Self-sustained metabolic pathways in microcompartments are the corner-stone for living systems. From a technological viewpoint, such pathways are a mandatory prerequisite for the reliable design of artificial cells functioning out-of-equilibrium. Here we develop a microfluidic platform for the miniaturization and analysis of metabolic pathways in man-made microcompartments formed of water-in-oil droplets. In a modular approach, we integrate in the microcompartments a nicotinamide adenine dinucleotide (NAD)-dependent enzymatic reaction and a NAD-regeneration module as a minimal metabolism. We show that the microcompartments sustain a metabolically active state until the substrate is fully consumed. Reversibly, the external addition of the substrate reboots the metabolic activity of the microcompartments back to an active state. We therefore control the metabolic state of thousands of independent monodisperse microcompartments, a step of relevance for the construction of large populations of metabolically active artificial cells. Generating artificial cells able to carry out metabolic activities out-of-equilibrium is a current challenge in synthetic biology. Here the authors use a microfluidic platform for integration and analysis of minimal metabolic reactions in artificial microcompartments formed of water-in-oil droplets. The complexity of metabolism in living systems is a widely accepted feature2. Attempts to modify or simplify these complex networks of reactions—the task of metabolic engineering—face the problem that any modification within these networks affects the overall behavior of the cell3. While metabolic engineering is centered on the genetic modification of living cells4, a bottom-up approach to build controlled cell-like systems from soft matter constituents by integrating synthetic pathways in microcompartments has become a promising alternative6. The organization of metabolic processes in microcompartments is a mandatory prerequisite for the construction of artificial cells8. Realizing such an assembly in a bottom-up approach would provide an unprecedented level of control on the constituents of the metabolic functions and allow a fine control of its waste and side products. To date, the bottom-up approach for the creation of life-like artificial microsystems is still in its infancy14 but key building blocks are step by step assembled16, from the creation of microcompartments18 to the in vitro integration of complex artificial metabolic pathways19. Under the assumption that primary living cells have emerged from prebiotic systems made of soft matter6, one should be able to integrate elementary metabolic activities in minimal systems to maintain these systems out-of-equilibrium. Under these conditions, even simple reactions are relevant in a protocell context, as evidenced by recent models of protocells divisions based on metabolic activity20.Metabolic activity is a hallmark of living systems. The chemical transformations of molecules present in the environment provide the energy required for the cell to maintain its out-of-equilibrium state and thereby prevent its decay toward a state of minimal energy23. Microfluidics provides means to quantitatively manipulate minute volumes of biological materials in the form of soft-matter systems such as (multiple) emulsions or vesicles26. The technology enables the miniaturization and parallelization of assays for high-throughput biological experiments in protein engineering27, cell screening29, molecular diagnostics30, or sequencing33. The same tools used to perform complex chemical assays and analysis are now integrated to build-up and analyse large population of artificial microcompartments having functions and properties mimicking those of living systems35.Microfluidics has become a key technology for the creation, manipulation and analysis of microcompartments, as well as for the control and programming of in vitro biochemical processes36. A key feature to integrate is the ability to function under conditions where the reaction is sustained independently of the cofactor stoichiometry. Here, the full conversion of the metabolic substrate requires the regeneration of the cofactor NAD+. The regeneration module is made of inverted membrane vesicles (IMVs) extracted from E. coli. We monitor the state of the microcompartments (active vs sleeping) by the readout of the microcompartments fluorescence. We analyze the kinetics of the system both in bulk experiments and over thousands of monodisperse microcompartments. A sustained active state is maintained for times varying between several minutes to hours depending on the experimental conditions, such as the concentrations of the IMVs and of the substrate. The decay of the active state is determined by the initial amount of the substrate in the microcompartments. We show that the active state is recovered by the injection of fresh substrate in the microcompartments, using a high-throughput targetted delivery of substrate in each droplet37. This reboot of activity actually confirms that the end-state upon full substrate consumption is a sleeping state that can indeed be reactivated. Our experiments therefore provide quantitative measurements of the metabolic state of the microcompartments, a measurement of these states over thousands of microcompartments and the on-and-off switching of the metabolic activity of the microcomparment through a chemical regeneration, all elementary steps required for the construction of autonomous metabolically active artificial cells.Here we use microfluidic systems for the integration of minimal metabolic reactions in man-made microcompartments. We design a platform for the production, manipulation and analysis of millions of individual monodisperse microcompartments in a water-in-oil emulsion. We develop an assay based on nicotinamide adenine dinucleotide (NADH) fluorescence to quantify the metabolic state of the microcompartments. The minimal metabolism is constructed from a reaction converting glucose-6-phosphate (G6P) into 6-phosphogluconolactone (GLP). The reaction is catalysed by glucose-6-phosphate dehydrogenase (G6PDH), an enzyme involved in the pentose phosphate pathway24. Droplet-based microfluidics provides both control of individual droplets and the capacity to process millions of them in automated and parallel processes. Although, the w/o interface acts as an almost impermeable membrane, a mediated transport of chemicals can be induced, either passively through the external oil phase38 or actively using microfluidic injection techniques39 to precisely control the composition of the microcompartments.We use water-in-oil (w/o) droplets stabilized by a block-copolymer surfactant as artificial microcompartments to host enzymatic reactions involved in metabolic pathways (such as the pentose phosphate pathway or the Krebs cycle). W/o droplets offer a powerful means of compartmentalization and they are produced, manipulated and analyzed at very high-throughput using droplet-based microfluidics42. We generate either 1-bit, 4-bit or 8-bit emulsions when one, four or eight experimental conditions are simultaneously assayed on chip. The optical setup for the fluorescence analysis of the microcompartments is described in Supplementary Figure 37. The droplets are incubated on-chip in delay-lines43. Reaction kinetics is monitored over time in the microcompartments using fluorescence measurements at specific incubation points. The system is calibrated for all the fluorophores used here using 8-bit emulsions  based on previously reported systems  into galactose and fluorescein  to monitor NAD-dependent metabolic reactions without designing specific fluorogenic assays. To demonstrate that our approach is valid, we use three independent systems involving NADH. We consider a single-step reaction and monitor the activity of compartmentalized glucose-6-phosphate dehydrogenase, which oxidizes G6P (1) into GLP (2) with the concomitant reduction of NADADH Fig. . The kinADH Fig. . We thenIn summary, the developed platform is designed as a generic system and allows for the monitoring of biochemical processes involved in metabolic pathways, with a versatile optical readout of the metabolic state of microcompartments using NADH fluorescence. This high-throughput methodology is then used to monitor the metabolic state of millions of biomimetic microcompartments based on NAD or NADH-dependent enzymatic activities.+ would provide the chemical energy needed for a continuous metabolization of uptaken substrate. In this sense, a self-sustained system is required for the in situ regeneration of the cofactor.Bioactive microcompartments need a constant energy supply in order to stay thermodynamically out-of-equilibrium and be able to activate their metabolism in presence of a given substrate. In the case of NAD-dependent reactions, the constant supply of NADH/NAD+/NADPH redox balance in synthetic biochemistry systems44. Here, we follow a bottom-up strategy for the integration of functional modules based on microcompartments. We use Inverted Membrane Vesicles (IMVs) extracted from E. coli as functional microcompartments for the regeneration of NAD+ cofactor.The use of NADH oxidase has been reported as an efficient method to maintain the NADP46 and the ATP synthase for oxidative phosphorylation47. Apart from active transport studies49, the use of the respiratory chain functionality of IMVs has only been employed for cell-free protein production47. In our experiments, we aim at using the NADH dehydrogenase activity of the bacterial respiratory chain complexes of IMVs as cofactor regeneration module for our enzymatic reactions ons Fig. . IMVs are + Fig. . The rate + Fig. .Fig. 2NAN of vesicles per compartment . Under the assumption of a uniform concentration of vesicles in the bulk, we expect the number of vesicles in the droplet to be distributed as a Gaussian with a width of order 29, Supplementary Note The activity of the compartmentalized IMVs is then measured in microfluidics Fig. . The IMVnts Fig.  and the 16 protons towards the core of the IMVs. For 100 IMVs in a droplet, each IMV would contain ~1014 protons in a volume of 2 aL, corresponding to a volume much larger than the volume of an IMV filled with water. This unphysical situation implies that a leakage of protons has to spontaneously occur from the vesicle to the droplet bulk. In living cells, the inward and outward proton fluxes are balanced by the coupling of the respiratory chain with the oxidative phosphorylation of ADP. However, the respiratory chain is also known to be functional even when uncoupled from the ATP synthase activity in a state referred to as basal or state 4 respiration. This state is characterized by the leakage of protons through proton pumps and/or through the membrane52. From the estimation of the proton flux, our experiments suggest that a similar process occurs with the IMVs.The complete regeneration of NADH at a concentration of 250 μM in a 30 pL volume requires the transfer of ~10+ regeneration systems in droplet microcompartments. Our next step consists in the coupling of this cofactor regeneration module to a NAD-dependent enzymatic module in order to obtain a self-sustained metabolism in the microcompartments.These results indicate that IMVs are efficient NADWe first test the experimental conditions under which the NAD-dependent enzymatic reaction is sustained using IMVs Fig. . The conτ1  activity network , NADH (500 μM), IMVs (about 40 vesicles per droplet) and G6P at different concentrations . We monitor the metabolic state of the microcompartments over 26 h as a function of the G6P initial concentration  with a constant concentration of the substrate. The regeneration of NADH and therefore the rate of the enzymatic conversion depend on the microcompartment in which the reaction occurs Fig. . The out44: the stoichiometry is self-controlled in the metabolicaly active state. We produce large populations of independent microcompartments, each having their own reservoir of chemical fuel or their own fitness. As long as the fuel is available the compartment is active. When the fuel is consumed, the microcompartments reach an inactive state. We will refer to this state as a sleeping state. Indeed, while the IMVs are not functional and no reaction occurs in the microcompartments, the regeneration of the cofactor leads to a high concentration of NAD+. As a consequence, the microcompartments should reboot upon the addition of fresh substrate.The important message is that—similar to the case of metabolism in living cells—the metabolic system functions under conditions where the cofactor is the limiting compound. With the IMVs regeneration module, we do not depend on a 1:1 stoichiometry of substrate and cofactor; we also do not rely on a perfect balance of the two oxidative states of the cofactorsHere, we demonstrate that feeding the microcompartments with fresh susbstrate reactivates the metabolic activity. So far our approach has shown that the metabolic state of the microcompartments can be controlled by the experimental conditions. However, living systems make use of the supply of chemical energy by their surrounding environment to maintain their out-of-equilibrium state.We first demonstrate that the system can be reactivated by an external supply of substrate in bulk experiments. The bulk system is initially in an active metabolic state composed of IMVs, G6PDH, NADH and G6P in 384-well plate Fig. . When alTo demonstrate that the microcompartments can also be reactivated, we integrate an injection mechanism on-chip to control the addition of fresh substrate molecules in the microcompartments Fig. . The micWe produce and control microcompartments which display a controllable and self-sustained metabolic activity. For the sake of clarity, we want to point out that the activity is self-sustained as long as an energy source, in the form of the substrate, is present in the system and consumed. In this respect, our system is dissipative and maintained out-of-equilibrium for a finite time. First, we design a microfluidic platform to produce large populations of microcompartments in the form of picoliter water-in-oil droplets. Our platform provides statistically relevant data through quantitative and non-invasive fluorescence measurements of the NADH level. We use this high-throughput methodology as a quantitative measurement of the metabolic state of each individual microcompartment in populations. Our method is generic and usable in a wide range of NAD-dependent enzymes, provided that the chemicals do not optically interfere with the NADH fluorescence.+ regeneration. The reaction is maintained out-of-equilibrium for hours until full consumption of the substrate. Oxygen is an essential element in the reaction :1(water). The oil therefore acts as a large reservoir of oxygen that constantly replenishes the amount consummed by the reaction in the aqueous phase. We have shown previously that chemical equilibrium in water-in-oil emulsion is reached within seconds even for larger organic molecules, several orders of magnitude faster that the kinetics of the reaction38: the kinetics of oxygen transport at the droplet scale is not a limiting factor. As a note, this particular effect is used in other contexts to control polymerization54 or to improve fermentation processes55. The kinetics of the reaction and the concentration profiles inside the compartments are recovered in a minimal kinetic model , ATP (Sigma A26209), glycerol , sulforhodamine B sodium salt , glucose-6-phosphate , FDG , glycerokinase from Cellulomonas sp. , glycerol-3-phosphate dehydrogenase from rabbit muscle , glucose-6-phosphate dehydrogenase from L. mesenteroides , β-galactosidase from E. coli , citrate synthase from porcine heart , l-(-)-malic acid , Dextran-Cascade Blue (3000 MW) , acetyl-CoA , l-malate dehydrogenase from pig heart  solutions were prepared by dissolution in millipore water, NaOH-Tricine buffer , NaOH-glycine buffer or KOH-Tricine buffer .Fluorescein sodium salt , NADH , NAD64. The surfaces of the microfluidic channels were treated using fluoro-silane  before use.Devices were made of poly-(dimethylsiloxane)  from SU8-3000 negative photoresist (MicroChem Corp) molds (20 or 70 μm depth) produced using a soft-lithography procedure as per standard techniques65. All microfluidic devices were used at controled room temperature (20 °C). Three microfluidic workflows were implemented depending on the experimental needs:Either Nemesys syringe pumps (Cetoni) or a pressure driven pump  were used to control the flows in the microfluidic devices. Devices were connected to flow controllers with PTFE tubing (Fisher Scientific) with an inner diameter (ID) of 0.3 mm and an outer diameter (OD) of 0.76 mm. Droplets were produced in fluorinated oil  and stabilized against coalescence by a perfluoropolyether-polyethyleneglycol block-copolymer surfactant (PFPE-PEG-PFPE), synthesized as previously described−1) with the fluorinated oil containing 3 wt% of surfactant (600 μL h−1). Droplets are produced at 1.9 kHz at each production nozzle and are collected in a glass vial. Several emulsions can be produced sequentially and combined in the same collection vial. Droplets are then reinjected in the kinetics module. Droplets are co-flown (0.5–0.8 bar) with fluorinated oil containing 3 wt% of surfactant (0.5–0.8 bar) and picoinjected with an aqueous phase  by applying an AC field . A fraction of fluorinated oil was extracted (−20–50 μL h−1) and the droplets are incubated on-chip in a delay line. Droplet fluorescence is measured at different time points along the delay line up to about 1 h, corresponding to a tenfold increase in the maximal incubation time compared to previously reported systems41.Workflow 1: Short-term multiplexed kinetics  with the fluorinated oil containing 3 wt% of surfactant (300 μL h−1). Droplets are produced at 1.9 kHz and are collected in a glass vial on ice to freeze the metabolism. Several emulsions can be produced sequentially and combined in the same collection vial. Droplets are then warmed up to room temperature and reinjected in a simple reinjection module for fluorescence analysis. Droplets (25 μL h−1) are co-flown with fluorinated oil (150 μL h−1) to be spaced for droplet fluorescence measurement. Droplets are this way continuously analyzed over hours.Workflow 2: Long-term multiplexed kinetics  with the fluorinated oil containing 3 wt% of surfactant (200 μL h−1). Droplets are produced at 450 Hz. A fraction of fluorinated oil was extracted (−20–50 μL h−1) and the droplets are incubated on-chip in a delay line. Droplet fluorescence is measured at different time points along the delay line.Workflow 3: Short-term single kinetics  in 45–50 μL of solution. The fluorescence was monitored at room temperature using a spectrofluorometer .Images were taken with a digital camera . A light emitting diode  combined to an epifluorescence cube composed of an excitation bandpass filter , a beamspliter  and an emission bandpass filter  were used for the excitation of NADH.E. coli were done by disintegration of the bacterial membrane and subcellular fractionation. The procedures are fully described in Supplementary Note E. coli (MG1655) were grown in LB medium and collected by centrifugation. The cells were lysed and homogenized either by ultrasonication  or French press . The membrane was isolated by three ultracentrifugation  steps  on a qNano device . For the measurement a NP200 stretchable nanopore was used, which was calibrated with carboxylated polystyrene beads (mean size 350 nm). The lower fluid cell was filled with 80 μL membrane buffer and 30 μL of the vesicles, diluted with membrane buffer, were added to the upper fluid cell. Size distribution and concentration were calculated from the measurement data with the instrument software . The average concentration was 2.2 ± 0.4 × 10The data that support the findings of this study are available from the corresponding author upon request.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2


Our quantitative insights into the methanotrophic carbon and energy metabolism will pave the way for future FBA studies and set the stage for rational design of methanotrophic strains for industrial applications. Further, the experimental strategies can be applied to other methane or methanol utilizers, and the results will offer a unique and quantitative perspective of diverse methylotrophic metabolism.Methanotrophic metabolism has been under investigation for decades using biochemical and genetic approaches. Recently, a further step has been taken toward understanding methanotrophic metabolism in a quantitative manner by means of flux balance analysis (FBA), a mathematical approach that predicts fluxes constrained by mass balance and a few experimental measurements. However, no study has previously been undertaken to experimentally quantitate the complete methanotrophic central metabolism. The significance of this study is to fill such a gap by performing  13C tracer analysis, major gaps still exist in our understanding of methanotrophic metabolism at the systems level. Particularly, direct measurements of system-wide fluxes are required to understand metabolic network function. Here, we quantified the central metabolic fluxes of a type I methanotroph, “Methylotuvimicrobium buryatense” 5GB1C, formerly Methylomicrobium buryatense 5GB1C, via 13C isotopically nonstationary metabolic flux analysis (INST-MFA). We performed labeling experiments on chemostat cultures by switching substrates from 12C to 13C input. Following the switch, we measured dynamic changes of labeling patterns and intracellular pool sizes of several intermediates, which were later used for data fitting and flux calculations. Through computational optimizations, we quantified methane and methanol metabolism at two growth rates (0.1 h−1 and 0.05 h−1). The resulting flux maps reveal a core consensus central metabolic flux phenotype across different growth conditions: a strong ribulose monophosphate cycle, a preference for the Embden-Meyerhof-Parnas pathway as the primary glycolytic pathway, and a tricarboxylic acid cycle showing small yet significant fluxes. This central metabolic consistency is further supported by a good linear correlation between fluxes at the two growth rates. Specific differences between methane and methanol growth observed previously are maintained under substrate limitation, albeit with smaller changes. The substrate oxidation and glycolysis pathways together contribute over 80% of total energy production, while other pathways play less important roles.Methanotrophic bacteria are a group of prokaryotes capable of using methane as their sole carbon and energy source. Although efforts have been made to simulate and elucidate their metabolism via computational approaches or IMPORTANCE Methanotrophic metabolism has been under investigation for decades using biochemical and genetic approaches. Recently, a further step has been taken toward understanding methanotrophic metabolism in a quantitative manner by means of flux balance analysis (FBA), a mathematical approach that predicts fluxes constrained by mass balance and a few experimental measurements. However, no study has previously been undertaken to experimentally quantitate the complete methanotrophic central metabolism. The significance of this study is to fill such a gap by performing 13C INST-MFA on a fast-growing methanotroph. Our quantitative insights into the methanotrophic carbon and energy metabolism will pave the way for future FBA studies and set the stage for rational design of methanotrophic strains for industrial applications. Further, the experimental strategies can be applied to other methane or methanol utilizers, and the results will offer a unique and quantitative perspective of diverse methylotrophic metabolism. Methylotuvimicrobium buryatense” 5GB1C, formerly Methylomicrobium buryatense 5GB1C . FBA involves a genome-scale reconstruction model, which is subject to experimental constraints and mass balance based on reaction stoichiometry, and this approach has been applied to M. buryatense 5GB1C  and se 5GB1C , 13. These 5GB1C –16. Howe13C tracers to measure in vivo enzymatic reaction rates during isotopic and metabolic steady states . During 13C INST-MFA, the carbon source is switched from a 12C substrate to its corresponding 13C isotopologue without perturbing the bacterial metabolism, and then the dynamic changes in labeling patterns of intracellular metabolites are measured. Since those changes are flux dependent, carbon fluxes can be calculated based on experimental measurements. 13C INST-MFA has been applied to cyanobacteria and plants feeding on CO2 as the sole carbon source , were tested to investigate whether metabolic fluxes at different growth rates would be correlated with each other. Under substrate-limiting conditions, methanol or methane concentration was low in the chemostat cultures, resulting in rapid 12C substrate deprivation in the medium after we switched to 13C substrate input. This ensures that the labeling pattern of the substrate does not vary temporally throughout the labeling experiment, making it possible to obtain a good fit to experimental data and a reliable flux calculation. As mentioned above, each substrate required a different strategy for switching substrates  CH4 balanced with air was delivered into the bioreactor at a flow rate of 100 cm3/min. GC sampled off-gas every 15 min. We calculated the methane/O2 consumption rates and CO2 production rate (in mmol/h) based on the gas compositions between the inlet gas and off-gas. (b) 13CH3OH labeling experiment. The above figure shows a typical 13CH3OH labeling experiment. The NMS2 medium contained 1g/liter methanol, and air was delivered into the bioreactor at a flow rate of 100 cm3/min. The CO2 detection limit of the GC is ∼0.12% (vol/vol), below which the CO2 content would be measured as 0.00%. However, the zero percentage of CO2 displayed at the bottom figure does not mean that no CO2 was produced from M. buryatense 5GB1 cultures. Download FIG S1, TIF file, 1.4 MB.Bioreactor experiments. (a) Copyright © 2019 He et al.2019He et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the M. buryatense 5GB1C consumes 30% to 40% more methane than methanol on a molar basis, partly because methane requires energy input to be oxidized to methanol. In addition, the formate production rate is over 7 times higher during growth on methanol than on methane. This trend is consistent with our previous reports for growth under substrate excess  than that at 0.1 h−1 (2.4), as expected for a gaseous substrate when the gas flow rate is kept constant. For the same amount of methane provided per unit time (100 cm3/min), the biomass dilution is half, so the steady-state OD600 should double. During growth on methanol, the OD600 (2.5) is independent of dilution rate, again as expected. In this case, the substrate is in the medium, such that increased dilution rate also increases the substrate input rate, and so the steady-state OD600 stays the same. For growth on both methane and methanol, the yield is similar at the two growth rates but different when comparing growth on methane to that on methanol. The O2/substrate ratio is an important parameter, as an indicator of the extent of oxidative phosphorylation. For growth on methane, one O2 is required for every methane utilized as part of the methane monooxygenase reaction, while any O2 utilized above that is used for oxidative phosphorylation. For growth on methanol, all of the O2 consumed is used for oxidative phosphorylation. The O2/methane ratio was about 1.1, while the O2/methanol ratio was about 0.6. The carbon conversion efficiency, which represents the percentage of carbons from the substrate that is converted to biomass, is higher for growth on methanol. These values suggest more CO2 production from methane metabolism. Finally, we show that both methane and methanol residues were extremely low  seems to stabilize between 10% and 20% after a 10-minute exposure to 13C substrates across all the growth conditions (13CH4 for over five generations (approximately 15 h), R5P was fully labeled . The results show that pool sizes of intracellular metabolites in these substrate-limited cultures span from 10−5 to 10−2 mmol/g dry weight (gDW) (13C enrichment rates (2 fold changes (Metabolic pool sizes can affect ht (gDW) . Glutamant rates . R5P andnt rates . These r changes  of metab10.1128/mSystems.00748-19.6TABLE S2Table S2, XLSX file, 0.01 MB.Metabolite pool sizes. Download Copyright © 2019 He et al.2019He et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the M. buryatense 5GB1C, MID data, pool sizes of central metabolites, and measured fluxes  were fitted through computational optimizations. The resulting flux maps of M. buryatense 5GB1C are illustrated in To quantify the central metabolic fluxes of 10.1128/mSystems.00748-19.3FIG S3FIG S3, TIF file, 1.1 MB.Data fitting results. Simulated MID data are plotted against corresponding measured MID data . Each ciCopyright © 2019 He et al.2019He et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00748-19.7TABLE S3Table S3, XLSX file, 0.01 MB.Metabolic network and atom transitions. Download Copyright © 2019 He et al.2019He et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00748-19.8TABLE S4Table S4, XLSX file, 0.02 MB.Flux distributions. Download Copyright © 2019 He et al.2019He et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00748-19.9TABLE S5Table S5, XLSX file, 0.01 MB.Sum of squared residuals (SSR) across different growth conditions. Download Copyright © 2019 He et al.2019He et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 13C enrichment rates in those pathways. M. buryatense 5GB1C has two possible glycolytic pathways: the EMP pathway and the ED pathway. Based on our results, the EMP pathway is always the predominant glycolytic pathway under these growth conditions, while the ED pathway has a minimal flux. This result is also qualitatively supported by the observation that 3PG, an intermediate in the lower EMP pathway, has a much larger pool size  and 6-phosphogluconate (6PG) across different growth conditions. Black bars are 13C enrichments of 3PG, and white bars are 13C enrichments of 6PG. Error bars represent standard deviations from at least two biological replicates. Download FIG S4, TIF file, 1.0 MB.Copyright © 2019 He et al.2019He et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the M. buryatense 5GB1C, a type I methanotroph, this cycle is incomplete, lacking reactions converting acetyl coenzyme A (CoA) to glyoxylate. It functions to convert two one-carbon units to acetyl-CoA, and it can also function as an alternate metabolic route for glycine and serine synthesis.These results also suggest that fluxes through the TCA cycle are low, accounting for only 3 to 8% of substrate uptake rates. The best fits indicate that the TCA cycle is branched at the malate dehydrogenase reaction, and the remaining part operates in the oxidative direction. A large fraction of oxaloacetate (OAA) is replenished through the pyruvate carboxylase (PC) reaction. During growth on methanol, OAA seems to be exclusively produced from the PC reaction. Compared to the TCA cycle, a much lower carbon flux can be found in the serine cycle for both growth substrates . The serine cycle serves as the major carbon assimilation pathway in type II methanotrophs. In contrast, in 4MPT) pathway  and the RuMP cycle in different proportions, suggesting a flexible metabolic branch point at the formaldehyde node. About 30% of the methane-derived formaldehyde is directly oxidized to CO2 via the H4MPT pathway and formate dehydrogenase (FDH) reaction, with the rest entering the RuMP cycle, while less than 8% methanol is directly converted to CO2 by this route during growth on methanol. Accordingly, even though the methanol uptake rate is lower than the methane uptake rate at the same growth rate  measurements  pathways sometimes show broader confidence intervals than other reactions.Major differences between methane and methanol metabolism can be found in the methane/methanol oxidation pathways. wth rate , a largeurements . SpecifiP value < 0.01, 4MPT fluxes, as mentioned above. Moreover, for a single substrate, we observed nearly perfect linear correlations of flux distributions between the two dilution rates . Download Table S6, XLSX file, 0.01 MB.Quantification of energy production and expenditure in the central metabolism of Copyright © 2019 He et al.2019He et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the M. buryatense 5GB1C. Compared to methane growth conditions, a greater fraction of energy is devoted to reproduction during growth on methanol; however, the absolute energy requirements are very similar for the two substrates at the same growth rate. Unlike a genome-scale reconstruction, our network includes reactions mostly in central metabolism, and thus, we cannot distinguish where M. buryatense 5GB1C uses the rest of the energy. For brevity, we combined all the energy surplus into the “other activities” category , malic enzyme (ME), and malate thiokinase (MTK) reactions. As shown in M. buryatense 5GB1C metabolism. To maintain mass balance, malate produced from the fumarase reaction must be consumed via the other three pathways. As noted above, fluxes through the partial serine cycle are always low, and hence, the MTK reaction is low. The ME and MDH reactions are more flexible. The MDH reaction can operate in either direction according to the 95% confidence intervals. Furthermore, it has been confirmed that single knockout mutants of either MDH, ME, or serine glyoxylate aminotransferase (which is involved in the serine cycle) do not show any growth defect , have shown that they have a strong Calvin-Benson-Bassham cycle (which plays a similar role as the RuMP cycle in the type I methanotrophs) yet a weak TCA cycle. This resemblance is partly attributed to a similar architecture of the central metabolic network between type I methanotrophs and cyanobacteria, in which they both employ a metabolic cycle overlapped with the pentose phosphate pathway for consuming one-carbon substrates. Additionally, both can obtain adequate energy from either light or substrate oxidation/respiration, and thus, a strong TCA cycle is not vital. On the other hand, metabolites in M. buryatense 5GB1C exhibit generally higher 13C enrichments, or lower dilution factors estimated  containing 1 liter NMS2 medium. Bioreactor setups . The inlet gas was sterilized by an autoclavable filter  before being delivered to cultures. Off-gas composition was measured by a gas chromatograph (GC) , which sampled the off-gas every 15 min. An antifoam agent  was continuously applied into the bioreactor via a syringe pump  at a rate of 20 μl/h. Under chemostat conditions, 10 liters NMS2 medium was prepared in a glass carboy and stirred at 500 rpm constantly. A peristaltic pump  was used to deliver fresh medium from the carboy into the bioreactor. Dilution rates, which were the same as the specific growth rates under chemostat conditions, were controlled by the feeding rate of fresh medium. The bioreactor, probes, tubes, and liquid medium were all autoclaved before use.2 medium  at 30°C r setups  were as r setups : agitati13C labeling experiments were performed under chemostat conditions. Under the methanol-limiting conditions, cultures were grown on 1 g/liter methanol at two dilution rates: 0.05 h−1 and 0.1 h−1. At time point zero, 12C medium was switched to 13C medium. The latter was kept in a 60-ml syringe and delivered to the bioreactor via the syringe pump. Following the substrate switch, cell samples were collected from 0 to 40 min. At each time point, ∼20-ml cell cultures were harvested on a Nylaflo filtration membrane  through fast filtration. The membrane was immediately placed in sterile 50-ml tubes and quenched with liquid nitrogen. Under methane growth conditions, M. buryatense 5GB1C was supplied with 2.5% (vol/vol) methane gas balanced with air . These labeling experiments were also performed at 0.1 h−1 and 0.05 h−1. Two separate mass flow controllers were used to maintain steady flows of 13CH4 gas (2.5 cm3/min) and air (97 cm3/min), which converged at a Y-shape connector. After 13CH4 and air gas tanks were opened, the flow rate of the mixture was first stabilized for 1 min before it was delivered to the bioreactor. This was carried out because the 13CH4 gas flow fluctuated in the beginning, which would perturb labeling patterns at the early time points. Cells were sampled at consecutive time points. For both methane and methanol growth, supernatant samples were taken from chemostat cultures before and after 13C labeling experiments, which were used for measuring formate and methanol concentrations. 13C-labeled substrates, 13CH3OH and 13CH4, were purchased from Sigma-Aldrich .2 consumption rates were calculated as follows:V represents the flow rate, subscripts “in” and “out” represent inlet and outlet gases, x represents volume fraction which was measured by the GC, and ΔV represents consumed gas volume per unit time. As described earlier, Vin was 100 cm3/min. Nitrogen gas was inert in our experiments, and thus, it can be used to correct the flow rate of other gas species (V by the molar volume (24.5 liters/mol at 25°C).Methane or O species  to 3. ThMethanol concentrations were measured by a methanol assay kit . Formate concentrations in the supernatant were determined by an ICS-5000 ion chromatography system  .2O) was added into each sample, and then all samples were incubated in boiling water for 20 min. Samples were then cooled on ice for 30 min. Samples were then vortexed for ∼1 min, and the filter membranes were removed from the tubes. The resulting samples were centrifuged at 3,000 × g and 4°C for 30 min. Supernatants were collected, frozen by liquid nitrogen, and lyophilized. One milliliter sterile ddH2O was added to each dried sample, which was vortexed for ∼30 s and then centrifuged at 3,000 × g and 4°C. Supernatants were transferred to 1.5-ml microtubes, frozen, and lyophilized again. After overnight freeze-drying, 100 μl sterile ddH2O was added, and the samples were centrifuged at 20,000 × g for 30 min. The supernatant was collected and filtered using 0.22-μm Spin-X centrifuge tubes . The final samples were stored in 200-μl vials at −20°C before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis.Cell samples were first lyophilized overnight. Hot-water extraction procedures were applied for intracellular metabolite extraction , 34. TweIntracellular metabolites were separated by a SeQuant ZIC-pHILIC (5MYm polymer 150- by 2.1-mm polyether ether ketone [PEEK] coated) high-performance liquid chromatography (HPLC) column , and labeling patterns were determined by a Waters Xevo mass spectrometer . Mobile phase A was 20 mM biocarbonate solution, and mobile phase B was acetonitrile. The flow rate was 0.15 ml/min. The initial gradient for mobile phase A was 15% for 0.5 min, and the gradient was increased to 85% in 20 min and held at 85% for 5 min. Then, the mobile A gradient was switched back to 15% for another 5.5 min. Multiple reaction monitors were the same as reported before  growing on LB agar medium were inoculated into M9 minimal medium supplied with 2 g/liter fully labeled glucose . After overnight growth, seed cultures were inoculated into fresh 13C-labeled M9 medium at an initial OD600 of ∼0.02. About 4 h later, E. coli cultures (45 ml and OD600 of ≈0.32) were harvested on Nylaflo membranes through fast filtration, and these were placed in the same tubes together with nonlabeled M. buryatense 5GB1C samples obtained before 13C labeling experiments. Intracellular metabolites were extracted from the combined E. coli and M. buryatense 5GB1C samples, and their labeling patterns were analyzed by LC-MS/MS.To measure the metabolite pool size,  reports , 36. Sin13Coverall is the 13C enrichment of a metabolite from a combined sample; ME. coli and M5G represent the total amount of such metabolite in E. coli and M. buryatense 5GB1C samples, respectively; and 13CE. coli and 13C5G represent the 13C enrichment of this metabolite in E. coli and M. buryatense 5GB1C, respectively. The total amount of a central metabolite in E. coli or M. buryatense 5GB1C equals intracellular metabolite concentration (mmol/gDW) × OD600 × sample volume × conversion factor. The conversion factor is 0.20 gDW/OD/liter  were calibrated independently. Specifically, in the first step of extraction, 13C-labeled E. coli was added with both boiling ddH2O and 12C standards of known concentrations. The rest of the procedures were the same as described earlier. All the chemicals were purchased from Millipore-Sigma .We calculated the intracellular metabolite pool size of each metabolite by the following equation:. coli)+MG×C135G MOD/liter , 38 for in vivo flux distributions by minimizing the sum of squared residuals (SSR) between experimentally measured and computationally simulated variables, including labeling patterns, metabolite pool sizes, specific growth rates, and formate production rates. Calculations were carried out in MATLAB version R2017a . The minimum standard deviation for MID data was set to 0.01. At least 100 different initial guesses were tested to find the optimal fit. The chi-square test was used to examine if the SSR values were statistically acceptable.The metabolic network includes the methane or methanol oxidation pathway, the RuMP cycle, the EMP pathway, the ED pathway, the oxidative pentose phosphate (OPP) pathway, the TCA cycle, the anaplerotic pathway, and the partial serine cycle. Since copper was not limited in culture, particulate methane monooxygenase (pMMO) was the major enzyme responsible for methane oxidation into methanol , and thuThe following assumptions were applied in the model. (i) For methanol growth, glycogen content accounted for 42% of total biomass, while this value was lower for growth on methane . (ii) ThAll the data are presented either in the main text or in the supplemental material.


Bacteria, Archaea, and Eukarya organisms were evaluated. To this end, the content of enzymes belonging to a particular pathway and their abundance and distribution in 1507 organisms that have been annotated and deposited in the KEGG database were assessed. In addition, we evaluated the consecutive enzymatic reaction pairs obtained from metabolic pathway reactions and transformed into sequences of enzymatic reactions, with catalytic activities encoded in the Enzyme Commission numbers, which are linked by a substrate. Both analyses are complementary: the first considers individual reactions associated with each organism and metabolic map, and the second evaluates the functional associations between pairs of consecutive reactions. From these comparisons, we found a set of five enzymatic reactions that were widely distributed in all the organisms and considered here as universal to Bacteria, Archaea, and Eukarya; whereas 132 pairs out of 3151 reactions were identified as significant, only 5 of them were found to be widely distributed in all the taxonomic divisions. However, these universal reactions are not widely distributed along the metabolic maps, suggesting their dispensability to all metabolic processes. Finally, we found that universal reactions are also associated with ancestral domains, such as those related to phosphorus-containing groups with a phosphate group as acceptor or those related to the ribulose-phosphate binding barrel, triosephosphate isomerase, and D-ribose-5-phosphate isomerase (RpiA) lid domain, among others. Therefore, we consider that this analysis provides clues about the functional constraints associated with the repertoire of enzymatic functions per organism.In order to identify common and specific enzymatic activities associated with the metabolism of the three cellular domains of life, the conservation and variations between the enzyme contents of  Stepwise scenario . From this, a Z-score of ≥5 suggests that the frequency of the pair in the real ESS is significantly greater than expected by chance, leaving a set of 132 EC pairs as significant, suggesting that they are involved in a large number of consecutive reactions in the organisms considered in this analysis.To evaluate the functional associations between all the EC numbers, we analyzed the distribution of all nonredundant EC consecutive pairs in a database of nrESS. We consider that this analysis is important to determine how EC numbers are functionally linked to other EC numbers. To determine the most significant EC pairs, frequencies of consecutive reaction types (EC:a.b.c → EC:w.x.y) were compared against the expected values, using a set of random ESS. In this regard, 10 random databases were constructed by shuffling the real nrESS, maintaining the EC composition and lengths, as similar to the random ESS constructed for Proteobacteria comparisons , 17. TheBased on the distribution pattern associated with the enzymatic pairs in all the genomes, we identified five pairs  as widely distributed among the organisms or “universal” enzymatic pairs. . These rBacteria, Archaea, and Eukarya. Therefore, the five reactions were identified in the glycerolipid metabolism, probably because this pathway is a fundamental pathway associated with the origin and evolution of cell membranes and linked to the central structural component of the major classes of biological lipids, triglycerides, and phosphatidyl phospholipids, which are involved in the composition of membranes [Finally, to evaluate the roles of these enzymatic pairs in all the metabolic maps, these “universal” reactions were traced along the complete metabolism of embranes . In thisembranes . TherefoFinally, two ancient metabolic maps, for glycolysis and methane, contain two and three reaction pairs, respectively, 5.4.2:4.2.1 and 4.2.1:5.4.2 and 5.4.2:4.2.1, 4.2.1:5.4.2, and 2.7.7:2.7.8; whereas the 2.7.7:2.7.1 pair is preferentially associated with eight metabolic maps, outstanding among which are the amino sugar and nucleotide sugar metabolism, fructose and mannose metabolism maps, among others.Bacteria, Archaea, and Eukarya, although they are constrained to specific metabolic maps . In addition, we identified that those reactions are associated with ancient folds, as the P-loop-containing nucleoside triphosphate hydrolase (SF:52540), suggesting that universal reactions could be also ancestral in the evolution of metabolic pathways. When we analyzed the functional association between the enzymatic reactions, 132 EC pairs of reactions were identified as significant, and only 5 of them were identified as universal to the cellular domains. In summary, we found that conserved enzymatic reactions are mainly related to phosphorylation reactions, which are an essential on the modern metabolism.Enzymatic activities reflect the organization of metabolism in all organisms, and their analysis can provide clues about how reactions have shaped to their actual form. In this regard, we evaluated the abundance and distribution of enzymatic reactions in organisms from three cellular domains, and we found five EC numbers (at the first three levels of EC classification) were universal to S1 TableColumns correspond to: 1. Name of organism at NCBI database; 2. Organism ID in KEGG database; 3. Total number of proteins by organism; 4. Total number of non redundant ESS by organism; 5. Total of non-redundant EC numbers; 6. Total of maps by organism; 7. Taxonomy according of NCBI classification system; 8. Organism lifestyle; and 9. Other features of lifestyles(TXT)Click here for additional data file.S2 TableColumns correspond to: 1. Metabolic map ID in KEGG database; 2. Metabolic map name; 3. Total number of reactions; 4. Total number of non-redundant reactions; 5. Total number of ESS by metabolic map; and 6. Total number of non-redundant ESS by metabolic map.(TXT)Click here for additional data file.


Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and With the advent of bioinformatics and computational biology, computational and mathematical techniques can provide accurate simulation of biological processes. The most widely used approaches to analyze omics data mainly focus on genomics, transcriptomics, and proteomics, through differential expression or network-based coexpression analysis. However, genes and their expression alone do not always constitute a reliable indicator of cellular phenotype. When characterizing a phenotypic outcome, relying solely on gene or protein expression profiles will miss the highly nonlinear interaction between these biological layers. Such approaches often overlook the metabolic level, the dense network of biochemical reactions occurring in a cell with the aim of converting nutrients into energy and cellular building blocks.Being the best-characterized network in biological systems and also the closest to the phenotype, metabolism is arguably the best indicator for the cell physiological state . Once coThe recent availability of high-throughput data regarding multiple layers of biological organization (omics) allows mapping cellular processes at the levels of genes, mRNA, proteins, and metabolites . In a siConstraint-based modelling is the most widely used approach to model the behavior of metabolism, often assuming that cells have to fulfill a given task  or to optimize the production of a given compound. Such models have two main advantages: first, they do not need dynamic or kinetic data as they are based on mass balance across the metabolic network; second, they are suitable for integration of different omic layers at genome scale to improve their predictive performance. In particular, multiomic “vertical” integration methods have been proposed to include omic layers (mainly transcriptomics and proteomics) –6. ConveIn this review, we focus on metabolic systems biology, and in particular on human metabolic modelling. Originally tested and validated with microorganisms, metabolic systems biology is now becoming widespread for human tissues and biomedical applications . We provUnderstanding the role of individual components in a biological system is an important step to elucidate or predict its behavior. However, a major theme in systems biology is investigating these systems with an integrated approach. In fact, studying the interactions between different components also gives insights into the functioning and behavior of the components taken independently . in vitro.Metabolism is the biggest biological network fully described. It is a surrogate of the phenotype, being able to elucidate phenotypic changes caused by single or combined omics or physiological factors. It also has the advantage of being the only system that can be entirely modelled at genome-scale. As a result, it plays a central role in elucidating the genotype-phenotype relation. Systems-based approaches have been successfully applied over the last decade to investigate metabolic networks, composed of a set of chemical reactions and a pool of metabolites. The first examples of metabolic systems biology appeared in 1999 and were focused on modelling, connecting, and simulating several cellular processes –24. In mThere are different methods to model a metabolic system: steady-state analysis  involves a set of linear equations, while kinetic simulations involve ordinary differential equations (ODEs). Each variable represents the variation of a metabolite concentration, in a dynamic or steady state, where the concentration depends on the rates of the reactions that produce and consume that metabolite . KineticFor large systems, however, the use of kinetic modelling remains challenging. The increasing demand for systems-level genome-scale analyses has recently led to the widespread use of constraint-based steady-state models and their unsteady-state extensions. This has also been facilitated by the increasing availability of multiomics data and phenotypic information, used to constrain the model dynamically and therefore often compensating for the lack of regulatory and kinetic modelling.Following a number of successful attempts at building multiscale kinetic and constraint-based models , 29, achGenome-scale metabolic models contain all known metabolic reactions in an organism and can therefore serve as functional databases of cell-specific metabolism. A genome-scale model is built using the following process. First, a draft reconstruction is generated starting from the genome and including all the genome-encoded metabolic reactions. The draft reconstruction also includes annotated enzyme, reaction, and pathway data from databases like KEGG , BioCyc  in silico experiments are performed to further improve and validate the model. For more details on how to build a metabolic model from the DNA sequence of an organism, the reader is referred to full protocols [The construction of genome-scale model represents the starting point for flux balance analysis . Finally, the model is run and validated by comparing its predictions with existing experimental results, and newrotocols –36.Most genome-scale models are annotated with curated gene-protein-reaction associations (GPR rules), linking genes with enzymes . Such anFlux balance analysis (FBA) is the most widely used constraint-based technique to predict flux distributions and network capabilities in genome-scale models . FBA hasS, containing the stoichiometric coefficients of each metabolite (on the rows) in each reaction of the network (on the columns). A stoichiometric coefficient of a metabolite in a given reaction is positive if the metabolite is produced by the reaction and negative if consumed. An underdetermined linear system of equations Sv = 0 is defined from the stoichiometric matrix, where the unknowns are represented by the vector v of reaction flux rates. Additional constraints are included as lower and upper bounds of the fluxes in v (vmin ≤ v ≤ vmax). These account for the growth or physiological condition, and can be used to incorporate omics data. One or more cellular objectives , or a linear combination thereof, are finally selected to be maximized or minimized under the above-mentioned constraints, therefore solving a linear program.FBA is a linear programming technique that models the steady-state condition in a chemical reaction network . The FBAHomeostatic assumption: the organism has reached a steady state where the metabolite concentrations are constant and a set of nutrients are being constantly converted to generate biomass.(Multilevel) optimality: in each state, the organism tends to maximize one or multiple objectives, usually related to growth, biotechnologically relevant compound production  and important energy-carrying molecules .FBA is therefore based on two main assumptions:FBA is widely used in systems biology to quantify the entire metabolic steady state of a cell and calculate its flux distribution. All known metabolic reactions in a given cell are considered, and they are mathematically described in a way that allows simulation of various states and configurations of the chemical reaction network. Intuitively, the steady-state constraints used in FBA can be thought of as Kirchoff's laws applied to any node representing a metabolite in the network: the flux through each metabolite in the network must be constant, namely the input flux must equal the output flux. The combination of steady-state constraints and capacity constraints on reaction fluxes is a system of linear homogeneous equations and inequalities; thus, its solution space is a convex polyhedral cone representing the feasible flux distributions.The assumptions of FBA and the reduction of the problem to a linear program can cause some limitations. First, incorporating or predicting metabolite concentrations is challenging, and requires a dynamic FBA , relaxinWhen solving linear programs for FBA, different solvers can give different solutions due to numerical implementation differences and to the existence of multiple alternate optimal solutions –51. WhilSeveral tools are available for metabolic model reconstruction, constraint-based modelling, FBA and related analyses. These include COBRA , its PytAlthough some biological outcomes can be elucidated through single-omic analysis , the vast majority of phenotypic observables are a result of more comprehensive interconnections among multiple omics . Regulat in silico models of metabolism, it is possible to obtain a metabolism-enriched view of any given omic profile. In this context, as well as permitting the simulation of large, usually genome-scale, systems in a few seconds of CPU time, FBA also has the advantage of facilitating the introduction of additional omic layers of experimental data that can be overlaid onto the model, using GPR rules or metabolite annotations to place constraints on the flux bounds. Omic-informed metabolic models consider the mechanistic relation between omics, therefore correctly representing the prior information available on biochemical networks [The idea of multiomic FBA is that by mapping omic data ontonetworks . MultiomFollowing this approach, FBA and its multiomic modifications have been used to predict the metabolic response to a given condition in light of the multiple cellular objectives that a cell is required to meet , 68. Rec a priori an equivalent flux increase/decrease in all the reactions. Codon usage can also be integrated through the GPR rules [Ongoing modelling efforts are aimed at incorporating further biological knowledge in the models. To account for enzyme promiscuity when overexpressing or underexpressing a gene, GPR rules have been proposed as additional rows of the stoichiometric matrix, as an enzyme-by-reactions submatrix . For insPR rules . Splice PR rules , 83.Soft constraints can be used to integrate omics data in a reaction-specific fashion, rather than with a single mathematical rule modifying the constraints for all the reactions . FurtherThe selection of an appropriate objective function is still a challenge in metabolic modelling. A common assumption in systems biology is that cells tend to optimize their metabolic network in order to maximize the growth rate (biomass). This is however still a matter of debate , both in ‘what does a particular cell do?' has often more than one correct answer. There is increasing evidence that cells have to cope with multiple, usually competing, objectives to optimize simultaneously [Especially in the context of human metabolism, the questionaneously . A singlaneously , and evoaneously .Optimization processes have therefore been proposed that take into account multiple objectives  with the advantage of ensuring metabolic flexibility for possible reorganizations performed during adaptations to changes in the environmental conditions . These mHaemophilus influenzae [ Escherichia coli [The study of human metabolism is becoming increasingly important for biomedical applications as an approach for understanding many diseases and aspects of health , 110. A fluenzae , followehia coli ), due tohia coli , 114.HumanCyc   to 5.17 (in Recon 3) and 1.67 (in Recon 2.2). The table of ATP yields and the RMSE calculations are provided as Supplementary Material.Over the last few years, the curation efforts have yielded a reduction in the number of unbalanced reactions, as well as in the number of blocked reactions and dead-end metabolites. For instance, Recon 2.2 has no unbalanced reactions apart from the biomass objective functions, and Recon 3 has now less than 15 blocked reactions and dead-ends. Furthermore, as generating context-specific models using omics relies on GPR rules, the accuracy of these models strictly depends on the accuracy of the GPR in the baseline model. As shown in Starting from human generic metabolic reconstructions, a number of successful efforts have been published on reconstructing tissue-specific models. These include brain , adipocyTissue-specific models are usually built as a reduction of a generic human model. The reactions that are removed in the construction of the tissue-specific model are found to be not active in that tissue. Reactions are removed according to transcriptomic or proteomic data collected in that tissue, and therefore the tissue-specific model contains fewer reactions than the generic one. Unsurprisingly, in most cases they outperform the generic counterpart where they stemmed from. At this point, a new tissue-specific biomass equation needs to be established, or the model needs to be set so that the cell can find alternative pathways to sustain its life. Although many methods for generating tissue-specific models set the goal of creating the minimal metabolic model that satisfies viability or a set of metabolic tasks, the use of nonminimalistic methods for generating tissue-specific models should be preferred. For instance, considering essentiality before removing reactions from a generic model yields a nonminimal tissue-specific model, but improves model functionality and agreement with experimental data . Since tContext-specific human metabolic models have shed light on disease onset and progression in a range of recent case studies. In this context, the renewed interest in disease metabolism is due to the fact that a holistic genome-scale view, rather than a single-gene approach, is necessary to fully characterize most diseases. For instance, in cancer, the differences in the metabolic pathways between cancer cells and their parent tissue have been characterized using omics data and a human metabolic model , 151. AsFor specific diseases, tissue-specific models have been used successfully to identify biomarkers and therapeutic targets . Using RFollowing the same direction, personalized models  hold promise to become key for precision medicine . The larGenotyping coupled with patient-specific metabolic modelling offers a new opportunity for personalized medicine. In fact, new biomarkers can be predicted in a patient-specific fashion, and personalized therapies can therefore be designed and subsequently assessed in terms of their metabolic mechanistic effects . As showDue to the importance of the gut microbiome composition in human health, a set of modelling approaches has been proposed recently . Using hResearch in computational biology has led to detailed models for a better understanding of the individual biological components, but arguably to a less clear picture of the interactions among the components that result in a given phenotype . Genome-Since metabolic homeostasis and observable phenotype are strictly linked, metabolism is nowadays considered diagnostic of the phenotype, and therefore arguably the best indicator of the functional state of a cell. Metabolism can also be used to prioritize genes and assess their function and the role of gene perturbations (including knockouts). Without such integrated analysis, a gene may e.g., incorrectly be regarded as important only due to its highly variable expression value. Mycoplasma genitalium. The model includes metabolism, replication of the genome, and cell division. Several metabolic models can be combined in frameworks to investigate the metabolic exchanges between individual cells and the emerging community behavior [Metabolic models are increasingly being used to construct multiscale, multicellular or multitissue models. In 2012, a research effort by Karr et al.  providedbehavior , with apbehavior –170.The availability of tissue-specific models has also led to the generation of multitissue metabolic models . FurtherGiven the recently renewed interest of the scientific community in understanding disease metabolism, it is highly likely that cancer metabolism (and metabolism in other diseases) will become the main research topic in drug development. This will complement rather than replace standard transcriptomic-only studies, and will provide a proxy for the phenotypic and observable outcome. The idea is that to improve phenotypic predictions, the signatures need to be taken from sources of data that are closer to the phenotype. The integration of omics data into metabolic models has enabled the prediction and successively the validation of biomarkers and therapeutic targets. In drug design, although genomics, transcriptomics and proteomics are often deemed sufficient, metabolomics can elucidate mechanisms that are not visible from genes and protein activity. More importantly, it can address cases where the genes responsible for a disease are well known but not druggable, but their corresponding downstream reactions are . in silico clinical trials [Genomics analysis is usually able to identify known diseases through gene mutations. However, it may not be able to flag variants of known diseases, or identify novel diseases. This can happen, for instance, if (i) a gene mutation is not flagged as important; (ii) the gene is not screened at all; (iii) the disease is not a direct effect of a gene perturbation; (iv) individuals respond differently to the same mutation . Genome-l trials .The efforts of the metabolic modelling community are far from being complete. In this regard, we envisage a joint effort from metabolic modelling and genome-wide association studies (GWAS) communities to identify gene-metabolite interactions that are currently not included in metabolic models using state-of-the-art association mappings . Computa Escherichia coli, single-omic integration in FBA has been reported to give similar accuracy to pFBA without omic integration [Likewise, the methods for integration of omics data in genome-scale models still show room for improvement. For instance, in yeast andegration . Reassuregration . In multegration . In mammegration , 182, whegration , 184. Whegration . Therefoegration , 186, oregration . E. coli and yeast fluxomic data is publicly available from several experiments [Compared to the well-characterized microbial metabolic reconstructions, gaps still present in our knowledge of human metabolism—including characterization of enzymes, the definition of cell-specific metabolic functions and tissue-specific growth mechanisms—make it more difficult to test (and compare) metabolic models and omic integration methods. Human metabolic models and methods have been tested and cross-compared for gene essentiality through CRISPR-Cas9-mediated loss-of-function screens, and for their ability to predict growth rate and recapitulate known metabolic functions . A dataseriments –191, to Finally, despite many recent advances, gathering insights from the data generated through omic-informed models remains a bottleneck in systems biology. We therefore envisage that genome-scale metabolic models will be increasingly investigated with machine/deep learning algorithms in a patient-specific fashion , 192. In


Lignin is a crucial molecule for terrestrial plants, as it offers structural support and permits the transport of water over long distances. The hardness of lignin reduces plant digestibility by cattle and sheep; it also makes inedible plant materials recalcitrant toward the enzymatic fermentation of cellulose, which is a potentially valuable substrate for sustainable biofuels. Targeted attempts to change the amount or composition of lignin in relevant plant species have been hampered by the fact that the lignin biosynthetic pathway is difficult to understand, because it uses several enzymes for the same substrates, is regulated in an ill-characterized manner, may operate in different locations within cells, and contains metabolic channels, which the plant may use to funnel initial substrates into specific monolignols.Brachypodium distachyon and permits explanations for some counterintuitive observations. The model predicts the lignin composition and label distribution in a BdPTAL knockdown strain, with results that are quite similar to experimental data.We propose a dynamic mathematical model that integrates various datasets and other information regarding the lignin pathway in Given the present scarcity of available data, the model resulting from our analysis is presumably not final. However, it offers proof of concept for how one may design integrative pathway models of this type, which are necessary tools for predicting the consequences of genomic or other alterations toward plants with lignin features that are more desirable than in their wild-type counterparts.The online version of this article (10.1186/s13068-018-1241-6) contains supplementary material, which is available to authorized users. The moFor implementing these criteria, a match was defined as “good” if the simulation result fell within a range bounded by the mean value plus/minus 25%. We set a slightly more relaxed admissible range for wall-bound phenolics, because their effluxes have not been characterized with a sufficient degree of precision and because they are used by the plant for other purposes outside lignin production. Beyond the steady-state ensemble of flux distributions, the analysis led to an ensemble of parameter sets that rendered dynamic model simulations consistent with the experimental results.p-coumaric acid to the medium to elucidate further to what degree Phe and Tyr enter distinct reaction chains of lignin biosynthesis. Again, labeled Phe or Tyr was supplied, and the authors traced how the unlabeled cinnamic acid and p-coumaric acid dilute each labeling feed source. Figure In a separate set of experiments, Barros et al.  added unBdPTAL knockdown data  input that is actually taken up by the plants from the medium and enters the lignin pathway, we used the steady-state model and recorded all input values of CA and pCA that led to lignin and wall-bound phenolic profiles matching the experimental data. These data came from labeling experiments in wild-type lines and include labeled and unlabeled lignin monomer profiles, as well as the label incorporation level in wall-bound p-coumaric acid and ferulic acid  if metabolite i was a substrate or activator of flux j , and  if they acted as inhibitors of flux j [gi,j, the rate constants, aj, of the enzymatic reactions are then computed via Eq. Kinetic orders f flux j . Using tDue to the high number of unknown parameters, dilution experiments were parameterized one at a time. The thus obtained working parameters from one experiment were used to regenerate new parameters in the reduced parameter space, employing random Monte Carlo sampling. Therefore, hundreds of thousands of parameter combinations were simulated to satisfy all model criteria.Specifically, we used a variation of an explore-and-exploit algorithm –31, starp-coumaric acid and ferulic acid to a satisfactory degree.For the dynamic model, the criteria of admissibility were the same as the criteria in the static model. Thus, parameter values were deemed admissible if they met the model criteria presented in “Ideally, it would be possible to determine the concentrations and activities of all enzymes involved in the pathway. However, in situ measurements of protein concentrations of the magnitudes available in lignin biosynthesis are extremely difficult to obtain, and as of yet no reliable information is available. It, therefore, appears that reliable and rather precise transcripts of genes coding for the enzymes of interest are our best option.p% change in transcript results in a change in enzyme activity that is much lower than p%. To account for this uncertainty, we used the measured transcript profile of BdPTAL transgenics as an upper bound for the corresponding enzyme activity levels. Thus, we randomly generated 20,000 altered enzyme activity profiles bounded between the wild type and BdPTAL transcript level, simulated all these profiles with the dynamic model, and retained those close enough to the BdPTAL observations of lignin amounts and labeling levels. Figure It is known that changes in gene expression are not necessarily translated one-to-one measure into the corresponding enzyme activity, and it is in fact likely that a Lignin is a fascinating organic compound that is essential to the life of terrestrial plants. Its molecular composition and structural toughness are critical ingredients for the roles of lignin in plants and they are also increasingly valued by industry, which has begun to use lignin as a starting substrate for a variety of products .Brachypodium appears to be functionally separated into two locations, namely the cytosol and the outer surface of the ER. Otherwise, the Brachypodium pathway seems similar—although not entirely identical—to the corresponding pathways in alfalfa and switchgrass. The assumption of different locations for enzymatic activity actually seems very reasonable, because experimental observations report that three of the pathway enzymes, namely C4H, C3′H and F5H, are bound to the outer surface of ER, whereas other enzymes are commonly assumed to be free in the cytosol. This has been reported in Brachypodium [Arabidopsis [Both targeted reductions and increases in lignin, as well as any changes in its molecular composition, require a detailed understanding of how lignin is synthesized in vivo. This understanding is difficult to gain from wet experiments alone, as the biosynthetic lignin pathway is complex and reuses the same enzymes several times for different substrates. This multiple use makes intuitive predictions regarding introduced alterations in these enzymes difficult and unreliable. Not surprisingly, the pathway also contains regulation, as well as functional channels, which permit some control over the flux into specific monolignols, and these nonlinear features confound explanation and prediction capabilities. In addition to these features, we have suggested here and elsewhere  that thehypodium , tobaccohypodium , and Arabidopsis . WhetherBrachypodium. Also, in switchgrass and Medicago truncatula, caffeoyl shikimate esterase (CSE) converts caffeoyl shikimate into caffeic acid [Brachypodium or black cottonwood. Specifically for Brachypodium, it was, furthermore, shown that phenylalanine (Phe) is not the only substrate for the lignin pathway, but that this organism can also use tyrosine (Tyr) in almost equal amounts. It is unknown whether this secondary pathway is a matter of redundancy, whether there are specific internal or environmental reasons for this organism needing this alternative, or whether the organism uses these pathways to control its S/G ratio.In terms of different species and their own peculiarities with respect to the lignin pathway, experimental work suggests that there are indeed distinctions with respect to the presence or absence of some metabolites and reactions. For instance, alfalfa converts caffeoyl-CoA into caffeoyl aldehyde by means of CCR, and COMT catalyzes the subsequent reaction converting caffeoyl aldehyde into coniferaldehyde . These teic acid , while tBrachypodium is only consistent with the available data if it is distributed over two compartments. While the similarities are encouraging toward a streamlined understanding of lignin biosynthesis, the differences should not be overinterpreted. It is quite possible that the pathways in alfalfa and switchgrass might be compartmentalized as well, and that products inhibit reactions in Brachypodium, but our modeling efforts, adhering to the simplicity of Ockham’s razor, do not require these features, possibly only because the same types of data are not available for the various species. Thus, much further exploration and analysis will be needed to determine whether all models designed so far are special cases of one common model that simultaneously contains all these features or whether evolution has led to distinct implementations of a basic model structure with species-specific variations that respond to different environmental demands.Over the past decade, we have developed models of lignin biosynthesis in different plant species –11, 13. While it will be interesting to explore similarities and true differences among a variety of species further, it is becoming clear that, even in the face of scarce data and substantial information gaps, dynamic models are gaining in relevance and importance. They not only integrate different datasets and other auxiliary information, but they are in the process of becoming obligatory tools for making reliable predictions regarding natural and introduced alterations in the metabolic pathway systems that generate lignin in different organisms.Additional file 1. Additional materials.Additional file 2. Additional table.