The variable compartmental origin of traced metabolites is a feature that allows application of the model to yeasts with uncertain genomic and transcriptional backgrounds. The presented test case includes the baker's yeast Saccharomyces cerevisiae and the methylotrophic yeast Hansenula polymorpha. Highly similar flux solutions were computed using either a model with undefined pathway localization or a model with constraints based on curated (S. cerevisiae) or computationally predicted (H. polymorpha) localization information, while false solutions were found with incorrect localization constraints. These results indicate a potentially adverse effect of universally assuming Saccharomyces-like constraints on amino acid biosynthesis for non-conventional yeasts and verify the validity of neglecting compartmentation constraints using a small-scale metabolic model. The model was specifically designed to investigate the intracellular metabolism of wild-type yeasts under various growth conditions but is also expected to be useful for computing fluxes of other eukaryotic cells.Recent advances in the availability and applicability of genetic tools for non-conventional yeasts have raised high hopes regarding the industrial applications of such yeasts; however, quantitative physiological data on these yeasts, including intracellular flux distributions, are scarce and have rarely aided in the development of novel yeast applications. The compartmentation of eukaryotic cells adds to model complexity. Model constraints are ideally based on biochemical evidence, which is rarely available for non-conventional yeast and eukaryotic cells. A small-scale model for  •Compartmentation influences computed intracellular fluxes.•Improper localization constraints potentially produce false flux solutions.•Minimal compartmentation constraints result in high-quality flux computations. The investigation of intracellular reaction rates has become accessible to a wide range of scientists, rather than exclusively experts, and has been applied to expand detailed knowledge on cellular physiology  as well ased MFA  or non-sased MFA . Further balances . An MDV describes the relative abundance of mass isotopomers differing in the number of included 13C-isotopes of a single metabolite or a fragment thereof and are determined by mass spectrometric analyses. The equation system is solved, i.e., the fluxes are computed, by least squares parameter estimation, in which an initial random flux distribution is iteratively adapted to minimize the sum of the squared residuals (SSR) between experimentally and simulated MDVs. A χ2-cut-off defines the maximum SSR value for a statistically acceptable flux solution. The solution with the smallest SSR in a sufficient number of minimizations is then assumed to be the optimal flux solution. Flux confidence intervals as a result of a sensitivity analysis indicate the resolvability and thus the robustness of the flux solutions  and H. polymorpha (Pichia angusta) CLIB 421  were used in all experiments. Growth experiments were conducted in 1.3 L shake flasks filled with 50 mL Verduyn medium containing per liter, 5 g (NH4)2SO4, 3 g KH2PO4, 0.5 g MgSO4·7H2O, 4.5 mg ZnSO4·7H2O, 0.3 mg CoCl2·6H2O, 1.0 mg MnCl2·4H2O, 0.3 mg CuSO4·5H2O, 4.5 mg CaCl2·2H2O, 3.0 mg FeSO4·7H2O, 0.4 mg NaMoO4·2H2O, 1.0 mg H3BO3, 0.1 g KI, 15 mg EDTA, 0.05 mg biotin, 1.0 mg calcium pantothenate, 1.0 mg nicotinic acid, 25 mg inositol, 1.0 mg pyridoxine, 0.2 mg p-aminobenzoic acid, and 1.0 mg thiamine  and an air-tight sample port, and were shaken at 130 rpm with an amplitude of 30 mm. 13C-tracer experiments were performed with a mixture of 80% 1-13C-glucose and 20% U-13C-glucose , reported to have a high potential for resolving the network of central carbon metabolism at a reasonable prize per experiment (600) of 0.05 from precultures grown in the same minimal medium, but supplemented with naturally labeled glucose after cells were harvested and washed with 0.9% NaCl solution. Precultures were grown in 100 mL shake flasks at a shaking frequency of 200 rpm at 30 °C and 40 °C for S. cerevisiae and H. polymorpha, respectively. Samples of the main culture were taken by connecting a syringe to the sample port, opening the sealing clamp, and flushing the sampling line with air before creating a vacuum with the syringe and allowing approximately 1 mL to flow into the sample tube. The sampling line was flushed with air before the sealing clamp was closed to minimize disturbances of the closed headspace and corresponding gas analysis. After the cultures reached stationary phase, the headspace analysis was continued for several hours to verify the tightness of the system by stable gas analysis signals.thiamine . The medperiment . The mai2.2600 was measured, and the samples were centrifuged at 13,000 rpm for 5 min in a tabletop centrifuge. The biomass pellet was separated from the supernatant, and both fractions were stored at −20 °C until further analysis. The supernatant was used to determine the concentrations of glucose and the excreted metabolites ethanol, glycerol, and acetate. The metabolites were separated on an Aminex HPX-87H column  at 60 °C with a flow rate of 0.8 mL/min of 5 mM H2SO4. Glucose, ethanol, and glycerol were detected on a Shodex RI-101 detector, and acetate was detected at a wavelength of 210 nm in a variable wavelength detector of an UltiMate 3000 HPLC system .Immediately after sampling, the OD600 and a calibration curve, which was recorded for both yeasts under corresponding experimental conditions. The correlation curves were generated by weighing the biomass from duplicates of 10 mL culture samples with five different OD600 values ranging from 0.5 to 6. The samples were cooled on ice before centrifugation in glass tubes at 3500 rpm for 20 min in a Heraeus Megafuge 16 R . The supernatant was discarded, and biomass pellets were washed with water and centrifuged again. Finally, the supernatant was discarded again and biomass pellets were dried at 60 °C for 48 h before weighing.The cell dry weight (CDW) of samples was calculated from the measured OD2 and BCP-CO2 sensors connected to a BACCom12 communication box using the FermVis software . The signals were obtained in units vol% (H) and transformed into molar concentrations as described by R is the real molar volume, and Vm is the volume of one mole of ideal gas.The off-gas was measured using BCP-O0, s0, and p0 are the biomass, substrate, and product concentrations, respectively, at the beginning of the experiment; xt is the biomass at time point t, and YSX and YPX are the substrate and product yields on biomass, respectively. The equations were solved with SigmaPlot Version 12.5 . For experiments in which the formation of glycerol, acetate or ethanol was not observed, the corresponding equations were excluded from the fit.Exponential growth rate (µ), uptake rates, and production rates (r) were calculated by a simultaneous nonlinear fit of the time-dependent concentration (c) changes using Eqs. 2 production rate was not used as an input constraint for 13C-MFA because the CO2 sensor was calibrated for unlabeled carbon and was therefore insensitive to 13C-labeled CO2. Specifically, due to the calibration of the sensors, only 6% of 13CO2 was detected. A pre-simulation correction of this rate was not feasible because the labeling of CO2 is not necessarily equal to the total labeling of substrate carbon, but rather depends on the actual flux distribution. Once a flux distribution was found, the simulated CO2 labeling was used to correct the original CO2 rate, and this rate was used as a quality indicator for the obtained flux solution. The corrected CO2 production rates  were calculated from the measured rates (rCO2) and the simulated fraction of unlabeled and labeled CO2  derived from the best flux solution using Eq. The calculated CO2.3tert-butyldimethylsilyl-trifluoroacetamide , followed by incubation at 85 °C for 1 h to allow derivatization. The derivatized samples were analyzed on a gas chromatography-mass spectrometry (GC-MS) single quadrupole system consisting of a TRACE™ GC Ultra and an ISQ single quadrupole MS with electron impact ionization  equipped with a TraceGOLD TG-5SilMS fused silica column . The GC-MS was operated as described by Biomass that was sampled during the mid-exponential growth phase was used to determine the MDVs of proteinogenic amino acids. Approximately 0.3 mg of biomass was resuspended in 150 µL of a 6 M HCl solution, then hydrolyzed at 105 °C for 6 h and dried overnight. The dried hydrolysate was resuspended in 30 µL of acetonitrile and 30 µL of N-methyl-N-The raw GC-MS data were transformed into MDVs and corrected for the influence of unlabeled biomass from the inoculum and the natural abundance of heavy isotopes using iMS2Flux , as desc2.4OpenFLUX was used to compute the intracellular fluxes . The sofS. cerevisiae ATCC 18824  was included with an undefined ratio. In this manner, the model could compensate for possible small deviations from the intended ratio of the two isotopomers, which may occur due to weighing errors or inaccurate declaration of 13C-enrichment. As OpenFLUX requires a flux basis for starting the parameter estimation, the biomass-building drain of glycine was fixed in every model. The complete models, including MDVs, measurement errors, and flux ranges of extracellular reaction rates defined in the preSolverScript files, are included in the supplement.The model was constrained with glucose and oxygen uptake, byproduct secretion rates, and precursor drains into biomass. The glucose uptake rate was normalized to 1000, while all other constraining rates were scaled accordingly. These rates were defined in a separate MATLAB file (‘preSolverScript.m’) that contained a matrix with the lower and upper boundaries of the respective rates. In the presented model, one uptake reaction for each of the glucose isotopomers . Java 7  was used to convert the model in text file format into the respective MATLAB files. The resulting flux distributions were visualized on metabolic pathway maps created with Omix® .2.5Saccharomyces Genome Database (www.yeastgenome.org)  . Hits from the BLASTP analysis and annotation information from the JGI database were compared. Resulting BLASTP hits were computationally analyzed with TARGETP 1.1 (TP) (The ome.org)  was used1.1 (TP)  and WoLF1.1 (TP) . Further33.12 exchange reaction according to 13C label by naturally labeled CO2 contained in the headspace of the shake flask at the beginning of the experiment. A similar compensation was applied for methyltetrahydrofuran (MTHF), allowing label adjustment. A simplified respiratory chain reaction was used to allow reoxidation of NADH and balancing of O2. The model was compartmented into cytosol and mitochondrion and featured transport reactions between the compartments for isocitrate, malate, acetyl-CoA (ACCOA), pyruvate (PYR), and oxaloacetate (OAA). The amino acids serine (SER), glycine (GLY), and threonine (THR) were exceptions in the model, as they contributed measured MDVs and were featured as mass-balanced intermediates of the modeled network. These exceptions were made because of the complex interactions among the three amino acids and with the rest of the network. Furthermore, the model definition allowed to simulate the origin of each amino acid independently and uncoupled from other amino acids stemming from the same precursor, e.g., alanine and leucine, which share the common precursor pyruvate.For metabolic flux analysis, a model was developed that described the central carbon metabolism, including glycolysis, the pentose phosphate pathway (PPP), and the tricarboxylic acid (TCA) cycle, with approximately 50 reactions and linked biomass production to twelve intermediates of this central network. A COThe model reactions for valine (VAL), leucine (LEU), and isoleucine (ILE) biosynthesis shown in To independently simulate the labeling patterns of leucine and valine, which share the common precursor 2-oxoisovalerate (ISV), the synthesis pathways of these two amino acids had to be uncoupled. To this end, leucine synthesis was defined by two reactions, pyruvate condensation (r128) and acetylation (r129), to account for a possible exchange of the intermediate 2-oxoisovalerate between compartments. In contrast, valine synthesis was aggregated into one reaction, thereby skipping the intermediate 2-oxoisovalerate  and, hen3.2S. cerevisiae was obtained from the Saccharomyces Genome Database. Eleven genes that contribute to nine anabolic pathways or pathway segments that link amino acids to precursors with separate cytosolic and mitochondrial pools were identified. The localization in the non-conventional yeast H. polymorpha was not reported and was inferred with the computational localization analysis tool WoLF PSORT and TargetP based on the protein sequences of the eleven identified genes. The comparison with protein annotations from the JGI database confirmed the BLASTP results and yielded no further protein entities suitable for analysis. The computational prediction of the most likely compartmental pathway localization was used to generate a pathway localization pattern for H. polymorpha. A summary of genes and proteins with the results of the computational analysis of compartmental localization is provided in Curated information on the localization of amino acid biosynthesis for S. cerevisiae model was constrained to the mitochondria, while lysine biosynthesis was constrained to the cytosol. For lysine biosynthesis, this concerned only the carbon atoms stemming from acetyl-CoA, as the second precursor α-ketoglutarate (OGA) was exclusively located in the mitochondria. The uncertainties in the localization of leucine and lysine biosynthesis were neglected, as it is has been shown by H. polymorpha BLASTP hits were consistently mitochondrial for valine and alanine biosynthesis and consistently cytosolic for lysine biosynthesis. In the cases of leucine and isoleucine biosynthesis, inconsistent results were neglected, as such inconsistencies were neglected for S. cerevisiae as well, and localizations were assumed to be mitochondrial. The localization of aspartate, threonine, and methionine biosynthesis was left variable based on parallel cytosolic and mitochondrial prediction results. This resulted in an overall pathway map of H. polymorpha that was identical to that of S. cerevisiae. The final compartmentation constraints are indicated in the corresponding models in the supplement.Based on the curated localization information provided in 3.313C-MFA is applicable to cultures grown in a (pseudo-)metabolic and isotopic steady state. This was ensured here by growing S. cerevisiae and H. polymorpha in glucose minimal media in batch shake flask experiments and sampling for proteinogenic amino acid labeling analysis during the mid-exponential phase, approximately five generations after inoculation. Physiological data were monitored throughout the entire growth period ; specifically, the acetolactate synthase involved in the biosynthesis of valine and leucine was defined as uniquely mitochondrial because of the unambiguous enzyme localization prediction results. The acquired physiological data  or without these additional constraints (Sf). Additionally, one solution was generated using a minimal set of localization constraints .To further investigate the differences between the differently constrained models, the labeling patterns of compartmental pools of the amino acid precursors pyruvate, oxaloacetate, and acetyl-CoA were calculated by a forward isotopomer simulation in OpenFLUX (task 8) based on the computed flux distributions . The resThe differences between Sf and Sd were ascribed to a vast switch in the simulated compartmental origin of amino acids produced from pyruvate as a precursor . With the unconstrained model, these amino acids were simulated to be synthesized, to a great extent, from cytosolic pyruvate, in contrast to the curated mitochondrial origin. This discrepancy can be attributed to the additional freedom of the unconstrained model, Sf, but does not result in deviations between flux solutions Sf and Sd beyond the confidence of the properly constrained solution, Sd.The complete flux solutions for each model, the results of the simulated labeling of acetyl-CoA, pyruvate, and oxaloacetate, and the sensitivity analyses are included in the supplement .Fig. 5Si3.5H. polymorpha  was simulated. A high PPP activity of ca. 50% was previously reported for this H. polymorpha strain during growth at 30 °C (The flux solutions of lymorpha  differedat 30 °C . One posat 30 °C . High PPat 30 °C .Fig. 6SiS. cerevisiae dataset, the majority of differences between the H. polymorpha flux solutions Sf and Sd were found downstream of the pyruvate kinase reaction. In contrast to the unconstrained solution, Sf, the constrained solution, Sd, showed pyruvate decarboxylase activity and import of the cytosolic acetyl-CoA produced via this pathway into the mitochondria. With the free model, minimal activity of the malic enzyme and the glyoxylate shunt was computed, while no activity was simulated with the constrained model. The significance of all differences between Sf and Sd was evaluated by a sensitivity analysis of the fluxes, which revealed that the confidence intervals of all fluxes computed for Sf included the optimal solution of Sd, with the respiratory chain reactions being the sole exceptions (S. cerevisiae flux solutions, the majority of biomass precursors were simulated to originate from the mitochondrial pools. This simulation result is questionable and is certainly a subject to be addressed for further improvement of the constraints, but it was not a determinant of flux ratios in the present study. In contrast to the S. cerevisiae dataset, the Sdmin solution of H. polymorpha was not explicitly more similar to Sd compared with Sf; rather, the Sdmin solution showed flux values that were often in between the values of Sf and Sd, which in some instances were closer to Sf and in others closer to Sd.As in the case of the ceptions . There wmin. The absent exchange flux of acetyl-CoA between the mitochondria and cytosol prevented an equilibration of these two pools. However, as cytosolic acetyl-CoA was not used as an amino acid precursor in the flux solution, this difference had no further impact on the flux estimates. The simulated extensive exchange of acetyl-CoA across the mitochondrial membrane in Sd was reflected in equivalent labeling patterns of the respective parallel pools (S. cerevisiae flux solutions, the absent exchange of oxaloacetate resulted in significant differences in the compartmental labeling of oxaloacetate for all three solutions. The activity of the malic enzyme was reflected in a slightly different pyruvate labeling in Sf. The ratios of simulated compartmental origins of amino acids in Sf were reversed in comparison to Sd for all amino acids stemming from pyruvate; however, the rather poor resolvability of malic enzyme activity (The labeling patterns of acetyl-CoA, pyruvate, and oxaloacetate differed only marginally between the three solutions, with the exception of cytosolic acetyl-CoA computed for the solution Sdel pools . In contactivity , which iS. cerevisiae dataset. To further strengthen this indication, four flux solutions were simulated, each with a different set of randomly assigned constraints regarding the compartmental origin of amino acids. Due to a lack of experimental data from a strain with definitive differences in the compartmentation of amino acid biosynthesis from the assumed compartmentation in S. cerevisiae, these simulations were meant to represent scenarios of improper use of compartmentation assumptions. These simulations resulted in a large number of fluxes outside of the flux confidence intervals of Sd or Sf (The differences between labeling patterns of mitochondrial and cytosolic acetyl-CoA, pyruvate, and, especially, oxaloacetate in all three solutions showed the potential for a higher influence of compartmentation constraints on the flux solutions compared with that seen in the Sd or Sf .r3 exceeding the χ2-cut-off values (r3 would be rated as statistically acceptable, although it resulted in significant inaccuracy of the flux estimates.In contrast to the differences between Sf and Sd, the deviations of the flux values found in these additional solutions were often more significant. Most remarkably, there were fluxes with confidence intervals that did not overlap at all with those of Sf or Sd. Likewise, the SSRs of these additional solutions were significantly increased compared with those of Sf or Sd, with all but Sdf values . A solutThese analyses indicate that improper use of constraining compartmentation assumptions can yield inaccurate results that may not always be revealed as false solutions by statistical measures.The complete flux solutions for each model, the results of simulated labeling of acetyl-CoA, pyruvate, and oxaloacetate, and the sensitivity analyses are included in the supplement.4The presented analysis highlights the potential disadvantage of extrapolating established constraints, which were verified for cognate organisms or under different experimental conditions without reconfirmation for the application case. The comparison of flux solutions computed with and without compartmental localization constraints revealed that the retraction of these constraints did not significantly alter the simulated flux distributions, as the computed flux solutions showed extensive consistency. In contrast, an alarmingly adverse effect of randomly chosen constraints was shown, which represents the potentially equivalent negative consequence extrapolated curated or computationally predicted constraints would have when they are improperly assumed. These results do not allow a recommendation of total dismissal of compartmentation constraints but strongly support the demand for a more rigorous evaluation of such constraints and a rejection of constraints where there are no good reasons to extrapolate assumptions from other organisms or only vague computational indications.


Ascomycin is a 23-membered polyketide macrolide with high immunosuppressant and antifungal activity. As the lower production in bio-fermentation, global metabolic analysis is required to further explore its biosynthetic network and determine the key limiting steps for rationally engineering. To achieve this goal, an engineering approach guided by a metabolic network model was implemented to better understand ascomycin biosynthesis and improve its production.Streptomyces species was first investigated by comparing the metabolic enzymes of Streptomyces coelicolor A3(2) with those of 31 Streptomyces strains, the results showed that more than 72% of the examined proteins had high sequence similarity with counterparts in every surveyed strain. And it was found that metabolic reactions are more highly conserved than the enzymes themselves because of its lower diversity of metabolic functions than that of genes. The main source of the observed metabolic differences was from the diversity of secondary metabolism. According to the high conservation of primary metabolic reactions in Streptomyces species, the metabolic network model of Streptomyces hygroscopicus var. ascomyceticus was constructed based on the latest reported metabolic model of S. coelicolor A3(2) and validated experimentally. By coupling with flux balance analysis and using minimization of metabolic adjustment algorithm, potential targets for ascomycin overproduction were predicted. Since several of the preferred targets were highly associated with ethylmalonyl-CoA biosynthesis, two target genes hcd (encoding 3-hydroxybutyryl-CoA dehydrogenase) and ccr (encoding crotonyl-CoA carboxylase/reductase) were selected for overexpression in S. hygroscopicus var. ascomyceticus FS35. Both the mutants HA-Hcd and HA-Ccr showed higher ascomycin titer, which was consistent with the model predictions. Furthermore, the combined effects of the two genes were evaluated and the strain HA-Hcd-Ccr with hcd and ccr overexpression exhibited the highest ascomycin production (up to 438.95 mg/L), 1.43-folds improvement than that of the parent strain FS35 (305.56 mg/L).The metabolic conservation of S. hygroscopicus var. ascomyceticus showed that the general metabolic network model of Streptomyces species could be used to analyze the intracellular metabolism and predict the potential key limiting steps for target metabolites overproduction. The corresponding overexpression strains of the two identified genes (hcd and ccr) using the constructed model all displayed higher ascomycin titer. The strategy for yield improvement developed here could also be extended to the improvement of other secondary metabolites in Streptomyces species.The successful constructing and experimental validation of the metabolic model of The online version of this article (doi:10.1186/s12934-017-0787-5) contains supplementary material, which is available to authorized users. Ascomycin FK520) is a 23-membered polyketide macrolide antibiotic with immunosuppressant activity, which has similar characterization to FK506 (tacrolimus), exhibiting various therapeutic applications, including antifungal , immunos0 is a 23Recently, considerable efforts have been made to enhance the ascomycin titer of the production strains, including random mutagenesis and selection , fermentStreptomyces coelicolor A3(2), the model organism of the Streptomyces genus, is the first one to be completely sequenced species of the genus Streptomyces [S. coelicolor A3(2) was first constructed in 2005 (iIB711) [S. coelicolor A3(2) with that of Streptomyces avermitilis [S. avermitilis and some of the non-conserved ORFs might have synteny-conserved isoenzymes with S. avermitilis. The results illustrated that metabolic genes were highly conservative in the above two Streptomyces strains. In addition, it has been demonstrated that the size of panmetabolism  in Escherichia coli was limited to the set of reactions already known and approached a constant [Streptomyces species, the size of panmetabolism might not approach a plateau because they contain large number of genes in their secondary metabolism. Nevertheless, the primary metabolic genes in Streptomyces species are more conserved, which has been illustrated by comparing genome and pangenome analysis [Streptomyces strain would not significantly expand the size of the set of known primary metabolic reactions in Streptomyces species. That is, the construction of a general metabolic network model for Streptomyces strains only requires the addition of limited diversity to the current primary metabolic network of the model strain S. coelicolor A3(2).ptomyces . The GSM(iIB711) , and was(iIB711)  and 2014rmitilis . It was constant . This phconstant , 22. Howanalysis , 24. ThuStreptomyces species has so far not been fully analyzed, and the primary reactions in model iMK1208 of S. coelicolor A3(2) have not been investigated to establish if these reactions are conserved and could be applied to construct a general metabolic network model for Streptomyces strains. To resolve these issues, the metabolic enzymes in the updated model iMK1208 of S. coelicolor A3(2) were compared with those of 31 Streptomyces strains. And high conservation of the primary metabolic enzymes had been found in the all selected Streptomyces strains. Then, a metabolic network model of S. hygroscopicus var. ascomyceticus for ascomycin biosynthesis was attempted to construct by using the universal primary metabolic reactions from model iMK1208 and adding the reactions associated with the synthesis of ascomycin in S. hygroscopicus var. ascomyceticus. Using the thus constructed metabolic network model, some potential targets for ascomycin overproduction were predicted with the aid of FBA [hcd and ccr) located in the ethylmalonyl-CoA pathway showed high promoting effects on ascomycin overproduction. After gene overexpression manipulation, all the engineered strains had shown higher ascomycin production than control, and the highest ascomycin titer reached to 438.95 mg/L in the strain HA-Hcd-Ccr. These findings not only suggested a general metabolic network model could be construed for Streptomyces species, but also provided an effective method for the titer improvement of other products in different Streptomyces species.Nevertheless, the metabolic conservation of d of FBA  and MOMAd of FBA . Among aStreptomyces strains with completely sequenced genomes (deposited in the NCBI by September 2015) were used in the conservation analysis. All the available protein coding sequences of the 31 Streptomyces strains were retrieved from the National Center for Biotechnology Information (NCBI), and plasmids were omitted from the analysis. The accession numbers of all 31 strains are listed in Additional file S. coelicolor A3(2) in metabolic model iMK1208 were used as the reference, and the sequence similarities on protein level were calculated from a pair-wise comparison of homologous sequences between the protein sequences in the model iMK1208 and the protein coding sequences of the 31 Streptomyces strains using BLAST v2.2.30+. The top hit for each protein sequence  was selected as the optimal comparison result. The protein sequences were considered as conserved at the threshold of 40% identity, 50% of query protein length aligned and e value < 1e − 05.A total of 31 S. hygroscopicus var. ascomyceticus FS35 was constructed based on the model iMK1208 of S. coelicolor A3(2). Several secondary metabolism subsystems actinorhodin, undecylprodigiosin, calcium dependent antibiotic, and germicidin biosynthesis were removed from the model iMK1208. Some specific secondary metabolites biosynthesis reactions in other subsystems such as membrane transport, cofactor and prosthetic group biosynthesis were also removed from the model. The reactions related with ascomycin specific precursors biosynthesis and overall biosynthesis reactions were added to the new model.The initial model of S. coelicolor [S. hygroscopicus var. ascomyceticus [S. lividans [S. coelicolor [As a kind of constraints-based metabolic model, the necessary constraints were used to obtain the optimal flux solution space. For example, 6-phosphogluconate dehydrogenase was shown to be NADP-dependent and no detectable activity with NAD , the uppelicolor , S. hygryceticus  and S. llividans , the lowelicolor . MenaquifPH value  was performed to calculate the flux distributions throughout the metabolic network by defining a maximized-biomass equation as the objective function using COBRAToolbox-2.0  in MATLAalue Eq. , definedS. hygroscopicus var. ascomyceticus FS35 was isolated after femtosecond laser irradiation [S. hygroscopicus var. ascomyceticus mutants used in this study were derived from FS35. Escherichia coli JM109 was used to propagate all plasmids. E. coli ET12567/pUZ8002 was used as the nonmethylating plasmid donor strain for intergeneric conjugation with FS35. All E. coli strains were grown in Luria–Bertani (LB) medium at 37 °C. The integrative E. coli-Streptomyces shuttle vector pIB139 containing the ermE* promoter (PermE*) was used for gene overexpression in S. hygroscopicus var. ascomyceticus FS35. S. hygroscopicus var. ascomyceticus strains was grown at pH 7.0 and 28 °C, MS medium was used for the preparation of Streptomyces spores suspensions, seed cultures were prepared as described previously [2SO4, 1.0 g/L K2HPO4·3H2O, 2.0 g/L CaCO3, 1.0 g/L MgSO4·7H2O, 0.001 g/L FeSO4·7H2O, 0.001 g/L MnCl2·4H2O, 0.001 g/L ZnSO4·7H2O, MOPS 5.0 g/L, pH 7.0. The production medium contained 20 g/L soluble starch, 40 g/L dextrin, 5.0 g/L peptone, 5.0 g/L yeast powder, 6.0 g/L corn steep liquor, 12 mL/L soybean oil, 1.0 g/L MgSO4·7H2O, 0.5 g/L MnSO4·1H2O, 1.5 g/L (NH4)2SO4, 0.5 g/L K2HPO4·3H2O, and 2.0 g/L CaCO3. Streptomyces coelicolor A3(2) were cultured in YEME at 28 °C for the isolation of genome. For conjugation experiments, S. hygroscopicus var. ascomyceticus strains were performed according to the Streptomyces standard protocols [S. hygroscopicus var. ascomyceticus were selected on 2CMY contained starch 10 g/L, casein tryptone 2.0 g/L, NaCl 1.0 g/L, (NH4)2SO4 2.0 g/L, K2HPO4 1.0 g/L, CaCO3 2.0 g/L, FeSO4·7H2O 0.001 g/L, MgCl2·6H2O 0.001 g/L, ZnSO4·7H2O 0.001 g/L, agar 20 g/L, pH 7.0. Agar plates by overlaying with an appropriate antibiotic or combination of antibiotics.All strains and plasmids used in this study are summarized in Table adiation . All S. eviously . SynthetE. coli with Streptomyces were performed according to standard protocols [hcd and ccr gene overexpression. The plasmids pIB/ccr and pIB/hcd were constructed as follows. The coding sequence of hcd and ccr were amplified from the genomic DNA of S. coelicolor A3(2) by PCR using the primer pairs hcdF1/hcdR1 and ccrF1/ccrR1 respectively. The hcd and ccr PCR products were digested using the restriction enzymes NdeI and XbaI, and the resulting fragments produced by digestion were cloned into the pIB139 vector between NdeI and XbaI respectively to obtain pIB139R and pIB139D, respectively. The constructed plasmids pIB/hcd and pIB/ccr were both confirmed by DNA sequencing. The conformed plasmids pIB/hcd and pIB/ccr were introduced into the parent strain S. hygroscopicus var. ascomyceticus FS35 to obtain the hcd and ccr overexpression strains HA-Hcd and HA-Ccr, respectively. To construct the vector pIB/hcd/ccr, the primer pairs HcdF2 and HcdR2 were used for amplifying the gene hcd and the primer pairs CcrF2 and CcrR2 were used for amplifying the gene ccr. The hcd PCR product was digested using NdeI and PstI, and then the fragment was cloned into the pUC18 vector to form pUC/hcd. The resulted plasmid pUC/hcd was cut using PstI and XbaI, and linked with the ccr PCR product, which was digested with the same restriction enzymes, to form the plasmid pUC/hcd/ccr. The plasmid pUC/ccr/hcd was excised by NdeI/XbaI and the fragment including gene hcd and ccr was cloned into pIB139 to generate the plasmid pIB/hcd/ccr. The resulting plasmid was introduced into the parental strain S. hygroscopicus var. ascomyceticus FS35 via conjugal transfer, generating the overexpression strain HA-Hcd-Ccr. The parental strain S. hygroscopicus var. ascomyceticus FS35 with pIB139 (FS-PIB) was used as the negative control. All the positive mutants were confirmed by PCR amplification and DNA sequence analysis.General DNA manipulation and intergeneric conjugation of rotocols . All therotocols , pIB139,g for 10 min at 4 °C. Cell pellets were washed 3 times by 100 mM Tris·HCl (pH 7.0) containing 5 mM MnSO4, 20 mM KCl, 2 mM DTT and 0.1 mM EDTA, and then resuspended in the same buffer [g for 20 min at 4 °C was stored at − 80 °C for assays of enzyme activity and total protein concentration. The concentration of total protein was detected by the Bradford method [The culture samples for enzyme activity analysis in vitro were harvested at the end of exponential phase (72 h) by centrifugation at 8000×e buffer . Cell sud method . One uni3-Hydroxybutyryl-CoA dehydrogenase  activity was assayed by monitoring the decrease in NADH concentration at 340 nm using acetoacetyl-CoA as the substrate, the reaction mixture contained 100 mM Tris–HCl buffer (pH 7.5), 100 μM acetoacetyl-CoA, 150 μM NADH, and extract. The reaction was started by the addition of acetoacetyl-CoA , 47. ActS. hygroscopicus var. ascomyceticus FS35 and its derivatives were carried out as described previously [g for 10 min, washed once with 0.1 M HCl and twice with distilled water separately, and dried at 80 °C to constant weight. The concentration of ascomycin was quantitated by high-performance liquid chromatography (HPLC) analysis as described previously [Fermentations of eviously . Biomasseviously . The reseviously .S. coelicolor A3(2), which is an important model strain for Streptomyces species, were used as the standard for metabolic conservation analysis of 31 Streptomyces strains. Except for the exchange and teichoic acid biosynthesis subsystems, the 1208 proteins in the other 43 metabolic subsystems of the model iMK1208 were preformed blastp homology analysis against 31 Streptomyces strains  and the stain S31-sxi had lowest homology (72%) with the all 1208 metabolic enzymes. Specially, 24 strains had higher homology more than 80%, and only 2 strains had homology least than 75% with the analyzed proteins in S. coelicolor A3(2). The conservation of metabolic enzymes from different metabolic subsystems in Streptomyces species was also investigated by cluster analysis. The conservation of different subsystems showed a similar distribution in all strains  and S. hygroscopicus limoneus KCTC 1717 (S16-shyl) as examples. Homology analysis of 1166 primary metabolic enzymes from S. coelicolor A3(2) was preformed against S14-shy and S16-shyl. Both strains showed high numbers of enzymes homologous with those of the primary metabolic enzymes in S. coelicolor A3(2) . However, among these 146 proteins, there were 95 proteins with at least one isozyme present in S14-shy. Moreover, among the remaining 51 non-homologous proteins, 15 had similar protein annotation information with proteins in S14-shy. Taking all these factors into consideration, there were only 36 proteins without homologs among all 1166 primary metabolic related proteins from S14-shy, which represents a mere 3% of all proteins in the primary metabolism subsystems of the metabolic network model of S. coelicolor A3(2). Similar results were also obtained for the strain S16-shyl , and the details are listed in the Additional file −4 mM/g DCW/h respectively. Based on the above constraints, the maximal specific growth rate calculated by the model was 0.0653/h, which was very close to the experimental result of 0.0616/h  and d-fructose 6-phosphate. In transaldolase-overexpressing strain, the carbon flux to E4P was enhanced which resulted in an increased supply of shikimate acid [fPH = 1.906) was catalyzed by crotonyl-CoA carboxylase/reductase (Ccr). In this reaction, crotonyl-CoA could be transformed into ethylmalonyl-CoA, an important precursor in ascomycin biosynthesis [After model verification, the potential overexpression targets for ascomycin overproduction were predicted by coupling the model with FBA and MOMA algorithms. In order to improve the accuracy, targets located in the inorganic ion transport and metabolism, exchange and nucleotide salvage pathway subsystems were excluded in advance. The targets related to ATP maintenance requirements were also been excluded. Using their respective H values , 29 poteonyl-CoA , 50. Theate acid . The tarate acid –53. The ynthesis . Overexpynthesis –58.Fig. hcd and ccr genes were individually overexpressed and co-overexpressed in S. hygroscopicus var. ascomyceticus respectively . In the ccr-overexpressing strain HA-Ccr, the crotonyl-CoA carboxylase/reductase activity was 0.28 U/mg protein, an increase of 2.5 times over the control (0.08 U/mg protein). The enzymes activity in each mutant strain was higher than parental strain, which confirmed that both hcd and ccr genes were overexpressed. In addition, in the strain HA-Ccr, the 3-hydroxybutyryl-CoA dehydrogenase activity was 0.27 U/mg protein, a similar level with that of the control (0.25 U/mg protein). By contrast, in the hcd-overexpressed strain HA-Hcd, the crotonyl-CoA carboxylase/reductase activity was 0.13 U/mg protein, which was 1.62-fold higher than that of the control (0.08 U/mg protein). In the strain HA-Hcd, the ascomycin production reached up to 375.32 mg/L at 168 h, increased 1.23 times compared with that of the parent strain FS35 (305.56 mg/L) . The crotonyl-CoA carboxylase/reductase activity was 0.35 U/mg protein, and thus was increased 3.37 times compared with that of the parent strain FS35 (0.08 U/mg protein). Enzyme activity analysis confirmed that the activity of 3-hydroxybutyryl-CoA dehydrogenase and crotonyl-CoA carboxylase/reductase activity in the strain HA-Hcd-Ccr was significantly improved compared with the control strain FS35. The final biomass concentration of the double-overexpression strain (16.92 g/L at 168 h) exhibited a slightly decrease compared with the parent strain FS35 (18.02 g/L at 168 h)  involved in the ethylmalonyl-CoA pathway were selected for the experimental overexpression. HACD1 and CCCR were catalyzed by 3-hydroxybutyryl-CoA dehydrogenase (Hcd) and crotonyl-CoA carboxylase/reductase (Ccr), respectively  has been constructed and updated for 2 times and successfully used in the guide of metabolic engineering targets identity [Streptomyces species [Streptomyces species, the conservation of these metabolic reactions existed in the reported GSMM had not been investigated completely. Previous studies showed that primary metabolism might be highly conserved between Streptomyces species [S. coelicolor A3(2) are conserved in other Streptomyces species, a more comprehensive, deeper metabolic gene analysis of Streptomyces species was performed in this study.In previous reports, GSMM was confirmed as an effective method for better understanding of intracellular metabolic behavior and identifying the key limiting steps for target metabolites biosynthesis. In identity –20. It h species , 59. How species , 24. To Streptomyces species was investigated by comparing the metabolic enzymes in the latest reported model of S. coelicolor A3(2) with those of 31 completely sequenced Streptomyces strains. The results showed that the metabolic enzymes were highly conserved within the 31 Streptomyces strains, especially for primary metabolism. This was consistent with the previous comparative genome and pangenome analyses of Streptomyces species [Streptomyces species, and metabolic reactions showed higher conservation than the data obtained through protein homology analysis. Therefore, majority of the primary metabolic reactions in the model of S. coelicolor A3(2) are likely universal across Streptomyces species. In addition, due to the conservation of metabolic genes and our limited knowledge of gene functions and biochemical characteristics, the set of primary metabolic reactions in Streptomyces strains is expected to converge to a constant value with the addition of analyzed genomes, as was demonstrated for E. coli strains [Streptomyces species, it is reasonable to reference the primary metabolic reactions in the model of S. coelicolor A3(2) to construct general metabolic network models for other Streptomyces strains.The metabolic conservation of  species , 23, 24. strains , 22. ConS. hygroscopicus var. ascomyceticus was firstly attempted to be constructed and applied to identify the genetic engineering targets for improving ascomycin production. The constructed metabolic model of S. hygroscopicus var. ascomyceticus facilitated the understanding of the ascomycin biosynthesis network. Using this model in conjunction with the FBA and MOMA algorithms, we identified 29 potential gene overexpression targets according to a ranking of their respective fPH values. Among all the predicted targets, several targets with high fPH values  were found in the ethylmalonyl-CoA pathway, which had also been confirmed to be an limiting pathway for other products synthesis in some Streptomyces species [S. hygroscopicus var. ascomyceticus FS35 could utilize acetate and ethylmalonic acid as a sole carbon source (data not shown), indicating the presence of the ethylmalonyl-CoA pathway in this strain. However, the ethylmalonyl-CoA pathway was incomplete in the most reported model of Streptomyces species. According to the conservative analysis of primary metabolism, the related metabolic reactions had been supplied into the general metabolic model by adding additional constraints and the missing reactions in the pathway  biosynthesis , HACD1 could converts acetoacetyl-CoA to 3-hydroxybutyryl-CoA through 3-hydroxybutyryl-CoA dehydrogenase encoded by sis Fig. . PHB hadyceticus . CCCR ismonensin , tacrolimonensin , and salmonensin . Furtherhcd and ccr were overexpressed and co-overexpressed in S. hygroscopicus var. ascomyceticus. The experimental results suggested that all the engineered strains had a higher ascomycin production than control, and the highest ascomycin production could reach to 438.95 mg/L. The genetic manipulation about the target genes further verified the feasibility the strategy about model construction. However, the increase of production titer was different from the predicted improvements. This may be due to some limitations of the constraint-based metabolic network simulation, such as the lack of detailed knowledge on metabolic regulation, as well as thermodynamic and kinetic information. The strategy for metabolic model construction in Streptomyces species will make the construction process of metabolic network easier and make it possible for the strain without genomic information.The two target genes S. hygroscopicus var. ascomyceticus, the ethylmalonyl-CoA pathway is also found in many Streptomyces species, and the corresponding enzymes can be found in 30 of the 31 investigated Streptomyces strains  as model, a general metabolic model of Streptomyces species was attempted to be constructed based on the conservative analysis of the primary metabolic reactions of 31 Streptomyces strains. Using this general metabolic model, the GSMM of S. hygroscopicus var. ascomyceticus was reconstructed and applied to identify the potential targets for ascomycin overproduction. According to the simulated results, two targets hcd and ccr from the ethylmalonyl-CoA pathway were selected for overexpression, the experimental results indicated ascomycin production showed higher improvement in both of single- and double-overexpression strains. These findings could not only indicate that improving the supply of ethylmalonyl-CoA could improve ascomycin yield effectively, but also suggest that a general metabolic model could be existed and applied to guide for the yield improvement of other products in Streptomyces species.In this study, using the latest reported model of Additional file 1. Conservation analysis of the proteins in the model iMK1208 against 31 Streptomyces strains.Additional file 2. Metabolic network model of S. hygroscopicus var. ascomyceticus FS35 and potential targets identified using this model.Additional file 3: Table S1. Primers used in this section. Figure S1. Heat map illustrating the conservation of metabolic enzymes in different metabolic subsystems among 31 Streptomyces strains. Figure S2. Homology analysis of proteins related to the primary metabolism of S. coelicolor A3(2) and S16-shyl. Figure S3. Production profiles of the stain S. hygroscopicus var. ascomyceticus FS35 in batch fermentation. Table S2. Genetic targets chosen for experimental implementation. Table S3. The encoded enzymes involved in the ethylmalonyl-CoA pathways of the 31 Streptomyces strains.Additional file 4. The updated model including the complete ethylmalonyl-CoA pathway, and the potential targets identified using this model.


A model based solely on mass-balance constraints refines our understanding of the trade-offs, energy requirements, leaf-level fluxes, and plasticity mechanisms in different biochemical types of assimilation. 3 plants. However, detailed quantification of the biochemical activities in the bundle sheath is lacking. This work presents a general stoichiometric model for C3, C2, C2+C4, and C4 assimilation (SMA) in which energetics, metabolite traffic, and the different decarboxylating enzymes  are explicitly included. The SMA can be used to refine experimental data analysis or formulate hypothetical scenarios, and is coded in a freely available Microsoft Excel workbook. The theoretical underpinnings and general model behaviour are analysed with a range of simulations, including (i) an analysis of C3, C2, C2+C4, and C4 in operational conditions; (ii) manipulating photorespiration in a C3 plant; (iii) progressively upregulating a C2 shuttle in C3 photosynthesis; (iv) progressively upregulating a C4 cycle in C2 photosynthesis; and (v) manipulating processes that are hypothesized to respond to transient environmental inputs. Results quantify the functional trade-offs, such as the electron transport needed to meet ATP/NADPH demand, as well as metabolite traffic, inherent to different subtypes. The SMA refines our understanding of the stoichiometry of photosynthesis, which is of paramount importance for basic and applied research.The goal of suppressing photorespiration in crops to maximize assimilation and yield is stimulating considerable interest among researchers looking to bioengineer carbon-concentrating mechanisms into C These then diffuse to the BS where they are decarboxylated. C4 plants have traditionally been grouped into three subtypes (Zea mays) has been shown to operate two BS decarboxylation enzymes (NADP-ME and PEPCK) and two CO2 delivery pathways   . Both th3 to C4 has recently been studied ; (ii) is based only on stoichiometry and therefore does not rely on kinetic measurements; and (iii) includes all main photosynthetic reactions, but is user-friendly for non-specialists. The theoretical underpinnings of the SMA are described in detail and a range of simulations to exemplify the model rationale and behaviour are provided. Numerous topics are covered, including (i) an analysis of C3 and C2 photosynthesis and all subtypes of C2+C4 and C4  in operational conditions; (ii) the energetics involved in manipulating photorespiration in a C3 plant; (iii) the consequences of progressively upregulating a C2 shuttle in a background of C3 photosynthesis; and (iv) the consequences of progressively upregulating a C4 cycle in a background of C2 photosynthesis. Results quantify ATP and NADPH demand, which link dark and light reactions; refine our understanding of the stoichiometry of photosynthesis and the trade-offs inherent to different photosynthetic subtypes; and represent a useful framework for the integration of existing biochemical models.The aim of this work was to develop a stoichiometric model of assimilation (hereafter SMA) which (i) generalizes all pathways of assimilation , and PEP carboxylation rate (VP). The ATP and NADPH requirements are SMA outputs and they are not related to light reactions at this stage. Reactions are typically grouped by the biochemical function of the pathways, of which only the entry point is calculated. The SMA is based only on well-established reaction stoichiometry. The SMA accounts for the interactions between C2 and C4 cycles, including the fluxes associated with amino group rebalancing, and for the NADPH and ATP demand of assimilatory processes. The theory underpinning the SMA can be followed in The SMA was developed on the basis of a stoichiometric model of NADP-ME CA; respiration in the light, RLIGHT; and Rubisco rate of oxygenation relative to carboxylation, rO/C), two define the activity of the CCM , and seven partition key processes between the BS and M . In addition, for NAD-ME subtypes it is possible to constrain malate dehydrogenase (MDH) activity to zero. Inputs can be constrained in different ways depending on the research questions. When the goal is to refine the analysis of a particular metabolic state of the leaf, input quantities may represent realistic biochemistry, otherwise inputs can be freely manipulated to simulate bioengineering or explore hypothetical scenarios. A, RLIGHT, and rO/C can be measured with accuracy , or based on the rate of ATP and NADPH made available by light reactions (and referred to as light-limited). Integrating enzyme-limited formulations in the SMA is straightforward, because the A, rO/C, and VP output by the biochemical model can be directly input into the SMA (see references below). Integrating light-limited formulations is more complicated and will be addressed in a dedicated paper. Physiological values are available for rPEPCK, gained through extensive biochemical work , predicting fC and fO requires that the increased CO2 concentration in the BS be modelled, for instance using the validated models for C4, C3, C2, and C2+C4 photosynthetic subtypes . For each type, two sets of simulations are presented: static scenarios, where the SMA is calculated for one set of inputs, representing realistic operational conditions  cycle operate in the M, while no carbon fixation is occurring in the BS. Although photorespiration and GDC activity are high, no glycine (GLY) is exported in the BS, and there is no net metabolite flux at the M–BS interface. All ATP and NADPH demand is in the M.Outputs for C3 photosynthetic type was simulated by varying rO/C between 0 and 1 while keeping other quantities at 0  diffuses back to the M, exporting half of the GLY amino groups. The excess ammonia produced by GDC in the BS is fixed by glutamine synthetase/glutamine oxoglutarate aminotransferase (GS/GOGAT), transaminated to alanine (ALA), and diffuses back to the M so as to re-balance the amino groups. For the C2 shuttle to operate, an import flux of pyruvate (PYR) equimolar to R is required in the M. The C4 CCM is not operational. In this example, PGA is reduced mainly in the M and a substantial BS↔M triose phosphate exchange occurs. Because the CCM is not operational, there is no net export of NADPH from M to BS and the NADPH demand in BS of 1.1 μmol m−2 s−1 must be met by linear electron flow (LEF); however, cyclic electron flow (CEF) may be preponderant as the ratio of ATPBS to NADPHBS was ~8.Outputs for CfGDC was varied from 0 to 1 in an idealized C3 plant, thus simulating the transition to C2 photosynthesis through intermediate states of GDC compartmentalization, which are generally referred to as proto-Kranz  cycle to the BS results in changing the locality of ATP and NADPH demand  in the M and transaminated to ASP, which diffuses back to the BS and is transaminated back to OAA to supply MDH in the BS. The pair PYR/ALA balances the amino groups resulting from OAA/ASP transamination, and the amino groups resulting from the flux of GLY and SER are directly dependent on the operation of the C2 shuttle. Alternative scenarios may involve NADH resulting from GDC activity sustaining a minimal level of PR in the BS . PR is mainly located in the M, and triose phosphate trafficking is higher than for the C2 photosynthetic type. Because a large part of Rubisco is mainly located with GDC in the BS, and photorespiration is reduced by the activity of the CCM, the effectiveness of the C2 shuttle (as R) is reduced relative to C2 photosynthesis. PR in the BS consumes all NADPH available through MDHM, therefore T ≈ 0; however, there is no residual NADPH deficit, and LEF is not required in the BS.An example of NADP-ME C2+C4 photosynthetic subtype  remained zero . The net effect of the C2 shuttle (as R), which depends on Rubisco oxygenating activity in the M, is zero. The CCM supplies all NADPHBS (6.54 μmol m−2 s−1), no LEF is required in the BS, and there is excess OAA that is not reacted with by MDH and is subsequently transaminated (T).Outputs for a typical NADP-ME subtype are shown in ATP/GA than for the NADP-ME subtype. However, PEPCK activity is generally low, all NADPHBS is supplied by the CCM, and no LEF is required in the BS.4 photosynthetic subtype with no engagement of PEPCK. The CCM exports CO2 at the same rate as the NADP-ME subtype; however, MDH activity in M is zero, all OAA is transaminated, and the NADPH demand in the BS has to be generated in the BS through LEF.rPEPCK = 1). The CCM exports CO2 at the same rate as the other subtypes. Because MDH is present in the M, the CCM would export reducing power to the BS if rPEPCK < 1, but, here, PEPCK activity in the BS diverts OAA produced by PEPC in the M, making OAA unavailable for MDH activity in the M, which is consequently zero. Furthermore, because MDH activity is zero, the CCM does not export reducing power to the BS, and the NADPH must be supplied through LEF in the BS.rPEPCK = 1). The CCM exports CO2 at the same rate as the other subtypes. Here, MDH in M is not operational, and the CCM does not have the potential to export reducing power to the BS.rO/C was set at a typical C4 value of 0.05  required the engagement of LEF in the BS. In the NAD-ME subtype, the demand for LEF in the BS increased linearly for fPR > 0.The fraction of PGA reduced in the BS was manipulated through the input parameter subtypes , B. The rPEPCK. fPR was set at 0.25 to highlight differences between NADP-ME and NAD-ME subtypes, while fPPDK and fCS were kept at zero  and Rubisco  activity, leaf-level Rubisco rates of carboxylation and oxygenation (as rO/C), and PEP engagement (as VP) are known. While previously published models are tailored for a particular species or a discrete photosynthetic type that is fixed a priori, the SMA allows rates to vary continuously between boundaries defined by what is biochemically feasible. The SMA is designed for integration with existing biochemical models. However, these models rely on assumptions that are specific to a particular photosynthetic type and, as such, they were not included in the SMA at this stage. The SMA is based upon logical constraints and well-established reaction stoichiometry, and is, consequently, of general use. Compared with other recent flux-balance models [e.g. Flux-balance analysis models are constraint-based models in which steady state fluxes are predicted in a metabolic network by applying mass-balance constraints based on reaction stoichiometry . Lately 4 plasticity mechanisms, 4 plants can exploit to stabilize their energy and redox state were recently reviewed  and can be followed in Supplementary file 2 ; the exchange of triose phosphate and PGA and the effect of switching decarboxylase (ME versus PEPCK) are covered in simulations 4.2.1 and 4.2.2. In addition to the list of fPR (simulation 4.2.3), and I suggest a possible role for PPDK in the BS, which was investigated by manipulating fPPDK (simulation 4.2.4). The simulations were carried out as a single factor analysis in which only one input was varied at a time , to virtually complete engagement in the atypical C4Alloteropsis semialata . Similarly, in NAD-ME subtypes the gradient spans Panicum species with virtually no PEPCK engagement, Cleome C4 species with intermediate PEPCK activity, and Spartina maritima where PEPCK engagement is virtually complete . Although PEPCK plants have numerous chloroplasts in the BS  contribute to fine-tuning ATP and NADPH demand in the BS in response to illumination of the BS chloroplast. Light availability in the BS is determined by anatomy and changes dynamically according to light intensity and quality. The locality of ATP production is therefore largely independent of metabolic control [2, and PCO cycles in the BS impose a limit, and a threshold of ATP demand in the BS (ATPBSMIN = RuPphospBS + VOBS + R), corresponding to an ATP partitioning ratio of ATPBSMINATPM≈0.3. If ATP supply in the BS is lower than the minimum demand, C4 photosynthesis ceases, leading to stunted phenotypes  in a single tool. A range of examples detailed the energetics and metabolite fluxes involved in the gradual activation of the CSMA is coded in an Excel workbook that is freely available to download. Macros are avoided.JXB online.Supplementary data are available at 3 photosynthesis: Figure S2.File 1. SMA development and equations S1–S34; simulation of static scenarios under physiological operational conditions: Table S1 and Figures S1, S3–S5E; simulation 1.2, a dynamic scenario for CFile 2. Excel workbook coding the SMA.Supplementary_Table_S1_Figures_S1_S5Click here for additional data file.Supplementary_Data_ModelClick here for additional data file.


Recently, some in vitro engineering approaches were proposed as alternative strategies to solve this problem. In brief, by reconstituting a biosynthetic pathway in a cell-free environment with the supplement of cofactors and substrates, the performance of each biosynthetic pathway could be evaluated and optimized systematically. Several value-added products, including chemicals, nutraceuticals, and drug precursors, have been biosynthesized as proof-of-concept demonstrations of in vitro metabolic engineering. This mini-review summarizes the recent progresses on the emerging topic of in vitro metabolic engineering and comments on the potential application of cell-free technology to speed up the “design-build-test” cycles of biomanufacturing.With the breakthroughs in biomolecular engineering and synthetic biology, many valuable biologically active compound and commodity chemicals have been successfully manufactured using cell-based approaches in the past decade. However, because of the high complexity of cell metabolism, the identification and optimization of rate-limiting metabolic pathways for improving the product yield is often difficult, which represents a significant and unavoidable barrier of traditional  This has become an attractive, sustainable way to produce molecules e.g., H2 production from glucose and water) and prohibit them from achieving the theoretical yield of commodity chemicals.As high-valued products, biologically active compound is one kind of the most attractive engineering targets nowadays because many of them demonstrate important pharmacological activities or biotechnological significance Escherichia coli and Saccharomyces cerevisiae, which are well studied and equipped with a broad spectrum of biomolecular tools to allow metabolic engineering easily, the effects of heterologous expression of pathways are often unpredictable to guarantee a high productivity, as witnessed in metabolic engineering of S. cerevisiae to produce n-butanol E. coliThe unsatisfactory results of large-scale biomanufacturing of high-value products and commodity chemicals are largely due to two challenges: complex cell-wide regulation of metabolic pathways, and difficulty in balancing biosynthesis of target products and innate cell physiology. First, a lot of organisms are difficult to be engineered because of unknown regulation patterns and the lack of engineering tools for non-model organisms in vivo metabolic engineering, especially in its potentials of speeding up the DBT cycles in vitro metabolic engineering approaches: cell-free synthetic enzyme engineering and cell-free protein synthesis (CFPS)-based metabolic engineering  enzymes and the acyl carrier protein (ACP) in the natural E. coli host, and purifying the enzymes to homogeneity. Upon supplementing the ten protein species with acetyl-CoA, malonyl-CoA and NADPH, C14-C18 fatty acids were observed in the system, evidenced by 14C-isotope incorporation experiments and subsequently via UV-spectrophotometry in vitro. Basically, in order to develop a route to synthesize the jet fuel farnesene, Zhu and colleagues reconstituted the mevalonate (MVA) pathway in a cell-free synthetic enzyme system in vitro by expressing and purifying eight enzymes of the MVA pathway as well as the α-farnesene synthase from an E. coli host As a powerful method to investigate natural enzymes and metabolisms, some remarkable achievements have been reported. One remarkable example is the study of the bacterial fatty acid synthases. Although being investigated extensively at the genetic and enzymatic level, it is still not easy to manipulate enhanced production of specific fatty acids because of the complex cell-wide regulation of fatty acid synthesis. In 2010, Liu et al. revealed the strong dependence of fatty acid synthesis on malonyl-CoA availability and several important phenomena in fatty acid synthesis by a quantitative investigation of the fatty acid biosynthesis and regulation in a cell-free synthetic enzyme system in vitro. The biosynthetic pathways of dhurrin, which plays an important role in plant defense against pathogens in vitro using enzymes from the natural host organism Sorghum bicolor lysates. It was found that the microsomal environment could allow functional expression of catalytically active CYP79 and P450ox, and thus dhurrin synthesis was observed by radioactive TLC analysis when combining the three enzymes with 14C-tyrosine, UDP-glucose, and NADPH. In another study, camalexin pathway was constructed in vitro by purifying three enzymes: CYP79B2, which catalyzes decarboxylation and N-hydroxylation of tryptophan to indole-3-acetaldoxamine (IAOx); a second P450 enzyme, which was previously unknown and is believed to catalyze an oxidative coupling of cysteine to IAOx; and CYP71A15, which decarboxylates and cyclizes the resulting cysteine-indole-3-acetonitrile (Cys-IAN) compound to form the thiazole ring structure within camalexin. By using a combination of gene expression data and protein sequence analysis, Klein and coworkers were able to identify a P450 enzyme capable of performing the C–S coupling reaction and to reconstitute the entire camalexin pathway in vitro for the first time In addition to bacterial pathways, some eukaryotic pathways were also reconstituted 2.2in vivo systems 2 was 11.2 mol per mole of anhydroglucose unit of cellobiose, corresponding to 93.1% of the theoretical yields. This yield was more than 2 times higher than the yield from microbial fermentations which is limited to 4 H2 per mole of glucose in vitro non-natural, ATP balanced pathway for n-butanol production from glucose n-butanol production from two acetyl-CoAs. As a result, one molecule of glucose was able to produce one molecule of n-butanol, two molecules of CO2 and one molecule of water. Recently, Opgenorth et al. described a robust, efficient synthetic glucose breakdown pathway and implemented it to produce bioplastic PHB 2. Because the PBG pathway generated more reducing equivalents than are needed to produce PHB (4 NADPH per glucose produced but only 1 NADPH needed), the authors designed a NAD(P)H purge valve regulatory nodes which composed of a mixture of dehydrogenases to prevent the buildup of NADPH. Reactions were initiated with 60.7 glucose and continuously monitored in 10-h cycles by absorbance at 600 nm. It was observed that by the end of the third cycle, the reaction stopped by the depletion of glucose with a production of 57 ± 6 mM PHB , corresponding to a 94% yield. When reactions were initiated with 109.2 mM glucose, the system maintained > 50% of the maximum activity over the entire 55 h run at room temperature and generated 93.8 ± 6.1 mM PHB, corresponding to an 86% yield. The high yield emphasized the importance of cofactor recycling for SyPaB system. Compared to the microbial production of PHB using Cupriavidus necatorPerhaps a more advanced and systematic application of cell-free synthetic enzyme engineering, especially for reconstituting long biosynthetic pathways that involves a large number of enzymes for chemical production purposes in vivo cell metabolism, the relative simplicity of in vitro biological systems makes them far easier to simulate processes and predict optimal enzyme ratios for maximizing product yield and accelerating volumetric productivity. This simplicity could be concluded into five aspects: 1) it is free of complex transcriptional or translational regulations; 2) lower background noises in the defined system; 3) accurate measurements of metabolic components; 4) better defined model parameters, and 5) smaller modeling scales compared to in vivo systems. With the development of high-speed computers and the accumulation of huge biological data, numerous computational tools have been developed to simulate the in vivo cell metabolism and to facilitate the design of in vivo metabolic engineering e.g., metabolites, mRNA, and peptides) and are solved by a set of differential equations with defined kinetic parameters of biological reactions or processes in vivo metabolic engineering with tremendous success for rational design of the host cell in vitro synthetic systems, even with the fact that in vitro synthetic systems could be easier and more precisely described via kinetic models compared to in vivo systems 2 and CO2, by estimating the kinetic parameters with the best fitting of experimental data e.g., E. coli) based on the advantages of kinetic modeling in cell-free synthetic enzyme systems i.e., product-induced feedback regulation) and the complex allosteric machinery . Recently, a forward design method has been reported to establish an in vitro glycolysis biological process, which constituted of 10 enzymes In order to further understand and predict the performance of biological systems, computational modeling has been commonly applied in vivo metabolic engineering in speed, simplicity, and easiness of manipulation. However, there are still several drawbacks associated with cell-free synthetic enzyme engineering such as SyPaB. For example, in order to get the purified enzymes, researchers still need to spend numerous time and effort in plasmids construction, expression optimization and protein purification. Also, SyPaB was assembled in a complete artificial manner, which could lead to the instability of certain purified enzymes and coenzymes in vivo metabolic engineering. The cell-free synthetic enzyme system itself, on the other hand, is arguably difficult in being scaled up for biomanufacturing In summary, cell-free synthetic enzyme engineering is advantageous to 3in vivo protein expression and purification steps could be bypassed in the latter, which further speed up the DBT cycles. After decades of improving, current CFPS is well established, which could yield 200–2300 mg/mL protein in the batch mode reaction in vitro with the use of cell-free lysates and mixing multiple crude lysates to initiate the DBT cycle. A panel of cell-free lysates are selectively enriched and prepared in parallel, in each of which a target enzyme is overexpressed by using CFPS technology. Their cell-free lysates were next mixed in a combinatorial manner to construct a mevalonate biosynthetic pathway involved in isoprenoid synthesis n-butanol biosynthesis E. coli lysates could support a highly active 17-step CoA-dependent n-butanol pathway derived from Clostridia metabolism involving CoA intermediates in vitron-butanol synthesis, another E. coli enzyme (AtoB) converts acetyl-CoA to acetoacetyl-CoA, and heterologous enzymes  convert acetoacetyl-CoA to n-butanol. It was found that by adding both NAD and CoA with glucose to initiate n-butanol synthesis, the cell-free system could produce 1.2 g/L n-butanol. In order to improve pathway performance, the researchers replaced some of initial Ter and AdhE enzymes with a variety of homologs. In less than a day, they studied 4 Ter and 3 AdhE homologs by using CFPS-based metabolic engineering framework. Also they demonstrated the possibility of using linear DNA templates  instead of plasmids for pathway prototyping , which would further expedite the process as the laborious cloning steps could be avoided. Finally, the n-butanol production was improved by 200% of the initial starting conditions (up to 1.5 g/L) by optimizing the performance of different enzymes' sets and adjusting the physicochemical environment.A key difference between the cell-free synthetic enzyme engineering and the CFPS-based metabolic engineering is that the laborious i.e., cell free protein synthesis and the SyPaB. In this case, it is possible to combine a CFPS model with the SyPaB models that are described in previous section to simulate and predict the performance of CFPS-based metabolic engineering. In spite of the unknown kinetic parameters of CFPS systems and the unclear composition of cell lysates in vitro protein synthesis and enabled the investigation of the polysome dynamics under the non-steady-state and non-continuum conditions e.g., peptide chain elongation Currently, no computational modeling approach has been reported to model the CFPS-based metabolic engineering framework e.g., transcription factors) by using the kinetic parameters with constant values e.g., CFPS experimental designs and properties of target proteins) and outputs , the trained algorithm can predict the outputs from system inputs with high accuracy. Although CFPS is already simplified from the in vivo protein synthesis, it still has highly non-linear regulations and large-dimensional impact factors for the protein production E. coli CFPS kits with fixed basic reaction system. Next, the authors chose 11 variable components in CFPS system and specified a vector of values for each component to build up a space of possible experiments. By using a robotic workstation for liquid handling, a larger number of CFPS experiments were implemented in a high-throughput manner When using kinetic models to simulate CFPS process, one of the major limitations is the ignorance of the transcriptional and translational regulations  used in the cell-free system, the large-scale biomanufacturing is too expensive even when producing high-value products. Additionally, when using cell-free synthetic enzyme engineering, the stability of the enzymes could cause the reduced productivity during biosynthesis. Nevertheless, novel strategies from synthetic biology and protein engineering are being developed to address both challenges. For example, Caschera et al. have coupled polyphosphate and maltodextrin for bypassing substrate level phosphorylation based on expensive energy sources (phosphoenolpyruvic acid (PEP) and 3-PGA) in vivo expression levels in vitro metabolic engineering remains a challenge to be demonstrated, a milestone of the scale-up of CFPS has been achieved to expression complex high valued proteins in a 100 L reactor in vitro optimized pathway back into the host cells might be a future direction to address this scale-up problem. However, issues of lethality, toxicity of some metabolic intermediates and the compartmentalization of some pathways in the eukaryotic organisms should be aware during this process and might need additional DBT cycles to further improve the productivity. Meanwhile, to simulate and guide the design of in vitro metabolic engineering, data-driven algorithms  represent promising approaches, especially with the fast and high-throughput biological measurements of experimental data in vitro metabolic engineering have been implemented and published, there is no database that curates these studies in a standardized manner, which obstructs the development of data-driven algorithms. The construction of such database requires both time and labors. However, it is still feasible to construct large-scale database including thousands of datasets in three to five years. With sufficient experimental data and appropriate data-driven algorithms, the internal complex interactions in the in vitro biological systems could be captured and explicitly elucidated in near future. It is worth noting that, biased data for training the data-driven algorithm will mislead the data-driven models. Therefore, using the equally distributed data to train the data-driven models is necessary to derive a data-driven algorithm with high prediction accuracy. To conclude, in vitro metabolic engineering, although still being on the infant stage, has great potentials in speeding up the DBT cycles of biomanufacturing and serves as an alternative approach to in vivo metabolic engineering.Compared to traditional


Scheffersomyces stipitis naturally produces ethanol from xylose, however reaching high ethanol yields is strongly dependent on aeration conditions. It has been reported that changes in the availability of NAD(H/+) cofactors can improve fermentation in some microorganisms. In this work genome-scale metabolic modeling and phenotypic phase plane analysis were used to characterize metabolic response on a range of uptake rates. Sensitivity analysis was used to assess the effect of ARC on ethanol production indicating that modifying ARC by inhibiting the respiratory chain ethanol production can be improved. It was shown experimentally in batch culture using Rotenone as an inhibitor of the mitochondrial NADH dehydrogenase complex I (CINADH), increasing ethanol yield by 18%. Furthermore, trajectories for uptakes rates, specific productivity and specific growth rate were determined by modeling the batch culture, to calculate ARC associated to the addition of CINADH inhibitor. Results showed that the increment in ethanol production via respiratory inhibition is due to excess in ARC, which generates an increase in ethanol production. Thus ethanol production improvement could be predicted by a change in ARC.The yeast  Scheffersomyces stipitis [S. stipitis is Crabtree-negative [S. stipitis, the yield of ethanol from xylose decreases with high oxygen supply and increases at low oxygen supply, nonetheless, productivity could drop in the latter case [Xylose fermentation to ethanol is essential to achieve economically viable biofuel production from lignocellulosic biomass –5. Schefstipitis  is a xylstipitis , 8, transtipitis , proteomstipitis  and genostipitis , 12. S. negative , 14 whicnegative –17. In Ster case , 19. HerS. stipitis is able to grow the equivalent of just one doubling time before growth and ethanol production stop [S. stipitis are in a narrow range of oxygen concentrations, so that an effective control of oxygen supply is required [S. stipitis has the proton translocating NADH dehydrogenase complex I (NDH1) and an alternative oxidase (AOX) in the respiratory chain, both absent in the Crabtree-positive yeast Saccharomyces cerevisiae [S. stipitis a higher oxidative capacity for NADH at the respiratory chain than S. cerevisiae [+) cofactors, several reports indicate that cofactor manipulation is a useful tool to improve fermentation [E. coli that an increase in NADH availability induces fermentation by stimulating pathways which are normally inactive under aerobic conditions [et al. [S. cerevisiae. Furthermore, Hou et al. [S. cerevisiae strain disabled for formate anabolism, and modified to overexpress the native NAD dependent formate dehydrogenase, were able to induce fermentation by increasing intracellular NADH concentration via formate supplementation.Also, at complete anaerobic conditions, ion stop , 21. Culrequired –28. S. srevisiae . This ferevisiae . Fermententation . In thisnditions . Besides [et al.  were ablu et al.  using a S. stipitis may prevent NADH excess via its high NADH oxidative capacity at the respiratory chain and by converting NADH to NADPH through a bypass at the tricarboxylic acids cycle (TCA) [S. stipitis may be able to use the arabinose assimilation pathway backwards, oxidizing NADH and producing polyols, in fact, polyol accumulation in S. stipitis is several times greater than in S. cerevisiae; 317 times for arabitol and 46 times for ribitol [S. stipitis by modifying enzymes of the respiratory chain increases yield even under aerobic conditions [+) redox pair affect the dependency between ethanol production and oxygen supply. The different phenotypic phases of S. stipitis metabolism had been studied in an experimentally validated core metabolic model [Since xylose metabolism yields a positive NADH balance, cofactor usage has been shown to be important on ethanol production when xylose is used , 36. S. le (TCA) , 11. Mor ribitol . On the nditions , 38, 39,et al, [S. stipitis. The metabolic models were obtained from genome-scale metabolic reconstructions [In-silico analyses made on S. stipitis were also performed on S. cerevisiae, which it is well-known for its ethanol production capacity. Also, deletions found in-silico by Acevedo et al, [S. stipitis to improve ethanol production were analyzed using the above mentioned tools.An useful way to study metabolic behaviour is by analyzing the flux distribution in the stoichiometric network, the method most extensively used to this purpose is the Flux Balance Analysis (FBA) , 41. Thret al,  proposedructions , Flux Baructions , 41 was ructions  was usedo et al,  in S. stIn-silico results indicated that inhibiting components of the respiratory chain modifies ARC and it allows to associate ethanol production to growth. Subsequently, S. stipitis batch cultures were performed adding respiratory inhibitors in order to improve yield. Batch culture essays showed that yield increases by 18% when inhibiting the mitochondrial NADH dehydrogenase complex I. Next, by using data from batch cultures and a kinetic model of the culture, uptakes rates were determined and were used as constraints in the FBA of S. stipitis in order to determine the ethanol sensitivities associated to batch cultures. Results showed that the observed increase in ethanol production via respiratory inhibition is related to an excess in ARC for biomass production and to a positive response of ethanol production to this excess.S. cerevisiae the IMM904 model [S. stipitis the IBB814 model [The following genome scale metabolic reconstructions were used: for 04 model  which ha14 model  which ha14 model , 48–53.et al., [To determine metabolic states FBA was used , 41. Looet al.,  method set al., , 51–53.The flux-carrying set (FCS) is defined as the all non-zero fluxes obtained from a FBA. The changes in the FCS are characterized by the fluxes which are increased, decreased, turned on or turned off due to a imposed modification in any given flux.Constraint-Based Reconstruction and Analysis) Toolbox version 2.0 [http://opencobra.sourceforge.net/.Computational methods were performed using Matlab2012a as programming environment. FBA routines were carried out using COBRA (sion 2.0 . TomLab/Phenotypic phase planes (PhPPs) analysis  was perfZ to a perturbation on the availability of a metabolite i. Each sensitivity is defined as:bi corresponds to the mass balance for metabolite i and super index r denotes a relaxation in the steady state. The sensitivity corresponds to the Lagrange multipliers associated to the dual optimization problem. These values were obtained from the solution vector of the dual problem (vector m × 1 where m is the number of metabolites) in the COBRA function optimizeCbModel. From de Z to an increment in Z decreases Z does not change Z  is define as:As described by , the senThe sensitivity of ethanol production to ARC changes S. stipitis batch culture was modeled using differential equations in order to represent biomass, xylose and ethanol mass balances. Thereby the culture model was defined as:x is biomass concentration (g⋅L−1). S: is xylose concentration (g⋅L−1) and P is ethanol concentration (g⋅L−1). Monod equation was used to describe specific growth as follows:μ is the specific growth rate (h−1), S is xylose concentration (g⋅L−1), Ks is the substrate saturation constant (g⋅L−1) and μmax is the maximum specific growth rate. The specific uptake rate of xylose was determined based on substrate usage mass balance as follows:S is the specific uptake rate of xylose (g⋅gcel−1 h−1), Yo is the theoretical yield of biomass from xylose, YP is the theoretical yield of ethanol from xylose and qP is the specific productivity of ethanol (g⋅gcel−1 h−1). The ethanol specific productivity was represented by the Luedeking and Piret equation:α (g⋅gcel−1) and β (g⋅gcel−1 h−1) correspond to the Luedeking and Piret coefficients. It was considered representing in the culture model the lag phase of growth and the delay of ethanol production. Thus two delay factors were added to the model as follows [Where  follows :fr1=q1qfr1 is the delay factor associated to growth, q1 is the delay parameter associated to growth, v1 is the exponent parameter associated to growth, fr2 is the delay factor associated to ethanol production, q2 is the delay parameter associated to ethanol production and v2 is the delay parameter associated to ethanol production.Where, CL is the oxygen concentration in the liquid (g⋅L−1), C* is the oxygen concentration at saturation in water at 30°C (g⋅L−1), kLa is the volumetric oxygen transfer coefficient (h−1), qO2 is the specific oxygen transfer rate (g⋅gcel−1 h−1), x is the biomass concentration (g⋅L−1). Assuming that under oxygen limiting conditions the magnitude of specific oxygen uptake rate is determined by the oxygen transfer rate since it is assumed that all oxygen transferred from gas to liquid phase is consumed, qO2 may be calculated as follows:Therefore, Eqs  and 8)  8) can bμmax, Ks, Yo, m, α, β, q1, q2, v1 and v2 were determined by least squares fitting of the equations to experimental data. Least squares method was implemented in Matlab2012a.The culture model parameters Yeast extract Peptone xylose) medium composed of 7 g⋅L−1 of yeast extract, 14 g⋅L−1 of peptone and 14 g⋅L−1 of xylose. The ethanol production medium consisted of 6.0 g⋅L−1 of yeast extract, 0.20 g⋅L−1 of (NH4)2SO4, 5.0 g⋅L−1 of KH2PO4, 0.40 g⋅L−1 of MgSO4⋅ 7H2O, 0.038 g⋅L−1 de CaCl2 and 15 g⋅L−1 of xylose. By adding HCl 2N and NaOH 2N, pH was adjusted to 5.0. All mediums and the fermenter were autoclaved at 121°C for 15 min.Inoculum propagation was conducted using YPX  was used for all batch cultures.The yeast Inoculum propagation was performed from cells previously stored in 1.5 mL cryovials at −80°C YPX medium with 50% V/V glycerol. The propagation was conducted in YPX medium Erlenmeyer flasks with 500 mL culture volume of 150 mL on a rotary shaker incubator  with temperature controlled to 30°C and agitation of 250 rpm.BioBundle Cultivation Systems w/ ez-control fermenter was used. Agitation was applied using an Rushton turbine impeller, the inlet air flow was controlled by a mass flow controller (MFC), pH was controlled by supplying NaOH 2 N with a peristaltic pumps integrated to the control system and temperature was controlled with a heating jacket. Relative oxygen concentration was measured using a polarographic oxygen sensor. Cultures were carried out at 30°C, 1 L of culture volume and pH 5.An Applikon-Biotechnology model Sampling was performed using a sample taking connected to a syringe of 5 mL. The biomass concentration was estimated by turbidimetry measuring absorbance at 620 nm in a spectrophotometer .μL were centrifuged at 12,000 rpm for 10 min, then supernatant was filtered through a membrane of 0.22 μm pore size and 200 μL were deposited in glass vials.From each sample 300 Infinity Quaternary LC System was used with UV/IR detector and automatic sample injection. A column Aminex BioRad HPX-97H and H2SO 5 mM as mobile phase were used, conditions were 0.6 mL⋅min−1 as flow and temperature of 45°C. Retention times were 9.5 and 21.3 min for xylose and ethanol, respectively.Xylose and ethanol concentrations were measured by high-performance liquid chromatography (HPLC). An HPLC system Agilent Model 1260 et al. [−1; a condition with kLa = 25 h−1 established at 250 rpm and another with kLa = 15 h−1 established at 150 rpm.The absorption method described by García-Ochoa et al.  which coμ) as a function of the oxygen (qO2) and xylose (qxyl) uptake rates. A single phenotypic phase is defined as the region where the derivative of μ with respect to qxyl and qo2 is constant. Each single phenotypic phase is associated to a particular active set of reactions (non-zero fluxes). On the other hand, each single point on a given phenotypic phase corresponds to a metabolic state, which is characterized by the flux values of the active set.Phenotypic Phase Plane (PhPPs) analysis  was usedμ the specific ethanol productivity (qetoh) and the ethanol yield from xylose (Yetoh/xyl) are shown over the PhPP as colored contours, respectively. Three relevant subsets of metabolic states can be distinguished on the PhPP (see i) The line of optimality for biomass production, which is represented by the dashed black line, where the uptake rates for oxygen and xylose are in exact proportion to allow maximum biomass yield. ii) The phenotypic phase to the right hand side of the dashed black line, where growth is limited by xylose uptake rate. iii) The phenotypic phases to the left hand side of the dashed black line, where oxygen uptake rate is below the value necessary to achieve fully aerobic growth and ethanol is produced. The dashed blue line between the contours of Yetoh/xyl in i and ii do not allow ethanol production, corresponding to fully aerobic metabolism. On the other hand, subset iii corresponds to fermentative metabolism since ethanol is produced.As shown in PhPP see : i) The etoh to changes on ARC is referred as i) Increasing ARC generates an increment in qetoh, then ARC limits ethanol production. ii) qetoh decreases when increasing ARC, thus ARC is in excess for ethanol production. iii) Changes in ARC do not affect qetoh. Whether case i, ii or iii occurs is determined as follows (see methods section for details): If The sensitivity of qS. stipitis using⋅glucose and xylose, respectively. The following regions can be observed starting from the line of optimality for biomass toward the left: First, a region where ARC limits ethanol production (g xylose  than wheg xylose  as carboetoh) and the PhPP for the ethanol yield from xylose of S. stipitis using⋅glucose and xylose, respectively. It can be seen that the line of optimality for ethanol production is over the line that divides the regions where On the other hand, RCetoh=0 .S. cerevisiae was also determined in order to compare it with the PhPP of S. stipitis. As S. cerevisiaeThe PhPP of S. stipitis, unlike S. cerevisiae, an increment in ARC does not always increase ethanol production,The sensitivity analysis of the both yeasts indicates that in et al. [in-silico reaction deletions able to associate ethanol production (qetoh) to biomass production (μ) in S. stipitis, all of them related to the respiratory chain. S. stipitis with the cytochrome oxidase (COX) and the alternative oxidase (AOX) deleted. It can be seen that the highest values for μ and qetoh coincide, the same was observed for the other deletions.Previous work by Acevedo et al. , reporteOn the other hand, sensitivity analysis showed that there is an excess of ARC for ethanol production through all the PhPP . Hence, S. stipitis were done. Inhibitors of the respiratory chain were added to batch cultures of S. stipitis using xylose at conditions of limited oxygen supply. Salicylhydroxamic acid (SHAM) was used to inhibit AOX, while potassium cyanide and sodium azide were used to inhibit COX. As shown in in-silico deletions corresponded to the respiratory chain, the inhibition of the NADH dehydrogenase complex I was also considered. As it can be seen in Experimental essays to determine whether the inhibition of the respiratory chain improves ethanol production in kLa = 25 h−1 and kLa = 15 h−1 were performed. kLa = 15 h−1 are shown in kLa = 25 h−1 are shown in etoh/xyl) increases by ∼24% at kLa = 25 h−1 and by ∼18% at kLa = 15 h−1 (etoh) decreased ∼41% and ∼35% at kLa = 25 h−1 and kLa = 15 h−1, respectively.Batch cultures in fermenter with limited oxygen supply considering = 15 h−1 . On the etoh are slightly higher in cultures with rotenone (red lines), this effect is more pronounced at kLa = 15h−1 . Solid lines in  = 15h−1 . Fig 7c ll cases .xyl and qO2 were plotted over the PhPP, this allowed to identify trajectories on the phenotypic phases. As neither xylitol nor acetic acid was detected under the studied conditions, the possible effect of the xylitol production on the balance of redox cofactors can be left aside. Then the effect of the respiratory chain, in this case the Complex I NADH Dehydrogenase, can be considered as the main contributor to changes in redox balance. μ and qetoh, respectively. All trajectories cover the three subsets previously defined over the PhPP, starting from subset ii  are associated to lower values for μ than the trajectories at kLa = 25 h−1 (solid and dashed red lines). According to the colored contours of the PhPP in La = 15 h−1 reaches a specific growth rate of about 0.035 h−1, while trajectories at kLa = 25 h−1 reaches about 0.06 h−1. These values predicted by the PhPP are close to the values for μ obtained from the batch cultures at kLa = 25 h−1 and at kLa = 15 h−1 shown in La = 15 h−1 reaches an ethanol specific productivity about 0.040 mmol⋅gcel−1⋅h−1 of, while at kLa = 25 h−1 about 0.050 is obtained over the PhPP. These values from the PhPP are close to the values for qetoh calculated from the cultures at kLa = 25 h−1 and at kLa = 15 h−1 shown in iii is shown in etoh is predicted if ARC increases. Therefore, considering that inhibiting complex I NADH dehydrogenase implies an increase in ARC, the latter sensitivity predicts that ethanol production should improve.With the purpose of characterizing the metabolic states associated to the batch cultures, the values of qetoh but it does not characterize subsequent metabolic states with an inhibited complex I. Then it is necessary to analyze the subsequent metabolic states considering the inhibition of complex I, in order to verify whether changes in sensitivities are consistent with states favoring ethanol production. To this purpose, qxyl and qO2 trajectories were used as constraints for the FBA and the inhibition were simulated by applying a constraint on the upper bound of the flux associated to complex I. Thus by using FBA metabolic states were successively calculated and the sensitivities associated to the trajectories were obtained. Along with The results shown in hibition , meaningChanges in the flux-carrying set (FCS) were analyzed in order to explain a change in the ethanol production generated by the inhibition of the CINADH. The FCS of a metabolic state with inhibited the CINADH was compare against the FCS of a metabolic state without inhibition in order to obtain the changes in the FCS caused by the inhibition of the CINADH.According to the analysis made, when CINADH is inhibited, NADH becomes available to other reactions, including ethanol production reaction. This does not mean that ethanol flux would increase significantly because its production is dependent as well on carbon flux. in-silico in S. stipitis and S. cerevisiae. Flux Balance Analysis (FBA) was used to characterize reachable metabolic states in both yeasts at different glucose and oxygen uptake rates. These states are represented as a Phenotypic Phase Plane (PhPP). Metabolic states, ranging from aerobic growth with no ethanol production to metabolic states with low oxygen availability and high ethanol production, using xylose as carbon source were determined (The effect of the Available Reducing Capacity (ARC) on ethanol production rate was studied termined .S. stipitis; one in which ARC is in excess, another where ARC has no effect on ethanol production, and a third where ARC is limiting the ethanol production . Based on the obtained results, three regions were identified for oduction . Whereasoduction . Therefoe et al,  have preterature , 44, altterature –60. Hencet al, [S. stipitis, were analyzed using PhPP and sensitivity analysis. The resulting PhPP and its corresponding sensitivities showed that partial inhibition of the reactions reported by Acevedo et al, [S. stipitis for ethanol production. Consequently, inhibitory essays in batch cultures were done in order to verify the effect of inhibiting the respiratory chain. Values for kLa of 25 h−1 and 15 h−1 were considered, which are equivalent to OTR values of 5.9 and 3.5 mmol O2 L−1⋅h−1, respectively. These microaerobic conditions allowed to reach yields between 0.16 g⋅g−1 and 0.32 g⋅g−1. [2 L−1⋅h−1 to reach a yield of 0.29 g⋅g−1. On the other hand, [2 L−1⋅h−1 as optimal to reach a yield of 0.40 g⋅g−1. [2 L−1⋅h−1 to achieve a yield of 0.39 g⋅g−1. In the present work rotenone addition allowed to increase yield from 0.16 g⋅g−1 to 0.20 g⋅g−1 at an OTR of 5.857 mmol O2 L−1⋅h−1 and from 0.32 g⋅g−1 to 0.38 g⋅g−1 at an OTR of 3.514 mmol O2 L−1⋅h−1. Although yields were not higher than those reported by other authors, increasing ethanol production through respiratory inhibition can be considered as a complementary strategy to aeration control. [S. stipitis generates an increment of 40% in ethanol specific productivity and a decrease of 33% in maximum specific growth rate, final yields were not reported. Also, [S. stipitis, they reported an increment of 10% in final ethanol concentration.Reaction deletions reported by Acevedo et al,  aiming to et al,  generate2 g⋅g−1.  suggesteer hand,  reported0 g⋅g−1.  suggestecontrol.  reportedd. Also,  studied μ and qetoh over the PhPP were close to the ones obtain from the cultures (Figs λ ≤ 0 in Trajectories over the PhPP were obtained using the uptake rates calculated from the kinetic model, it was observed that the values for res Figs  and 9. AS. stipitis, to evaluate the effect of available reducing capacity on ethanol production. The inhibition of NADH dehydrogenase complex I with rotenone leads to an increase in ethanol production in batch cultures and it can be characterized by means of the PhPP and sensitivity analysis of the S. stipitis genome scale metabolic network. The analysis shows that cofactor availability, measured as the sensitivity to available reducing capacity In this work sensitivity analysis of genome-scale metabolic modeling and phenotypic phase plane analysis were used to characterize metabolic response on a range of uptake rates in