The model was then used to probe the cells metabolic state and dynamics, performing a dynamic metabolic flux analysis (dMFA) study. Indeed, MDSCs maturation correlates with a high glycolytic flux contributing to up to 95% of the global ATP turnover rate, while most of the glucose-derived carbon enters the TCA cycle. Model simulations also reveal that pentose phosphate pathway and oxidative phosphorylation activities were kept at minimal levels to ensure NADPH production and anabolic precursors synthesis. Surprisingly, MDSCs immunosuppressive activity, i.e. L-arginine uptake, metabolism and endogenous synthesis, only consumes sparse quantities of energy-rich nucleotides (ATP and NADPH). Therefore, model simulations suggest that MDSCs exhibit a heterogeous metabolic profile similar to tumour cells. This behavior is probably an indirect immunosuppressive mechanism where MDSCs reduce the availability of carbon sources in the tumour periphery microenvironment, which could explain the dysfuntion and death of immune effector cells.Recent years have witnessed an increasing interest at understanding the role of myeloid-derived suppressor cells (MDSCs) in cancer-induced immunosuppression, with efforts to inhibit their maturation and/or their activity. We have thus modelled MDSCs central carbon metabolism and bioenergetics dynamic, calibrating the model using experimental data on  One of the mechanisms of immunotherapy specifically targets the inhibition of myeloid-derived suppressor cells (MDSCs) maturation and activity. Briefly, MDSCs are a heterogenous cell population of macrophages, dendritic cells, granulocytes, to name only the most abundant cell types2. These cells metabolize the semi-essential amino acid L-arginine through the enzymatic activities of arginase 1 and inducible nitric oxide synthase 3. Both the sparse availability of L-arginine and the accumulation of nitric oxide derivatives  trigger the suppression of specific anti-tumour immune response4. Despite the considerable progress achieved in the comprehension of MDSCs biology that has resulted in multiple therapeutic approaches proposed to overcome MDSCs-mediated tumor escape, the metabolic events supporting MDSCs immunosuppressive potential are still ambiguous and poorly investigated.The invasiveness of conventional anti-cancer therapies appeals to the establishment of novel therapeutic strategies. Specifically, the immunotherapy approach is gaining in interest as it showed promising results in the treatment of cancer of various types5. Likewise, LPS-activated M1 macrophages display enhanced glycolytic metabolism and reduced mitochondrial activity6. Therefore, we concentrated our efforts to study the central carbon metabolism and bioenergetics of MDSCs, investigating their specific requirements to acquire an immunosuppressive phenotype8. The analysis of intracelullar metabolites concentration suggested that MDSCs exhibit a glycolytic metabolism and a high TCA cycle activity providing the immunosuppressive mechanisms with energy-rich nucleotides and carbon intermediates.Previous work on the activation of dendritic cells with lipopolysaccharides  showed that cells undergo a PI3K/Akt-dependent metabolic transition from oxidative phosphorylation to aerobic glycolysis11. However, such approach, as well as other metabolic modelling approaches, have not been applied yet to study the immunosuppression phenomenon, to the best of our knowledge. We thus propose here to apply a modelling approach11 previously validated on Chinese Hamster Ovary (CHO) cells11, on the maturation process of bone marrow cells into MDSCs, and thus perform dMFA to unreaveal metabolic traits of MDSCs’ maturation process.Dynamic metabolic flux analysis (dMFA) have shown being useful, as a quite unique tool, to describe extracellular and intracellular nutrients and metabolites concentration as well as fluxes transient behavior in mammalian cells8. We are thus first presenting a descriptive model as well as evaluating its simulation capacity. Sensitive kinetic parameters and their confidence intervals were determined to identify the key kinetic parameters and biochemical reactions to be considered as potential targets for modulating MDSCs maturation and immunosuppressive activity. Then, a dMFA study was performed through model simulations for evaluating MDSCs metabolic performance.The dynamic metabolic model combines the central carbon metabolism (CCM) and cell energetics, redox states and metabolic regulation mechanisms, and includes MDSCs immunosuppressive machinery (ARG1 and iNOS enzymes) and L-arginine endogenous synthesis reactions. The model was calibrated on experimental data of extra- and intracellular metabolites generated in a previous work13.Model simulations confirmed the hypothesis that MDSCs maturation correlates with a high glycolytic flux sustaining ATP turnover and also supporting a high TCA cycle activity, with low but not suppressed pentose phosphate pathway and oxidative phosphorylation activities generating NADPH and anabolic precursors. Interestingly, MDSCs immunosuppressive machinery reveals not being energetically costly, while the overall nutritional profile and metabolic flux studies showed that MDSCs mimic tumour cells’ metabolic plasticity8 where experimental settings are well detailed.Experimental data used herein to verify the accuracy of the metabolic model were taken from our previous work2.Briefly, BM cells were extracted from 6- to 8- weeks old C57BL/6 mice . Animal experimentations were performed in accordance with the Canadian Council on Animal Care guidelines and the protocol was approved by the Université de Montréal’s ethical committee. Mice were kept under specific pathogen–free conditions prior to euthanasia by CO2 and 37 °C incubator. The maturation of BM-derived MDSCs was performed by culturing BM cells for 96 hours in the supplemented medium mentioned above in the presence of 40 ng/mL of GM-CSF (granulocyte macrophage - colony stimulating factor) and 40 ng/mL IL-6 (Interleukin-6) as shown by Marigo et al.14.BM cells were cultured in RPMI1640 medium  supplemented with 10% (v/v) irradiated fetal bovine serum , 1 mM Sodium Pyruvate (Sigma), 50 µM β-Mercaptoethanol (Sigma), 100 U/mL Penicillin, 150 U/mL Streptomycin (Cedarlane) in a 5% COIt should be noted that the term “BM-derived MDSCs” is used when referring to experimental and simulation results on BM cells derived into MDSCs. When referring to literature, the more general term MDSCs is used because it is the terminology normally accepted for these cells.et al.15.Every 24 hours, extracellular nutrients and metabolites in medium were measured using a dual-channel immobilized oxidase enzyme biochemistry analyzer  and metabolites were extracted using cold methanol and sonication on ice method modified from Kimball 4OAc at pH 7.0. Organic acids concentrations were assessed using the above-mentioned UPLC-MS/MS system with a Hypercarb column . Mobiles phases were 20 mM amoonium acetate at pH 7.5 and 10% (v/v) methanol in water.Nucleotides concentrations were measured using a 1290 UPLC system coupled to a 6460 triple quadruple mass spectrometer. A Symmetry C18 column  was employed for the separation. Mobile phases consisted of 10 mM ammonium acetate with 15 mM DMHA  at pH 7.0 and 50%/50% (v/v) acetonitrile, 20 mM NH17 and CHO cells18 where it showed satisfactory results simulating experimental data. The global procedure for model development is described as Supplementary material. Briefly, we have here developed a model integrating metabolic pathways known to be expressed in MDSCs, by applying a modelling approach thus previously validated with eukaryotic cells. Basically, the network of biochemical reactions was first built including central carbon metabolism as well as biochemical reactions known to be of major importance in MDSCs. Metabolic flux kinetics and regulation mechanisms were then described, based on literature on MDSCs when available, or on previous works on animal cells. The model was then describing known metabolic network in MDSCs as well as it was anchored specifically onto MDSCs by performing model structure calibration and parameters value identification thanks to experimental in vitro metabolomic data with these cells.The construction steps while building the structure of the metabolic model presented in this study is based on previous models describing the central carbon metabolism of plant3. Furthermore, the catabolic pathways related to the amino acids metabolism  were also considered since these are known to contribute as carbon source. Therefore, the model describes major inputs, such as major nutrients, and outputs, such as cell growth and the management of cell metabolites from the immunosuppressive phenomenon. This biosystem network is thus highly simplified but it considers the major set of interlinked biochemical reactions allowing to describe MDSCs behaviour.The model metabolic network Fig.  see nom represenFlux kinetics consider the effect of substrates as well as co-factors (i.e. energetic and redox nucleotides).19. Therefore, intra- and extracellular metabolites that are consumed or secreted were taken as being at the same concentration across the cell membrane; a reliable assumption we have demonstrated in past works dealing with similar models18.Except for the amino acids alanine, asparagine, aspartate, glutamine and glutamate, all other extracellular metabolite concentrations are considered higher that the affinity constants of associated cell membrane transportersThe reactions of the adenylate kinase and ATPase enzymes are used to balance the time evolution of AMP, ADP and ATP energetic shuttles, with the synthesis of AMP being described.+, NADH and NADP+, NADPH. NAD+ synthesis is thus described, from which NADP is synthesized.The model assumes non-constant energetic metabolites concentration: NAD+/NADPH and NAD+/NADH, respectively.Enzymatic activity of the NADPH oxidase and the mitochondrial proton leak phenomenon are employed to balance the time evolution of NADP9.The stoichiometry of the reaction describing cell division and growth was based on average cell composition for CHO cells, since this information for MDSCs was not availableMoreover, the modelling strategy is based on the following considerations/assumptions:20. The fluxes are modeled by multiplicative Michaelis-Menten type equations and a factor of non-competitive inhibition is added when metabolites show inhibitory effects. Nutrients and metabolites as well as cell initial concentration (i.e. at t = 0) are presented in Table The final  dynamic model includes 44 reactions Table  and mass11 (and from references therein), a first sensitivity analysis was performed on the resulting model, aiming to identify the most critical (i.e. sensitive) parameters. In brief , because of the large number of parameter values to be determined, an iterative procedure including manual trials on sub-groups of parameters and a model simulation global error minimization step, allowed identifying optimal model parameter values. Sensitivity analysis of model parameters on the model acuity to simulate experimental reality was performed as follow. Values of model parameters were changed by ±5 or 10%, once at a time, from their optimal value, and the normalized sum-squared differences were calculated as described below. A model sensitivity analysis on initial conditions has also been performed to evaluate the effect of measurement errors. Using the parameter values determined in a previous work on CHO cellsFinally, the 95% confidence intervals on the estimated model parameters were computed, using the Matlab functions “nlinfit.m”, “nlparci.m” and “nlpredci.m”. Following the parameter estimation step (“nlinfit.m”), the confidence intervals were computed for the estimated model parameters (“nlparci.m”). The 30 most sensitive parameters Table  have beeThe resulting metabolic model has then been used to simulate metabolites concentration with time, supported Fig.  or not  with exp11. Taking all of the above, the model structure was not reduced.The sensitivity analysis Fig.  procedur18. In complement, we also observed that the model is less sensitive to errors on initial conditions . These TDFs indirectly induce a similar metabolic behavior to tumor cells and this is further confirmed in this work from the dynamic metabolic flux analysis (dMFA) performed using our model.Dynamic metabolic flux analysis confirms MDSCs mimick tumour cells metabolic profile. Indeed, model simulation of experimental data confirm that BM-derived MDSCs exhibit a high glycolytic activity level as revealed by the continuous increase of the flux through pyruvate kinase Fig. . Moreove23. The high glycolysis-TCA cycle activity is probably associated to the acquisition of the immunosuppressive phenotype, and the maintenance of inflammatory environment at the tumour edge, by supporting enzyme and surface markers expression, cytokine/chemokine synthesis, etc. In addition, some of the TCA cycle intermediates have roles other than being a carbon source. For instance, the accumulation of fumarate and succinate stabilizes hypoxia induction factor 1α24 that was shown to be expressed in MDSCs to adapt to low oxygen tension in tumour25. Since BM-derived MDSCs exhibit a low growth rate during the 96 hours of the study, a longer culture period or the analysis of flux distribution in in vivo generated MDSCs is required to confirm if this behavior, i.e. high TCA cycle activity, is associated to proliferative purposes as previously reported in cancer cells22. Moreover, the analysis of the G6P branch point shows that only 20% of the glucose-derived carbon enters the pentose phosphate pathway, since the ratio of the flux through G6PDH to the one through HK was kept constant at 20%  only contributes sparsely to the replenishment of TCA cycle intermediates Fig. . However+ and superoxide (O2−), the latter being a precursor for the generation of immunosuppressive species ROS and RNOS28. A similar decreasing trend from 6 to 1% was simulated for the percentage of ATP consumed by the L-arginine metabolism  do not use considerable quantities of ATP and NADPH. In fact, the percentage of NADPH used by the L-arginine metabolism is decreasing from 5 to 1% Fig. . This deism Fig. . These fism Fig.  on the 1in silico platform thus allows prospecting MDSCs dynamic behaviour under stimulation or inhibition conditions, as well as it represents a complementary tool to existing tools for studying the immunosuppression phenomenon29. Cell metabolomic studies provide new and complementary insights enabling to better understand the immunosuppression phenomenon, and such dynamic model may be useful to the identification of novel biomarkers as well as to define therapeutic strategies to modulate this phenomenon. The validation of the predictive capacity of the model will, however, be possible with the accumulation of large experimental data sets, as well as further implementing the metabolic network. This first modelling work on MDSCs thus brings a basic framework for the study of the immunosuppression phenomenon, with the most sensitive parameters as keys of further model development.This work on model simulation of MDSCs maturation, used a wide set of experimental data for extra- and intracellular metabolites concentration to develop a descriptive and predictive dynamic metabolic model. The model then enabled performing a dMFA study, which results allowed highlighting the carbon distribution dynamics during MDSCs maturation and also in fully mature and active cells. Mainly, MDSCs revealed to be dependent on the glycolysis and the glucose-derived carbon to ensure high central carbon metabolism activity and sustained production of ATP. Pentose phosphate pathway and oxidative phosphorylation showed being active at the minimal level allowing to replenish the NADPH pool and anabolic precursors. This metabolic behaviour was not justified by specific requirements of MDSCs immunosuppression machinery neither at the carbon intermediate or energy level. However, this metabolic profile indirectly favors tumour invasiveness and shall be considered as a target for anti-cancer immunotherapy. In this work, the interpretation of carbon flux distribution simulated by the model leads to the identification of the specific metabolic signatures of mature MDSCs that are directly associated to the immunosuppression phenotype. This Supplementary information


Oenococcus oeni, the bacterium responsible for the malolactic fermentation (MLF) of wine, is still scarcely understood. Here, we characterized the global metabolic response in O. oeni PSU-1 to increasing ethanol contents, ranging from 0 to 12% (v/v). We first optimized a wine-like, defined culture medium, MaxOeno, to allow sufficient bacterial growth to be able to quantitate different metabolites in batch cultures of O. oeni. Then, taking advantage of the recently reconstructed genome-scale metabolic model iSM454 for O. oeni PSU-1 and the resulting experimental data, we determined the redistribution of intracellular metabolic fluxes, under the different ethanol conditions. Four growth phases were clearly identified during the batch cultivation of O. oeni PSU-1 strain, according to the temporal consumption of malic and citric acids, sugar and amino acids uptake, and biosynthesis rates of metabolic products – biomass, erythritol, mannitol and acetic acid, among others. We showed that, under increasing ethanol conditions, O. oeni favors anabolic reactions related with cell maintenance, as the requirements of NAD(P)+ and ATP increased with ethanol content. Specifically, cultures containing 9 and 12% ethanol required 10 and 17 times more NGAM (non-growth associated maintenance ATP) during phase I, respectively, than cultures without ethanol. MLF and citric acid consumption are vital at high ethanol concentrations, as they are the main source for proton extrusion, allowing higher ATP production by F0F1-ATPase, the main route of ATP synthesis under these conditions. Mannitol and erythritol synthesis are the main sources of NAD(P)+, countervailing for 51–57% of its usage, as predicted by the model. Finally, cysteine shows the fastest specific consumption rate among the amino acids, confirming its key role for bacterial survival under ethanol stress. As a whole, this study provides a global insight into how ethanol content exerts a differential physiological response in O. oeni PSU-1 strain. It will help to design better strategies of nutrient addition to achieve a successful MLF of wine.The effect of ethanol on the metabolism of  L-malate into L-lactate, a reaction that decreases wine acidity. Oenococcus oeni is the main bacterial species that carries out the MLF, due to its ability to grow under the harsh conditions present in wine, such as high ethanol content (>13% v/v), low pH (<3.5), and high sulphite concentration (<50 ppm)  performed by lactic acid bacteria (LAB). MLF allows wine deacidification and improves flavor complexity and microbiological stability . This pr<50 ppm) . Howeverdictable .Survival of microorganisms under stress conditions requires the maintenance of the main functions of the cell membrane, which is essential to control ion permeability and to regulate solute exchange between the cell and the external medium. In wine, ethanol is considered the main stressor, because it can injure cell membrane integrity and impact cell viability.O. oeni survival in wine have focused on the deleterious effect of ethanol on the integrity of cell membrane and changes in the cell wall composition. O. oeni strains able to express a functional gtf gene – involved in exopolysaccharide synthesis - were more resistant to ethanol and other stressors. Other authors determined that cells grown in the presence of 8% v/v ethanol modified the membrane fatty acid profile, resulting in an increment of membrane cyclopropane fatty acids (CFAs) (cfa gene. The correlation between cfa induction and ethanol resistance has been demonstrated in O. oeni cells (Most studies on s (CFAs) . The bioni cells .O. oeni under winemaking conditions is still scarcely understood. Indeed, several related studies did not include ethanol in the culture medium  analysis. The results show that strains are not genetically adapted to regions but to specific types of wines. More recently, O. oeni strains isolated from Burgundy, identified two different O. oeni lineages associated to either red or white wines in this French region.Besides, several studies have grouped O. oeni strains cultivated in wine-simulated cultures showed that the environment strongly affects O. oeni stress responses at this level. Transcriptomic and proteomic analyses of Escherichia coli and Saccharomyces cerevisiae; since then, its use has been expanded to other, less characterized species, as well as to complex biological systems. Nowadays, genome-scale models are widely used for studying the metabolism of numerous organisms, including LAB such as Lactococcus lactis  have been reconstructed, which has been of substantial help in the study and applications of corresponding biological systems. Initially, GSMM have considered only highly characterized organisms, such as s lactis , Lactobalantarum , Streptomophilus  and, morfaecalis  and Strepyogenes . These mi strain . We repoO. oeni’s growth at different levels of ethanol; then, we characterized the evolution of different nutrients and metabolic products during the fermentation. Finally, taking advantage of the recently constructed genome-scale metabolic model (GSMM) of O. oeni PSU-1 strain (named iSM454)  , we predOenococcus oeni (® BAA-331TM) was obtained from the American Type Culture Collection (ATCC) . Cryogenically preserved (-80°C) strains were cultured and maintained on MRS plates    and storO. oeni PSU-1 preculture was prepared from a frozen stock by inoculating 100 ml Erlenmeyer flasks containing 75 ml MRS medium supplemented with 0.5 g L-1 of cysteine. Before inoculation, the cells were subjected to ethanol adaptation. For this purpose, we serially passaged every culture, starting from 1% ethanol v/v to reach 0, 3, 6, 9, or 12% v/v ethanol concentration in each culture. O. oeni’s cells were inoculated in each culture medium to achieve an initial optical density at 600 nm (OD600) of approximately 0.1. When the cultures reached OD600 = 0.2, they were transferred to other cultures containing higher ethanol content.An O. oeni to grow in the presence of ethanol. However, the concentration of carbon and nitrogen used in MaxOeno was higher than that found in wine, because these concentrations allowed the growth of O. oeni avoiding its arrest or slowing. The vitamins and minerals were those of The developed chemically defined culture medium, named MaxOeno, was designed with the aim of simulating the wine environment, allowing -1) with the aim of studying their metabolic fate in O. oeni. Meanwhile, malate and citrate, the main organic acids in wine, were included at a concentration of 5 and 1 g L-1, respectively. We verified from the literature that these concentrations were not inhibitory for the bacterium, and that they also allow bacterial cells to grow in the presence of ethanol  glucose, fructose, malate and citrate. Glucose and fructose are the main residual sugars present in wine. Both were added in equal concentrations  0.4, magnesium sulfate 1.3, fructose 12.5, glucose 12.5, dipotassium phosphate 2.0. Tween 80 was also added at 1 ml L-1, as well as a nitrogenous bases solution, 100 mL L-1; a mineral salts solution, 5 ml L-1; and a vitamins solution, 1 ml L-1.The MaxOeno culture medium contained, in g L-1: thiamine, 1; biotin, 1; nicotinic acid, 2; pyridoxine hydrochloride, 2; C-D-pantothenate, 2; folic acid, 1; choline chloride, 2; riboflavin, 1; 4-aminobenzoic acid, 0.1; cyanocobalamine, 0.1; and xanthine, 5. The nitrogenous bases solution contained: adenine sulfate, 0.5; uracil, 0.5; cytosine, 0.5; thymine, 0.5 and guanine, 0.5. Mineral salts solution contained: MgSO4∗7H2O, 60; FeSO4∗7H2O, 12; CuSO4∗5H2O, 0.015; and ZnSO4∗7H2O, 0.135. The vitamin solution was sterilized by membrane filtration .The vitamin solution contained the following, in g L-1 of assimilable nitrogen prepared with the following amino acids, in g L-1: L-arginine 0.4, L-serine 0.24, L-threonine 0.27, L-glutamic acid 0.33, L-aspartic acid 0.3, L-lysine 0.33, L-asparagine 0.3, L-leucine 0.30, L-glutamine 0.50, L-alanine 0.2, cysteine 0.54, glycine 0.27, histidine 0.53, isoleucine 0.30, methionine 0.34, phenylalanine 0.37, proline 0.67, tryptophan 0.46, tyrosine 0.41 and valine 0.27.Besides, the culture medium was supplemented with 1,060 mg LBefore sterilization, the pH of the medium was adjusted to 4.8 using KOH.Ethanol-adapted cells were inoculated in 100 mL flasks containing 70 mL of MaxOeno culture medium to achieve an initial optical density at 600 nm (OD600) of approximately 0.2. The flasks were of glass, airtight and with a sampling port. The cultures were incubated at 25°C, without stirring. Samples were collected aseptically through the flask sampling port, and bacterial growth was estimated by OD600, the optical density of the culture measured at 600 nm. At the same time, the content from each flask was centrifuged; the supernatant was collected and frozen at -20°C, for future chemical analyses.600 versus dry weight (g L-1). The latter was previously carried out as described in The biomass was determined as dry weight of cells through a calibration curve of ODL-lactate, D-lactate and amino acids were quantified by UHPLC/MS using a Dionex unit model Ultimate-3000  coupled to a mass spectrometer ExactiveTM plus . The UHPLC system was controlled using the XcaliburTM 2.13 software . The methods utilized for compound identification and quantification are detailed below:O,O′-diacetyl-L-tartaric anhydride solution (≥97%) (DATAN)  . The samples were incubated for 40 min at 75°C, under agitation. Subsequently, the samples were dried and reconstituted in 200 μl of a solution of acetonitrile and water (1:2). L-lactate and D-lactate (≥98%)  were used as external standards. A 10 μl derivatized sample was injected in the equipment and separated using a UPLC BEH C18  analytical column at a flow of 0.5 ml min-1 and oven temperature of 31°C. Solvents used for separation were: solvent A: ammonium formate 1.5 mM, pH was adjusted at pH 3.6 using formic acid. Solvent B: acetonitrile.Fifty microliter of sample were dried and then derivatized by adding 50 μl of (+)-® 100 RP-18 (5 μm) (Merck) analytical column with a flow of 0.35 ml min-1 and oven temperature of 30°C. Solvents used for separation were: solvent A: formic acid 0.1% v/v. Solvent B: methanol.A 10 μL sample was directly injected in the equipment and separated using a LiChrospherA 50 μl sample was derivatized using 100 μl 5,5′-dithiobis(2-nitrobenzoic acid) (Ellman’s reagent) . The reagent solution for derivatization was prepared using 4 mg Ellman’s reagent dissolved in 10 ml buffer phosphate 0.01 M (pH 7.0). All samples were derivatized at the time of being taken. The chromatographic conditions were the same of those employed for amino acids analysis.L- + D-) lactate and alcohols  were separated and quantified in a Lachrom L-700 HPLC system  equipped with Diode Array and Refractive Index detectors . An Aminex HPX-87H ion exchange column  was used, as described previously , organic acids  where a pseudo steady state is accomplished. Thus, we modeled each of the three differentiated growth phases observed during growth of O. oeni PSU-1 . However, experimental results suggested that intracellular accumulation of some metabolites occurred during growth, and thus the assumption of no accumulation could not be applied in this case .Genome-scale metabolic models can be used to find flux distributions under the assumption of steady state. In steady state, the concentration of intracellular metabolites remains constant and all the mass produced must be consumed in order to fulfill the mass balances. No accumulation of intracellular metabolites is allowed. GSMMs can also be used to model the exponential phase . To determine the variation range of these accumulation reactions, an extended model was built, able to simulate simultaneously the three growth phases, in which the assumption of S∗v = 0 was still valid . This extended model was employed to calculate the maximum and minimum flux that these accumulation reactions were able to carry under the restrictions fixed by the experimental results. Then, these constraints were included in the iSM454 model and used for further analysis.In the most widely used approach, Flux Balance Analysis, a matrix S, which summarizes the biochemical reactions occurring in a metabolic network, is used. In this matrix, the stoichiometric coefficients are used to describe each reaction. The assumption of steady state indicates that mass balances must be accomplished, which is equal to state SOenococcus oeni PSU-1 recently developed by our group was employed in this work ; thus, this approach was used here for modeling each of the four differentiated growth phases (three growth phases + stationary phase) observed during growth of O. oeni PSU-1 . However, experimental results suggested that intracellular accumulation of some metabolites occurred during growth, and thus the assumption of no accumulation, as well as the restriction S∗v = 0, could not be applied in this case , unless the accumulation was represented in the model. Therefore, to include the latter in the S matrix, accumulation reactions were added in the form of sink reactions for phases II and III, and demand reactions for phases I and II . To determine the variation range of these reactions, an extended model was built, able to simulate simultaneously the three growth phases, in which the assumption of S∗v = 0 was still valid . This extended model was employed to calculate the maximum and minimum flux that these accumulation reactions were able to carry under the restrictions fixed by the experimental results. Then, these constraints were included in the iSM454 model and used for further analysis.The GSMM of his work . GSMM alFigure 1, step 4). This model was used to determine the maximum and minimum fluxes required through the accumulation reactions so that each independent phase simulation was able to fulfill the S∗v = 0 assumption. This extended model possesses a stoichiometric matrix Z, which contains three “S” sub matrices , each associated to a specific growth phase. These “S” sub matrices were taken from iSM454 and represent three independent problems solved simultaneously in the extended model. Then, to allow interaction between these three problems and thus lose independency, accumulation reactions were added, which allow flux to pass from phasen to phasen+1. These were selected based on experimental results, and corresponded to the following compounds: mannitol, malate, cysteine, threonine, phenylalanine, and valine.An extended model was generated to allow simultaneous simulation of the three phases observed for each ethanol level  were set in the extended model, according to the corresponding phase, with experimental values: the substrates glucose, fructose, citrate, malate, cysteine, threonine, valine, phenylalanine and serine; and the products mannitol, erythritol, L-lactate, D-lactate and acetate . To minimize experimental error, linear regressions were determined for each of the metabolites at each growth phase and used to predict the phase’s final value. Initial values were taken from the initial concentration of the experiment for phase I, and from the final values of the former phase for phases II and III.To determine the maximum and minimum values that the accumulation reactions were able to carry under the restriction of the experimental constraints, the following fluxes  was carried out to determine the maximum and the minimum flux that each accumulation reaction was able to carry under these constrained conditions.Figure 1, step 5), to represent the mass difference observed experimentally as an input or/and output of the system. From now on in the text, every time the iSM454 model is mentioned, it will refer to the model in which the S’ matrixes are included.When the ranges of the accumulation reactions were determined, the extended model was divided into three S’ matrix, each one representing one of the growth phases. These were used to carry out all the further analysis. Each of these matrixes include the S matrix from iSM454, and also a set of the accumulation reactions as sink reactions for phases II and III, and demand reactions for phases I and II (-1 h-1) were set with experimental values: the substrates glucose, fructose, citrate, malate, cysteine, threonine, valine, phenylalanine and serine; and the products mannitol, erythritol, L-lactate, D-lactate and acetate. The accumulation reactions were constrained with the ranges determined with the extended model. Also, experimental biomass and NGAM estimated in this work were fixed.Flux balance analysis was carried out in the iSM454 model (with the S’ matrixes) to analyze each of the different growth phases at each ethanol level. In order to do so, the following fluxes H utilization. In O. oeni, ATP is produced through the F0F1-ATPase and/or through three reactions of the phosphoketolase pathway that involve the following enzymes: acetate kinase, pyruvate kinase and 3-phosphoglycerate kinase. To quantify the NAD(P)+/NAD(P)H utilization, the reactions that produce NADH and NADPH were analyzed, that correspond to reactions that involve the following enzymes: malate dehydrogenase, glyceraldehyde-3P dehydrogenase, threonine dehydrogenase, NADH quinone reductase, NAD(P)+ transhydrogenase and the pathway for methylglyoxal degradation for NADH formation; and Glucose-6P dehydrogenase, phosphogluconate dehydrogenase and GMP reductase for NADPH formation. This knowledge was considered for the determination of the maximum intracellular fluxes for NAD(P)+/NAD(P)H and ATP synthesis at different growth phases.The flux distribution obtained through FBA was used to quantify ATP, and NAD(P)0F1-ATPase. To assess variation of ATP, NADH and NADPH, the sum of the reactions described above was subjected to FVA for each cofactor.Flux variability analysis was carried out by maximizing and minimizing the flux under the same constrained conditions used for FBA. This technique was applied to each of the accumulation reactions present in the extended model, and in iSM454 to analyze changes of production of ATP, NADH, NADPH; and separately, in FO. oeni PSU-1, that includes: phosphoketolase pathway, fructose reduction, citrate degradation and MLF. It also included energetic reactions such as F0F1-ATPase, NGAM, and the pathways for amino acid degradation present in O. oeni PSU-1, which correspond to serine, threonine and cysteine. A complete list of the reactions included is shown as Supplementary File S1.As our culture medium mimics the wine composition, it contains different carbon sources and thus it is difficult to elucidate which substrate is being used to synthesize a particular product. Therefore, we used EFMA to find which metabolic products can be generated from each substrate, separately. EFMA was carried out with CellNetAnalyzer version 2017.4 . In ordeTo determine the flexibility of the metabolic network under ethanolic conditions, a Random Sampling Analysis was conducted. In this analysis, the solution space is uniformly sampled, and flux distributions can be explored to assess the flexibility of different pathways. In contrast to EFMA, random sampling can be conducted in the range of minutes to hours for large metabolic network resulting in a valuable tool to explore the solution space without requiring the use of a reduced model.Flux distributions were calculated using optGpSampler . For eacO. oeni to grow in the defined, wine -like culture medium designed here, named MaxOeno, was followed by biomass concentration  and specific growth rates. Additionally, we compared the performance achieved in this medium with the defined medium previously described by O. oeni PSU-1 cultivated in MaxOeno’s grew more (OD600nm = 0.75 vs. OD600nm = 0.25) and faster (μ = 0.012 h-1 vs. μ = 0.004 h-1) than Terrade’s. Therefore, the MaxOeno culture medium was employed for all further experiments of this work.The ability of Oenococcus oeni PSU-1 was grown in MaxOeno culture medium with increasing ethanol concentrations , at pH 4.8. Cell growth kinetics featured four growth phases for all culture conditions, (i.e.) phases I, II, III and stationary; ranging from 0 to 48 h, 48 to 104 h, 104 to 168 h and 168 to 264 h cultivation, respectively. These phases were characterized by different substrate consumption, e.g., during phase I, over 90% of malate and citrate were consumed; phase II presented a higher consumption rate of fructose and glucose, if compared to phase III; and phase III presented a slower specific growth rate, due to a strong decrease in sugar consumption rates . It is worth mentioning that during growth, a lag phase was not observed in all cases . Besides, all cultures reached maximum biomass titers after 164 h (6.8 days) cultivation; thereafter, growth arrested.r2 = 0.98) between ethanol concentration and cell growth was found, when considering each phase. Moreover, cultures with higher ethanol content showed slower specific growth rate and lower biomass production compared to cultures without ethanol (Table 1). The maximum biomass production and specific growth rate were observed during phase I.A clear, linear relationship , MLF and citrate degradation. The end-products of these catabolic pathways are D-lactate, L-lactate, acetate, ethanol, mannitol, erythritol and CO2. To assess the metabolic response of O. oeni to ethanol concentration, we followed the consumption and production of the major metabolites of these pathways.Supplementary Figure S1), although specific consumption rates for fructose were twofold faster than for glucose, independently of ethanol concentration present in the culture medium .As expected, the higher the ethanol concentration in the medium, the lower the sugar consumption. However, whatever the ethanol concentration was, the largest amounts of sugars were consumed during phase I. In this phase, total sugar consumption was similar for all culture conditions . Furthermore, during this phase, O. oeni PSU-1 consumed glucose faster than fructose. Specific consumption rate of fructose was reduced by 50% compared to the previous phase, although specific glucose consumption rate decreased less, regardless of culture conditions .During phase II, both glucose and fructose consumption increased when the ethanol concentration present in culture medium was lower . Besides, specific fructose consumption rates in cultures without and with 3% v/v ethanol decreased around 90 and 60%, respectively. Similar rates than in phase II were found for cultures with 6, 9, and 12% ethanol.Finally, in phase III, specific consumption rate of glucose was generally faster than fructose. Specific glucose consumption rates in cultures without and with 3 and 6% v/v ethanol were similar than phase II; but decreased significantly in cultures with 9 and 12% v/v ethanol . Total mannitol concentration - the most abundant metabolite produced - was higher than erythritol concentration. In the absence of ethanol, specific production rates for mannitol and erythritol were 1.25 mmol gDCW-1h-1 and 0.95 mmol gDCW-1 h-1, respectively. Production rates of both polyols raised concomitantly with ethanol increment, during phase I .These polyols are mainly produced through fructose catabolism - although they could also be synthesized from glucose. Again, the fastest production of mannitol and erythritol was achieved during phase I. The production of both metabolites increased concomitantly, with ethanol content . On the contrary, erythritol production sharply decreased in this phase for cultures containing 9 and 12%v/v ethanol .In phase II, production rates of both polyols dropped by half of the previous phase in the absence of ethanol in the culture medium. Once again, mannitol production increased with ethanol content, except for cultures with 6% ethanol , their consumption being triggered at the very beginning of the cultivation. Total acid consumption, expressed in concentration units (g L-1), suggests that all cultures behaved similarly, both acids being completely consumed at the end of phase I (i.e.) after 48 h cultivation . In relation to specific consumption rates, L-malate was consumed faster when ethanol concentration was higher in culture medium; however, citrate was not differentially consumed.L-lactate production; and the specific consumption rate of the former was circa 30% faster than the latter, for all culture conditions . This suggests that one of these compounds is either accumulating intracellularly or being transformed into another metabolite .Surprisingly, malate consumption was larger than D-lactate (0.15–0.20 mmol gDCW-1 h-1) was more than ten fold slower than the corresponding one for L-lactate (1.5–3.0 mmol gDCW-1 h-1), at least during phase I. Also, during this phase D-lactate production is lower at 9 and 12% ethanol content. Contrary to L-lactate, D-lactate is still significantly synthesized in later phases, although at variable rates .Otherwise, the specific production rate of -1 in cultures without ethanol. O. oeni produces acetate from citrate and from sugars through the phosphoketolase pathway. During phase I, citrate uptake and acetate synthesis rates were higher than other phases , suggesting that acetate was formed using all citrate consumed. However, acetate synthesis was faster than citrate uptake, which indicates that another pathway as phosphoketolase pathways was used additionally for its production. In later phases, however, acetate was mainly produced through phosphoketolase pathway, using other compounds, such as glucose.Acetate was the second most abundant metabolite produced, reaching a maximum of 50 mmol gDCWO. oeni in MaxOeno culture medium at increasing concentrations of ethanol; however, none was totally consumed (Table 2). Histidine, cysteine, lysine and aspartic acid were the most consumed amino acids. The highest specific consumption rates were determined for histidine and proline  and their rates increased concomitantly with increasing ethanol contents in culture medium.All the amino acids were metabolized during the cultivation of Figure 5 and Supplementary Figure S3). Interestingly, specific consumption rate of cysteine was the only one that was higher for phase II than for phase I, even increasing with ethanol content .Amino acids were mostly consumed , during phase I, with the exception of cysteine, which was consumed during both, phases I and II .Threonine was consumed faster in cultures with 9 and 12% ethanol; and serine was consumed faster in cultures with 6% ethanol . However, their specific consumption rates increased with ethanol content . Alanine and aspartic acid were also consumed faster in cultures with higher ethanol content .Cysteine, lysine, methionine, and tryptophan were up taken at similar amounts, whatever the ethanol concentration .Estimated NGAM values significantly increased in all phases, although differently, depending on the ethanol content of the culture medium. In phase I, NGAM increased threefold in cultures with 3 and 6% ethanol; and 10- and 17-fold in cultures with 9 and 12% ethanol. In phase II, it sequentially increased from 6 to 11 fold in cultures from 3 to 12% ethanol, respectively. In phase III, a maximum NGAM increase of sevenfold was achieved for cultures with 12% ethanol content. Whatever the case, at any ethanol level, NGAM was the lowest for phase I and the largest for phase III (Supplementary File S2). Phase I was the most sensitive phase at every ethanol level, reaching the highest NGAM prediction variation in the absence of ethanol (130%). The largest variations were obtained when fructose and mannitol fluxes were varied.Sensitivity of NGAM values toward the experimental data was evaluated by varying each constrained flux independently in 1% of its value, and then repeating NGAM prediction. NGAM prediction variation was assessed by calculating the relative prediction error (in %), and the highest prediction error was taken as representative of the NGAM’s sensitivity of the corresponding data set  produced by F0F1-ATPase was higher than ATP produced through the phosphoketolase pathway. In cultures with 0 to 12% ethanol, fluxes through F0F1-ATPase increased from 2.9 to 4.8 mmol gDCW-1 h-1, respectively; and fluxes through the phosphoketolase pathway decreased from 2.1 to 1.3 mmol gDCW-1 h-1, for the same cultures .ATP is synthesized either by FATP occurring through F0F1-ATPase decreased by 50% in all cultures. On the contrary, rATP through the phosphoketolase pathway increased in all cultures with ethanol content. Finally, during phase III, rATP by F0F1-ATPase continues to decrease – by another 50% compared to phase II; and rATP through phosphoketolase pathway also decreased in cultures containing less than 6% v/v of ethanol content but remained constant in cultures with higher ethanol contents .In phase II, r0F1-ATPase increased from 58% to almost 80% of total ATP flux in cultures without ethanol and cultures with ethanol content, respectively. Besides, during phases II and III there was a preference for synthesis by the phosphoketolase pathway; on average, 56% of ATP was produced from this pathway .Interestingly, during phase I, the ATP flux through FFigure 6B). The reactions that produce NAD(P)H involve the following enzymes: malate dehydrogenase, glyceraldehyde-3P dehydrogenase, threonine dehydrogenase, NADH quinone reductase, NAD(P)+ transhydrogenase and the pathway of methylglyoxal degradation for NADH formation; and glucose-6P dehydrogenase, phosphogluconate dehydrogenase and GMP reductase for NADPH formation. A clear trend of the use of cofactors in relation to the presence of ethanol in the culture medium could not be found .We determined the impact of ethanol on the cofactor usage by analyzing the NAD(P)H flux during growth (Table 3). Results showed that serine, threonine and cysteine can be used for diacetyl formation, as well as citrate, which can also be a precursor for acetate production. L-malate can be directed into either D- or L-lactate, and therefore it is the only substrate that can be used for L-lactate synthesis. Both glucose and fructose are able to generate all products but L-lactate.As the MaxOeno medium contains several substrates, a EFM was carried out to determine possible substrate-product relationships between the substrates present in the medium and the possible products, including but not limited to products measured experimentally . Fluxes were normalized over specific growth rates.Distribution of carbon intracellular fluxes was analyzed for all the growth phases, considering the major central metabolic pathways, which includes phosphoketolase pathway, fructose reduction, citrate degradation and MLF. FBA showed that a significant redistribution of intracellular fluxes occurred in Figure 7 and Supplementary Figures S4, S5).The model showed that fructose, the sugar that was most consumed, was mainly used – around 70% of the total – for mannitol biosynthesis during phase I; however, in later phases, fructose was redirected to glucose-6P synthesis, and then into the phosphoketolase pathway . It was highest during phase I, and increased with ethanol content, from 42 to 84 mmol gDCW-1 in cultures without and with 12% ethanol, respectively.Erythritol biosynthesis arises from ribulose-5P and glyceraldehyde-3P in almost all cases . All of these fluxes also incremented as ethanol increased in the culture medium .Interestingly, pyruvate production increased in phase II, and even more in phase III by the following routes: amino acid degradation, citrate degradation, and O. oeni to ensure its growth. For this end, we cultivated O. oeni PSU-1 strain in a wine-like, defined culture medium spiked with 0, 3, 6, 9, or 12% v/v ethanol. Moreover, we took advantage of our recently constructed genome-scale metabolic model  to ethanol content. Specific growth rate and maximal biomass content decreased progressively as ethanol concentration in the medium increased. Moreover, both decreased during the time course of the batch cultivation. In general, specific growth rates and biomass production were larger during phase I, which was coincident with the highest metabolic activity of O. oeni observed during this phase, for any culture condition.As expected, cell growth was closely related +/NAD(P)H cofactors and energy, reflected in higher production of ATP by F0F1-ATPase.Changes in ethanol level strongly impacted O. oeni cells cultured under alcoholic stressful conditions. Indeed, cultures containing 9 and 12% ethanol required 10 and 17 times more NGAM during phase I, respectively, than cultures without ethanol. The latter indicates that the ATP produced was principally used to cope with cell maintenance resulting from this stress. This agrees with our previous work, where the genome –scale metabolic model (GSMM) indicated that NGAM was 30-fold higher in cultures with 12% ethanol than in cultures without ethanol limiting ; therefoxidation . Several O. oeni . The lat O. oeni . NeverthO. oeni PSU-1 uses fructose, glucose, and citrate as electron acceptors for cofactors reduction. In particular, the specific production rates of mannitol and erythritol incremented as ethanol concentrations increased during phase I. However, fructose, which is the main precursor of mannitol and erythritol, showed similar specific consumption rates during phase I, whatever the culture conditions; and glucose, that can also be used in erythritol formation, displayed slower consumption rates at higher ethanol concentrations. This indicates that there is a preferential consumption of carbon sources toward mannitol and erythritol formation at higher ethanol concentrations rather than biomass formation, as a result of higher cofactor requirements. Indeed, the model predicts that cofactor regeneration due to mannitol and erythritol formation countervail for almost 50% of the cofactors used for sugar catabolism in conditions without ethanol, and for 51–57% of this usage in ethanol-containing cultures.When ethanol production pathway is non-functional, a lack of reduced cofactors occurs. Our results show that D-lactate or diacetyl. D-lactate is used for NAD(P)+ regeneration, while diacetyl is related with consumption of intracellular protons and thus increase of internal, as well as external pH, as diacetyl is less acidic than citric acid  function is reduced, pyruvate accumulates, and diacetyl is produced produced . Thus, hO. oeni PSU-1 in the presence of 12% of ethanol, showing that genes related with malate and citrate consumption were up-regulated while genes related with fructose consumption were down-regulated, which was associated to mechanisms of ethanol resistance +, NGAM and ATP increase with ethanol content, unrelated with biomass increment.We found that under ethanol stress conditions, O. oeni in each of the growth phases. To the best of our knowledge, this is the first report where experimental data from the entire exponential curve has been integrated to the model (O. oeni PSU-1 during its growth in a culture medium containing various carbon sources, as the wine-like medium MaxOeno. The results highlight the flexibility of the metabolism of this bacterium, which could not have been carried out with a canonic approach (Finally, in this work we were able to integrate in the model specific consumption/production rates and specific growth rates for each of the determined growth phases, and thus, the model was able to represent the different phenotypes of he model . Even ifhe model . The appapproach ; for exa2 stresses, are the most relevant conditions that affect the growth of O. oeni. For instance, O. oeni SD-2a strain grown at pH of 4.8 and pH 3.0, several genes related with the metabolism of amino acids, carbohydrates, membrane transport and energy metabolism were differentially expressed. Therefore, the resulting quantitative transcriptomics can be incorporated into the iSM454 metabolic model as restrictions and, together with experimental data, could allow to simulate the redistribution of metabolic fluxes resulting from this environmental perturbation.Several perspectives derived from this study can be foreseen. For example, a similar approach can be employed to tackle the metabolic response to acidic conditions in this bacterium, which, together with alcohol and SOO. oeni from different geographical regions, which is particularly relevant to understand the relationship between the wines of a certain geographical location and what has been termed as the “terroir” (e.g., O. oeni PSU-1. However, it is known that most O. oeni strains produce these deleterious compounds (O. oeni’s GEM to simulate their biosynthesis under different cultural and environmental conditions, to understand and minimize their production will be particularly promising.Moreover, important efforts have been made to identify the genetic characteristics of different communities of r” e.g., . The chaompounds . Therefo2 and pH, as well as efficient consumption of malic acid. For example a recent study carried out in 16 wineries from different Chilean valleys showed that autochthonous strains present a unique number of genes with respect to commercial strains. Moreover, some of these strains do not contain some genes related to off-flavors, such as phenolic decarboxylase (O. oeni strains might unravel new organoleptic patterns, as well as environmental responses, which complement the current knowledge regarding the differences at genomic level and, eventually, discover new starter cultures.Finally, and in accordance with the above, determination of new ecotypes will permit to define new nutritional formulations that might be relevant for the wine industry, such as tolerance to alcohol, or resistance to SOboxylase . Thus, pEA designed the research and provided guidance throughout the investigation. AC wrote the paper, designed, performed, and coordinated the batch experiments as well as processed the HPLC data. AC and GG designed and evaluated the defined culture medium MaxOeno. MR, SM, and PC developed the modeling framework. MR developed the extended model and executed the modeling simulations and analyses. SM supervised these analyses. PC designed the figures of metabolic flows and contributed to biochemical interpretation of metabolic reaction added to the model. All the authors read, corrected, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Microbial communities are ubiquitously found in Nature and have direct implications for the environment, human health and biotechnology. The species composition and overall function of microbial communities are largely shaped by metabolic interactions such as competition for resources and cross-feeding. Although considerable scientific progress has been made towards mapping and modelling species-level metabolism, elucidating the metabolic exchanges between microorganisms and steering the community dynamics remain an enormous scientific challenge. In view of the complexity, computational models of microbial communities are essential to obtain systems-level understanding of ecosystem functioning. This review discusses the applications and limitations of constraint-based stoichiometric modelling tools, and in particular flux balance analysis (FBA). We explain this approach from first principles and identify the challenges one faces when extending it to communities, and discuss the approaches used in the field in view of these challenges. We distinguish between steady-state and dynamic FBA approaches extended to communities. We conclude that much progress has been made, but many of the challenges are still open. Thus, we do not discuss interactions between microorganisms that have no direct metabolic component, such as physical interactions or signalling processes . The approaches we describe are all constraint-based, stoichiometric, modelling methods for metabolic networks. The associated models are essentially mappings of genes to metabolic enzymes, the reactions of which form a  metabolic network where the edges and nodes represent  reactions and metabolites, respectively.Constraint-based modelling has been developed for sequenced microbes in monoculture, and many methods have been developed to reconstruct, query and predict the metabolic capacities of a species based on its genome sequence –24. The As we explain later in more detail, the results of FBA-type approaches mainly depend on (i) the quality of the reconstructed metabolic model, including the biomass composition of the associated microorganism and the assumed gene–protein reaction rules, (ii) the chosen objective function, (iii) the considered constraints for exchange and intracellular rates, and (iv) the considered nutrients that can be used for biomass and product formation. If FBA is extended to analyse metabolism on a community level, then additional layers of complexity are added to all four of these points.With respect to reconstructions, despite the progress that has been made in automating the reconstruction of metabolic models on the genome scale –34, modeOnce a genome-scale model has been reconstructed, it is essential for community modelling that it can be combined with other models in a meaningful way. The construction of community models is complicated by the fact that, in most cases, such models will have been created by different authors and research groups. Consider, for example, that all models should be encoded to be readable by the same simulation software; model components  must be unambiguously annotated such that they can be used to link models together and community models themselves should be reproducible and exchangeable. Fortunately, with regards to model interoperability, the constraint-based modelling community has recently adopted a standard model encoding format that allows for efficient model exchange . HoweverAnother difficulty is to define a community objective function in both biological and mathematical terms; typically, a combination of the objective functions of individual species is optimized but the exact formulation can differ ,17,36–3913C-labelling experiments Desulfoviates. In , pairwisates. In . Consideates. In ,38,100. ates. In .Under the constraint that two organisms have to be able to produce biomass at a rate greater than a certain threshold—which allows growth rates to differ—medium compositions that can induce interactions between two species were determined . With thThese examples show that a lot of valuable information can be gained without assuming equal growth rates, but there are two issues regarding this kind of approach. First, as discussed earlier, these results will describe only a snapshot of the community dynamics, because unequal growth rates will result in rapid changes of shared external metabolites and the biomass abundances of the species. This means that stable relative biomass abundances cannot be inferred when the species' growth rates differ. If this is desired to compare with experimentally determined data, then the equal growth rate constraint needs to be taken into account.iw. In other words, all resources will be invested in the least expensive biomass. Technically, this issue can be avoided by assuming equal growth rates [There is a second point to consider when an approach such as equation (3.5) is used, which occurs for communities whose members compete for shared metabolites. If the community species differ in their biomass composition, then a weighted sum of biomass-forming rates will lead to a solution where all available resources are invested in the growth of the organism with the highest biomass yield, i.e. there will be only one biomass formation reaction that carries an actual flux, and which one this is depends on the chosen weights th rates , by settth rates ,99, or bth rates .One might argue that this problem could be solved by interspecies feeding mechanisms. The reasoning behind this is that then resources cannot only be invested in one organism, but also in the other one because they depend on each other. While this will indeed lead to several metabolically active networks and an exchange of metabolites, still only the biomass-forming reaction of the organism with the highest biomass yield will carry a non-zero flux (if both organisms compete for the same resource and have different biomass yields for this substrate). The network of the other organism will only be used to provide the required metabolite, but resources will not be invested in growth. This is rather artificial as these fluxes require a catalyst—biomass. Coupling of excretion flux to growth rate is one way this problem has been tackled .Besides using explicit constraints on the growth rates, one can also overcome this issue by merging the species' biomass equations into one, so that all the required macromolecules are produced. Instead of maximizing 4.Escherichia coli [Steady-state cFBA based on balanced growth is well suited to make predictions about communities in fairly constant environments, such as chemostats, regarding their metabolic interactions and optimal relative biomass abundances. However, in batch cultures and many natural communities, organisms are exposed to dynamically changing conditions and can grow sequentially, something this framework cannot accommodate. For such an analysis, dFBA can be used—under simplifying conditions. dFBA was first used to describe the diauxic growth of hia coli  and has hia coli ,102–107.dFBA models are similar to FBA models, except that the constraints on the input fluxes are made dependent on extracellular concentrations through kinetic rate expressions. Usually Michaelis–Menten kinetics are used, which are sometimes extended with inhibitory terms ,4.1jK parameter is equated to the affinity of the transporter, equation (4.1) describes purely substrate-limited uptake, and thus assumes that the transport step is fully controlling the downstream metabolic pathway. From metabolic control analysis, we know that this is not necessary and in fact not very likely, as control over steady-state flux tends to be distributed [jK can be interpreted as the Monod constant for growth . In praall biomass components, so also proteins, are in steady state. As long as proteome reallocations are not explicitly modelled in genome-scale metabolic models—but they will be in the future—this complication is not an issue. The concept of dFBA is summarized in One important assumption in dFBA is that the time constants related to intracellular dynamics are much smaller than those that describe changes of external concentrations. This is the basis for the quasi-steady-state (QSS) approximation inherent in the approach. The assumption that metabolism is in QSS during the relatively slow medium transitions, invoked by the cell's own metabolic activities, is very reasonable and generally accepted in the field. However, for adaptations that require gene-regulatory processes that may act at comparable time scales to the environmental changes, this does not apply, and so cells need not to be in balanced growth, in the sense that Extending dFBA to communities is then relatively straightforward: one defines input fluxes for which kinetic expressions are required, and solves the set of differential equations, using FBA for each organism individually. However, here we also face a challenge in the interactions between the species, and different solutions that researchers have suggested.4.1.Rhodoferax ferrireducens and Geobacter sulfurreducens in an anoxic subsurface environment leading to predictions about the conditions under which one can outcompete the other [E. coli and Saccharomyces cerevisiae that—in this study—exclusively consume xylose and glucose, respectively, to engineer a co-culture system in which both sugars are consumed simultaneously [et al. [dFBA has been proven to be a very useful tool to analyse metabolism on a community level –107,110.he other . Later oaneously . Chiu et [et al.  combinedIn these studies, the important assumption is made that communities are spatially homogeneous. Often, however, natural occurring communities tend to be structured, which can give rise to interesting dynamics –114. ThiAs for any application of FBA, the choice of the objective function has certain implications. It has been debated for a long time on which level natural selection occurs: on the species or community level –97. WithD), even though, in the long run, providing D to another organism would provide a growth benefit later on. The models cannot predict the optimal strategy over a longer period of time, only at each time instance. If another organism is auxotrophic for this compound D and it is not provided in the medium, the growth dynamics of this organism cannot be described without explicitly setting the model up for this purpose. In contrast, in steady-state cFBA, this interaction would show up without explicitly enforcing it.One very interesting—and challenging—consequence of optimizing organisms individually is that one needs to hardwire the interactions between the organisms, as they cannot be predicted. The reason for this is that at each time instance FBA will predict the currently most optimal flux distribution for growth. It will not predict the suboptimal excretion of a metabolite  and where metabolic interactions are known. In these cases, modelling the community with dFBA provides flux distributions within and between species, and can provide insight into the strength of metabolic interactions that cannot be measured directly.5.In this paper, we illustrated and discussed how steady-state cFBA can be derived from classical FBA without any additional assumptions. We made a clear distinction between different types of fluxes—specific internal fluxes, exchange fluxes and fluxes towards biomass—as they have different units that are relevant in a community setting. We concluded that stable biomass abundances in communities can only be modelled if (i) the species' relative biomass abundances are explicitly considered and (ii) all organisms grow with the same growth; this ‘community growth rate’ can then serve as the objective function.cFBA allows optimal biomass abundances and the associated community growth rate at a steady state of balanced growth to be calculated. Furthermore, this approach can be used to infer essential interactions between species in a certain environment without making any assumption about how and whether species interact. However, the cases in which community members all grow at the same rate may be rather limited because that can only be achieved in fairly stable environments. In natural occurring communities, however, dynamically changing environmental conditions are prevalent which cannot be incorporated into cFBA. Here, dcFBA can be used. This approach is, however, limited in its predictive power of unknown interactions and usually requires that the entire system structure is defined beforehand. This is, in particular, true for mutual interactions based on suboptimal growth strategies, i.e. if the excretion of a by-product comes at a growth cost but would benefit an organism in the long run, as it would allow growth of another organism that conversely provides growth-promoting metabolites. While dcFBA is a very powerful tool to describe small synthetic well-characterized communities, its extension to larger communities is not straightforward, owing to the number of parameters required to model transport rate dynamics and the associated numerical instability. One way to keep the number of parameters in a reasonable range is to use explicit exchange kinetics only for metabolites that are essential for the community to grow optimally. This information could be revealed by performing FVA on the solution computed by steady-state cFBA. This combination of cFBA and dcFBA therefore seems promising.We still face many challenges in modelling metabolism in communities that go significantly beyond insufficient experimental data or reconstructed genomes. We require advances in how to define the objective function of communities, how to deal with dynamics and subsequent (sub)optimal dynamic strategies within the constraint-based modelling format, and how to incorporate the recent advances made in modelling monocultures –84. It s


During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants.Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response.The thale cress Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed.The successful application of predictive modelling in The online version of this article (doi:10.1186/s12918-016-0347-3) contains supplementary material, which is available to authorized users. Firstly, in silico metabolic modelling has proven impressively successful in guiding systems metabolic engineering of other biological systems, including bacteria and fungi [in silico based analyses, such as elementary flux mode analysis [13C-metabolic flux analysis (13C-MFA) [in silico simulation appears particularly interesting due to its high speed, given e.g. the relatively long time of experiments with growing plants.Increasing world population, shortage of arable land and the resulting growing demand for food, feed and raw materials are major drivers to create plant lines with increased performance, e.g. better resistance to disease and drought . In addind fungi –14. Second fungi  provide nd fungi –18. And nd fungi . Among tanalysis , 21 and analysis , 23, as 13C-MFA) –26, high13C-MFA) , flux ba13C-MFA)  and meta13C-MFA) . Among tin silico analysis of plant central carbon metabolism. We use A. thaliana as a widely applied model plant to study plant biology and biotechnology, reflected also by the fact that it was the first plant sequenced [A. thaliana leaf was constructed, which represented 511 reactions and 1567 metabolic genes. Its reactions and pathways were localized in different subcellular compartments: cytosol, plastid, mitochondrion and peroxisome. Through simulation, relevant physiological scenarios of plant metabolism were studied. This involved the detailed investigation of the diurnal metabolism through comparison of metabolic capabilities in the light and in the dark. In addition, the modelling results were integrated with previous experimental fluxomic and transcriptomic data of A. thaliana leaves in order to explore the systems-wide regulation of the metabolism. This provides an enhanced understanding of plant metabolic pathways and their contribution to growth and product formation.Here, we conduct detailed equenced . In shorA. thaliana leaves. For re-construction, the initial draft of the network built on recent genome-scale models of the organism [http://www.genome.jp/kegg), MetaCrop [http://metacrop.ipk-gatersleben.de/) and AraCyc [http://pmn.plantcyc.org). This provided state-of-art gross information on the genomic pathway repertoire. Where needed, the network was updated with experimental data and primary literature as described in detail below. Individual additions and specifications considered enzyme localization, cofactor usage, and inter-compartmental transport. Subsequently, the model was condensed, which, however, did not reduce its information content , which were added. Both models, the detailed version and the condensed version, used for the simulations, are provided as SBML files in the Additional files The metabolic network used in this study comprises the central carbon metabolism of organism –32. The organism   and an absolute log2-value > 0.5.Elementary flux modes (EFMs) were calculated with efmtool, based on the null space approach and recursive enumeration with bit pattern trees . The EFMA. thaliana rosettes [Experimental transcriptome data were taken from a recent study on the shift of gene expression between day and night in rosettes . In thisA. thaliana rosette [−1 to enable a straightforward comparison with the respective flux modes, obtained in this work, also given in (C-mol) (C-mol)−1.The experimentally measured metabolic fluxes of an illuminated  rosette  were conArabidopsis and of other C3 plants under ambient atmospheric conditions at different light intensity [The quantum yield of photosynthesis was derived from previous measurement of the rate of photosynthesis of ntensity –43. Due ntensity . This prA. thaliana’s core carbon pathways reflected 511 metabolic conversions in total . The chosen topology is specific for green leaf tissue. The network contained the Calvin-Benson-Bassham (CBB) cycle for CO2 assimilation and photosynthesis, the gluconeogenesis, the glycolytic Embden-Meyerhof-Parnas (EMP) pathway, the oxidative and the non-oxidative pentose phosphate (PP) pathway, the tricarboxylic acid (TCA) cycle, the reactions of photorespiration, of starch biosynthesis and degradation, of energy metabolism, as well as the anabolic pathways for biomass synthesis  (http://pmn.plantcyc.org) [http://metacrop.ipk.gatersleben.de) [The network reflected the four major compartments of plant leaves that contribute to biochemical conversions: cytosol, peroxisome, mitochondrion and plastid (cyc.org) , 45–48. cyc.org) , the oxicyc.org) . The placyc.org) , the oxicyc.org) . The TCAcyc.org) . The phocyc.org) . Additioeben.de) . Pyruvateben.de) , whereaseben.de) . The supeben.de) . Malic eeben.de) .2 was assumed to freely diffuse within the cell [The separation of the metabolic routes in distinct organelles requires the translocation of specific compounds across cellular membranes. Based on experimental evidence, unidirectional or bidirectional transport between cytosol and mitochondrion was assumed for pyruvate, malate, inorganic phosphate, glycine and serine, respectively, whereas antiporters were considered for malate/oxaloacetate, citrate/oxaloacetate and ATP/ADP , 52–55. the cell , photonsthe cell  and inorthe cell .Redox, energy and phosphate metabolism were compartmentalized across the different organelles. This included the confinement of the photosynthetic light reactions to the plastid and of the oxidative phosphorylation system to the mitochondrion. A vacuolar storage pool for inorganic phosphate was considered . InorganA. thaliana leaves was collected from previous work , and for metabolism in the dark using internal starch (dark-metabolism). The solution space consisted of 1.2 million elementary flux modes for the light-metabolism and of 5.7 million elementary flux modes for the dark-metabolism  (C-mol substrate)−1 closely resembles experimental values for A. thaliana leaves of about 25.2 (g biomass) (C-mol substrate)−1 [2 assimilation, deducing that degradation of a heterotrophic substrate, such as starch, is the only feasible phenotype in the dark.The full sets of elementary flux modes, which span the entire space of feasible flux distributions, were calculated both for metabolism in the light using COtrate)−1 . The max2)−1. This corresponds to a quantum yield between 0.09 - 0.12 (mol CO2) (mol photons)−1, obviously supporting optimal photosynthesis.The computed set of elementary modes was used to investigate the impact of the light influx on growth in more detail. First, energetically inefficient modes that included massive cycling of resources were eliminated. Although stoichiometrically possible, these modes involve futile cycling of carbon across the internal membranes to dissipate energy , as compared to light-metabolism (5 %), whereby overall, both values were rather low. It was interesting to note that also the origin of the released CO2 differed. Under both conditions, the carbon loss by photorespiration was negligible, whereas the largest contributors to CO2 release were the TCA cycle and pyruvate and malate dehydrogenase. During the dark, additional carbon was released during the generation of NADPH in the oxidative PP pathway, whereas during illumination, previously assimilated CO2 was lost via PEP carboxykinase.As shown in the previous section, the metabolism in the dark recruited all compartments to supply ATP and reducing power, whereas the plastid was the dominant compartment in the light Fig. . Next, wThe striking differences in the localization of energy and reducing power supply among the cellular compartments now suggested to also inspecting anabolic metabolism. Therefore, the metabolic pathways, responsible for the synthesis of each anabolic precursor, were investigated and compared between the optimum growth modes in the light and in the dark . Interestingly, the biosynthetic origin of many of the twelve anabolic precursors strongly depended on the physiological growth mode, i.e. light and carbon acquisition Fig. . As examIt is generally accepted that cyclic and non-cyclic electron flow both contribute to the in vivo photosynthetic light reaction –67. HoweA. thaliana, such as growth and photosynthetic efficiency, which can be taken as indication of its high quality and validity  a diurnal expression pattern was identified, however only the former two are linked to the observed flux changes. This indicates that At3g49160 likely has a functionally different purpose from mediating the pyruvate phosphorylation in central carbon metabolism. For instance, pyruvate kinase could also catalyze several metabolic conversions in ribonucleotide and nucleotide biosynthesis. As isoenzymes might have distinct functions , correlain silico through modelling [Plants adapt to changes in light intensity through photo protection and optimization of energy conversion . In thisodelling . Likely,odelling . When anArabidopsis can operate close to theoretical pathway optimum and that this is mediated by a fine-adjustment of metabolic flux, strongly under transcriptional control. In this light, the present work is one of the very few examples so far, which link in-vivo with in-silico flux data to a higher-level understanding [The created condensed network model adequately described leaf physiology, and thus, because of its reduced network size as compared to genome-scale models, permits straightforward, comprehensive analysis of the entire elementary flux mode solution space. Taken together, the metabolic simulations provide detailed molecular insights into plant functioning. standing , 115. It


Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb). For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data and do not focus on target protein production optimization. This paper explores an experimental case study where hybridoma cells are grown in a sequential batch reactor. The simplest macroscopic reaction scheme translating the data is first derived using a maximum likelihood principal component analysis. Subsequently, nonlinear least-squares estimation is used to determine the kinetic laws. The resulting dynamic model reproduces quite satisfactorily the experimental data, as evidenced in direct and cross-validation tests. Furthermore, model predictions can also be used to predict optimal medium renewal time and composition. Therapeutic products  are subject to exponential demands and cost-lowering process improvements, leading to the intensification of growth conditions in the bio-pharmaceutical industry and the sharp increase of the related market economy. For instance, monoclonal antibody (MAb) market amounts to several billion dollars and is still increasing.To improve bioprocess yield and repeatability, monitoring and control tools are required. The latter implies the availability of dynamic models, which can predict the process trajectory and support the design of software sensors or control strategies. Previous optimization studies of hybridoma cell cultures for MAb production were usually conducted using simple mathematical models based on macroscopic reaction schemes such as in ,2.More recently, a macroscopic model with kinetics accounting for overflow metabolism, where glucose and glutamine are the main substrates, was proposed in . Indeed,A more practical alternative is to directly deduce the model structure from the available data, assuming that the data sufficiently cover the process operating range. In , PrincipA simple dynamic model of cultures of hybridoma cells in SBR is developed and validated with experimental data. Confidence intervals for the parameters and the estimated trajectories are provided.A systematic model identification procedure, based on rigorous yet simple to use tools—MLPCA to determine the stoichiometry, nonlinear least squares to identify the parameters of the kinetic laws, sensitivity analysis and Monte Carlo analysis to infer the confidence intervals—is assessed in a real case study, showing good performance and promise for future applications.The simple dynamic model is further exploited to optimize the medium renewal strategy in the sequential batches.The main contribution of this study is threefold:This paper is organized as follows. This section first reviews the basic concept of overflow metabolism and then presents a systematic procedure to infer candidate macroscopic models from principal component analysis of the data at hand.The glycolysis which is a series of degradation reactions of glucose (the main substrate) taking place in the cytoplasm and leading to a final product, i.e., pyruvate.TCA) cycle or citric acids cycle, which takes place inside the mitochondrions and uses pyruvate to product the cells energy units (Adenosine triphosphate or ATP) and reduced cofactors .The Krebs cycle, also called the tricarboxylic acids  principally used for cell growth and division. This limitation comes from the capacity to oxidize the main nutrients: glucose (main carbon source) and glutamine (main nitrogen source). The excess amounts of these nutrients are assumed to follow other metabolic pathways more commonly known as “fermentation”, producing a side byproduct.This “Overflow Metabolism” or “short-term Crabtree effect” ,9,10,11,The byproduct formation usually inhibits the oxidative capacity of the cells, slowing down growth for increasing concentrations. In turn, it depends on the oxidative capacity of the cells and on the medium composition. A generic mechanistic model that would, in principle, allow the representation of the culture of different strains presenting overflow metabolism, can be described through the following main reactions:jth substrate consumption:jth substrate overflow:jth byproduct consumption:X, jS and jP are the concentrations of cell biomass, jth substrate and jth side byproduct, respectively. The nik coefficients represent the yield (or pseudo-stoichiometric) coefficients of component n in reaction i. Overflow metabolism assumption involves that, for each concerned substrate, these reactions take place in pairs (1a and 1b) or triplets (1a–1c) if the considered byproduct can be reconsumed by the biomass as a substitute substrate source when the oxidative capacity is not fully exploited.S < critS and Sφ < Smaxφ), substrate is consumed with biomass growth and a relatively low metabolite byproduct production (1a) without overflow, which is defined as respiratory metabolism and the consequent remaining respiratory capacity can be used to oxidize byproduct as substitute carbon source as in (1c). Indeed, Sonnleitner and Käppeli  assume tS > critS and Sφ > Smaxφ), the respiratory capacity is saturated, resulting in overflow metabolism towards excess metabolite production (reactions (1a) and (1b)). The state at which overflow metabolism is initiated (S = critS and Sφ = Smaxφ) is referred to as critical metabolism. For instance, yeast metabolism is described by the bottleneck assumption of Sonnleitner and Käppeli , i.e., is the medium renewal time trenewal and the glucose and glutamine concentrations in the medium Grenewal and Gnrenewal. Fmincon also allows to specify box constraints for trenewal Є [3 14] days. These values are selected in accordance with the previous experimental results: medium renewal should on the one hand not occur too soon and on the other hand before the end of the experiment set at day 14. Grenewal Є [1 15] g/L and Gnrenewal Є [0.1 1] g/L so as to avoid cell starvation or growth inhibition through the accumulation of byproduct.This section intends to provide the best medium renewal time and composition to maximize the monoclonal antibody production and substrate savings. Using the validated model of renewal and Gnrenewal are respectively set to 6 and 0.4 g/L . Moreover, the MAb production, defined as the sum of the final batch concentrations, amounts to 60.92 µg/mL which represents a production gain of 30% with respect to the experiments of refresh is reached. Indeed, when G is depleted, Gn still limits biomass decay and therefore maintains an efficient MAb production rate. However, since ammonium production (byproduct formed by glutamine overflow) is not considered in the model obtained in refresh are not recommended.Even when considering substrate savings, the upper bound of GnInterestingly, approximately the same renewal time as in Since MAb production clearly appears as a function of substrate saving penalization, new optimizations considering α in the range 0 to 500 with incremental steps of 50 are achieved in order to assess the impact of α on MAb production and select a good compromise. Results displayed in In this work, a simple dynamic model of hybridoma sequential batch cultures is developed, which can be used to optimize the production of monoclonal antibodies. Maximum likelihood principal component analysis allows assessing the information content of the experimental data, providing the minimum number of reactions and the corresponding stoichiometry by solving an optimization problem under a few a priori biological constraints. An original formulation of the method is presented, allowing biomass to occur in several reactions. Advantages of the method are (a) to limit the number of reactions, i.e. to avoid a useless complication of the model with respect to the experimental field and the involved biological phenomena ; (b) to offer the possibility to proceed to a quick first estimation of the stoichiometry independently of the kinetics, in turn reducing the number of unknown parameters ; (c) to provide a “divide and conquer” approach where the stoichiometry and kinetics can be estimated separately or simultaneously, in an iterative way, starting from estimates obtained at the previous step.The procedure can be supplemented by parametric sensitivity analysis, which allows further model simplification, whenever needed, by isolating parameters with low sensitivities.A Monte-Carlo study, where parameter variations are considered in accordance with the resulting estimation error variances, shows that model trajectories are globally kept inside a corridor defined by measurement confidence intervals . As a practical illustrative outcome of the present study, the obtained dynamic model is used for a two-sequential batch process optimization . The results show that the importance of substrate savings drives the location of the optimum. A renewal time scheduling can therefore be established based on the user will to save medium components such as substrates. Further experimental validations of the optimization method are important perspectives of this still on-going work as well as estimation and on-line control issues.