Bacillus subtilis by quantifying fluxes, transcripts, and metabolites in eight metabolic states enforced by different environmental conditions. We find that most enzymes whose flux switches between on and off states, such as those involved in substrate uptake, exhibit large corresponding transcriptional changes. However, for the majority of enzymes in central metabolism, enzyme concentrations were insufficient to explain the observed fluxes—only for a number of reactions in the tricarboxylic acid cycle were enzyme changes approximately proportional to flux changes. Surprisingly, substrate changes revealed by metabolomics were also insufficient to explain observed fluxes, leaving a large role for allosteric regulation and enzyme modification in the control of metabolic fluxes.One of the key ways in which microbes are thought to regulate their metabolism is by modulating the availability of enzymes through transcriptional regulation. However, the limited success of efforts to manipulate metabolic fluxes by rewiring the transcriptional network has cast doubt on the idea that transcript abundance controls metabolic fluxes. In this study, we investigate control of metabolic flux in the model bacterium  Saccharomyces cerevisiae  concentrations. For instance in the case of irreversible Michaelis–Menten kineticsJ represents the flux, E and S the enzyme and substrate concentrations, and k and v the binding constant and turnover rate, respectively. More generally, for almost any model of enzyme kinetics, we can writewhere M represents the metabolic state of the cell . To analyze the relative flux and enzyme levels between two conditions as a linear problem, we move to log spacewhere h, and that of the metabolic state ρm following the notation of and define the relative contributions of enzyme concentration, ρm directly is impossible without knowing the function f(M), we can directly estimate ρh by quantifying flux and enzyme levels. Such analysis has been performed previously using flux and enzyme measurements in two different conditions  in at least three conditions led to 46 pairwise comparisons where we could examine the relationship between flux changes and enzyme concentration changes. In order to restrict the influence of outliers on the linear fitting, we use the weighted Theil-Sen estimator  were in this pathway: SucC, SucD, Icd, and CitB  and compensates with higher vmax  . As such, a more direct test of control of flux by transcriptional regulation comes from analyzing the split ratio at a particular branch point and relating it to the concentrations of the enzymes catalyzing the reaction on each branch. To consider this quantitatively, we constructed a simple mathematical model to calculate this split ratio as a function of enzyme concentrations for the case when one substrate can be converted by one of two irreversible enzymes, and scanned a large range of values for the enzyme kinetic parameters to determine the relationship between the enzyme concentration ratio and the flux split ratio, again assuming Michaelis–Menten kinetics and ignoring product inhibition. We find that for a wide range of parameters, the change in flux ratio between two conditions will be exactly equal to the change in the enzyme concentration ratio. This relationship only breaks down for extreme cases, such as when one enzyme has orders of magnitude lower affinity (high ic rate) .B. subtilis central carbon metabolism are likely to be transcriptionally controlled. We considered branch points where we could correlate at least five data points and we eliminated the split involving malic enzyme because of the high uncertainty in the estimation of this flux  is ruled out.Because of the mostly linear relationship between flux ratio and enzyme ratio, we can apply the analysis developed in the previous section to determine whether flux splits in his flux . We findWe note that our simple model assumes only one enzyme for each branch of the split, whereas, for instance, citrate synthase occurs as two isozymes. While CitZ is thought to be responsible for the majority of the activity , we neveThe central result from the regulatory analysis above is that most reactions in central carbon metabolism are consistent with a minority contribution of transcriptional regulation to control of flux. To further elucidate the remaining contribution, which may come from allosteric regulation, post-transcriptional modifications, or substrate concentration changes, we quantified metabolite concentrations in all eight conditions using mass spectrometry. If changes in flux are driven by increased substrate concentration, then flux changes should correlate with changes in substrate levels. Furthermore, metabolites may regulate flux in distant reactions through allosteric interactions, and we can also investigate these distant correlations.B. subtilis, similarly to its recently proposed role in E. coli  or cooperativity (λ>1). This formulation is identical to the commonly used S-system approach for modeling metabolic networks (J)−log(E)=λ log(S). The remaining analysis paralleled the earlier analysis of the flux–enzyme relationship and we introduce the corresponding coefficient ρes to represent the combined contribution of enzyme and substrate concentrations to flux.To determine whether the observed metabolite changes could explain flux changes through modulation of substrate concentrations, we derived a simplified model for the quantitative relationship between the two. In contrast to the relationship between flux and enzyme concentration, which is linear under virtually any model of enzyme kinetics, the relationship between substrate concentration and flux is generally non-linear. As such, we allowed for a free parameter to represent the contribution of substrate concentration to flux, hypothesizing a relationship of the form Δnetworks . The opth=0.70, R2=0.41), but with mild outliers corresponding to the glucose and fructose conditions  as growth on glucose or fructose led to relatively high citrate concentrations. However, very surprisingly, for most reactions the effect of including substrate concentrations was virtually negligible, as demonstrated by ρes values far below one , likely due to highly growth rate-dependent σA-based transcriptional regulation (R>0.8 and none with ρh between 0.8 and 1.2). This is consistent with the results of a detailed analysis of a constitutively expressed protein in E. coli  despite 20-fold or more variation in fluxes.In this study, we provide evidence that most central metabolic enzymes are available in excess, meaning that changes in central metabolic fluxes are not primarily realized through transcriptional regulation by modification of enzyme concentration. This evidence comes from the fact that fluxes and enzyme concentrations (inferred from transcripts) in central carbon metabolism do not change proportionally across conditions. While previous analysis commonly showed a mismatch between flux and enzyme changes when considering two conditions at once , by consThis finding is somewhat surprising in light of the fact that cells clearly face constraints on the amount of protein they can produce , and alsE. coli and S. cerevisiae  between our conditions. The most obvious reactions falling into this category are those involved in substrate uptake. Almost all of these genes exhibited large (over 10-fold) changes in inferred protein concentration between conditions where they were required and others where they were not. Similar effects were seen for two enzymes, GapB and PckA, that were required under gluconeogenic but not under glycolytic conditions. However, among the three gluconeogenic conditions, there was virtually no change in enzyme expression despite significant changes in flux, suggesting that when these enzymes are necessary they are present in excess.With the exception of the two examples discussed above, even large changes in flux through enzymes in central metabolism were not caused by the corresponding changes in enzyme concentration. If central metabolic enzymes are generally in excess as we suggest, then the next most intuitive picture is that flux is regulated far upstream, such as at the level of uptake, and propagates through mass action through the available enzymatic reactions. In this scenario, substrate concentration changes would be the primary drivers of flux changes. Surprisingly, we find that including the effects of substrate concentration changes (based on a simplified kinetic model) is still, in general, insufficient to explain the changes in metabolic fluxes across conditions. The combination of substrate and enzyme changes was able to perfectly explain only a handful of fluxes in central metabolism, most notably the fluxes through the reversible glycolytic enzymes under gluconeogenic conditions, while the remainder of the reactions mostly showed flux changes that were much larger than the combination of enzyme and substrate changes.B. subtilis phosphoproteome showed that glycolytic enzymes are significantly enriched for in vivo phosphorylation  of these interactions have been characterized . It is lWe have introduced a framework for the analysis of transcriptomic, metabolomic, and fluxomic data sets to deduce the regulation likely to be responsible for the modification of cellular metabolism. Such data sets are becoming more commonly available due to advances in experimental techniques, but methods to integrate different types of data are lacking. By using an extended version of regulatory analysis, we are able to state with high confidence that most reactions in central metabolism are not controlled by transcriptional regulation in response to significant environmental perturbations. Future advances in understanding the regulation of metabolic fluxes are likely to come from more careful examination of the relationship between fluxes and metabolite levels, aided by metabolomics methods that can concurrently quantify large numbers of different compounds , as wellB. subtilis BSB168, a prototrophic derivative of B. subtilis 168 trpC2 : 1 ml 0.1 M CaCl2, 1 ml 1 M MgSO4, 1 ml 50 mM FeCl3 and 10 ml trace salts solution. The trace salts solution contained (per liter): 170 mg ZnCl2, 100 mg MnCl2·4H2O, 60.0 mg CoCl2·6H2O, 60.0 mg Na2MoO4·2H2O and 43.0 mg CuCl2·2H2O. Filter-sterilized carbon sources were added separately to the medium, pH neutralized with 4 M NaOH where necessary. For 13C-labeling experiments, the same final concentrations were used, but the carbon source was added directly to the shake flask as a mixture of 20% (w/w) uniformly labeled carbon source (>98% isotopic purity) and 80% (w/w) naturally labeled carbon source.The M9 minimal medium consisted of the following components (per liter): 8.5 g Na600  in combination with an Aminex HPX-87H polymer column . Sugars were detected with a refractive index detector and organic acids with an UV/Vis detector. Substrate or product yields were calculated by linear regression of external concentration against biomass, and specific rates were calculated as yield multiplied by the growth rate. At least five time points during the exponential growth phase were used for the regression analysis. Cell growth was monitored photometrically at 600 nm and cell dry weight was inferred from a predetermined conversion factor of 0.48 g cells/OD600 . All meaB. subtilis were previously established  (Sample collection and RNA purification were performed as previously described . Three rL009126) . SignalsL009126) . For eac600 between 0.8 and 1.2. In all, 2 ml of culture was vacuum filtered on a 0.45-μm pore size nitrocellulose filter (Millipore) and immediately washed with two volumes of fresh M9 medium containing the respective carbon source and adjusted to the pH of the culture at the time of sampling. Sampling was performed in a room kept at 37°C. After washing, the filter was directly transferred for extraction into 4 ml of 60% (v/v) ethanol/water and kept at 78°C for 2 min. The metabolite extract was separated from cell debris and nitrocellulose by centrifugation at 14 000 g at 4°C for 10 min. The supernatants were dried at 0.12 mbar to complete dryness in a speed vac set-up . Dry metabolite extracts were stored at −80°C until analysis.Two samples for intracellular metabolite quantification were taken within 5 min of each other from the shake flask cultures during exponential growth at an ODMetabolite concentrations were determined by using an ion-pairing ultrahigh performance liquid chromatography-tandem mass spectrometry method . Dry metxi,xj),} and the estimated slope is the weighted median of the pairwise slopes, using weights proportional to the distance (xi−xj)2. To estimate ρh while obtaining a confidence interval and P-value, we obtain a distribution of slopes by performing the fitting multiple times (N=2000), each time sampling a random subset of the data and concurrently perturbing the data by adding Gaussian noise with standard deviation given by the estimated measurement errors. The reported ρh is the median of this distribution, and the 95% confidence interval and the P-value for ρh>1 are obtained directly from the distribution. Additional analysis of ρh values under random permutation of conditions is shown in To calculate the best linear fit while allowing for outliers, we use the weighted Theil-Sen estimator . We calcSupplementary Figures S1–5


Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modeling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of  Flux balance analysis (FBA), a constraint-based modeling approach, is widely used in predicting metabolic fluxes based on stoichiometric metabolic models, in particular, large-scale or genome-scale metabolic models  and nucleotide triphosphates (NTPs) according to the fraction of DNA and RNA , TCA cycle, and mitochondrial electron transport chain (ETC)]. This is because these existing large-scale metabolic networks of plants provided mostly qualitative predictions of intracellular fluxes for primarily central carbon metabolism. Furthermore, previous work has shown that fluxes of central carbon metabolism dominate the FBA results, with little to no flux through the secondary metabolic pathways  were taken into account in the three models via representative metabolites or corresponding precursors, the biomass components included in these three models are not exactly the same. For example, xylose and γ-aminobutyric acid (GABA) were not considered in Poolman model, but were included in AraGEM and AraCore models. Similarly, soluble metabolites were only considered in AraCore model, but not in Poolman and AraGEM models. To keep the list of biomass components as consistent as possible between the three models, we added some additional biomass transporters to the models if required. It is noted that not all newly added biomass components can be produced by all models, e.g., maltose, xylose, and GABA cannot be produced in Poolman model, coniferyl-alcohol, coumaryl-alchol, sinapyl-alcohol, and xylose cannot be produced by AraCore model as these metabolites are not present in AraCore. Therefore, in this study, we only added new biomass components that can be produced by all the three Arabidopsis models. In total, we added nine new biomass transporters to Poolman model and seven for AraGEM , the biomass compositions were normalized to enable a fair comparison. These calculations were performed by weight, defining 1 unit of flux through the biomass equation equals to 1 g of biomass. The details of these calculations are provided in Supplementary Data http://www.gurobi.com, version 5.0.2) solver in combination with the COBRA toolbox were used to solve the linear programming problems. In our study, the biomass equation was maximized to obtain the optimal solution of the metabolic model as described elsewhere  and hydrogen sulfide (H2S) were constrained to be utilized as the sole N and S sources, respectively, because AraGEM and AraCore can only grow with H2S and NH3 as the S and N sources, respectively. Since SO2S transporter. We therefore added an H2S transporter to Poolman model, namely “H2S_tx”, enabling H2S as sole sulfur source in all models. Pi is the sole P source input in all three models. Additionally, non-growth associated maintenance (NGAM) was included in all simulations with a value of 2.02, which is a normalized flux unit referring to the value reported in Poolman model  is generated by defining all of the biomass constituents, in which all the precursor metabolites are assembled in one single reaction with corresponding coefficients  was conducted for the nine model-biomass combinations, which determines the range of possible solutions for each reaction while giving rise to the same optimal value for the objective function  in Poolman model operates in the opposite direction compared to other two models, which was corrected in a subsequent publication . In particular, we analyzed how central metabolic reactions listed in Supplementary Table +-dependent glyceraldehyde-3-phosphate dehydrogenase , a reaction belonging to the TCA cycle, whereas this reaction did not carry flux with PoolmanBOF and AraCoreBOF. The flux distributions through the TCA cycle reflect the function of the metabolic network, and its operation largely depends on the cell type and the considered physiological context , a measure of the linear dependence between two variables, for all predicted growth rates simulated by decreasing and increasing of each single biomass component by 30%  no maintenance, (ii) sole GAM, (iii) sole NGAM, and (iv) GAM and NGAM. We then compared the growth rates of scenario (i), (ii), and (iii) with that of scenario (iv), separately. The analyses showed that the influence of maintenance parameters varies with the used model. The exclusion of GAM and/or NGAM changes the growth rate by, at most, 29.6 and 1.3% in AraGEM model and AraCore model, respectively  were tested to cover multiple possibilities for biomass composition. Our results demonstrated that flux predictions of the central metabolic network are fairly insensitive to changes in biomass composition, regardless of the employed metabolic networks Figure . BiomassE. coli model that maintenance cost had larger impact on growth rate prediction (Feist et al., Our results also showed that 30% variations in individual biomass precursor had a minor effect on the growth rates with Arabidopsis models (Supplementary Tables E. coli model of Feist et al. (E. coli model of Varma and Palsson (Sensitivity analyses of biomass composition performed on different models often yields conflicting results. According to the sensitivity analysis on the t et al. , small c Palsson  noted th Palsson  found th Palsson . Moreove Palsson . These oAlthough, our analysis indicated the robustness of the growth rate and central metabolic fluxes in response to changes in biomass composition and maintenance cost, it does not suggest that it is sufficient to generalize biomass composition from any cell type within a plant or from other closely related organisms. This is because the biomass composition for different tissues and organisms may include metabolites specific to them, which need specific determination. Notably, variables other than model structure, such as the P/O ratio have considerable impacts on flux distributions according to previous reports (Feist et al., In this study, we reviewed the characteristics of published Arabidopsis metabolic models, concentrated on three particular models with distinct model structures and different compositions of biomass components. We examined the sensitivity of predicted fluxes to biomass composition, which is particularly relevant to plant metabolic models for which the biomass data is often collected from various sources. Our analysis showed that central metabolic fluxes as well as growth rates were insensitive to the variations in biomass composition, but were significantly affected by model structure. This work represents a thorough set of analyses performed in plants by means of constraint-based modeling, thereby providing relevant information about how critical FBA solutions can be affected by biomass composition, and more importantly by the structure of the models. Despite differences in several aspects such as model structure and number of metabolites and reactions included, each of the evaluated models has its own merits. Comparative analysis of the models paves the way for exploring the existence of principles that are relevant for the regulation and robustness of plant central carbon metabolism.HY, CC, PH, and NV conceived and designed the work. HY carried out all simulations and drafted the manuscript. CC, PH, and NV revised the manuscript. All authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The morphology, crystal phase, and photoelectrochemical performance of the flower-like CdS and pure CdS nanocrystals are carefully investigated via various characterizations. Superior photocatalytic activity relative to that of pure CdS is observed on the flower-like CdS photocatalyst under visible light irradiation, which is nearly 13 times of pure CdS. On the basis of the results from SEM studies and our analysis, a growth mechanism of flower-like CdS is proposed by capturing the shape evolution. The imidazole ring of L-Histidine captures the Cd ions from the solution, and prevents the growth of the CdS nanoparticles. Furthermore, the photocatalytic contrast experiments illustrate that the as-synthesized flower-like CdS with L-Histidine is more stable than CdS without L-Histidine in the hydrogen generation.Morphology-controlled synthesis of CdS can significantly enhance the efficiency of its photocatalytic hydrogen production. In this study, a novel three-dimensional (3D) flower-like CdS is synthesized via a facile template-free hydrothermal process using Cd(NO Recently, morphology-controlled synthesis of semiconductor nano-/micro-crystals has attracted essential interests because it enables the development of the addition of flexibility to existing systems in many areas, such as catalysis, optics, magnetism, biology, and so onet al. has reported a facile L-cysteine-assisted method for synthesis of CdS in spherical nanostructuresControlling the shape of nanocrystalline materials is a crucial issue in the exploitation of novel properties in nanoscience research. CdS is one of the II–VI group semiconductor materials with a direct band gap of 2.4 eVXRD analysis is used to investigate the phase structure and crystalline size of the products. The morphology of the as-synthesized 3D CdS nanoarchitectures was examined by SEM and TEM. 2+ and thiourea might form Cd-thiourea complex because of the strong coordinating capacity of the thiourea. With increasing temperature during the hydrothermal process, the Cd-thiourea complex may tend to decompose to release active Cd2+ and S2–. Then the active Cd2+ reacts with active S2– to form CdS seeds. L-Histidine plays a key role in the formation of flower-like CdS. The nitrogen atom of the imidazol ring promotes its solubility in aqueous solution and is also an important element to produce hydrogen-bond interaction between particlesOn the basis of the SEM observation and the experimental process, a possible mechanism is proposed to explain the formation of flower-like CdS, as depicted in 172-production activity.2g–1, which is 1.7 times larger than that of pure CdS. Generally, the photocatalyst with a higher specific surface area is indispensable in the enhancement of photocatalytic performance, due to more surface active sites and photocatalytic reaction centersThe specific surface area and porosity of the samples CdS prepared without L-Histidine and with L-Histidine were studied by nitrogen sorption measurements. The BET specific surface area of the CdS prepared with L-Histidine is about 3.32 m2, whereas the value for the CdS with L-Histidine is 2 times higher than that of CdS without L-Histidine. This can be attributed to the fact that the photoinduced charge carriers of CdS with L-Histidine are separated more efficiently than in CdS without L-Histidine29The photoelectrochemical performance of semiconductors mainly depends on the generation of photoinduced electron, separation of electron–hole pairs and efficiency of charge carrier transferPhotoluminescence (PL) analysis is also employed to investigate the migration, transfer and separation efficiencies of photo-generated electrons and holes in semiconductors, since PL emission of semiconductor mainly arises from the charge carrier recombination313233342S and 0.5 M Na2SO3 as the sacrificial reagents. Expectedly, for CdS with L-Histidine, the photocatalytic H2-production rate is markedly enhanced to 376.7 μmol/h, nearly 13 times of pure CdS (29.2 μmol/h). Because CdS with L-Histidine has a higher specific surface area and possesses more surface active sites and photocatalytic reaction centers, it exhibits a better photocatalytic performance to produce H2. Meanwhile, the photoelectrochemical and PL spectra validate that photo-generated electron-hole pairs of CdS with L-Histidine can be separated more efficiently than pure CdS. These behaviors are beneficial for photocatalytic performance to produce H2 and are the mainly reason for higher photocatalytic activity of flower-like CdS, while the detailed mechanism study still needs further investigations. Additionally, platinum (Pt), as an excellent co-catalyst, is loaded onto the CdS samples via a photodeposition method. The experimental result  shows that the photocatalytic H2-production rate of dendritic CdS decorated with 0.3% Pt is 53.1 μmol/h, while the flower-like CdS is 731.1 μmol/h, having a high photocatalytic activity for H2-production. In order to check the reproducibility of photocatalytic material and remove the dissolved gases, the reactor was evacuated and the photocatalytic experiment was repeated in every 5 h of reaction  in L-Histidine can draw the photogenerated holes of CdS, so the introduction of L-Histidine into the system can remove holes efficiently and avoid the photocorrosion problem of CdS. The slight decrease in the rate of hydrogen evolution might be related to the deactivation of the photocatalyst or attributed to the consumption of the sacrificial reagents in the solutionreaction . The act2-evolution rate is 376.7 μmol/h under visible light irradiation, which is 13 times of that of pure CdS.In summary, we have successfully synthesized the novel three-dimensional (3D) flower-like CdS nanoarchitectures via a facile template-free hydrothermal process using L-Histidine as a chelating agent. The pure CdS and flower-like CdS samples present regular hexagonal and cubic phases structure. Based on the SEM studies, we propose the growth mechanism of flower-like CdS nanoarchitectures. The imidazole ring of L-Histidine captures the Cd ions from the solution, and prevents the growth of the CdS nanoparticles. The as-synthesized flower-like CdS sample slightly extends the photoresponse to visible light region and exhibits an excellent photoelectrochemical performance. The photocurrent of flower-like CdS is 2 times of pure CdS. Enhancement in photocurrent displayed by flower-like CdS is attributed to the reduction in the electron-hole pairs recombination process. The above factors are assumed to be the main origins that lead to the enhanced photocatalytic activity of the flower-like CdS sample in hydrogen generation under visible light illumination. The flower-like CdS photocatalytic H3)2.4H2O, 99.0%), Thiourea , L-Histidine (C6H9N3O2), Dehydrated Alcohol , Sodium sulfate anhydrous , Sodium sulfide , Sodium sulfite anhydrous  were purchased from Sinopharm chemical Reagent Co., Ltd. All the chemicals were used as received without further purification. Deionized (DI) water was used in all experiments.Cadium nitrate tetrahydrate (Cd(NO3)2•4H2O, and 3 mmol of thiourea were dispersed in 50 mL distilled water. Next, the homogeneous solution was transferred into a 100 mL Teflon-lined autoclave and held at 180 oC for 4 h after vigorous stirring and sonication. After that, the precipitates were collected by centrifugation and washed with distilled water and ethanol several times. The final product was dried in an oven at 80 oC for 12 h.In a typical procedure, 1 mmol of Cd(NO3)2•4H2O, 3 mmol of thiourea, and 0.3 mmol of L-Histidine were dispersed in 50 mL distilled water. Next, the homogeneous solution was transferred into a 100 mL Teflon-lined autoclave and held at 180 oC for 4 h after vigorous stirring and sonication. After that, the precipitates were collected by centrifugation and washed with distilled water and ethanol several times. The final product was dried in an oven at 80 oC for 12 h.In a typical procedure, 1 mmol of Cd. Scanning electron microscopy (SEM) images were obtained using Carl Zeiss Ultra Plus. Transmission electron microscope (TEM) study was carried out on a TECNAI TF20 instrument. UV–vis absorption spectra were measured using Shimadzu UV-3100 spectrophotometer. Nitrogen adsorption-desorption isotherms and the Brunauer-Emmett-Teller (BET) surface areas were collected at 77 K on a TriStar II 3020 system. Steady and time-resolved fluorescence emission spectra were recorded at room temperature with a fluorescence spectrophotometer .X-ray diffraction patterns (XRD) of the samples prepared were recorded on a Rigaku X-ray diffractometer D/MAX-2200/PC equipped with Cu K2 aqueous solution) to cut off the light in the UV region. The fabrication of the working electrodes refers to the reported literature2) and then dried slowly in room temperature until a layer of film formed on the surface. The electrolyte solution used for all measurements was 0.5 M Na2SO4. Pt electrode and Ag/AgCl electrode were used as the counter and reference electrodes, respectively. The photocurrent responses of the working electrodes were recorded by sudden light on and off under visible light illumination at the bias voltage of 0.5 V.The photocurrent of the photocatalysts were measured by electrochemical works station , using a 300 W Xe lamp which was equipped with an optical filter  which was equipped with an optical filter (0.1 M NaNO2 aqueous solution) to cut off the light in the UV region. The amounts of H2 evolution were measured by using a gas chromatography  with thermal conductivity detector(TCD) and Ar as carrier gas.The photocatalytic reactions were carried out in a Pyrex reaction cell. 0.15 g powder was dispersed in 100 mL aqueous solution containing 0.5 M NaHow to cite this article: Wang, Q. et al. Highly Efficient Photocatalytic Hydrogen Production of Flower-like Cadmium Sulfide Decorated by Histidine. Sci. Rep.5, 13593; doi: 10.1038/srep13593 (2015).


SBM2 – Framework) implements the developed method and, as an example, is applied for the integrative analysis of the crop plant potato.The characterization of biological systems with respect to their behavior and functionality based on versatile biochemical interactions is a major challenge. To understand these complex mechanisms at systems level modeling approaches are investigated. Different modeling formalisms allow metabolic models to be analyzed depending on the question to be solved, the biochemical knowledge and the availability of experimental data. Here, we describe a method for an integrative analysis of the structure and dynamics represented by qualitative and quantitative metabolic models. Using various formalisms, the metabolic model is analyzed from different perspectives. Determined structural and dynamic properties are visualized in the context of the metabolic model. Interaction techniques allow the exploration and visual analysis thereby leading to a broader understanding of the behavior and functionality of the underlying biological system. The System Biology Metabolic Model Framework ( At least 54 bacterial, 6 archaeal, and 16 eukaryotic reconstructions are available to-date while many others are under development  includes detailed quantitative descriptions on the biochemical processes and therefore requires often difficult to obtain kinetic rate equations and parameters. Due to this, kinetic modeling is generally limited to smaller models, but leads to quantitative predictions and reveals dynamic behavior of the underlying biological system , which are related to each other through edges.specific graphs representing qualitative and quantitative metabolic models . Using our method, the analysis results from different formalisms are visualized in the context of the metabolic model through data assignment functions  is defined as follows.With the aim to formally represent qualitative and quantitative metabolic models a directed, attributed, bipartite graph . The unified graph GUnified =  is a directed, attributed, bipartite graph consisting of two finite, non-empty sets M of metabolites and R of reactions, whereby both sets are disjoint M ∩ R = ∅. Other finite sets are directed edges E ⊆ (M × R) ∪ (R × M) and attributes A = {type, stoichiometry, localization, label, concentration, capacity, rate, boundaries, objective function}, which are assigned to nodes and edges using the following functions:type: E → {ci, pi, cr, pr, i} is a function, which assigns a type to each edge . A directed edge from a metabolite to a reaction is of type ci, cr, or i  and a directed edge from a reaction to a metabolite is of type pi or pr . To easily distinguish between reversible and irreversible edges, reversible edges are illustrated using a double-headed arrow, with the black arrow-head denoting the main direction from substrate (consumed metabolite) to product (produced metabolite) of a reaction.stoichiometry: E′ → ℝ>0 is a function, which assigns a positive real number greater than 0 to each edge of type ci, cr, pi, or pr out of the set E′ = {e ∈ E| ¬ (type(e) = i)}.label: M ∪ R → Σ* is a function, which assigns a word over the alphabet to each metabolite and each reaction.localization: M → Σ* is a function, which assigns a word over the alphabet to each metabolite.capacity: M → ℝ≥0 ∪ {∞} is a function, which assigns a positive real number or infinity {∞} to each metabolite.concentration: M → ℝ≥0 is a function, which assigns a positive real number to each metabolite. Additionally, the concentration of a metabolite has to be less than or equal to the capacity of the metabolite, ∀m ∈ M: concentration(m) ≤ capacity(m).rate: R →{{h, j}, h, j, {}} is a function, which assigns a kinetic rate equation j ∈ J, whereby J is a set of all kinetic rate equations or a positive real number (stochastic rate) h ∈ ℝ≥0 or the empty set to each reaction.boundaries: R → , with lower, upper ∈ ℝ≥0, and lower ≤ upper is a function, which assigns an ordered pair of positive real numbers to each reaction, whereby the lower bound has to be smaller than or equal to the upper bound.objective function: R → {0, 1}, with ∀r, r′ ∈ R: objective function (r) = 1 ∧ objective function (r′) = 1 ⇒ r = r′, is a function, which assigns 0 or 1 to each reaction, whereby only one reaction receives the value 1 (for optimization).Furthermore, the following requirements must be fulfilled:r ∈ R applies: (1) there exists at least one incoming and one outgoing edge (whereby the incoming edge is not of type i) and (2) if one incoming or outgoing edge is reversible (irreversible) than all incoming and outgoing edges are reversible (irreversible). With this rule a reaction is either connected to reversible edges or irreversible edges but not a combination of them.For all reactions m ∈ M and a reaction r ∈ R there are at most two edges e, e′ ∈ E of different types. If two edges e and e′ connect m with r the type of e is ci and the type of e′ is i. This case describes a substrate inhibition at high substrate concentrations, whereby a metabolite is substrate and inhibitor at the same time.Between a metabolite e connects r with m and another edge e′ connects m with r the type of e is pi and the type of e′ is i. In this case, a product inhibition is modeled with a metabolite as product and at the same time inhibitor of a reaction.If one edge An explicit formulation of both cases for reversible reactions is not needed because the reaction mechanisms already provide implicit substrate- and product inhibition.unified graph into specific graphs for analysis. The edge set E is composed of three subsets, E = iE ∪ irE ∪ rE. The subset of inhibitory edges is iE = {e ∈ E|type(e) = i}, the subset of irreversible edges is irE = {e ∈ E|type(e) = ci ∨ type(e) = pi} and the subset of reversible edges is rE = {e ∈ E|type(e) = cr ∨ type(e) = pr}. The set of metabolites M consists of a subset of metabolites cpM, which are either consumed or produced in reactions cpM = {m ∈ M|∃r ∈ R:  ∈ rE ∨  ∈ irE} ∪ {m′ ∈ M|∃r ∈ R: ∈ Er ∨ ∈ Eir}.Moreover, the following sets are defined to simplify the transformation of the unified graph, data assignment functions that integrate calculated structural and dynamic data are used (this will be described in detail in the Transformation section).To assign analysis results to nodes and edges of the unified graph with a rich attribute set qualitative and quantitative metabolic models can be represented and additionally visualized using standards. Figure unified graph and the corresponding visualization in SBGN-PD.Due to the definition of the 2.2unified graph the Systems Biology Graphical Notation  is utilized. SBGN has been developed to interpret biological models easily without the need for extensive descriptions using three sub-languages. SBGN-PD  are visualized using simple chemicals ∈ entity pool nodes (process ∈ process nodes). All elements of the edge set e ∈ E are visualized using arcs of the set connecting arcs based on the assigned type. Edges of type ci are visualized using consumption arc, pi using production arc, cr using production arc in the opposite direction, pr using production arc and i using inhibition arc, respectively.The translation of the stoichiometry is visualized using cardinality and the metabolite attribute localization is visualized using compartment, which is a container for metabolites defined for this location. The localization of reactions is independent of a compartment, hence, a reaction could be located within, outside or on top of the border of a compartment. Import or export reactions in SBGN-PD are defined using the additional symbol source and sink ∈ entity pool nodes, see Figure The edge attribute Furthermore, interaction techniques allow the exploration and subsequent visual analysis leading to a broader understanding of the behavior and functionality of the underlying biological system (which will be described in detail in the Results and Discussion section).2.3unified graph (GUnified) into the specific graphs  have to be performed as a prerequisite to analyze a metabolic model using different modeling formalisms. The different models, modeling formalisms and the transformation from GUnified into GStoichiometric are described in the following. The transformations from GUnified into GKinetic, GPetri net, and into both of the topological graphs GMetabolite, GReaction are defined in the Supplementary Material.Overall, five transformations from the 2.3.1kinetic graph (GKinetic), which is transformed from the unified graph (GUnified), see Figure A kinetic metabolic model (ODE model) consists of a structural description of relations between metabolites and reactions and is extended with detailed kinetic data including rate equations, metabolite concentrations, and additional kinetic parameters. The kinetic model is represented by the kinetic graph is converted in ODEs, which are numerically solved  and extended quantitative stochastic Petri nets (eSPNs). The extension includes continuous tokens (to model metabolite concentrations), continuous arc weights (to model non-integer stoichiometry), continuous place capacities (to model limited resources), and inhibitor arcs (to model inhibition). An inhibition is modeled using an inhibitor arc from a place to a transition meaning that the transition can only fire if no token is on that place. The transition may only fire when the place is empty.A Petri net metabolic model can be defined using different Petri net types. Here, we refer to extended qualitative place/transition Petri nets  assigned to transitions. For further details on Petri nets for modeling metabolic models the reader is referred to Baldan et al. (Petri net graph (GPetri net), which is transformed from the unified graph (GUnified), see Figure Both Petri net types share the same structure, but n et al. . The PetPetri net graph is converted into a linear equation system, which can be solved to derive invariants describing main pathways (T-invariants) or metabolite conservation (P-invariants) of a metabolic model [more details in Murata . The unified graph GUnified is transformed in a directed, attributed, bipartite stoichiometric graph GStoichiometric =  with a metabolite set SM = cpM, which is a subset of the set M in GUnified. Metabolites with only inhibitory interactions to reactions are not considered. The reaction set in GStoichiometricSR = R equals the reaction set R set in UnifiedG and the edge set in GStoichiometricSE = irE ∪ rE is a subset of the set E in UnifiedG. Edges of type i are excluded. The attribute set in GStoichiometricSA ⊆ A is a subset of the set A in GUnified with SA = {type, stoichiometry, localization, label, boundaries, objective function}.GUnified into GStoichiometric and thereby detailing the difference between both graphs. This transformation results in structural differences (no inhibitions) and a reduced attribute set. Thereby, all regulatory information and quantitative data are lost.Figure stoichiometric graph, a metabolic model can be validated utilizing the Dead-End analysis or Gap-Finding analysis revealing blocked reactions or dead-end metabolites. To examine the flow of metabolites through a metabolic model the stoichiometric graph is converted into a system of mass balance equations at steady-state, which are solved by minimizing or maximizing an objective function. This optimization can be conducted using a linear optimization instead of a non-linear optimization to handle the problem of alternate optimal solutions. Applicable optimization-based methods are FBA, flux variability analysis (FVA), robustness analysis (RA), and knockout-analyses (KA) resulting in a flux distribution, minimal and maximal fluxes, sensitivity curves, and sensitivity values, respectively. For a detailed description of optimization-based methods the reader is referred to  and reaction graph  are predominantly used for topological analysis . This transformation results in structural differences (unipartite graphs) and a reduced attribute set. Thereby, all regulatory information and quantitative data are lost.Metabolic models are analyzed according to topological properties in order to understand the importance of key elements, structure, and robustness against disturbances. Since the metabolite graph or reaction graph is conducted using the corresponding adjacency matrix. A shortest path analysis results in paths (subgraphs which could be the graph itself). Furthermore, centrality analysis with different centrality measures leads to a ranking of graph elements according to different importance concepts. For further details on different centrality measures the reader is referred to Koschützki and Schreiber  are assigned to elements of the unified graph  using the assignment function zn: M, R, E → NV, whereby the vector could comprise numeric values , pairs of numeric values , and a set of time, step, and flux value dependent numeric values .Numeric values of the vector  of independent modeling formalisms as explained in the Introduction section and leads to major (+) or minor (±) improvements of an integrated analysis as already depicted in Figure In detail, using the integrated approach it is not required to define detailed kinetics to derive quantitative predictions and reveal dynamic behavior of the underlying biological system. Instead, using some parameters the Petri net simulation or stoichiometric modeling method FBA could be performed to approximate kinetic simulations. Thus, larger models are applicable in the integrated approach leading to analysis results, which could be again integrated to analyze the model further. Additionally, qualitative analysis can be conducted for extended Petri nets using another integrated formalism such as Dead-End analysis or centrality analysis. Quantitative predictions can be revealed for a qualitative model with a static description using stoichiometric analysis.Dead-End metabolites. Additionally, Petri net T-invariants correspond to flux modes, which could be directly calculated using the stoichiometric analysis method elementary flux modes (not presented here).Hence, different modeling formalisms complement each other even through, overlaps between the introduced metabolic modeling formalisms exist. For example, the stoichiometric matrix used in the stoichiometric modeling formalism to derive mass balance equations corresponds to the incidence matrix of the Petri net formalism used to derive an equation system solved for, e.g., invariant analysis. In the case of structural analysis, both the stoichiometric and the Petri net formalism could be utilized to reveal, for example, VANTED system . It utilizes and extends VANTEDs functionality for the interpretation of experimental data and for analyzing metabolic models with different modeling formalisms.The described method is implemented as an Add-on for the Solanum tuberosum) an integrative analysis is performed using the described method. Due to its main component, starch in the potato tuber, potato is of great importance as food and in industry, for example, for the production of fuel. Therefore, a major aim of plant breeding is to improve the distribution of biomass within the plant in favor of harvestable plant parts. Based on the homogeneous tissue of the potato tuber the main flux of metabolites is from sucrose to starch  is converted into hexose phosphates  utilized in glycolysis (Glyc) and as precursors for starch synthase (StaSy). The pathways Glyc, starch biosynthesis, and energy consumption (ATPcons) are modeled as summarized reactions. This is a necessary simplification to avoid unknown transport processes into additional compartments. To describe the environment the model is extended through sucrose import (Imp) and starch export reactions (Exp).Using a kinetic model representing the sucrose breakdown in the developing potato tuber Junker,  the inteInv) as described in Junker . The results indicate the production of starch and the utilization of reactions with different probabilities.Glyc and starch biosynthesis.Additionally, the invariant analysis reveals beside 3 P-invariants (reflecting substance conservation) 19 T-invariants, which can be grouped in trivial and non-trivial T-invariants. Each of the seven trivial T-invariants corresponds to a reversible reaction. The non-trivial T-invariants can be differentiated in a group of nine representing the cleavage of sucrose by invertase and another group of three where the sucrose is cleaved by sucrose synthase. These T-invariants reflect the main processes that are pathways taking place in the metabolic model in reality  to constrain the fluxes for these reactions, results in a flux distribution, which is comparable to the kinetic analysis results. In Figure AdK is not utilized as can be seen in results of the kinetic and Petri net analysis.The stoichiometric analysis (irrespective regulatory processes), using only three steady-state reaction rates  centrality analysis is conducted. As a result, the table in Figure Suc and G6P, which are selected to be highlighted in Figures Glyc could not be processed.Using the In summary, using the integrative analysis allows different modeling formalisms to be investigated in one workflow. An integrated and interactive visualization of the analysis results leads to an advantage over the use of each modeling formalism independently. This helps to compare analysis results from different formalisms within one metabolic model and allows for the investigation of analysis results from one formalism in another, as mentioned in the use case.4unified graph, enabling graph transformations, and a visualization in a standardized and formalized way. The unified graph supports user interaction and thereby allows different analysis results to be explored in the context of the metabolic model. The application reveals structural and dynamic properties of the crop plant potato utilizing the integrative analysis. The method has been implemented as an extension of the VANTED system and could also be applied to other model types, but we have focused here on metabolic models as an application area.We described a method, which is able to bring together different metabolic modeling formalisms. The integration is realized by a unified graph, adding of appropriate modeling formalisms, and corresponding transformations. Furthermore, the visualization has to be adapted to represent different types of models in SBGN using, for example, the sub-language SBGN-AF for gene regulatory models.Combining different modeling formalisms opens many possibilities for future research. Additional analysis algorithms can be added to study metabolic models in more detail. We plan to extend the method for different types of models such as gene regulatory models to investigate further cellular processes. This extension requires the adaptation of the SBM2 – Framework software. Falk Schreiber supervised the project and gave conceptual advice. Both authors wrote the manuscript.Anja Hartmann developed the theoretical framework, the use case, and implemented the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.http://www.frontiersin.org/Journal/10.3389/fbioe.2014.00091/abstractThe Supplementary Material for this article can be found online at Click here for additional data file.


Chlamydomonas reinhardtii have elucidated some novel metabolically important genes and this enhances the lipid production in this microalgae. Adding to it, various other aspects in metabolic engineering using OptFlux and effectual bioprocess design also gives an interactive snapshot of enhancing lipid production which ultimately improvises the oil yield. This article reviews the current status of microalgal based technologies for biofuel production, bioreactor process design, flux analysis and it also provides various strategies to increase lipids accumulation via metabolic engineering.The current scenario in renewable energy is focused on development of alternate and sustainable energy sources, amongst which microalgae stands as one of the promising feedstock for biofuel production. It is well known that microalgae generate much larger amounts of biofuels in a shorter time than other sources based on plant seeds. However, the greatest challenge in a transition to algae-based biofuel production is the various other complications involved in microalgal cultivation, its harvesting, concentration, drying and lipid extraction. Several green microalgae accumulate lipids, especially triacylglycerols (TAGs), which are main precursors in the production of lipid. The various aspects on metabolic pathway analysis of an oleaginous microalgae i.e.,  Identification of genes involved in enzyme and integrating with complex metabolism is really difficult without base/model system. This difficulty has led to development of The commercially viable biofuel recovery from microalgae is not realistic due to the (i) little biomass recovery (ii) cost of downstream processing and validation. Furthermore, the viability of microalgae based biofuel can be achieved only by the (i) designing advanced photobioreactors (ii) developing low cost technologies for biomass harvesting, drying, and oil extraction (iii) development of biorefinery approach.The overall production of such biofuel from microalgae can be enhanced by the genetic engineering approaches and adopting metabolic pathways engineering for augmented lipid production. Besides above techniques for improvement in the possibility to harnessing microalgae for biofuel a new emerging technologies i.e., biotic or algal-bacterial interactions for enhancement of microalgae growth and lipid production are also explored  and Acetyl CoA Carboxylase (ACCase) are found to be linked with fatty acid synthesis pathway. These enzymes are present in chloroplast and ACCase is a rate limiting pathway for fatty acid biosynthesis. ACCase, carboxylate Acetyl CoA to form malonyl CoA and FAS elongate the fatty acid chain by two units  and then finally to triacylglycerol (TAG).In this pathway acyl group is transferred from phospholipids by phospholipid:diacylglycerol acyltransferase  production is not restricted to specialized cells but in microalgae it can be triggered by stress. (a) specific lipid like betaine lipid which was not reported in plants  and mevalonate (MVA) pathway Engineering different enzymes toward lipid biogenesisIdentifying rate limiting enzymes/committed stepCarbon partitioning/captureMathematical modelingOver expression of a gene/multiple geneTranscription factor engineeringMetabolic engineering strategy is defined as tuning of metabolic pathways in a cell to trigger the target metabolite production. Achieving such targets various strategies can be adopted which are listed below:Chlamydomonas reinhardtii . Whole genome sequencing of model as well non model microalgae is required in order to reconstruct the metabolism. Reconstruction of metabolic fluxes using stoichiometric model i.e., S.v = 0; Where v is a vector of fluxes and S is matrix, and matrix is constructed by balancing the masses in each of the cell compartment of Pseudomonas fluorescens  formation. In this facet the major disadvantage are slow growth rate and low photosynthetic activity. Since lipid productivity is directly proportional to cell number therefore two stage cultivation approach may be employed to circumvent the above stated problem but photosynthetic one still remains. Metabolic flux analysis using GC-MS and LC-MS/MS under photoautotrophic growth in Brassica napus from Arabidopsis in order to increase the fatty acid content  as a promoter will also increase TAG accumulation under phosphorus starvation , genome editing through modern tools have been described and is established in Advantages of microalgae for biofuel application over the other fuel crops have been thoroughly reviewed , so more focus has to be put on genome sequencing, and rigorous genome scale metabolic flux based analysis is required toward TAG accumulation in different lineages of microalgae. In our opinion, first we should target the key genes responsible for lipid biosynthesis. As we had genome sequenced for plant as well for algae the methods that are belong employed in plant can easily be replicated in microalgae also.The metabolic engineering of microalgae is an significant area of research due to enormous interest on generating efficient biofuel. As algae has shown the highest divergence (67.7% distinct) so these findings regarding genes divergence will through a light toward a positive hope for lipid bio-genesis. A survey of literature above has established the idea that research has been carried out only in model microalgae (Certainly, the various type of stress responses on microalgae is a key tool for increasing the lipid droplets. Moreover, the accessibility of nutrients during stress condition will have a definite role in lipid productivity. It is well documented now that during stress conditions the microalgal metabolism will shift toward the storage of energy rich essential molecules in form of triacylglycerol are which could be efficient source of lipid. But definitely, writing these aspects in research article and to get it done practically is not impossible but it's tough in case of complex lipid biogenesis pathway.All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom  Diatoms are unicellular photosynthetic eukaryotes ubiquitous in marine and freshwater habitats and are responsible for about 20% of the photosynthetic carbon fixation on Earth . DiatomsPhaeodactylum tricornutum is an emerging model diatom because of its relatively small genome (27.4 megabases) [P. tricornutum may be the most advanced in microalgae, with the recently developed ability to assemble whole chromosomes in yeast [The marine diatom in yeast , knock-oin yeast ,10, and in yeast . Previouin yeast  and genein yeast . The devP. tricornutum is the metabolic network reconstruction, which enables computational analysis of systems-level responses. Genome-scale metabolic network reconstructions are derived from the annotated genome and contain information about all known metabolic reactions in an organism including the stoichiometry, subcellular localization, and the gene products by which they are catalyzed. The reconstruction process itself is laborious and iteratively and described, for example, in detail in [One promising strategy that investigates the yet unexplored metabolic capabilities of distinct organisms such as etail in . The recetail in ,16.P. tricornutum which provides a comprehensive insight into yet unexplored metabolic capabilities. We constrained the model with organism-specific biomass equations generated by Fourier transform infrared spectroscopy. The model predicts the presence of a surprising chloroplast glutamine-ornithine shunt that transfers reducing equivalents generated by photosynthesis to the mitochondria. Our findings demonstrate the utility of whole genome metabolic reconstructions to uncover unexpected biochemistries and to provide an important in silico template for directing future metabolic engineering efforts.Here, we report the reconstruction of a detailed and compartmentalized genome-scale metabolic model for Phaeodactylum tricornutum was obtained from JGI (http://genome.jgi-psf.org/Phatr2/Phatr2.home.html). We used the “finished chromosomes” (Phatr2) and “unmapped sequence” (Phatr2_bd) protein sequences to generate the draft reconstruction. While working on the reconstruction, an updated genome annotation, Phatr3, with refined gene models and improved functional annotation became available and exploited as well. Using a Phatr2 to Phatr3 gene ID mapping table provided by the JCVI, the Phatr2 gene IDs in the reconstruction were replaced by their corresponding Phatr3 IDs. Phatr3 is available at Ensembl Protistis (http://protists.ensembl.org/Phaeodactylum_tricornutum/Info/Index).The genome annotation of P. tricornutum genome annotation contains many putative enzymes with unknown function. To facilitate the manual curation of our draft reconstruction we used protein BLAST (with default settings) to reannotate the predicted proteins. Using the BLAST command line tool we created a local BLAST database containing all reviewed UniProtKB/SwissProt sequences having protein evidence at the protein or transcript level [P. tricornutum proteins and the created UniProt BLAST database.The pt level . Using aTo predict a subcellular localization for each protein we used a refined version of a previously developed pipeline. We used the updated Phatr3 protein sequences as input for TMHMM 2.0 , MitoproWe extended the pipeline by i) removing nuclear targeted proteins using predictNLS , ii) scret al. [P. tricornutum biochemical composition  as done by Mayers et al. [Based on the experimental approach of Mayers et al.  and [26–et al. , we detes et al. .By conducting these measurements over a growth curve including samples from nitrogen replete during exponential growth phase to nitrogen starved during stationary phase, we were able to achieve large changes in the cellular contents for all of these cellular components in smooth gradients , and tw (iJN678  and Knoo (iJN678 ). Before Toolbox . We usedP. tricornutum genome annotation Phatr2 (Phatr3 was not yet available) and the reconciled reference networks we obtained a draft reconstruction based on homology using the RAVEN Toolbox [Chlamydomonas or Synecchocystis for which no homologs in P. tricornutum were found and verified whether these reactions are present in P. tricornutum or not, ii) merged expanded reactions, iii) removed compartments not relevant for P. tricornutum, e.g., the eyespot, iv) removed duplicated metabolites and reactions which were introduced due to incorrectly reconciled information, and v) edited annotations.Starting from the  Toolbox . Before We manually curated the draft reconstruction pathway-by-pathway and verified the given information and added any missing information using the COBRA Toolbox . BesidesP. tricornutum reconstruction, the involved metabolites were characterized according to their chemical formula and charge determined at a pH of 7.3 using MarvinSketch . The pH was presumed to be constant across all compartments due to missing information for P. tricornutum. All reactions were elementally and charge balanced. Reaction reversibility was chosen based on published reconstructions such as iRC1080 or according to databases such as BIGG [™ .For each reaction in the  as BIGG  or SimPhProtein subcellular localization was assigned based on the prediction pipeline and indirect physiological evidence. If available, protein localization data from experiments with transgenic diatoms expressing protein-fluorescent protein fusions was exploited. Gene-reaction associations were identified from the literature, genome annotation, or genome sequence using BLAST and formulated as Boolean logic statements. Based on the biological evidence found we assigned a confidence score to each reaction reflecting the available information and evidence for its inclusion . Here, tSince naming might be ambiguous, different identifiers were used to annotate the reactions and metabolites. Reactions were annotated with EC numbers and KEGG reaction identifiers, metabolites were annotated with KEGG compound, ChEBI, and InChI identifiers.Each reaction was associated with at least one subsystem similar to the subsystem naming convention used in the KEGG database . Exchang2 and photon uptake. To validate that NAD(P) production did not occur without nutrient uptake we introduced an artificial reaction NAD(P)H → NAD(P) + H and again closed CO2 and photon uptake. If we found ATP production in the absence of nutrients, we identified all reactions contributing to the flux and produced a metabolic map using Escher [Quality control was performed during the reconstruction process. We ensured that ATP could not be produced without inputs. This was tested according to established standards  by optimg Escher  in orderS (m x n) where m is the number of metabolites and n is the number of reactions. The entries in the stoichiometric matrix, sij, represent the stoichiometric coefficients for the participation of the ith metabolite in the jth reaction. A negative value indicates consumption of metabolite i in reaction j whereas sij > 0 represents production of metabolite i. Flux balance analysis  was useTo constrain the space of possible solutions, the biomass objective function accounting for the ratios of biomass components  and biomass precursors  as well as energetic requirements to produce 1 g of biomass, is optimized for.2 exchange reaction to the experimentally determined total organic carbon  solver in MATLAB  with the COBRA Toolbox .2 and NO3 uptake. The ATP maintenance reactions were set to a range of 0–1 mM based on experimental results indicating negligible maintenance requirements [Dark period culture measurements were taken after the cells completed division; evidenced by consistency in the cell counts between dark and light period samples. Therefore, we hypothesized all biomass increases during the light period resulted from assimilation of extracellular nutrients. Elemental analysis indicated the culture fixed 1.57 mM C and assimilated 0.535 mM N during the light phase on culture day 5 , and C > 0 represents the minimal fraction of total photosynthetically fixed electrons that have to be directed to the mitochondria.However, unlike the traditional biomass function where the stoichiometry is pre-determined, dynamic allocation of fixed carbon was possible through the implementation of demand reactions for a β-1,3-glucose molecule representing the diatom storage glycan, chrysolaminarin, and TAG(16:1Δ9/16:1Δ9/16:0), the most abundant storage TAG observed during nutrient replete growth in cornutum . Additiox values . To simuGenome-scale network reconstructions are biochemically, genetically and genomically structured knowledge-bases which provide a framework to analyze and predict genotype-phenotype relationships. The reconstruction process is divided into four main steps  and summP. tricornutum genome annotation and protein homology to template organisms having reconstructions [P. tricornutum, attractive candidates for the analysis of cellular processes at a systems level, as they add to the biochemical diversity of microbes in a biotechnology setting, thereby increasing available production systems. Second, the draft reconstruction was manually curated and refined using additional resources such as the genome annotation, subcellular localization predictions and external databases . DMSP levels are known to increase with light intensity or nitrogen starvation but its metabolism is not well understood in diatoms and while the biosynthetic pathway is currently unknown [First, we generated a draft reconstruction based on the ructions –41. Diatructions . Althougases see . Once thases see . Qualitaammonium ) and orgammonium ). Theref results . Histidi unknown , a sensiiLB1027_lipid, see P. tricornutum.Leveraging a genome-scale model in the exploration and contextualization of lipid metabolism requires an accurate representation of the metabolic pathways and intermediate metabolites. To this end, a lipid module was developed  was constructed. The reduced lipid metabolism subsystem accounts for 1,029 reactions compared to 3,325 reactions involved in lipid metabolism in iLB1027_lipid. The core model yields comparable flux distributions and is suitable, for example, if detailed data on the lipid composition under the simulated condition are missing.The curated genome-scale metabolic network for One challenging aspect of eukaryotic reconstructions is the subcellular localization prediction of proteins. Due to their endosymbiotic origin, photosynthetic heterokonts including diatoms possess chloroplasts that are surrounded by four membranes. This complex structure concurs with distinct plastid targeting signals in diatoms that restrict the use of available subcellular prediction tools for other eukaryotes. We enhanced a previously developed pipeline which combined different bioinformatics programs to predict the subcellular localization of proteins in diatoms  see Fig.et al.’s results and experimentally validated subcellular protein localizations taken from [To evaluate the accuracy of the improved pipeline, we compared our predictions to Sunaga ken from ,12,58–64in silico pipeline; original refers to the version as published by Sunaga et al. [The table compares predictions of protein localizations to experimental data. For all considered proteins, Phatr2 and Phatr3 IDs and the status of the gene model in Phatr3 are given. If the gene models were modified, the pipeline predictions for both gene models are given. We distinguish between two versions of the a et al.  and the In order to mathematically solve the genome-scale model using FBA, the observed cellular phenotype is manifested as a biological objective function . This obP. tricornutum, biomass changes in the light period is dominated by the generation of carbon storage compounds, while the dark period is dominated by the anabolic processes necessary for cell division [The biomass composition used in heterotrophic genome-scale models is typically fixed based on experimentally derived values at a given culture condition . Howeverdivision . There idivision .P. tricornutum’s biomass composition over a growth curve that resulted in nitrogen deprivation after the high accumulation of biomass  than the median of all models (6%). B. napus (bna572+) has a comparable ratio of genes in the reconstruction (996) to predicted genes in the genome (9873) but contains far fewer reactions (671). The only model with a higher ratio belongs to the well-studied model organism S. cerevisiae, though this model iTO977 also contains fewer total reactions.In order to assess musculus . The conP. tricornutum involve environmental stress, such as nitrogen or phosphorous limitation [Recently, there has been a focus on using diatoms for biotechnological applications such as biofuel production, because of their high rate of neutral lipid accumulation ,81. Maximitation . Howevermitation .2 uptake  accounted for approximately 30% of total electron flow at low irradiances and as low as 5% at high irradiances [in silico, 5% of electron flow through the cytochrome b6f complex (CBFC_u) was routed to elemental oxygen mimicking the electron drain to PTOX while 5% of the electron flow through photosystem I (PSI_u) was committed to a Mehler-like reaction. Combined, these accounted for the 10% of electron flow to water-water reactions. Independent PTOX and Mehler reactions in the model are blocked by default but the boundaries can be adjusted to fit experimental results that deviate from the 10% value. In accordance with Bailleul et al.’s findings, the model predicts the use of mitochondrial oxidative phosphorylation to balance ATP and NADPH ratios.A recent, in depth characterization of photosynthetic electron flux in tosystem . Resultsadiances . Fixing et al. suggest that this ratio is fixed over a range of low to moderate light intensities. To simulate the observed energetic coupling between the mitochondria and plastid, an inequality constraint was added to the model. This constraint forced a minimum amount of the photosystem flux to be routed to the mitochondrial electron transport chain. Upon adding energetic coupling, the model predicted AOX was a primary electron sink at high irradiances. Additionally, the energetic coupling affected accumulation of neutral lipid biomass. Storage of lipid biomass was inversely proportional to energetic coupling with TAG accumulation being abolished when at least 35% of photosynthetically fixed electrons were redirected to the mitochondria at super-saturating photon uptake  to vent excess reducing equivalents. Our results predicted that flow of reductant from the plastid to the mitochondria was dependent on the ATP needs of the cell; however the results of Bailleul n uptake . Since let al., our reconstruction uncovered a previously undescribed plastid ornithine biosynthetic pathway  and two via oxidation of NADPH by AGPR_h. Ornithine is then proposed to be shuttled from the plastid to the mitochondria. The activity of 1-pyrroline-5-carboxylate dehydrogenase  and glutamine dehydrogenase  produce NADH further suggesting that this novel ornithine-glutamate pathway coupling these two organelles is possible.The mechanism by which reducing equivalents are shuttled to the mitochondria during energetic coupling is still unknown. In addition to the malate shuttle as proposed by Bailleul  pathway  that mayStorage of metabolites such as glutamine and ornithine could serve a photoprotective role by sequestering reducing equivalents as well as assimilated nitrogen. Indeed when intermediates of this ornithine shuttle were allowed to accumulate during simulations, the model predicted they were preferred over TAG biosynthesis. Ornithine concentrations were previously investigated in the context of the diatom ornithine-urea cycle (OUC) . AlthougP. tricornutum and contains information such as reaction stoichiometry and associations between genes and reactions. We especially focused on lipid metabolism since diatoms are attractive candidates for industrial-scale lipid production [P. tricornutum for biotechnological applications.Our assembled reconstruction represents the current, comprehensive biochemical, genetic, and genomic knowledge about oduction ,84. The S1 File(DOCX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(ZIP)Click here for additional data file.S4 File(ZIP)Click here for additional data file.


High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype–phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted. Cardiovascular disease (CVD) includes acute and chronic conditions, such as stroke and coronary heart disease . CVD resA vascular surface that normally is thromboresistant, anti-inflammatory, vasodilatory, and antiproliferative can turn into a surface that is thrombogenic, proinflammatory, vasoconstricive, and stimulatory of smooth muscle cell proliferation. Often this change is reactive and transient restoring vascular homeostasis. However, in diseases such as atherosclerosis, hypertension, and diabetes mellitus (DM) such changes, known as endothelial dysfunction, may be prolonged and critical for disease progression. The extent of pathological metabolic perturbation is determined by an interaction of lifestyle factors, such as diet and exercise with underlying genetic factors –12. ConsMetabolic modeling offers insights into cellular metabolism . Below, Endothelial cell metabolism has been investigated in multiple contexts including angiogenesis, hypoxia, shear stress, glycemia, and response to perturbations with mediators of vascular health including thrombin, sphingosine-1-phosphate, and more –19. The via the transcription factor Kruppel-like factor 2  is an intracellular fatty acid chaperone protein that impacts the peroxisome proliferator-activated receptor transcription pathway . Circulavia the tricarboxylic acid (TCA) cycle. When CPT1A was knocked down in vitro, vessel sprouting was impaired due to low levels of deoxy ribonucleotides. CPT1A knockdown in mice produced impaired retinal vessel formation (Fatty acid oxidation (FAO) accounts for roughly 14% of ATP production in cultured EC . Carnitiormation .via the TCA cycle  encourages NOS uncoupling leading to endothelial dysfunction. ADMA levels, and the ratio of ADMA to arginine, have been connected to several aspects of CVD risk –80.Genetic variation in eNOS affects some measures of recovery of blood flow control in acute myocardial infarction . Inhibitl-lysine, l-homoarginine, l-glutamine, l-leucine, or l-serine – decreases NO and increases endothelium-dependent vascular resistance. This effect is reversible by adding arginine to the medium and was shown to be dependent on y+L and y+ family amino acid transporters . While reconstructions afford a mechanistic description of genotype–phenotype relationships, they are not context specific. However, when constrained with cell or context-specific data, for example gene expression information of ECs, reconstructions afford GEMs that are descriptive of the biological event and cell of interest. Gene expression data of a HUVEC cells at normoxia vs. hypoxia would for instance generate two GEMs based on the same reconstruction thereby providing two snapshots descriptive of metabolic flux through reactions as defined by the two expression datasets. Essentially, reconstructions define the biochemical components of an organism, while context-specific polyomic data are required to generate a GEM of a particular cell or cellular event. Genomic, proteomic, and/or metabolomic fingerprints can thus be analyzed and compared within the context of GEMs .in silico and affords realistic hypotheses of biochemical mechanisms  from ECs could be hypothesized and related to alterations in core energy metabolism induced by global metabolic expression profiles. The release of sphingosine-1-P from ECs and its contribution to individual vascular health could thus be proposed on biochemical alterations on the systems level as opposed to mutations in sphingosine kinase alone.Computational modeling can contribute to decisions regarding the suitability of a treatment for individual patients. GEMs could be produced for individuals based on genomics and subsequently used to stratify patients and personalize medical interventions for CVD. GEMs maybe based on generalized transcriptomic data from a pool of samples from a cell type  or a setIndividualized hepatocellular carcinoma models have been used to predict patient outcomes based on the predicted production of acetate, identified as a key metabolic pathway for survival . Twenty-Developing personalized CVD therapeutic interventions relies on the ability to account for genotypic and phenotypic variation. Variability in disease phenotypes can be captured and understood in the context of GEM’s to facilitate this process.Genome-scale metabolic models provide an integrated approach in studying EC metabolism. They allow analysis of the multiple factors affecting ECs in the body, facilitating the exploration of the relationship of genotype to metabolic phenotype. This offers the possibility of producing personalized predictions of CVD risk and treatment, that account for both genetic and lifestyle factors. Currently, GEMs are the only biochemical model type that can account for both of these factors within a predictive modeling framework .2+ signaling (Genome-scale metabolic models are only one type of model used to account for EC function. Focused and mechanistic computational models of various aspects of vascular biology have also been made. These address some important biophysical parameters that are currently outside the scope of GEM modeling. This includes assessing the effects of shear stress on blood vessel reactivity and growth as well as the effects on blood cell/endothelium interactions of flow –122. Modignaling  along wiignaling  and mechignaling  signalinignaling –132.in silico querying of the effect of LDL deposition on global endothelial metabolism. Indeed, computational analysis of LDL metabolism has already proposed novel approaches to combat CVD (Integrating biophysical and signaling parameters with GEMs would generate a more complete understanding of the role of endothelial metabolism for CVD. In addition, these future GEMs would allow retrospective analysis of biophysical and genomic data that have been generated in the last few decades from population studies , 133, whmbat CVD –136.in silico in a data-driven manner, to optimize future clinical interventions.Realistic computational predictions of the effects of genetic and environmental perturbations on endothelial metabolism are possible and beneficial. There has been some exploration of CVD with GEMs and analysis of EC metabolism with GEMs; however, the full potential of this technique is only just beginning to be explored. Existing and future models will allow clinicians and researchers to investigate variable endothelial function SM and OR wrote the manuscript and conceived the ideology. HH, SP, and PJ conceived the ideology and contributed to writing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.