Saccharomyces cerevisiae.Natural and industrial environments are dynamic with respect to substrate availability and other conditions like temperature and pH. Especially, metabolism is strongly affected by changes in the extracellular space. Here we study the dynamic flux of central carbon metabolism and storage carbohydrate metabolism under dynamic feast/famine conditions in DW−1 h−1 is observed, no overflow metabolites are detected. From famine to feast the glucose uptake rate increased from 170 to 4788 μmol gDW−1 h−1 in 24 s. Intracellularly, even more drastic changes were observed. Especially, the T6P synthesis rate increased more than 100-fold upon glucose addition. This response indicates that the storage metabolism is very sensitive to changes in glycolytic flux and counterbalances these rapid changes by diverting flux into large pools to prevent substrate accelerated death and potentially refill the central metabolism when substrates become scarce. Using 13C-tracer we found a dilution in the labeling of extracellular glucose, G6P, T6P and other metabolites, indicating an influx of unlabeled carbon. It is shown that glycogen and trehalose degradation via different routes could explain these observations. Based on the 13C labeling in average 15% of the carbon inflow is recycled via trehalose and glycogen. This average fraction is comparable to the steady-state turnover, but changes significantly during the cycle, indicating the relevance for dynamic regulation of the metabolic flux.The metabolic flux reacts fast and sensitive to cyclic perturbations in substrate availability. Compared to well-documented stimulus–response experiments using substrate pulses, different metabolic responses are observed. Especially, cells experiencing cyclic perturbations do not show a drop in ATP with the addition of glucose, but an immediate increase in energy charge. Although a high glycolytic flux of up to 5.4 mmol gComparable to electric energy grids, metabolism seems to use storage units to buffer peaks and keep reserves to maintain a robust function. During the applied fast feast/famine conditions about 15% of the metabolized carbon were recycled in storage metabolism. Additionally, the resources were distributed different to steady-state conditions. Most remarkably is a fivefold increased flux towards PPP that generated a reversed flux of transaldolase and the F6P-producing transketolase reactions. Combined with slight changes in the biomass composition, the yield decrease of 5% can be explained. Systems biology pursues the understanding of biological systems by unraveling their structure and dynamics . In part13C wash-in experiments a bias in the labeling enrichment of glycolytic intermediates was observed, caused by a transient influx of 12C into glycolysis [It has been observed that storage carbohydrates react sensitive and fast to changes in extracellular glucose concentration . Severalycolysis . Recentlycolysis , 8. Henc13C-labeling experiment where the system is at both, metabolic and isotopic non-stationary state, would significantly increase the accuracy of the kinetic parameter estimation. Recently, Abate et al. [To study the dynamic interaction of trehalose and other storage intermediates with the central carbon metabolism, intracellular flux estimation is required. An interesting approach consists in collecting time-series of metabolic concentration measurements from dynamic experiments to describe the dynamic response of a pathway to a particular stimulus , 10 compe et al.  have pro13C tracer experiments are performed to quantify the short-term in- and outflows of the large intracellular trehalose pool as well as central carbon metabolism. The concentration of glycolytic and PPP metabolites as well as enrichment dynamics are monitored over several cycles and a piecewise affine flux approximation in time is used to quantify a dynamic flux profile. Since we focus on the short-term response by monitoring the metabolic activity (intracellular concentrations and fluxes) during cycles of 400 s, it is assumed that the metabolic flux is mainly controlled by metabolic interactions.Here, dynamic Saccharomyces cerevisiae CEN PK 113-7D  was used. Cells from one cryovial (glycerol at −80 °C) were used as seed culture and incubated  for 10 h in 1 L-Erlenmeyer flasks containing 100 mL of minimal medium that contained 13.6 g L−1 glucose. The pre-culture was used to inoculate a 7 L bioreactor  with a working volume of 3.894 L. A low-salt Verduyn minimal medium [−1 was used. No ethanol was added to the medium because oscillations were not observed during cultivation. The broth was sparged with pressurized air at 0.992 L min−1 (approx. 0.25 vvm) and the vessel was maintained at an overpressure of 0.3 bar. The pH of the broth was controlled at 5.0 by adding either 4 M KOH or 2 M H2SO4. Temperature and agitation were kept at 30 °C and 600 rpm, respectively, during the complete cultivation. The culture was starved for 2 h after the end of batch phase, which was determined by a strong and fast CO2 decrease and dissolved oxygen (DO) increase to almost saturation. The chemostat phase at a dilution rate of 0.1 h−1 was started and run for about 5 residence times. At this time, the culture was assumed to be at steady-state as indicated by a constant CO2 and O2 concentration in the off-gas , and a constant biomass density (3.64 ± 0.05 gDW L−1).In this work, the haploid yeast l medium  with a g−1, the same amount of fresh medium was added , but block-wise. The feed was switched on for 20 s under control of an automatic timer  feeding 43 mL. In the following 380 s there was no feed addition to complete a total cycle time of 400 s. To maintain the broth volume constant (max. 3.894 + 0.043 L), weight control was used, i.e., 43 mL were withdrawn but during the first 260 s at a flow rate of 0.166 ± 0.001 mL s−1.After 5.3 residence times of running in chemostat mode, the continuous feed was changed to a block-wise regime to start the feast/famine cultivation Fig. . In orde2, O2 and DO in consecutive cycles. Samples were taken for determination of metabolite concentrations and/or dry weight (3.46 ± 0.01 gDW L−1). Right after the sampling for concentrations, the medium was shifted to perform the labeling experiment. The new medium was formulated to have the same components and concentrations as the base medium but with uniformly 13C labeled glucose . The labeled medium feed line was prefilled to avoid any perturbation on the cycle time.Initially, unlabeled glucose was used as substrate during the feast/famine cycles for 50 h. After 5 residence times a repeating dynamic state was reached indicated by stable profiles of COBroth samples for the measurement of extracellular and intracellular metabolite concentrations were rapidly withdrawn using two separate sampling ports during three subsequent cycles at defined cycle times  to obtain duplicate measurements. Samples for the measurement of mass isotopomer fractions were withdrawn at comparable cycle times after switching to the labeled feed.2 and CO2 volume fractions in the off gas were measured by a combined paramagnetic/infrared NGA2000 analyzer .For the measurement of extracellular metabolite concentrations, approximately 1.5 mL of broth were withdrawn into a syringe containing pre-cooled (−20 °C) stainless steel beads (approx. 26 g). The cooled broth (~ 1 °C) was immediately filtered as described by . Extraceg for 5 min at 1 °C, processed and stored according to [13C cell extract.For the measurement of intracellular metabolite concentrations, 1.0 ± 0.05 mg-broth was rapidly withdrawn and quenched in 5 mL cold (−40 °C) pure methanol  and procrding to . Samplesrding to  and/or Lrding to , 19. ForThe metabolic network used for constructing the stoichiometric model was based on the consensus model reported for yeast  but signBeing the entry gate to central metabolism, the dynamics of glucose uptake play a critical role . With th13C-labeling enrichment showed a decreasing pattern after the feeding phase, indicating a  source of unlabeled glucose. There was no unlabeled glucose added from the feed and putative artifacts from sampling where excluded . The dilution in the 13C-enrichment of extracellular glucose has also been observed in cultures of Penicillium chrysogenum [Interestingly, the ysogenum . We hypoMetabolites of the upper glycolysis showed a concentration pattern resembling the time-course of extracellular glucose, though with a delay of about 40 s to reach the concentration maximum (i.e. around 60 s after the start of the cycle). The concentration of metabolites in the lower glycolysis exhibited dynamics inverse to upper glycolytic intermediates, which is in agreement with previous findings during pulse perturbations . ComparaMetabolites of the pentose phosphate pathway showed a response, in both concentration and labeling, that was similar to glycolytic metabolites with the exception of Sed7P Fig. .Fig. 3MegDW−1 to a maximum at 12.39 ± 0.18 μmol gDW−1 in 60 s, which confirms the highly dynamic nature of the feast/famine experiment. The concentration and 13C-labeling of G6P, F6P and G1P are very comparable, indicating that PGI and PGM operate near equilibrium, i.e. these are bidirectional as reported previously [Acting as a first branchpoint, G6P plays an important role in the upper part of the glycolytic pathway, PPP and storage carbohydrate synthesis , 25 obseeviously , 25. In 13C-labeling pattern of G1P and UDPG resembled the G6P labeling, the enrichment profile of T6P showed significant differences, especially a delay of about 40 s and then, after about 100 s, a strong de-enrichment , and the 13C-enrichment reached 4.3% at the end of the first cycle . The constant labeling enrichment indicates that there was no synthesis during this period (180–400 s). At the same time, there is a decrease in the concentration which is in agreement with the observed entry of low 13C-enriched material into glycolysis (G6P).Trehalose showed the highest concentration of all the measured pools  suboptimal placement of breakpoints for some metabolites and (2) potentially missing reactions with influence on the labeling patterns. Nevertheless, key features of the measurements are reproduced over the complete three cycles, which increases the confidence that the most important features are included and the calculated fluxes are a reasonable estimation.The model can reproduce most of the DW−1 h−1 during the first 24 s after the perturbation. The average flux during the cycle, 1253 μmol gDW−1 h−1, was the same as during the steady-state at the corresponding growth rate (1252 μmol gDW−1 h−1 at D = 0.1 h−1). This rate is higher than the glucose feeding rate (1156 μmol gDW−1 h−1) which originates from a glucose recycle through the extracellular space as will be discussed later.Calculations show that the net fluxes in glycolysis followed the pattern of glucose uptake (reaction r1_1) as response to glucose perturbation Fig. . Some glDW−1 h−1), a steep decrease between 24 and 69 s and then further decreasing to the value at the end/beginning of the cycle, 171 µmol g-DW−1 h−1. The downstream glycolytic reactions  resembled the uptake profile with flux values that were about two-thirds of the glucose uptake rate. In addition, with each step the flux profile became smoother, i.e. lower maximum peaks and slower decrease indicating a slight buffering effect of the intermediates.The dynamics of the uptake rate mostly follows the extracellular glucose concentration, a strong increase with the onset of the feeding (max of 4788 µmol g-−1). Similar behavior was observed for transaldolase (reaction r2_5) and the E4P-consuming transketolase (reaction r2_6).The metabolic flux of the pentose phosphate pathway is different to that of the glycolysis Fig. . After aAlthough exhibiting some delay, reactions of the trehalose synthesis (reactions tre_1 and tre_2) were activated by the glucose perturbation Fig. . Especia profile . CorrespThe intracellular degradation of trehalose (reaction tre_deg) decreased directly after the glucose addition. The extracellular degradation of trehalose (reaction r7_2) seemed to be active in phases with low glucose availability  and subsequently degraded in the extracellular space (reaction r_7_2),A glycogen breakdown followed by the export of the resulting glucose (reactions r14_4). Upon inspection of both routes, it can be observed that extracellular trehalose degradation accounts for about 40%, while glycogen degradation contributes 60% of the unlabeled glucose. Trehalose export and degradation (when other carbon sources were depleted) has been observed previously by  under baComing back to the initial hypothesis for the sources of the observed glucose de-enrichment, the model contained Fig. :A glucosThe synthesis of glycogen followed a different pattern to that of trehalose during the feast/famine. While the trehalose was mostly synthesized during high glucose availability, glycogen synthesis from UDPG (reaction r_14_3) showed a minimum of 53 orted by , 24.S. cerevisiae there could be side-activities of another, related kinase.The measured T6P enrichment pattern Fig. , showing13C flux estimation to the thermodynamic driving force (1-Q/Keq) of some reactions operating near equilibrium.Metabolic reaction directions follow the thermodynamic driving forces (TDF) , 14. Esp(in vivo) Keq. There may be changes in TDF, especially when changing conditions are expected . Here, we found that the mass action ratio (Q-ratio) of the PGI reaction tends to keep constant for all the calculated fluxes, as it is usual for reactions close to equilibrium. However, there is a slight tendency to decrease when the flux is higher. While the TDF would approach a zero value in case of a Keq as reported by [Contrary to the suggestion of a negative TDF for the PGI reaction , we founorted by , the TDForted by . Maximalorted by .Fig. 8AcThe metabolic flux estimation suggests a change in flux direction in PGM (r14_1) from G1P synthesis to G6P production towards the end of the cycle. Our results are in agreement with expectations from  since thTo compare the metabolic activity with steady-state observations, the average fluxes during the cycle were calculated. The average flux of the glucose uptake (r1_1) equals to 1253 The imported glucose is then distributed over different pathways at the level of G6P Fig. . Based oIn contrast to steady-state, under the dynamic feast/famine regime flux direction changes are observed. Especially, the PGM flux changes to the direction G1P → G6P between 364 s of the previous cycle to 2.5 s of the subsequent cycle. Similarly, a change in the flux direction of transaldolase (TA) and the F6P-producing transketolase (TK2) is observed, although only for very short periods. While during the steady-state TA run backwards (producing Sed7P) with a flux of about 9 In this work we studied the dynamics of central carbon metabolism as well as trehalose and glycogen under repetitive perturbations, i.e. a block-wise feeding regime. The intracellular metabolites respond rapidly to the extracellular perturbations with up to ninefold change during the cycle. The perturbation led to a highly dynamic flux response, especially, the glucose uptake increased from 170 to 4788 13C in extracellular glucose. A further part of the de-enrichment originates from the estimated glycogen degradation flux.We found that in average about 15% of the consumed glucose is recycled, a significant fraction through trehalose in the intra- but also extracellular space. This was surprising, but in line with the reported trehalose Agt1p transporter  and a thNext to slightly reduced storage turnover, there were differences between the reference steady-state flux distribution and the average flux during the feast/famine regime:(1) A fivefold increase in the oxidative pentose-phosphate flux. This higher flux generated a reversed flux of transaldolase and the F6P-producing transketolase reactions as compared to flux distributions at steady-state conditions.Keq seems to be still inconsistent in literature.Interestingly, during the short period of the cycle, some reactions do change direction. Especially, ribulose-phosphate-3-epimerase, transketolase and aldolase operated backwards towards the end of the cycle when glucose availability was low. Similarly, PGM switches from G6P consumption to G6P production. The observed concentration changes are proportional to the flux estimations, but in vivo 13C-profile was found for T6P that suggests a putative mechanism for trehalose phosphorylation. If this reaction was not considered, it was not possible to reproduce the experimental observations since the 13C-enrichment of G6P and UDPG remained high. The flux of this putative reaction was about 30% of the T6P-phosphatase flux and was estimated higher when glucose availability was low.(2) Next to a de-enrichment in extracellular glucose, a decreasing 13C-labeling patterns to deviate dramatically. Nevertheless, the ability of the model and the estimated fluxes to reproduce most of the metabolic pools suggest that the method is robust and reliable. Deviations in labeling patterns of some pools could be expected due to missing reactions involving these pools or errors due to experimental conditions .Although the accuracy of flux estimations in this work have not been evaluated statistically due to its inherent complexity, the fluxes reported here correspond to those with the least-squares that simultaneously fit both concentration and labeling enrichment measurements. Thus, small changes in flux values would cause To obtain a more comprehensive understanding of the complete central carbon metabolism under dynamic conditions, a next step could be extending the present work to cover TCA cycle. This would demand more advance analytic and experimental techniques than the ones used here. Especially with relation to compartmentation of TCA cycle metabolites, a big challenge that can be resolved in the near future .13C labeling experiments shows that the metabolic response to changing substrate availability can unravel unreported metabolic behaviors.In this work the successful implementation of an experimental platform plus a calculation methodology for estimating fluxes based on dynamic 13C labeling patterns during multiple cycle perturbations. The approach can be applied to study metabolic responses to industrial conditions like substrate gradients but also changes in oxygen availability. While laborious, the approach can reveal intracellular cycles that can impact product yields and rates. In particular, upon strong changes in concentrations, some metabolic fluxes did not show strong responses indicating that the biological system is robust enough to withstand such changing environments. On the other hand, some reactions were found to change the direction of their metabolic flux as compared to steady-state fluxes. In addition, the observed dynamic behavior in glycogen and trehalose metabolism shows that at the beginning of the cycle, when substrate depletion has been reached, both glycogen and trehalose pools serve as a carbon source. This carbon utilization will proceed until a new carbon excess condition is present again.The proposed approach was robust and it was possible to reproduce experimental data of most dynamic metabolite concentrations and dynamic S. cerevisiae exhibits a different metabolic behavior from both batch and continuous cultures, that is, the feast/famine regime does not necessarily resemble the features typically observed in chemostats or in batch. For instance, this can be noted in the no presence of ethanol and glycerol during periods of carbon excess when S. cerevisiae is grown under mild feast/famine conditions.Finally, emphasis has to be made on the fact that a feast/famine cultivation of 2PG, 2-phosphoglycerate; 3PG, 3-phosphoglycerate; 6PG, 6-phospho gluconate; ALA, alanine; DHAP, dihydroxy acetone phosphate; E4P, erythrose-4-phosphate; F6P, fructose-6-phosphate; FBP, fructose-1,6-bis-phosphate; G1P, glucose-1-phosphate; G6P, glucose-6-phosphate; GAP, glyceraldehyde-3-phosphate; PEP, phospho-enol-pyruvate; PYR, pyruvate; Rib5P, ribose-5-phosphate; Ribu5P, ribulose-5-phosphate; SER, serine; S7P, sedoheptulose-7-phosphate; T6P, trehalose-6-phosphate; UDPG, UDP-glucose; UDP, uridine-5-diphosphate; UTP, uridine-5-triphosphate; X5P, xylulose-5-phosphate;ENO, phosphopyruvate hydratase; FBA, fructose-bisphosphate aldolase; G6PDH, glucose-6-phosphate dehydrogenase; GAPDH&PGK, glyceraldehyde-3-phosphate dehydrogenase + phosphoglycerate kinase; GPM, phosphoglycerate mutase; PFK, 6-phosphofructokinase; PGI, glucose-6-phosphate isomerase; PGM, phosphoglucomutase; PMI, mannose-6-phosphate isomerase; PYK, pyruvate kinase; RPE, ribulose-phosphate 3-epimerase; RPI, ribose-5-phosphate isomerase; TPP, trehalose- phosphatase; TPS, alpha,alpha-trehalose-phosphate synthase; TA, transaldolase; TK1, S7P-producing transketolase; TK2, F6P-producing transketolase.CER, Carbon evolution rate; DO, dissolved oxygen; IDMS, Isotope dilution mass spectrometry; OUR, Oxygen uptake rate; PPP, pentose phosphate pathway; SRE, Stimulus response experiment.


Arabidopsis thaliana with corresponding metabolic specificities of respective tissues in terms of sucrose and proton production/utilization. An ability of the model to operate under different light modes (‘light’ and ‘dark’) and correspondingly in different energy producing modes is particularly important in understanding regulatory modules.Sucrose translocation between plant tissues is crucial for growth, development and reproduction of plants. Systemic analysis of these metabolic and underlying regulatory processes allow a detailed understanding of carbon distribution within the plant and the formation of associated phenotypic traits. Sucrose translocation from ‘source’ tissues (e.g. mesophyll) to ‘sink’ tissues (e.g. root) is tightly bound to the proton gradient across the membranes. The plant sucrose transporters are grouped into efflux exporters (SWEET family) and proton-symport importers . To better understand regulation of sucrose export from source tissues and sucrose import into sink tissues, there is a need for a metabolic model that takes in account the tissue organisation of 2 under light) and heterotrophic (growing on starch in darkness), and the root was always considered as heterotrophic tissue dependent on sucrose supply from the mesophyll compartment. In total, the model includes 413 balanced compounds interconnected by 400 transformers. The structured metabolic model accounts for central carbon metabolism, photosynthesis, photorespiration, carbohydrate metabolism, energy and redox metabolisms, proton metabolism, biomass growth, nutrients uptake, proton gradient generation and sucrose translocation between tissues. Biochemical processes in the model were associated with gene-products (742 ORFs). Flux Balance Analysis (FBA) of the model resulted in balanced carbon, nitrogen, proton, energy and redox states under both light and dark conditions. The main H+-fluxes were reconstructed and their directions matched with proton-dependent sucrose translocation from ‘source’ to ‘sink’ under any light condition.Here, we describe a multi-compartmental model consisting of a mesophyll cell with plastid and mitochondrion, a phloem cell, as well as a root cell with mitochondrion. In this model, the phloem was considered as a non-growing transport compartment, the mesophyll compartment was considered as both autotrophic  contains supplementary material, which is available to authorized users. Arabidopsis thaliana. The integrated model should describe biomass growth both in light and in dark phases with corresponding formation and consumption of starch and sucrose. Furthermore, the structured metabolic model should take into account major pathways of primary metabolism such as sugar metabolism, central carbon metabolisms, photosynthesis, photorespiration, energy and redox metabolism, proton turnover, sucrose translocation from source to sink tissues and biomass growth. In future, we will use the model to recognize cause-effect relationships and describe regulatory processes in carbon metabolism and transport.The aim of this research was to build a multi-compartmental metabolic model of growing Arabidopsis thaliana ) –72. We cWhile interpreting of the FBA results, main attention was placed on the metabolic activity of the mesophyll, since in the model the mesophyll constituted 85 % (w/w) of the mass fraction within the plant biomass.2 to yield triose phosphate (GAP or DHAP) in the Calvin-Benson cycle requires a maximal theoretical ATP/NADPH ratio of 1.5  was higher in the dark condition than in the light condition  glycolysis in the cytoplasm, (ii) oxidative phosphorylation in the mitochondrion and (iii) ATP synthase in the plastid. The FBA of our model predicted that in light conditions  83.2 % of the cytoplasmic ATP turnover was supplied from mitochondrial metabolism and 16.8 % was provided by glycolysis and other cytoplasmic processes  and only 7.7 % of the cytoplasmic ATP in dark conditions  and consuming  reactions. The FBA of the model predicted active export of reduced equivalents from the plastid to the mitochondrion via the malate/oxaloacetate shuttle in form of malate and oxaloacetate during light conditions Fig. . HoweverThe main objective of this model was the reconstruction of the proton-fluxes created by the activities of the redox and energy metabolism under different light conditions and their relationship with the translocation of sucrose. The model accounted for main metabolic processes (e.g. reactions and exchange transport) that participated in proton turnover in all compartments and sub-compartments Table . For exaArabidopsis thaliana from the environment in symport with nutrients into the root , the major consumer of cytoplasmic protons was the mitochondrion Fig. . For exaAlso many metabolic processes resulted in proton production/utilization and therefore additionally contributed to the proton balance in the cytoplasm. Particularly in the light , the plastid provided GAP, which entered glycolysis downstream of sugar phosphorylation reactions, which are producing substantial amounts of protons  and a release of protons contributing to approximately 17 % of the cytoplasmatic proton pool. This proton excess was mainly utilized by the fully functional TCA cycle in mitochondria. Correspondingly, the mesophyll in dark conditions pumped less protons to the apoplast Fig. .FBA of proton fluxes allowed quantifying the net-fluxes of protons from phloem to mesophyll and root. Further, taking in account pH differences between compartments, the protons enter the root cytoplasm from the phloem along their concentration gradient allowing symport of solutes including sucrose via SUC and STP transporters. The sucrose flux to the root (as the sink tissue) was in accordance with proton symport mechanisms of SUCs and STPs and the proton balance of the tissues involved Figs. , 4 and 7Arabidopsis thaliana. The flux balance analysis (FBA) of the model quantified sugar metabolism, central carbon metabolism, photosynthesis, energy and redox metabolism, proton turnover, sucrose translocation from mesophyll to root and biomass growth under both dark- and light-growth conditions with corresponding growth either on CO2 (in the light) or on starch (in darkness). The model showed that in light conditions, interplay between photorespiration and photosynthesis including both cyclic and non-cyclic electron flow defined the ATP balance of the plastid. The plastid was found to be either deficient, self-supported or producing a surplus of ATP. The excess of redox potential from the photosynthetic light system was translocated to the mitochondrion via the malate/oxaloacetate shuttle. Thus, photosynthetically active plastids could achieve the ATP/NADPH ratio required for CO2 fixation. Also, the FBA predicted that the mitochondria were the main ATP provider for cytoplasmic processes together with glycolysis under both light- and dark-growth conditions. At the same time, the mitochondria were the main sink for reduced equivalents translocated both from plastid and from cytoplasm as well as for the protons.In this work we present a multi-compartmental metabolic model of growing Arabidopsis thaliana in light and dark conditions. The FBA was used to optimize the network structure and to prepare it for further analysis of the distribution of the intracellular fluxes based on experimental measurements by Metabolic Flux Analysis (MFA).The model described all main metabolic processes participating in the proton metabolism. The translocation of sucrose among plant tissues was associated with an integral balance of protons, which in turn was partially defined by operational modes of the energy metabolism . The proton fluxes predicted by FBA generally corresponded to the molecular mechanisms and functional peculiarity of the sucrose transporters SWEET and SUC/STP proton-symporters and net-flux of sucrose from source to sink tissue. Thus, our multi-compartmental model adequately described the growth stoichiometry and sucrose translocation processes of The primary information on the metabolic reactions regarding stoichiometry, direction, metabolite elemental formula, charge state, and associated genes was collected from AraCyc  using thArabidopsis thaliana was reconstructed based on the decision to take in account only those metabolic processes, which contributed to better understanding of sugar metabolism, central carbon metabolism, energy metabolism, proton turnover, biomass growth and sucrose translocation among tissues in Arabidopsis thaliana. Thus, metabolic reactions from the following pathways were used in the reconstructed network: 2-oxoglutorate decarboxylation to succinyl-CoA; adenosine nucleotides de novo biosynthesis; amino acids biosynthesis; aspartate degradation; Calvin-Beson cycle; chorismate biosynthesis; fatty acids biosynthesis; folate metabolism; glutamine biosynthesis; glycolysis; homoserine biosynthesis; inosine-5’-phosphate biosynthesis; maintenance; malate/oxaloacetate shuttle; nitrate reduction; ornithine biosynthesis; oxidative phosphorylation; PRPP biosynthesis; pentose-phosphate pathway; phosphorus metabolism; photosynthesis light reactions; photorespiration; purine nucleotides de novo biosynthesis; pyrimidine ribonucleotides interconversions; pyruvate decarboxylation to acetyl-CoA; pyruvate fermentation; starch biosynthesis; starch degradation; sucrose biosynthesis; sucrose degradation; sulphate reduction; TCA cycle; UDP-glucose biosynthesis; UDP-glucoronate biosynthesis; uridine-5’-phospahte biosynthesis. Many biosynthetic reactions concatenated in linear pathways (e.g. nucleotide or amino acid biosynthesis) were lumped in order to reduce model complexity. All reactions in the model were manually transferred from the database, and manually curated.The model reconstruction was performed with Insilico Discovery™ . The structured metabolic network of cis-octadecenoate, 34.7 % cis-hexadecenoate, 2.4 % octadecanoate, 0.006 % Linoleate and 32.4 % glycerol. RNA composition was derived by reversed sequence analysis of protein amino acid sequences in FASTA format available from TAIR: 32 % A, 26 % U, 9 % C and 33 % G. Based on these data, the biomass macromolecular composition was considered to be (w/w) 20 % cellulose, 20 % pectin, 10 % starch, 12 % sucrose, 25 % proteins, 9 % lipids, 1 % RNA and 3 % ash. The mass ratio between shoot and root was accepted as 85/15 (w/w) based on own measurements of fresh weight of 21 days old plants grown under long-day (16 h light) conditions in hydroponic culture.The biomass was assumed to be composed of the following polymers: proteins, lipids, RNA, carbohydrates  and ash , 40. TheIn accordance with concept of sucrose emitting tissues and sucrose demanding tissues as well as under consideration of the molecular mechanism of sucrose translocation mechanisms Fig. , under dIn the model, each biochemical reaction (classified with E.C. number) was referred to corresponding KEGG reaction (KeggID). Additionally, reactions were annotated with accession numbers of genes whose products perform this biochemical process (GeneID). In case of lumped reactions, all corresponding GeneIDs were associated with lumped reaction. This annotation was later used to match the model with mass-spectrometry based proteomic data  and to vThe final version of the model was exported in SBML and MATLAB format  as well as the stoichiometric matrix of the model and high-resolution printout (JPG) of the model with all marked reactions. Corresponding databases of the compounds/metabolites, transformers/reactions and genes used in the model are provided in in ASCII format .Sucrose was considered as the only carbohydrate that is exchanged among tissues.The anatomy of the whole plant was reduced down only to three tissues: autotrophic mesophyll, heterotrophic root, interconnected by transport phloem. The model was restricted to the mesophyll as always being the ‘source’ tissue of sucrose both in light conditions (during photosynthesis) and in dark conditions . The root was always defined as the ‘sink’ tissue for sucrose. The root took up sucrose and used it for biomass formation, but did not store it with a purpose to emit it back to phloem. Thus, we have consciously constrained the exchange of sucrose to a unidirectional net-flux from autotrophic mesophyll to heterotrophic root Figs.  and 4.Sucrose was considered to be also a part of the formed biomass. Nevertheless, we did not consider the possibility that deposited sucrose can be returned to sucrose pool into the phloem for its exchange. Thus, sucrose, which was directed to biomass, was not considered as available for growth or exchange through the free sucrose pool.Starch was considered as a metabolite that can be formed and deposited during light growth phase and used as the carbon and energy source during dark growth phase. We assumed that starch was accumulated only in autotrophic tissues (mesophyll), but not in heterotrophic tissues (root).Starch was formed only during the light growth phase, and stored as a part of biomass. It was not degraded even partially during light growth phase.Starch was considered as the only carbon source for growth of the plant during dark conditions.In this version of the model, we did not consider exchange of amino acids and organic acids  between tissues.Fatty acid biosynthesis was considered to be cytoplasmic to reduce complexity of redox balance in plastid.ATP/ADP was made exchangeable between compartments Fig.  through NAD(P)H/NAD(P) could not be exchanged between compartments. However, the reduced equivalent could be transferred between compartments through malate/oxaloacetate shuttle.Phloem played only a role in transport processes and deliver nutrients and water from root to mesophyll, and sucrose from mesophyll to root as unidirectional net-fluxes Fig. .Nutrient uptake  followed a proton-symport mechanism.Proton transport against know pH gradient was always active (ATP-dependent), unlike transport along known pH gradients, which was considered as being passive Figs.  and 4.The only exchangeable metabolites from central carbon metabolism between plastid and cytoplasm in the mesophyll were D-glyceraldehyde 3-phosphate (GAP) and maltose. Other potentially exchangeable metabolites  were not considered in the model to reduce the complexity.Gas exchange between plant and environment was considered as being passive.In order to reduce the complexity in the description of processes within the multi-tissue plant and to focus mainly on the mechanisms of sucrose translocation, the model was formulated under the following assumptions:Before proceeding to advanced network analysis, the detailed consistency check of the network stoichiometry through elemental and charge balance analysis was performed with Insilico Discovery™ . In addition to known reaction stoichiometry, this analysis relied on known elemental composition and charge (at specific pH) of metabolites. Consistency check of elemental and charge balances allowed identifying and revising/eliminating: (i) compounds for which elemental composition and charge were unknown and (ii) inconsistent reactions, polymerizations and transport steps with unbalanced elements or charges. A Check for dead-ends and unused transformers allowed identifying and eliminating transformers and network regions which were not capable of operating at steady state. A check for irreversible transformers allowed visualizing and approving those transformers, which were irreversible from thermodynamic point of view. Irreversibility of a transformer was important constraint for the Flux Balance Analysis.S of the network is very powerful tool in optimization of the metabolic network structure, because it identifies and illustrates how substances flows within a network. This analysis also allowed identification of false or/and unused network structures. The degree of freedom estimated the dimensions of the network, and further requirements to solve it with experimentally derived measurements. The lower degree of freedom, the easier solutions can be found. Analysis of conserved moiety revealed sets of compounds, whose sum always remained the same even under dynamic conditions. The number of conserved moieties equalled the number of linearly dependent balance equations. Analysis of conserved moieties can reveal false-positive conserved moieties which had accidentally appeared due to designed network structure. Accidental false-positive conserved moieties were eliminated and only biologically relevant conserved moieties (e.g. NAD(P)H/NAD(P), acetyl-CoA/CoA/succinyl-CoA) were remain in the network. Analysis of blocked transformers identified regions of the network, which were not capable of operating at steady state due to the irreversibility constraints of the transformer equation. Computation of the base vector of the nullspace revealed “functional” and “non-functional” regions of a metabolic network. Nullspace of S is a set of all solutions of the linear equation system S × v = 0. S was the stoichiometric matrix of the network  and v was the rate vector of metabolic fluxes. Computation of parallel routes and cycles revealed a set of reactions/transporters/polymerizators which (i) were capable of maintaining a steady state, (ii) could not be decomposed, and (iii) in total did not consume or produce an external substrate/product. From condition (iii) followed that the net reaction of parallel routes and reaction cycles was zero. In the complex networks, there was a high chance for the accidental formation of parallel routes and cycles which increased internal degree of freedom. Parallel routes and cycles substantially increase the number of elementary flux modes without leading to new phenotypic behaviour. Therefore, their number had to be minimized in order to use elementary mode analysis for evaluation of maximal product yield. Computation of elementary modes revealed a unique set of smallest sub-networks that allowed a reconstructed network to function in steady state. Elementary mode analysis took into account stoichiometry and thermodynamics when evaluating whether a particular metabolic route or (sub-)network was feasible and likely for a given set of proteins/enzymes. The topological analysis of the model was performed with Insilico Discovery™ .Topological analysis of the stoichiometric matrix S × v = 0) using constraint-based Flux Balance Analysis (FBA). In order to calculate metabolic flux distribution, maximization of biomass formation was formulated as objective function. The FBA provided a method for computing flux distributions in the metabolic networks by linear optimization of the objective function:Z was the objective function, c was a vector of weighting factors, vi was the ith element of v and νimin and νimax were the minimum and maximum constraints on vi. The topology of stoichiometric matrix (S), together with lower (νimin) and upper (νimax) boundaries for the vector of metabolic fluxes (v), in combination with thermodynamic properties of reactions (e.g. irreversibility), and steady state condition (S × v = 0)provided with sufficient constraints to optimize objective function. The designed metabolic network was subjected to two specific constraints provided in Supplemental data (Table The distribution of intracellular metabolic fluxes through the metabolic network was investigated under pseudo steady state assumption (


Saccharomyces cerevisiae, it has long been known that a functional trehalose pathway is indispensable for transitions to high glucose conditions. Upon addition of glucose, cells with a defect in trehalose 6-phosphate synthase (Tps1), the first committed step in the trehalose pathway, display what we have termed an imbalanced glycolytic state; in this state the flux through the upper part of glycolysis outpaces that through the lower part of glycolysis. As a consequence, the intermediate fructose 1,6-bisphosphate (FBP) accumulates at low concentrations of ATP and inorganic phosphate (Pi). Despite significant research efforts, a satisfactory understanding of the regulatory role that trehalose metabolism plays during such transitions has remained infamously unresolved. In a recent study, we demonstrate that the startup of glycolysis exhibits two dynamic fates: a proper, functional, steady state or the imbalanced state described above. Both states are stable, attracting states, and the probability distribution of initial states determines the fate of a yeast cell exposed to glucose. Trehalose metabolism steers the dynamics of glycolysis towards the proper functional state through its ATP hydrolysis activity; a mechanism that ensures that the demand and supply of ATP is balanced with Pi availability under dynamic conditions. In the model eukaryote  In response, regulatory mechanisms are required to ensure that ATP production by lower glycolysis can keep up with ATP consumption by the upper-part; failure to do so results in a dysfunctional state, which we call an imbalanced glycolytic state, and loss of cell viability. The consequences of this failure have been extensively studied in S. cerevisiae, where the trehalose pathway has been implicated as a key component of the regulatory machinery that ensures robust transitions to high glucose.The general design of glycolysis, with an ATP consuming upper-part and an ATP producing lower-part, comes with major risk if sudden increases in flux are not managed properly. When excess glucose becomes available, its rapid phosphorylation results in the decrease of ATP and PS. cerevisiaetps1Δ mutants lack a functional trehalose cycle and are unable to grow on glucose. A shift to high glucose leads to the accumulation of FBP with low ATP and Pi levels, suggesting an imbalance in glycolytic fluxes. Mechanisms ascribed to the trehalose pathway implied an involvement in the regulation of substrate demand by the upper- and lower-parts of glycolysis, either by (1) direct inhibition of hexokinase (hxk) - the first ATP consuming step - by an intermediate of this pathways, trehalose 6-phosphate (T6P), or (2) enhancement of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) activity - the first reaction in lower glycolysis - through supply of Pi. However, neither of these mechanisms could fully explain the metabolic deficits of tps1Δ mutants.S. cerevisiae from a systems-level perspective. A detailed kinetic model of glycolysis allowed us to simulate the tps1Δ metabolic behavior. This model reproduced the most relevant metabolic features of the tps1Δ phenotype . Surprisingly, we found that with the same parameter settings a second state, with normal glycolytic function, could be achieved under different initial conditions (e.g. higher Pi concentration). This bistable behavior of the model suggested the possibility that two phenotypes, viable and non-viable, could be present in the same population. We pursued this observation experimentally, reasoning that cells in the normal state should be viable. Using plating assays we found that, not only did such a small fraction of cells exist (~1 in 103 cells), but it appeared spontaneously and reproducibly in any population of sufficient size. Propagation experiments showed that this glucose tolerance was non-genetic in nature. Additionally, this result provided an explanation for population level growth behaviors, which showed extremely long lag phases (> 20hrs). Further testing showed that such mixed population structures also explained the growth profiles and lag behaviors of tps1Δ populations grown on galactose with increasing amounts of glucose. In evaluating previous studies we found these results to be particularly informative in explaining population-level measurements for tps1Δ and related mutants.In this study, we started by exploring the consequences of a defective trehalose cycle in tps1Δ cultures. Significantly, our results revealed that a surprisingly large fraction of wild-type cells (7 %) also failed to properly initiate glycolysis.Growth happens on much longer time scales than the metabolic processes for which we predicted the bistable behavior; could we also show distinct metabolic states at the single cell level? We exploited the fact that cells in the imbalanced state cannot maintain pH homeostasis, a consequence of low ATP levels, to identify the two states  within a single population . For this we used a pH-dependent GFP reporter (pHluorin), combined with high throughput flow cytometry, and confirmed the presence of two distinct metabolic subpopulations in 6 unique tps1Δ models, each with slightly varied initial metabolite concentrations and enzyme levels (Vmax); a situation likely for a population consisting of millions of individual cells. In response to a simulated glucose pulse, approximately 1 in 103 models reached a functional steady state. Importantly, this approach allowed us to identify metabolic variables that could compensate for the absence of a trehalose pathway. We found that the successful initiation of glycolysis depended not so much on a single mechanism, but is determined by the interplay between components that tend to reduce the flux through the upper part of glycolysis (ATP consuming ) and those that enhance the flux through the lower (ATP producing) part. Further analysis showed that enhanced futile cycling of ATP would provide a consolidation of the identified mechanisms that together would achieve a balance between upper- and lower glycolytic activities .Next, we showed how this type of phenotypic heterogeneity could arise in metabolism through small continuously distributed variations in metabolic variables, rather than stochastic fluctuations. We generated > 10i availability. Estimations of in vivo fluxes, using a 13C-tracer approach, confirmed that this cycle is indeed transiently activated, with a highly dynamic response during shifts to high glucose. As much as 28% percent of the uptake flux was channeled toward trehalose, significantly impacting both the overall glycolytic flux and phosphate homeostasis. The flux profiles were fully consistent with our model’s prediction that various features inherent in this futile cycle  all contribute  to the robust startup of glycolysis. We could now also understand the seemingly incomplete mechanistic descriptions offered by previous interpretations.We then realized that the full trehalose pathway constitutes a futile cycle, and that its temporary activation in response to glucose would be an effective way to balance the demand and supply of ATP with Pi. Depending on the rate of glucose phosphorylation, the system may reach critically low Pi and ATP levels that could result in an imbalance between fluxes through the upper- and lower-parts of glycolysis. If, however, mechanisms kick in that slow down the upper part and/or Pi recovery is enhanced (i.e. stimulating the lower part), a balanced state can be reached. Importantly, this only made sense after the dynamics of the whole glycolytic pathway - its bistability - was considered.A dynamic picture of regulation emerged: if glucose is suddenly available, its rapid phosphorylation in the upper part of glycolysis results in a drain on ATP and PWhile this study solved the long standing puzzle of the trehalose cycle’s involvement in glycolytic transitions, it was intriguing that 7 % of wild-type cells also failed to cope. Apparently the proper startup of glycolysis is not guaranteed even when the necessary regulatory components are available. How can this failure be understood? We speculate that the wild-type failure reflects a trade-off between efficient start-up and failsafe regulation. Although cells could reduce the startup risk by always maintaining the trehalose cycle in a high state of activity, effecting tighter control of glucose influx will come at a permanent cost of reduced ATP yield and flux capacity. From a population perspective a 7 % loss in viability could be justified if the remaining 93 % survives without a permanent sacrifice in capacity. Such a trade-off scenario has been shown, in many other contexts, to be an outcome of evolution in dynamic environments.This work underscored the importance of specific mechanisms which dynamically regulate metabolism during environmental changes. It seems likely that such mechanisms are of general importance in natural environments, where changes can be transient and occur very suddenly without much forewarning. It may not be a coincidence that a pathway implied in heat and osmostress response is used by yeast also to cope with “glucose stress”.


Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies. The pioneering experimental work of Mamer and Horning  or unsupervised tools . Instead, we focused on systems biology approaches like network inference and metabolic modelling.An important aspect underlying most if not all the methods for the analysis of metabolomics data that we will address in the next sections is to properly identify the metabolites in the MS or NMR spectra and map them within the metabolic context of the organism. Often the peaks detected in experiments are assigned based on reference spectra contained in large chemical database. However, the analytical methods used in metabolomics do not allow coverage of the whole range of small molecules produced by an organism, introducing possible biases in the interpretation of whole-organism metabolism. Although this is a very broad theme, in this section we will try to summarize the features of some tools for metabolite mapping that can be exploited in the context of systems biology approaches.http://procyc.westcent.usu.edu/cgi-bin/MetaboSearcher.cgi) to directly search genome-constructed metabolic databases. Its aim is to enhance the identification of MS data by using compound databases derived empirically. Incorporating information on genome-encoded metabolism facilitates the identification of MS peaks that may not be present in standard chemical databases. Only the compounds that the organism of interest is able to produce, based on its genome, are investigated for potential matches. The output metabolites are mapped also to known metabolic pathways.Metabolome Searcher  addresses the annotation of putative metabolites by providing a hypothesis-driven approach to interpret MS data. MassTRIX processes the submitted list of raw mass peaks by comparing the input experimental masses against all chemical compounds of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database , the user can interpret the metabolic state of the organism in the context of its actual or potential enzymatic capacities.The MassTRIX web server  is the Polyomics integrated Metabolomics Pipeline (PiMP)   allows users to combine datasets from heterogeneous multiple-omics sources. This tool visualizes and combines datasets from transcriptomics, proteomics, and metabolomics on user defined metabolic pathway maps. ProMeTra supports pathway maps designed and annotated by the users.The web-based ProMeTra system  using dedicated experiments. In this review, we focus on the application of network approaches to analyze metabolomics data that usually have not been gathered with the aim of reconstructing entire metabolic networks. For the latter purpose, the typical starting point is genome data  and their relationships (described by links or edges). It can be conveniently described using a matrix, termed adjacency or connectivity matrix Table ρ = 0.0243, Spearman). Instead, valine and methionine are strongly correlated (ρ = 0.951) even if they are not metabolic neighbors  based on some similarity measure: in metabolomics the similarity between metabolites, and thus their association, is usually expressed using Pearson or Spearman’s correlation indexes. Consequently, the elements of the corresponding adjacency matrix are in the interval   and glucose in high and low risk cardiovascular risk networks. They applied a combined method, by analysing association networks with a multivariate approach to highlight differences among networks pertaining to different risk phenotypes  can be affected by data pre-treatment  such as scaling, transformation and normalization. Such pre-treatments are routinely applied to metabolomics data in order to correct for systematic and unwanted variation such as sample-to sample to variability induced by dilution effects (e.g. in the case of urine) or differences in experimental settings (like different sample titration or different number of scans in NMR experiments). The literature on the topic is huge: we refer the reader to Bijlsma et al. , Goodacrwij of TOM is defined asmi and mj denote metabolite i-th and j-th, ki and kj denote the connectivity of metabolite i-th and j-th. The dissimilarity is defined as 1 − wij, which is a measure of interaction between metabolites weighted by the strength of their correlation. The parameter Weighted gene correlation network analysis (WCGNA) is a systems biology method for describing the correlation patterns among genes across microarray samples. WCGNA can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the so-called module eigengene, i.e. a representative gene summarizing the expression profile of the module  and the clustering coefficient C(k) to follow a power law. The authors found P(k) to follow a power law but not C(k), indicating the absence of modular structure in the network of urinary metabolites. They suggested that this may be caused by (i) urine being a waste product in which little to no active metabolism occurs and (ii) the limited number of metabolites considered (n = 60) which is less than the content of the full urinary metabolome. A further hypothesis put forward in the same work was that networks constructed from metabolite profiles derived from biological samples that are metabolically active, such as blood or tissue, may exhibit power law (i.e. a few metabolites connected with many metabolites) behaviour in both connectivity and clustering coefficients. However, in contrast to gene regulatory network, expression networks or metabolic networks, the metabolite correlation networks have not been fully characterized in terms of network topology (i.e. the patterns of interconnection among the nodes). Therefore, it is not very clear what are the expected or more likely network properties . We refer the reader to , the mutual information MI between A and B is defined asp is the joint probability distribution function of A and B, and p(ai) (respectively p(bj)) indicates the probability that A = ai (respectively B = bj). It should be noted that the mutual information between two variables is not independent from correlations, since, under some conditions, the two variables can be functionally related  and pruned, i.e. set to 0, if the difference between the two lowest weights is above a cut-off value ξ. In practice, the following two conditions are evaluated for each triplet i, j, k:ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks)  between two metabolites by defining a null model that considers all the possible MI values [MIi] and [MIj] for metabolites i and j. The following equations define the likelihood fµi and σi are, respectively, the mean and the standard deviation of the distribution of the [MIi] values: a weighted adjacency matrix is built with entries f.The CLR algorithm  to analyse and interpret untargeted liquid chromatography–mass spectrometry (LC–MS) data from lipidomics and phosphoproteomics experiments in a cell-line model of Huntington’s disease. Grounding on database information, the algorithm infers the identity of unassigned metabolites corresponding to features and the molecular mechanisms underlying their dysregulation. This innovative approach helps to reduce the bias towards well-studied metabolites typical of targeted metabolomics. The algorithm takes as input a list of LC–MS peaks that differ between two different conditions and searches for them in a databases containing over 42,000 nodes (either proteins or metabolites) connected by over one million weighted edges representing interactions between proteins as well as enzymatic and transporter reactions. The output is a subnetwork of the database representing metabolic pathways that are dysregulated under the conditions considered.Pirhaij et al.  used theThe metabolism is a network structure that can be approached as a system of interdependent variables that enable mathematical modelling through kinetic models. These models are defined as systems of ordinary differential equations describing the time course of metabolite concentrations as a function of rate laws that account for enzyme catalysis. The development of these models requires to know both the network structure and the reaction kinetics and parameters   of transformation or transport. Mechanistic changes at the level of the metabolism are likely to produce changes in the distribution of fluxes. Intracellular fluxes are not directly measurable, but the use of stable isotope-enriched nutrients, such as 1,2-13C) relies upon a combination of different methods, going from the direct interpretation of the labeling patterns to computational model-based approaches . Instead, the entry into the non-oxidative pentose phosphate pathway results in the synthesis of ribose phosphate molecules that contain two 13C atoms . The subsequent entry of M+1 pentose-phosphate into glycolysis contributes to the synthesis of triose phosphate and lactate molecules with one 13C atom (M+1). An approximate estimation of the relative importance of oxidative versus non-oxidative pentose phosphate pathway fluxes can be inferred from the M+1/M+2 ratio of the RNA-derived ribose. During the last years, the use of this and other isotopic labelling tracers have been applied to unveil the different metabolic pathways activated in cancer cells . Progressively, these products are enriched in 13C, with concomitant decrease in M0. Isotopic steady state   where one checks whether a group of differentially expressed genes is enriched for a pathway or ontology term by using overlap statistics such as the cumulative hypergeometric distribution . As the first step of the analysis, metabolites are assigned to specific sets based on one or more reference databases. A group of metabolites are assigned to the same set if they are known to be: (i) involved in the same biological processes   of p metabolites are measured, of which m metabolites belonging to the same pathway are selected. The question whether these metabolites behave differently in the two conditions being compared can be translated into the question whether the metabolite levels are predictive for the outcome  testing (e.g. Bonferroni). The Globaltest detects consistent differences in patterns of metabolite levels between two conditions. It does not test in which direction a pathway is regulated (up or down), nor it determines how many metabolites have changed concentration levels between two conditions. If the tested pathway is activated or inhibited by the tested condition , the differences in metabolite levels will result in a large Q-score and a small P-value. However, the results may change, depending on which metabolites are included, i.e. on the completeness of the database(s) from which prior knowledge has been obtained. If the correlation of the missing metabolite(s) with the outcome is almost equal to the average correlation between the outcome and the metabolites included in the pathway, this has almost no effect on the Q-score. Instead, if a metabolite that has a much higher or lower correlation to the outcome than average is missing then the Q-score will change upon its inclusion. This is an aspect inevitably intrinsic to the MSEA strategy. Databases contain metabolites from only a limited number of pathways, compared to the whole metabolic network of an organism. Consequently, it is possible to test only a relatively small number of pathways and this is an inherent limitation of MSEA.Different strategies exist for performing MSEA depending, among others, on the statistical test applied. In the popular ome Fig. . In othen et al.  dealt win et al. , which iF-test is applied to test the null hypothesis and a P-value is computed using permutations. A correction for multiple testing is also used. Differently from the Globaltest, GlobalANCOVA evaluates the impact of group membership on the observed metabolite concentration patterns. In other words, GlobalANCOVA practically tests the null hypothesis that the information on the group level does not improve the fitting. The GlobalANCOVA approach allows the inclusion of time-dependent information in a straightforward manner constructed . GlobalANCOVA exploits linear logistic regression and Analysis of Variance (ANOVA) in the framework of a global assessment for a group of metabolites. GlobalANCOVA aims to evaluate the relationship between the metabolite concentrations and the phenotypic covariates. In particular, the aim of GlobalANCOVA is to prove the relevance of certain covariates in explaining the observed metabolite concentration patterns, called covariates of interest. Therefore, two models are compared: the full model (FM), which contains all covariates and the reduced model (RM), which does not have the covariates of interest. The null hypothesis is that both models explain the data equally well. The relevance of the covariates of interest in explaining the observed pattern is proven if the full model explains the observation better than the reduced model. To do so, a squared error is computed for the fitting of the concentration levels of each metabolite. Subsequently, the residual sum of squares (RSS) over all metabolites in the group is computed. Finally, a multivariate test statistic is built based on the RSS values for the full and reduced models  terms are often used for this purpose. The annotation terms passing the enrichment P-values). The analysis focuses on whether common terms tend to occur towards the top or the bottom of the list , and this can make difficult to grasp potential interrelationships of relevant terms. In addition, the quality of the pre-selected metabolite lists has a deep influence on the enrichment analysis, making the output unpredictably sensitive to changing statistical methods or cutoff thresholds. In particular, it is inappropriate to use all the metabolites of the metabolite set library as the reference metabolome, because there is no analytical platform that can measure all these metabolites with the same probability. Thus, the choice of the platform rather than the experimental conditions may cause the observed metabolite enrichment. To tackle this problem, the user may upload a platform-specific reference metabolome. This is an option provided, for example, in the implementation available in MetaboAnalyst  (Hulsegge et al. t-test or by ANOVA. PAPi can provide information regarding the impact of environmental conditions and stimuli on metabolite uptake and intracellular metabolic overflow. Metabolic pathway activity is directly related to metabolic flux distribution and thus this kind of analysis can tie directly to fluxomics.PAPi allows users to compare the activity of metabolic pathways under different experimental conditions (Aggio et al. The systems biology approach to the interpretation of metabolomics has the potential to unravel the causative mechanisms leading to the observed metabolomics profile. In this way, there is a paradigm shift from the chemometrics framework that makes metabolomics a hypothesis-generating research field to a framework where metabolomics can provide insights into the biological properties of cell and organism functioning. This shift will unlock the potential of metabolomics and related omics disciplines, such as fluxomics and lipidomics, to fully contribute to the advancement of our understanding of health and disease. In this review, we addressed approaches based on association networks and on pathway analysis. These are useful tools to grasp the complexity of metabolomic profiles; however, they are not sufficient to understand fully the intricacies of the metabolism without dedicated experiments.Many of the methods described here exploit the lessons learned in other, more mature omics, mainly genomics and transcriptomics, e.g. regarding the validation of their theoretical frameworks. As mentioned several times, a major caveat in untargeted metabolomics is the impossibility of measuring all metabolites in the sample, whose consequences are very difficult to predict.