Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons.Arabidopsis. The dynamic behaviors obtained by predictive simulations agreed with actually available metabolomic time-series data, identified probable errors in the experimental datasets, and estimated probable behavior of unmeasurable metabolites in a qualitative manner. The model could also predict metabolic responses of Arabidopsis with altered network structures due to genetic modification.We show here a streamlined approach for the construction of a coarse model that allows us to set up dynamic models with minimal input information. The approach uses a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of biochemical systems theory (BST) with rate constants of 1, normal kinetic orders of 1, and -0.5 and 0.5 for inhibitory and activating effects, named Unity (U)-system. The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme. The U-system approach was validated with small, generic systems and implemented to model a large-scale metabolic reaction network of a higher plant, The U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network. Thus, it can be a useful first-line tool of data analysis, model diagnostics and aid the design of next-step experiments. Systems biology have advanced far enough that it is becoming possible to gain a comprehensive understanding of a metabolic system, and to use this knowledge for developing rational treatment options for diseases or devising strategies for increasing the productivity of foods and chemicals. While the main hallmark of systems biology is often portrayed as a means for predicting systems responses to external or internal stimuli, a similarly important aspect is its abilities to envisage all relevant information and integrate experimental data into logical and computable structures. As an example, suppose that three metabolites, A, B, and C, form a linear pathway Figure . SupposiThe exploitation of logical concepts for the prediction of probable behaviors of unmeasurable data and identification of uncertainties in datasets becomes much more complicated and important if the pathway systems are large. Large-scale datasets, and in particular time series data characterizing metabolic systems, are also becoming more and more commonplace to assist the investigation. To acquire such datasets with reasonable efforts, the high-throughput analytical instruments have been developed in recent times -3. GivenIt is therefore useful to explore to what degree it might be possible to employ the logic of the pathway to address these issues. Such an analysis seems to be feasible in principle, because the biologist executing the experiments usually has a relatively well-supported concept of the topology of the pathway system. The challenge is that even small pathways quickly become too complicated for intuitive assessments, and larger and more complicated pathway systems simply prevent us from testing assumptions or hypotheses without computational support. Such computational support of course requires mathematical models, which immediately leads to the challenge of setting up detailed, quantitatively appropriate models with a minimum of input requirements. The goal of such models is not necessarily to provide a quantitatively perfect picture of a pathway or to fit all data with great accuracy. Instead, these models should be easy to construct, robust, and merely able to identify how data within a large dataset could probably be.Power-law models within Biochemical Systems Theory  possessArabidopsis. This pathway system contains 351 metabolites and 441 fluxes, which are subject to various regulatory mechanisms. Apart from these, we have measurements from two different analytical methods, one comprising 268 metabolites, including 21 amino acids, and the other one accounting for 16 amino acids. As expected, although a large number of data can be measured, it is clear that not all data can be obtained. Also, none of the obtained data contain exactly the same values even in the replicated samples, mainly due to high biological variation among samples. In most cases, the measured time series are qualitatively the same, but in some cases they differ. We show that the U-system analysis estimates the probable behaviors of unmeasurable data and identifies at least some of the unreliable data. Besides, the U-system model is also applicable to predict metabolic responses when a metabolic reaction is altered.Here we capitalize on these two features of BST and propose a coarse test for the prospect of metabolic time-series data and consistency between the data and an alleged pathway diagram. The purpose is not to establish optimally fitting models but to identify how the unmeasurable data of metabolites located in a focused pathway probably behave, which time-series data within the dataset may be inconsistent with the understanding of pathway, as well as which data within a large dataset are probably reliable and which, if any, are most likely imprecise. We use a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of BST with kinetic orders of 1, which allows for inhibitory and activating effects that are modeled with kinetic orders of -0.5 and 0.5. The rate constants are at first set to 1, but may be later subjected to order-of-magnitude adjustments, which obviously improve the model representation of the data. Once the parameter values are fixed, their effects are to some degree compensated by adjustments in metabolite concentrations according to the network structure so that we named this simplified method unity (U)-system. The diagram for U-system approach is also exhibited in Figure in vitro experiment . Additig13 and h34, respectively. When g13 is changed, only the magnitudes of normalized metabolite concentrations are changed but almost no difference is observed in temporal changes in time-course patterns of metabolite concentrations. Similar results are also observed for g21, g32, g41, h111, h112, h22, h33 and h44 , is a major primary methyl-group donor and also a precursor for a plant hormone ethylene and polyamines. To increase the sulfur-containing amino acids methionine and cysteine in crops, numerous researches in the field of genetics and metabolic engineering have been carried out for more than two decades, and the objectives have been partially realized. However, the results are not quite satisfactory yet, because the amino acids and related metabolites in the obtained transgenic crops are not well balanced. One of the major causes for not achieving a good balance is an incomplete understanding of the whole regulatory mechanisms of amino acid accumulation, as well as the regulatory feedback structure of the system, although it is well known that the accumulation of methionine, a member of the aspartate-family amino acids together with lysine and threonine, is tightly controlled by negative feedback by lysine and threonine [in vitro data into kinetic models for specific pathways [Arabidopsis metabolic system. In this study, therefore, we exploited the U-system approach to construct a large-scale mathematical model based on available metabolic reaction networks  for 351 metabolites was constructed by U-system approach on the basis of only structures of metabolic reaction networks available in databases          Figure  does not) Figure  clearly ) Figure  and AdoM) Figure  temporar) Figure  and sacc) Figure  increase Figure X0  exhibited small biological fluctuations among sample replicates, increasing from its steady-state level to a maximum at around 24 h, from where it decreased back to its steady-state level. The lysine concentrations (X93) contained more biological fluctuations among the sample replications, but it is still possible to detect a clear pattern over time. The glutamate concentration (X37) increased within 2 h and then seemed to be constant. The aspartate concentration (X78) increased slightly, then decreased, and finally tended to increase considerably.The modified U-system includes various approximations so that it is necessary to validate whether it is practicability and conformable to experimental data. Figure X26) and lysine (X93) agreed with the metabolic behaviors of experimental data obtained by metabolome analysis with significant correlations  was not consistent with metabolome data , lysine (X93) and glutamate (X37), both methods yielded similar results in terms of the metabolic trajectories, although the simulations were in better agreement with the time-courses of metabolite concentrations measured by amino acid analysis. For example, lysine concentration (X93) measured by metabolome analysis  exhibited high standard deviations. This assumption may be indirectly supported by the results of correlation analysis. For example, the correlation between the U-system simulation and the time-series data obtained by the amino acid analysis  slightly increased and then decreased to its initial concentration level or steady-state  after lysine and threonine supplementation were also indirectly supported by the increase of proline (X50) observed both in simulation and metabolome analysis  in this case) agree well with the experimental data, those of intermediate metabolites (glutamate (X37) and aspartate (X78) in this case) are likely to be correct. This consistency implies that the prediction of metabolic behavior by our approach is reliable even if the quality of the experimental data for some metabolites is not very high.As mentioned above, the relative concentrations observed for several amino acids such as aspartate and glutamate were scattered mainly because of biological variability and possibly due to analytical limitations, and it was difficult to precisely capture the time courses of their concentrations Figure  and 6d. e Figure  and 2. Te Figure . The fluntration . Thus, is Figure . One couThe 86 out of 268 metabolite concentrations measured by metabolome analysis  measured by metabolome analysis   started increasing at around t=55 h and then 4-methoxy-3-indolylmethyl GSL concentration (X261) increased around t=60 h. In this case, the dynamic behaviors of 1-hydroxy-3-indolylmethyl GSL (X257) from model prediction were inconsistent with the relative concentrations obtained by metabolome analysis. In the metabolic map, the reactions between X257 and X261 are linearly connected without any regulations, i.e., inhibition and activation  might have not be reliably analyzed.Figure s Figure  did not ) Figure  started X80)   Figure , which cArabidopsis metabolic system predicted that the supplementation of lysine and threonine did not affect 4-methoxy-3-indolylmethyl GSL concentration (X261) until t=60 h  in pyrimidine biosynthesis until t=40 h  and ureidoproprionate (X344) in purine and pyrimidine biosynthesis , glutarate (X152), and pipecolate (X141) in lysine degradation  the U-system model is presented in a normalized form, and 2) the U-system model constructed only using partial, currently-available metabolic pathways. Recent advances in metabolomics technology have enabled us to obtain large-scale semi-quantitative datasets for hundreds of metabolite concentrations. The first characteristic is essential to establish mathematical models for actual metabolic systems consisting of hundreds to thousands of metabolites, whose concentrations cannot be measured practically by existing methods of measurement other than metabolomics. The second characteristic is desirable for the coarse characterization of complicated systems, because metabolic responses mainly depend on the network structure, rather than parameter values, as we have shown in this study. Thus, the U-system approach is not only informatively useful for data analysis and model diagnostics but it also can aid the design of next-step experiments.BST uses ordinary differential equation systems of a specific format: Each process is represented with a product of power-law functions. This format is the result of Taylor approximation in a logarithmic space ,9,19,20.i X are dependent variables , j Y are independent variables , αik  and βik  are the non-negative rate constants for influxes and effluxes, respectively, ijk g and ijk h are real-valued kinetic orders associated with dependent variables in influxes and effluxes, respectively, while ijk G and ijk H are those associated with independent variables. n and m are the numbers of dependent and independent variables, respectively, p and q are the maximum numbers of influxes and effluxes, respectively, and t is time. Details have been documented in the literature many times, e.g. in [Here,  e.g. in ,7,9,21.Since the power-law format in BST appears to be quite restrictive, the accuracy of different model variants within BST has been assessed many times. As Taylor approximations, the models are mathematically guaranteed to be correct at an operating point of choice, but their ranges of validity were also tested against results from more traditional formulations like the Michaelis-Menten formalism. These comparisons demonstrated sufficient accuracy of BST models over quite large ranges in variation of the involved variables -25. ApplVmax, Michaelis constant KM, etc.) are not explicitly visible, but they are instead distributed to the rate constants and kinetic orders. The BST parameters have their own meaning either as turn-over rates or as the specific effect that a variable has on the flux term in which it is involved. Thus, if a kinetic order is negative, the variable with which it is associated has a negative (inhibitory) effect on the flux term. This effect is given exclusively by the one appropriate kinetic order, thereby reducing the number of kinetic parameters in the system, which can be much higher in traditional rate functions. Still, the simplified BST parameters are able to capture time-transient behaviors of metabolite concentrations and other characteristics of metabolic reaction systems very well.Model parameters of pathway systems are usually estimated from kinetic information or from metabolic time series data. In Eq. (5), the familiar kinetic parameters involved in Michaelis-Menten kinetics  can be estimated directly from time-series data of metabolite concentrations, and subsequently convey important information about the system characteristics. In some cases, traditional kinetic parameters from the literature, such as 1 is converted into X2 and X4, so that the equation is dX1/dt = VinX1 - VoutX1-1 - VoutX1-2. Since most of the parameters are set at unity in the proposed approach, we name this system as Unity-system or U-system.It was recently shown  that a cFixing the parameters values is to some degree compensated by adjustments in metabolite concentrations. In other words, if the kinetic orders are forced to be 1, +0.5, or -0.5, a data fit will lead to adjusted metabolite concentrations.Monte-Carlo simulations were employed to test in an unbiased manner whether the qualitative behavior of a model is consistent with the U-system representation. Most Monte-Carlo simulations were calculated for 1,000 loops. The parameter values were generated randomly from uniform distributions. The ranges for rate constants and kinetic orders were between 0.5 and 20, and ±0.2 and ±0.8, respectively.Differential equations were solved using automatic switching for stiff and non-stiff problems (LSODA)  using th(Scientific Tools for Python) . All visArabidopsis thaliana liquid callus culture derived from accession Col-0 was prepared as described in Murota et al. [a et al.  with slia et al.  was usedFor both metabolome and amino acid analyses, calli were collected prior to lysine and threonine treatment (0 h), and 2, 6, 12, 24, 36, 48, 60, 72, 84 and 96 h after the treatment. The calli were immediately frozen in liquid nitrogen and stored at -80°C. Prior to analyses, the frozen samples were lyophilized using a freeze dryer  in a vacuum.Metabolites were extracted by homogenizing lyophilized callus in 500 uL 80% methanol in 0.1% formic acid per 2 mg dry weight callus with 5 mm zirconia beads  in 2.0 mL sampling tubes  for 5 min using shake master NEO . After centrifugation using a high speed refrigerated micro centrifuge (TOMY MX-300) at 14,000 r.p.m. at 4°C, 250 uL supernatant was dried up in 96 well plate and the residue was dissolved in 120 uL ultrapure water . One uL of the solution was subjected to widely targeted metabolome analysis  by LC-MSMetabolites were extracted by homogenizing lyophilized callus in 500 uL of the extraction solution (methanol: milliQ water = 4:1) per 2 mg dry weight in 2.0 mL sampling tubes using Mixer Mill MM300. After centrifugation at 15,000 r.p.m. at 25°C for 10 min, the 400 uL supernatant was used for the analysis performed according to the protocol based on the EZ:faast amino acid derivatization technique for GC-MS  with slight modification. The reagent 1  was diluted to 10-fold while the reagent 6 (re-dissolution solvent) was added only 50uL to concentrate amino acid concentrations in samples. Then, 1 uL of solution was subjected to amino acid analysis by GC-MS using GCMS-QP2010 Plus .t continuous random variable based on a survival function were calculated for comparing model simulations with experimental data.The correlation coefficients, testing for the significance of the correlation coefficients, and student's http://www.genome.jp/kegg/) [http://arabidopsis.org/tools/aracyc/) [Information for metabolic pathways including their inhibition and activation was assembled from KEGG (p/kegg/) ,38 and Aaracyc/)  databaseThe present study employs a GMA-system model to which the U-system approach was applied for characterizing metabolic behaviors in a large-scale metabolic reaction network related to aspartate-derived amino acids, namely, aspartate, lysine, threonine, and methionine. The effects of feedback inhibition and activation were also included based on information available from the literature and pertinent databases ,40. KineThe authors declare that they have no competing interests.SN and MYH conceived the experimental concepts and designed callus culture experiment. KS, EOV, FS and MYH conceived the theoretical concepts and designed modeling approach. YC, YY and HO planned and performed callus culture experiments. YS conducted metabolome analysis by LC-MS. KS conducted amino acid analysis by GC-MS, and performed modeling and simulations. KS, TF, SN, EOV, FS and MYH performed data analyses. KS, SK, HO, TF, SN, EOV, FS and MYH performed data interpretation. KS, YS, SN, EOV, FS, MYH wrote and edited the manuscript. All authors read and approved the final manuscript.X1 and X2 represent metabolites whereas Y1 and Y2 represent enzyme activities for the X1 influx and X2 efflux, respectively. Figure S2. Comparisons of time courses of X1 and X2 obtained by the Michaelis-Menten model and those by the simplified U-system approach of the corresponding GMA model. The x-axis represents time, while the y-axis represents the concentrations of iX. The red lines pertain to the actual system in left and bottom axes whereas the blue dotted lines are for U-system in right and top axes. a The X1 U-system concentration compared with real concentration when the values of X1 was increased two-fold at t=0. b The X2 U-system concentration compared with real concentration when the values of X1 was increased two-fold at t=0. c The X1 U-system concentration compared with real concentration when the values of X2 was increased two-fold at t=0. d The X2 U-system concentration compared with real concentration when the values of X2 was increased two-fold at t=0. Figure S3. Normalized concentrations of i Xwith variations of parameters i αwhich value from 0.5 (red), 1, 5, 10, 15, 20, 25, 50, 75 to 100 (blue). Figure S4. Normalized concentrations of i Xwith variations of parameters i βwhich value from 0.5 (red), 1, 5, 10, 15, 20, 25, 50, 75 to 100 (blue). Figure S5. Normalized concentrations of i Xwith variations of parameters ij gwhich range from 0.1  to 1.0 . Figure S6. Normalized concentrations of i Xwith variations of parameters ij hwhich range from 0.1 (red) to 1.0 (blue).Usystem_SupplementaryInformation.pdf. Additional figures for the branched pathway model. Figure S1. Linear pathway with inhibition (⊖). Click here for fileArabidopsis model.Usystem_SupplementaryModel.pdf. The details for Click here for fileUsystem_SupplementaryFigures.pdf. The U-system simulations comparing with experimental data from metabolome and amino acids analysis.Click here for file


Rhodosporidium toruloides can accumulate lipids up to 70% of its cell mass under certain conditions. However, our understanding of lipid production in this yeast is still much limited, especially for growth with mixed substrates at the level of metabolic network. In this work, the potentials of several important carbon sources for TAG production in R.toruloides are first comparatively studied in silico by means of elementary mode analysis followed by experimental validation.Microbial lipids  have received large attention for a sustainable production of oleochemicals and biofuels. R.toruloides was reconstructed based on a combination of genome and proteome annotations. Optimal metabolic space was studied using elementary mode analysis for growth on glycerol, glucose, xylose and arabinose or in mixtures. The in silico model predictions of growth and lipid production are in agreement with experimental results. Both the in silico and experimental studies revealed that glycerol is an attractive substrate for lipid synthesis in R. toruloides either alone or in blend with sugars. A lipid yield as high as 0.53 (C-mol TAG/C-mol) has been experimentally obtained for growth on glycerol, compared to a theoretical maximum of 0.63 (C-mol TAG/C-mol). The lipid yield on glucose is much lower  vs. 0.58 (predicted) C-mol TAG/C-mol). The blend of glucose with glycerol decreased the lipid yield on substrate but can significantly increase the overall volumetric productivity. Experimental studies revealed catabolite repression of glycerol by the presence of glucose for the first time. Significant influence of oxygen concentration on the yield and composition of lipids were observed which have not been quantitatively studied before.A simplified metabolic network of R.toruloides and its detailed in silico analysis for growth on different carbon sources for their potential of TAG synthesis. Experimental studies revealed the phenomenon of catabolite repression of glycerol by glucose and the importance of oxygen supply on the yield and composition of lipids. More systematic studies are needed to understand the mechanisms which should help to further optimize the lipid production in this strain of industrial interest.This study provides for the first time a simplified metabolic model of The online version of this article (doi:10.1186/s12934-015-0217-5) contains supplementary material, which is available to authorized users. During the past years fuel production from biomass has gained enormous interest due to the escalating cost and scarcity of fossil fuels. Biodiesel industry has become important and the use of plant oils as raw materials is not ecologically sustainable because of the relatively high production costs and their competition with global supply of food. Despite the expected high production capacity of biodiesel the anticipated target has increased at a slower rate , mainly nd generation biofuels based on cheaper and more abundant lignocellulosic biomass. In fact, several oleaginous microorganisms were reported for efficient lipid production using industrial wastes and biomass hydrolysate [On the other hand, microbial lipids are being explored as raw materials for the production of biodiesel and functional oils with comparable properties of those produced from plant oils -11. Morerolysate ,13-18. SCryptococcus albidus, Lipomyces lipofera, Lipomyces starkeyi, Rhodosporidium toruloides, Rhodotorula glutinis, Trichosporon pullulan, and Yarrowia lipolytica [R. toruloides and R. glutinis were shown to be the most promising. Y. lipolytica was also proven to be a potential lipid producer, especially after improvement with genetic tools.Several yeast strains are known for lipids production, which include polytica . Ageitospolytica  comparedRhodosporidium toruloides has a high capability for growth and lipid synthesis on a range of carbon sources from glucose, fructose and xylose to glycerol [w/w) in cell mass of R. toruloides. Its ability to simultaneously assimilate sugars, especially glucose and glycerol has not been studied in detail. Studies on a closely related species R. glutinis showed that glucose and glycerol can be utilized simultaneously with preference toward glycerol which is similar to Y. lipolytica [The red yeast glycerol -22. Lipipolytica .R. toruloides grown on different substrates and substrate mixtures, using detailed biochemical knowledge on genome scale. Elementary mode analysis was used to elucidate the optimal pathway and the various fluxes for lipid accumulation from different substrates. Validation experiments were then performed and the kinetics of growth, lipid production and substrate uptake were evaluated. Experiments with biomass hydrolysate as pure substrates or with glycerol were also compared. Since lipid production is a process requiring intensive reducing power and energy, the control of oxygen supply on lipid accumulation and composition is important and studied in a well-controlled bioreactor system. An understanding of these metabolic processes can open doors for metabolic engineering of this yeast and for process optimization.With substrate cost and availability being continually changing, the utilization of multiple feed stocks is crucial for process viability. Therefore the aim of this study was to analyze the metabolic network and fluxes of in silico modeling of R. toruloides. With the genome annotation and other metabolic capacities in available literature and experiments, a draft metabolic network model was validated and curated manually until a high-quality metabolic network model of R. toruloides was constructed . The numbers of elementary modes obtained from the metabolic network for each substrate are shown in Table The large quantity of information featured in public databases, like details about genomes, pathways and proteins were first used for the reconstruction of the metabolic network and the −1 on the basis of an assumed TAG molecule size of 51 carbons . Taking the molecular weight of the assumed TAG (C51H92O6 = 801.27 g.mol−1) into account the resulting maximum yield is 0.30 g.g−1 glucose. For the production of one mol of TAG, 42 moles of NADPH and 21 moles of ATP are required. From the flux distribution map . About 10% ATP is generated by the respiratory chain reaction using the mitochondrial NADH.With glucose as the sole carbon and energy source, the computed optimum flux distribution is shown in Figure 2 formation. Glycerol is channeled into glycolysis via the dihydroxyacetone to glyceraldehydes 3-phosphate. Most of the fluxes pass through the lower part of the glycolysis into the pyruvate dehydrogenase complex. Two more optimal modes (data not shown) were identified where similar maximum TAG yields were observed with a succinate to fumarate interconversion in the mitochondrion and cytoplasm. The required ATP is also generated by both the respiratory chain reactions in the mitochondrion.On the other hand, with glycerol as the sole substrate Figure , the NADSimilarly, the optimal flux distributions with xylose and arabinose as substrates are shown in Additional file R. toruloides were performed using blend of substrates. The maximum TAG yield obtained is 0.58 Cmol. Cmol−1 substrate with a blend of glucose and glycerol , whereas glycerol as a sole carbon source has the lowest CO2 production (30.7 mol/mol TAG), indicating that glycerol is a desirable substrate in terms of efficiency of carbon utilization. CO2 production is proportional to the flux entering into the PP pathway as clearly observed from the in silico predictions.Using the same method, optimal flux distributions and EFM analysis with the proposed metabolic network of l Figure , comparein silico predictions regarding the use of different substrates an initial set of cultivations was performed using glucose and glycerol as the sole carbon and energy source. The time profiles of the cultivations are shown in Figure To compare the −1 compared to 0.12 h−1 with glycerol as the sole carbon source. The lipid concentration on the other hand, was similar in both substrates  of cell mass. On the other hand lower cell mass but higher lipid content was observed with glycerol and a maximum lipid content of 57% (on cell mass basis) was achieved. Moreover, the lipid yield on glycerol consumed (0.27 g.g−1) is more closer to the theoretical maximum value (0.32 g.g−1) predicted than the lipid yield on glucose . It should be emphasized that the maximum yield of TAG given in Table R. toruloides strains on different substrates and conditions. The lipid yield on glucose could be improved by proper process control, especially by applying nitrogen or phosphorous limitations as demonstrated in literature. We found in this work that the lipid yield on glucose can be significantly improved by using blend with glycerol.The results shown that glucose supported a higher cell mass concentration with a maximum specific growth rate of 0.16 hin silico analysis was all done with a fixed P:O ratio of 1.2. The effects of oxidative phosphorylation (with varied P:O ratio) on TAG yields using glucose as a substrate were analyzed using the proposed model. The change in P:O ratios (from 1.2 to 2) neither showed significant effect on the TAG yields (from 0.58 to 0.60 Cmol.Cmol−1 glucose), nor cell mass yield (0.67 to 0.71 Cmol.Cmol−1 glucose), respectively. Hence, it can be concluded that ATP generated by oxidative phosphorylation is not a major limiting factor for TAG synthesis. In agreement with the in silico analysis, controlling the oxygen concentration at 50% air saturation in the fermentation broth of R. toruloides grown either on glucose or glycerol throughout the cultivation process did not have any significant effect on the yield  with higher lipid content . The lipid yields on the substrate consumed and the specific lipid productivities on cell mass are higher when glucose and glycerol are used in mixture rather than only glucose  with the lowest lipid content (32%). Similarly, with a closely related yeast strain R. glutinis, Easterling [R. glutinis. However, it should be stressed that, on mixtures of carbohydrates and with the sequential utilization of sugars, the sugar is being consumed with more nitrogen available (and hence more cell mass formation), followed by glycerol consumption during a period of nitrogen restricted growth which will have a positive effect on the lipid yield. Still, the mechanism of the positive role of glycerol for the lipid synthesis of cells grown in blends deserves more detailed studies. Glycerol can be utilized as a source of carbon and energy. But under certain conditions (e.g. in pO2-controlled culture with glucose as the sole carbon source), R. toruloides can produce glycerol extracellularly up to 5 g/L (data not shown). The two most important functions of glycerol synthesis in yeast are related to redox balancing and the hyperosmotic stress response [Saccharomyces cerevisiae, production of glycerol occurs to maintain the redox balance. Interestingly, glycerol production is found to be higher in a minimal medium than in a complex medium [de novo synthesis of amino acids from glucose and ammonia leads to an excess of NADH which is re-oxidized by glycerol formation. In osmotolerant yeasts, glycerol plays a crucial role in their survival [S. cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation, production and import. Moreover, it was also shown that glycerol transport depends on the strain and the growth conditions. Glycerol production has been never reported in R. toruloides, and hence transcriptomic analysis is being further performed in our laboratory.In the above blend substrate fermentation experiments, glucose was used at a concentration of 10% of the added carbon source. The effect of different combinations of the substrates on the lipid yield was further investigated in flask experiments. As compared to mono-substrate fermentation with glucose, a 26% increase of lipid yield was observed if glycerol is added , about 65% of the fatty acid measured is unsaturated (oleic acid and linoleic acid) if glycerol or blend are used, compared to 57% of saturated fatty acids  if glucose is the sole carbon source. Oxygen limitation has significantly influenced the fraction of saturated and un-saturated fatty acid composition in the lipid produced by R.toruloides especially on glycerol or mixed substrates. Oxygen controlled conditions derived fatty acid compositions that are suitable for biodiesel conversion [The effects of different growth substrates and culture conditions on the fatty acid composition were also studied Figure . In oxygR. toruloides is a known lipogenic yeast and the results presented here confirm its ability to accumulate over 60% of its cell mass as lipid. However, lipid accumulation was strongly affected by the nature of the carbon source provided. The recently sequenced and annotated genome of R. toruloides, its proteomic analysis and the available protein annotation have opened doors to understand the underlying mechanism of this organism for lipid production [R. toruloides. Maximum theoretical TAG yields on hexoses (glucose), pentose (xylose and arabionse), glycerol and on mixture of hexoses & glycerol, pentose & glycerol have been estimated  which is inactive in the predicted optimal flux distribution of glucose  on glucose and glycerol (0.32 g.g−1 to 0.27 g.g−1). However, the theoretical maximum yield retained to 0.30 g.g−1 on glucose and 0.32 g.g−1 on glycerol if a NADP-dependent acetaldehyde dehydrogenase (ALD6) is incorporated. This enzyme constitutively catalyzes the conversion of acetaldehyde to acetate, generating an NADPH in the cytoplasm. Another alternate route for supplying NADPH as proposed by Ratledge [−1 glucose. A third possible target could be a heterologous NADPH generating enzyme or an endogenous engineered NADPH generating enzyme, both in the glycolytic pathway [One of the key processes for fatty acid biosynthesis is the provision of reducing power (e.g. NADPH) to reduce the acetyl group and channel it into the acyl chain of fatty acid. In c enzyme  and the c enzyme . In R. te Figure . A singlRatledge  can be a pathway  which coRhodotorula glutinis ATCC 204091 [GOT2B1. This protein is 84% identical to an uncharacterized protein of R. toruloides (RHTO_06438). A theoretical analysis of the metabolic implications of a transhydrogenase reaction was investigated using the R. toruloides model. About 10% higher theoretical maximum yields were calculated on all the substrates when a transhydrogenase activity was introduced  in C 204091  with a U342 in C in silico analysis for determining and understanding the potential and metabolic pathways for lipid production in R. toruloides grown on different substrates, especially for substrate blends. Cultivations of R. toruloides were carried out with different substrates and in a well-controlled bioreactor system. The experimental results are in general agreement with model predictions in terms of lipid yield.The present work describes the successful application of reconstruction of metabolic network and it’s The constructed metabolic model can be used to guide further optimization of lipid synthesis in this strain. The maximal lipid yield experimentally achieved is still significantly below the theoretical maximum, especially for growth on glucose (0.29 vs. 0.58 C-Mol/C-Mol) and its blend with glycerol. Several strategies to increase the lipid yield are identified and discussed with the help of the metabolic model.R. toruloides DSMZ 4444 was used for the current study. The strain was maintained at −80°C on potato dextrose medium with 20% glycerol (v/v). The medium for seed cultures and main cultures was a nitrogen limited medium which is similar to that reported previously [3 and CaCl2 solutions and inoculated immediately. Spruce BH  was also used in co-substrate fermentation with glycerol. An enzymatic hydrolysis of spruce was done without buffer. The hydrolysate was then heated at 80°C for 15–20 min to inactivate the enzymes. The supernatant was removed and filtered with a centrifuge with filter bag. The sample was then concentrated by vacuum evaporation at 60°C. The concentrated hydrolysate contained 550 g/L glucose and 35 g/L xylose.eviously . Carbon Seed cultures and cultivations in flasks were performed in baffled shake flasks incubated at 30°C and 180 rpm for 24 h. Batch cultivations were performed in a 1.5 L well-equipped parallel bioreactor system  with 1 L initial working volume. Cultivations were started by inoculating 30 mL (3%) from the seed cultures grown for 24 h. pH was maintained at 6.0 using 5 N NaOH and 2 M HCL. Dissolved oxygen was maintained at 50% air saturation. Carbon dioxide evolution rates and oxygen uptake rates were automatically evaluated by the online DASGIP off gas analyzing system equipped with sensors from Bluesens.-1, [2SO4 and detection via refractive index or by UV absorption at 210 nm. Ammonia concentration in the supernatant was determined by photometric measurements using a kit from Macherey Nagel, Germany. GC analysis of the fatty acid methyl esters was performed as reported [Cell growth was recorded as optical density at 600 nm. Cell mass was harvested during the cultivations after centrifugation . Cell dry weight was determined gravimetrically after drying the harvested cells in an oven at 80°C to a constant weight. Extraction of lipids was performed with a modified method described previously using Folsch solution  as shown in the reaction network , the influx of substrates  and efflux of products  are considered. The P/O ratio is taken as 1.2 and the cell mass equation is taken from S.cerevisiae’s data [A metabolic network with the most important reactions of e’s data . EFMs wee’s data  in MATLAe’s data ,46.Figur−1.h−1 normalized to the substrate uptake rate.The metabolic network with D-glucose as a substrate comprises of 69 reactions, of which 27 are reversible with 61 internal metabolites and 8 external metabolites. With glycerol as a sole carbon source, the network is comprised of 71 reactions (28 reversible) of which 61 are internal and 8 external metabolites. The network with D-xylose and L-arabinose comprises 71 and 73 reactions respectively. Theoretical maximum yields were calculated based on the obtained fluxes. All fluxes are given in mmol.g


Recent technological advances in accurate mass spectrometry and data analysis have revolutionizedmetabolomics experimentation. Activity-based and global metabolomic profiling methods allowsimultaneous and rapid screening of hundreds of metabolites from a variety of chemical classes,making them useful tools for the discovery of novel enzymatic activities and metabolic pathways. Byusing the metabolome of the relevant organism or close species, these methods capitalize onbiological relevance, avoiding the assignment of artificial and non-physiological functions. Thisreview discusses state-of-the-art metabolomic approaches and highlights recent examples of their usefor enzyme annotation, discovery of new metabolic pathways, and gene assignment of orphan metabolicactivities across diverse biological sources. The functional annotation of the wealth of genetic information generated during the genomic erais currently considered one of the grand challenges in molecular biology. Although bioinformaticsand modelling have contributed considerably to the functional assignment of proteins, significantportions of sequenced genomes  in an unbiased manner usingcovalent active-site-directed probes A significant proportion of unannotated or misannotated genes encode enzymes, the catalyticactivity of which is usually fundamental to their physiological function. Techniques that candirectly exploit or monitor the native activity of a candidate enzyme are therefore powerful toolsin accurate functional assignment of unannotated gene products: several approaches that havehistorically been used to annotate enzyme function, as well as newly developed techniques, aredescribed in Metabolites constitute the substrates and products of enzymatic reactions, and the study of thetotal metabolite pool of a given organism or cell type is known as metabolomics. As metabolitesrepresent the final outcome of gene expression and activity, the metabolome can be perceived as theultimate readout of the biochemical and physiological state of a cell, that is, a direct linkbetween mechanistic biochemistry and cellular phenotype The diversity and quantity of metabolites that comprise a metabolome varies depending on theorganism, and can range from several hundred (bacteria) to several thousand  uniquelow-molecular-weight (< 1000 Da) chemical entities Escherichia coli. Consequently, the discovery of novel metabolic pathways is anongoing effort A significant impediment to large-scale assignation of enzymatic function is our incompleteunderstanding of primary and secondary metabolism, even among well-studied organisms such as13C and15N) is an essential component of most studies utilizing discovery metabolomictechniques. Two types of information can be uniquely gathered from well-designed SIL experiments:(a) metabolic fate, and (b) metabolic pace.Stable isotope labelling or SIL into succinate, there are at least two known potential metabolic pathways in which this enzyme couldserve . The firMetabolic pace refers to the flux (rate) of biochemical reactions and pathways in living cells. Time-coet al recently used ametabolomics approach to delineate several novel isoflavonoid and phenylpropanoid pathways in theleguminous plant species Medicago truncatula after stimulation with methyljasmonate and yeast elicitor, two agents known to influence plant secondary metabolite synthesisOur current understanding of central metabolism has been influenced by studies performed in asmall number of model organisms under a limited set of test conditions, suggesting thatconditionally induced pathways are poorly represented in metabolic charts. Varying growthconditions, or applying abiotic or biotic stimuli to cultures of interest, are therefore usefultactics for the elucidation of cryptic pathways. Farag Geobacter metallireducensR-)stereoisomer of citrate in the Krebs cycle of Desulfovibrio vulgarisMethylobacterium extorquensSimilarly, new pathways and functions are likely to be found in non-model organisms, particularlythose inhabiting unique physical environments—such as thermophiles and halophiles.Importantly, non-canonical metabolic processes that occur in these organisms, or those able tooperate under non-mesophilic conditions, are often highly sought after as biotechnological orindustrial tools. Stable-isotope probing combined with metabolomics and fluxomics was recently usedto uncover an unusual isoleucine biosynthesis pathway in E.coli glycolysis and the tricarboxylic acid (TCA) cycle are essential for growth understandard laboratory conditions etal recently analysed the biochemical mechanisms of pathway compensation in E.coli strains deficient in primary enzymes of central carbon metabolism pfkA). Thecombination of genetic tools and metabolomics has also been used to identify a previouslyunrecognized uridine monophosphate (UMP) degradation pathway in E. coli strainsthat are deficient in a negative feedback regulator of pyrimidine homeostasis Alternatively, molecular genetics can be used in combination with metabolomics as an effectivehypothesis-generating tool to discover new metabolic functions: the disruption of a pathway ofinterest might lead to the upregulation of alternative pathways to compensate for the lostfunction(s). For example, despite the essentiality of central carbon metabolism in all livingsystems, studies have shown that just 4 of the more than 70 enzymes involved in Arabidopsis,respectively The understanding of novel metabolic activities and pathways is incomplete without theidentification of the genes responsible. As already mentioned, untargeted investigations of newbiochemical pathways can rarely associate metabolic reactions with the enzymes that catalyse themwithout subsequent genetic intervention or recombinant protein studies. One method that can overcomethis is co-expression analysis. This transcriptomics-based method allows the identification of genesinvolved in a defined metabolic pathway based on their co-expression with genes of known function,through the assumption that genes involved in the same biochemical pathway are co-regulated In vitro studies with purified recombinant protein often provide the mostdefinitive proof of a bona fide activity for a specific enzyme, as consumption ofsubstrate and generation of product can be monitored directly in an isolated system, free ofcontaminating species. The disadvantages of this approach are that putative substrates need to betested individually  and are often not commercially available, or, if so,prohibitively expensive. A possible solution is to engage the metabolome of the parent organism as asubstrate library substitute. The metabolome represents a chemically diverse and rich mixture ofphysiologically relevant compounds, including those that are unknown or not commercially available,and is easily and cheaply obtained in large quantities (depending on the culturability of the parentorganism). After the incubation of a recombinant enzyme of interest with a cellular metabolomeextract (including any required co-factors or co-substrates), mass changes can be monitored by massspectrometry to identify putative native substrates, which decrease in abundance, and products,which increase in abundance -MS on mixtures of recombinant enzyme and E. colismall molecular extract to identify the phosphatase and phosphotransferase activities of twoE. coli uncharacterized enzymes, YbhA and YbiV ance Fig . Furtheret al have extensively characterized the substrate specificity of hP450 enzymes,with successful application of metabolome-based in vitro assays in many cases invitro reactions to facilitate the positive identification of substrates and products afteranalysis by MS 18O and 16O-labelled oxygen gas, causing theresulting enzymatic product to emit a 1:1 ratio of native (M) and isotopically labelled (M +2) m/zs on a mass spectrum plot. Although this approach is applicable to enzymesthat have other common co-substrates, such as ATP, NAD(P)H, or SAM , the prerequisite of knowing the identity of the co-substrate limits itsapplication to putative enzymes of known functional class.The human cytochrome P450 monooxygenase (hP450) family of enzymes comprise more than 50 membersand have significant roles in the normal physiological metabolism of a variety of lipids, sterols,vitamins and xenobiotics in vitro metabolomic methods are particularly useful for characterizingenzyme function. For example, although genetic tools are relatively well developed for the humanpathogen Mycobacterium tuberculosis, the slow growth rate (~20 h doubling time) ofthis organism makes gene knockout protocols cumbersome. Consequently, we have used invitro metabolomic methods to identify the functions of two uncharacterized M.tuberculosis gene products. One is a 2-hydroxy-3-oxoadipate synthase that was previouslyannotated as an oxoglutarate decarboxylase component of the Krebs cycle oxoglutarate dehydrogenasecomplex Genetic techniques are powerful tools in functional genomics, allowing target gene disruption andsubsequent assessment of the resulting phenotype or metabolic status to infer important informationon enzymatic function. However, genetic methods are not always applicable, such as when thecandidate gene is essential for growth or the host organism is not genetically tractable. In thesecases, in vitro technologies are generally insufficient, in isolation, to attain anaccurate and detailed picture of the physiological function of a candidate gene. Instead,in or ex vivo approaches are becoming increasingly popular asprimary or complementary screens in enzyme function or pathway discovery, with metabolomics at theforefront of these advances. As already mentioned, ex vivo metabolomics has longbeen a major tool in the delineation of cellular metabolic circuitry and is still a key experimentalplatform in pathway and metabolite discovery ex vivo metabolomics to conditional knock-down, stable geneticmutants, over-expression strains or to chemical knock-downs of a candidate gene product allows amore precise and specific assessment of the function of a gene in cellular metabolism and polyketide synthase (PKS) pathways, the products of which are of substantial biomedical interestdue to their pharmacological properties and roles in virulence. In these cases, bioinformatics canreliably predict gene clusters likely to code for NRPS or PKS operons, and subsequent metabolomicsof individual gene mutants provides the necessary means of both identifying the final metabolite andassigning each of the chemical steps involved in its biosynthesis to individual gene products etal recently used genetics and metabolomics to identify the catalytic roles of each of theeight gene products responsible for the production of a new siderophore and virulence factor,hexahydroastechrome, in the filamentous fungus Aspergillus fumigatusex vivo metabolomics need not require genetic disruptionof the gene of interest. This is particularly useful in cases where gene function is essential forcell survival, the host organism does not have readily available genetic tools, or where geneticinactivation of the candidate gene does not cause significant metabolomic changes due to inherentgenetic robustness of the test organism et al recently used a mechanism-based inhibitor to inactivate andinterrogate the physiological function of an integral membrane hydrolase (KIAA1363) that had beenshown to be upregulated in certain aggressive cancers in situ and subsequent metabolomics has been used by Cravatt etal to investigate the function of a series of unannotated human serine hydrolase familyproteins In addition to the canonical approach described above, several groups have demonstrated that genefunctional assessment by The experimental workflows and examples discussed above implemented a ‘reversegenetics’ approach, whereby the coding gene of interest is initially chosen by bioinformaticmeans and the cognate function subsequently sought with activity-based metabolomics. Although thisis a logical process for assigning function to unannotated genes, the reverse process, that is,assigning gene sequences to predetermined orphan metabolic activities or pathways, is largelyexcluded.et al overcame this problem by initially selectingfor mutants with a known phenotype that was predicted to correlate with the metabolic pathway ofinterest, in their case impaired starch metabolism in Arabidopsis thaliana.Subsequent metabolomics was then used to compare, by principal components analysis, mutants of knownstarch metabolizing genes with the experimentally derived mutants and subsequently to discriminatebetween strains with a bona fide deficiency in starch metabolism and thosecontaining mutations in alternative pathways that coincidentally had pleiotropic effects on starchmetabolism Orphan metabolic activities are an alternative and growing problem within functional genomics, aconsequence of many decades of pre-genomics enzymology research, as well as the recent and rapidrise in untargeted metabolomics data accumulation. For example, recent studies found that30–40% of all enzymatic activities currently assigned an enzyme commission (EC) numberare not associated with a defined genetic locus How can we best prioritize large-scale metabolomic-based functional genomics efforts?Which organisms have a large number of orphan enzymes and are therefore good models for thediscovery of new enzymatic functions and new metabolic pathways?Which are the most complementary methods to confirm metabolomic results?Which enzyme classes present the larger potential for new catalytic functions?et al recently used a technique termed ‘metabolic footprinting’to substantially enhance the throughput of metabolomic analysis of a library of randomly mutagenizedbacterial strains of interest, for the identification of genes involved in the metabolism of testcompounds E.coli and Shewanella oneidensis  were grown inmedia containing compounds of interest, and a shortened LC-MS protocol (2 min per sample) was thenused to analyse the supernatant from spent cultures. This streamlined methodology was possible dueto the substantially reduced quantity and diversity of metabolites present in the supernatantrelative to cell lysates. Strains that had reduced metabolism (higher remaining levels) of the testcompound of interest were analysed further to confirm the role of the target gene in the predictedmetabolic pathway(s). This approach allowed the authors to assign ergothioneine histidase activityto the S. oneidensis gene SO3057.Defined phenotypic traits are not always associated with the presence or absence of metabolicactivities of interest, nor are the global metabolic pathways to which they belong commonly known.Baran in vitro and ex vivo metabolomicexperiments is the loss of information regarding the subcellular and cellular localization of themetabolites/pathways under scrutiny. For example, compartmentalized metabolite pools  and/or cellular heterogeneity among groups of cells ina tissue cannot be differentiated in a typical metabolomic experiment. In practical terms, thechanges in abundance of putative substrates and products are diminished due to dilution ofnon-reacting pools of other cellular or tissue compartments. Imaging mass spectrometry (IMS) is apowerful technique that can complement traditional metabolomic experiments and provide informationabout spatial distribution, in addition to mass and abundance data  imaging and secondary ion mass spectroscopy (SIMS) imaging. MALDI imagingcurrently has a spatial resolution of 5–50 μm, allowing analysis of single cellswithin tissue samples, for example. A lateral resolution under 50 nm can be achieved using SIMS, andup to five different ions can be detected simultaneously, making SIMS-based ‘ionmicroscopy’ a unique technique to follow the fate and organization of metabolic pathwaysinside and outside cells. However, with the available technology, only mono- or diatomic ions can bedetected, in contrast to entire molecular ion detection obtained with MALDI imaging. Directmonitoring of new enzymatic activities with subcellular resolution is a goal that is yet to beachieved, but holds the promise to improve our understanding of cellular physiology and enzymefunction at their correct compartment within cells.An intrinsic disadvantage of in vitro or ex vivo. The future ofmetabolomics as a tool in enzyme functional assessment is inextricably linked to the future of MS asan analytical platform. As the technology improves, its reliability and accuracy as a functionalgenomics tool will follow suit. Similarly, methodological improvements in how metabolomicsexperiments are executed are constantly being sought, including increases in the breadth ofmetabolic diversity detected with a single analytical method Enzymes control metabolism and are responsible for the formation of many of the most elaborateand interesting chemical structures in the natural world. Tools that enable rapid and unbiasedfunctional assignment of unannotated enzymes are therefore valuable commodities in almost everyfield of natural science research. Metabolomics has recently emerged as a leading technology infunctional genomics, where in combination with modern genetic tools and recombinant proteintechniques, it has enabled a unique perspective on the identification of the mechanistic propertiesof test enzymes either


E. coli and Synechocystis sp. andvalidated via existing networks for these species. Then, it is applied to reconstructgenome-scale metabolic network models for 36 sequenced cyanobacteria amenable forconstraint-based modeling analysis and specifically for metabolic engineering. Thereconstructed network models are supplied via standard SBML files.Genome-scale metabolic network reconstructions are considered a key step inquantifying the genotype-phenotype relationship. We present a novel gap-fillingapproach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifiesmissing network reactions by integrating metabolic flux analysis and functionalgenomics data. MIRAGE's performance is demonstrated on the reconstruction ofmetabolic network models of  Genome-scale metabolic network reconstructions are considered a key step in quantifyingthe genotype-phenotype relationship . While tThe two major computational challenges in metabolic network reconstruction are (i) theidentification of missing reactions in a metabolic network, and (ii) the association ofgenes with network reactions. The identification of missing reactions, referred to as'gap-filling', is commonly performed based on a pre-defined metabolic capability thatthe network is expected to be able to fulfill. For example, the capability to generateessential biomass products under various genetic and environmental conditions -25, the Escherichia coli and the cyanobacteria Synechocystis sp. PCC 6803,validated via existing networks for these species. Then, it is applied to reconstructgenome-scale metabolic network models for 36 sequenced cyanobacteria (supplied viastandard Systems Biology Markup Language (SBML) files [44]), amenablefor constraint-based modeling analysis and specifically for metabolic engineering. Todemonstrate the utility of the reconstructed cyanobacteria networks, a strain designmethod was applied to predict gene knockouts whose implementation is expected tosignificantly elevate the production rate of an important nutritional product,astaxanthin.Here, we present a novel approach, MetabolIc Reconstruction via functionAl GEnomics(MIRAGE), for reconstructing metabolic network models and specifically addressing theproblem of gap-filling, by searching for missing reactions whose presence is supportedby various functional genomic data. Specifically, to reconstruct a metabolic networkmodel for an organism of interest, MIRAGE starts from a core set of reactions, whosepresence is established via strong genomic evidence, and identifies missing reactionsthat are required to activate the latter core reactions (in addition to biomassrequirement) by identifying additional reactions, whose presence is further supported byphylogenetic profiles and gene expression data. The performance of MIRAGE, in comparisonto previous methods, is demonstrated on the reconstruction of network models forMIRAGE is a functional genomics-based model reconstruction approach that aims togenerate a genome-scale metabolic network model for an organism of interest, given acore set of reactions that are known to exist in its network, and optionally, adefinition of a biomass reaction. The core set of reactions can be automaticallyderived strictly from genomic data, based on strong sequence similarity with knownenzyme-coding genes in other species. The method then aims to find missing reactions), supported by functional genomics data,whose addition to the network would lead to a functional model. The method follows atwo-step procedure, starting with the utilization of functional genomics data toestimate the probability of including each reaction from the universal database inthe reconstructed network, and then, metabolic flux analysis that selects the mostlikely set of reactions whose addition to the network would satisfy the abovedescribed objectives.For the first step, we utilize two functional-genomics data sources to estimate thelikelihood that a reaction from a universal reactions database should be included inthe target metabolic network: (i) enzymes' phylogenetic profiles, and (ii) geneexpression. Specifically, we define a weight for each reaction in the universaldatabase ,based on the functional similarity between neighboring enzymes, in terms ofresemblance of phylogenetic profiles, and correlation in gene expression of theenzyme-coding genes .Enzyme phylogenetic profiles were extracted from KEGG, representing a pattern ofenzyme presence or absence across an available collection of species. For eachreaction in KEGG, we computed a phylogenetic weight, representing the likelihood forits inclusion in the network reconstruction. Specifically, the phylogenetic weight ofa certain reaction is calculated based on the maximal Jaccard coefficient between itsphylogenetic profile and the corresponding profiles of its neighboring core reactionsin the network . Similarly, an expression weight for eachreaction was calculated by evaluating gene expression profiles (measured in thetarget organism) of potential enzyme-coding genes , compared with the expression profiles of known genes associatedwith neighboring core reactions. The sum of the phylogenetic and expression weightsafter proper normalization was used as input for the second reconstruction step.de novo synthesis of all metabolites that exist inthe network) [The second reconstruction step aims to find a set of high weight gap-fillingreactions that satisfy the objectives described above. Towards this goal, we employedthe following reaction pruning procedure. Starting from a metabolic network modelconsisting of all reactions in the universal reaction database, we iteratively removepotential gap-filling reactions, as long as the removal does not affect theconsistency of the model. In each iteration, the probability of choosing a certainreaction for removal is inversely proportional to its weight - that is, low weightreactions have a higher probability to be chosen first for removal. The modelconsistency check procedure involves the usage of constraint-based modeling to verifythat the remaining network (i) enables each core reaction to carry non-zero metabolicflux within a stoichiometrically balanced flux distribution, accounting for reactiondirectionality constraints, (ii) enables the production of all essential biomassconstituents, and (iii) accounts for the growth-associated dilution of all networkmetabolites  . Since tet al. [Notably, the presented method extends upon the Model Building Algorithm (MBA) ofJerby et al.  that waset al. [et al. resulted from the markedly large size andcomplexity of the universal reaction database in comparison to the human networkmodel used by Jerby et al., and the additional reconstruction objectivespreviously not accounted for.The described method is computationally demanding since each trial of the randomreaction pruning procedure , requires eliminating each reaction from the universalreactions set in turn, and checking the consistency of the resulting model.Implementing the speedup heuristic suggested by Jerby et al. , which aTo overcome this, we implemented the following additional speed-up techniques. First, the model consistency check procedure is based onidentifying a set of flux distributions in which all core reactions are activated, and is applied following the removal of each reactionin the reaction pruning procedure. The first speed-up involved the utilization offlux distributions computed in one call to the model consistency check procedure insubsequent calls to this procedure  to avoid time-consuming linear programmingoptimizations. Second, to further minimize the number of performed linearoptimizations, the latter are now formulated with the objective of minimizing fluxthrough subsequent gap-filling reactions in the pruning order. These two speed-uptechniques, significantly elaborated upon in Additional file 1, provide a 100-foldimprovement in running time.Figure E. coli, for which a comprehensively curated model(iAF1260) is already available for validation [E. coli reactions to be used by MIRAGE, we considered KEGGreactions annotated as existing in E. coli and also belonging to iAF1260,plus the known biomass and all exchange reactions from iAF1260. Then we removeddead-end reactions that cannot be activated within a feasible flux distribution whenconsidering the entire universal reactions set, yielding a core set of 812 reactions.Performing standard flux variability analysis [To evaluate the performance of MIRAGE, we applied it to reconstruct a metabolicnetwork model for lidation . Towardsanalysis  when focE. coli with iAF1260shows a predictive precision of 41.9% and recall of 24.3%, which is significantlybetter than random sampling of gap-filling reactions . Without utilizing functionalgenomics data, the predictive performance was also markedly lower, with a precisionof 27.5% and recall of 20.6% . Using only geneexpression [E. coli, without prior knowledgeof exchange reactions , finding an overall similar predictive performance, showing an improvementof MIRAGE compared to other approaches .Comparison of MIRAGE's reconstructed network model for pression  or phyloE. coli with that of Model SEED [E. coli is close , the number of predicted gap-filling reactions byMIRAGE was 62, in comparison to only 10 by SEED. This results from MIRAGE's aim toresolve all gap-filling problems instead of just enabling biomass production asperformed by SEED. The precision of MIRAGE's predictions was significantly higherthan that of SEED, reaching 41.9% for MIRAGE versus 10% for SEED. Re-running MIRAGEgiven the very same definition of a biomass reaction used in the SEED reconstructionof E. coli's model (rather than the biomass definition taken from iAF1260)still resulted in a higher number of 76 predicted gap-filling reactions, with asignificantly higher precision of 34.2% than that achieved by SEED.Comparing the predictive performance of MIRAGE on reconstructing the metabolicnetwork of del SEED  has showSynechocystis, Synechococcus,Cyanobacteria, Prochlorococcus, Anabaena, and so on [Synechocystis sp. PCC 6803 [2 is the sole carbon source. Due to lack ofcomprehensive gene expression for most cyanobacteria species, we utilized here onlyphylogenetic data  to define reaction weights.To demonstrate the utility of MIRAGE, we applied it to reconstruct genome-scalemetabolic network models for 36 cyanobacteria for which genomic data are available todefine core reactions sets. Our analysis spans all cyanobacteria for which enzymeannotations are available in KEGG, including nd so on . For allPCC 6803 , assuminThe average size of a core reactions set for a cyanobacteria network is 570 reactionsFigure , out of Synechocystis sp. PCC 6803 withthe manually curated models of Knoop et al. [Synechocystismodel by further utilizing gene expression data obtained from Tu et al. [et al. model [P-values are 2.99 × 10-27 and 3.59 ×10-31 for Knoop et al.'s model and iSyn811, respectively).Again, we find that the predictive performance of either the functional genomics dataor the flux analysis alone is far worse . Moreover,20.3% of predicted reactions showed E-values below 10-100, compared to9.7% of randomly sampled reactions, testifying the overall correctness of thepredicted set of reactions.To evaluate the performance of MIRAGE in reconstructing cyanobacteria models, wecompared a reconstructed network model for p et al.  and iSynp et al. ,52. In tu et al.  as part l. model  and precl. model ,52. These Figure . AcompaSynechocystis model, we appliedit to predict gene knockout lethality data provided by [et al. : out of 39 genesknown to be non-essential, Knoop et al. correctly predicted 35, while ourmodel correctly predicts 38. Out of 11 known essential genes, Knoop et al.correctly predicted 7, while our model correctly predicts 6. The fact that ourautomatically generated model reaches a similar level of prediction performance tothat of a manually curated model demonstrates the applicability and importance of ourmodel reconstruction approach.As a further evaluation of our reconstructed vided by . We findTo demonstrate the applicability of the reconstructed cyanobacteria networks, weapplied a computational metabolic engineering approach called Optknock  on these-1 h-1 inProchlorococcus marinus 9601, representing a carbon utilization of 40% forastaxanthin production (considering a CO2 uptake rate of 0.22 mmolgDW-1 h-1 [2 to produce astaxanthin is predictedto reduce growth rate by 57% relative to the wild-type Prochlorococcus strain (TableThe application of Optknock for astaxanthin production identified double geneknockouts in 15 species that are expected to lead to astaxanthin secretion (TableDW-1 h-1 ). This uE. coli [Synechocystis sp. PCC 6803 [Our paper presents a novel method, MIRAGE, for reconstructing metabolic network modelsby integrating metabolic flux analysis and functional genomics data to resolve networkgaps. MIRAGE was validated based on a comparison of its predictions with manuallycurated metabolic networks for E. coli  and SynePCC 6803 -52. ThenOur results show that functional genomics data enable the marked improvement ofgap-filling in metabolic networks. Furthermore, we show that the integration of morethan one type of functional genomics data can further improve the performance of MIRAGE.Naturally, MIRAGE can be extended to account for additional functional genomics data,including protein-protein interactions and genomic context data, which were previouslyused for the identification of missing gene annotations in metabolic networks . MetabolSeveral existing gap-filling methods work by searching for a minimal set of missingreactions that would enable the network to perform a certain task . MIRAGE Metabolic models generated by automated methods such as MIRAGE should be regarded asfirst draft models, requiring further manual curation to bring them up to comparablelevel with standard manually curated models. The growing interest in reconstructingmetabolic network models for hundreds of species raises the challenge of developingimproved such gap-filling approaches that could speed up the reconstruction process,while the approach presented here shows a marked improvement in this direction over thestate-of-the-art, supporting the advantage of integrating functional genomic data aspart of model reconstruction. We expect MIRAGE to be used for automatic reconstructionsof many other species, leading to a significant boost in the understanding of theirmetabolism.Binary vectors describing reaction phylogenetic profiles were acquired from KEGG. Aphylogenetic weight for each non-core reaction is defined as the maximal Jaccardsimilarity with a phylogenetic profile of a core reaction that shares a metabolitesubstrate with the reaction at hand. The Jaccard values are normalized based on thefrequency of appearance of the shared metabolite in the universal reaction database.An expression weight for a given non-core reaction is computed by evaluating thecorrelation between profiles of genes that may potentially code for an enzymecatalyzing the reaction at hand and expression profiles of genes associated withneighboring reactions. Specifically, for each gene in the genome of the targetspecies, we compute the average Pearson correlation between its expression profileand profiles of genes associated with neighboring reactions, with the expressionweight defined as the maximal such correlation obtained. All Pearson correlations arenormalized by the frequency of appearance of the connecting metabolites (as doneabove).The distribution of phylogenetic and expression weights are normalized to having thesame mean and standard deviation. Final edge weights are defined based on the sum ofnormalized phylogenetic and expression weights. Reactions for which either weight ismissing are assigned the median normalized value.First, we create a random reaction pruning list by iteratively sampling the nextreaction with probability proportional to its weight normalized by the sum of weightsof the remaining non-sampled reactions. Next, we scan through the obtained reactionlist and try to remove each reaction in turn from the model, as long as the resultingmodel remains consistent. The consistency check involves verifying that: (i) eachcore reaction can carry non-zero flux under steady-state and reaction directionalityconstraints; (ii) there can be non-zero flux through the biomass reaction; and (iii)the growth-dilution of each metabolite in the network is accounted for. Once wefinish scanning through the pruning list, we are left with a minimal functionalmodel. We repeat the random pruning procedure 500 times and count the number of timesthat each non-core reaction appeared in the final model. Finally, we order thenon-core reactions based on their frequencies (from low to high) and repeat thepruning step to obtain the final model.The details of the above and the implementation of the various speed-up techniquesthat makes this algorithm computationally tractable are described in Additional file1. The implementation of MIRAGE is available at .KEGG, Kyoto Encyclopedia of Genes and Genomes; MBA, Model Building Algorithm; MIRAGE,MetabolIc Reconstruction via functionAl Genomics; SBML, Systems Biology MarkupLanguage.EV and TS conceived the research and wrote the paper. EV performed the computationalanalysis. Both authors read and approved the final manuscript.


The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.http://identifiers.org/biomodels.db/MODEL1603150001).Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (The online version of this article (doi:10.1007/s11306-016-1051-4) contains supplementary material, which is available to authorized users. Metabolic processes are implicated in many important aspects of human health. Models are critical in the exploration and understanding of the complexity underlying human metabolism. Genome-scale models of human metabolism . The use of R-groups is especially prevalent in Recon 2 in defining lipid metabolism, where in the interests of simplicity, multiple reactions involving fatty acids of differing chain lengths were condensed into a single reaction. The R-groups that remain are those representing conserved moieties such as acyl-carrier protein (ACP), which cannot be represented as a defined chemical formula but whose presence do not affect the mass and charge balancing of reactions.Recon 2 included reactions with incomplete mass and charge balancing Table . DespiteReversible reactions that thermodynamics suggest should be unidirectional under typical physiological conditions can also impact the accuracy of model predictions. Many of these have been discovered through multiple rounds of manual curation, driven by the requirement to predict realistic ATP yields. This iterative process involved performing a flux-balance analysis (FBA) test, inspecting the resulting flux pattern for anomalous reactions, and correcting their directionality based on literature searches and definitions in pathway databases.Previous work has highlighted the advantages of augmenting metabolic reconstructions with unambiguous, publicly available identifiers mapping elements to entries in persistent, external data resources . Regarding simplicity, updates are supplied in simple text files that are parsed and interpreted by custom software. By providing the facility to update models in such an automated manner, the potential user base for the model building process expands beyond those that have an intimate understanding of software, and of formats such as SBML and the COBRA Toolbox. The benefits in terms of promoting reproducible science are also clear: all changes made to a given model to produce a new version are catalogued in text files, which essentially act as a Supplementary material 1 (XML 23979 kb)Supplementary material 2 (PDF 18 kb)Below is the link to the electronic supplementary material.