Escherichia coli cells growing in a defined medium. We extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media. We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect , while FBA without proteomics constraints predicts respirative metabolism almost exclusively. The comparisons to the 13C study showed improvement upon inclusion of the correlations and motivated a technique to systematically identify inconsistent kinetic parameters in the literature. The minor secretion fluxes for glycerol and acetate are underestimated by our method, which indicate a need for further refinements to the metabolic model. For yeast cells grown in synthetic defined (SD) medium, the calculated broad distribution of growth rates matches experimental observations from single cell studies, and we characterize several metabolic phenotypes within our modeled populations that make use of diverse pathways. Fast growing yeast cells are predicted to perform significant amount of respiration, use serine-glycine cycle and produce ethanol in mitochondria as opposed to slow growing cells. We use a genetic algorithm to determine the proteomics constraints necessary to reproduce the growth rate distributions seen experimentally. We find that a core set of 51 constraints are essential but that additional constraints are still necessary to recover the observed growth rate distribution in SD medium.Using protein counts sampled from single cell proteomics distributions to constrain fluxes through a genome-scale model of metabolism, Population flux balance analysis (Population FBA) successfully described metabolic heterogeneity in a population of independent  No two living cells are exactly the same. Even cells from a clonal population with identical genomes living in the same environment will express proteins in different numbers simply due to the random nature of the chemistry involved in gene expression. The consequences of this stochastic gene expression are complex and not well understood, especially at the level of large reaction networks like metabolism. Here we investigate how variability in the copy numbers of metabolic enzymes affects how individual cells extract nourishment from their environment and grow. We model 100,000 independent yeast cells, each with their own set of enzyme copy numbers sampled from experimental distributions, and use flux balance analysis (FBA) to compute the optimal way that each cell can use its metabolic pathways—an approach we dubbed Population FBA. We find that enzyme variability gives rise to a wide distribution of growth rates, and several metabolic phenotypes—subpopulations relying on diverse metabolic pathways. Most importantly, we compare the predicted fluxes through the different pathways to experimental values; we find that Population FBA is able to correctly predict Crabtree effect, while traditional FBA, which lacks the proteomics constraints our method imposes, differs both qualitatively and quantitatively from experiment. A cell’s phenotype—its set of distinguishing observable traits—can be as much an emergent property of the cell’s environment and gene expression state as it is a result of the cell’s genotype. While some observables, like an organism’s response to Gram staining, can be immutable and tied to specific genes, others can be more fluid, varying from cell-to-cell with the random fluctuations in each cell’s molecular makeup –4. A celi.e. the flux into and out of each metabolite is balanced). Parsimonious FBA (pFBA) improves on the predicted flux distribution shown in13C medium. Sampling from protein distributions that have been rescaled for 13C medium allows us to predict such a curve . In eac−1, see Fig A9 and Fig A6 in The fast-growing peak was predominantly the result of limitation in glucose uptake . While slow-growing cells utilized glucose at rates below the maximum allowed rate; see Fig A5 and Fig A8 in E. coli [E. coli, for example, the glucose transporter ptsG might be expressed under the control of the lac system—the shape and location of any peak in the growth rate distributions of cells grown with and without IPTG  could then indicate whether glucose uptake limitation is responsible.A similar glucose-associated peak arose in work modeling the growth rate distribution of E. coli . In lighE. coli  could beE. coli [In order to identify the features of protein distribution which play important role in obtaining the shape of the distribution, we performed some control simulations. We randomly omitted 50% (267) and 66% (356) of the 535 available protein distributions and used the rest of the protein distributions to perform Population FBA. The resulting growth rate distributions are very similar to experimental growth rate distributions with a broad tail at slow growth rates and a peak at fast growth rates  respired little even under highly aerobic conditions. Using the wild-type yeast 7.6 model in 13C medium calculations were performed using our Population FBA methodology  were performed using our Population FBA methodology. The central metabolic fluxes of 1,000 simulated cells were compared to experimental values. The mean predicted flux through each of the experimentally characterized reactions showed strong qualitative agreement . One reaction in particular proved absolutely essential for prediction of significant fermentation in presence of oxygen and high glucose concentration or Crabtree effect. −1 hr−1. This is a key reaction in the electron transport chain within the mitochondria, and as a result of the low expression level of one of the proteins involved in catalyzing it  the simulations predicted a predominantly fermentative metabolismThe mean upper bound imposed on the ferricytochrome c reductase reaction, based on proteomics and kinetics data, was 1.20 mmol gDwt. Several other mitochondrial proteins were expressed in small numbers and had similarly limiting effects on oxidative phosphorylation, including PDX1 (expressed at approximately 232 per cell) which catalyzes the pyruvate dehydrogenase (PDH) reaction—an important step linking glycolysis and the TCA cycle, and CIT3 (approximately 356 per cell) which catalyzes the citrate synthase (CS) reaction which is part of the TCA cycle. In general the protein copy numbers involved in the fermentation pathway are considerably higher. Had these flux constraints not been imposed, the model would have predicted greater amounts of respirative metabolism (see FBA without Proteomics Constraints Spuriously Predicts Respiration in S. cerevisiae), and would have been unable to recover the experimentally observed behavior. Importantly, we note that the agreement we found between experimental and simulated fluxes could not have been realized without raising several dubious turnover rates that had initially been collected from the BRENDA database .Identical calculations  was important but not essential to the agreement we see between our simulated fluxes and the experimental ones. As examples, the glucose uptake and ethanol secretion reactions were overestimated without imposed correlations by about 1.6 and 4.5 mmol gDwt−1 hr−1, respectively. With correlated sampling, these errors fell to only about 0.8 and 3.1 mmol gDwt−1 hr−1 ; while our simulations do qualitatively predict small fluxes, we underestimate both production rates by a significant fraction. Acetate secretion, for example, was predicted to occur at a rate approximately 230-fold slower than was measured  was largely canceled. NADH and NADPH metabolism remains an active area of investigation [There are a few points of quantitative disagreement we observe between our predicted fluxes and those seen experimentally, most importantly in acetate and glycerol secretion, both of which were directly measured . The expured see . In ordetigation .13C modeled media  and the Analysis of sub-populations). The first two PCA components combined accounted for almost 85–95% of the total variability in pathway usage  in a manner similar to that described in   and relied more heavily on the efficiency of their mitochondrial oxidative phosphorylation machinery. This resulted is a slight drop-off in ethanol and CO2 production (decreased fermentation) and marked increase in oxygen uptake, flux through ATP synthase  (via methylenetetrahydrofolate dehydrogenase), eventually forming 10formylTHF, and 3) phosphorylated ADP producing formate and the THF necessary to complete the cycle. The net effect of these coupled mitochondrial and cytoplasmic pathways, as summarized in + molecules, the production of ATP, and the formation and subsequent excretion of formate and some ammonium  was associated with increased mitochondrial NADH production and subsequent oxidative phosphorylation. Interestingly, this behavior was also associated with increased succinate and formate production—metabolic byproducts usually made during fermentation  see Fig. The mainium see . While tnium see . Additioal study , 72. TheIn addition to some taken up from the modeled SD media, a significant portion of the glycine necessary to drive this system was found to originate from environmental citrate  would threonine be taken up at rates in between. We found that whether or not threonine was being heavily utilized correlated strongly with whether or not the protein-associated glyceraldehyde-3-phosphate dehydrogenase (GAPDH) or pyruvate decarboxylase (PDC) reaction bound was constraining glycolytic flux. When it was not constraining, the cells engaged in as much glycolysis as was needed to serve their energy requirements (NAD+ reduction and ADP phosphorylation) and threonine was utilized at its basal rate in order to cover protein synthesis. Conversely, when the GAPDH or PDC reaction bound was constraining glycolysis threonine uptake was high. We found that much of this threonine was used by the cells to form glycine  which in turn was used to fuel the glycine cleavage system . This enabled the cells to make up some of the capacity for NAD+ reduction and ADP phosphorylation that was lost due to their limited glycolytic flux. Because of the relatively small amount of threonine uptake allowed, its use was essentially always at its maximum when the cells engaged in this behavior. Flux variability analysis was performed on over 400 cells growing slower than than 0.4 hr−1. It confirmed that predicted threonine uptake is robust and not a randomly chosen flux distribution among many possible degenerate solutions  we chose for raising overly-constraining kcat values focused on identifying constraints that most strongly limited cellular growth rate. This has the benefit of keeping as many of the experimental parameters available intact, but it also raises some questions, the most important of which is whether the set of turnover rates that are doubled is the only set that would lead to a mean growth rate similar to the measured one. Similarly, are all of the turnover rates that are kept intact after the doubling procedure actually necessary? In order to address these questions, we implemented an Evolutionary Algorithm based approach for finding constraint sets that yield growth rate distributions that approximately recover the one seen experimentally. This method made use of the Micro Genetic Algorithm formalism, a type of genetic algorithm (GA) that uses relatively small numbers of “genomes” and dispenses entirely with “mutations” [Genetic algorithm for constraint selection).The main doubling algorithm  was lifted in some but not all of our GA runs. By comparing threonine usage across GA runs we found that when the GAPDH-associated kcat was raised the bimodality among slow-growing cells essentially disappeared; instead, at growth rates lower than about 0.3 hr−1, the modeled cells all took up threonine at its basal rate and none were found to utilize the glycine cleavage system , these studies have shown that proteins are expressed in varying numbers from cell to cell. In order to understand how any given cell’s protein expression state effects its behavior, and how that behavior relates to the overall behavior at the population level, these protein distributions must be sampled and realistic subpopulations of individual cells must be modeled. Our Population FBA approach provides such a method; allowing us to carry out the generation of realistic populations of cells and subsequent analysis of their intracellular fluxes and exchanges with the environment.With the development of single cell and micro-colony imaging experiments –38, 42, −1), and a dominant peak of fast-growing cells . Moreover, we find excellent agreement between experimental fluxomics data [Simulations of the steady-state growth rates attainable by the cells in our modeled populations gives rise to a distribution that is in excellent agreement with the experimentally observed growth rate distribution . In partics data  and the ics data , 41.13C experiments on wild-type yeast contained no amino acids in the media; our simulations showed that cells under these conditions depended on ammonium, sulfate, and phosphate salts taken up from the media. The gene knockouts that differentiated the the strains used in the 13C and SD experiments required the addition of uracil, leucine and histidine to the SD media. The SD media also contained an additional 17 amino acids, several of which were taken up and catabolized as a energy source. Despite the differences, both simulated populations displayed very similar growth rate distributions.The simulations presented here also allowed us to make quantitative predictions about the effects of growth in media where the main difference was the presence or absence of amino acids. The S. cerevisiae. One of the most important result of this study is that it underscores the need for imposing biologically realistic internal constraints in flux balance models. Without the types of constraints Population FBA imposes, the yeast 7.6 model gave fluxes, growth rates, and metabolic byproducts that were qualitatively and quantitatively inconsistent with the results of a 13C fluxomics study. Our study has shown that yeast populations exhibit the same types of cell-to-cell diversity in behavior that is coming to be recognized across the microbial world, and that although the particular sets of constraints that are necessary to recover the experimental growth rate distribution are not unique, any set that does recover the growth rate distribution also recovers the main metabolic behaviors we observed, including the Crabtree effect and the noted shift toward respiration seen among our fast-growing cells.We have employed our Population FBA methodology to study metabolic heterogeneity in S1 TextExtended figures and tables for further analysis and validation of experimental data, as well as further analysis of our results. The text also covers a more detailed explanation of our methods.(PDF)Click here for additional data file.S1 Filekcat values, final kcat values after doubling procedure in both SD and 13C media as well as scaling ratios to convert sampled SD protein count to 13C count. There is also listing of final kcat values obtained after 10 independent GA optimizations in SD media. Correlation matrix imposed while sampling distributions as well as its Cholesky decomposed factor is also provided.Model files contain all modifications to reflect gene deletions and media conditions. Parameter files with all shape and scale parameters for fluorescence distributions of proteins used in our simulations, along with their original (ZIP)Click here for additional data file.S2 Fileet. al. [Full parameter set acquired after removing all unreliable fluorescence distributions for protein abundances observed in Dénervaud et. al. , includi(ZIP)Click here for additional data file.S3 Filewww.escher.github.io, [JSON file for the metabolic model yeast 7.6 (y76Model.json) and corresponding metabolic map (y76Map.json) to be used with ESCHER ((ZIP)Click here for additional data file.


Synechocystis 6803 with growth kinetics, cell movements, and a light distribution function. The hybrid platform not only maps flux dynamics in cells of sub-populations but also predicts overall production titer and rate in PBRs.Genome-scale models (GSMs) are widely used to predict cyanobacterial phenotypes in photobioreactors (PBRs). However, stoichiometric GSMs mainly focus on fluxome that result in maximal yields. Cyanobacterial metabolism is controlled by both intracellular enzymes and photobioreactor conditions. To connect both intracellular and extracellular information and achieve a better understanding of PBRs productivities, this study integrates a genome-scale metabolic model of 2 mass transfer limitations. Second, fluxome in a single cyanobacterium may show stochastic changes due to random cell movements in PBRs. Third, insufficient light due to cell self-shading can activate the oxidative pentose phosphate pathway in subpopulation cells. Fourth, the model indicates that the removal of glycogen synthesis pathway may not improve cyanobacterial bio-production in large-size PBRs, because glycogen can support cell growth in the dark zones. Based on experimental data, the integrated GSM estimates that Synechocystis 6803 in shake flask conditions has a photosynthesis efficiency of ~2.7 %.Analysis of the integrated GSM demonstrates several results. First, cyanobacteria are capable of reaching high biomass concentration (>20 g/L in 21 days) in PBRs without light and COThe multiple-scale integrated GSM, which examines both intracellular and extracellular domains, can be used to predict production yield/rate/titer in large-size PBRs. More importantly, genetic engineering strategies predicted by a traditional GSM may work well only in optimal growth conditions. In contrast, the integrated GSM may reveal mutant physiologies in diverse bioreactor conditions, leading to the design of robust strains with high chances of success in industrial settings.The online version of this article (doi:10.1186/s12934-015-0396-0) contains supplementary material, which is available to authorized users. To enhance mass transfer, people often use CO2-enriched air in combination with high intensity mixing. Many models have been developed to understand how cyanobacterial physiological dynamics are influenced by the light intensity, CO2 supply, temperature, and geometry of PBRs  glucose and 4 g/L NaH13CO3 . The TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide) method ’ and ‘d-lactate [c] → d-lactate [external]’, which were respectively used to simulate glycogen storage/consumption and d-lactate production by an engineered cyanobacterial strain , and half-saturation constant for dissolved CO2, Km; f2 is a function of the cell’s local position l, biomass concentration X, and photon influx on the PBR surface vphoton,0. The linear optimization problem was solved by the MATLAB® (2012b) built-in function ‘linprog’ using the ‘simplex’ algorithm. To estimate the flux distribution in engineered cyanobacterial strains, we used the MOMA (minimization of metabolic adjustment) algorithm  represents dissolved CO2 concentration. This study assumed that pH was constant at 8.0, and that the dissolved CO2 and cell culture were homogeneous in PBRs. Because we assumed that cell metabolism was pseudo-steady in each interval  is the overall glycogen concentration in the PBR, in mmol/L; KLa is the mass transfer rate of CO2, in h−1; [CO2] represents the dissolved CO2 and HCO3− concentrations, in mmol/L; [CO2]* is the combined concentrations of dissolved CO2 and HCO3− in equilibrium with atmospheric CO2 , in mmol/L; and µapp, vglycogen,app, and v,appCO2 are fluxes determined previously. The updated values of the biomass concentration, dissolved CO2 concentration, etc., were then used to constrain the FBA model in the next interval. The kinetic parameters are given in Table KLa was determined by the following equation: [N is the rotation speed, in rpm; V is the shake flask volume, in mL; and L is the culture volume, in mL. MATLAB code of the integrated GSM is provided in Additional file The ODEs were resolved in their numerical discrete form (Euler-like integration scheme):mentclass2pt{minimquation: Table 1LiAdditional file 1. Flux distribution database.Additional file 2. Supplementary figures and tables.Additional file 3. MATLAB file for simulating cyanobacterial performance in PBRs.Additional file 4. Matrix of flux distribution database.


Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties  of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli’s central metabolism.Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism  Escherichia coli. The first E. coli genome-scale model, iJE660iJR904iAF1260iJO1366E. coli network reconstruction. Since extensive and reliable data on the biochemistry and genome is available for this model organism, the reconstructed genome-scale models turned out to be exceptionally accurate and proved to be invaluable to gain insights into the metabolism of E. coli or to identify intervention strategies for a targeted modification of the metabolism for biotechnological applications1Constraint-based and stoichiometric modeling has become a standard tool for analyzing key properties and capabilities of metabolic networks and numerous studies have demonstrated the power and wide applicability of this approach12456891011NetworkReducer, a network reduction algorithm that allows the derivation of stoichiometrically consistent submodels from genome-scale modelsNetworkReducer has been demonstrated before, the goal of the current contribution is to derive a widely applicable core model of the central metabolism of E. coli from a reference genome-scale network and to analyze its properties in relation to the genome-scale parent model.However, the increasing size and complexity of genome-scale models have set restrictions on the computational feasibility of some stoichiometric modeling techniques. For example, exhaustive elementary modes analysisE. coli have been used in the literature. However, almost all of them were derived in a bottom-up manner and are therefore not related to and consistent with a corresponding genome-scale model. One exception is the E. coli core model presented by Orth et al.EColiCore1, was manually derived from the genome-scale model iAF1260 for educational purposes. Whereas EColiCore1 perfectly fits the demands it was developed for, the stoichiometric properties are closely related but not fully consistent with its genome-scale parent model iAF1260NetworkReduceriAF1260 having the same scope as EColiCore1. However, even this core model has several drawbacks. First of all, it was not derived from the latest reference genome-scale model iJO1366. Secondly, it lacks some central metabolic pathways such as the Entner-Doudoroff and the methylglyoxal pathway. Furthermore, we wanted to protect a larger set of phenotypes in the core model including feasibility of growth on a selection of standard substrates as well as producibility of standard (fermentation) products by E. coli. One major goal of the present contribution was therefore to derive a stoichiometric model of the central metabolism of E. coli from iJO1366 that fulfills these requirements and therefore holds the potential to become a reference model of the central metabolism of E. coli accessible for all types of constrained-based modeling. Another central goal of this study is to systematically compare key properties of the derived core model, EColiCore2, with those from its genome-scale parent model iJO1366. This analysis, which is the first of this kind, includes a comparison of the solution space with respect to flux ranges, reaction essentialities, and production capabilities. We demonstrate that EColiCore2 is capable to describe several key properties of iJO1366 including those that were not explicitly accounted for. Generally, as intended, the derived core model reflects the degrees of freedom in the central metabolism of E. coli very well while neglecting redundancies along biosynthetic routes. A full enumeration of the complete set of elementary modes or of metabolic intervention strategies becomes possible in EColiCore2. Using ethanol as a relevant product, we also compare metabolic engineering intervention strategies in the core and the genome-scale model and demonstrate how the core model can even be used as a basis for the identification of metabolic engineering strategies with higher cardinalities in the genome-scale model that were computationally not tractable before.Several different small-scale models of the central metabolism of E. coli core model we used the recently introduced network reduction algorithm NetworkReducer which takes a given large-scale metabolic network as input and derives a smaller subnetwork while keeping desired features of the full network intactk is described by a corresponding set of linear inequalities:For deriving the s denoting the number of protected functions and phenotypes. In a first (pruning) step, NetworkReducer iteratively removes non-protected reactions and metabolites while checking that none of the protected functions are violated. Afterwards, an optional network compression step can be applied which, for example, lumps reaction sequences (unbranched pathways) into single reactions. This second operation further reduces the size of the network but does not change the space of feasible steady-state flux distributions. For further details on NetworkReducer we refer to the original workwith iJO1366 was used as the gold standard for a well-curated genome-scale stoichiometric network model for E. coli’s metabolism. We introduced some minor modifications to iJO1366 prior to the application of the network reduction routine:We added external metabolites for all reactions where compounds are secreted into or taken up from the environment. This allows a more convenient consideration of mass balances of those metabolites  but does not change the solution space.Further, we allowed reaction R_nadh16tpp (NADH dehydrogenase) to be reversible as otherwise succinate excretion is obligatory under anaerobic conditions  excretion became essential reactions because they are needed to consume formate and glyoxylate formed as byproducts of some synthesis pathways for biomass precursors. This behavior would not reflect reality. In fact, in the genome-scale model, there are different routes that consume these metabolites , however, these pathways would be lost in the reduction process. To avoid such an undesired behavior of the reduced model, we demanded that growth without formate excretion and malate synthase is possible and replaced metabolites M_glx_c (glyoxylate) and M_for_c (formate) in the reactions R_GLYCTO4, R_GLXCL, R_AMPMS2, R_DB4PS, R_FTHFLi, R_GTPCI and R_GTPCII2 by auxiliary metabolites M_glxBIO_c and B_forBIO_c, respectively. These changes reflect the assumption that consumption of glyoxylate and formate produced during biomass synthesis is realized via these reactions. Importantly, this modification does not produce new steady-state flux distributions, it only excludes those solutions that would render malate synthase and formate (or hydrogen) production essential.When applying the The latest reconstruction iJO1366 model will be denoted by ECGS  throughout the manuscript. Flux bounds were set as in the original model iJO1366, in particular, a maximal substrate uptake rate of 10 mmol/gDW/h was used (if not stated otherwise) and the ATP maintenance demand was set to 3.15 mmol/gDW/h. The flux range of all reactions with unknown capacities were set to  for reversible and  for irreversible reactions.For clarity, the slightly modified NetworkReducer. As the goal is to construct a core model that represents the central metabolism of E. coli in detail while neglecting redundancies in anabolic synthesis pathways we protected the reactions and metabolites involved in glycolysis, pentose phosphate pathway, Entner-Doudoroff pathway, tricarboxylic acid cycle, methylglyoxal pathway and key reactions of the electron transport chain. In addition, six major anaplerotic reactions (including the glyoxylate shunt) and two NAD(P) transhydrogenase reactions were protected. To account for biomass synthesis we demanded that the reaction R_Ec_biomass_iJO1366_core_53p95M of ECGS must be maintained. In total, 65 reactions and their reactants have been protected in the central metabolism as listed in the Next we specified the protected parts and phenotypes of the genome-scale model to be retained by NetworkReducer to protect not only parts but also behaviors or phenotypes. We therefore demand that the reduced network must allow for growth on four representative carbon substrates  with at least 98% of the maximal growth rate achievable with the ECGS model  are not included in this set. Here we exploit the possibility of GS model . This enGS model . For allCellNetAnalyzerEColiCore2) and the compressed pruned (EColiCore2comp) networks are provided in SBML format in CellNetAnalyzer projects on the repository website of CellNetAnalyzer (http://www2.mpi-magdeburg.mpg.de/projects/cna/repository.html).The network reduction and all other calculations were done with NetworkReducer to the iJO1366-based genome-scale model ECGS as described in the Methods section , we obtained EColiCore2 (in the following abbreviated by ECC2), a core model of E. coli’s metabolism where all the specified protected parts and phenotypes have been maintained. ECC2 contains 499 reactions (thereof 198 reversible) and 486 internal as well as 33 external metabolites  preserves major metabolic phenotypes. Therefore, we consider ECC2 as a qualified solution suitable for further in-depth analysis.We emphasize that network ECC2 is not the only possible solution and other subnetworks can solve the reduction problem equally well. However, by definition, all alternative networks share the set of protected parts (metabolites and reactions) and protected phenotypes. Further, by altering the order of removed reactions, we found that differences in alternative reduced networks occur mainly in alternative routes for the synthesis of biomass precursors. Our focus was to obtain a core model that (a) represents possibility of anaerobic production of lactate, succinate and ethanol was demanded, their maximal production rates correspond exactly to those of ECGS . The maximal product for formate, hydrogen, and acetate in ECC2 compare also well with what is known (and expected) from experimental observations, however, they are much higher in ECGS (iJO1366 for aerobic growth on glucose is 2.93 (but only 2.0 in ECC2). We refrained from demanding that ECC2 reproduces these extremely high product yields as they are not expected to be of relevance under physiological conditions .The demanded biomass yields for the different substrates are achieved by ECC2. For all substrates, the maximal biomass yields in ECC2 are even above 99.9% of the genome-scale maximum except for aerobic growth on acetate where 98.8% of the maximal biomass yield of ECGS is achieved . Regardi in ECGS . For exaNetworkReducer to ECC2 generates an even more reduced network, ECC2comp, where coupled reactions  are lumped into overall reactions. The lumped reactions can be classified into two different categories. The vast majority (399) are reactions that are fully coupled (form an enzyme subset) with the biomass synthesis reaction. In addition, 21 reactions of the ECC2 model involved in chains of transport reactions (secreting metabolites from the cytosol via periplasm to the extracellular compartment) have been lumped as well. The compression thus further reduced the number of reactions and metabolites down to 79 reactions and 61 internal and 33 external metabolites. Initially, ECC2comp contained 25 degrees of freedom as ECC2, however, for better handling, we split up the reversible exchange reactions for carbon dioxide (CO2), acetate, and succinate into two irreversible (uptake and excretion) reactions respectively thereby increasing the number of reactions to 82 and the degrees of freedom to 28  and biomass byproducts compared to ECC2 and ECGS. However, the adjusted stoichiometric coefficients and the new precursors reflect the metabolites and their amount needed to produce all the building blocks contained in the original biomass synthesis reaction of ECC2 and ECGS . The growth rate was fixed to its near optimal value (0.98 h−1) and for all remaining reactions the minimal and maximal flux values were calculated for both networks. With ECC2 being a subnetwork of ECGS, it follows that the flux range of a reaction in ECC2 will be contained in the flux range of this reaction in ECGS. For 290 out of the 499 reactions in ECC2 (58.1%) a (close to) perfect agreement with the corresponding flux ranges in ECGS can be observed (difference less than 0.1 mmol/gDW/h) and for additional 153 reactions (30.7%) a good agreement can be found   . For onlmax) of the mutant is still above 95% of the wild type μmax. If μmax is reduced by more than 5% but growth still possible with more than 5% of the wild type μmax then the reaction is considered growth-limiting but not essential. Finally, if μmax falls below 5% of the maximal growth rate in the wild type, the reaction is considered to be lethal.Reaction essentialities for growth represent another important functional network property. We focused on reaction essentialities for growth on glucose. Separately for each reaction we fixed the rate to zero and maximized the growth rate. A reaction knockout is considered to be without significant effect if the maximal possible growth rate . For each internal metabolite a corresponding excretion reaction was temporarily introduced and then maximized.2 fixation would be conducted by E. coli which is unlikely as such a behavior has, to our knowledge, not been reported yet for these metabolites.Similar as for reaction essentialities, the maximal yield of any metabolite in ECC2 can never be greater than in ECGS since all feasible flux distributions in ECC2 are also possible in ECGS. For the vast majority of metabolites 77.8%,  we foundProduction envelopes (PEs) are projections of the solution space of feasible steady-state flux distributions on a two-dimensional plane. The growth rate is typically projected on the x-axis and a selected product synthesis (excretion) rate on the y-axis. PEs can be computed by means of sequential FVA where the growth rate is incremented in discrete steps from its minimum to its maximum and the minimal and maximal product synthesis rate is computed for each step. It should be noted again that, since ECC2 is a sub-network of ECGS, any PE of ECC2 will be contained in the corresponding PE of ECGS.2 release under aerobic conditions. It can be seen that the maximal growth and CO2 production rates are well represented by the reduced model while some deviations occur for the lower boundaries of CO2 release. For example, while the genome-scale model ECGS allows for CO2 fixation with acetate and succinate as substrates this behavior is not reflected by ECC2. The second and third row show the PEs for growth versus synthesis rates of typical fermentation products with glucose as substrate under any and under exclusively anaerobic conditions, respectively. Except for acetate, the PEs are either identical or, for the case of ethanol production under anaerobic conditions, show a large overlap confirming that the maximal yields of those products in ECGS are also reachable by ECC2. As was already mentioned above, whether the predicted maximal acetate yield with ECGS of almost 3 mol acetate per mole glucose is a realistic value remains questionable. In particular, homoacetic acid fermentation in E. coli (without incorporation of heterologous pathways) as predicted by ECGS has, to our knowledge, not been observed so far. Therefore, we did not protect this property in ECC2 and consider this deviation rather as correction than as restriction. The last row in A selection of representative PEs is given in As one goal of the whole reduction process, with the reduced degrees of freedom in ECC2 and ECC2comp, these models become accessible for the enumeration and subsequent analysis of elementary modes (EM). In the following we will discuss the EM of ECC2 keeping in mind that, as already mentioned, the EM are equivalent for both networks  and storage of EM from ECC2comp will require less than 20% of the memory needed to store the EM of ECC2 (82 vs. 499 reactions). A comparison of the set of EM from ECC2 and ECGS is not possible. So far, the computation of EM for genome-scale models is limited to approaches that compute the shortest EMIn the following we want to study how metabolic engineering strategies identified in the ECC2 model relate to the corresponding intervention strategies computed in ECGS. As a relevant product we chose ethanol which has previously been used as a “benchmark” case in other studies279293027−1.Here we consider production of ethanol from glucose under anaerobic conditions (oxygen uptake rate set to zero). The maximal glucose uptake rate was increased to 18.5 mmol/gDW/h to reflect the higher glucose conversion rates under anaerobic conditions. We excluded pseudo (transport and excretion) reactions from the list of possible knockout targets leaving only reactions that can be targeted by genetic modifications as knockout candidates. We designed the MCS problem as follows: the engineering goal with respect to product formation was formulated by demanding a minimal product yield of 1.4 mol ethanol per mol glucose consumed while growth should be feasible with a minimal rate of 0.05 hFor these constraints, we calculated the complete set of MCS with up to ten reaction deletions for both models, ECGS and ECC2. In total, we found 2176 MCS in ECC2 and 44066 MCS in the genome-scale network ECGS . Each MCECC2 and a cut set of ECGS with MECGS. We first consider the case that we are given one specific MCS of ECC2 (denoted by M*ECC2) and classify each single MECGS with respect to M*ECC2 , hence, the knockouts of M*ECC2 are also sufficient in ECGS to obtain the desired phenotype. An MCS of ECGS belongs to subclass A.2 if it contains all reactions of M*ECC2 complemented by further targets exclusively contained in the ECGS (and not in the ECC2) network to block additional unfavorable pathways in ECGS. Importantly, if the given M*ECC2 is not a valid MCS in ECGS (no MECGS of type A.1 exists for M*ECC2), then some MECGS of type A.2 must exist: knocking out the targets from M*ECC2 and all reactions from ECGS that are not contained in ECC2 (as they were deleted during the reduction process) will lead to a solution space in ECGS that is identical to the one obtained when deleting only M*ECC2 in ECC2. Clearly, not all the reactions exclusively contained in ECGS need normally be deleted to block flux distributions feasible in ECGS but not in ECC2; a minimal set of reaction deletions will be sufficient which is then combined with M*ECC2. We can thus conclude that any MCS of ECCS, that is not a cut set in ECGS, can always be extended to a valid MCS of type A.2 in ECGS. Hence, for a given M*ECC2, there is always either one MECGS of type A.1 or one or several MECGS of type A.2. The other two possible subclasses A.3 and A.4 arise if M*ECC2 is complemented by deletions of at least one additional reaction from ECC2 either without  or may not (B.1) be combined with deletions of reactions exclusively contained in the ECGS network.Cut sets in ECGS which are not supersets of the given Mgiven cut set M#ECC2 in ECC2 with respect to each cut set in ECGS. Every cut set of ECGS belongs then to exactly one subclass. Notably, for different cut sets from ECC2, a certain cut set M#ECGS can be of class A or B. If it is of type A.1 or A.2 for one particular M*ECC2, then it cannot be of class A for any other MCS of ECC2 . If it is of type A.3 or A.4 for M*ECC2 then it can be of type A.3 or A.4 for other MCS of ECC2 (but it cannot change from A.3 to A.4 or vice versa). Of special interest are MCS of ECGS that are exclusively of type B for all MCS of ECC2 and thus not a superset (extension) of any M*ECC2. Those cut sets of ECGS involve deletion patterns in the ECC2 part that were not allowed in cut sets of ECC2 because they would violate some of the specified desired phenotypes, for example, if some of the reactions knocked-out would be essential for growth or product synthesis in ECC2 (but not in ECGS). For this reason, such MCS in ECGS may, in the extreme case, comprise fewer interventions than the smallest MCS in ECC2 . All relationships between cut sets discussed above hold true not only for ECGS and ECC2 but for any pair of a (full) metabolic network and a subnetwork of it.So far we discussed the relationships between one must exist in ECGS) will have at least 11 reaction deletions. 12 MCS of ECGS are of type A.3 and thus extensions of MCS from ECC2 involving additional reaction deletions only in the ECC2 part. Finally, 1,070 MCS of ECGS establish relationships of type A.4. All found 1,082 MCS in ECGS of class A are supersets of 18 distinct MCS of ECC2. For each of the 4 MCS in ECC2 with cardinality 5 there exist MCS in network ECGS with 9 reaction deletions which include all deletions of the respective MCS of network ECC2 and additional deletions. Further, for 14 MCS in ECC2 with cardinality 6 there exist superset MCS in ECGS with two to four additional reaction deletions. For the remaining 2,154 MCS of ECC2, no supersets have been found among the calculated MCS in network ECGS. Since, as explained above, MCS of type A.1 or A.2 must exist, this indicates that the corresponding MCS in ECGS have cardinalities above ten. Lastly, 44,066 − 1,082 = 42,984 of the MCS of cardinality up to ten in ECGS are not a superset of any MCS in ECC2, hence, are exclusively of type B.1 (221) or B.2 (42763).We analyzed the relationships between the calculated MCS in ECC2 and ECGS for the ethanol case study  with restop-down approach, we could initially combine the knockouts of the seed MCS with knockouts of all reactions from ECGS that are not contained in ECC2. As already discussed, this would result in a valid but, most likely, non-minimal cut set. A constrained minimal cut set for ECGS can afterwards be found by iteratively removing knockout candidates from this cut set and validating a posteriori if the undesired phenotype is still blocked. If the removal of a reaction from the cut set violates the target constraints this reaction is reinserted and the cut set is eventually identified as a valid MCS if no further reaction can be removed.The fact that any MCS found in the reduced network ECC2 will be a subset of one or several corresponding MCS in the full network ECGS can be used to construct MCS for the genome-scale model. The strategy is to augment a “seed” MCS from ECC2 by additional knockouts to obtain valid MCS for ECGS. Although all cut sets of type A.2, A.3, and A.4  could, iseed of reaction knockouts for ECGS and the corresponding reactions are inactivated in the ECGS model. Subsequently, all MCS in this model are computed but allowing knockouts only in reactions contained in the ECGS but not in the ECC2 model. By excluding all reactions from ECC2 as knockout candidates for ECGS, the remaining search space is considerably reduced (here by 499 reactions). The union of the seed MCS together with the MCS found in the ECGS network yields a valid MCS for the full ECGS network. In principle, this procedure can be used to determine all MCS in ECGS of type A.2 (and MCS of type A.1 can be easily identified beforehand).A “bottom-up” or “expansion” approach would tackle the problem of finding smallest MCS in a more directed manner: Again, a MCS of ECC2 is used as a We pursued the expansion approach to identify additional knockout strategies for ECGS based on the MCS from ECC2. Each of the 2176 MCS of ECC2 was used as seed in ECGS and corresponding MCS for ECGS with up to 15 additional reaction deletions were identified. Since the (up to 10) reactions of the respective seed MCS from ECC2 are inactivated prior to the computation of corresponding superset MCS in ECGS, the computed MCS can contain up to 25 interventions. The results of these calculations can be summarized as follows. For 1020 (of the 2176) seed MCS from ECC2 we found at least one MCS in ECGS with maximal 15 additional reaction deletions. For 1156 seeds no MCS could be identified in ECGS indicating that the allowed 15 reaction deletions do still not suffice to obtain valid MCS for network ECGS. If the MCS of ECC2 with cardinality 5 are used as seeds, at least 8 additional knockouts (of reactions only contained in ECGS) were necessary to achieve a valid MCS in ECGS . However, the smallest MCS results from seeds of cardinality 6 where it is sufficient to delete 5 additional reactions resulting in valid MCS for network ECGS with cardinality 11. Recall here that we predicted before that the smallest MCS of type A.2 would need at least 11 reaction deletions as no MCS of this type A.2 exist up to cardinality 10. The biggest MCS found in this way for network ECGS had as much as 23 reaction deletions: 8 from the seeds and 15 additional knockouts. Again, all additional knockouts were restricted to ECGS reactions not contained in ECC2.We exemplarily discuss specific knockout strategies that have been identified by the different approaches. A specific MCS identified in the reduced network ECC2 suggests deletion of the six reactions i) acetate kinase (R_ACK), ii) phosphogluconate dehydrogenase (R_GND), iii) hexokinase (R_HEX1), iv) D-lactate dehydrogenase (R_LDH_D), v) pyruvate formate lyase (R_PFL), and vi) glucose-6-phosphate isomerase (R_PGI) (we denote this MCS by M1). This knockout set is a valid MCS in ECC2 but does not yet enforce the desired phenotypic behavior (coupling of growth with ethanol synthesis) in network ECGS. However, by enumerating MCS up to size ten in ECGS we found a valid MCS of ECGS that extends M1 with two further reaction deletions, namely acetaldehyde dehydrogenase (R_ACALD) and either xylose isomerase (R_XYLI2) or hexokinase (HEX7). Reaction R_ACALD is contained in network ECC2 whereas reactions R_XYLI2 and R_HEX7 are only part of ECGS. These two MCS are thus of type A.4 and cannot be found with the described expansion approach starting with M1 as a seed in ECGS. However, if we used the above MCS M1 from ECC2 as a seed in ECGS and allow only reactions from ECGS that are not contained in network ECC2 as knockout candidates, at least six additional reaction knockouts are necessary to obtain a valid MCS. For example, additional deletions of R_XYLI2 or HEX7 together with NAD dependent aldehyde dehydrogenase (R_ALDD2x), NADP dependent aldehyde dehydrogenase (R_ALDD2y), citrate lyase (R_CITL), gluconokinase (R_GNK) and acetyl-CoA:hexanoate-CoA transferase (R_HXCT) result in a valid MCS for ECGS and is found by our procedure. These knockout strategies reveal that the deletion of R_ACALD (in the ECC2 subnetwork) is equivalent to the knockout of R_ALDD2x, R_ALDD2y, R_CITL, R_GNK and R_HXCT in the ECGS network.adhE)) was identified as a knockout candidate in several MCS in ECGS but never in ECC2 (where it is the only pathway to produce ethanol). Some alternative ethanol synthesis pathways must therefore exist in ECGS that, with the suggested knockout strategies, would lead to a high ethanol yield. In fact, in ECGS, acetaldehyde can be derived from acetyl-CoA and glycine that are condensed to L-2-Amino-3-oxobutonate (R_GLYAT) which is then reduced to L-allo-threonine via reaction R_ATHRDHr and finally split to acetaldehyde and glycine by R_THRA2i. However, since the acetaldehyde dehydrogenase (adhE) is part of the main route for ethanol production in E. coli and since adhE knockout mutants are known to show no relevant ethanol excretion at all32Generally, elimination of the R_ACALD reaction  MCS in ECGS require a huge and unrealistic number of knockouts or if a calculation of (large sets of) MCS for a given intervention problem is not feasible at all in the ECGS model, it would thus be practical to start with a MCS calculated in the core network ECC2. If experimental implementation of all knockouts contained in the MCS of ECC2 is not sufficient, one may use our expansion algorithm to expand the seed MCS from the ECC2 model to obtain a valid MCS for the ECGS model (whose existence is guaranteed).To summarize, our case study showed that metabolic intervention strategies can be exhaustively enumerated in the reduced network ECC2. The MCS found in the ECC2 network for enhancing ethanol synthesis show significant overlap with the smallest MCS identifiable in ECGS. Discrepancies mainly arise due to (a) additional pathways in ECGS having a zero or low ethanol yield (which must be deleted in ECGS) and (b) additional high-yield ethanol synthesis pathways (which give rise to new intervention strategies in ECGS). For a product of the central metabolism such as ethanol, we expect that a large number of MCS found in ECC2 are sufficient to reach the desired production phenotype. In fact, successful E. coli that is stoichiometrically consistent to and maintains several key properties of the most recent and well-curated genome-scale model of this organism (iJO1366). In addition, for the first time, we systematically compared key properties of the derived metabolic core model and of its genome-scale parent model.The central goal of this contribution was the development of a reference metabolic core model of the central metabolism of NetworkReducer algorithm together with a list of protected parts and phenotypes. This list was carefully compiled to ensure that the core model reflects the degrees of freedom in the central metabolism of E. coli while it was allowed to neglect redundancy along biosynthetic routes. The derived EColiCore2 network is a subnetwork of iJO1366 still comprising 499 reactions and 486 internal metabolites. However, ECC2 is small enough for detailed studies that would not be possible in the genome-scale model. In particular, all elementary modes for growth on the included substrates can be calculated on a standard personal computer in reasonable time. In addition, by compressing (mainly biosynthetic) linear reaction chains to condensed overall reactions a compressed network version of ECC2, ECC2comp, was derived which contains 82 reactions and 54 internal metabolites. The solution space of ECC2comp is equivalent to ECC2 since every steady state flux distribution in ECC2comp can be uniquely mapped to one of ECC2 and vice versa. ECC2comp might be more practical and efficient for some applications. For example, less working memory is required for sophisticated calculations. Further, the reduced number of reactions and metabolites allows for a clear and concise graphical representation of the whole network . These examples demonstrate the trade-off in analyzing a core vs. a full genome-scale network: unrealistic or overoptimistic behaviors represented in the genome-scale model  can be excluded in a core model, however, unknown and non-intuitive pathways of possibly higher relevance might be missed when focusing only on the well-examined part of the metabolism.We performed a comprehensive analysis and comparison of different properties of ECC2 and its genome-scale parent model ECGS to demonstrate that ECC2 is a reasonable reduction of ECGS. ECC2 preserves key phenotypic behaviors and properties and allows for the analysis of major metabolic fluxes and the elucidation of engineering strategies in the central metabolism of We also studied the value of the core model for calculating relevant metabolic engineering strategies by means of a case study with ethanol as product of interest. As expected, cut sets found in ECC2 for overproduction of ethanol are usually not sufficient to completely block all potentially undesired phenotypes (with low product yield) in ECGS but already indicate key targets (and combinations thereof) which are also relevant for ECGS. Analysis of the smallest cut sets in ECGS showed that non-intuitive intervention strategies may exist in the genome-scale model which either target additional unfavorable routes that were not contained in the ECC2 model or/and redirect carbon flux to hidden (unknown) product synthesis pathways in the full network. While the cut sets of the ECGS model are complete and ensure that the desired phenotypes are really achieved in the full model, the relevance of some of the found alternative targets  and pathways is questionable. Generally, one can expect that intervention strategies calculated in ECC2 for products of the central metabolism  will have a better predictivity (most relevant pathways for their production are contained in the model) than those calculated for products of the anabolism where relevant - favorable and unfavorable - pathways might have been removed in the pruning step.We further clarified the relationships between cuts sets of the full and of a subnetwork. With respect to a given subnetwork (here ECC2) one can distinguish two major classes of cut sets in the full (ECGS) model: a cut set in ECGS is either a superset of one  cut sets found in ECC2 , or it is not a superset of any cut set in ECC2 and therefore involves other deletion patterns in the ECC2 subnetwork possibly combined with further knockouts in reactions exclusively contained in ECGS. Whereas cut sets of the latter type can therefore not be obtained or deduced from ECC2, we demonstrated that cut sets in ECGS that are supersets of cut sets from ECC2 can always be obtained by expanding seed cut sets in ECC2. Thus, ECC2 can initially be used to find intervention strategies leading to desired phenotypes in the core model. If a particular cut set was experimentally not successful (the mutant shows still an unfavorable phenotype in the experiment) or if one is interested to determine (some or more) full intervention strategies in the ECGS model, the respective knockout strategy of ECC2 can be used as a seed and then be extended by further knockouts to a valid MCS for ECGS. We demonstrated that this approach can be used to determine thousands of additional valid MCS for ECGS with higher cardinalities which could not be calculated before.E. coli are well reflected in the reduced model.A major advantage of ECC2 is that it is a strict submodel of its genome-scale parent model ECGS so that results from ECC2 can be directly related to ECGS. In fact, this property was used to determine cut sets in the ECGS model from seed cut sets calculated in ECC2. Furthermore, all flux distributions in ECC2 are valid solutions in ECGS and, likewise, all EM of ECC2 are equally valid in ECGS. Hence, analyzing ECC2 means analyzing a subset of the feasible phenotypes of ECGS and ECC2 was derived in such a way that metabolic fluxes in the central metabolism of As desired, ECC2 allows for the full enumeration of EM and metabolic engineering strategies and it is readily usable for educational purposes due to its appropriate scope, size, and clarity. If certain pathways or/and metabolites contained in ECGS but not in the ECC2 model become of interest in a particular application, they might be added to the core model which should still keep the model size low enough to perform detailed calculations (e.g. EM enumeration). Likewise, if one wants to protect other phenotypes from the genome-scale model, the reduction process can be repeated with adapted constraints.E. coli, similar to its well-curated and evolved genome-scale parent model iJO1366.With the results presented in this study, we believe that ECC2 holds the potential to become a reference metabolic model for the central metabolism of How to cite this article: Hädicke, O. and Klamt, S. EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model. Sci. Rep.7, 39647; doi: 10.1038/srep39647 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The physiology of cells, determined by all extracellular production and consumption rates, intracellular reaction rates and the growth rate, is of fundamental interest in such different fields of the life sciences as microbiology, biotechnology and medical applications. In this Crystal Ball, I emphasize the importance of metabolic reaction rates, that is fluxes, for our understanding of metabolic network operation and envisage a time in which fluxes can be changed at will. Indeed, the ultimate goal of many fields of the life sciences is to understand cell physiology and hence the flux distribution, which characterizes the metabolic state of a cell. Based on this information, it will be possible to design strategies, for example, to avoid certain phenotypes (applications in infection biology and cancer research) or to redirect fluxes .I argue that we are now in the lucky position that we can measure extra‐ and intracellular fluxes, that we have well‐characterized genetic tools for changing enzyme activities, and that we have fermentation capabilities to maintain optimal environmental conditions for a microorganism. Rationally changing a flux, however, requires a solid understanding of how the interplay of all mentioned aspects (and more) result in the observed flux distributions.Before describing some of these developments, we have to agree on the importance of the goal to precisely manipulate cellular fluxes. In many research projects, fluxes are in fact ignored, despite the ultimate goal implicitly being to understand metabolic network operation. For example, complex interactions between signalling pathways is often studied while ignoring cell physiology, although many cellular processes are growth rate dependent. And in industrial biotechnology, often the titre of the product of interest is communicated as this value quantifies overall performance of the biocatalytic reaction, implicitly even the product production rate (i.e. the flux towards the product) assuming a defined experimental time. Of course, when the fermentation is reported in the context of the entire process, titre is central; as it considers physical parameters including product solubility, the yield of the product on a given substrate and product inhibition of the biocatalyst besides other aspects. However, this is not the only critical value relevant to the process.−1) and not specific rates normalized to the cell dry weight used (e.g. gproduct per gCDW per hour) are reported. When manipulating enzyme activities (e.g. using a stronger promoter or a more active enzyme variant), one implicitly aims for a change in flux at a given network node; the subsequent outcome of this flux change being a change in product concentration. A focus on end‐point assays potentially misses interesting clones that show a significant improvement in production rate, hence achieve the maximal titre long before the end‐point. However, there currently is no facile method to observe cellular fluxes on a broad scale.In Metabolic Engineering, we propose to execute rational strain engineering, with the performance of the  cell in focus. Still, often end‐point concentrations  metabolism may be simple or almost impossible, depending on the cell and environment studied. Pathway redundancy and the lack of experimental measurements, which could be otherwise used to close metabolite mass balances might hamper identifiability of particular fluxes. For example, the flux through glycolysis in lactic acid bacteria (a linear pathway from glucose to lactate) can be resolved by simple flux balancing, while aerobic glucose metabolism in http://seva.cnb.csic.es/) give an indication of the effort of the research community. One has not to be too optimistic to state that we are slowly but surely entering the area of writing the genetic code, as compared to only reading it.With the concepts of Synthetic Biology, orthogonality, modularity, but foremost standardization, we can finally progress towards truly rational strain engineering. The rate of transcription and translation can be changed significantly and – importantly – in a generally predictable manner. For example, the affinity of the ribosome via the 16S rRNA to ribosome binding sites was computed, promoters characterized in detail, and other genetic elements were introduced to deliver standardized tools that facilitate flux rerouting . While many strategies including transporter overexpression failed, 15 years ago Peter Ruhdal Jensen and colleagues reported an E. coli with a 1.7‐fold increased glucose uptake rate  in Pseudomonas putida , but notably produces only biomass and CO2, with no ‘overflow’ metabolites encountered  and activated beta‐OH‐fatty acids (i.e. ACP‐fatty acid) resulted in a strain with 55% theoretical yield of rhamnolipid on sugar, a flux increase of 300% in the dTDP‐rhamnose pathway, and 50% increase in de novo fatty acid synthesis.The many aspects that ultimately define the flux distribution in a given metabolic network are so complex that considerable effort is still needed to achieve, for example, a significantly increased glucose uptake rate (actually a good idea in many whole‐cell catalysts, as the substrate uptake rate determines the maximal achievable specific flux towards the product and influences the space time yield, e.g. gConsidering fluxes in many more biotechnological projects and even life science research in general will certainly yield great rewards, and many surprises are waiting for us. We have many of the tools at hand, and are developing many more; now, we must use them to their full effect to illuminate metabolism.


Synechococcus elongatus PCC 7942 with a synthetic metabolic pathway. The strain into which the synthetic metabolic pathway was introduced produced 3.48 mM (0.265 g/L) 1,3-PDO and 14.3 mM (1.32 g/L) glycerol during 20 days of incubation. In this study, the productivities of 1,3-PDO were improved by gene disruption selected by screening with in silico simulation.Production directly from carbon dioxide by engineered cyanobacteria is one of the promising technologies for sustainable future. Previously, we have successfully achieved 1,3-propanediol  production using S. elongatus PCC 7942. A genome-scale model of S. elongatus PCC 7942 constructed by Knoop was modified by the addition of a synthetic metabolic pathway for 1,3-PDO production. Next, the metabolic flux distribution predicted by metabolic flux balance analysis (FBA) was used for in silico simulation of gene disruption. As a result of gene disruption simulation, NADPH dehydrogenase 1 (NDH-1) complexes were found by screening to be the most promising candidates for disruption to improve 1,3-PDO production. The effect of disruption of the gene encoding a subunit of the NDH-1 complex was evaluated in the 1,3-PDO-producing strain.First, a stoichiometric metabolic model was applied to prediction of cellular metabolic flux distribution in a 1,3-PDO-producing strain of ndhF1-null 1,3-PDO-producing strain showed the highest titers: 4.44 mM (0.338 g/L) 1,3-PDO and 30.3 mM (2.79 g/L) glycerol. In this study, we successfully improved 1,3-PDO productivity on the basis of in silico simulation of gene disruption.During 20 days of incubation, the The online version of this article (10.1186/s12934-017-0824-4) contains supplementary material, which is available to authorized users. Synechococcus elongatus PCC 7942 to produce 1,3-propanediol  by introduction of a synthetic metabolic pathway [Direct production of chemicals and fuels from carbon dioxide by cyanobacteria is one of the promising technologies to reduce carbon dioxide emissions and the dependence on fossil fuels , 2. Comp pathway . 1,3-PDO pathway . In the  pathway . Althoug pathway  among vaEscherichia coli. For screening of effective candidates for gene manipulation, various strategies have been established for prediction of the effects of a gene knockout, knockdown, and overexpression [Synechocystis sp. PCC 6803. So far, the metabolic models of some cyanobacteria used for bioproduction  have been constructed on the basis of an annotated genome sequence [Synechocystis sp. PCC 6803. FBA was used for validation of this genetic modification to increase theoretical productivity of butanol. Furthermore, some simulations based on FBA have predicted various metabolic modifications (knockout and overexpression) to increase theoretical production by cyanobacteria, for example, of biofuels  and succinic acid by Synechocystis sp. PCC 6803 [Synechococcus sp. PCC 7002 [In silico simulation is an powerful methodology to improve productivity with low experimental costs. The stoichiometric metabolic model not requiring kinetic parameters of metabolic reactions is comparatively applicable to analysis of whole-cell metabolism. Flux balance analysis (FBA) can predict metabolic flux distribution under constraint conditions . FBA usipression . Shastripression  were thesequence –32. AnfePCC 6803 –35 and bPCC 7002 . NonetheS. elongatus PCC 7942 constructed in our previous work [In this study, we applied FBA to prediction of metabolic flux distribution in a 1,3-PDO-producing strain of ous work . The canous work . The posAll the chemicals were purchased from Wako Pure Chemical Industries, Ltd.  unless specified otherwise. Restriction enzymes, phosphatase , ligase , and DNA polymerase  were used for cloning. Oligonucleotides were synthesized by Life Technologies Japan, Ltd. .S. elongatus PCC 7942 . Hereafter, this medium is referred to as the BG11 medium. The composition of the BG11 medium used in this study was the same as previously reported [A modified BG11 medium supplemented with 20 mM HEPES–NaOH (pH 7.5) for pH stabilization was used for cultivation of reported . Antibio730) was measured using Infinite 200 PRO . For determination of dry cell weight (DCW), cells in culture were harvested on the Ominipore™ membrane filter  by filtration under reduced pressure. Cell harvest filters were dried at 65 °C for 12 h. The weight differences before and after cell harvesting were determined as DCW in culture.The incubation conditions for 1,3-PDO production were the same as those described previously . Cell dendhD1, ndhD2, ndhD3, ndhD4, and ndhF1 were T2711–T2712, T3189–T3190, T2723–T2724, T2719–T2720, and T3208–3209, respectively. The primer sets for the downstream region of ndhD1, ndhD2, ndhD3, ndhD4, and ndhF1 were T3187–T3188, T3191–T3192, T2787–T2726, T2721–2722, and T2713–T2714, respectively. The amplified upstream and downstream regions except for ndhF1 were digested by KpnI–HindIII and HindIII–BamHI, respectively, and ligated into KpnI–BamHI sites of pTA703. Only for ndhF1, the upstream and downstream regions were digested with SalI–HindIII and HindIII–BamHI, respectively, and ligated into SalI–BamHI sites of pTA703. A chloramphenicol resistance cassette was amplified from pZA31-luc  by PCR using primers T2128–T2129. The amplified fragment digested with HindIII was ligated into the HindIII site of each plasmid harboring upstream and downstream regions of a target gene, thus creating plasmids pTA1811 (ndhD1 disruption), pTA1812 (ndhD2 disruption), pTA1641 (ndhD3 disruption), pTA1640 (ndhD4 disruption), and pTA1828 (ndhF1 disruption).Plasmids and primers used in this study are listed in Additional file The constructed plasmids  were used for gene disruption in TA1297 (wild-type strain), TA2984 , and TA3800 (glycerol-producing strain). TA2984 and TA3800 were constructed in our previous studies , 38. Theg, 10 min) and filtered using Minisart RC4 . Concentrations of glycerol and 1,3-PDO in filtered samples were determined using a high-performance liquid chromatograph  equipped with an autosampler , a SUGAR SP-G guard column , a SUGAR SP0810 column (Shodex), and a refractive index detector . Operating conditions were as follows: injection volume, 5 µL; mobile phase, MilliQ water; flow rate, 1.0 mL/min in isocratic mode; column temperature, 80 °C.Culture supernatants were centrifuged  as a solver.2/s) and growth light (100 μE/m2/s) provided by a digital LED source  were used for measurement of photosynthetic activity. Photon flux density was measured with IKS-27 . Respiratory activity was similarly measured in complete darkness.Chlorophyll concentrations were measured by extraction in methanol . Oxygen 730 of 1.0) was collected, and ATP was extracted from pelleted cells by resuspension in a 1% (w/v) trichloroacetic acid solution. After centrifugation at 20,000×g for 10 min at 4 °C, the supernatant was neutralized with 1 M Tris–acetate buffer (pH 7.8). The concentration of ATP in the neutralized sample was determined using the ATP determination kit .ATP extraction and measurement were basically performed by the method of Wang et al. . The appndhF1-null 1,3-PDO-producing strain) were measured by LC-QqQ-MS and GC-Q-MS analyses. Sample preparation and analysis were performed by the method described by Soma and Hanai [Intracellular metabolites of TA2984  and TA4058 . The additional information on the introduced synthetic metabolic pathway for 1,3-PDO production  was added to GSM_7942, resulting in a metabolic model of the 1,3-PDO-producing strain composed of 603 genes, 672 reactions, and 536 metabolites .We previously constructed a 1,3-PDO-producing strain, designated as TA2984, by introduction of a synthetic metabolic pathway. A four-step reaction of the introduced pathway converts DHAP, one of the metabolites in the Calvin cycle, into 1,3-PDO via glycerol  was composed of 74 strains that could not satisfy the constraint conditions and lower limit of 1,3-PDO production. The second group (triangles) included 254 strains showing a higher specific production rate of 1,3-PDO, but a null value of the specific growth rate. This may also mean that each gene disrupted in the 254 strains was essential for growth. It was likely that the disruption of any of the 328 genes classified into these two groups was not applicable to improvement of 1,3-PDO productivity. The metabolic flux distributions of the other 267 strains with single-gene disruption could show a certain specific growth rate and higher specific production rate of 1,3-PDO as compared with the experimental data. We screened the 267 candidates for more desirable ones using the product of the specific growth rate and specific production rate as an indicator. Out of the 267 candidates, 231 showed a lower product of the specific growth rate and specific production rate and were classified into the third group (squares).c oxidase were classified into the fourth group. Second, two cases indicating the same metabolic flux distribution were observed in different two-gene disruptions . These genes were annotated as genes encoding an enzyme catalyzing a serial reaction without any blanching pathway. The other six metabolic flux distributions were examined by single-gene disruption. Based on the value of the product of the specific growth rate and specific production, the disruption of 22 genes involved in the NDH-1 complex was found by screening as the most promising candidate for improvement of 1,3-PDO production among the 10 patterns of the fourth group.Finally, 36 genes were classified into the fourth group (circles) as promising candidates to be disrupted for increasing 1,3-PDO productivity  in our experiment. On the other hand, it was reported that four mutants in which each gene encoding the unique subunit in four NDH-1 complexes is disrupted can grow under photoautotrophic and ambient air conditions [NDH-1 complexes in cyanobacteria are involved in several cellular functions  and are subdivided into four variants  by function and subunit composition according to reverse-genetic studies . The phenditions –52. Indenditions . TherefondhB disruption, the gene knockout strains of ndhD1 , ndhD2 , ndhD3 , and ndhD4  based on the 1,3-PDO-producing strain (TA2984) were successfully constructed  showed the highest titers of 1,3-PDO  and glycerol  during 20 days of incubation  did not increase  was the lowest among the four gene knockout strains and TA2984 (Table ndhD4-null strain) showed the highest production rate of 1,3-PDO  produced from DHAP by engineered cyanobacteria [Previously, we have constructed a glycerol-producing strain TA3800) into which only the pathway converting DHAP to glycerol was introduced [00 into wbacteria –20, 38 abacteria , 2,3-butbacteria . AlthougndhF1 successfully increased metabolic flux of DHAP into the 1,3-PDO-producing pathway as expected on the basis of the in silico simulation  and TA2984  were measured. As the result, TA4058 showed lower respiratory activity in the dark and almost the same photosynthetic activity under saturated light conditions (1000 μE/m2/s) as compared with TA2984 , the photosynthetic activity of TA4058 was lower than that of TA2984, being consistent with the growth defect of TA4058  mainly produced by glycolysis and the TCA cycle is used for generating a proton gradient and H2O from O2. The generated proton gradient functions as a driving force of ATP synthase for ATP production. Whole or partial loss of cyclic electron flow and respiratory activity under the influence of the ndhF1 disruption may decrease cellular ATP content.The physiological role of cyclic electron flow is known to increase the ATP/NADPH ratio produced by photosynthetic electron flow . So, nexThe intracellular metabolites involved in a reaction with ATP seemed to reflect the ATP deficiency in TA4058. For example, ATP is consumed in reactions producing RuBP and ADP-glucose, and is produced in the reaction producing pyruvate. In ATP-consuming reactions, substrates (ribulose-5-phosphate: Ru5P and glucose-1-phosphate: G1P) and products (RuBP and ADP-glucose) were detected at high and low concentrations, respectively. Conversely, lower concentration of a substrate (phosphoenolpyruvate: PEP) and higher concentration of a product (pyruvate) were observed in an ATP-producing reaction. In particular, the metabolites around the glycogen synthesis pathway seemed to be affected by the ATP deficiency. Fructose-6-phosphate (F6P) is a metabolite of the branching point for glycogen synthesis from the Calvin cycle and is sequentially converted to glucose-6-phosphate (G6P), glucose-1-phosphate (G1P), and ADP-glucose. In this glycogen synthesis pathway, significant accumulation (more than fivefold) was observed for metabolites except for ADP-glucose produced with ATP consumption. This result suggests that glycogen synthesis is repressed in TA4058 caused by ATP deficiency. Because other studies showed that higher accumulation of glycogen is observed in stationary phase , 59, thendhF1 disruption in the simulation result  in heterotrophic organisms. We hypothesized that the repressed respiratory activity can decrease the metabolic flux in catabolic metabolism generating reducing power in photosynthetic organisms, too. The decreasing of 3-PG-consuming flux into glycolysis by the ult Fig.  was consndhF1 disruption. Hasunuma et al. [Synechocystis sp. PCC 6803 by overexpression of the flv3 gene involved in NADPH consumption. The strategy of acceleration of NADPH consumption also showed the energy balance to change cellular metabolism of cyanobacteria. In the present study, the metabolism changes intended to adapt to the energy imbalance (ATP deficiency) were applied to chemical production . Taking into consideration of the points mentioned above, ndhF1 disruption is a good strategy for enhancement of production of other chemicals  from metabolites in the Calvin cycle in cyanobacteria [Not only greater flux of 3-PG into the Calvin cycle but also repression of glycogen synthesis may increase the productivity values of 1,3-PDO and glycerol. These results indicate that the productivity improvement was caused by the metabolism adapted to energy imbalance induced by the a et al.  successfbacteria , 38, 55.ndhF1-null and wild type strain  from pyruvate than the wild type strain with the same pathway (0.191 g/L). It is difficult to explain clearly the reason why the suggestion based on our simulation result is different from their result, but one possible reason would be the driving force by ATP generation by the reaction from PEP to pyruvate to compensate the ATP deficiency in the ndhF1-null strain.The simulation result of the metabolic flux distribution of the ain Fig.  showed ta et al. , howeverRecently, Broddrick et al.  construcS. elongatus PCC 7942 were successfully improved by gene manipulation based on in silico simulation of gene disruptions. The best candidate for improvement of 1,3-PDO productivity was found to be the elimination of NDH-1 complexes involved in several cellular functions: respiration, cyclic electron flow, and carbon dioxide uptake  was found to produce 1.28-fold more 1,3-PDO  and 2.13-fold more glycerol  than TA2984 did (Fig. ndhF1 disruption may enhance the 3-PG-consuming flux into the Calvin cycle and can be suitable for 1,3-PDO and glycerol production (Additional file In this study, the productivity of 1,3-PDO and glycerol in did Fig. . The metAdditional file 1. Additional tables.Additional file 2. Additional figures.


Klebsiella pneumoniae and identify missing biosynthetic pathways in models of Mycobacterium tuberculosis. This work provides fundamental insights into the essentiality of organic cofactors and has implications for minimal cell studies as well as for modeling genotype-phenotype relations in prokaryotic metabolic networks.The composition of a cell in terms of macromolecular building blocks and other organic molecules underlies the metabolic needs and capabilities of a species. Although some core biomass components such as nucleic acids and proteins are evident for most species, the essentiality of the pool of other organic molecules, especially cofactors and prosthetic groups, is yet unclear. Here we integrate biomass compositions from 71 manually curated genome-scale models, 33 large-scale gene essentiality datasets, enzyme-cofactor association data and a vast array of publications, revealing universally essential cofactors for prokaryotic metabolism and also others that are specific for phylogenetic branches or metabolic modes. Our results revise predictions of essential genes in  •Seventy one biomass equations of manually curated genome-scale metabolic models are compared.•Eight classes of universally essential prokaryotic organic cofactors are proposed.•Conditionally essential organic cofactors are presented and discussed.•Klebsiella pneumoniae are revised.Gene essentiality predictions for •Mycobacterium tuberculosis is predicted.A missing essential pathway in models of  Indeed, it can be used in taxonomical classification  and is iin silico. This is performed through a growth reaction wherein necessary constituents are combined in stoichiometric amounts producing new biomass. Maximization of the flux through this reaction, the so-called Biomass Objective Function (BOF), is the most commonly used method for simulating growth phenotypes through the Flux Balance Analysis methodology  and multi-species comparative assays in determining  which components make up the cell's biomass, in contrast with the advanced picture in terms of the elemental composition . Genome-hodology .The BOF can be formulated as a direct biosynthesis from precursor metabolites ; biosyntThe utility of metabolic models is tied to the accuracy of the biomass composition used . Yet, mo22.1Bacillus subtilis and Escherichia coli), as shown in E. coli built in 2003. Another is the cluster that includes all the three models of the genus Methanosarcina, built after the first model, iAF692. However, the majority of models group independent of their phylogenetic relatedness. Most of the clusters are thus artifacts rather than representing underlying biology. For example, the node on the bottom of the tree  represents the three BOFs with the most unique compounds among all compounds represented in the 71 BOFs analyzed.We first extensively assessed biomass compositions in published prokaryotic GEMs. In total, 71 detailed biomass compositions were gathered, covering 9 phyla with 5 classes of Proteobacteria and one phylum of Archaea (Supplementary Dataset 1). To enable comparison across different models, we reconciled diverse nomenclatures and representation styles, ranging from lumped stoichiometry to reaction-level inclusion. This exercise resulted in 551 unique metabolites (nomenclature as per BiGG database ) that arE. coli in 2011  some which are represented in a high level form, tagged with the species’ initials (phosphoethanolamine is named pe_CB in C. beijerinckii and pe_HP in H. pylori). We also identified gaps, for example in the network of C. beijerinckii regarding the production of Spermine, Spermidine, Glutathione, Dimethylbenzimidazole and Adenosylcobalamin. Even under the rich media conditions used   was usedused see , whereinused see b attestiSynechocystis sp., between 29.4% and 32.8% of essential reactions were different when using an alternative biomass composition (Klebsiella pneumoniae) with the BOF of iAF1260 (E. coli), wherein 82 (67.7%) of the predictions were new essentials. The BOF of iAF1260 brings 19 new components that iYL1228 can produce    , the median presence of a gene increases to 50%  are annotated to functions related with biosynthesis of cofactors and prosthetic groups c. Moreovs to 50% d. This i2.4The true-positive  rate of cofactor-related essential genes of iYL1228 in γ-Proteobacteria when using the biomass composition of iAF1260 indicates organic cofactors as crucial but also missing biomass components in prokaryotic GEMs. To close this gap, we set out to identify universally essential cofactors (or classes thereof) for prokaryotes that will improve accuracy and comparability of GEMs. For this, we integrated multiple large-scale datasets a. The co2.5Mycobacterium tuberculosis iNJ661v , only one form could be included for the sake of simplicity. When a class of cofactors includes active and non-active interconvertible forms, the active forms should be preferred. A simple example case is the representation of flavins: FAD and FMN are the preferred active forms to be included in the BOF, oppositely to riboflavin, the non-active precursor. More details on the different approaches on modeling biomass compositions are discussed in the Supplementary Discussion.in vivo) predictions.The comparison of gene essentiality in different species has some limitations. These can occur due to biological causes, as the alternative presence of isozymes and transporters that introduce redundancy in different networks, and different media conditions where the large-scale assays were performed. Other limitations can occur due to errors and incompleteness in databases, which we examine further in the Supplementary Discussion. By overlapping several levels of evidence in the prediction of essential cofactors  our apprWe here propose a standardized and detailed core biomass composition for prokaryotes . This is a conservative proposal and thus includes only the three most prevalent lipid components as representative species , and these should be adapted according to the species being modeled. Our proposal excludes other non-universal macromolecules such as cell wall peptidoglycans (more details can be found in the Supplementary Discussion). We further suggest that the pipeline used here a can be The cofactors here identified as universally essential play fundamental roles in biochemistry. In most cases, they are related with the transfer of small units: hydride groups for NAD(P)(H), methyl groups for SAM, electrons for FAD and FMN, acyl groups for CoA and one-carbon units in C1 carriers. The two special cases of P5P and THMPP correspond to direct intervention in catalysis, stabilizing intermediate metabolites and assisting in the formation of new chemical bonds, respectively. Our classification of universally essential is conservative, excluding cofactors for which we found minor exceptions in the data analyzed, e.g. biotin  component is included in the BOF, it implies that this component needs to be provided, either through the biosynthetic pathway or via transport reactions. The construction of more complete and standardized BOFs will thus have a great impact not only in the predictions of essential genes but also in the construction of minimal media required for growth. Both applications are of utmost importance for identifying metabolic vulnerabilities of pathogens, being in fact the most common motivations to construct GEMs for those organisms.Overall, this work lays foundations for improving the definition of biomass composition in the current and future metabolic reconstructions – an important step towards biochemically more accurate models with higher predictive power. Moreover, it is the first large-scale systematization of essential metabolic organic cofactors for prokaryotes, which we hope will be useful for several fundamental and applied studies.44.1We searched for manually-curated GEMs of prokaryotes in four major online databases: BiGG , MetRxn The ModelSEED template for universal biomass components was obtained from the original publication .4.2Hierarchical clustering was performed using ‘pvclust’ R package  with bin4.3We chose five different GEMs by sampling high and low phylogenetic dissimilarity pairs in order to assess the impact of BOFs in predictions of essentiality a.4.4Simulations of maximum growth rates for single-deletions of reactions and genes were performed using Flux Balance Analysis (FBA) . All mod4.5Searches in DEG  were per4.6We extracted all enzyme-cofactor association data for prokaryotes using the Python SOAP access methods for BRENDA . Biosynt4.7All changes described in the main text were performed manually on the original SBML file for iNJ661v. To simulate Middlebrook media as used in the genome-scale experimental assay for validation of the predictions , new tra


The IMGMD database is freely available, without any registration requirements, at http://imgmd.jiangnan.edu.cn/database.Genome-scale metabolic models (GSMMs) constitute a platform that combines genome sequences and detailed biochemical information to quantify microbial physiology at the system level. To improve the unity, integrity, correctness, and format of data in published GSMMs, a consensus IMGMD database was built in the LAMP (Linux + Apache + MySQL + PHP) system by integrating and standardizing 328 GSMMs constructed for 139 microorganisms. The IMGMD database can help microbial researchers download manually curated GSMMs, rapidly reconstruct standard GSMMs, design pathways, and identify metabolic targets for strategies on strain improvement. Moreover, the IMGMD database facilitates the integration of wet-lab and  Since the first GSMM of Haemophilus influenzae Rd was constructed in 19994, more than 300 GSMMs for over 100 organisms have been built6.Genome-scale metabolic models (GSMMs) are a kind of a mathematical model that integrates multiple types of omics data, such as genomics, transcriptomics, proteomics, and metabolomics. GSMMs can clarify the relations among genes, proteins, and reactions. Models can be used to describe all biochemical reactions, metabolites, and genes involved in the metabolism of a specific organism. They have been used to decipher metabolic, regulatory, and signaling networks at the whole-organism level9. Nonetheless, owing to differences in nomenclature10, integrity, and correctness11 as well as the format12 of published GSMMs, these models cannot be directly applied by other researchers. To reduce the manual labour needed for model construction and to increase the quality of GSMMs, some databases and tools, such as BIGG Models11 and MetaNetX13, have been developed. Nonetheless, they are limited by the quality and quantity of models. For example, BIGG Models includes only 80 high-quality GSMMs (as of 30 November 2016), which is far from the number of published models. In MetaNetX, only 24 (14.7%) models have been published, which were validated by experimental results (http://www.metanetx.org/cgi-bin/mnxweb/repository).GSMMs have been developed for over 15 years, and four steps are involved in their construction: creation of a draft model, manual refinement, conversion to a mathematical format, and network evaluationIn silico Microbial Genome-scale Metabolic Models (IMGMD) in the LAMP (Linux + Apache + MySQL + PHP) system. It provides a platform for integration and standardisation of all published microbial GSMMs. In IMGMD, users are not only able to browse and download standardised GSMMs but can also reconstruct GSMMs automatically. In addition to pathway mining and mutation library functions, users can access information that can guide pathway design and metabolic target identification.In this study, we built a database named in silico and in vivo metabolic engineering results, and accordingly, it provides guidance for target searches  has a user-friendly website for the following applications: (1) It can be used to download standardised GSMMs; this module integrates model-related information, such as gene–protein–reaction relations, genome information, and references Fig. . (2) It hes Fig. .Figure 1All web interfaces of the IMGMD database were tested in various browsers, such as Google Chrome, Mozilla Firefox, Internet Explorer, Opera, and Safari on Windows or Linux platforms. Despite minor differences in appearance, all tools functioned normally in all the tested browsers and on all platforms. Among the browsers tested, Google Chrome and Mozilla Firefox provided the best user experience. Hence, we recommend that users access the database using one of these two browsers.Saccharomyces cerevisiae as an example to demonstrate the use of ‘Search for organism’. All 8 S. cerevisiae models are returned. Then, by clicking on ‘Saccharomyces cerevisiae S288c’ for model iND750, a user can find detailed information about the organism , model , and reference , PSORTb23 was employed to predict protein subcellular localisation.Five steps are needed to construct a model in IMGMD: (1) choosing three models for reference; (2) uploading the genome sequence; 3) choosing a threshold ; (4) entering an e-mail address to receive results ; (5) submitting the job to the IMGMD database. Once the job is complete, the results contain three parts, including the model, transport proteins, and prediction of protein subcellular localisation  is a Web service that includes the RAST genome annotation tool. Based on the annotation results, a model for a specific organism can be reconstructed automatically. Given that the RAST service (http://rast.nmpdr.org/rast.cgi) can annotate only prokaryotes, ModelSEED has limited applicability to eukaryotes. Besides, model construction by ModelSEED will take a long time, according to the job numbers. IMGMD is also a web platform that serves for model construction. It is based on the results of genome homologous alignment. Users can upload a target organism’s genome sequence and choose relevant parameters. After submission of the job to IMGMD, results will be returned within 1 day. Nonetheless, a model constructed by IMGMD is a draft model. It still needs to be further processed to obtain a GSMM. The COBRA Toolbox is based on the Matlab platform, which is commonly used for model construction. The COBRA Toolbox requires users to have basic Matlab knowledge and an advanced computer configuration for model analysis , other pathways also could generate pyruvate. On the web page of pathway-mining results, information about the substrate and production can be linked to some metabolic databases, like KEGG29, ModelSEED24, ChEBI30, and PubChem31. Besides, on the page of detailed pathway information, reactions participating in a pathway are shown, including Reaction ID, Formula, Genes, Subsystem, and EC numbers 32 and Methanosarcina barkeri (iMG746)33, there were 8 and 12 pathways from glucose to pyruvate, respectively. (3) Pathways that generate highly valuable products may exist in typical organisms. To mine these potential pathways, users can choose all collected models for the search, and then choose reactions in which species and corresponding genes can serve as references for a target strain to guide strain design. Considering these three levels, the function of pathway mining may be useful in synthetic biology and systems metabolic engineering.In this module, users can explore metabolic pathways at three levels. (1) According to the input metabolites as substrates and products, total pathways from a substrate to product in a GSMM can be output. For example, in the 35. In IMGMD, a library that combines in vivo and in silico results to guide metabolic engineering was created.The pathway prediction tool enables new pathway design for metabolic engineering; additionally, the mutation library function can be used for optimisation of the host strain. It can help to identify targets that couple cell growth with product formation, e.g., targets for gene upregulation, downregulation, and gene deletioniAF1260, 217 results can be found. The effect of a knockout of b4025, which encodes glucosephosphate isomerase  in E. coli, the growth rate and production rate can be viewed on another webpage  were related to various knockout strategies, involving different algorithms, such as OptKnockhttp://systemsbiology.ucsd.edu/InSilicoOrganisms/OtherOrganisms, http://synbio.tju.edu.cn/GSMNDB/gsmndb.htm) and by scrutinizing the primary scientific literature , Microsoft Excel, Microsoft Word, or PDF. To read these models using the COBRA Toolbox, models in all formats were rewritten in the Excel format. Word and PDF files were manually transformed into Excel files. The SBML models were transformed into Excel, using the COBRA Toolbox on the Matlab platform. Nonetheless, during this process, some models written in SBML could not be read using the COBRA Toolbox. Eventually, 265 of total 328 (80.8%) published models were standardised in the Excel and SBML formats and can be downloaded from our database.E. coli model iAF126043, pyruvate was represented as pyr. In Saccharomyces cerevisiae model Yeast 1.044, and Yarrowia lipolytica model iNL89545, it was indicated by PYR and s_1277, respectively. In the IMGMD database, according to their unique IDs in various biochemical databases , the metabolites from different models were unified using IMGMD metabolite IDs. Then, 8367 metabolites from these different models were integrated. Additionally, 77.65% of metabolites can be linked to at least one of these databases  were collected by referring to information such as EC numbers, reaction descriptions, and formulas, in the other columns for the models. Finally, 17 of 19 (89.5%) GSMMs were filled out, except for models of Streptomyces lividans (GMD-TK24) and Pseudomonas putida (PpuMBEL1071). For the Formula column, metabolites were first replaced with the unified IMGMD metabolite IDs. For example, the reaction catalysed by alcohol dehydrogenase (EC: 1.1.1.1) was unified as ‘M00442[c] + M00003[c] 〈=〉 M00083[c] + M00079[c] + M00004[c]’. In all models, to arrange metabolites on the left and right of ‘〈=〉 ’ in order, a MATLAB script developed in our lab was used. Lastly, the reaction was unified as ‘M00003[c] + M00442[c] 〈=〉 M00004[c] + M00079[c] + M00083[c]’. If we ignore the cell compartments and do not count transport and exchange reactions, the database contains 21436 reactions.A list of reactions, including the Gene–Protein–Reaction relations for the models, should contain 15 columns of information, e.g., a reaction description, formula, and genesin vivo or in silico production, and in vivo or in silico growth rate is collected. Additionally, amino acid and nucleic acid sequences of related genes collected from the KEGG database are also stored in this Excel file.During the process of literature mining, mutation information is stored in an Excel file. Information such as organisms, models, genes, operations, All processed data are stored in a MySQL database and are available through a Web server built in the standard LAMP (Linux + Apache + MySQL + PHP) system to provide fast and secure data access. XAMPP for Linux 5.6.15 (https://www.apachefriends.org/index.html) was installed on CentOS Linux 5.8 (https://www.centos.org/download/). BLAST 2.2.28 (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/release) and Python 2.7.11 (https://www.python.org/) were used for model auto-reconstruction, and a C++ script based on a depth optimisation algorithm was written to explore the metabolic pathways in a particular GSMM.The IMGMD database (http://imgmd.jiangnan.edu.cn/database) provides a platform that integrates the names of metabolites and metabolic reactions from common biochemical databases and existing model repositories. This database includes 328 models for 139 microorganisms and provides 265 standardised models for downloading. Based on a homologous sequence alignment method, models can be reconstructed automatically in the IMGMD database, which can accelerate the process of model construction. Furthermore, IMGMD provides a pathway mining tool for pathway design and a mutation library for strain optimisation.Compared with other GSMM databases, the IMGMD database is specific for microorganisms. It is user-friendly and feature-rich; accordingly, the scientific community can easily use and extend the knowledge base. Thus, IMGMD will be a useful database for the design phase of systems metabolic engineering. Future developments include integration of the COBRA Toolbox, which will allow users to directly simulate gene deletion or over-expression, on the IMGMD platform. Besides, the IMGMD database is maintained by our lab and will be updated annually, to keep pace with the advances of GSMMs.