and titres necessary for commercial production, however, system-level metabolic engineering will be required. Genome scale metabolic reconstructions have revolutionized microbial metabolic engineering and are used routinely for Chlamydomonas reinhardtii genome. AlgaGEM is a comprehensive literature-based genome scale metabolic reconstruction that accounts for the functions of 866 unique ORFs, 1862 metabolites, 2249 gene-enzyme-reaction-association entries, and 1725 unique reactions. The reconstruction was compartmentalized into the cytoplasm, mitochondrion, plastid and microbody using available data for algae complemented with compartmentalisation data for Arabidopsis thaliana. AlgaGEM describes a functional primary metabolism of Chlamydomonas and significantly predicts distinct algal behaviours such as the catabolism or secretion rather than recycling of phosphoglycolate in photorespiration. AlgaGEM was validated through the simulation of growth and algae metabolic functions inferred from literature. Using efficient resource utilisation as the optimality criterion, AlgaGEM predicted observed metabolic effects under autotrophic, heterotrophic and mixotrophic conditions. AlgaGEM predicts increased hydrogen production when cyclic electron flow is disrupted as seen in a high producing mutant derived from mutational studies. The model also predicted the physiological pathway for H2 production and identified new targets to further improve H2 yield.We have developed a genome-scale metabolic network model (named AlgaGEM) covering the metabolism for a compartmentalized algae cell based on the in silico functional analysis and can be used to derive new, non-trivial hypotheses for exploring this metabolically versatile organism. Flux balance analysis can be used to identify bottlenecks and new targets to metabolically engineer microalgae for production of biofuels.AlgaGEM is a viable and comprehensive framework for  Microalgae are receiving increased attention as the search for sustainable and profitable biofuel feedstocks progresses. Algae-derived hydrogen, methane, triacylglycerols, and ethanol could all serve as potential biofuels -3, but min silico. A GEM maps annotated metabolic genes and proteins to reactions based on the current best understanding of a given organism. A growing number of metabolic engineering studies have demonstrated the use of well-curated GEMs to generate strain designs that are neither intuitive nor obvious -hydrogenases, HydA1 and HydA2, catalyze H2 production using electrons from ferredoxin. In several species of anaerobic microbes the decarboxylation of pyruvate to acetyl-coA by PFR1 is linked to hydrogen production via the reduction of ferredoxin  and  and 2 prC. Reinhardtii produces hydrogen under heterotrophic and mixotrophic conditions  and minimum energy resource usage to maintain normal growth rate (wild type). Flux simulations are provided in detail , the model predicted about an 8-fold increase in H2 production, if cyclic electron flow is inhibited. This is in line with observations for the stm6 mutant, which produced 5-13 fold more H2 compared to the wild type [2 production becomes a release valve for excess redox.We used AlgaGEM to simulate the metabolic changes when Hild type . Cyclic Stm6 mutant, the model predicted increased linear electron flow and reduced mitochondrial TCA cycle activity  and the resolution of gene product targeting where this is yet to be established. In its current version, the model covers the primary metabolism including some of the fermentative pathways. Importantly, while the model shares 85% of the reactions with AraGEM and while AraGEM was used to compartmentalise many genes, the model predicts distinct algal behaviours such as the catabolism or secretion rather than recycling of phosphoglycolate in photorespiration.in silico flux predictions illustrates the potential of using genome scale models to explore complex, compartmentalized networks and develop non-trivial hypotheses. The metabolic changes highlighted by AlgaGEM to increase H2 yield show agreement with evidence found in the literature and the model predicted the magnitude of change observed in a stage transition mutant, Stm6. Further experimental investigations are also suggested to test new targets. Such results support the potential use of this framework for algae metabolic engineering.The use of AlgaGEM for The authors declare that they have no competing interests.in silico flux analysis and wrote the manuscript. LQ developed the method for the reconstruction and contributed in insightful discussions. RP worked on the data mining process and analysis. LKN conceived the study, performed analyses and revisions of the manuscript. All authors read and approve the final manuscript.CGO carried out the reconstruction and curation of AlgaGEM, performed Supplemental material file. Table S1. List of inter-organelle transporters and enzymes’ localization based on biochemistry information.Click here for fileSupplemental material file. AlgaGEM-COBRA tool box. The folders include AraGEM (sbml format) and constraints to run flux balance analysis for different metabolic scenarios using COBRA tool box.Click here for fileSupplemental material file. Table S2. Metabolic flux simulations.Click here for file


Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment. Many serious diseases have a strong metabolic component. The abnormal metabolic states of diseased cells could therefore be targets for treatment. However, metabolism is a highly complex and interconnected system in which thousands of metabolic reactions occur simultaneously in any given cell type. In order to understand how metabolism of a diseased cell differs from its healthy counterpart we must therefore study the system as a whole. We have developed an algorithm that integrates several types of data in order to generate active metabolic networks; catalogues of the metabolic reactions that are likely to be active in a given cell type. We applied this algorithm to data for 69 healthy cell types and 16 cancer cell types. These metabolic networks can form the basis for simulation of metabolic interactions between organs or as scaffolds for interpretation of high-throughput data. We used these networks to perform an analysis between cancer and healthy cell types in order to identify cancer specific metabolic features that constitute potential drug targets. Several of the resulting targets were already known and used clinically, but we also found high-ranking reactions and metabolites which have not yet been investigated as drug targets. Abnormal metabolic states are at the origin of many diseases such as diabetes, hypertension, hearth diseases and cancer, which can be seen in many aspects as a metabolic disease. Cancer and coronary diseases are the two main causes of death in the developed countries. It is expected that by 2030 close to 200 million persons  will be obese in the EU alone, and many of these will have one or more of the following co-morbidities: diabetes, hypertension, heart disease and increased risk of cancer, and the direct  and indirect (inability to work) costs are estimated to amount to more than €100 billion per year There are three generic genome-scale human metabolic networks currently available, namely Recon1 Here we present a pipeline for automatic identification of expressed cell type specific genome-scale metabolic networks . A key ewww.metabolicatlas.com, will be periodically updated as new reactions are added to KEGG or MetaCyc or expression profiles for more proteins become available in HPA or other databases.The output of our analysis is a cell type specific metabolic network for each of the cell types profiled in the HPA. As we are using HPA and gene expression data our networks do not represent the complete metabolic network that may be expressed in each cell type, but solely the part of the metabolic network that is expressed and hence the part of the network that is likely to be active. In order to provide a reliable and up to date genome-scale model template for our tissue/cell type specific metabolic networks, we first constructed the Human Metabolic Reaction (HMR) database containing the elements of previously published generic genome-scale human metabolic models In order to evaluate the capability of our pipeline to generate reliable tissue specific metabolic networks, the metabolic model generated for hepatocytes was compared to HepatoNet1 Human Metabolic Reaction database (HMR), containing the two existing genome-scale metabolic models, Recon1 and EHMN, as well as incorporating information from HumanCyc and KEGG.The existing methods for the inference of tissue specific active metabolic networks have only used Recon1 as a scaffold. In order to integrate other sources of information we constructed the iHuman1512.The HMR database has a hierarchical structure in which the genes are at the top and are linked to information about their tissue specific expression profiles reported by Su et al www.metabolicatlas.com.Using the INIT algorithm , genome-scale active networks for 69 different cell types and 16 cancers were automatically generated. The resulting active metabolic networks are provided in SBML iHepatocyte1154) shows a p-value of 1e-200, which is similar to the value obtained by comparing the manually reconstructed HepatoNet1 to BRENDA. 55% of the genes in iHepatocyte1154 are also in BRENDA, while only 43% of the genes in HepatoNet1 are in BRENDA. The comparatively high p-values are for tissues for which there are very few annotated enzymes in BRENDA.The tissue specific models generated were compared with the BRENDA iHepatocyte1154) contains 1,154 genes, of which 452 are also included in HepatoNet1 and 702 are absent. The evidence for the expression and translation of the 702 absent genes is as good as the evidence for the 452 genes that are in both networks, and we are therefore confident that the presence of most of the 702 extra genes has been correctly inferred by our algorithm. The HepatoNet1 network contains 261 genes not included in iHepatocyte1154, of which 156 were absent from our initial connected human network. Our algorithm could therefore not have assigned these genes to the hepatocyte sub-network and their existence reveals just a limitation of the data that were used as an input and not a limitation of our algorithm. 80 of these genes were not in HMR  such as spermidine, spermine, and putrescine in cancer cells. PAs play a variety of roles, of which several are related to oxidative stress prevention and suppression of necrosis Another high-ranking target is the isoprenoid biosynthesis pathway, in particular the intermediate geranylgeranyl diphosphate. This metabolite has been shown to promote oncogenic events due to its role in prenylation of important cancer proteins such as Ras and Rho GTPases A third prominent group among the Reporter Metabolites is prostaglandins and leukotrienes together with the intermediate HPETE. These autocrine compounds are synthesized from arachidonic acid and are elevated in connection with inflammation. They have been shown to aid in cancer progression by promoting metastasis and by influencing the immune system The fact that so many of the identified targets correspond to well known and used drug targets, indicates that the method is able to generate biologically relevant hypotheses. Of particular interest are therefore the Reporter Metabolites that are currently not targeted in cancer treatment. Among the top-scoring Reporter Metabolites we identified biliverdin and bilirubin . BiliverWe here present a method that is able to integrate different sources of biological evidence to generate high quality cell type specific metabolic networks. We used this method to generate genome-scale metabolic networks for 69 different human cell types and 16 cancer types, and this is the first step towards the establishment of a Human Metabolic Atlas, which may become a central portal for further advancing human metabolic models with the capability of performing tissue-level and organism-level metabolic simulations, allowing for a better understanding of complex diseases. The Human Metabolic Atlas and will be made publicly accessible for the medical and scientific community and may hereby become a valuable resource in the development of personalized medicine based on system-level analysis. An example of system-level analysis is the identification of cancer specific metabolic features that we have performed by comparing the networks generated using the INIT algorithm.iHuman1512. The numbers of genes maintained in each of the above mentioned steps are listed in iHuman1512 and some published human metabolic networks is available in In order to have an unambiguous characterization of metabolites and reactions, KEGG and InChI identifiers were used for standardization. Metabolites lacking identifiers to external databases were left out of the HMR database together with their corresponding reactions. The metabolite identifiers were used to infer if two reactions coming from different sources were the same. Each reaction was assigned to one or several of the eight compartments included in the HMR database: nucleus, cytosol, endoplasmatic reticulum, Golgi apparatus, peroxisomes, lysosomes, mitochondria and extracellular. In cases where the sub-cellular localization was absent from the template models it was inferred from immunohistochemical staining in the HPA. For enzymes that were not in the HPA, Swiss-Prot and GO were used to infer localization  algorithm The INIT (Integrative Network Inference for Tissues) algorithm is formulated as a mixed integer-linear problem (MILP) and is specially tailored to use the evidence from the HPA as input. The problem is formulated so that all reactions in the resulting model are able to carry flux. The stoichiometric matrix S contains the stoichiometric coefficients for each internal metabolite in each reaction. By multiplying the stoichiometric matrix by the vector of reaction rates we obtain a vector of net accumulation or consumption rates for each internal metabolite. Instead of imposing the steady state condition for all the internal metabolites, as it is usually done, we allow for a small positive net accumulation rate. The net productions of metabolites will be given positive weights in the optimization. The reason for this choice is that we prefer to have a network able to synthesize molecules such as NADH or NADPH, rather than only being able to use them as cofactors. If a metabolite is present in a cell type (according to the HMDB) a positive net production of this metabolite will be imposed to the network in order to assure that all the reactions necessary for its synthesis are included in the tissue specific model.Up to this date there is not a human biomass equation available in the literature (for example Recon1 incorporates a mouse biomass equation). On the other hand, human cells (with the exception of cancer cells), in contrast to microorganisms, do not tend to proliferate or do so slowly in comparison with the rest of their metabolic functions. This makes the biomass equation less relevant, unless the aim is to model cancer proliferation. Also human cells secrete into the blood a much broader spectrum of compounds than microbial cells secrete into their environment (which are mainly fermentation products). We therefore chose to generate networks allowing for secretion (or accumulation) of all their metabolites. If we had used the stricter steady state constraint, many reactions would have been removed from the models just because they were leading to dead end metabolites. These end metabolites could in fact be added to biomass or just be secreted into the blood stream, therefore we have aimed for a more flexible approach by allowing secretion (or net accumulation) of metabolites.iw of 20, 15, 10 and −8 for high, medium, low and absent proteins respectively. These scores are arbitrary and have been chosen to quantify the evidence colour codes that appear in the HPA. We have tested the sensitivity of the algorithm to the variation in these weights by perturbing them by 20% up and down and the impact on the output of the algorithm resulted only in small changes of the resulting networks. If the evidence comes from gene expression levels which were retrieved from BioGPS i in tissue j is divided by the average signal across all the tissues. If the signal in a particular tissue is higher than its average across all the tissues the weight will be positive, if it is lower it will have a negative weight.The MILP used in INIT can be specified as:For the reactions that are related to several genes or proteins the highest evidence score is used. If no gene is associated to a particular reaction, or there is no proteomic or transcriptomic evidence, a weight of −2 is used in order to avoid adding unnecessary reactions without evidence and keep the network as parsimonious as possible. If a reaction linked to several genes is added to the final tissue specific network, only the genes showing a positive evidence score are kept in the tissue specific reaction-gene association.www.mosek.com) and its Matlab interface.The MILP problem was solved using MOSEK Click here for additional data file.Figure S2The relative pathway enrichment profiles, based on KEGG pathways, for each of the models. Blue corresponds to underrepresentation and red to overrepresentation. Note that it is the number of enzymes present for each pathway that underlie the comparison, not the abundances of the proteins.(PDF)Click here for additional data file.Table S1Evaluation of the models by comparison to curated tissue-specific enzymes. For each model, the set of genes is compared to the set of genes annotated as existing in the corresponding tissue in BRENDA. The p-values are derived from hypergeometric distribution.(PDF)Click here for additional data file.Table S2Investigation of the 80 genes that were present in HepatoNet1 but missing in HMR. The 80 missing genes were associated with 746 reactions in HepatoNet1, of which 597 metabolic and transport reactions were related to the sinusoidal space compartment. 117 metabolic reactions existed in HMR with different gene or no gene association and 32 (5 unique) reactions were altogether absent in HMR. KEGG reaction identifiers or Transporter Classification database identifiers (TCDB) are provided for the missing reactions.(PDF)Click here for additional data file.Table S3Investigation of the 76 genes that were removed during the pre-processing steps. 76 genes which were present in both HepatoNet1 and the HMR database were removed in order to get a fully connected input network for the INIT algorithm. This table summarizes which genes were removed during each of the pre-processing steps (see (PDF)Click here for additional data file.Table S4Investigation of the 105 genes which are present in HepatoNet1 but missing in iHepatocyte1154 due to the INIT algorithm. The 105 missing genes were associated with 182 reactions in HepatoNet1, of which 5 metabolic reactions are related to the sinusoidal space compartment. 108 metabolic reactions existed in iHepatocyte1154 with different gene or no gene association and 69 (60 unique) reactions are absent in iHepatocyte1154 due to INIT algorithm. KEGG reaction identifiers are provided for the missing associated reactions to the genes.(PDF)Click here for additional data file.Table S5List of reactions that were significantly more present in cancer tissues compared to their corresponding normal tissues .(PDF)Click here for additional data file.Table S6List of genes for which their corresponding reactions were significantly more present in cancer tissues compared to their corresponding normal tissues .(PDF)Click here for additional data file.Table S7List of Reporter Metabolites .(PDF)Click here for additional data file.Table S8Versions of the databases used in the creation of the Human Metabolic Reaction database (HMR).(PDF)Click here for additional data file.Table S9Number of the genes after each pre-processing step during the generation of iHuman1512. Since the INIT algorithm provides a connected and functional model, reactions that are unconnected in the template model can never be included. In order to separate between reactions that were excluded due to connectivity reasons and those that were excluded due to negative evidence, a number of preprocessing steps were performed on the data in the HMR database. In the first step reactions that contain very generic metabolites such as “lipid” or “alcohol” were removed. Reactions that were elementally unbalanced were fixed or removed. Simulations were performed to ensure that the network could not gain carbon, energy or redox power in an unbalanced manner. In the second step reactions where directionality information was lacking were removed. In the third step reactions where one or more of the substrates could not be synthesized through some other reaction (unconnected reactions) were removed. Finally, in the fourth step reactions that couldn't carry flux when the model had access to all exchange metabolites (as defined in the EHMN) were removed. Consequently, iHuman1512, a connected human network with 5,535 reactions, associated with 1512 protein coding genes, was generated. The number of genes associated to the remaining reactions after each pre-processing steps are presented below.(PDF)Click here for additional data file.Table S10Comparison between iHuman1512 and some other published human metabolic networks.(PDF)Click here for additional data file.


Cancer cells can have a broad scope of proliferation rates. Here we aim to identify the molecular mechanisms that allow some cancer cell lines to grow up to 4 times faster than other cell lines. The correlation of gene expression profiles with the growth rate in 60 different cell lines has been analyzed using several genome-scale biological networks and new algorithms. New possible regulatory feedback loops have been suggested and the known roles of several cell cycle related transcription factors have been confirmed. Over 100 growth-correlated metabolic sub-networks have been identified, suggesting a key role of simultaneous lipid synthesis and degradation in the energy supply of the cancer cells growth. Many metabolic sub-networks involved in cell line proliferation appeared also to correlate negatively with the survival expectancy of colon cancer patients. Cancer metabolism has been the object of a substantial amount of interest during the last years158Genome-scale metabolic models13This paper is aimed at identifying metabolic sub-networks, as well as regulatory mechanisms and protein interaction sub-networks that control the growth rate of cancer cells. A previous studyGenome-scale metabolic networks or protein interaction networks can be used for a contextualized data analysis. For example if the expression levels of several metabolic genes linked to reactions that are stoichiometricaly coupled between each other (for example reactions in a linear metabolic pathway), are positively correlated with the growth rate, it is likely that the activity of the pathway has a causal relationship with the cell growth rate. This is not the case if only a single gene linked to this pathway shows a significant correlation.Another advantage of using genome-scale biological networks is the fact that instead of single genes it allows identifying sub-networks. This helps to choose combinations of targets that would disable the function of one or several of the relevant sub-networks and lead to a synergistic effect against cancer proliferation, this phenomenon is known as synthetic lethality1A new algorithm to identify metabolic sub-networks showing a correlated activity with the growth rate has been developed. Contrarily to protein interaction networks, metabolic networks are topologically Petri nets and not graphs. The proximity between two reactions cannot be just defined by the existence of shared metabolites. If two reactions are stoichiometrically coupled (e.g. their fluxes are fully correlated) they should be considered as neighbours even if they do not share metabolites. A new algorithm taking account of this fact has been developed. The details of the algorithm are presented in the https://gdoc.georgetown.edu). This study was chosen based on the high number of patients, which allows increasing the statistical power of the test used to identify differentially expressed genes. The newly developed algorithm was used to identify metabolic sub-networks showing higher activity in the group of patients who died in the first 5 years. The results evidenced that many of the metabolic processes involved in the faster proliferation of cancer cell lines are also involved in higher mortality of colon cancer patients. This confirms the suitability of targeting these metabolic processes.There is an underlying assumption in using correlations observed in the NCI-60 panel to extract general conclusions about cancer metabolism. This assumption is that although cancer cells are heterogeneous even within the same tumorWe present here the main results and conclusions of several forms of integrated analysis of gene-expression data. Our main focus is cell metabolism and the integrated analysis of gene expression patterns within the context of genome-scale metabolic networks; however, in order to put our results in the context of the regulatory mechanisms triggering cell growth, we start by identifying transcription factors and protein interaction sub-networks that show an increased activity in cells with higher proliferation rates.http://discover.nci.nih.gov) were used as input data to our analysis. We started computing a Spearman correlation coefficient with the cell growth rate for each probe in the mentioned microarrays. The histograms of correlation coefficients and p-values for the HG-U133 Plus 2.0 microarray are shown in The normalized HG-U133 Plus 2.0 and the HG-U95 microarrays available at Cell-Miner  in both microarrays, was used to identify enriched transcription factor binding motifs. We used PscanThe identified binding motifs from JASPAR corresponded to ELK1, ELK4, GABPA and E2F1. The two TRANSFAC motifs were E2F_02 and E2F_03. These motifs are potential targets of the E2F family of transcription factors. The lists of regulated genes and the position of the binding motifs in their promoters are reported in the The same kind of analysis was performed for the set of genes that showed significant negative correlations with the growth rate  in both microarrays. This led to the identification of Egr1 and SP1 with p-values of 2.7e-9 and 2.5e-8 respectively.Among the 288 genes that showed significant positive correlation with the growth rate  in both microarray platforms, 124 showed at least one protein interaction with another of the genes. Among those genes, 109 formed a connected sub-network . This neWe have developed a new algorithm for the integration of gene-expression data with genome-scale metabolic networks. Metabolic networks are topologically Petri nets and transforming them into graphs (in which the adjacency between two reactions is defined by sharing metabolites) results necessarily in a loss of information and a misrepresentation of their structure. Here we define a measurement of the functional similarity between metabolic reactions. This measurement is based on projecting the vector representing each reaction into the kernel of the stoichiometric matrix of the metabolic network and computing the angles between the projections of different reactions. In this way, reactions that are fully stoichiometrically coupled (even if they do not share any metabolite) have a zero angle between them  is well known, however the function of the identified sub-networks seems to go well beyond the synthesis of lipids as biomass building blocks. The sub-network analysis identified the synthesis of several acyl-ACPs  such as octanoyl-ACP , which iSeveral sub-networks indicate not only an increased rate of fatty acid synthesis but also an increased degradation of very diverse fatty acids. This is in agreement with previous observationsThe largest sub-network (network 2 in https://gdoc.georgetown.edu) because it contains the highest number of patients and it allows obtaining the highest statistical confidence for the differential expression of genes between both groups of patients.In order to assess to which extent the identified sub-networks can be extrapolated from a panel of cancer cell lines to in-vivo tumors; we have performed a differential expression analysis of 145 colon cancer patients who survived more than 5 years and 85 patients who died within 5 yearsA t-test between the two groups of patients was performed and the p-values obtained were used to carry out the same analysis as in the case of growth correlated genes in the NCI-60 panel. In general the statistical significance of differential expression between the patient groups is much lower than the significance of correlation across the NCI-60 panel. Only 8 genes passed a threshold of 0.05 false discovery rate compared with 288 in the previous analysis. However, the metabolic sub-network analysis is still possible because it is not based on a stringent significance cut-off . For a more realistic P/O ratio of 1.5 it could be possible to obtain 4 ATP molecules per cycle, which is still enough to compensate for the loss of 2 ATP molecules necessary to drive the cycle.In order to assess if the apparently futile cycle formed by the simultaneous fatty acid synthesis and degradation can constitute an energy source for the cell, we have calculated the global stoichiometric balance of a cycle involving the synthesis of acyl-CoAs from cytosolic acetyl-CoA, the transport of acyl-CoAs to the mitochondria via the carnitine shuttle, the mitochondrial β-oxidation, the transformation of the resulting mitochondrial acetyl-CoA into citrate, the transport of citrate to the cytosol via the citrate-malate antiporter and finally the cytosolic conversion of citrate into acetyl-CoA. The whole cycle consumes two molecules of ATP per unit of cycled acetyl-CoA and results in the consumption of two molecules of cytosolic NADPH and the production of one mitochondrial NADH and one mitochondrial FADHThe mentioned hypothesis is supported by the fact that the drug etomoxir, an inhibitor of β-oxidation, shows anti-cancer effectsThe impact of fatty acid oxidation on patient survival has been previously shown in breast cancerThe growth correlated metabolic sub-networks can be used for the identification of suitable anti-cancer drug targets. A common method for drug design is based on mimicking the chemical structure of a metabolite in order to use competitive inhibition of metabolic enzymes to decrease the metabolic flux in a particular pathway. The structure of the growth-correlated sub-networks can be used as a guide for the design of new drugs. First of all it is possible to identify metabolites that are present only in the growth correlated metabolic sub-networks, in order to avoid interferences with other metabolic pathways. Each of the metabolites unique to a sub-network, if it is assumed to be in steady state, can be used to define a mass balance equation that relates the fluxes of the reactions producing the metabolite and the fluxes of the reactions consuming it. The number degrees of freedom of the system of equations that results for each sub-network can be used as a measurement of its robustness. For example, a sub-network formed by a set of reactions in series has a robustness of 1 while a sub-network formed by a set of reactions operating in parallel would have as many degrees of freedom as reactions. Disabling a single reaction, in the first case would stop the flux through the whole pathway, while in the second case it would only have a minor impact. This can be a criterion to decide what metabolic sub-networks are more fragile and therefore constitute better targets for drugs. Finally, it is also possible to compute the degrees of freedom of a sub-network after blocking all the reactions consuming and producing a particular metabolite (which mimics the effects of a drug showing competitive inhibition against the enzymes processing the metabolite). If the remaining degrees of freedom are zero, the metabolite can be considered to be essential for the network operation and a possible scaffold for drug design.As a conclusion we can say that in this paper we show how human genome-scale biological networks, and in particular genome-scale metabolic networks, can be used as analysis scaffoldss of gene-expression data, in order to provide deeper insights into the molecular mechanisms associated to cancer cell proliferation. This kind of analysis has been made possible thanks to the recent availability of high quality genome-scale metabolic networks with comprehensive gene annotations. In particular the HMR database contains 3668 metabolic genes, allowing an almost full mapping of transcriptional changes of genes into metabolic reactions.This has led to the identification of possible regulatory mechanisms controlling cancer proliferation. We also identified over 100 growth related metabolic sub-networks, which can be seen as an atlas of metabolic pathways whose complexity goes well beyond the metabolic features typically associated to cancer1Several of the identified metabolic processes had been already shown to be relevant in particular cancers. Our analysis allows putting these processes in a broader perspective and reveals interactions between them, such as the simultaneous synthesis and degradation of fatty acids and the synthesis of carnitine.Based on the analysis of gene expression and observations of the secretion pattern of the NCI-60 cell panelA very important question is the extrapolability of the results obtained from the contextualized analysis of the gene expression patterns of the NCI-60 panel, to in vivo tumors. In order to address this point we have used the differentially expressed genes between two groups of colon cancer patients to identify metabolic sub-networks whose activity has a negative impact on patient survival. The analysis revealed a substantial overlap between the sub-networks identified in both analyses, which suggests that the same metabolic sub-networks involved in sustaining higher proliferation rates in-vitro are also more active in more aggressive in-vivo tumors. Comparing the sub-networks at a metabolite level we obtain a very statistically significant overlap.We also suggested some criteria to identify suitable drug targets against cancer proliferation, which are based in the topological structure of the metabolic sub-networks, which determines their robustness.http://discover.nci.nih.gov). The specific growth rates of each cell line are calculated from the doubling times reported at Cell-Miner and they are reported in the The normalized HG-U133 Plus 2.0 and the HG-U95 microarrays were obtained from Cell-Miner (S.B. designed the project, performed the analysis and wrote the manuscript, A.F. prepared the figures and contributed to the improvement of the manuscript.function Clustersfunction expandfunction Graphstructurefunction multiplesetS1S2S3S4S5S6S7S8S9S10Supplementary methods


Enabled by rapid advances in sequencing technology, metagenomic studies aim to characterize entire communities of microbes bypassing the need for culturing individual bacterial members. One major goal of metagenomic studies is to identify specific functional adaptations of microbial communities to their habitats. The functional profile and the abundances for a sample can be estimated by mapping metagenomic sequences to the global metabolic network consisting of thousands of molecular reactions. Here we describe a powerful analytical method (MetaPath) that can identify differentially abundant pathways in metagenomic datasets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge.p values (abundp and structp) using nonparametric approaches to answer two different statistical questions: (1) is this subnetwork differentically abundant? (2) What is the probability of finding such good subnetworks by chance given the data and network structure? Finally, significant metabolic subnetworks are discovered based on these two p values.First, we introduce a scoring function for an arbitrary subnetwork and find the max-weight subnetwork in the global network by a greedy search algorithm. Then we compute two In order to validate our methods, we have designed a simulated metabolic pathways dataset and show that MetaPath outperforms other commonly used approaches. We also demonstrate the power of our methods in analyzing two publicly available metagenomic datasets, and show that the subnetworks identified by MetaPath provide valuable insights into the biological activities of the microbiome.http://metapath.cbcb.umd.edu.We have introduced a statistical method for finding significant metabolic subnetworks from metagenomic datasets. Compared with previous methods, results from MetaPath are more robust against noise in the data, and have significantly higher sensitivity and specificity (when tested on simulated datasets). When applied to two publicly available metagenomic datasets, the output of MetaPath is consistent with previous observations and also provides several new insights into the metabolic activity of the gut microbiome. The software is freely available at  Metagenomics is a new scientific field that involves the analysis of organismal DNA sequences obtained directly from an environmental sample, enabling studies of microorganisms that are not easily cultured in a laboratory . MetagenTo address these problems, we introduce a general method (MetaPath) for searching the global metabolic network to find differentially abundant finer-level subnetworks. For the purposes of this paper we define a subnetwork to be a connected set of genes that is statistically enriched or depleted in one group of samples. Underlying our approach is a statistical scoring system that captures the differential abundance for a given subnetwork, combined with a greedy search algorithm for a maximum weighted subgraph, to indentify the highest scoring subnetworks. Unlike previous approaches, MetaPath explicitly searches significant subnetwork in the global metabolic network (rather than the KEGG defined pathways), enabling us to detect subnetworks spanning predefined “containers”. In addition, we developed rigorous statistical methods that take into account the topology of the network when testing the significance of the subnetworks.http://metapath.cbcb.umd.edu.Using simulated datasets, we demonstrate that Metapath outperforms previously described approaches for comparing biological networks based on abundance data. We show that our findings are more robust to noisy data than the results of single gene comparisons, and that MetaPath can find finer-level subnetwork than can be found by comparing predefined KEGG pathways. We also discuss the biological significance of the results derived from the application of MetaPath to actual metagenomic datasets, demonstrating that the output from MetaPath is easy to interpret and provides valuable biological insights. The software is freely available at We tested our methods on two previously published metagenomic datasets, which were downloaded from the NCBI Trace Archive or Short Read Archive databases: 1) gut microbiomes from obese and lean twins ; (2) met gut micrp value for each feature (metabolic reactions) follows a uniform distribution from 0 to 1. Based on this assumption, p values can be converted to Z scores [p values can be converted back to the original Z values using the following equation:To score the biological activity of a particular subnetwork, we first use Metastats  to calcuZ scores  using thZ score will be positive and vice versa. We are specifically interested in finding a pathway whose reactions are either enriched or depleted as a whole, as apposed to previous approaches [Z scores over all reactions contained within it: k is the size (number of metabolic reactions) of the subnetwork.where proaches ,14 that proaches  we definet al. 2002 [et al, 2008 [As proposed in , we atteal. 2002  used simal, 2008  used intj which has the highest weight of the edges that are adjacent to and have the same direction with ej-1”. Both searching algorithms are implemented in our program and can be selected through command-line parameters. To find all significant subnetworks (computing significance is discussed below), we iteratively remove the edges in the global network that are contained in previously found significant subnetworks, and rerun our greedy search on the rest of the network until we can no longer find any additional significant subnetworks. Note, that unlike the original version of our code [This algorithm tries to find a connected metabolic subnetwork, which can have any arbitrary structure, with maximum weight. However, it is believed that in metabolic networks, chains are especially more biologically meaningful and interesting, because they attempt to capture the structure of a series of reactions that are successively connected. To allow this idea, we modify line 8 of the above algorithm to “Pick an edge eour code , the seap value is estimated as the number of random permutations that produce higher scores than the original subnetwork. The p value computed through this approach (termed abundp throughout the rest of the paper), however, ignores the topology of the underlying global metabolic network, and potentially leads to incorrect conclusions. For example, assume we have a densely connected metabolic network, in which every edge is connected with all other edges. Then, the best subnetwork is simply composed of the top differentially abundant metabolic reactions. This indicates that whenever there are significant reactions, which may simply come from random noise given the large number of edges, they will form a significant subnetwork because of the biases from the network topology , relying on a topological definition of the null distribution of subnetwork scores. Specifically, instead of treating each subnetwork as a bag of genes, we estimate the distribution of scores for actual subnetworks identified within the underlying global metabolic network. Since this null-distribution depends on the size (number of edges) of the subnetwork, let k be the size of a subnetwork generated by the greedy search algorithm described above, and Z be the corresponding Z-score. The structp value for this subnetwork can be calculated as follows: (i) permute the edge weights (row labels of the abundance matrix) of the global metabolic network; (ii) perform greedy search to find a maximal weighted subnetwork of size k; (iii) repeat step 1 and step 2 for 1000 times, and generate 1000 weights of the max-weight subnetwork (null distribution); (iv) the structp value is the proportion of the 1000 times in step 3 that we see scores higher than our original observation Z.The null score distribution for a specific subnetwork can be estimated by permuting the sample labels (columns of the abundance matrix) of the reactions and computing the subnetwork scores from the permuted abundance matrix. The significance ogy Fig. . To addrTo summarize the methods described above, the MetaPath algorithm proceeds as follows:1. Differential abundance is assessed on an edge-by-edge basis (reaction-by-reaction) using Metastats;p-values) from Metastats are fed into a greedy search algorithm to determine all maximally weighted subnetworks in the global metabolic network;2. The significance estimates (abundp), and a topology-dependent bootstrapping approach (structp);3. The significance of each subnetwork detected by the greedy search algorithm is assessed using both a topology-independent bootstrapping approach (abundp ≤ 0.05 and structp ≤ 0.05) are reported to the user (Note: the threshold for significance can be adjusted through command-line parameters). The pathways are ranked by abundp values.4. The subnetworks determined to be significant  identifying significantly active subnetworks using simulated annealing and greedy search ; (ii) dip value of the difference for the two distributions is less than a predefined value (0.05 and 0.01 were used). In this study, we have chosen a subnetwork (a series of reactions with length 5 or 10) to be enriched in one population. The goal is to compare different methods in recovering this significant subnetwork (a set of significant reactions) based on the simulated abundance matrix. Biologically, the enriched pathways indicate functional enrichment of certain biological processes in a microbial community.We designed a simulated metabolic pathways dataset in which five subjects are created for each of the two groups with distinct phenotypes. To generate the artificial reaction abundance matrix (where rows represent reactions and columns represent subjects), a Gaussian distribution is created for each reaction, whose mean is randomly chosen from a real metagenomic dataset (gut microbiome from obese and lean subjects ). The vaThe receiver operating characteristic (ROC) curve is plotted for each method Fig. . Fig. 3 -5, bitscore > 50, and %identity > 50; parameters suggested in the original study), resulting in total 1832 unique reactions within the 12 metagenomic samples. First, we computed p values [p value cutoff of 0.05, 92.7±9.1 (mean±standard deviation) reactions are significant including 37.1±6.6 and 55.6±3.1 enriched reactions in obese and lean groups, respectively, based on 10 runs of Metastats. The high variance of the number of significant genes can be primarily explained by two reasons: (1) some reactions are slightly below or above significance cutoff (0.05), thus p values computed through bootstrapping will jump between being considered significant and not significant , information that is not used by MetaPath. The q values for all reactions are 1 (except R01676 where q=0.73), i.e. a literal interpretation of Metastats results would indicate no pathways are significantly different between the two populations. This result can be explained by the flat distribution of the p values  theues Fig. , from whabundp and structp. All these subnetworks are enriched in obese subjects; none was found to be enriched in lean subjects. These 9 significant subnetworks contain 48 unique reactions, 22 of which are significant. It is worth pointing out that the number of significant reactions varies between different runs of statistical permutations (using Metastats) as shown above, but the significant pathways identified by Metapath stay the same , resulting in total 1781 unique reactions within the 13 metagenomic samples. Based on 10 runs of Metastats, 383.7±1.56 reactions are significant using p value cutoff of 0.05, including 268.7±1.56 and 115±0 reactions enriched in infant and adult subjects respectively.Using a q value cutoff of 0.05, 167.2±2.7 reactions are significant, including 133.2±2.7 and 34±0 reactions enriched in infant and adult subjects respectively.Compared with the previous dataset (obese and lean twins samples), the predictions of significant reactions are much more consistent across different permutations.A second data-set comprises gut microbiome samples from 4 infants and 9 adults individuals which were sequenced by Kurokawa, Applying MetaPath to search for significant subnetworks using the same parameters as before, we have found that 6 are enriched in infant subjects Fig.  and 4 arThe pathway in Fig. We have introduced a statistical method for finding significant metabolic subpathways from metagenomic datasets. Compared with previous methods, results from MetaPath are more robust to noise in the data, and have significantly higher sensitivity and specificity (when tested on simulated datasets). When applied to two publicly available metagenomic data-sets the output of MetaPath is consistent with previous observations and also provides several new insights into the metabolic activity of the gut microbiome. Finally, MetaPath is efficient: a typical metagenomic dataset and the corresponding metabolic network (about 2000 edges) can be analyzed in half an hour on a single processor.http://metapath.cbcb.umd.eduWhile showing promising results, our methods have several limitations that we plan to address in the near future. First, and foremost, we restrict ourselves to pathways of a fixed length — a restriction necessary for accurately computing the null distribution of pathway scores. This can severely affect our ability to discover long pathways whose abundance differs only slightly, but significantly, between samples. Second, we currently estimate gene abundances by simply counting the number of sequencing reads that map to a certain gene. Such an approach ignores differences in the length of genes, potentially leading to incorrect conclusions. We plan to address this issue by incorporating a recently-published  method tThe authors declare that they have no competing interestsBL and MP conceived the project, designed the algorithm and wrote the manuscript. BL implemented the algorithm and analyzed the data. Both authors read and approved the final manuscript.