Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. Many large-scale mathematical models describe metabolism to understand how microbes and other organisms (including humans) function and interact with each other and with their environment. Making these models is extremely time- and effort-intensive; it requires gathering and combining information from many sources, including the organism’s genome sequence, biological databases, scientific literature, and expert advice. The exact procedure and resources used depend on the model creators’ expertise and research interests, such that independently created models for the same organism are often very different and can hardly be compared. However, each model typically contains unique information that is ‘lost’ when working with a different model. To integrate the available knowledge, we developed a computational tool to build consensus metabolic models. Our tool—COMMGEN- combines independently generated models by matching identical parts and resolving differences between inconsistent parts. We apply our tool to four sets of models of different organisms. In all these sets, COMMGEN identified and resolved hundreds of inconsistencies. COMMGEN can be generally applied to standardize and improve models of metabolism, in particular for complex scenarios, such as those involving microbial communities and host-pathogen interactions. Genome-scale constraint-based metabolic models (GSMs) are curated organism-specific knowledge repositories . They inGenome-scale metabolic reconstructions consist of metabolites, metabolic reactions (including boundary reactions and a biomass reaction), cellular compartments, and genes ,16. The Saccharomyces cerevisiae or Bacillus subtilis, many uncertainties remain during GSM construction. These uncertainties are typically manually addressed based on expert knowledge and scientific literature, which involves a laborious iterative process that can take several years, for example, for eukaryotes [However, even for well-studied organisms such as karyotes . The maikaryotes , alternakaryotes –21, and karyotes ; (ii) sukaryotes , and thekaryotes .As a consequence, when several GSMs for the same organism are developed independently, they are complementary and only partially overlapping ,25. The To represent metabolism more comprehensively, and thereby improve our understanding of a target organism, alternative GSMs of a target organism can be integrated into a so-called consensus model of the respective organism, one per organism. Consensus models have an increased scope  and they are more consolidated . When discrepancies exist between GSMs, these must be carefully examined to select the most appropriate modeling alternative. However, while consensus models have been generated successfully for several (model) organisms such as budding yeast and human, this required extensive manual curation by communities of domain experts ,24,25,27Here, we present COMMGEN, a tool for COnsensus Metabolic Model GENeration that reconciles two or more distinct GSMs of the same organism beyond a common namespace. COMMGEN automatically identifies similarities, dissimilarities, and complements of the metabolic networks based on an extensive classification of problems that typically arise during GSM integration and on novel algorithms to resolve these problem classes. For several model organisms, we show that semi-automatically created consensus GSMs in a standardized namespace  are subsOur analyses addressed model building, testing and refinement in a stepwise fashion. We started by identifying the classes of inconsistencies that exist between models for four widely different albeit representative microbes. We subsequently set up the framework for COnsensus Metabolic Model GENeration, and tested it on the four case studies for functionality and predictability.To systematically resolve inconsistencies between two or more Initial GSMs (IGSMs) to be integrated, we defined three main (coupled) inconsistency categories: metabolites, reactions, and compartments. We explain these categories and the inconsistency classes they contain using examples from four sets of IGSMs that cover gram-positive and gram-negative bacteria as well as yeast .2+’, or ‘glucose’ and ‘alpha-D-glucose’. Common metabolites may also have different chemical sum formulas in different IGSMs, for example, depending on whether functional groups are specified or not  and unique reactions. We further categorize unique reactions according to whether they are unrelated to any inconsistency, related to a single inconsistency, or related to multiple inconsistencies . Because the identified inconsistencies ultimately depend on namespace consistency, user-defined settings, and user choices, we quantified the resolution of inconsistencies by automatic processing to remove user bias as much as possible. After creating the BCM from the IGSMs and merging the identical reactions, the fraction of consensus reactions was low (11%) and approximately half of the unique reactions were associated with at least one inconsistency . The incP. putida GSMs , and they were outperformed by the RCM (except for B. subtilis). For B. subtilis, resolving inconsistencies in the BCM decreased all scores except sensitivity. This can be explained by one IGSM (iBSu1103) being largely based on a predecessor (iYO844); in addition, iBSu1103 was optimized for correct growth predictions using GrowMatch [The performance of GSMs as mathematical models for cellular metabolism is typically evaluated by assessing their ability to correctly predict wild type and mutant growth phenotypes across different growth conditions . We perfrowMatch ,36. InfoFinally, we wanted to evaluate how automatic consensus model generation compares to its (largely) manual counterpart. We focused on the community approach to establish a yeast consensus model  based onGenome-scale constraint-based metabolic models are both integrated knowledge repositories and predictive mathematical models. In terms of knowledge representation, a consensus model should be more consolidated than individual GSMs due to shared parts, more comprehensive due to unique parts, and more accurate due to reconciliation of inconsistencies in similar parts. A consensus model, however, can propagate errors in the initial models’ unique parts, and it may be less consistent than the initial models, especially when inconsistencies in similar model parts were not identified or reconciled.Inconsistencies in GSMs are typically nested, not mutually exclusive, and therefore difficult to address, which so far prevented the development of methods for the automated generation of consensus models . Manual Regarding a GSM’s predictive mathematical model character, it is important to note that remaining inconsistencies in a consensus model can have severe effects, for example, when inconsistencies resulting from model merging are not adequately addressed. As a consequence, individual GSMs may outperform a consensus model in terms of predictive ability even though the latter is more representative of the available information. COMMGEN’s aim (and design) is to compare and reconcile IGSMs in order to obtain a high-quality representation of the IGSMs’ combined information. In contrast to model optimization methods such as GrowMatch , COMMGENP. putida, we argue that (semi-) automatically generated consensus models provide the basis for additional improvements due to their comprehensiveness and standardized naming system. Gap-filling methods [As demonstrated by our case study for  methods ,39 may b methods ,40 to re methods  can reso methods ,42 may a methods .More generally, the systematic integration of heterogeneous information is an essentially unsolved challenge in (post-)genomic biology. For metabolism, consensus GSMs are formalized means for complementing incomplete information, and for identifying and addressing errors through the comparison of independently generated GSMs for the same organism. COMMGEN automatically identifies and semi-automatically resolves widespread and highly interlinked inconsistencies between initial GSMs, thereby moving beyond existing approaches for manual and computer-aided consensus model generation. It can therefore facilitate the construction of new models by comparing and combining information from automatic model construction tools such as the modelSEED  and manuWhile we focus here on the reconciliation of multiple GSMs for the same species, we argue that COMMGEN’s methods and standardization are more widely applicable. The identification of similar, yet distinct, biochemical entities can help to compare metabolic capabilities of organisms in detail via their GSMs, or even to compare entire pathway databases. However, dealing with different species will require new, systematic preprocessing steps to map gene sets in different organisms functionally to each other , which is a topic of future research. In addition, COMMGEN’s methods for identifying redundancies and hierarchical relationships in networks can be used to further advance standardization of terms and ontologies. We therefore expect COMMGEN to be of substantial aid in future integration of knowledge for metabolic networks, to greatly accelerate model-building processes and to thereby improve subsequent high-throughput model-based network analyses. Although COMMGEN will not directly address the domain-specific problems, these capabilities will lay a solid foundation for the systematic, genome-scale comparison of metabolic spaces within and across genera and will have substantial impact for large-scale evolutionary analyses, design of microbial communities, and understanding of host-microbe  interactions.http://sysbio3.fhms.surrey.ac.uk/. iNJ661 was obtained from the supplementary files of the corresponding paper. The remaining models were taken from the model repository at www.metanetx.org. See iJN746 and iJP962 were requested from and received by email from the first authors of the corresponding papers. GSMN-TB was downloaded from −6) was used whereas a minimal relative growth rate (30%) to the wild type was used for mutant strains. See For comparison to experimental data, the models were loaded into the COBRA toolbox . The bouIn a metabolic network, reaction nodes are only connected to the metabolite and gene nodes that are involved in the corresponding reaction. Similarly, metabolite and gene nodes are only connected to reaction nodes. However, reaction nodes are not informative for the identity of metabolites as two metabolites representing the same chemical compound are non-overlapping in their connected reaction nodes. Therefore, we characterize metabolites by the other metabolite and gene nodes that are connected to the same reactions. We use this information to quantify how similar metabolites from different models are based on their network context. These similarity scores are then compared to the scores of metabolites that are known to match because they are present in both models: pairs of metabolites that score comparable to these shared metabolites may consist of functionally equivalent chemical compounds. We use a user-defined percentile of shared metabolite scores as a threshold to identify similar metabolites. The method is described in the following:m (m x m) where a 1 indicates that the two metabolites share a reaction.We create a Boolean metabolite-to-metabolite matrix Mg (g x m) where a 1 indicates that the metabolite and gene share a reaction.We create a Boolean gene-to-metabolite matrix Ma ((m + g) x m) by vertically concatenating Mm and Mg.We create an attribute matrix Ma by dividing each row by its sum such that the numbers in each row sum up to 1. Thereby, the values in Ma reflect both that a metabolite is connected to a metabolite or gene and how rare (defining) this connection is.We normalize Ma that correspond to metabolites and genes that are not included in both models for these cannot aid in the identification of common metabolites between the models.We discard rows from Ma that correspond to metabolites that are identified to be the same in both GSMs.We discard the columns from Ms (m x m) where the number at position i,j corresponds to the Pearson’s correlation coefficient between columns i and j of Ma.We create a scoring matrix Ms using a minimal score. The minimal score equals a user-defined percentile of scores for metabolites that are present in both models.We distinguish between similar and non-similar metabolites in MA lumped reaction is an artificial reaction that represents the net effect of multiple individual reactions. Therefore, if the lumped and non-lumped representations carry flux in opposite directions, steady state is maintained as they cancel each other out. We use this property to identify lumped reactions by linear programming. The method is described in the following:We determine the directionality for each reaction as forward, backward, or reversible.We transform each reaction such that it only runs in the forward direction; backward reactions are reversed and reversible reactions are split into two reactions.We update the stoichiometric matrix S (m x r) accordingly.We remove the boundary reactions from S as these reflect exchanges of metabolites between the organism and the medium.We define the linear programming (LP) problem:We initiate the variables of the LP problemc: Vector (1 x r) containing the objective coefficient for each reaction. We set each value to -1 to penalize flux through each reaction; this ensures that the total flux in the network is minimized.lb: Vector (1 x r) containing the lower bounds of each reaction. As all reactions are forward reactions, every value is set to 0.ub: Vector (1 x r) containing the upper bounds of each reaction. As all reactions are forward reactions, every value is set to 1000.b: Vector (m x 1) containing the desired accumulation or dissipation of each metabolite. Each value in this vector is set to 0 to ensure a steady-state flux distribution.LR to be considered as a lumped reaction. We set:We select a reaction LR with index ic(iLR) = -1000lb(iLR) = -1.LR is thus now allowed to carry flux in the backward direction, which results in a positive contribution to the objective value.We run the LP problem as defined under step v.x that either only contains zeros (no non-lumped representation available), or contains a flux distribution such that the flux through LR is maximized in the reverse direction while having a minimal flux through the rest of the network. In the first case, we skip steps ix and x. In the latter case, we identified a set NL1 of corresponding non-lumped reactions.The LP problem returns a flux distribution 1 for future reference.We save the set NLx may be identified.We modify the LP problem such that any alternative sets NLc(NL1) = 3 x c(NL1)1 such that it becomes more ‘rewarding’ to use other reactions.This effectively further penalizes flux through the reactions of NL1 by NL2, NL3, …) until:We repeat steps viii-x , orThe number of reactions in NLx.There is a recurring set NLx such that only sets remain that overlap to a pre-defined extent in gene associations with LR.We filter the different sets NLWe repeat steps v-xi such that we obtain sets NL for each reaction in the model.Alternative transport reactions result in the transport of a metabolite between two compartments with a different net reaction. We identify metabolites with alternative transport reactions one metabolite at a time. If a metabolite is present in two or more compartments, we identify all transport reactions for this metabolite by selecting reactions where the metabolite is on both sides of the equation. If two of these reactions transport the metabolite between the same two compartments, these reactions are alternative transport reactions.Invalid transport reactions are reactions that transport metabolites between two unconnected compartments. We identify these by forming a list of all compartments that are directly connected through transport reactions in the IGSMs and asking the user to indicate if any of these are invalid. For any of the invalid compartment connections, we identify reactions that contain metabolites from both compartments; these reactions are invalid transport reactions.cmp (m x r) for each compartment. These matrices only contain reactions of which all metabolites are in the same compartment. Columns (reactions) that are identical between these matrices represent identical reactions with an alternative compartmentalization.We create a separate stoichiometric matrix SIn the MnXRef namespace, metabolites with an unclear compartmentalization are placed in the compartment UNK_COMP. For each reaction that contains a metabolite in UNK_COMP, we identify reactions from the other IGSM(s) that involve all metabolites with known compartmentalization similarly to the identification of alternative stoichiometries. These reactions are then filtered for reactions that also involve the metabolite with the unknown compartmentalization.Boundary (exchange) reactions are artificial reactions that represent the exchange of metabolites with the medium. They only involve a single metabolite, and have no metabolites on the other side of the equation. In some models these reactions are lumped together with transport reactions that import metabolites from the extracellular compartment. After the MnXRef namespace conversion these reactions are still annotated as boundary reactions, and are thus easily identified in COMMGEN by searching for boundary reactions with non-extracellular metabolites.To combine GSMs with an alternative compartmentalization, it is sometimes most straightforward to remove a compartment ‘RC’ from a GSM and move its reactions to a different target compartment ‘TC’. We defined four categories of reactions in RC, which are treated differently when RC is removed: (i) Reactions that only involve metabolites from RC are moved to TC; (ii) Multi-compartment reactions that transport a metabolite between RC and TC are removed; (iii) Multi-compartment reactions involving RC and TC that involve a chemical conversion are kept, but all metabolites from RC are placed in TC; (iv) Multi-compartment reactions involving RC and a metabolite other than TC are kept, and all metabolites from RC are placed in TC.dbl (m x 2r) that contains the normal stoichiometric matrix S (m x r), as well as its negative -S (m x r). We then identify columns (reactions) in Sdbl that are identical.Identical net reactions are reactions that involve the same set of metabolites in the same stoichiometries, but they may be defined in opposing directions. Therefore, we create a double stoichiometric matrix Slog (m x r). We then identify columns in Slog that are identical; these correspond to reactions involving the same metabolites, but in different stoichiometries.We convert the S (m x r) matrix to a Boolean (0/1) representation Srdx (m+1 x r) by adding an artificial metabolite ‘redox pair’. Then, for each reaction that involves a redox pair, we put the stoichiometric coefficients of the redox metabolites in Srdx to ‘0’, and add a ‘1’ in the ‘redox pair’ row instead. We then use the same approach as for the identification of alternative stoichiometries to identify reactions that only differ in stoichiometries and redox pairs.GSMs often differ in their involvement of redox pairs in any particular reaction. The first step in identifying these inconsistencies is the creation of a list of redox pairs. COMMGEN comes with a list of commonly used redox pairs in the MnXRef namespace, and this list can be expanded by the user. COMMGEN can suggest expansions for this list by selecting metabolite pairs that co-occur frequently (≥ 80% of reactions). We identify reactions that are identical except for their redox pairs by expanding the stoichiometric matrix S (m x r) to Slog (m x r). For each column (reaction) we then identify other columns that contain nonzero elements on each row where the respective column has a nonzero element. These sets of columns (reactions) are potentially nested reactions. We then confirm these sets by detecting sets where two or more metabolites that are on the same side of the equation for one reaction, are on the same side of the equation for the other reaction.We convert the S (m x r) matrix to a Boolean (0/1) representation SSimilar reactions are reactions from different IGSMs that share a predefined number of genes, substrates and products. We identify similar reactions by constructing three sets of pairs of reactions: (i) reactions that originate from different IGSMs, (ii) reactions that share the required number of substrates and products, and (iii) reactions that share the required number of genes. All combinations of two reactions in each of these three sets are considered similar reactions.All computational simulations and analyses were performed using MATLAB . Gurobi COMMGEN uploads SBML files to MetaNetX.org , where thttps://gitlab.com/Rubenvanheck/COMMGEN.The COMMGEN version used for this paper is freely available as MATLAB code as S1 DatasetThis file contains the original models, the input models, the BCMs, and the RCMs for COMMGEN as well as an overview of the changes made between original and input models.(ZIP)Click here for additional data file.S1 ProtocolThis file contains the code that was used in order to obtain the data for (ZIP)Click here for additional data file.S2 ProtocolUpon the merging of reactions differing in gene rules a choice has to be made in how the final gene rule looks. This file shows how the consensus procedure as applied for this study affects the use of ‘OR’ and ‘AND’ operators.(ZIP)Click here for additional data file.S3 ProtocolThis file contains the scripts and reference data for the prediction of growth and no-growth phenotypes and subsequent creation of (ZIP)Click here for additional data file.S4 ProtocolThis file contains two example scripts of how to start with COMMGEN.(ZIP)Click here for additional data file.S5 ProtocolThis file contains the code that was used in order to obtain the ROC curve in (ZIP)Click here for additional data file.S6 Protocolhttps://gitlab.com/Rubenvanheck/COMMGEN.This file contains the code for COMMGEN and is recommended to use when running scripts from other additional files. However, we recommend obtaining the current version of COMMGEN from (ZIP)Click here for additional data file.


Chromohalobacter salexigens is a natural producer of ectoines, compatible solutes with current and potential biotechnological applications. As production of ectoines is an osmoregulated process that draws away TCA intermediates, bacterial metabolism needs to be adapted to cope with salinity changes. To explore and use C. salexigens as cell factory for ectoine(s) production, a comprehensive knowledge at the systems level of its metabolism is essential. For this purpose, the construction of a robust and high-quality genome-based metabolic model of C. salexigens was approached.The halophilic bacterium C. salexigens metabolic model (iFP764). This comprised an exhaustive reconstruction process based on experimental information, analysis of genome sequence, manual re-annotation of metabolic genes, and in-depth refinement. The model included three compartments , and two salinity-specific biomass compositions, partially based on experimental results from C. salexigens. Using previous metabolic data as constraints, the metabolic model allowed us to simulate and analyse the metabolic osmoadaptation of C. salexigens under conditions for low and high production of ectoines. The iFP764 model was able to reproduce the major metabolic features of C. salexigens. Flux Balance Analysis (FBA) and Monte Carlo Random sampling analysis showed salinity-specific essential metabolic genes and different distribution of fluxes and variation in the patterns of correlation of reaction sets belonging to central C and N metabolism, in response to salinity. Some of them were related to bioenergetics or production of reducing equivalents, and probably related to demand for ectoines. Ectoines metabolic reactions were distributed according to its correlation in four modules. Interestingly, the four modules were independent both at low and high salinity conditions, as they did not correlate to each other, and they were not correlated with other subsystems.We generated and validated a high quality genome-based C. salexigens metabolism at low and high salinity conditions, driving to low and high production of ectoines. In addition, it can be useful to optimize the metabolism of other halophilic bacteria for metabolite production.Our validated model is one of the most complete curated networks of halophilic bacteria. It is a powerful tool to simulate and explore The online version of this article (10.1186/s12934-017-0852-0) contains supplementary material, which is available to authorized users. Chromohalobacter salexigens is a moderately halophilic gamma-proteobacterium of the family Halomonadaceae, phylogenetically related to the genus Halomonas . Interestingly, all the four modules were independent both at low and high salinity conditions, as they did not correlate to each other, and they were not correlated with other subsystems , the second module corresponded to the futile cycle for ectoine synthesis and degradation  , the thiems Fig. .P-uridylyl transferase) and glucose-6P . Both products are precursors of trehalose-6-phosphate. As observed for ectoines metabolism, this correlation pattern of the trehalose metabolism module did not vary with salinity  formed a separate module including internal reactions of central C subsystem, particularly those involved in the conversion of glucose-1-P into UDP-glucose , independent from other subsystems, and with no interdependence pattern variation in response to salinity. It is plausible that reactions included in each module respond to a similar regulation, and/or that a coordinated functioning of cyclic modules is necessary to adapt solute compatible metabolism, and therefore metabolic osmoadaptation, to external conditions. Our results are only an example (“the tip of the iceberg”) of the potential of the joint rational use of computational tools, together with experimental data, in order to explore metabolism in halophilic microorganisms.The predicted modular distribution of correlated compatible solutes metabolism sets of reactions is very intriguing, and deserves experimental work to better understand iFP764 model is a powerful tool to simulate and explore C. salexigens metabolism at different salinity conditions. This halophilic bacterium has one of the broadest salinity range of growth, and is considered as a model to study osmoadaptation. Notwithstanding the existing limitations in reconstructing and validating a C. salexigens specific network, the iFP764 model is one of the most complete and curated networks of halophilic bacteria, and accounts for all existing relevant experimental information. Thus, it may become an important tool to understand the metabolism of halophilic microorganisms, helping to drive the generation of new experimental hypotheses. In addition, C. salexigens is considered as a good cell factory for ectoines, whose production is salinity-dependent. Therefore, this model could be a robust and reliable tool to be used for the design of new C. salexigens strains optimized for the production of ectoines and other biotechnologically interesting compounds, avoiding the expenditure of resources.The information resulting from this work showed that the Additional file 1: Table S1. Biomass composition at high salinity (´´BIO_H``). Table S2. Biomass composition at low salinity (´´BIO_L``); Table S3. Composition of M63 minimal medium and the exchange reactions formulated to simulate the uptake of metabolites from medium; Table S4. In silico  constraints used for simulation at high or low salinity; Description of the bottom-up building and exhaustive manual refinement of the C. salexigens metabolic reconstruction iFP764; Table S5. The total number of dead ends metabolites found in the iFP764 model: root no-production metabolites and root no-consumption metabolites; Table S6. In silico prediction of utilization of various metabolites as carbon sources in C. salexigens; Figure S1. Selected histograms of possible flux values obtained in the first scenario by Monte Carlo sampling at low and high salinity relative to salinity-specific glucose consumption rate.Additional file 2. Genome-scale metabolic reconstruction of C. salexigens. Details of iPF764 containing all genes associated-reactions and metabolites.Additional file 3: Table S7. Essential genes at low and high salinity and their associated COGs.


Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum, a leading cause of malaria, has a complex life cycle [ Anopheles genre is responsible for all the transference of malaria from one patient to another [ Plasmodium falciparum lifecycle comprises three (3) important developmental stages: the mosquito stage, the liver stage, and the blood stage [ Sporozoites injected into a host by a mosquito that is infected travel to the liver and begin the hepatic stage of the life cycle of the Plasmodium by invading hepatocytes. Here, they get to increase and segregate into schizonts, then comprising numerous hepatic merozoites. All of these merozoites are successively set loose into the blood where the erythrocytic stage is initiated and begins by invading and duplicating inside the red blood cells (RBCs) [fe cycle , 2. The  another , 2. Desp another –5. The Pod stage . Sporozos (RBCs) , 7. Plasmodium falciparum (P. f.) in cells that are infected would be quite a potential source of targets for novel drugs but it is complex and difficult to understand intuitively. In silico methods can handle and take care of this complexity. They also give room for integrative analyses of the cell metabolism [ P. f.'s resistance to most identified antimalarial drugs has given rise to the increase of antimalarial drug discovery research [ P. f. genome, the reconstruction of the metabolic network is required for a comprehensive understanding of the molecular mechanisms of the organism [ P. f. This study identifies new reactions as potential targets for drugs in the metabolic network of P. f. that contributes to its survival in the host and validates predicted drug targets. The computational model used in this paper is an enhancement of the computational model used in our preceding paper [Evidences abound to the fact that the parasite is already developing resistance to many front-line antimalaria therapies. Therefore, novel antimalaria cures are in immediate need to combat the drug-resistant malaria parasite , 8. The tabolism . In silitabolism  and facitabolism . They hatabolism , 8, 12. research , 13, 14.research . Due to organism , 17. Thoorganism . Thus, tng paper . The comThe malaria parasite metabolic pathways are quite different from those of its human host. This uniqueness can be exploited in the design of therapeutic strategies –21. MetaIdentifying essential reactions in a metabolic network allows the identification of potential drug targets in the network , 37. Essk-shortest paths passing through metabolites and its closest neighbour links is defined as the load point of a particular metabolite in a metabolic network. The usefulness or the importance of a particular metabolite in the metabolic network of an organism is determined by load points and choke points [k-shortest paths passing through them. When a choke point in an organism is absent, the organism can rarely survive [Flux balance analysis (FBA) is an in silico method used in gaining deepened understanding into the abilities and the metabolic behaviour of a cell , 42, 43.e points –60. The  survive , 60. The survive , 62. TMF survive , 62. TMF survive , 61, 62. survive . MCA is  survive . MCA stu survive . The app survive .Perturbation is an approach generally applied to study the conduct and atomic components underlying cellular systems . A pertu Plasmodium falciparum metabolic network was identified and proposed as potential drug target for Plasmodium falciparum.In this study, the essentiality of the different reactions was determined. Therefore, a list of indispensable reactions in the Plasmodium falciparum which was extracted from the BIOCYC flat file database version 19.5 [ Plasmodium falciparum from [ P. falciparum 3D7 from PlasmoDB and the version of KEGG as of July 2014. The BIOCYC identifiers were chosen for this study as the generally accepted means of identification. Plasmodium falciparum reactions gotten from Chiappino-Pepe were mapped to BIOCYC reactions via enzymes commission numbers and common name. The reconstructed metabolic network of Plasmodium falciparum is made available as SBML file with some reactions considered to be reversible and some considered to be irreversible. In this study, currency metabolites of the 24 currency metabolites outlined by [In this study two different resources were considered for the reconstruction of the metabolic network, which is the genome-scale metabolic dataset of the 3D7 strain ofion 19.5  because lined by  were rem Plasmodium falciparum metabolic network was created leading us to perform an in silico knockout experiment and analysis on the metabolic network enabling us to analyse the network for the essentiality and perturbation of the knocked-out reactions and we also moved further to determine the perturbations and essentiality of all reactions in the network. When a transition (reaction) is knocked out, all reactions that have a corresponding reactant or product of the knocked-out reaction are equally knocked out, helping to ascertain the effect of that reaction to the network. Single knockout analyses were performed and our results were outputted in  .txt formats to list the reactions that were affected after knocking out a specific reaction.Once the metabolic network was reconstructed, aIn the network a reaction is knocked out to determine the dependent reaction on the knocked-out reaction. The procedure for determining which reaction is connected to the knockout is outlined in the following steps.Start.Get the SBML file.Extract all reactions in the file.Extract all products and reactants related to the various reactions.Identify reaction to knockout initially.Get other reactions that are linked to the identified reaction which can be knocked out.Determine if the other reactions are linked to other reactions.If true, do not knock out the reaction; else knock out the reaction.Repeat Steps If there are no other reactions to be knocked out, attach the resulting reactions to the initial reaction.Assign “knocked-out reactions” as the list of resulting reactions attached to the initial knocked-out reaction.Repeat Steps Determine the essentiality of each reaction by comparing the knocked-out items to the total network.Determine the most essential reactions by comparing the results of Determine the least essential reactions by comparing the results of Extract result to a spreadsheet file.Make visualisation of the result in the spreadsheet file.Stop.All above steps are performed for all reactions in the network enabling this study to predict the essentiality of all reactions in the network and exporting them in hierarchical order. ri represents the list of reactions, E(r) represents the essentiality of a reaction in the reconstructed metabolic network, k(ri) represents the knocked-out reactions in the reconstructed metabolic network, N(ri) represents a reaction in the network, ∑k(ri) represents the summation of the knocked-out reactions in the reconstructed metabolic network, and ∑N(ri) represents the summation of all reactions in the network.In this study reconstructed network, we determined the essentiality of every reaction in the network and proposed some essential reactions and validated the essentiality of previous proposed reactions in literature. The essentiality of all reactions is saved in a  .txt file for easy access. The formula for determining the essentiality of every reaction in the network is outlined in the following steps: Plasmodium falciparum which mostly have been predicted in several literatures to be druggable was considered when testing and validating our model and these 77 essential reactions are given in the supplement  predicted most essential reactions based on our method in the network are represented in Computationally predicted essential reactions from six different literatures were compared with our method. Ten reactions that were common to over 80% of all literature considered in this study were identified and validated to be essential by our method, thereby confirming our method as valuable computational technique for validation of predicted drug target as given in The reactions that were knocked out from the network when the top seven (7) most essential reactions from our network were knocked out are given in the Supplementary  Plasmodium falciparum which includes 24 new potential candidates for drug targets of which 9 are orphans and 7 potential candidates for drug target which has been predicted computationally in literature of which 1 is an orphan. The reactions are presented in The predicted essential reactions based on the analysis of our network are given in the Supplementary  Plasmodium falciparum and identifies reactions in the network that are essential to the survival of the network according to its essentiality. These essential reactions are predicted as potential drug targets for Plasmodium falciparum; the essentiality of the reaction in the network is listed according to its effect on the network when knocked out (the amount of reactions knocked out when the parent reaction is knocked out). This study identifies that there are over 200 essential reactions in the network of which, among the top 7 predicted most essential reactions, 4 were identified to be found in the gold standard which includes superoxide dismutase, 3-phosphoshikimate 1-carboxyvinyltransferase, 5-O-(1-carboxyvinyl)-3-phosphoshikimate phosphate-lyase, and adenosylhomocysteinase and 1 was already predicted as essential computationally which is methionine adenosyltransferase. The result of our study was compared with an extensive list of 77 essential reactions with biological evidence. We finally present a polished list of 31 including 24 new potential candidates for drug targets of which 9 are orphans and 7 potential candidates for drug target which has been predicted computationally in literature of which 1 is an orphan. This model also helps to improve the understanding of the biological processes within this network and any other metabolic network. It would be quite exciting to further our research by confirming our in silico predictions experimentally and also test if our essential reactions can be successfully targeted without collateral partial or complete targeting of the corresponding human reactions. Potential candidates for drug targets for Plasmodium falciparum already biologically proven were disregarded from our list as our method validated a number of them. The method developed could handle multiple knockouts but we plan to do this when we have some amount of drug combination to validate our method. The method used in this study is capable of predicting essential reaction in any other organisms of a robust genome-scale metabolic network.This study established a novel method that performed an analysis on the genome-scale metabolic network ofThe dominance of malaria in resistance to identified antimalarial drugs in current circulation has given rise to the increase of antimalarial drug discovery research. Hence, researches on the development of novel drug targets which would serve as effective solutions for malaria treatment are urgently needed. P. f. metabolic reactions under different growth conditions and perturbations. We have been able to identify new essential reactions as possible targets for drugs in the metabolic network of P. f. that contributes to its survival in the host and validate predicted drug targets. The computational model used in this study enhances and enables computational analysis of large dataset.In this study, a novel computational model was constructed which makes the validation of predicted antimalarial drug target cheaper, easier, and faster as well as the validation of P. f. available and provide guidance to experimental studies helping to develop a better description of P. falciparum metabolism and to identify antimalarial drug targets.Generally, the results from this study make a deep understanding of the metabolism of


Saccharomyces cerevisiae.Natural and industrial environments are dynamic with respect to substrate availability and other conditions like temperature and pH. Especially, metabolism is strongly affected by changes in the extracellular space. Here we study the dynamic flux of central carbon metabolism and storage carbohydrate metabolism under dynamic feast/famine conditions in DW−1 h−1 is observed, no overflow metabolites are detected. From famine to feast the glucose uptake rate increased from 170 to 4788 μmol gDW−1 h−1 in 24 s. Intracellularly, even more drastic changes were observed. Especially, the T6P synthesis rate increased more than 100-fold upon glucose addition. This response indicates that the storage metabolism is very sensitive to changes in glycolytic flux and counterbalances these rapid changes by diverting flux into large pools to prevent substrate accelerated death and potentially refill the central metabolism when substrates become scarce. Using 13C-tracer we found a dilution in the labeling of extracellular glucose, G6P, T6P and other metabolites, indicating an influx of unlabeled carbon. It is shown that glycogen and trehalose degradation via different routes could explain these observations. Based on the 13C labeling in average 15% of the carbon inflow is recycled via trehalose and glycogen. This average fraction is comparable to the steady-state turnover, but changes significantly during the cycle, indicating the relevance for dynamic regulation of the metabolic flux.The metabolic flux reacts fast and sensitive to cyclic perturbations in substrate availability. Compared to well-documented stimulus–response experiments using substrate pulses, different metabolic responses are observed. Especially, cells experiencing cyclic perturbations do not show a drop in ATP with the addition of glucose, but an immediate increase in energy charge. Although a high glycolytic flux of up to 5.4 mmol gComparable to electric energy grids, metabolism seems to use storage units to buffer peaks and keep reserves to maintain a robust function. During the applied fast feast/famine conditions about 15% of the metabolized carbon were recycled in storage metabolism. Additionally, the resources were distributed different to steady-state conditions. Most remarkably is a fivefold increased flux towards PPP that generated a reversed flux of transaldolase and the F6P-producing transketolase reactions. Combined with slight changes in the biomass composition, the yield decrease of 5% can be explained. Systems biology pursues the understanding of biological systems by unraveling their structure and dynamics . In part13C wash-in experiments a bias in the labeling enrichment of glycolytic intermediates was observed, caused by a transient influx of 12C into glycolysis [It has been observed that storage carbohydrates react sensitive and fast to changes in extracellular glucose concentration . Severalycolysis . Recentlycolysis , 8. Henc13C-labeling experiment where the system is at both, metabolic and isotopic non-stationary state, would significantly increase the accuracy of the kinetic parameter estimation. Recently, Abate et al. [To study the dynamic interaction of trehalose and other storage intermediates with the central carbon metabolism, intracellular flux estimation is required. An interesting approach consists in collecting time-series of metabolic concentration measurements from dynamic experiments to describe the dynamic response of a pathway to a particular stimulus , 10 compe et al.  have pro13C tracer experiments are performed to quantify the short-term in- and outflows of the large intracellular trehalose pool as well as central carbon metabolism. The concentration of glycolytic and PPP metabolites as well as enrichment dynamics are monitored over several cycles and a piecewise affine flux approximation in time is used to quantify a dynamic flux profile. Since we focus on the short-term response by monitoring the metabolic activity (intracellular concentrations and fluxes) during cycles of 400 s, it is assumed that the metabolic flux is mainly controlled by metabolic interactions.Here, dynamic Saccharomyces cerevisiae CEN PK 113-7D  was used. Cells from one cryovial (glycerol at −80 °C) were used as seed culture and incubated  for 10 h in 1 L-Erlenmeyer flasks containing 100 mL of minimal medium that contained 13.6 g L−1 glucose. The pre-culture was used to inoculate a 7 L bioreactor  with a working volume of 3.894 L. A low-salt Verduyn minimal medium [−1 was used. No ethanol was added to the medium because oscillations were not observed during cultivation. The broth was sparged with pressurized air at 0.992 L min−1 (approx. 0.25 vvm) and the vessel was maintained at an overpressure of 0.3 bar. The pH of the broth was controlled at 5.0 by adding either 4 M KOH or 2 M H2SO4. Temperature and agitation were kept at 30 °C and 600 rpm, respectively, during the complete cultivation. The culture was starved for 2 h after the end of batch phase, which was determined by a strong and fast CO2 decrease and dissolved oxygen (DO) increase to almost saturation. The chemostat phase at a dilution rate of 0.1 h−1 was started and run for about 5 residence times. At this time, the culture was assumed to be at steady-state as indicated by a constant CO2 and O2 concentration in the off-gas , and a constant biomass density (3.64 ± 0.05 gDW L−1).In this work, the haploid yeast l medium  with a g−1, the same amount of fresh medium was added , but block-wise. The feed was switched on for 20 s under control of an automatic timer  feeding 43 mL. In the following 380 s there was no feed addition to complete a total cycle time of 400 s. To maintain the broth volume constant (max. 3.894 + 0.043 L), weight control was used, i.e., 43 mL were withdrawn but during the first 260 s at a flow rate of 0.166 ± 0.001 mL s−1.After 5.3 residence times of running in chemostat mode, the continuous feed was changed to a block-wise regime to start the feast/famine cultivation Fig. . In orde2, O2 and DO in consecutive cycles. Samples were taken for determination of metabolite concentrations and/or dry weight (3.46 ± 0.01 gDW L−1). Right after the sampling for concentrations, the medium was shifted to perform the labeling experiment. The new medium was formulated to have the same components and concentrations as the base medium but with uniformly 13C labeled glucose . The labeled medium feed line was prefilled to avoid any perturbation on the cycle time.Initially, unlabeled glucose was used as substrate during the feast/famine cycles for 50 h. After 5 residence times a repeating dynamic state was reached indicated by stable profiles of COBroth samples for the measurement of extracellular and intracellular metabolite concentrations were rapidly withdrawn using two separate sampling ports during three subsequent cycles at defined cycle times  to obtain duplicate measurements. Samples for the measurement of mass isotopomer fractions were withdrawn at comparable cycle times after switching to the labeled feed.2 and CO2 volume fractions in the off gas were measured by a combined paramagnetic/infrared NGA2000 analyzer .For the measurement of extracellular metabolite concentrations, approximately 1.5 mL of broth were withdrawn into a syringe containing pre-cooled (−20 °C) stainless steel beads (approx. 26 g). The cooled broth (~ 1 °C) was immediately filtered as described by . Extraceg for 5 min at 1 °C, processed and stored according to [13C cell extract.For the measurement of intracellular metabolite concentrations, 1.0 ± 0.05 mg-broth was rapidly withdrawn and quenched in 5 mL cold (−40 °C) pure methanol  and procrding to . Samplesrding to  and/or Lrding to , 19. ForThe metabolic network used for constructing the stoichiometric model was based on the consensus model reported for yeast  but signBeing the entry gate to central metabolism, the dynamics of glucose uptake play a critical role . With th13C-labeling enrichment showed a decreasing pattern after the feeding phase, indicating a  source of unlabeled glucose. There was no unlabeled glucose added from the feed and putative artifacts from sampling where excluded . The dilution in the 13C-enrichment of extracellular glucose has also been observed in cultures of Penicillium chrysogenum [Interestingly, the ysogenum . We hypoMetabolites of the upper glycolysis showed a concentration pattern resembling the time-course of extracellular glucose, though with a delay of about 40 s to reach the concentration maximum (i.e. around 60 s after the start of the cycle). The concentration of metabolites in the lower glycolysis exhibited dynamics inverse to upper glycolytic intermediates, which is in agreement with previous findings during pulse perturbations . ComparaMetabolites of the pentose phosphate pathway showed a response, in both concentration and labeling, that was similar to glycolytic metabolites with the exception of Sed7P Fig. .Fig. 3MegDW−1 to a maximum at 12.39 ± 0.18 μmol gDW−1 in 60 s, which confirms the highly dynamic nature of the feast/famine experiment. The concentration and 13C-labeling of G6P, F6P and G1P are very comparable, indicating that PGI and PGM operate near equilibrium, i.e. these are bidirectional as reported previously [Acting as a first branchpoint, G6P plays an important role in the upper part of the glycolytic pathway, PPP and storage carbohydrate synthesis , 25 obseeviously , 25. In 13C-labeling pattern of G1P and UDPG resembled the G6P labeling, the enrichment profile of T6P showed significant differences, especially a delay of about 40 s and then, after about 100 s, a strong de-enrichment , and the 13C-enrichment reached 4.3% at the end of the first cycle . The constant labeling enrichment indicates that there was no synthesis during this period (180–400 s). At the same time, there is a decrease in the concentration which is in agreement with the observed entry of low 13C-enriched material into glycolysis (G6P).Trehalose showed the highest concentration of all the measured pools  suboptimal placement of breakpoints for some metabolites and (2) potentially missing reactions with influence on the labeling patterns. Nevertheless, key features of the measurements are reproduced over the complete three cycles, which increases the confidence that the most important features are included and the calculated fluxes are a reasonable estimation.The model can reproduce most of the DW−1 h−1 during the first 24 s after the perturbation. The average flux during the cycle, 1253 μmol gDW−1 h−1, was the same as during the steady-state at the corresponding growth rate (1252 μmol gDW−1 h−1 at D = 0.1 h−1). This rate is higher than the glucose feeding rate (1156 μmol gDW−1 h−1) which originates from a glucose recycle through the extracellular space as will be discussed later.Calculations show that the net fluxes in glycolysis followed the pattern of glucose uptake (reaction r1_1) as response to glucose perturbation Fig. . Some glDW−1 h−1), a steep decrease between 24 and 69 s and then further decreasing to the value at the end/beginning of the cycle, 171 µmol g-DW−1 h−1. The downstream glycolytic reactions  resembled the uptake profile with flux values that were about two-thirds of the glucose uptake rate. In addition, with each step the flux profile became smoother, i.e. lower maximum peaks and slower decrease indicating a slight buffering effect of the intermediates.The dynamics of the uptake rate mostly follows the extracellular glucose concentration, a strong increase with the onset of the feeding (max of 4788 µmol g-−1). Similar behavior was observed for transaldolase (reaction r2_5) and the E4P-consuming transketolase (reaction r2_6).The metabolic flux of the pentose phosphate pathway is different to that of the glycolysis Fig. . After aAlthough exhibiting some delay, reactions of the trehalose synthesis (reactions tre_1 and tre_2) were activated by the glucose perturbation Fig. . Especia profile . CorrespThe intracellular degradation of trehalose (reaction tre_deg) decreased directly after the glucose addition. The extracellular degradation of trehalose (reaction r7_2) seemed to be active in phases with low glucose availability  and subsequently degraded in the extracellular space (reaction r_7_2),A glycogen breakdown followed by the export of the resulting glucose (reactions r14_4). Upon inspection of both routes, it can be observed that extracellular trehalose degradation accounts for about 40%, while glycogen degradation contributes 60% of the unlabeled glucose. Trehalose export and degradation (when other carbon sources were depleted) has been observed previously by  under baComing back to the initial hypothesis for the sources of the observed glucose de-enrichment, the model contained Fig. :A glucosThe synthesis of glycogen followed a different pattern to that of trehalose during the feast/famine. While the trehalose was mostly synthesized during high glucose availability, glycogen synthesis from UDPG (reaction r_14_3) showed a minimum of 53 orted by , 24.S. cerevisiae there could be side-activities of another, related kinase.The measured T6P enrichment pattern Fig. , showing13C flux estimation to the thermodynamic driving force (1-Q/Keq) of some reactions operating near equilibrium.Metabolic reaction directions follow the thermodynamic driving forces (TDF) , 14. Esp(in vivo) Keq. There may be changes in TDF, especially when changing conditions are expected . Here, we found that the mass action ratio (Q-ratio) of the PGI reaction tends to keep constant for all the calculated fluxes, as it is usual for reactions close to equilibrium. However, there is a slight tendency to decrease when the flux is higher. While the TDF would approach a zero value in case of a Keq as reported by [Contrary to the suggestion of a negative TDF for the PGI reaction , we founorted by , the TDForted by . Maximalorted by .Fig. 8AcThe metabolic flux estimation suggests a change in flux direction in PGM (r14_1) from G1P synthesis to G6P production towards the end of the cycle. Our results are in agreement with expectations from  since thTo compare the metabolic activity with steady-state observations, the average fluxes during the cycle were calculated. The average flux of the glucose uptake (r1_1) equals to 1253 The imported glucose is then distributed over different pathways at the level of G6P Fig. . Based oIn contrast to steady-state, under the dynamic feast/famine regime flux direction changes are observed. Especially, the PGM flux changes to the direction G1P → G6P between 364 s of the previous cycle to 2.5 s of the subsequent cycle. Similarly, a change in the flux direction of transaldolase (TA) and the F6P-producing transketolase (TK2) is observed, although only for very short periods. While during the steady-state TA run backwards (producing Sed7P) with a flux of about 9 In this work we studied the dynamics of central carbon metabolism as well as trehalose and glycogen under repetitive perturbations, i.e. a block-wise feeding regime. The intracellular metabolites respond rapidly to the extracellular perturbations with up to ninefold change during the cycle. The perturbation led to a highly dynamic flux response, especially, the glucose uptake increased from 170 to 4788 13C in extracellular glucose. A further part of the de-enrichment originates from the estimated glycogen degradation flux.We found that in average about 15% of the consumed glucose is recycled, a significant fraction through trehalose in the intra- but also extracellular space. This was surprising, but in line with the reported trehalose Agt1p transporter  and a thNext to slightly reduced storage turnover, there were differences between the reference steady-state flux distribution and the average flux during the feast/famine regime:(1) A fivefold increase in the oxidative pentose-phosphate flux. This higher flux generated a reversed flux of transaldolase and the F6P-producing transketolase reactions as compared to flux distributions at steady-state conditions.Keq seems to be still inconsistent in literature.Interestingly, during the short period of the cycle, some reactions do change direction. Especially, ribulose-phosphate-3-epimerase, transketolase and aldolase operated backwards towards the end of the cycle when glucose availability was low. Similarly, PGM switches from G6P consumption to G6P production. The observed concentration changes are proportional to the flux estimations, but in vivo 13C-profile was found for T6P that suggests a putative mechanism for trehalose phosphorylation. If this reaction was not considered, it was not possible to reproduce the experimental observations since the 13C-enrichment of G6P and UDPG remained high. The flux of this putative reaction was about 30% of the T6P-phosphatase flux and was estimated higher when glucose availability was low.(2) Next to a de-enrichment in extracellular glucose, a decreasing 13C-labeling patterns to deviate dramatically. Nevertheless, the ability of the model and the estimated fluxes to reproduce most of the metabolic pools suggest that the method is robust and reliable. Deviations in labeling patterns of some pools could be expected due to missing reactions involving these pools or errors due to experimental conditions .Although the accuracy of flux estimations in this work have not been evaluated statistically due to its inherent complexity, the fluxes reported here correspond to those with the least-squares that simultaneously fit both concentration and labeling enrichment measurements. Thus, small changes in flux values would cause To obtain a more comprehensive understanding of the complete central carbon metabolism under dynamic conditions, a next step could be extending the present work to cover TCA cycle. This would demand more advance analytic and experimental techniques than the ones used here. Especially with relation to compartmentation of TCA cycle metabolites, a big challenge that can be resolved in the near future .13C labeling experiments shows that the metabolic response to changing substrate availability can unravel unreported metabolic behaviors.In this work the successful implementation of an experimental platform plus a calculation methodology for estimating fluxes based on dynamic 13C labeling patterns during multiple cycle perturbations. The approach can be applied to study metabolic responses to industrial conditions like substrate gradients but also changes in oxygen availability. While laborious, the approach can reveal intracellular cycles that can impact product yields and rates. In particular, upon strong changes in concentrations, some metabolic fluxes did not show strong responses indicating that the biological system is robust enough to withstand such changing environments. On the other hand, some reactions were found to change the direction of their metabolic flux as compared to steady-state fluxes. In addition, the observed dynamic behavior in glycogen and trehalose metabolism shows that at the beginning of the cycle, when substrate depletion has been reached, both glycogen and trehalose pools serve as a carbon source. This carbon utilization will proceed until a new carbon excess condition is present again.The proposed approach was robust and it was possible to reproduce experimental data of most dynamic metabolite concentrations and dynamic S. cerevisiae exhibits a different metabolic behavior from both batch and continuous cultures, that is, the feast/famine regime does not necessarily resemble the features typically observed in chemostats or in batch. For instance, this can be noted in the no presence of ethanol and glycerol during periods of carbon excess when S. cerevisiae is grown under mild feast/famine conditions.Finally, emphasis has to be made on the fact that a feast/famine cultivation of 2PG, 2-phosphoglycerate; 3PG, 3-phosphoglycerate; 6PG, 6-phospho gluconate; ALA, alanine; DHAP, dihydroxy acetone phosphate; E4P, erythrose-4-phosphate; F6P, fructose-6-phosphate; FBP, fructose-1,6-bis-phosphate; G1P, glucose-1-phosphate; G6P, glucose-6-phosphate; GAP, glyceraldehyde-3-phosphate; PEP, phospho-enol-pyruvate; PYR, pyruvate; Rib5P, ribose-5-phosphate; Ribu5P, ribulose-5-phosphate; SER, serine; S7P, sedoheptulose-7-phosphate; T6P, trehalose-6-phosphate; UDPG, UDP-glucose; UDP, uridine-5-diphosphate; UTP, uridine-5-triphosphate; X5P, xylulose-5-phosphate;ENO, phosphopyruvate hydratase; FBA, fructose-bisphosphate aldolase; G6PDH, glucose-6-phosphate dehydrogenase; GAPDH&PGK, glyceraldehyde-3-phosphate dehydrogenase + phosphoglycerate kinase; GPM, phosphoglycerate mutase; PFK, 6-phosphofructokinase; PGI, glucose-6-phosphate isomerase; PGM, phosphoglucomutase; PMI, mannose-6-phosphate isomerase; PYK, pyruvate kinase; RPE, ribulose-phosphate 3-epimerase; RPI, ribose-5-phosphate isomerase; TPP, trehalose- phosphatase; TPS, alpha,alpha-trehalose-phosphate synthase; TA, transaldolase; TK1, S7P-producing transketolase; TK2, F6P-producing transketolase.CER, Carbon evolution rate; DO, dissolved oxygen; IDMS, Isotope dilution mass spectrometry; OUR, Oxygen uptake rate; PPP, pentose phosphate pathway; SRE, Stimulus response experiment.


Arabidopsis thaliana with corresponding metabolic specificities of respective tissues in terms of sucrose and proton production/utilization. An ability of the model to operate under different light modes (‘light’ and ‘dark’) and correspondingly in different energy producing modes is particularly important in understanding regulatory modules.Sucrose translocation between plant tissues is crucial for growth, development and reproduction of plants. Systemic analysis of these metabolic and underlying regulatory processes allow a detailed understanding of carbon distribution within the plant and the formation of associated phenotypic traits. Sucrose translocation from ‘source’ tissues (e.g. mesophyll) to ‘sink’ tissues (e.g. root) is tightly bound to the proton gradient across the membranes. The plant sucrose transporters are grouped into efflux exporters (SWEET family) and proton-symport importers . To better understand regulation of sucrose export from source tissues and sucrose import into sink tissues, there is a need for a metabolic model that takes in account the tissue organisation of 2 under light) and heterotrophic (growing on starch in darkness), and the root was always considered as heterotrophic tissue dependent on sucrose supply from the mesophyll compartment. In total, the model includes 413 balanced compounds interconnected by 400 transformers. The structured metabolic model accounts for central carbon metabolism, photosynthesis, photorespiration, carbohydrate metabolism, energy and redox metabolisms, proton metabolism, biomass growth, nutrients uptake, proton gradient generation and sucrose translocation between tissues. Biochemical processes in the model were associated with gene-products (742 ORFs). Flux Balance Analysis (FBA) of the model resulted in balanced carbon, nitrogen, proton, energy and redox states under both light and dark conditions. The main H+-fluxes were reconstructed and their directions matched with proton-dependent sucrose translocation from ‘source’ to ‘sink’ under any light condition.Here, we describe a multi-compartmental model consisting of a mesophyll cell with plastid and mitochondrion, a phloem cell, as well as a root cell with mitochondrion. In this model, the phloem was considered as a non-growing transport compartment, the mesophyll compartment was considered as both autotrophic  contains supplementary material, which is available to authorized users. Arabidopsis thaliana. The integrated model should describe biomass growth both in light and in dark phases with corresponding formation and consumption of starch and sucrose. Furthermore, the structured metabolic model should take into account major pathways of primary metabolism such as sugar metabolism, central carbon metabolisms, photosynthesis, photorespiration, energy and redox metabolism, proton turnover, sucrose translocation from source to sink tissues and biomass growth. In future, we will use the model to recognize cause-effect relationships and describe regulatory processes in carbon metabolism and transport.The aim of this research was to build a multi-compartmental metabolic model of growing Arabidopsis thaliana ) –72. We cWhile interpreting of the FBA results, main attention was placed on the metabolic activity of the mesophyll, since in the model the mesophyll constituted 85 % (w/w) of the mass fraction within the plant biomass.2 to yield triose phosphate (GAP or DHAP) in the Calvin-Benson cycle requires a maximal theoretical ATP/NADPH ratio of 1.5  was higher in the dark condition than in the light condition  glycolysis in the cytoplasm, (ii) oxidative phosphorylation in the mitochondrion and (iii) ATP synthase in the plastid. The FBA of our model predicted that in light conditions  83.2 % of the cytoplasmic ATP turnover was supplied from mitochondrial metabolism and 16.8 % was provided by glycolysis and other cytoplasmic processes  and only 7.7 % of the cytoplasmic ATP in dark conditions  and consuming  reactions. The FBA of the model predicted active export of reduced equivalents from the plastid to the mitochondrion via the malate/oxaloacetate shuttle in form of malate and oxaloacetate during light conditions Fig. . HoweverThe main objective of this model was the reconstruction of the proton-fluxes created by the activities of the redox and energy metabolism under different light conditions and their relationship with the translocation of sucrose. The model accounted for main metabolic processes (e.g. reactions and exchange transport) that participated in proton turnover in all compartments and sub-compartments Table . For exaArabidopsis thaliana from the environment in symport with nutrients into the root , the major consumer of cytoplasmic protons was the mitochondrion Fig. . For exaAlso many metabolic processes resulted in proton production/utilization and therefore additionally contributed to the proton balance in the cytoplasm. Particularly in the light , the plastid provided GAP, which entered glycolysis downstream of sugar phosphorylation reactions, which are producing substantial amounts of protons  and a release of protons contributing to approximately 17 % of the cytoplasmatic proton pool. This proton excess was mainly utilized by the fully functional TCA cycle in mitochondria. Correspondingly, the mesophyll in dark conditions pumped less protons to the apoplast Fig. .FBA of proton fluxes allowed quantifying the net-fluxes of protons from phloem to mesophyll and root. Further, taking in account pH differences between compartments, the protons enter the root cytoplasm from the phloem along their concentration gradient allowing symport of solutes including sucrose via SUC and STP transporters. The sucrose flux to the root (as the sink tissue) was in accordance with proton symport mechanisms of SUCs and STPs and the proton balance of the tissues involved Figs. , 4 and 7Arabidopsis thaliana. The flux balance analysis (FBA) of the model quantified sugar metabolism, central carbon metabolism, photosynthesis, energy and redox metabolism, proton turnover, sucrose translocation from mesophyll to root and biomass growth under both dark- and light-growth conditions with corresponding growth either on CO2 (in the light) or on starch (in darkness). The model showed that in light conditions, interplay between photorespiration and photosynthesis including both cyclic and non-cyclic electron flow defined the ATP balance of the plastid. The plastid was found to be either deficient, self-supported or producing a surplus of ATP. The excess of redox potential from the photosynthetic light system was translocated to the mitochondrion via the malate/oxaloacetate shuttle. Thus, photosynthetically active plastids could achieve the ATP/NADPH ratio required for CO2 fixation. Also, the FBA predicted that the mitochondria were the main ATP provider for cytoplasmic processes together with glycolysis under both light- and dark-growth conditions. At the same time, the mitochondria were the main sink for reduced equivalents translocated both from plastid and from cytoplasm as well as for the protons.In this work we present a multi-compartmental metabolic model of growing Arabidopsis thaliana in light and dark conditions. The FBA was used to optimize the network structure and to prepare it for further analysis of the distribution of the intracellular fluxes based on experimental measurements by Metabolic Flux Analysis (MFA).The model described all main metabolic processes participating in the proton metabolism. The translocation of sucrose among plant tissues was associated with an integral balance of protons, which in turn was partially defined by operational modes of the energy metabolism . The proton fluxes predicted by FBA generally corresponded to the molecular mechanisms and functional peculiarity of the sucrose transporters SWEET and SUC/STP proton-symporters and net-flux of sucrose from source to sink tissue. Thus, our multi-compartmental model adequately described the growth stoichiometry and sucrose translocation processes of The primary information on the metabolic reactions regarding stoichiometry, direction, metabolite elemental formula, charge state, and associated genes was collected from AraCyc  using thArabidopsis thaliana was reconstructed based on the decision to take in account only those metabolic processes, which contributed to better understanding of sugar metabolism, central carbon metabolism, energy metabolism, proton turnover, biomass growth and sucrose translocation among tissues in Arabidopsis thaliana. Thus, metabolic reactions from the following pathways were used in the reconstructed network: 2-oxoglutorate decarboxylation to succinyl-CoA; adenosine nucleotides de novo biosynthesis; amino acids biosynthesis; aspartate degradation; Calvin-Beson cycle; chorismate biosynthesis; fatty acids biosynthesis; folate metabolism; glutamine biosynthesis; glycolysis; homoserine biosynthesis; inosine-5’-phosphate biosynthesis; maintenance; malate/oxaloacetate shuttle; nitrate reduction; ornithine biosynthesis; oxidative phosphorylation; PRPP biosynthesis; pentose-phosphate pathway; phosphorus metabolism; photosynthesis light reactions; photorespiration; purine nucleotides de novo biosynthesis; pyrimidine ribonucleotides interconversions; pyruvate decarboxylation to acetyl-CoA; pyruvate fermentation; starch biosynthesis; starch degradation; sucrose biosynthesis; sucrose degradation; sulphate reduction; TCA cycle; UDP-glucose biosynthesis; UDP-glucoronate biosynthesis; uridine-5’-phospahte biosynthesis. Many biosynthetic reactions concatenated in linear pathways (e.g. nucleotide or amino acid biosynthesis) were lumped in order to reduce model complexity. All reactions in the model were manually transferred from the database, and manually curated.The model reconstruction was performed with Insilico Discovery™ . The structured metabolic network of cis-octadecenoate, 34.7 % cis-hexadecenoate, 2.4 % octadecanoate, 0.006 % Linoleate and 32.4 % glycerol. RNA composition was derived by reversed sequence analysis of protein amino acid sequences in FASTA format available from TAIR: 32 % A, 26 % U, 9 % C and 33 % G. Based on these data, the biomass macromolecular composition was considered to be (w/w) 20 % cellulose, 20 % pectin, 10 % starch, 12 % sucrose, 25 % proteins, 9 % lipids, 1 % RNA and 3 % ash. The mass ratio between shoot and root was accepted as 85/15 (w/w) based on own measurements of fresh weight of 21 days old plants grown under long-day (16 h light) conditions in hydroponic culture.The biomass was assumed to be composed of the following polymers: proteins, lipids, RNA, carbohydrates  and ash , 40. TheIn accordance with concept of sucrose emitting tissues and sucrose demanding tissues as well as under consideration of the molecular mechanism of sucrose translocation mechanisms Fig. , under dIn the model, each biochemical reaction (classified with E.C. number) was referred to corresponding KEGG reaction (KeggID). Additionally, reactions were annotated with accession numbers of genes whose products perform this biochemical process (GeneID). In case of lumped reactions, all corresponding GeneIDs were associated with lumped reaction. This annotation was later used to match the model with mass-spectrometry based proteomic data  and to vThe final version of the model was exported in SBML and MATLAB format  as well as the stoichiometric matrix of the model and high-resolution printout (JPG) of the model with all marked reactions. Corresponding databases of the compounds/metabolites, transformers/reactions and genes used in the model are provided in in ASCII format .Sucrose was considered as the only carbohydrate that is exchanged among tissues.The anatomy of the whole plant was reduced down only to three tissues: autotrophic mesophyll, heterotrophic root, interconnected by transport phloem. The model was restricted to the mesophyll as always being the ‘source’ tissue of sucrose both in light conditions (during photosynthesis) and in dark conditions . The root was always defined as the ‘sink’ tissue for sucrose. The root took up sucrose and used it for biomass formation, but did not store it with a purpose to emit it back to phloem. Thus, we have consciously constrained the exchange of sucrose to a unidirectional net-flux from autotrophic mesophyll to heterotrophic root Figs.  and 4.Sucrose was considered to be also a part of the formed biomass. Nevertheless, we did not consider the possibility that deposited sucrose can be returned to sucrose pool into the phloem for its exchange. Thus, sucrose, which was directed to biomass, was not considered as available for growth or exchange through the free sucrose pool.Starch was considered as a metabolite that can be formed and deposited during light growth phase and used as the carbon and energy source during dark growth phase. We assumed that starch was accumulated only in autotrophic tissues (mesophyll), but not in heterotrophic tissues (root).Starch was formed only during the light growth phase, and stored as a part of biomass. It was not degraded even partially during light growth phase.Starch was considered as the only carbon source for growth of the plant during dark conditions.In this version of the model, we did not consider exchange of amino acids and organic acids  between tissues.Fatty acid biosynthesis was considered to be cytoplasmic to reduce complexity of redox balance in plastid.ATP/ADP was made exchangeable between compartments Fig.  through NAD(P)H/NAD(P) could not be exchanged between compartments. However, the reduced equivalent could be transferred between compartments through malate/oxaloacetate shuttle.Phloem played only a role in transport processes and deliver nutrients and water from root to mesophyll, and sucrose from mesophyll to root as unidirectional net-fluxes Fig. .Nutrient uptake  followed a proton-symport mechanism.Proton transport against know pH gradient was always active (ATP-dependent), unlike transport along known pH gradients, which was considered as being passive Figs.  and 4.The only exchangeable metabolites from central carbon metabolism between plastid and cytoplasm in the mesophyll were D-glyceraldehyde 3-phosphate (GAP) and maltose. Other potentially exchangeable metabolites  were not considered in the model to reduce the complexity.Gas exchange between plant and environment was considered as being passive.In order to reduce the complexity in the description of processes within the multi-tissue plant and to focus mainly on the mechanisms of sucrose translocation, the model was formulated under the following assumptions:Before proceeding to advanced network analysis, the detailed consistency check of the network stoichiometry through elemental and charge balance analysis was performed with Insilico Discovery™ . In addition to known reaction stoichiometry, this analysis relied on known elemental composition and charge (at specific pH) of metabolites. Consistency check of elemental and charge balances allowed identifying and revising/eliminating: (i) compounds for which elemental composition and charge were unknown and (ii) inconsistent reactions, polymerizations and transport steps with unbalanced elements or charges. A Check for dead-ends and unused transformers allowed identifying and eliminating transformers and network regions which were not capable of operating at steady state. A check for irreversible transformers allowed visualizing and approving those transformers, which were irreversible from thermodynamic point of view. Irreversibility of a transformer was important constraint for the Flux Balance Analysis.S of the network is very powerful tool in optimization of the metabolic network structure, because it identifies and illustrates how substances flows within a network. This analysis also allowed identification of false or/and unused network structures. The degree of freedom estimated the dimensions of the network, and further requirements to solve it with experimentally derived measurements. The lower degree of freedom, the easier solutions can be found. Analysis of conserved moiety revealed sets of compounds, whose sum always remained the same even under dynamic conditions. The number of conserved moieties equalled the number of linearly dependent balance equations. Analysis of conserved moieties can reveal false-positive conserved moieties which had accidentally appeared due to designed network structure. Accidental false-positive conserved moieties were eliminated and only biologically relevant conserved moieties (e.g. NAD(P)H/NAD(P), acetyl-CoA/CoA/succinyl-CoA) were remain in the network. Analysis of blocked transformers identified regions of the network, which were not capable of operating at steady state due to the irreversibility constraints of the transformer equation. Computation of the base vector of the nullspace revealed “functional” and “non-functional” regions of a metabolic network. Nullspace of S is a set of all solutions of the linear equation system S × v = 0. S was the stoichiometric matrix of the network  and v was the rate vector of metabolic fluxes. Computation of parallel routes and cycles revealed a set of reactions/transporters/polymerizators which (i) were capable of maintaining a steady state, (ii) could not be decomposed, and (iii) in total did not consume or produce an external substrate/product. From condition (iii) followed that the net reaction of parallel routes and reaction cycles was zero. In the complex networks, there was a high chance for the accidental formation of parallel routes and cycles which increased internal degree of freedom. Parallel routes and cycles substantially increase the number of elementary flux modes without leading to new phenotypic behaviour. Therefore, their number had to be minimized in order to use elementary mode analysis for evaluation of maximal product yield. Computation of elementary modes revealed a unique set of smallest sub-networks that allowed a reconstructed network to function in steady state. Elementary mode analysis took into account stoichiometry and thermodynamics when evaluating whether a particular metabolic route or (sub-)network was feasible and likely for a given set of proteins/enzymes. The topological analysis of the model was performed with Insilico Discovery™ .Topological analysis of the stoichiometric matrix S × v = 0) using constraint-based Flux Balance Analysis (FBA). In order to calculate metabolic flux distribution, maximization of biomass formation was formulated as objective function. The FBA provided a method for computing flux distributions in the metabolic networks by linear optimization of the objective function:Z was the objective function, c was a vector of weighting factors, vi was the ith element of v and νimin and νimax were the minimum and maximum constraints on vi. The topology of stoichiometric matrix (S), together with lower (νimin) and upper (νimax) boundaries for the vector of metabolic fluxes (v), in combination with thermodynamic properties of reactions (e.g. irreversibility), and steady state condition (S × v = 0)provided with sufficient constraints to optimize objective function. The designed metabolic network was subjected to two specific constraints provided in Supplemental data (Table The distribution of intracellular metabolic fluxes through the metabolic network was investigated under pseudo steady state assumption (


Saccharomyces cerevisiae, it has long been known that a functional trehalose pathway is indispensable for transitions to high glucose conditions. Upon addition of glucose, cells with a defect in trehalose 6-phosphate synthase (Tps1), the first committed step in the trehalose pathway, display what we have termed an imbalanced glycolytic state; in this state the flux through the upper part of glycolysis outpaces that through the lower part of glycolysis. As a consequence, the intermediate fructose 1,6-bisphosphate (FBP) accumulates at low concentrations of ATP and inorganic phosphate (Pi). Despite significant research efforts, a satisfactory understanding of the regulatory role that trehalose metabolism plays during such transitions has remained infamously unresolved. In a recent study, we demonstrate that the startup of glycolysis exhibits two dynamic fates: a proper, functional, steady state or the imbalanced state described above. Both states are stable, attracting states, and the probability distribution of initial states determines the fate of a yeast cell exposed to glucose. Trehalose metabolism steers the dynamics of glycolysis towards the proper functional state through its ATP hydrolysis activity; a mechanism that ensures that the demand and supply of ATP is balanced with Pi availability under dynamic conditions. In the model eukaryote  In response, regulatory mechanisms are required to ensure that ATP production by lower glycolysis can keep up with ATP consumption by the upper-part; failure to do so results in a dysfunctional state, which we call an imbalanced glycolytic state, and loss of cell viability. The consequences of this failure have been extensively studied in S. cerevisiae, where the trehalose pathway has been implicated as a key component of the regulatory machinery that ensures robust transitions to high glucose.The general design of glycolysis, with an ATP consuming upper-part and an ATP producing lower-part, comes with major risk if sudden increases in flux are not managed properly. When excess glucose becomes available, its rapid phosphorylation results in the decrease of ATP and PS. cerevisiaetps1Δ mutants lack a functional trehalose cycle and are unable to grow on glucose. A shift to high glucose leads to the accumulation of FBP with low ATP and Pi levels, suggesting an imbalance in glycolytic fluxes. Mechanisms ascribed to the trehalose pathway implied an involvement in the regulation of substrate demand by the upper- and lower-parts of glycolysis, either by (1) direct inhibition of hexokinase (hxk) - the first ATP consuming step - by an intermediate of this pathways, trehalose 6-phosphate (T6P), or (2) enhancement of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) activity - the first reaction in lower glycolysis - through supply of Pi. However, neither of these mechanisms could fully explain the metabolic deficits of tps1Δ mutants.S. cerevisiae from a systems-level perspective. A detailed kinetic model of glycolysis allowed us to simulate the tps1Δ metabolic behavior. This model reproduced the most relevant metabolic features of the tps1Δ phenotype . Surprisingly, we found that with the same parameter settings a second state, with normal glycolytic function, could be achieved under different initial conditions (e.g. higher Pi concentration). This bistable behavior of the model suggested the possibility that two phenotypes, viable and non-viable, could be present in the same population. We pursued this observation experimentally, reasoning that cells in the normal state should be viable. Using plating assays we found that, not only did such a small fraction of cells exist (~1 in 103 cells), but it appeared spontaneously and reproducibly in any population of sufficient size. Propagation experiments showed that this glucose tolerance was non-genetic in nature. Additionally, this result provided an explanation for population level growth behaviors, which showed extremely long lag phases (> 20hrs). Further testing showed that such mixed population structures also explained the growth profiles and lag behaviors of tps1Δ populations grown on galactose with increasing amounts of glucose. In evaluating previous studies we found these results to be particularly informative in explaining population-level measurements for tps1Δ and related mutants.In this study, we started by exploring the consequences of a defective trehalose cycle in tps1Δ cultures. Significantly, our results revealed that a surprisingly large fraction of wild-type cells (7 %) also failed to properly initiate glycolysis.Growth happens on much longer time scales than the metabolic processes for which we predicted the bistable behavior; could we also show distinct metabolic states at the single cell level? We exploited the fact that cells in the imbalanced state cannot maintain pH homeostasis, a consequence of low ATP levels, to identify the two states  within a single population . For this we used a pH-dependent GFP reporter (pHluorin), combined with high throughput flow cytometry, and confirmed the presence of two distinct metabolic subpopulations in 6 unique tps1Δ models, each with slightly varied initial metabolite concentrations and enzyme levels (Vmax); a situation likely for a population consisting of millions of individual cells. In response to a simulated glucose pulse, approximately 1 in 103 models reached a functional steady state. Importantly, this approach allowed us to identify metabolic variables that could compensate for the absence of a trehalose pathway. We found that the successful initiation of glycolysis depended not so much on a single mechanism, but is determined by the interplay between components that tend to reduce the flux through the upper part of glycolysis (ATP consuming ) and those that enhance the flux through the lower (ATP producing) part. Further analysis showed that enhanced futile cycling of ATP would provide a consolidation of the identified mechanisms that together would achieve a balance between upper- and lower glycolytic activities .Next, we showed how this type of phenotypic heterogeneity could arise in metabolism through small continuously distributed variations in metabolic variables, rather than stochastic fluctuations. We generated > 10i availability. Estimations of in vivo fluxes, using a 13C-tracer approach, confirmed that this cycle is indeed transiently activated, with a highly dynamic response during shifts to high glucose. As much as 28% percent of the uptake flux was channeled toward trehalose, significantly impacting both the overall glycolytic flux and phosphate homeostasis. The flux profiles were fully consistent with our model’s prediction that various features inherent in this futile cycle  all contribute  to the robust startup of glycolysis. We could now also understand the seemingly incomplete mechanistic descriptions offered by previous interpretations.We then realized that the full trehalose pathway constitutes a futile cycle, and that its temporary activation in response to glucose would be an effective way to balance the demand and supply of ATP with Pi. Depending on the rate of glucose phosphorylation, the system may reach critically low Pi and ATP levels that could result in an imbalance between fluxes through the upper- and lower-parts of glycolysis. If, however, mechanisms kick in that slow down the upper part and/or Pi recovery is enhanced (i.e. stimulating the lower part), a balanced state can be reached. Importantly, this only made sense after the dynamics of the whole glycolytic pathway - its bistability - was considered.A dynamic picture of regulation emerged: if glucose is suddenly available, its rapid phosphorylation in the upper part of glycolysis results in a drain on ATP and PWhile this study solved the long standing puzzle of the trehalose cycle’s involvement in glycolytic transitions, it was intriguing that 7 % of wild-type cells also failed to cope. Apparently the proper startup of glycolysis is not guaranteed even when the necessary regulatory components are available. How can this failure be understood? We speculate that the wild-type failure reflects a trade-off between efficient start-up and failsafe regulation. Although cells could reduce the startup risk by always maintaining the trehalose cycle in a high state of activity, effecting tighter control of glucose influx will come at a permanent cost of reduced ATP yield and flux capacity. From a population perspective a 7 % loss in viability could be justified if the remaining 93 % survives without a permanent sacrifice in capacity. Such a trade-off scenario has been shown, in many other contexts, to be an outcome of evolution in dynamic environments.This work underscored the importance of specific mechanisms which dynamically regulate metabolism during environmental changes. It seems likely that such mechanisms are of general importance in natural environments, where changes can be transient and occur very suddenly without much forewarning. It may not be a coincidence that a pathway implied in heat and osmostress response is used by yeast also to cope with “glucose stress”.