E. coli. Glycerol production decreases the concentration of fructose-1,6-bisphoshate (FBP), which then activates Cra resulting in the downregulation of glycolytic enzymes and upregulation of gluconeogenesis enzymes. Because cells grow on glucose, the improper activation of gluconeogenesis and the concomitant inhibition of glycolysis likely impairs growth at higher induction of the glycerol pathway. We solve this misregulation by engineering a Cra-binding site in the promoter controlling the expression of the rate limiting enzyme of the glycerol pathway to maintain FBP levels sufficiently high. We show the broad applicability of this approach by engineering Cra-dependent regulation into a set of constitutive and inducible promoters, and use one of them to overproduce carotenoids in E. coli.Synthetic metabolic pathways are a burden for engineered bacteria, but the underlying mechanisms often remain elusive. Here we show that the misregulated activity of the transcription factor Cra is responsible for the growth burden of glycerol overproducing  Synthetic pathways represent a metabolic burden on host cells. Here the authors engineer Cra-binding sites to prevent misregulation in glycerol and carotenoid overproducing E. coli strains. However, synthetic metabolism interferes with the endogenous one, often in a way that impairs the cellular growth and fitness of the host. For example, overproduction pathways consume metabolites that are no longer available for the growth and metabolism of the host. This competition between synthetic and endogenous metabolism leads to a metabolic burden that causes stress responses and physiological changes of the host3. Eventually, metabolic burden and the accompanying perturbations to metabolism reduce the overall fitness and productivity of the engineered microbes. Therefore, the current challenge in metabolic engineering is to minimize metabolic burden, while maximizing flux through synthetic metabolic pathways.Engineering synthetic metabolic pathways by inserting new enzymes into the metabolic network of microbes is a common approach to expand the spectrum of chemicals that they can overproduce5. Non-growing microbes are less susceptible to metabolic burden, because they have a lower requirement for biomass building blocks and energy. However, unlike actively growing cells, non-growing cells have a low overall metabolic activity6, and this can limit the flux and productivity of synthetic metabolic pathways. Thus, the higher metabolic activity of growing cells is undoubtedly an advantage but requires the optimization of the enzyme levels in the synthetic metabolic pathway in such a way that sufficient resources remain for cell growth7. Optimal control of enzyme expression has been achieved at various levels of transcription and translation, for example by engineering promoters8 or ribosome-binding sites9. However, these methods are static because they do not allow adjusting the enzyme levels to the changing internal and external conditions10. To dynamically control the expression levels of enzymes, feedback mechanisms have been introduced into synthetic metabolic pathways. An approach to do so is to express enzymes in the synthetic pathway under the control of promoters that bind transcription factors (TFs). The activity of the TFs in turn is controlled by intermediates or precursors of the synthetic pathway. The resulting feedback between metabolism and gene expression improved overproduction of lycopene11, fatty acids13, and precursors of isoprenoids14. Another approach to engineer metabolic feedback regulation of gene expression is to combine CRISPR interference with transcriptional regulators that sense stress of the host15.An approach to avoid metabolic burden is to express synthetic pathways in non-growing microbes using two-stage bioprocessesE. coli as an example to systematically study the cause and consequences of metabolic burden in engineered bacteria. We chose the glycerol pathway because it is a simple two-step pathway that drains precursors from one of the most central pathways, glycolysis. According to the United States Department of Energy (DOE), glycerol belongs to the top ten value-added chemicals from biomass16, and can be a precursor for other bio-based products like acrylic acid17 and 1,3-propanediol18. First, we controlled the glycerol pathway with an arabinose-inducible pBAD promoter and observed that already low levels of inducer caused a growth burden. Metabolomics and proteomics data indicated that the growth burden was caused by a transcriptional response in glycolysis, notably the activation of gluconeogenesis by the transcription factor Cra. Next, we combined theoretical and experimental analysis to show that insertion of a Cra-binding site into the pBAD promoter enables higher growth rates at higher glycerol production rates. Finally, we show that this approach is generally applicable to synthetic pathways that utilize glycolytic metabolites such as carotenoid production.Here, we used glycerol production in E. coli  to prevent that glycerol is re-utilized as a carbon source  with an arabinose-inducible pBAD promoter, and the second enzyme (GPP2) with a strong constitutive promoter Fig. . ExpressE. coli metabolism19 predicted that growth and glycerol production rates follow a linear relationship  activated Cra and thereby changed gene expression and enzyme levels. To test this, we probed the proteome at the three induction levels, and inspected the abundance of a total of 38 enzymes in glycolysis and gluconeogenesis  as a reference . The strong effect of Cra on the expression of PpsA is consistent with previous studies, which showed that the ppsA promoter is under the direct control of Cra22. In our base strain, PpsA was one of the most strongly increased enzymes at 0.5% ara  and upregulated enzymes in gluconeogenesis . Because cells grew on a glucose minimal medium we hypothesized that activation of gluconeogenesis was responsible for the growth burden. We confirmed this hypothesis by deleting ain Fig. . The resrlower_glycolysis) and in the glycerol pathway (rglycerol) according to Michaelis–Menten kinetics, which are a well-established kinetic format for enzymatic reactions23. Similar to flux balance analysis  both lower glycolysis and the glycerol pathway were subject to Cra-regulation. We simulated Cra-regulation with a power-law term that affects the maximal enzyme expression rate. Since the power-law term equals one in the un-induced state, all models share the same parameter set.In total, we analyzed three different models, each with a different regulatory structure Fig. . A modelrglycerol,MAX) that can possibly be achieved given the specific set of parameters. To estimate rglycerol,MAX, we made use of a numerical continuation method24, which iteratively increases the expression rate of enzyme e2 (β2) and computes the new steady state for FBP, e1, and e2. After each iteration, the continuation method determines the stability of the model by inspecting the eigenvalues of the Jacobian matrix24, and terminates if instabilities occur in the model. If the model remains stable, the continuation method terminates at the maximal expression rate of e2 , which we defined as the rate were 20% of the ribosomes translate e2. Thus, rglycerol,MAX is the glycerol production rate at the termination point of the continuation method and we obtained 5000 values of rglycerol,MAX for each of the three models  with the Δcra model than with the base strain model . Thus the lower activity of the pBAD-Cra is not merely due to sequence changes but due to active inhibition by Cra.To obtain further evidence that the pBAD-Cra promoter is functional, we measured the activity of the pBAD promoter and the pBAD-Cra promoter with GFP, both in the wild-type and the Δter Fig. , thus inter Fig. . These rTo further demonstrate the broad utility of this approach we inserted Cra into a constitutive promoter and the pTet promoter  than in the base strain (6 mmol g−1 h−1). This suggested that it was not glycerol production per se, which impaired growth of the base strain, but rather other effects such as the higher expression levels of GPD1  if FBP falls below a critical value, Cra activity increases, (ii) higher Cra activity represses the pBAD-Cra promoter and decreases GPD1 expression, and (iii) lower expression of GDP1 will restore the concentration of FBP. Thus our data indicate that this feedback regulation is functional, because it enabled high FBP levels and at the same time high glycerol production rates, which presumably prevented that E. coli into farnesyl diphosphate (FPP). FPP is then further converted into carotenoids by heterologous enzymes from Pantoea ananatis  to control a synthetic metabolic pathway for the overproduction of carotenoids Fig. . Biosynttis Fig. . The firE. coli carried the pCarotenoid plasmid together with either the pController or pController-Cra plasmid  and expression rates, this was not observed for glycerol overproduction. In the latter case, the problem was that a small increase of inducer was sufficient to cause a strong growth burden that constrained productivity. A consequence of the growth burden was a low biomass concentration and, consequently, low glycerol titers at the end of the cultivations.In this study, we used the arabinose-inducible pBAD promoter to control synthetic metabolic pathways in 27. Recent multi-omics data suggest that synthetic metabolic pathways can place an additional burden on cellular metabolism by perturbing the proteome and metabolome28. Perturbations of the metabolome could be critical especially if they alter the concentration of regulatory metabolites that control the expression and function of proteins, for example via metabolite-protein interactions30. Here, we observed that glycerol overproduction decreased the concentration of glycolytic metabolites, and that this caused misregulation at the level of transcription. More specifically, low concentrations of the regulatory metabolite FBP activated the transcription factor Cra and thereby upregulated the expression of gluconeogenesis enzymes likes PpsA. These results demonstrate the importance of maintaining regulatory metabolites above a critical threshold in engineered microbes. However, synthetic pathways often lack the regulatory mechanisms that maintain metabolite concentration homeostasis in natural metabolic pathways, such as directed overflow31, allosteric enzyme regulation32, or transcriptional regulation33. Here we show that engineering transcriptional regulation (Cra-regulation) in a synthetic glycerol pathway can help to maintain regulatory metabolites above a critical threshold: the pBAD-Cra strain has higher FBP levels and at the same time a higher glycerol flux than the base strain. This supports the hypothesis that Cra-dependent regulation counteracts a decline in the concentration of FBP by downregulating the expression of the glycerol pathway in response to decreasing FBP levels. However, as it remains an open question whether this regulation is truly dynamic, we cannot rule out the possibility that, due to the constant inhibitory activity of Cra, the pBAD-Cra promoter simply functions as a weaker pBAD promoter. Future studies should clarify whether the pBAD-Cra promoter automatically adapts to new conditions, e.g., by shifting the glycerol producers between different environments.A classical view is that synthetic metabolic pathways are a burden for industrial microbes because they consume cellular resources35, or demonstrated the ability of engineered promoters to increase the productivity of synthetic pathways15. Here, we combined metabolomics and proteomics to study the consequences of engineered promoters at the level of host metabolism. From a biotechnological perspective, this approach will help to engineer improved production strains that can autonomously buffer external perturbations in industrial-scale bioreactors37, and internal perturbations like gene expression noise39.Previous studies mainly focused on the transcriptional consequences of promoter engineeringEscherichia coli K-12 MG1655  were used for glycerol and GFP production. Strains derived from E. coli BW25113 (KEIO collection40) were used for carotenoid production. Consensus sequences of Cra are AGCTGAAGCGTTTCAGTC (from epd gene). All oligonucleotides used for cloning were synthesized by Eurofins Genomics (Germany GmbH) and are listed in Supplementary Table 41 and Gibson assembly  were used for cloning. The No-SCAR system42 for genome editing was obtained from Addgene  into the pBAD promoter. The Cra consensus binding sequence was inserted into the pBAD-Cra plasmid by PCR. The pController plasmid carries dxs and dxr genes from E. coli MG1655. The pController-Cra plasmid was derived from the pController plasmid by inserting the Cra-binding site.Strains and plasmids are listed in Supplementary Table −1 glucose. M9 medium was composed of (per liter): 42.2 mM Na2HPO4, 22 mM KH2PO4, 11.3 mM (NH4)2SO4, 8.56 mM NaCl, 1 mM MgSO4·7H2O, 100 μM CaCl2·2H2O, 60 μM FeCl3, 7.6 μM CoCl2·6H2O, 7.1 μM MnSO4·2H2O, 7 μM CuCl2·2H2O, and 6.3 μM ZnSO4·7H2O. For pre-cultures, frozen bacterial stocks were plated on LB agar plates with the respective antibiotics and single colonies were used to inoculate 5 ml LB-pre-cultures in tubes. From this first pre-culture a second M9 pre-culture was inoculated 1:1000 and incubated overnight at 37 °C under shaking. For cultivations in microtiter plates, 96-well flat transparent plates  containing 150 µl M9 minimal medium were inoculated 1:150 from the overnight M9-culture. Online measurements of optical density at 600 nm (OD600) with the glycerol production strains were performed at 37 °C with shaking in a plate reader . Online measurements of OD600 and additional measurements of GFP fluorescence  of the GFP production strain were performed at 37 °C with shaking in a plate reader . Growth rates were calculated as dln(OD600)/dt by linear regression over the indicated time windows. For cultivations in shake flask, a 500 ml shake flask containing 35 ml M9 minimal medium (5 g l−1 glucose) was inoculated 1:150 from the overnight M9-culture and incubated at 37 °C under shaking at 220 rpm. Antibiotics were added as required: kanamycin (50 μg ml−1), ampicillin (100 μg ml−1), and spectinomycin (100 μg ml−1). For FACS (fluorescence-activated cell sorting) measurement, 10,000 cells were sorted per sample by BD LSRFortessa SORP flow-cytometer . A 488-nm laser (blue) at 100 mW was used for green fluorescent with a 510/20 band pass filters. BD FACS Diva software  and Flow Cytometry GUI for Matlab (version 1.3.0.0) by Nitai Steinberg  were used for the analysis of acquired data.All cultivations were performed using an M9 minimal medium with 5 g lGlycerol was measured in the culture supernatant with a glycerol enzyme assay kit . 10 μl supernatant were mixed with 100 μl reaction buffer and incubated for 20 min. Absorbance was measured at 570 nm in a plate reader .g. Cell pellets were resuspended in 120 µl DMSO43, and sonicated for 30 s. Samples were centrifuged again at 3220 × g and 50 µl of the supernatants were transferred to a 384-well plate, and carotenoids were quantified by measuring the absorbance at 470 nm. For absolute quantification, standards of β-carotene  were prepared at final concentrations of 5, 10, 25, and 50 mg l−1.Carotenoid production strains were cultivated in 96-well plates with M9 minimal medium containing 0.5% glucose and an additional 20% LB at 37 °C with continuous shaking. After 24 h cultivation, cells were harvested by centrifugation at 3220 × 600 of 0.5 and 2 ml culture aliquots were vacuum-filtered. For time-course metabolomics, volumes of samples were adjusted based on the OD600of the culture to obtain 1 ml with OD600 1. Culture aliquots were immediately filtered on a 0.45 µm pore size filter  and filters were transferred into an extraction solution consisting of acetonitrile/methanol/water (40:40:20 (v/v)). Extracts were centrifuged for 20 min at −9 °C at 17,000 × g to remove the cell debris. Centrifuged extracts were mixed with 13C-labeled internal standard and analyzed by LC–MS/MS, with an Agilent 6495 triple quadrupole mass spectrometer (Agilent Technologies)44. An Agilent 1290 Infinity II UHPLC system (Agilent Technologies) was used for liquid chromatography and controlled by the Agilent MassHunter Acquisition software (Version B.07.01). The temperature of the column oven was 30 °C, and the injection volume was 3 μl. LC solvents A were water with 10 mM ammonium formate and 0.1% formic acid (v/v) (for acidic conditions); and water with 10 mM ammonium carbonate and 0.2% ammonium hydroxide (for basic conditions). LC solvents B were acetonitrile with 0.1% formic acid (v/v) for acidic conditions and acetonitrile without additive for basic conditions. LC columns were an Acquity BEH Amide  for acidic conditions, and an iHILIC-Fusion(P)  for basic conditions. The gradient for basic and acidic conditions was: 0 min 90% B; 1.3 min 40% B; 1.5 min 40% B; 1.7 min 90% B; 2 min 90% B. Quantification of intracellular metabolite concentrations was based on the ratio of 12C and 13C peak heights.Shake flask cultivations on M9 glucose were performed as described above. For steady-state metabolomics, cells were grown to an OD2PO4, and 8 mM Na2HPO4). After washing, cell pellets were resuspended in 200 µl of lysis buffer containing 100 mM ammonium bicarbonate and 0.5 % sodium laroyl sarcosinate. Cells were again incubated for 15 min with 5 mM Tris(2-carboxyethyl)phosphine (TCEP) at 95 °C followed by alkylation with 10 mM iodoacetamide for 30 min at 25 °C. We used SP3 bead method46 for a large number of samples. Fixed protein amount of 50 µg measured by BCA assay  and mixed with 4 µl SP3 beads stock  with 100 µl ddH2O) in 96-well high-volume v-bottom plate . To initiate protein binding to the beads, 75 μl of 100% ethanol were added with the mixture of protein and beads for 15 min at room temperature. Tubes were placed in a magnetic rack for 5 min. The supernatant was discarded and the beads were rinsed two times with 200 µl of 70% ethanol and then 180 µl of 100% ethanol on a magnetic rack. For proteolytic digest, tubes were removed from the magnetic rack, and the beads were reconstituted in 28 µl 10% acetonitrile/10 mM NH4HCO3 with 1 µg trypsin (Promega) incubated shaking overnight at 30 °C. After incubation, the tubes were sonicated for 30 s and placed on a magnetic rack, and the supernatant containing tryptic peptides was recovered and transferred to new tubes. Recovered peptides were acidified by adding trifluoroacetic acid (TFA) to a 1.5% final concentration. Peptides were then purified through C18 microspin columns (Harvard Apparatus) according to the manufacturer’s instruction. The eluted peptides were dried and resuspended in 0.1% TFA for analysis of peptides. Analysis of peptides was performed by a Q-Exactive Plus mass spectrometer connected to an Ultimate 3000 RSLC nano with a Prowflow upgrade and a nanospray flex ion source (Thermo Scientific) as previously described47. Briefly, peptides were separated by a reverse-phase HPLC column (75 μm × 42 cm) packed with 2.4 μm C18 resin  at a flow rate of 300 nl/min by gradient model which is from 98% solvent A (0.15% formic acid) and 2% solvent B  to 35% solvent B over 84 min. The data acquisition was set to obtain one high-resolution MS scan at a resolution of 70,000 full width at half maximum (at m/z 200) followed by MS/MS scans of the 10 most intense ions. Label-free quantification (LFQ) of the data acquired from mass spectrometry was processed with Progenesis QIP (Waters), and MS/MS search was performed in MASCOT . The following search parameters were used: full tryptic search with two missed cleavage sites, 10 ppm MS1 and 0.02 Da fragment ion tolerance. Carbamidomethylation (C) as fixed, oxidation (M), and deamidation  as variable modification. Progenesis outputs were further processed with SafeQuant.Cultivations were performed as described above. Culture aliquots were transferred into 2 ml reaction tubes and washed two times with PBS buffer  were added to simulate the synthetic glycerol pathway. Constraints of the GLYK reaction were set to zero, to simulate deletion of the glyK gene. Additionally, constraints of glycerol-3-phosphate and glycerol dehydrogenases G3PD5, G3PD6, G3PD7, and GLYCDx were set to zero. The model was further constrained to stimulate growth on a minimal medium, with glucose as the sole carbon source at an uptake rate of 8 mmol g−1 h−1. The oxygen uptake rate was constrained at maximum of 20 mmol g−1 h−1 and uptake of inorganic ions was not constrained 19. FBA was performed with different glycerol production rates between 0 and 14 mmol g−1 h−1 and maximal growth was the objective function.Flux balance analysis (FBA) was performed with a genome-scale model of F, that is a temporal function of the state variables x and the kinetic parameters p:The stoichiometry of the model is shown in Fig. β and they are removed by dilution by growth. We assumed that enzyme degradation contributes little to the overall enzyme turnover and therefore can be neglected.The metabolite FBP is produced by −1 h−1 was estimated with FBA using a glucose uptake rate of 8 mmol g−1 h−1. With the specific cell volume for E. coli (2 µl mg−1)49 the reaction rate An upper glycolytic flux of 4.904 mmol grlower_glycolysis and rglycerol follow Michaelis–Menten kinetics:The reactions The expression rates of enzyme 1 (GapA) and enzyme 2 (GPD1) are:cra model, and the 2× cra model. Further, setting α to zero removes the regulation and therefore α2 was zero in the base model, while α1 and α2 were zero in the Δcra model.Cra-regulation was simulated with a power-law term rlower_glycolysis, because flux balance analysis showed a linear relationship between rlower_glycolysis and the growth rates 52 and was sampled between 0.33-fold and 3-fold of this literature value. The parameter kcat,1 followed from the steady-state constraint of the un-induced state where rglycerol = 0.The 1,max was derived from the mass balances of e1, assuming steady state:β53 resulting in β1,max = 0.000238 mM min−1.The concentration of e1 was 0.0238 mM, based on quantitative proteome data for GapAβ2,max) was defined by the translation rate of ribosomes according to:The maximal enzyme expression rate in the glycerol pathway (54: average translation rate (rt = 8.4 amino acids s−1), the median and abundance weighted protein length (L = 209 amino acids), the fraction of active ribosomes (fR = 0.8) and the cellular volume  and at a growth rate of µ = 0.6 h−1, the Avogadro number (NA = 6.02 × 1023 mol−1), the number of ribosomes per cell at that growth rate (R0.6 = 8000 ribosomes cell−1). The fraction of ribosomes (p) that synthesize GPD1 at full induction was assumed to be 20%, because only 50% of the ribosomes can translate a heterologous protein and this is associated with significant protein burden55.Equation  considerβ2,max and e2 were set to zero. Then 6 parameters were randomly sampled from intervals defined above. The 2 parameters  were calculated to ensure steady-state conditions. To test the stability of the steady states, eigenvalues of the Jacobian matrix were calculated, and tested if all eigenvalues are negative (λ < −10−5). The procedure was repeated until 5000 stable steady states were achieved. Next, induction  as a parameter p is varied. As the system is in steady state it follows that:To obtain steady states of the un-induced system, F with respect to the parameters is also zero:The derivative of F/δxSS) is required. The inversion is only possible as long as the matrix is regular. Once an eigenvalue reaches zero, the Jacobian becomes singular and matrix inversion is no longer possible. This bifurcation point defines the boundary between the stable and unstable parameter space. In other words: after this point is passed, the system cannot return to a stable steady state. Calculating the eigenvalues of the Jacobian at each step ensures that the iteration is terminated when one eigenvalue exceeds λ = −10−5. The 2nd criterion is that all variables are positive.After rearranging Eq. , Eq. 1414 is obtn) of each experiment can be found in the respective figure caption. For proteomics and metabolomics n represents the number of independent shake flask cultures. In growth assays, n represents the number of independent microtiter plate cultures.Statistical analysis was performed with Matlab (R2018b) and custom Matlab scripts. The number of replicates (Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


Lignocellulose is the most abundant organic carbon polymer on the earth. Its decomposition and conversion greatly impact the global carbon cycle. Furthermore, it provides feedstock for sustainable fuel and other value-added products. However, it continues to be underutilized, due to its highly recalcitrant and heterogeneric structure. Microorganisms, which have evolved versatile pathways to convert lignocellulose, undoubtedly are at the heart of lignocellulose conversion. Numerous studies that have reported successful metabolic engineering of individual strains to improve biological lignin valorization. Meanwhile, the bottleneck of single strain modification is becoming increasingly urgent in the conversion of complex substrates. Alternatively, increased attention has been paid to microbial consortia, as they show advantages over pure cultures, e.g., high efficiency and robustness. Here, we first review recent developments in microbial communities for lignocellulose bioconversion. Furthermore, the emerging area of synthetic ecology, which is an integration of synthetic biology, ecology, and computational biology, provides an opportunity for the bottom-up construction of microbial consortia. Then, we review different modes of microbial interaction and their molecular mechanisms, and discuss considerations of how to employ these interactions to construct synthetic consortia via synthetic ecology, as well as highlight emerging trends in engineering microbial communities for lignocellulose bioconversion. Lignocellulose, which is chiefly composed of cellulose, hemicellulose and lignin, is the most abundant organic carbon polymer in the biosphere. Take lignin for instance, with an estimated 300 billion tons present globally and an annual increase of ~ 20 billion tons . Its decMicroorganisms, which are present in nearly all habitats on the Earth, have evolved different, yet complementary, mechanisms to degrade these polymers. Therefore, they provide green and energy-efficient routes for lignocellulose bioconversion , 5.i)Clostridium strains, produce cell-bound cellulosomes for efficient cellulose hydrolysis .Fi.FiPseudoTrichoderma reesei secreted cellulase enzymes to hydrolyze lignocellulose into soluble saccharides, as the public goods, and Escherichia coli metabolized soluble saccharides into isobutanol . Ins) Fig. B 57]. S. ST. reeStaphylococcus aureus can oxidize cellulose and generate the electricity in MFC , via directed electron transfer [Meanwhile, microorganisms have evolved contact-dependent delivery systems  to transfer metabolites, including amino acids, proteins, DNA, and other small molecules Fig. B. In addtransfer , 62.Contact-independent communication allows molecules to reach many neighboring cells, as opposed to only one cell at a time . Therefore, it enables them to serve as nutrients or cues to nearby cells, and, in effect, lead to behavior similar to that of a multicellular entity . HoweverWith the rapid development of high-throughput sequencing, the top-down study of microbial communities efficiently reveals their composition and function, further guiding us to obtain minimal, yet efficient, synthetic consortia. However, the contributions of individual members and the highly inter-connected networks of metabolic and ecological interactions are still unclear, which greatly restricts development of the “bottom-up” study of microbial consortia to enhance lignocellulose bioconversion. Synthetic ecology, which is the integration of synthetic biology, ecology, and computational biology, presents a feasibility to rationally engineer microorganisms at the population-scale. There are three important factors that should be considered for “bottom-up” synthetic ecology study of microbial consortia. These factors and corresponding methods/tools are discussed below.Citrobacter freundii and Sphingobacterium multivorum, both of which are lignocellulosic degraders. S. multivorum w15 secreted extracellular enzymes  to generate public goods, while C. freundii so4 provided redox power and metabolic intermediates for S. multivorum w15 [C. thermocellum LQR1 and ethanol-producing Thermoanaerobacter sp. X514 to enhance cellulosic bioethanol fermentation [P. putida and C. fimi were lignocellulose degraders, while M. extorquens removed a toxic burden and fed back iron and methionine to the degraders for improvement of lignocellulose degradation , including growth kinetic model, cellulase secretion model and saccharide uptake model for T. reesei, as well as growth model, saccharide uptake model and isobutanol production for E. coli [T. reesei and E. coli for soluble saccharides was a key ecological interaction to drive community behavior, indicating an inherent tradeoff between cellulose hydrolysis rate and isobutanol yield [Saccharomyces cerevisiae and E. coli co-cultures [S. cerevisiae consumes glucose, while E. coli strain ZSC113 catabolizes xylose. Inhibitory interactions were observed: S. cerevisiae metabolizes some compounds, which are toxic to E.coli, and produces ethanol that inhibits the growth of E. coli. Thus, the efficiency of glucose/xylose utilization was not high. The dynamic flux balance model suggested that optimization of co-culture inoculum can address such problem, as optimized inoculum can accelerate xylose uptake rates at the expense of decrease glucose uptake. As a result, the co-culture system can exhaust 16 g/L glucose and 8 g/L xylose at 9.8 h, compared to original 11.5 h batch time [E. coli strains [Saccharomyces strains [The essence of design cooperation is understanding how metabolic tasks can be rationally allotted to individual members to achieve a desirable productive manner at a community-level. However, microorganisms have complex cross-feeding strategies for multiple metabolites, either simultaneously, or in an environment-dependent manner , 66, und E. coli . Fifty pol yield . Throughol yield . In addiol yield –69. GSMMol yield . Based ool yield , 71, 72.ol yield , 74. Morol yield . Hanly ecultures . In thistch time . Additiotch time . For ins strains . A multi strains . These mAspergillus niger biofilm, compared to suspended A. niger culture [Scheffersomyces stipitis in a biofilm reactor (BR) and continues plastic composite support (PCS)-BR, respectively [T. reesei, which was co-cultured with S. stipitis (cellulose degrader) and S. cerevisiae (ethanol producer), can contribute to ethanol production (9.8 g/L) from the undetoxified dilute acid pretreated wheat straw [T. reesei, which secretes cellulase to hydrolyze cellulose, formed biofilm on the surface of tubular membrane. As a result, biofilm consumed oxygen and generated anaerobic microenvironment for the growth of anaerobic Lactobacillus pentosus for lactate production (34.7 g/L) [Spatial and temporal assortment should be considered to promote cooperation, although microbial communities are generally cultured in well-mixed systems. Various microbial spatial and temporal segregations exist in nature. For example, aerobic and anaerobic microbial populations are separated by oxic/anoxic niches , whereas culture , 81. For culture , 56. Higectively , 83. Forectively . The bioat straw . Moreove4.7 g/L) .Zymomonas mobilis and the yeast Pichia stipitis for efficient lignocellulosic ethanol production  for each strain and avoid cross-inhibition, while hollow fiber bridges connected the bioreactors to exchange intermediate products  and Thermoanaerobacter (a cheater), the cooperation of cellulose-to-ethanol conversion was stable, since higher rewards (hydrolysates) are received by C. thermocellum and favor its growth. Such spatial self-organization of cellulases in the cooperator contributes to co-exist of the two strains. (ii) Regulation of relative cooperator/cheater benefits is another strategy to resist cheaters and maintain cooperation  [Stability of microbial consortia is essential, yet challenging. For microbial consortia in nature, the environment causes community change, where consortia may experience environmental fluctuations and exposure to competitive species. Moreover, succession of microbial consortia changes the community due to genome evolution and horizontal gene transfer . For synion Fig. C. Minty E. coli) .Moreover, precise population control is also a desirable method to maintain the stability of communities. Genetic parts, e.g., QS systems and bacteriocins, are useful elements to manipulate the growth rate or subpopulation fitness , 94, 95.With the increasing number of potential types of engineered interactions and community members, manual judgement of these considerations is cumbersome and inefficient, representing a major hurdle in synthetic microbial community. Automated design of synthetic microbial communities has been recently developed to counter this hurdle . This meBottom-up synthetic ecology study of microbial communities, which precisely consider the properties of individual functions, interactions, communities, and applications, yield a better understanding for diversity, interactions, and dynamics at the systems level. Hence, it presents an exciting opportunity to rationally engineer microbial consortia for lignocellulose bioconversion. However, we still lack the critical knowledge and technology to effectively characterize the contributions of individual members, reveal the highly inter-connected networks of metabolic and ecological interactions, as well as perform genetic manipulation at the systems-level. There are two major barriers, which need to be addressed in the next step. First, it’s difficult to gain a fine-scale understanding of functions of individual microbial species and a subsequent large-scale understanding of ecosystem function. So far, this has mainly relied on genome-scale and computational modeling approaches. The qualities of these in silico models greatly depend on the accuracy and completeness of information about metabolic networks and biochemical roles. For most microbial species in natural environments, such information is missing and requires significant time and effort to accumulate. Thus, it is restricted to constructed synthetic communities which are comprised a few  well-characterized species. Development of automated approaches for metabolic reconstructions of unmodelled strains would alleviate this bottleneck. Alternatively, it would be desirable for the extension of computational approaches to design communities which contain novel species without genome-scale metabolic reconstructions. Second, it’s challenging to genetically manipulate microbial communities with a wide range of specificities and magnitudes. So far, genetic engineering is focused at the level individual strains. In contrast to breakthroughs in high-throughput sequencing to obtain molecular information of microbiomes, microbiome genetic modulation has not seen widespread success. With the recent revolution in genome engineering toolboxes , genetic


Escherichia coli and Salmonella enterica that cooperate with each other by exchanging metabolites, and (ii) two E. coli with different production level of extracellular polymeric substances (EPS) that compete for the same nutrients. In the mutualistic community, our results demonstrate that crowding enhanced the fitness of cooperative mutants by reducing the leakage of metabolites from the region where they are produced, avoiding the resource competition with non-cooperative cells. Moreover, we also show that E. coli EPS-secreting mutants won the competition against the non-secreting cells by creating less dense structures (i.e. increasing the spacing among the cells) that allow mutants to expand and reach regions closer to the nutrient supply point. A modest enhancement of the relative fitness of EPS-secreting cells over the non-secreting ones were found when the crowding effect was taken into account in the simulations. The emergence of cell-cell interactions and the intracellular conflicts arising from the trade-off between growth and the secretion of metabolites or EPS could provide a local competitive advantage to one species, either by supplying more cross-feeding metabolites or by creating a less dense neighborhood.The metabolic capabilities of the species and the local environment shape the microbial interactions in a community either through the exchange of metabolic products or the competition for the resources. Cells are often arranged in close proximity to each other, creating a crowded environment that unevenly reduce the diffusion of nutrients. Herein, we investigated how the crowding conditions and metabolic variability among cells shape the dynamics of microbial communities. For this, we developed CROMICS, a spatio-temporal framework that combines techniques such as individual-based modeling, scaled particle theory, and thermodynamic flux analysis to explicitly incorporate the cell metabolism and the impact of the presence of macromolecular components on the nutrients diffusion. This framework was used to study two archetypical microbial communities (i)  Microbial communities play a key role in biogeochemical cycles, bioremediation, and human health. In crowded microbial systems such as biofilms and cellular aggregates, the close proximity between individual cells reduces the free space for the nutrients diffusion. To model the heterogeneous nature of these microbial systems, we developed CROMICS, a framework that integrates the information about the metabolic capabilities of each individual cell as well as the size and location of cells and macromolecules in the medium. The interactions among the individuals arise naturally through competition for or the exchange of metabolites. We show how the presence of mutants and a reduced diffusion in crowded environments can perturb the local availability of nutrients and therefore modify the dynamics of a microbial community. The discovered mechanisms underlying the microbial interactions in crowded systems together with the developed framework represent a valuable starting point for future studies of the interplay of human microbiome and host metabolism, the pathogen invasion, and the evaluation of antibiotic effectiveness. Microbial communities, such as biofilms, are involved in several processes ranging from beneficial bioremediation to the harmful fouling of industrial equipment, food contamination, and human chronic infections . One of Deff,met) x times slower than the diffusion in water, such that Deff,met is a function of the local volume not occupied by cells of a similar size and shape. However, experimental evidence shows that Deff,met is asymmetrical and depends on the local biofilm composition such as the abundance and size of EPS and cell species as well as the size of the diffusing molecule , and Mcell is the cell mass at time t [gDW]. The values of the maximal uptake flux  [mmol gDW-1 h-1] and the Michaelis-Menten constant  [mmol L-1] can be obtained from experimental data and/or databases (e.g. BRENDA database). Ceff,met [mmol L-1] represents the effective concentration of the metabolite in the box ijk, given byVbox = ΔxΔyΔz [mm3], 106 is the conversion factor from mm3 to L. Assuming that both cells and metabolites are hard spheres of different radii R [mm], the activity coefficient γmet (i.e. the ratio between the box volume and the available volume for met in the box ijk) can be estimated by SPT . Ea. EaE. co0-13 gDW  valid fo2 were supplied to the system to maintain a constant effective concentration of 2.92 mM and 0.21 mM in all boxes, respectively. Other metabolites such as galactose, acetate, and methionine were available in the system only when they were synthesized by microbial species. All metabolites are allowed to escape from the system through the four boundaries, where the metabolite concentrations were set equal to zero. Zero diffusive flux boundaries were applied for bacterial species. Well-mixed conditions were assumed inside each CN-box and IbM-box. All simulations were replicated 5 times. The diffusion of the metabolites were computed using the CN method.Lactose and OS. enterica and E. coli ΔmetB were constructed as described by Harcombe et al. [E. coli iJ01366 core [S. enterica iRR1083 [consume 0.5 mmol gDW-1 of intracellular methionine balanced by the production of the same amount of methionine that will be secreted to the medium. Furthermore, to simulate the metabolic variability of S. enterica (see below), the ratio of methionine: biomass (rmeth) in the biomass reaction was constrained either to 0.5 or 0 mmol gDW-1. In this way, different subpopulations were characterized by the methionine production, where a cell with rmeth = 0 corresponds to S. enterica wild type (WT) that does not secrete methionine (identified as meth-), while rmeth = 0.5 corresponds to methionine-secreting S. enterica mutant (meth+).GEM models for the methionine-secreting e et al. . For the366 core , the rea iRR1083 , the biovbio and vf,ex,met computed by TFA for 30,000 flux samples  requires as inputs vUf for acetate, galactose, O2, and also rmeth. The Pearson correlation coefficient and normalized mean squared error (nmse) were estimated as 0.9982 and 6.4 x 10−3 for E. coli, and 1 and 4.47 x 10−4 for S. enterica, respectively  using Matlab in a 12-core Intel Xeon E5, CPU 2.7 GHz. Comparatively, the runtime required by TFA for the parallel computation of metabolic distributions  of a similar number of bacteria is about 93 min, and with reduced GEM models [t, with a large number of cells and/or when the metabolic model used is computationally expensive . However, training the NNs requires previous knowledge of the prevalent metabolite exchanged among the species to select the most important metabolites to track. The use of TFA or other stochiometric-based models could be more appropriate in more complex problems, such as when the metabolic flux distributions of a species are very sensitive to small amounts of multiple substrates.NNs significantly reduce the runtime required for metabolic flux estimations, e.g. the exchange fluxes of 10,000 bacteria are computed in approximately 0.04 s . Twenty bacterial spots were randomly inoculated in the system with similar dimensions as described before. Each spot represents 200 metabacteria of E. coli and 200 of meth-. Only one spot (colony A) contains meth+ metabacteria. Three different initial number of meth+ metabacteria were tested: 70, 60, and 50 metabacteria. All metabacteria were randomly allocated in the spots.As a second case study, we simulated the co-growth of E. coli biofilms (identified as Beps+ and Beps++) on glucose and aerobic conditions were simulated in a 3D system. Biofilm Beps+ was composed by (non-EPS-secreting) wild type cells and mutants identified as eps+ that secreted 0.11 g gDW-1 of EPS, while biofilm Beps++ contained WT and mutants identified as eps++ that secreted 0.43 g gDW-1 of EPS. We assumed the same metabolic cost for the synthesis of 1 g of EPS than for 1 gDW of biomass.The growth of two VCN-box = 1.4 x 10−6 mm3), while the IbM-lattice was divided into 22 x 22 x 68 cubic boxes of Δx = 5.7 x 10−3 mm per side . Each CN-box contains 8 IbM-boxes, and 1 IbM-box can allocate at most one metabacterium. To take into account that eps+ and eps++ accumulated EPS, a therefore a lower number of cells can be allocated in IbM-box, the number of cells per metabacterium was computed as metaBsp = 0.52ΔxIbMΔyIbMΔzIbM/, where the denominator represents the maximum volume occupied by a cell of species sp and the EPS secreted by this. We assumed that the EPS amount MEPS,sp present in a box is proportional to the cell mass, thus MEPS,WT = 0 for the non-EPS-secreting WT. Thus, one metabacterim represents 27 cells of WT, 20 of eps+, and 12 of eps++. The volume occupied in a box by the cells and EPS were similar for the three species. The maximal metabacterium mass (or mass threshold to carry out the cell division) was then obtained by multiplying Mmax,sp of a single cells  are assumed to be spherical shape and with a volume proportional to their molecular weight see  S3. OnlyE. coli WT iJ01366 [DW-1 or 0.43 g gDW-1 of EPS that will be secreted to the medium by eps+ and eps++, respectively. NNs of 2 hidden layers with 15 neurons each were created using 30,000 flux solutions computed by TFA for the GEM models. The inputs for the NNs were the upper flux limits vUf for glucose, acetate, and O2. The Pearson correlation coefficient was estimated as 1, and nmse as 2.4 x 10−5  is the total biomass of species sp at t, and tsim is the final simulation time. Additionally, the relative fitness of species sp1 in competition with sp2 can be computed asThe average fitness or growth of a species S1 FigE. coli: S. enterica of (B) 99:1 and (C) 1:99.(A) Schematic representation of the microbial community in a 2D system.  The species ratio convergence predicted by CROMICS, COMETS, and the experimental observations  after 48(TIF)Click here for additional data file.S2 Fig2, and methionine, the cells produce a mean flux value of acetate, galactose, and growth rate. The Pearson correlation r was estimated as 0.9982, while the normalized mean square error between the fluxes predicted by TFA and NN was estimated to be 6.4 x 10−3. Fluxes vf are given in mmol gDW-1 h-1, and vbio in h-1.To train a NN with 2 layers of 15 neurons each, 30,000 flux samples were used. Training data were obtained by assuming that for a given uptake flux of lactose, O(TIF)Click here for additional data file.S3 Fig2, and methionine:biomass ratio rmeth, the cells produce a mean flux value of methionine and growth rate. The Pearson correlation r was estimated as 1, while the normalized mean square error between the fluxes predicted by TFA and NN was estimated to be 4.47 x 10−4. Fluxes vf are given in mmol gDW-1 h-1, and vbio in h-1.To train a NN with 2 layers of 15 neurons each, 30,000 flux samples were used. Training data were obtained by assuming that for a given uptake flux of acetate, galactose, O(TIF)Click here for additional data file.S4 Fig2, the cells produce a mean flux value of acetate and growth rate. The Pearson correlation r was estimated as 1, while the normalized mean square error between the fluxes predicted by TFA and NN was estimated to be 2.4 x 10−5. Fluxes vf are given in mmol gDW-1 h-1, and vbio in h-1. GEM models for the eps+ and eps++ mutants were constructed by modifying the biomass reaction to produce 0.11 g gDW-1 and 0.43 g gDW-1 of EPS that will be secreted to the medium. In comparison when the same metabolic upper flux limits were used, the growth rate computed by TFA for mutants were 2, and acetate) predicted were the same for the three type E. coli. Thus, for simplicity, the NN created for WT was modified to represent the eps+ and eps++, by multiplying the biomass computed by the original NNWT by a factor of 0.9 and 0.7, respectively.To train a NN with 2 layers of 15 neurons each, 30,000 flux samples were used. Training data were obtained by assuming that for a given uptake flux of glucose and O(TIF)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


The liver plays a major role in the metabolic activation of xenobiotics . Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them , sites of metabolism prediction tools , a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures.in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.Overall, a predictive toxicological model based on an The online version contains supplementary material available at 10.1186/s12859-021-04363-6. Heterocyclic Aromatic Amines (HAA) and their metabolites The liver plays a major role in the metabolic activation of xenobiotics . HAA are environmental contaminants formed during the cooking of meat or fish, in cigarette smoke or exhaust gas indole), are formed by a pyrolysis reaction of amino acids at temperature greater than 250C. Aminoimidazoarene HAA, such as MeIQx , PhIP  and IQ , are formed by Maillard reaction between hexose and amino acids at a temperature greater than 150C (O)cn2C ) is predicted to be derived from caffeine after one step reaction. Ten other compounds are predicted to be derived from caffeine after two step reactions. Among these ten metabolites, three  are also predicted to be metabolites of node 5. A single metabolite (node 22) is predicted to be derived only from the newly metabolite node 5, which has the specificity to lose the aromatic structure of the imidazole.Production probability scores of predicted metabolites In order to estimate the confidence of the predicted metabolites, we computed production probability scores for each of them. As detailed in the Methods section, the production probability score first takes into account scores associated with reactions, which are computed from several site of metabolism (SOM) scores predicted by dedicated tools , e.g. the chance that a transformation occurs on a given atom. In addition, the production probability score for the metabolites also takes into account the different pathways, e.g. chains of reaction, from caffeine to the considered metabolite, combined according to a Bayesian framework.The SOM score associated with reactions are indicated as an orange label on arrows in Fig. The 11 known metabolites (green nodes) have the largest scores. The 12 unknown predicted metabolites are represented with a blue bar.We observe that these predicted metabolites can be gathered into three groups, each of them is shown by an ellipse with a specific color. The first group (black ellipse) describes metabolites with a score greater than 0.70: it contains all known metabolites predicted by our method and two unknown metabolites, node 9 (Cn1c(=O)c2[nH]c[n+](O)c2n(C)c1=O) and node 11 (Cn1c(=O)c2c(ncn2C2OC(C(=O)O)C(O)C(O)C2O)n(C)c1=O). The second group (violet ellipse) contains three metabolites with a medium score (between 0.20 and 0.70) corresponding to the unknown metabolites node 14, node 5 and node 18. The last group of seven unknown metabolites (yellow ellipse) with low score (< 0.20) corresponding to the nodes 15, 20, 22, 10, 16, 19 and 21. The remaining nodes 10, 16, 19 and 21 have a score of 0.00, which is explained by the fact that all the pathways which produce them contain at least one reaction with a null score.Filtered caffeine metabolic map The production probability scores were used to filter the caffeine map of metabolism as follows. The metabolites of the filtered map are all metabolites of the first group (black ellipse) in Fig. The filtering procedure removed metabolites associated with nodes 5, 14, 18 and 22, which have a specific configuration regarding nitrogen atoms on the imidazole part. These four metabolites are the only ones that are oxidized metabolites of caffeine with a pattern N=C-N or N-C-N such that one of the nitrogens is methylated and the other nitrogen is linked to an oxygen atom.H–purin–7–yl)–3,4,5 –trihydroxyoxane–2–carboxylic acid), whose glucoronyl group is linked to the imidazole part.The other metabolites that were eliminated by the filtration procedures are nodes 10, 15, 16, 19, 20, and 21. They are all glucuronyl-conjugates of caffeine and caffeine metabolites whose glucoronyl group is not associated with the methylated nitrogen atom of the caffeine imidazole part. The fact that most of the glucuronyl conjugates are filtered is consistent with the literature because there is no glucuronyl-conjugate metabolite of caffeine described in humans yet. The only glucuronyl-conjugate appearing in the final map is node 11 (with SMILES formula : Cn1c(=O)c2c(ncn2C2OC(C(=O)O)C(O)C(O)C2O)n(C)c1=O, or IUPAC name: 6–, which is already known to be metabolized into nodes 6 (3-methylxanthine or 3MX), 7 (1-methylxanthine or 1MX) and 8  could also have the potential to be biotransformed into another new metabolite. However the 13U and 1MX which are the most observed metabolites from 13X , could cH –purin–9–ium). According to our method, this metabolite is formed from node 1, e.g. 1,3-dimethylxanthine (theophylline or 13x), with a SOM score predicted by Way2Drug of 0.714 for enzyme CYP3A4. This reaction from node 1 is an oxidation on an nitrogen atom of the imidazole part. Contrary to the oxidation of the carbon atom between the nitrogen atoms in the imidazole part which produced node 8 (13U) from node 1 with a predicted SOM score of 0.606 for enzyme CYP2D6, with the same order of magnitude, this reaction occurs on an un-methylated nitrogen atom of the imidazole part. This suggests that the oxidation on nitrogen atoms of caffeine could theoretically occur although it has never been experimentally observed.Apart from node 11, the only unknown metabolite of the initial map conserved after the filtration is node 9 (Cn1c(=O)c2[nH]c[n+](O)c2n(C)c1=O or 9–hydroxy–1,3–dimethyl–2,6–dioxo–2,3,6,7–tetrahydro–1The caffeine example suggests that there is an added value to building a map of metabolism by combining several approaches such as assembling reaction prediction tools, predicting sites of metabolism and filtering the map according to a production probability score. Based on this validation, we have further investigated how this method may facilitate the prediction of DNA adducts formation derived from xenobiotics. In this vein, we first constructed the predicted maps of metabolism of the 30 human HAA . The smallest maps correspond to the HAA Harman (70 metabolites) and norharman (50 metabolites). The size of these maps could be explained by the chemical structure of both HAA that have few sub-structures on which SyGMa transformation rules can be applied. The maps predicted for two of the three well characterized HAA in primary human hepatocytes  are associated with maps with similar characteristics: they have the same number of metabolites, reactions and metabolites reactive to DNA. The closeness of the chemical structure of isomers could explain such a similarity between maps: as planar isomers are composed of the same chemical substructures, the transformation rules contained in the SyGMa database have a high probability to occur equivalently on the metabolites of both HAA.We also noticed that the maps associated with pairs of HAA such as 7,8-DiMeIQx and 7,9-DiMeIgQx, MeIQx and 7,MeIgQx or IQ and IQ[4,5-XenoSite Reactivity score greater than 0.85 is reactive to DNA, following the criteria introduced in [2OH-8-MeIQx, 7,8-DiMeIQx, 7,9-DiMeIgQx and 4,7,8-TriMeIQx have both the largest map and the greatest amount of metabolites reactive to DNA . norharman characterized by the smallest map contains no metabolite reactive to DNA. Harman, AMPNH and APNH are also associated with a very small ratio of metabolites reactive to DNA . The ratio for the other HAA ranges from 38.8 to 55.8%. MeIQx and PhIP have a relatively high ratio of metabolites reactive to DNA (45.2% and 44.5%) while Ain vitro, (b) the reaction predictions performed by SyGMa may be incomplete, as we observed it for NAT2 reactions in the case of caffeine, (c) the reactions performed by SyGMa may not be homogeneous. As detailed below, the analysis of the production probability scores of the maps suggests that the last two hypotheses are highly probable.For each HAA, we estimated the number of metabolites reactive to DNA by assuming that each metabolite with a duced in . The fou or PhIP . This ob2OH-8-MeIQx and 4,7,8-TriMeIQx). We finally selected 7,8-DiMeIQx which represents the pair of isomers  having large maps. Note that the three selected HAA  have also the largest number of metabolites reactive with DNA. We applied the prediction pipeline and the annotated maps of metabolism obtained were explored to identify enzyme families and isoforms associated with reactions. We confirmed that most enzyme families  and CYPs isoforms  annotate at least one reaction in each map with the exception of GSTs.Manual annotation and filtering of six maps of metabolism As the pipeline for the study of maps of metabolism encompasses a part of manual annotations for atom number of reactions, we applied the pipeline to a selection of six HAA among the 30 HAA. We first selected Amentclasspt{minimaatocytes . We compThe annotated maps of metabolism obtained from A2-SO3H, in the map of metabolism of MeIQx, is the only experimentally identified metabolite associated with a production probability score of 0.0. A null score suggests that the metabolic pathways leading to the metabolite contains at least one reaction that has not been annotated either by Way2Drug for a reaction of rank 1, or by FAME 3 for a reaction of rank 2.Figure In general, we notice that PhIP and MeIQx have a high amount of metabolites associated with a low production probability score, compared to A2OH-8-MeIQx map of metabolism are filtered representing 66% of the map and more than half of metabolites are filtered in the maps of PhIP, MeIQx, 7,8-DiMeIQx and 4,7,8-TriMeIQx. By contrast, the filtration procedure had a lower impact on ATable abolites , 47.We observe that the filtering procedure based on prediction probability scores tends to homogenize the size of the final maps of metabolism while keeping a similar ratio of metabolites predicted to be reactive to DNA (between 40 and 50%). This suggests that there is a strong interest in using SOM scores (as included in the production probability scores) to homogenize maps of metabolism and eliminate unsupported DNA reactive metabolites.enzymatic contexts to be tables describing all the possible combinations of enzymes that may be considered to be available. In our study, there are 512 such different enzymatic contexts. Each enzymatic context is associated with a specific distribution of production probability scores. Indeed, when an enzyme is described as unavailable in an enzymatic context, the reactions annotated with this enzyme cannot be taken in account for the calculation of the production probability scores.Optimal enzymatic signature in terms of DNA reactivity As described previously, the pipeline relies on the computation of SOM scores on reactions involving manually annotated metabolites to reduce the maps of metabolism according to a Bayesian prediction probability. As SOM scores are directly related to enzymes, the production probability score is influenced by enzyme availability. We define global reactivity score of an HAA in a given context that we defined as the sum of the production probability score (in the considered context) of all metabolites reactive to DNA in the considered map of metabolism.Based on this assumption, for each of the six filtered maps of metabolism described above, and for each of the 512 enzymatic contexts, we computed the production probability scores of all metabolites of the map. This allowed us to determine the optimal enzymatic signatures in terms of reactivity, which are all the enzymatic contexts where the global reactivity score is maximized while they contain the smallest number of activated enzymes. Intuitively, optimal enzymatic signatures therefore correspond to enzymatic contexts where the chance to obtain at least one metabolite reactive to DNA is maximal according to our models.In this framework, it becomes possible to define Impact of enzymatic context to the production probability score and application on DNA adduct formation of HAA Fig. We observe that the enzymes UGTs, SULTs and CYP1A2 are present in all optimal signatures of the six HAA. This is consistent with the literature which describes the implication of SULTs and CYP1A2 in the formation of HAA DNA adducts . In addiument}αC .Conversely, the enzymes NATs and GSTs are absent in all optimal signatures. While the absence of GSTs is explained by the fact that annotated maps of metabolism do not contain any GSTs, the absence of NATs suggest that the resulting metabolites are not reactive to DNA since NATs are present in all maps of metabolism. However, NAT2 has been previously involved in formation of DNA adducts derived from HAA . We hypoFigure Impact of the XenoSite Reactivity threshold In order to test the impact of the reactivity threshold chosen to characterize all the compounds of a DNA reactive map, we decided to explore all the thresholds from 0 to 1.0. For the 101 threshold values considered (step of 0.01), we recalculated all the metabolites considered to be “reactive” , and then calculated the optimal signatures for reactivity to DNA. According to our previous results we did not consider NATs and GSTs. As shown in Fig. 2OH-8-MeIQx, 0.92 for MeIQx and PhIP, 0.93 for 4,7,8-TriMeIQx and 0.95 for 7,8-DiMeIQx. In addition, the use of any reactivity threshold lower than 0.89 returns the same optimal signature for each HAA. This suggests that enzymes involved in metabolic pathways leading to the most reactive metabolites (given by XenoSite Reactivity scores), are sufficient for activating all the pathways leading to the less reactive metabolites.We further observed a low variability between HAA since the largest optimal signature is reached at a reactivity threshold of 0.89 for A2OH-8-MeIQx, 7,8-DiMeIQx and 4,7,8-TriMeIQx, we noted that the reactivity thresholds for which SULTs is present in the optimal signature is greater than the one for UGTs. This suggests that SULTs-associated metabolites are more likely to form DNA adducts than UGTs-associated metabolites when the chemical structure of the HAA is close to the MeIQx structure.Figure enriched maps of metabolism. The main specificity of the pipeline is to use a production probability score to sort metabolites according to both the prediction of site of metabolisms and the topology of the maps predicted by bio-transformation rules. This score allows for the comparison of metabolite production in different physiopathological conditions which permit the exploration of the role of enzymes in the production of specific metabolites. In this study, we focus on metabolites and their DNA adduct formation capacity.In this study, we introduced a pipeline for predicting the metabolism of xenobiotics in humans. The pipeline was applied to six HAA of interest as well as caffeine. Our prediction pipeline is based on the construction of so-called 2-SO3H.The pipeline was used to reconstruct the maps of caffeine metabolism and of six HAA. Among the four xenobiotics for which the metabolism was known i.e;, MeIQx, PhIP, AIn the maps of metabolism predicted by our pipeline, most experimentally identified caffeine and HAA metabolites are associated with a high prediction probability score. On the contrary, a large number of predicted metabolites, which can be considered as over-predicted metabolites, are not supported by site of metabolism predictions.This fact allowed us to use prediction probability scores as filters that importantly reduced the size of the maps. In the case of HAA, half of the predicted metabolites were filtered out. In this way, maps of metabolism produced by other tools could be filtered by this production probability score.The prediction of the map of caffeine and HAA metabolism highlighted areas for improvement in our pipeline. First, the study of the caffeine map of metabolism evidenced that a metabolite (AFMU), resulting from a NAT2-directed reaction, is not predicted by the SyGMa tool, suggesting that the tool is incomplete in predicting reactions catalyzed by N-acetyl transferases. This assumption is also supported by the analysis of the optimal HAA signatures as detailed in Fig. We designed our methods for the study of HAA. As presented in Fig. 2-SO3H, an experimentally identified metabolite, is associated with a nul prediction probability score. This is explained by the fact that the reaction producing MeIQx-N2-SO3H is labeled as a rank 1 reaction, catalyzed by the SULTs. Our pipeline differentiate rank 1 and rank 2 reactions and could not annotate reactions of rank 1 with a SOM score only available for tools annotating reactions of rank 2. To overcome this issue, we plan on differentiating the SOMs prediction tools according to the enzymes annotating the reactions instead of the rank of the reactions. This, however will require the homogenization of the level of information about isoforms.A characteristic of the map of metabolism of MeIQx is that MeIQx-NThe production probability score that we defined allowed us to analyze the influence of enzymes on the production of DNA reactive metabolites and to propose a specificity of the CYP3A4 enzyme in the production of DNA adducts derived from AHAs close to MeIQx, which will be the subject of further experiments.In conclusion, our study describes a new method for the construction and analysis of maps of metabolism by combining prediction of biotransformation rules, predictions of site of metabolisms, and prediction of reactivity to DNA. The method was validated and applied to six xenobiotics. Further study will consist of applying the pipeline to the 24 other human HAA, which requires the automatization of the annotation of metabolites predicted by transformation rules. Moreover, our approach based on enzymes profile determination opens the perspective to predict various xenobiotics derived metabolites susceptible to bind to DNA adducts in both normal and physiopathological situations. The enzymatic contexts extracted from data repositories such as TCGA  and GTEXThe SyGMa python package   is a rulThe first parameter required by SyGMa to make predictions is the set of SMIRKS reactions to use. In this paper, we used the two sets of SMIRKS reactions, named “phase I” and “phase II”, obtained by data mining the Metabolite Database  and corrrank) that separates the original compound and a metabolite. If this maximal rank number is greater than 1, the metabolites obtained after a first iteration are used as a source for new reactions to obtain second rank metabolites, until the maximal rank is reached. In the paper, the maximal rank number was equal to 2. This allows the reproduction of the main observed xenobiotic metabolism with a first reaction associated to phase I enzymes of xenobiotic metabolism and a second reaction that conjugates the oxidized metabolite by enzymes of phase II metabolism of xenobiotics.The second parameter required by SyGMa is the number of reactions  prediction tools to annotate reactions that could be catalysed by phase I or phase II enzymes of xenobiotics metabolism. Each method used for predicting SOMs estimates the capability of an enzyme to react with an atom according to a reference database of reactions and their metabolites. Using these models, each method can evaluate each atom of a compound to see if it can be involved in a reaction similar to those described in the reaction database. This analysis results in a score for each atom of a compound that describes the probability to be transformed by each reaction. Therefore, the methods differ according to the parameters describing the atom configuration and the enzymes involved in the reactions (different enzyme family and/or isoforms).rank, enzyme label and atom number of reaction (see results section).In order to take advantage of the panel of existing methods, our procedure included different tools to annotate each edge (reaction) with a site of metabolism (SOM) prediction score depending on its Way2Drug SOMP  is used When input of a reaction is not the original compound, we used FAME 3  tool, whXenoSite Reactivity , 11 is uScoring metabolites with a SOM-based pathway production probability score A specific method was designed to compute a probability for each node to be formed for each metabolite according to all the annotations of the reactions of the metabolic map of metabolism. The approach relied on the formalism of Bayesian networks  which arThe graph (DAG) on which probabilities were computed was derived from the map of metabolism according to two principles. (1) The first principle applies when several isoforms of the same enzyme can transform a compound to another. In this case, the total affinity of the compound with the enzyme family is approximated by the maximal isoform enzyme affinity. (2) The second principle applies when several members of different enzyme families are competing to produce the same target metabolite from different input metabolites. In this case, the recruitment of enzymes in reactions is assumed to follow an exclusivity rule: each enzyme family can catalyze the production of the targeted metabolite with at most one reaction.production of the metabolite. The DAG was built according to the following rules, which are illustrated in Fig. Consequently, a variable of the Bayesian model was built for each node (e.g. metabolites) of the map of metabolism. It was therefore associated with the event For each edge, the SOM score was interpreted as a conditional probability, which is the probability of getting the output compound of the edge assuming the presence of input compound. The structure of the graph, which is both acyclic (because the pipeline for building metabolic maps cannot create cycles) and such that enzymes do not compete for the production of the same metabolite, yields that conditional probabilities are independent. This allowed creating a complete probability table associated with each metabolite production event.production probability score of each metabolite was defined to be the probability of a metabolite production. These scores were computed by using the conditional probability tables to expand joint probability function (Bayes formula) [The formula) .Additional file 1.: Extended Figure 2 The file provides the figure 2 (a) and (b) in a plain page to make structures and text more readable.Additional file 2.: Dictionary of SMIRKS rules The file provides a manually constructed catalogue of SMIRK rules required to map SMIRKS rules to enzyme family labels by taking into account the rank of the reaction in the pipeline for building enriched maps of metabolism.Additional file 3.: Extended Figure 4 The file provides the figure 4 in a plain page to make structures and text more readable.Additional file 4.: Extended Figure 5 The file provides the figure 5 in a plain page to make structures and text more readable.Additional file 5.: Description of thirty HAA and Caffeine structure used as pipeline input This pdf file describes the SMILES formula and 2D-structure of each of the thirty HAA and the Caffeine studied in the paper.Additional file 6.: Metabolites of AαC, PhIP and MeIQx founded in predicted metabolism maps The file provides a table describing for each metabolite experimentally described if they are predicted by SyGMa and if they are known to produce DNA adduct.Additional file 7.: Distribution of 3 HAA production probability score The file provides the distribution of production probability scores used in Fig. 6 but in full page format.Additional file 8.: Description of six HAA maps of metabolism The file provides a detailed description of metabolites for the six HAA filtered maps of metabolism built in the paper. For each metabolite of each map, the file provides the identifier of the metabolite, its SMILES formula, its production probability score, its reactivity to DNA and the score of XenoSite Reactivity.Additional file 9.: Metabolism map of AαC A representation of the filtered metabolism map of AαC with chemical structures.Additional file 10.: Metabolism map of MeIQx A representation of the filtered metabolism map of MeIQx with chemical structures.Additional file 11.: Metabolism map of PhIP A representation of the filtered metabolism map of PhIP with chemical structures.Additional file 12.: Metabolism map of 4,7,8-TriMeIQx A representation of the filtered metabolism map of 4,7,8-TriMeIQx with chemical structures.Additional file 13.: Metabolism map of 4-CH2OH-8-MeIQx A representation of the filtered metabolism map of 4-CH2OH-8-MeIQx with chemical structures.Additional file 14.: Metabolism map of 7,8-DiMeIQx A representation of the filtered metabolism map of 7,8-DiMeIQx with chemical structures.


Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data . In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data. The beginning of the 21st century has initiated a new era in the generation of Big Data. Major technological advances have enabled the generation of Big Datasets in a cost-efficient and high-throughput manner .The initial wave of biological Big Data was powered by the advancement and cost-effectiveness of sequencing technologies, leading to repositories of a large variety of genomes. It served as a foundation for the subsequent waves of omics, which has resulted in a growing wealth of “multi-omics” repositories. The growth of multi-omics Big Data can be perceived through the high number of published multi-omics research. For example, a simple keyword search of different omics research areas on NCBI PubMed  such as The exponential increase of Big Data in biology has been challenging to analyze due to the different types of omics data  , lack ofCellular components function through inter- and intra-cellular interactions that can be represented with an “interactome” network in which components such as proteins, genes, metabolites, and other macromolecules are represented as nodes, and the interactions between these cellular components correspond to the edges. These networks can represent transcriptional regulatory networks , proteinTraditionally, GEMs were developed for individual isolated organisms. However, over the last decades, the study of microbial communities has gained a lot of interest in the scientific community, especially to understand the complex interactions between host organisms and their associated microbiome ,20,21,22Here, we present a comprehensive review of the latest information of biological Big Data and how GEMs are a reliable tool to contextualize and understand them. We discuss how computational tools enable an in-depth understanding of experimental data to accelerate our knowledge of bacteria, archaea, and eukaryotes. We also discuss available tools for reconstructing context-specific GEMs using Big Data . We disc13C-metabolic flux analysis (13C MFA), and dynamic FBA (dFBA) [13C MFA uses labeled isotope tracers to predict the metabolic fluxes, FBA uses measurements of consumption rates as constraints to predict fluxes throughout the entire network [GEMs can be reconstructed using automatic and semi-automated tools. Over 6000 metabolic models have been generated either through semi-automatic or automatic genome-scale reconstruction tools, covering bacteria, archaea, and eukaryotes . GEMs coA (dFBA) . While 1 network . In the  network . Below, E. coli GEMs. They created a “core” model that was the intersection of all the genes, reactions, and metabolites of the individual models and a “pan” model that was a union of those models [Salmonella model from 410 individual GEMs of Salmonella strains and predicted its growth in 530 different environments [S. aureus and analyzed its growth under 300 different growth conditions [Klebsiella pneumoniae strains to simulate growth under 265 different carbon, sulfur, nitrogen, and phosphorus sources [Candidatus Liberibacter asiaticus (CLas) strains. They reported conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts [Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and Escherichia coli) pathogens [Pan-genome analysis unravels variability among genomes of multiple strains, resulting in divergent phenotypes across the strains ,33. Basee models . In anotronments . Bosi etnditions . Norsigi sources . In 2020ts hosts . These sathogens . MoreoveMethanobacterium formicicum (MFI) is a methanogen that is usually present in the digestive system of humans and ruminants [Methanosarcina barkeri str. Fusaro , Methan , and Me (iMM518 ), aidingA vast number of modeling efforts have been focused on using novel genomics information of eukaryotic organisms by expanding the number of metabolic networks for a broad range of organisms. Out of the 6000 metabolic models reconstructed to date, a total 215 metabolic models were reconstructions for eukaryotic microorganisms, and only 60 of them have been subjected to manual curation . Figure Chlorella vulgaris and Phaeodactylum tricornutum, several prediction tools were used  [Various computational tools that attempt to predict subcellular localization of proteins have been developed. These include peptide sequence motif prediction (ASAFind) , subcelld TMHMM) ,70.S. cerevisiae as a template. All models used the same biomass composition of S. cerevisiae in the modeling reaction. (iii) Merlin [Six different tools to reconstruct eukaryotic models have been developed so far. For example, (i) AuReMe, which had been tested for eukaryotic algae . The rec) Merlin , which r) Merlin  and UniP) Merlin . Finally) Merlin . (vi) Th) Merlin . The selEmerging applications of GEMs and increased demand for GEMs motivated the generation of automatic and semi-automatic computation tools to generate metabolic models of various organisms from all domains of life. A list of GEM reconstruction tools with their basic properties has been summarized in GEM reconstruction tools are distinct from each other due to features like (i) annotation/re-annotation of target genome sequences, (ii) reaction databases, (iii) presence/absence of gap-filling module, (iv) fully-automation or flexibility of customizing parameters, (v) annotation and addition of transport and exchange reactions, (vi) biomass reactions, (vii) presence/absence of subcellular localization module, and (viii) programming language. Additionally, some of them are more used than others, for example, The COBRA toolbox, which has been cited over 2700 times in its three versions see .Most reconstruction tools require an already annotated proteome to map it with reaction databases, whereas tools like merlin and ModelSEED  reannotaConsequently, merlin encompasses a function to visualize all the reactions in the draft model, and these reactions can also be mapped on the KEGG pathway browser. These functionalities provide opportunities to check and refine reactions and find candidate reactions for filling gaps in the network. RAVEN provides the options to set user-defined template models and blast parameters  during finding the homolog proteins between proteomes of target and template organisms . MetaDraExcept for merlin, all reconstruction tools rely on genome annotations, template models, and reaction databases for adding transporter and exchange reactions. Merlin directly annotates the transport genes and reactions using a transporter database, TCDB . For bioCoReCo contains a function to run comparative analysis on closely related organisms useful for conducting evolutionary studies . FAME caAs discussed earlier, multi-omics Big Data is expanding at an increasing rate. Machine learning methods have become an essential part to understand and handle the complex nature of Big Data. Recently, machine learning (ML) has been applied to improve the accuracy of GEMs by combining the knowledgebase of the biological system with the predictive power of ML . For exaUnsupervised ML approaches, such as principal component analysis (PCA), and clustering can help in reducing the dimensionality of omics data which can be applied to, for example, identify active reactions in GEMs , create The curation of an individual GEM is labor-intensive and time-consuming. The manual curation process can take several months for bacteria and years for eukaryotic organisms. Curation involves adding orphan reactions, refinement of specific model compartments or biomass functions, correct mass imbalanced reactions, etc. . This prSaccaromyces cerevisiae mutant strains [In another study, Oyentunde et al. developed a framework called BoostGAPFILL, which uses a combination of constraint-based and pattern-based methods for metabolic model refinement . They us strains . They cr strains .ML techniques have also been used to annotate genes . StiehleE. coli K-12 MG1655 has constantly been evolving. The initial model contained 660 associated genes [GEMs have become highly relevant during the last decades due to their ability to computationally simulate the complex metabolic processes carried out by different organisms . Metaboled genes , while ted genes .The continuous updating of GEMs, accompanied by biological Big Data has directly influenced the creation of well-curated modeling databases and tools to integrate the modeling results with omics data. There are databases focused on collecting and retrieving well-constructed and most recent models. The BiGG database compiles high-quality manually curated GEM databases. Additionally, CarveMe, a BIGG-based Database, has emerged as another important modeling database focused on the reconstruction and retrieval of bacteria and archaea microorganisms, facilitating the obtention and simulation of GEMs.GEMs have varying biological scope and coverage . GEMs miE. coli K12 MG1655, Saccharomyces cerevisiae, and other key organism GEMs have played an important role in understanding general metabolic pathways  and establishing the important relations among the elements of the GEMs . Based on this mathematical–biological relation, GEMs are used to elucidate the general metabolic mechanisms of the studied organisms using systems biology approaches. For instance, the first GEM of an acetogen, Clostridium ljungdahlii DSM 13528 [Azotobacter vinelandii DJ was developed to elucidate the nitrogen fixation pathway [Most of the initial GEM reconstructions have been targeted to establish the first models capable of linking the biological data of key organisms with their mathematical and computational representations (in silico). SM 13528 , modeledSM 13528 . GEM of  pathway . The pre pathway ,127 due E. coli K12 MG1655 using metabolic modeling predictions [iML1515) is capable of successfully predicting the tested conditions with more than 90% accuracy. Based on the metabolic estimations performed by iML1515, it is possible to establish new biological processes to describe the observed phenotypes. Lu et al. developed a comprehensive S. cerevisiae metabolic model Yeast8 along with a cluster of metabolic models , representing an ecosystem that can be integrated to understand the metabolism of yeast under different carbon and nitrogen sources and understand the genotype–phenotype relationship [C. reinhardtii (iRC1080) to simulate growth under different light sources. They created photon-utilizing reactions (prism reactions) that represent 11 different light sources used to study plant and algal growth, including solar, LEDs, and other light bulbs [GEMs of several organisms have been employed to test the metabolism of a wide range of different nutrients and substrates. Once the metabolic models are built and validated with experimental phenotypic data , they are usually tested with new carbon, nitrogen, phosphorus, and other elements as substrates to identify the specific mechanisms applied by the organisms to consume these nutrient sources. A recent example is the experimental validation of more than 3000 conditions for dictions . The newtionship . Chang eht bulbs . Their pAzotobacter vinelandii DJ [A. vinelandii. The new metabolic processes described to consume the different substrates by the metabolic model agree with the previous experimental data from different approaches . Ultimately, the model operated as a system validator to identify the active metabolic pathways during polyhydroxybutyrate and alginate production  in diazotrophic and non-diazotrophic conditions.Another relevant example are GEMs of bacteria with polytrophic metabolism. For example, the well-studied diazotroph bacterium andii DJ . More thCandida albicans (iRV781) with 1221 reactions, 781 genes, and 926 metabolites [Mycobacterium tuberculosis GEM iSM810 to predict essential genes that can be potential drug targets [Pectobacterium carotovorum (iPC1209) that contains 2235 reactions, 1113 metabolites, and 1209 genes [Plasmodium falciparum (iAM-Pf480), representing five life cycles of the malaria-causing pathogen [+ T cell-mediated diseases. They first identified essential genes and then perturbed the network using existing Food and Drug Administration (FDA) approved drugs and compounds. They were able to identify 55 potential drug targets for three autoimmune diseases, such as rheumatoid arthritis (RA), multiple sclerosis (MS), and primary biliary cholangitis (PBC) [GEMs can predict possible biological targets of an organism under a specific condition . The GEMabolites . They id targets . In anot09 genes . They idpathogen . They repathogen . Weglarzpathogen . In anotis (PBC) . These sA disease phenotype is usually a result of perturbations in cellular interaction networks, not only due to an abnormal gene . SystemsGEMs have been deployed to identify biomarkers for complex diseases such as cancers. In cancer, there are genetic and epigenetic alterations in the metabolism. By incorporating omics data into the metabolic models, cancer biomarkers can be predicted by estimating the exchange rates of different metabolites in the model . To undeThe acceleration of GEM reconstruction across several biological domains gave rise to new questions that could previously not be answered by GEMs, such as dynamic functional states and macromolecular expression. For example, dynamic metabolic models have been successfully used to characterize growth dynamics, time-dependent cycles, and organelle crosstalk . On the Thermotoga maritima, which included protein sequence and fold annotations [GEM-PRO models contain detailed annotation of protein structure without altering either the metabolic network or the numerical strategy to find metabolic flux distributions through Flux Balance Analysis (FBA). In a GEM-PRO model, structure annotations are added as a new layer on top of the biochemical reaction network, which allows for a systems-level analysis of protein structure trends within the network and the predicted metabolic fluxes. The first GEM-PRO model was generated for otations . These aEscherichia coli [E. coli GEM at different temperatures [E. coli identifying growth-limiting proteins and their associated pathways.The following GEM-PRO models were generated for hia coli ,156. TheStaphylococcus aureus [A similar, but more detailed, GEM-PRO with a residue-level resolution of protein structure was generated for a human GEM in Recon3D . The mods aureus .Another approach to include macromolecular information to GEMs was realized with the introduction of models of metabolism and gene expression (ME-models). In this case, the metabolic network itself is altered by adding reactions for enzyme synthesis and assembly proportional to the flux of the catalyzed metabolic reaction. The coupling of metabolic reactions with protein synthesis allows the calculation of a systems-level protein synthesis profile, which directly informs about the proteome composition of the organism with a particular metabolic phenotype . MoreoveT. maritima [The coefficients of proportionality between coupled reactions are called coupling coefficients derived from enzyme kinetics of catalysis and degradation, and their dilution to newly produced biomass. The first ME-model was reconstructed for maritima , which dE. coli in four iterations, namely Thiele et al. [iOL1650-ME [iJL1678-ME [iJL1678b-ME [iOL1650-ME successfully captured RNA-protein ratios at varying growth rates, as well as glucose uptake rates and phosphotransferase enzymatic activities. The effect of nitrogen, sulfur, phosphorus, and magnesium levels on growth rate was correctly captured by iOL1650-ME. Further, the model identified three growth modes resulting from nutrient availability: nutrient-limited, proteome-limited, and a transition between both. Third, iJL1678-ME [The following ME-models were reconstructed for e et al. , iOL1650L1650-ME , iJL1678L1678-ME , and iJL1678b-ME . The mod1678b-ME  correctlL1678-ME  accounteE. coli ME-model, iJL1678b-ME [iJL1678b-ME proved to be as accurate in its translation and transcription rate predictions as its predecessors and more so in the gene essentiality predictions, in only a fraction of the solution time. The COBRAme toolbox was used to reconstruct the ME-model of Clostridium ljungdahlii [The main limitation of these ME-models was their solution complexity and stability due to them being nonlinear large optimization problems, with over 70,000 reactions. The solveME package  was gene1678b-ME , drastic1678b-ME  and 70,71678b-ME  reactionngdahlii , which pLately, additional biological and biochemical layers have been added to ME-models to simulate the effect of stress conditions, e.g., temperature, pH, and oxidative stress. FoldME  integratThe steady-state assumption of FBA limits GEMs to capturing growth at a particular time during culture, though critical biochemical phenomena may occur in a time-dependent manner. Dynamic Flux Balance Analysis (dFBA) was the first approach to address non-steady-state simulations using FBA and GEMs. Mahadevan et al.  first prOn the other hand, the DOA alters the definition of the optimization function, where the new objective function is a concentration integrated over a timespan, e.g., the total production of biomass. Further work on the DOA was performed by Zhou et al.  by usingSaccharomyces cerevisiae and E. coli [A third strategy to solve dFBA was proposed by Höffner et al.  and is a E. coli .Even though dFBA can obtain stable and unique solutions of time-course concentrations, especially during nutrient-replete conditions, it alone cannot capture sub-optimal growth under stress or nutrient limitation. The underlying optimization problem of dFBA exchange fluxes is constrained by either observed fluxes in vivo or unconstrained. Naturally, flux uptake and secretion rate limitations must vary with time during the culture timespan. This led to the hybrid dFBA systems constrained by kinetic models of uptake and secretion, called multiscale models ,172. A dChlorella vulgaris coupled with kinetic models to predict growth dynamics. Chien-Ting et al. [C. vulgaris GEM iCZ946 [Multiscale models have been generated for the photosynthetic microalga g et al.  constraiM iCZ946  with timNonetheless, multiscale models are not limited to the simulation of sub-optimal growth, as any other model can be coupled with a GEM to capture the desired phenomenon . A multiC. vulgaris was measured during batch culture and was integrated into the GEM iCZ946. The resulting model accurately predicted growth rate under nitrogen-replete and nitrogen-deplete conditions and discovered a nitrogen pool in the microalga. A similar strategy was employed by Tibocha-Bonilla et al. [chlorophyll a absorption coefficients and abundances were used to constrain the GEM of the diatom Phaeodactylum tricornutum, thus capturing circadian clock oscillations and discovering mechanisms to release excess reducing power [Thalassiosira pseudonana under three light levels and integrated them in its GEM, effectively predicting the effect of light intensity on the growth rate. Furthermore, the model predicted the contributions of the cyclic and non-cyclic electron flows to the total electron flow.GEMs can also be given dynamicity using the biomass objective function (BOF), especially in organisms with drastically changing biomass compositions, such as photosynthetic microalgae . In a sta et al.  on five ng power . Moreoveng power  measuredNetwork-based tools have shown to be a reliable tool for big data analysis and contextualization. Different methods for metabolic flux analysis such as FBA, 13C MFA, and dFBA have some limitations. First, FBA assumes that the system is under steady-state . Second,findable using unique identifiers and mapped to known databases. The model should be accessible for the users to make and retrieve significant changes to draft reconstruction. The draft GEMs should be written in standard SBML formats. Moreover, the steps involved at different stages of draft reconstruction should be transparent to users so that the GEM is reusable and reproducible [Most of the constraint-based modeling methods can be seamlessly applied to prokaryotes. However, eukaryotic models and other non-model organisms are quite complex to reconstruct due to the lack of complete genome assemblies, diverse secondary metabolites and their intracellular complexity organized by compartments/organelles, such as cytoplasm, chloroplast, mitochondria, nucleus, periplasm, peroxisome, and thylakoid . Recent oducible .Parameter estimation and model fitting is another major challenge in effectively utilizing GEMs. Constraint based GEMs which are based on linear programming do not include any time dimensions and do not account of metabolite concentrations . DynamicAnother major challenge is the integration of omics data. As omics datasets represent different aspects of biological systems, there are challenges in developing a credible knowledge base for integration in GEMs, such as non-uniform and missing data, inefficient computation power to analyze omics data, signal-to-noise ratio in the data, inconsistent annotations, or storage and distribution of data . MoreoveDespite the challenges associated with omics data integration with GEMs, each integrated layer of omics data helps in minimizing metabolic gaps and providing realistic predictions for organism specific cellular metabolism .E. coli [E. coli is a model organism, new biological information is constantly being reported that helps in updating the model with a higher frequency. iML1515 contains 184 new genes and 196 new reactions compared to the older version. This is now the benchmark model for E.coli and, perhaps, can predict with higher accuracy comparable to experimental observations [It is well known that a model cannot predict and mirror the observations from experimental observations with 100% accuracy. Many models try to be more biologically relevant by adding experimental data based on various growth environments. Moreover, the models are updated as and when the biological information is available that provides new insights into the metabolism of a particular organism. For example, iML1515 is the most updated model of E. coli  with 271rvations . This efBig Data has enabled the fast development of systems biology tools. These advancements have triggered the reconstruction of genome-scale metabolic models for a wide range of organisms and applications. In this review, we have presented the current state of metabolic modeling in the context of biological Big Data. We have provided a comprehensive account of existing GEMs that utilize the vast repertoire of multi-omics data, available tools to reconstruct those GEMs, and their applications in different fields of biological research. GEMs are proven and robust platforms to understand the complex metabolic processes of biological organisms. Although, there are certain challenges associated with storing, analyzing, and interpreting Big Data to create a valuable knowledgebase, computational algorithms for data compression, distributed storage databases, and cloud computing can aid in solving these challenges . As the Since integration of biological Big Data provides several layers of biological knowledgebase to the GEMs, such as protein, the applications of GEMs can go beyond just understanding metabolic systems to include other host systems like the nervous systems or the immune system. This will help in understanding diseases linked to microbial environments, impact of probiotics , and die