Enterococcus faecalis during adaptation to low pH. Our results indicate reduced proton production in the central metabolism and decreased membrane permeability for protons due to different membrane composition. We conclude that proteomic data constrain genome-scale models to a physiological state and, in return, genome-scale models are useful tools to contextualize proteomic data.Genome-scale metabolic models represent the entirety of metabolic reactions of an organism based on the annotation of the respective genome. These models commonly allow all reactions to proceed concurrently, disregarding the fact that at no point all proteins will be present in a cell. The metabolic reaction space can be constrained to a more physiological state using experimentally obtained information on enzyme abundances. However, high-quality, genome-wide protein measurements have been challenging and typically transcript abundances have been used as a surrogate for protein measurements. With recent developments in mass spectrometry-based proteomics, exemplified by SWATH-MS, the acquisition of highly quantitative proteome-wide data at reasonable throughput has come within reach. Here we present methodology to integrate such proteome-wide data into genome-scale models. We applied this methodology to study cellular changes in  In reality, limitations in space, physical capacities or energetic constraints usually restrain gene expression as a function of environment, genetics, time and other factors.4–8 and new methods were developed to integrate this information.5–10 Integration of gene expression leads either to binary decisions,10 often involving reactivation of certain reactions , or heuristically determined changes in the flux bounds, e.g., E-Flux.8 However, transcript levels often represent the levels of their corresponding proteins poorly, particularly if perturbation induced changes are considered. mRNA levels explain only 29 to 55% of the protein levels in prokaryotes11 and 27% in human cells.13 Because this low correlation does not allow us to predict protein concentrations accurately, we expect that proteomic data constrains genome-scale models more effectively than transcript data. Constraining models generally leads to a limitation of the solution space, which can be quite large, and therefore constraints can help to increase the robustness of the results. Until recently it has been challenging to generate suitable proteomic data sets whereby a large fraction of the proteome is reproducibly detected and accurately quantified. A newly developed mass spectrometric (MS) technique, SWATH-MS can quantify a substantial fraction of the proteome with high reproducibility and at high throughput.15 This technique has been successfully applied to study dynamics during dormancy and resuscitation in Mycobacterium tuberculosis facilitating identification of potential drug targets.16In genome-scale models such limitations have typically been identified from by transcriptomic data (where messenger RNA (mRNA) levels serve as proxy for protein levels)18 only few studies integrated state-specific proteomic data into a genome-scale model.20Although protein data have served as a basis for cell-type-specific genome-scale reconstruction before,Enterococcus faecalis V583,22 sampled during a pH shift experiment.We describe a method to integrate whole-cell proteomic data into genome-scale models, facilitating both mapping of detected proteins and concentration changes thereof to constrain the solution space. To validate our method we obtained quantitative proteomic data for the pathogen E. faecalis quickly adapts to changing environmental conditions and resides in diverse native environments.22 The intrinsic versatility of E. faecalis enables it to survive extremely harsh conditions such as high temperature, salinity and alkaline pH.23 Specifically, in response to a decreasing environmental pH E. faecalis changes its fermentation pattern24 and increases the activity of the H+ translocating V-type ATPase.26E. faecalis V583 to a decreasing environmental pH by combining quantitative whole-cell proteomic data with genome-scale modeling. Specifically, we integrated the proteomic data into the validated genome-scale model of this organism27 inactivating undetected proteins (29% of the proteins annotated in the model) and applying significant protein concentration changes in the form of flux constraints. We further measured metabolite consumption and production rates throughout the experiment and used these to compute flux distributions to compare the different metabolic states . We identified three additional pH-dependent processes: an unspecific proton leak depending on the pH gradient,26 a proton influx via phosphate transport due to a change in protonation states (Ka2=7.21), and a retention of protons due to a stoichiometry change in L-lactate transport.29 On the basis of these processes, new reactions and species specific to the environmental pH were introduced to the genome-scale model .31 Flux boundaries for all pH-dependent reactions are summarized in To contextualize the pH adaptation during the pH shift, we adjusted a previously published modelle model . We alsoTo integrate the proteomic data into the genome-scale model, we first inactivated 194 proteins ; 29% codInactivation of undetected proteins created additional essentialities in the genome-scale model as we limited its ability to compensate for inactivated reactions. Therefore, 15 selected proteins were reactivated to ensure a feasible solution that fits the growth parameters of the chemostat culture . Except 32 which was used to analyze the experimental data, we obtained a probability To ensure that the number of reactivated proteins does not exceed an acceptable limit, we estimated the probability of a protein being present in the sample and going undetected P value threshold of Pi<0.05 and applied these as additional flux boundaries during FVA such that flux boundsnew=flux boundsold×(fold change±change tolerance). Here we assumed that changes in protein concentration equal changes in catalytic activity. To allow for variations in enzyme activity due to regulatory effects, we included a tolerance of 40%  only 21 significant protein concentration changes remained  and glutamate dehydrogenase (EF1415) had to be augmented assuming that neither enzyme was working at maximum capacity for the model to have a feasible solution with the existing metabolic and proteomic constraints.By changing both flux bounds concomitantly, we ensured recursive behavior for subsequent decreasing and increasing protein concentrations (and vice versa). In the experimental data, we observed the majority of significant protein concentration changes within 80 to 180 min after initiating the pH shift . After 2remained , 7 increL-lactate dehydrogenase 1, EF0255) and PFL  as well as PFL activating enzyme (EF1612). Although the level of LDH1 upregulation falls below the significance level , both enzymes are regulated by the Rex transcription factor EF2638 in a NADH-responsive manner34 prompting us to consider the change in LDH1 levels. In combination with the metabolic data, where we observed a shift from mixed acid to homolactic fermentation, the data suggest that an additional regulatory mechanism on the level of individual enzyme activities is likely to be in effect.We also observed an upregulation of enzymes in both fermentation branches : LDH1 (Lt2) and reach a significant increase 80 to 120 min after initiating the pH shift. The latter include enzymes required for biosynthesis of particular tRNA molecules, including those for L-alanine (EF1379), glycine (EF2406), L-isoleucine (EF1003) and L-valine (EF2931) and were upregulated  reaction by 36% from 1.65 to 25To contextualize both proteomic and metabolic adaptive changes we analyzed changes in flux distributions between pH 7.5 and 6.5 using the amended genome-scale model containing pH-specific environmental constraints, experimentally determined metabolite fluxes, and both detected proteins and significant concentration changes thereof. Using this model, we were able to predict the growth rates for both pH 7.5 and 6.5 correctly. In general, we observed that integrating the proteome data lead to smaller intervals for FVA and to a reduction of unconstrained reactions. The model also predicts an increase in the flux through the ATPS3r .Furthermore, we observed an increased uptake of 36 inactivating undetected proteins,37 or integrating significant quantity changes in the form of binary states.20This study represents the first effort of its kind to combine genome-scale modeling with whole-cell quantitative proteomic data and metabolic data. Previous efforts to integrate proteomic data with genome-scale models have been limited to integrating absolute quantitative data of core proteins,E. faecalis to a more acidic environment as in its native environment, the human gastrointestinal tract.38 The modeling was based on the validated genome-scale model of E. faecalis.27 We first integrated experimentally determined metabolite measurements and adjusted the model to include pH-gradient dependent processes to mimic changing extra- and intracellular pH changes. Second, we reduced the genome-scale model to a physiological state where all proteins that are absent at certain environmental conditions are switched off. Third, we integrated quantitative protein changes as additional constraints on flux bounds of the corresponding model reactions. In contrast to applied user-supplied thresholds to derive ‘on’ and ‘off’ states,9 we retained the quantitative information from the proteomic data to impose additional flux constraints. Both strategies are based on the assumption that the presence of a protein implies its unconditional activity. As this is not universally true, a few mismatches are expected and can even indicate new regulatory mechanisms on a post-translational or kinetic level.Here we developed a data integration method and combined quantitative whole-cell proteomic data with genome-scale modeling to study the response of the human pathogen 6 only the upper flux bound of a reaction is changed by calculating P×Vmax, where P is the probability of a gene being expressed, based on gene expression data, and Vmax is the maximal flux as estimated by FVA. Similar to our integration strategy and the one employed by Shlomi et al.,4 PROM also includes a tolerance to the changed flux bound to account for regulatory mechanisms affecting enzyme activity. Thus, we altered both upper and lower flux bound in our approach and used a global weighting factor to account for enzymes not working at maximal capacity or being restricted by regulatory effects in order to ensure recursive behavior when applying quantitative changes.Strictly speaking, quantitative proteomic data can only be used to define the upper bound of the flux through any metabolic reaction, where the maximal capacity of metabolic flux is determined by the enzyme concentration and the turnover rate of the enzyme. The same principle applies to the integration of transcriptomic data: In PROMde novo synthesis of purine nucleotides . As the medium includes precursors for these, the reactions do not carry a flux in the model. In fact, as FBA and FVA aim for the most efficient solution and the de novo synthesis of purine nucleotides is energetically less efficient, no flux in these reactions is to be expected.39 In other lactic acid bacteria, the expression of enzymes involved in the de novo purine synthesis is regulated by the PurR repressor,40 which is activated by purine hypoxanthine and PRPP. E. faecalis itself has such a PurR repressor (EF0058) with high sequence identity27 to the repressors of Bacillus subtilis41 and Lactococcus lactis.42 This suggests that the increased availability of purine precursors at pH 6.5 due to the decreased biomass causes a stronger repression of the genes involved in the de novo synthesis of purine nucleotides, which we see in the experimental data. Similarly, the enzyme glycerol kinase (R_GLYK) showed a significant decrease in protein levels but did not carry any metabolic flux in the genome-scale model. In Enterococcus faecalis the activity of this enzyme is regulated via phosphorylation by a component of the glycerol-specific PTS system. As glycerol is not present in the growth medium, glycerol is not phosphorylated and no flux through glycerol kinase is expected.43 Furthermore, the enyzme ribosylhomocysteinase , also abbreviated as LuxS, is involved in cell-to-cell signaling,44 which cannot be represented using a genome-scale metabolic model, thus, explaining the lack of flux through this reaction. However, the elevated level of RHC in the proteomic data could be explained by the need of cell population to accurately measure cell density in a more competitive environment at pH 6.5.A number of reactions did not carry a flux at pH 7.5 and, thus, were not affected by the application of the significant fold changes. Several of these reactions are involved in the 25 The increasing ATP requirement causes a decrease in biomass and different metabolic adaptations, such as increased glucose, L-arginine and L-serine consumption, and a shift in fermentation favoring homolactic fermentation. The change in fermentation products reduces the amount of protons produced in the central metabolism representing a significant cost reduction with respect to intracellular pH homeostasis. Owing to the pH ranges in cytoplasm and extracellular medium we exclude the possibility of a buffering effect by The amended genome-scale model successfully predicted growth rates of steady-state cultures at pH 7.5 and 6.5. The model showed that a shift to a lower environmental pH strongly increases ATP demand, mainly to maintain the pH gradient across the membrane. In accordance to the increased proton influx into the cell, the flux through the V-type ATPase reaction ATPS3r increases, confirming the experimental measurements of an increase in proton pump activity.26 it cannot affect enzyme activity to such a high degree.45 An alternative potential regulatory mechanism has been shown to exist in Lactococcus lactis where LDH is regulated by the NAD+/NADH ratio, which is determined by the glycolytic flux.46 An increase in glucose consumption results in a higher NADH concentration activating LDH and inhibiting glyceraldehyde-3-phosphate dehydrogenase. In combination with the inhibition of PFL by glyceraldehyde-3-phosphate, this results in an increased lactate production. Although this regulatory mechanism has yet to be observed for E. faecalis, it would explain the metabolic data in context of the proteome measurement.In combination with the proteomic data, where we observed a concomitant increase of PFL and LDH1, we suggest the existence of a regulatory mechanism on the level of enzyme activity independent of intracellular pH. As the change in intracellular pH within the observed external pH shift is minimalStreptococcus salvarius and Lactobacillus casei, a change in membrane composition in response to decreasing environmental pH has been reported47 to decrease membrane permeability with respect to protons. Thus, we propose that E. faecalis reduces its susceptibility to the pH gradient. Furthermore, the change in teichoic acid precursor synthesis remains elevated even after 21 h, suggesting a sustained change in cell wall composition to reduce proton leak into the cell.Furthermore, we observed transient increases of proteins involved in the synthesis of fatty acids and cell wall components. For other lactic acid bacteria, L-lactate/LDH1 and formate/PFL. In fact, the difference itself represents an opportunity to discern regulatory mechanisms and aspects for further studies. Finally, we conclude that the integration of proteomic data into genome-scale models aids in constraining the model in order to represent a more realistic picture of the organism’s state, and thereby limiting the often large solution space.In conclusion, we see that proteomic data can be understood and analyzed with the model as a representation of the metabolic network in a very comprehensive, systemic way. However, we also see that proteomic data do not eliminate the need for metabolite measurements because of the differences between proteomic and metabolic changes regarding the behavior of See Supplementary Information 1.1See Supplementary Information 1.2See Supplementary Information 1.3See Supplementary Information 1.4See Supplementary Information 1.5See Supplementary Information 1.6See Supplementary Information 1.7See Supplementary Information 1.8See Supplementary Information 1.9See Supplementary Information 1.10See Supplementary Information 1.11See Supplementary Information 1.12See Supplementary Information 1.13See Supplementary Information 1.14See Supplementary Information 1.15


M. tuberculosis has been shown to be influenced by experimental media conditions. Investigations of M. tuberculosis growth in physiological conditions have described an environment that is different from common in vitro media. Thus, elucidating the interplay between available nutrient sources and antibiotic efficacy has clear medical relevance. While genome-scale reconstructions of M. tuberculosis have enabled the ability to interrogate media differences for the past 10 years, recent reconstructions have diverged from each other without standardization. A unified reconstruction of M. tuberculosis H37Rv would elucidate the impact of different nutrient conditions on antibiotic efficacy and provide new insights for therapeutic intervention.The efficacy of antibiotics against M. tuberculosis H37Rv, named iEK1011, that unifies and updates previous M. tuberculosis H37Rv genome-scale reconstructions. We functionally assess iEK1011 against previous models and show that the model increases correct gene essentiality predictions on two different experimental datasets by 6% (53% to 60%) and 18% (60% to 71%), respectively. We compared simulations between in vitro and approximated in vivo media conditions to examine the predictive capabilities of iEK1011. The simulated differences recapitulated literature defined characteristics in the rewiring of TCA metabolism including succinate secretion, gluconeogenesis, and activation of both the glyoxylate shunt and the methylcitrate cycle. To assist efforts to elucidate mechanisms of antibiotic resistance development, we curated 16 metabolic genes related to antimicrobial resistance and approximated evolutionary drivers of resistance. Comparing simulations of these antibiotic resistance features between in vivo and in vitro media highlighted condition-dependent differences that may influence the efficacy of antibiotics.We present a new genome-scale model of M. tuberculosis H37Rv. As more experimental data and knowledge of M. tuberculosis H37Rv become available, a unified and standardized M. tuberculosis model will prove to be a valuable resource to the research community studying the systems biology of M. tuberculosis.iEK1011 provides a computational knowledge base for exploring the impact of different environmental conditions on the metabolic state of The online version of this article (10.1186/s12918-018-0557-y) contains supplementary material, which is available to authorized users. M. tuberculosis as a pathogen has been partially attributed to its unique metabolic capabilities. The metabolic network of M. tuberculosis has evolved to withstand and navigate the harsh environment imposed by the alveolar macrophage. Most bacteria cannot thrive in this hypoxic, acidic and nutrient-limited condition, yet it is in this harsh environment that M. tuberculosis encounters and evolves resistance to antibiotics. Elucidating the robust properties of metabolism that enable M. tuberculosis pathogenicity and drug resistance evolution has become a key area of research.The success of M. tuberculosis [M. tuberculosis to a nonreplicating, drug-resistant state [Recent studies have demonstrated that the choice of experimental media conditions plays an important role in understanding physiologically-relevant phenotypes of rculosis . Commonlrculosis . Perhapsrculosis –11. In pnt state –7. Specint state , 10.M. tuberculosis on the systems-level. GEMs of M. tuberculosis have helped interrogate a variety of biological phenomena, from understanding the transcriptional regulatory network [M. tuberculosis and the alveolar macrophage [While it is understood that differences in experimental media conditions lead to phenotypic variations, dissecting a mechanistic understanding of these different phenotypes remains challenging. Genome-scale models (GEMs) of metabolisms have emerged as powerful tools to computationally probe the effect of media composition on a cell’s phenotype . For the network  to elucicrophage .M. tuberculosis H37Rv GEMs have enabled novel insights, they have been constructed from different base models resulting in divergent representations of the metabolic network. For example, gene-protein-reaction rules (GPRs) ) differ within reactions shared amongst models . In addition to variation in network topology, divergent GEMs have a variety of identifiers used for metabolite and reaction names, making them difficult to compare and build from . While such differences may seem insignificant, the presence of multiple divergent M. tuberculosis H37Rv reconstructions hinders progress and may result in future wasted efforts [While new  efforts .M. tuberculosis H37Rv that unifies, standardizes, and updates previous divergent GEMs of this model organism. We assess the performance of iEK1011 to that of previous GEMs through gene essentiality prediction on two different datasets. iEK1011 is further characterized by performing simulations that examine the model’s predictions in physiological conditions and interrogate differences between in vitro and in vivo media conditions. Finally, in order to provide a comprehensive platform for elucidating antibiotic resistance (AMR), we integrate knowledge derived from experimental literature into iEK1011.Here, we present iEK1011, a new GEM of M. tuberculosis H37Rv. Since the first two M. tuberculosis H37Rv reconstructions released in 2007 [M. tuberculosis reconstructions, sMtb [In order to ensure a comprehensive unification, we first gathered and compared available reconstructions of  in 2007 , 17, a t in 2007  or GSMN-ns, sMtb , iSM810 ns, sMtb  and gal2ns, sMtb  were priM. tuberculosis H37Rv BioCyc Database [Using a variety of both quantitative and qualitative criteria  by 23% (579) Fig.  over sMtWith respect to the more recent DeJesus essentiality dataset, iEK1011 increases the number of true positives by 24% (666) Fig.  over sMtIn order to account for the variations in class sizes amongst models, we calculated the Matthews Correlation Coefficient (MCC) for each model’s prediction on both gene essentiality datasets Fig. . iEK1011M. tuberculosis. The extent of OADC’s impact remains unknown, which ultimately hinders the ability to rigorously define the inputs for GEMs, which are crucial components of COBRA methods [M. tuberculosis GEMs.Although the DeJesus essentiality dataset is more recent than the Griffin dataset by 6 years, the media condition used in determining essentiality on the DeJesus dataset was not as well defined because it utilized oleic-albumin-dextrose-catalase (OADC) in middlebrook 7H10/7H9 media supplemented with a variety of carbon sources . The con methods . Convers methods . TherefoM. tuberculosis behavior described in the literature. Specifically, an emphasis was placed on central carbon metabolism given its distinctive usage in M. tuberculosis and recent emergence as an unexpected research frontier [M. tuberculosis studies involving conditions relevant to pathogenicity [While the gene essentiality predictions are a useful metric to evaluate model quality, we prioritized the model’s ability to recapitulate frontier . In addigenicity . Therefogenicity  and randgenicity  to charaM. tuberculosis pathogenicity [3, see Additional file narG, and then exported out of the cell, a finding consistent with previous experiments [2, and Alanine. Fatty acids were chosen as the primary source of carbon in vivo due to the vast amount of literature evidence supporting the claim that M. tuberculosis uses host-derived fatty acids [2 was incorporated due to evidence showing it being fixated by M. tuberculosis in an approximated in vivo environment [2 due to a net gluconeogenic flux through phosphoenolpyruvate carboxykinase - a simulation result also found in Beste et al. [Taking advantage of recent studies investigating nitrogen metabolism within the context of genicity –38, we seriments . The choty acids , 40. iEKM. tuberculosis hypoxia adaptation [The differences in flux state simulations predicted by iEK1011 between the two conditions recapitulate key behavior described in the literature. Specifically, in the approximated in vivo condition involving hypoxia and growth on fatty acids, model-predicted flux decreases through TCA with an accompanying increase in succinate secretion Fig. . iEK1011aptation . Thus, i, which outcompetes ethambutol for embB binding spots [In order to incorporate specific antibiotic pressures into iEK1011, we evaluated each antibiotic and associated a biochemical objective function that approximates the evolutionary drivers of selection Table . In the ng spots . TherefoTaking advantage of the translation of antibiotic features to formats amenable by iEK1011, we simulated the evolutionary pressures induced by antibiotics and calculated the maximum and minimum fluxes for the AMR-associated reactions in both in vivo and in vitro conditions through FVA  and embB (EMB), as well as d-cycloserine resistance-conferring genes of alr (ALAR), ald (ALAD_L), and ddlA (ALAALAr), exemplify the differential effect of environmental L-alanine availability. Notably, L-alanine has been shown to be an important substrate in the macrophage environment (Beste 2013). While L-alanine and other amino acids may be available in LJ drug-testing media due to utilization of egg base or bovine serum, our analysis only accounted for metabolites that were explicitly stated in defined quantities within the media conditions. With respect to the efficacy of antibiotics, these results suggest that d-cycloserine and ethambutol may be less effective in vivo due to increased availability of L-alanine, which is a key precursor reaction catalyzed by AMR genes targeted by d-cycloserine and ethambutol, whereas in vitro conditions may increase susceptibility to ethambutol. In both cases, the significant decrease in model-predicted maximum ALAD_L  flux is in line with studies describing the deleterious mutations in ald that confer resistance to D-cycloserine [There were few differences in relative flux for a specific drug objective between these conditions. However, those that were uncovered highlighted potential impacts of environmental/media composition differences. In particular, we see major differences in the fluxes that correspond to optimizing the approximated ethambutol-induced evolutionary pressure see Fig. . Furtherloserine . AltogetM. tuberculosis H37Rv reconstructions has created an unnecessary obstacle in contextualizing the increasing growth of biochemical data for this troublesome pathogen. In order to address experimental insights to pathogenic conditions and alleviate roadblocks for future reconstruction efforts of M. tuberculosis H37Rv, we built a unified and updated GEM of M. tuberculosis, iEK1011. We tested the predictive potential of iEK1011 by comparing gene essentiality predictions with previous models and showed that iEK1011 outperforms previous models. We further assessed the predictive capabilities of iEK1011 by comparing simulated flux states between in vitro drug testing and approximate in vivo media conditions. Comparisons recapitulate specific phenomena indicative of biochemical flux states seen in physiological conditions. We incorporated antibiotic resistance knowledge in iEK1011, which enabled a network-based perspective of multi-antibiotic resistance evolution.The divergence of M. tuberculosis H37Rv reconstructions and encompassed the majority of genes within the two divergent groups of reconstructions. Additionally, iEK1011 incorporates new pathways including the incorporation of ergothioneine biosynthesis. This addition will aid a quantitative elucidation of the relationships between sulfur metabolism, bioenergetic homeostasis, and redox balance [M. tuberculosis H37Rv.iEK1011 unified previous  balance . As a unM. tuberculosis H37Rv reconstructions through gene essentiality predictions showed that iEK1011 achieves a higher MCC than previous models on two different datasets. While the two datasets were crucial in both assessing and driving iEK1011 reconstruction, experimental gene essentiality datasets derived from physiologically-relevant conditions are warranted for understanding the human-restricted lifestyle of M. tuberculosis.Functional assessment of previous M. tuberculosis. Future reconstruction efforts may target iEK1011’s lack of predicted CO2 fixation [Using iEK1011, we qualitatively determined differences in biochemical states between in vitro and approximated in vivo conditions. We showed that iEK1011 successfully recapitulates specific phenomena described in physiologically-relevant studies of fixation  and accofixation , 46. iEKM. tuberculosis H37Rv. We translated experimental knowledge of AMR genes and specific adaptation mechanisms to formats amenable to iEK1011. Comparing simulations of these AMR features between in vitro and in vivo conditions emphasized the potential impact of hypoxia and L-alanine availability on the pressures induced by antibiotics. Future constraint-based analysis of M. tuberculosis AMR may leverage new experimental approaches, such as those that have analyzed changes in essentiality under antibiotic exposure [An integrated knowledge-base of genome-scale metabolism and antibiotic resistance components may enable new perspectives for understanding and combating exposure .M. tuberculosis H37Rv. In this study, we computationally demonstrate that in vivo nutrient sources absent from in vitro media significantly alter the flux state of central carbon metabolism. As experimental insights to M. tuberculosis pathogenicity and antibiotic resistance continue to grow, this GEM will provide a foundation to connect disparate data types and knowledge.Taken together, iEK1011 is a new, comprehensive and predictive constraint-based model of M. tuberculosis H37Rv BioCyc Database [A variety of both quantitative and qualitative criteria was considered in determining which model would provide the base for the new model. The determining criterion included the amount of curated data, extent of previous model unification, gene essentiality predictions, standardized identifiers, cross-references to databases, and quality of physical representations, such as the use of an extracellular compartment and mass balanced reactions. Based on this criterion, the reconstruction of iEK1011 was based on the unification of iOSDD, sMtb, and portions of the Database   and non-growth (NGAM) associated ATP maintenance parameters. NGAM quantifies the energy required by Mtb to maintain itself in a given environment while GAM quantifies growth energy requirements not accounted for in the metabolic model. For iEK1011, the GAM was chosen to be 60 mmol gDwli model . For com E. coli  . We have provided a list of these 157 blocked reactions  was used across all models. Differences in MCC values between this study and that in Ma et al. [where TP (True Positive) represents the event where the model correctly simulates growth when a gene is nonessential. TN (True Negative) represents the event where the model correctly simulates no-growth when a gene is essential. FP  represents the event where the model simulates no growth with the gene knockout when the gene is in fact non-essential. FN  represents the event where the model simulates growth when the gene is in fact essential. While the a et al.  are due We further verified that the increase in accuracy for the iEK model is due to model curation as opposed to the addition of new non-essential genes by comparing only the gene essentiality predictions of the genes shared across all models. In particular, only 472 genes are shared across all the models. Using this set of genes for all model gene essentiality predictions, we find that iEK1011 still increases accuracy over other models , which outcompetes ethambutol for embB bindings spots [It has been shown that flux-increasing mutations in gs spots . Therefoald confer resistance to the d-cycloserine by increasing the pool of Alanine , thereby competitively inhibiting d-cycloserine [Analogous to the mechanism of ethambutol, it has been shown that loss-of-function mutations in loserine .folC, ribD, and thyA have been shown to confer resistance to PAS [thyA mutations are selected in order to decrease the utilization of folates. In addition, it was suggested that the up-regulation of ribD occurs as an alternative when dfrA is inhibited.Mutations in e to PAS . It was It has been shown that mycothiol biosynthesis is essential for ethionamide susceptibility . We apprFor both in vivo and in vitro media conditions, we simulated each of the drug objectives and compared the maximum and minimum fluxes of the reactions catalyzed by the curated antibiotic resistance genes , go to http://escher.github.io/, and load in the json map. If the model is loaded in as well, you will be able to modify reactions on the map or build your own. In addition, you can view the flux states generated by iEK1011 on the Escher map.In order to help future in silico simulations and reconstruction efforts of ynthesis  Additional file 2:A conversion of the sMtb model to BiGGs identifiers (JSON 476 kb)Additional file 3:Griffin et al. dataset. iEK1011_deJesusEssen_media.json - Used for essentiality testing on deJesus et al. dataset. iEK1011_m7H10_media.json - iEK1011 initialized with Middlebrook 7H10 media. iEK1011_drugTesting_media.json - Used for simulating on Lowenstein-Jensen media. iEK1011_inVivo_media.json - Used for simulating on approximated physiological media. (ZIP 310 kb)iEK1011 models: Genome-scale models of iEK1011 in json format initialized with different media conditions. iEK1011_griffinEssen_media.json - Used for essentiality testing on Additional file 4:M. tuberculosis metabolic subsystems. Central_carbon.json. Arabinogalactan_peptidoglycan_complex.json. Nitrogen.json. Sulfur_and_folate.json (ZIP 236 kb)Escher Maps: Contains four escher maps of Additional file 5:Table S1. Table describing changes in gene essentiality predictions according to changes in GAM and NGAM values that were utilized across different genome-scale reconstructions of M. tuberculosis .Table S2. List of reactions in iEK1011 that violate the law of mass conservation. Table S3. Examples of false negatives computed by iEK1011 on the DeJesus et al. gene essentiality dataset that are not within the iSM810 model, and reasoning for its inclusion [Table S4. Gene essentiality predictions using the shared set of 472 genes. (DOCX 17 kb)nclusion , 55. TabAdditional file 6:An ipython notebook that runs the simulations described in this study. (IPYNB 637 kb)


Streptomyces ambofaciens and to identify a list of potential metabolic engineering targets for the overproduction of the secondary metabolites in this microorganism. We focused on an often overlooked growth period  and, by integrating constraint-based metabolic modeling with time resolved RNA-seq data, we depicted the main effects of changes in gene expression on the overall metabolic reprogramming occurring in S. ambofaciens. Moreover, through metabolic modeling, we unraveled a set of candidate overexpression gene targets hypothetically leading to spiramycin overproduction. Model predictions were experimentally validated by genetic manipulation of the recently described ethylmalonyl-CoA metabolic node, providing evidence that spiramycin productivity may be increased by enhancing the carbon flow through this pathway. The goal was achieved by over-expressing the ccr paralog srm4 in an ad hoc engineered plasmid. This work embeds the first metabolic reconstruction of S. ambofaciens and the successful experimental validation of model predictions and demonstrates the validity and the importance of in silico modeling tools for the overproduction of molecules with a biotechnological interest. Finally, the proposed metabolic reconstruction, which includes manually refined pathways for several secondary metabolites with antimicrobial activity, represents a solid platform for the future exploitation of S. ambofaciens biotechnological potential.In this study we have applied an integrated system biology approach to characterize the metabolic landscape of  Constraint-based modeling is a widely adopted technique to study microbial metabolic features at the system level. Indeed, it has proven useful for addressing fundamental issues about the metabolic landscape of a variety of microorganisms, including the elucidation of non-trivial metabolic engineering strategies . Genome-Streptomyces representatives’ metabolism has been extensively studied  has been assessed in a recent work focused on S. coelicolor    and congma spp.) . More rema spp.) .in silico modeling with the aim to provide a system-level understanding of S. ambofaciens metabolic features, and to identify a list of potential metabolic engineering targets for the overproduction of the secondary metabolites in this microorganism. Besides including the first metabolic reconstruction of S. ambofaciens, the experimental validation of model predictions presented herein underlines the power of in silico modeling in the context of metabolic engineering.Here we have applied an approach that combines physiological, transcriptomic and Streptomyces ambofaciens ATCC 23877 was obtained from the American Type Culture Collection (ATCC). The identity of this strain was verified by sequence analysis using the 16S rRNA genes specific ‘universal’ primers 5′-CAGCAGCCGCGGTAATAC-3′ and 5′-CCGTCAATTCCTTTGAGTTT-3′ (8/ml. The composition (per liter) of the media used in this study for S. ambofaciens growth and manipulation is here reported. YS broth: 2 g yeast extract, 10 g soluble starch, (18 g agar in YS agar) (pH 7.0); SMII (medium for conjugation experiments): 15 g dextrose, 10 g soybean flour, 0.5 g MgSO4 ⋅ 7 H2O, 5 g CaCO3, 15 g agar; 2XYT (medium for spore resuspension): 16 g tryptone, 10 g yeast extract, 5 g NaCl.AGTTT-3′ . The strEscherichia coli strain DH5α [F- Φ80d lacZΔM15 endA1 recA1 hsdR17 supE44 thi-1 λ-gyrA96 λ (lacZYA-argF) U169] was used in cloning procedures. This strain was cultivated in Luria-Bertani broth supplemented with 50 μg/ml ampicillin when required. Conjugation experiments were performed with E. coli strain GM2929/pUB307::Tn7. Lennox Broth (LeB) was used to grow, wash and resuspend this strain. The composition (per liter) of the LeB is as follows: 10 g tryptone, 5 g yeast extract, 5 g NaCl, 1 g glucose.Streptomyces ambofaciens total genomic DNA was obtained as described by (E. coli DNA was performed as indicated by ribed by . ManipulS. ambofaciens ccr (srm4) (SAM23877_ RS26790 in S. ambofaciens ATCC 23877 genome) coding for crotonyl-CoA carboxylase/reductase was achieved by cloning the PCR-amplified coding region into an ad hoc engineered expression vector, pNGEM/OriT/P, under the control of groES promoter. To construct pNGEM/OriT/P we utilized the E. coli–Streptomyces shuttle vector pN702GEM3. pN702GEM3 (Neo/Kan resistance marker from Tn5 (for both E. coli and Streptomycetes), the pUC origin of replication (for E. coli) and a polylinker derived from pGEM3Zf(+) (Promega). This vector is replicative but not conjugative in Streptomycetes. To transform it into conjugative vector pNGEM/OriT a DNA fragment containing the oriT determinant was amplified by PCR from plasmid pTYM18 (HindIII restriction sites are underlined):Overexpression of N702GEM3  containsd pTYM18  using thAAGCTTCGCCCAACCTGCCATCACGAGATTTC-3′OriTHindIII-for: 5′-CCGACCAAGCTTCAGAAGCCACTGGAGCACCTC-3′OriTHindIII-rev: 5′-GAGCTGAHindIII, and the resulting 930 bp DNA fragment was cloned into pN702GEM3 using the HindIII restriction site of the polylinker.The amplified fragment was treated with groES gene promoter (groESP) region was amplified by PCR using S. ambofaciens ATCC 23877 genomic DNA as template, and the primer pair groES-for and groES-rev (XbaI and NdeI restriction sites are underlined):To construct the pNGEM/OriT/P plasmid, the TCTAGACCGTCCGGCGTTTCGAGGACGAGG-3′groES-for: 5′-GGCCGTTCTAGACATATGCCGACCTCCCCCTTCGGAGATCTCACG-3′groES-rev: 5′-GCTGGTXbaI, and the resulting 325 bp DNA fragment was inserted into the polylinker of pNGEM/OriT, generating pNGEM/OriT/P.The amplified DNA fragment was digested with srm4 (locus tag: SAM23877_ RS26790) was amplified from S. ambofaciens ATCC 23877 genomic DNA, using the primer pair CCR-for and CCR-rev (NdeI and EcoRI restriction sites are underlined):The crotonyl-CoA carboxylase/reductase (CRR) encoding gene CATATGCCCGAAAGCCATGCGCAGAGCGCG-3′CCR-for: 5′-GACCGTGAATTCGGAGCACCTGGTGCCGTCACCGGC-3′CCR-rev: 5′-CCGCACGNdeI and EcoRI and then ligated to NdeI and EcoRI –cleaved pNGEM/OriT/P, obtaining the resulting plasmid pNGEM/OriT/P/CCR.The amplified 1475 bp DNA fragment was digested with S. ambofaciens strain by conjugation with E. coli GM2929/pUB307::Tn7, as described previously Plasmids pNGEM/OriT/P and pNGEM/OriT/P/CCR were introduced into the S. ambofaciens CCR (Srm4) in E. coli, the entire ccr (srm4) gene was amplified by PCR using the pNGEM/OriT/P/CCR construct as template, and using the primer pair CCR-for (above reported) and pETCCR-rev (HindIII restriction site is underlined):To overproduce and purify His6-tagged pETCCR-rev: 5′-GAATTCAAGCTTGCGGAACCGGTTGATCGCGTCGA-3′NdeI and HindIII and then ligated to NdeI and HindIII –cleaved pET21b, obtaining the resulting plasmid pET/Srm4. pET/Srm4 was then introduced into E. coli BL21(DE3) competent cells.The amplified DNA fragment was digested with Escherichia coli BL21(DE3) harboring pET/Srm4 was grown in 1 l of LB medium supplemented with ampicillin (10 μg ml-1) to A600 = 0.6 at 37°C and 250 r.p.m. Culture was induced by the addition of 0.4 mM IPTG and incubated for 16 h at 20°C and 180 r.p.m. Cells were harvested by centrifugation at 5,500 RCF for 10 min, re-suspended in lysis buffer . Cells were lysed by passing the suspension through a French Press twice at 1000 psi, and the cell debris were removed by centrifugation at 10,000 × g for 20 min at 4°C. The clarified cell lysate was bound to 1 ml of Ni-IMAC resin (Bio-Rad Laboratories) for 1 h at 4°C. The resulting slurry was transferred to a gravity-flow column, and washed twice with 1 ml of lysis buffer. The recombinant protein His6-Srm4 was then eluted with 0.2 ml of elution buffer , and frozen for storage at -80°C.S. ambofaciens cultures grown in YS broth was assessed by HPLC. At different time intervals, supernatants were filtered through Phenex-RC membrane . Four hundred microliter filtrated samples were lyophilized and resuspended in a mixture water-acetonitrile 70:30. The concentration of spiramycins was determined by HPLC (Beckman System Gold Programmable Solvent Module125) equipped with a UV detector (232 nm). The column was a reverse phase LiChrospher®RP-8 HPLC Column (Supelco) and the mobile phase was a mixture of 100 mM phosphate buffer (pH 2.3) containing 50 mM sodium perchlorate and acetonitrile 70:30 v/v with a flow rate of 0.8 ml/min. Purified spiramycin (Sigma–Aldrich) freshly dissolved in phosphate buffer (pH 7) was used as standard. Two-tailed paired Student’s t-test was used to assess statistical significance of antibiotic titre differences between bacterial strains during the time course. Statistical significance was declared at p-value < 0.05.Spiramycin production by Streptomyces ambofaciens cultures (100 ml) were harvested by centrifugation at 10,000 × g at 4°C for 20 min. Supernatants were discarded and mycelial pellets were washed twice in homogenization buffer , 10% glycerol, 0.1 mM phenylmethylsulfonyl fluoride). After resuspension in homogenization buffer, mycelial extracts were prepared by mycelial disruption at 2000 psi using a French Press and subsequent centrifugation of the homogenates at 25,000 x g for 5 min at 4°C to remove debris. The supernatant was collected and used for the enzyme activity measurement. Protein concentrations were determined by using the colorimetric Bio-Rad colorimetric assays for protein determination with BSA as a standard. Protein extracts were analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) according to standard procedures.3 and 0.1–0.3 mg of mycelial extract proteins, or 0.10–0.2 μg of purified CCR protein. Upon equilibration at 30°C for 5 min, 6 mM NADPH was added to initiate the reaction. CCR activity was measured spectrophotometrically by following the decrease in absorbance at 340 nm (𝜀NADPH = 6.22 mM-1 cm-1), which corresponds to the oxidation of the NADPH. Specifically, the decrease in the absorbance due to CCR-independent NADPH oxidation was excluded from the calculation of CCR activity, by performing an analogous reaction in the absence of crotonyl-CoA. A unit (U) of enzyme activity is the amount of enzyme catalyzing the oxidation of 1 μmol NADPH min-1.The crotonyl-CoA carboxylase/reductase (CCR) activity was determined as described  in a reaS. ambofaciens growing in YS medium at 28°C on a rotary shaker at 200 rpm, by using the RNeasy Mini kit according to the manufacturer’s instructions (QIAGEN). RNase-free DNase I was used to eliminate traces of DNA in the samples, in accordance with the instructions of the manufacturer .Total bacterial RNA was extracted from ccr-specific transcripts, normalized to 16S rRNA, were carried out by Real-Time qPCR. Total RNAs (1 μg) were reverse-transcribed by using random hexamer (2.5 μM) with Superscript RT (Invitrogen). About 0.1–1% of each RT reaction was used to run real-time PCR on a SmartCycler System (Cepheid) with SYBR®Green JumpStart Taq ReadyMix (Sigma–Aldrich) and the oligonucleotide primer pairs 16Suniv-1/16S-univ-2 (specific for 16S rRNA gene) and ccr-rtF/ccr-rtR (specific for ccr). The primer sequences (and names) were: 5′-CAGCAGCCGCGGTAATAC-3′ (16Suniv-1); 5′-CCGTCAATTCCTTTGAGTTT-3′ (16Suniv-2); 5′-CGATCGTCACCTGCGCCTCCAC-3′ (ccr-rtF); 5′-CGAAGTGGGTGCCGACGATAC-3′ (ccr-rtR). Primers were synthesized as a service by MWG-Biotech AG Oligo Production. RT-PCR product lengths were 185 bp for 16Suniv-1/16S-univ-2 and 96 bp for ccr-rtF/ccr-rtR. Real-Time qPCR samples were run in triplicate. The Real-Time qPCR conditions were: 30 s at 94°C, 30 s at 60°C, 30 s at 72°C for 35 cycles; detection of PCR products was performed at 83°C. The relative transcript levels were calculated using the 2ΔΔct method  starting from 50 ng of previously rRNA depleted RNA from each biological replicate and for all the time points analyzed. Then each library was sequenced on a MiSeq Illumina sequencer and 76 bp reads were produced.For RNAseq experiments ribosomal RNAs were depleted starting from 1 μg of total RNA from each of the time points and biological replicates by using the RiboZero Gram positive kit , strand specific RNA-seq libraries were prepared by using the ScriptSeqS. ambofaciens ATCC 23877 genome (GCF_001267885.1). Multi-mapping reads alignment was forced using non-deterministic option to obtain a single mapping locus for the reads located on the terminal arm region (nt 1-202694/8101246-8303940), which is known to be duplicated. High quality reads were selected imposing the following criteria: mapping quality for uniquely mapping reads MAPQ greater than 30 and alignment score greater than -15; for multi-mapping reads the alignment score was set equal or greater than -15. These criteria define high quality reads only those showing a maximum of three variations (mismatches or short indels): the sum of penalty scores assigned to mismatches and short indels should not be greater than -15.Bowtie 2 (v2.2.6)  was usedS. ambofaciens ATCC 23877 RefSeq annotation were considered in each sample. For protein coding genes located in duplicated regions it was impossible to determine the exact chromosomal origin of the reads. To overcome this problem we reported only one of the two homologs in the final list of genes and the relative raw count is the sum of all the reads aligning to both loci (strand specificity was not considered). The R   and SAMt). The R  package ). The R  was thenTime course analysis was performed with maSigPro . To findp-value ≤ 0.05 were considered significantly enriched .The enrichments of functional categories were calculated using Fisher test and BH correction for multiple testing, the categories with an adjusted PRJNA342588.Raw data are publicly available at Sequence Reads Archive under accession number BioProject S. ambofaciens ATCC 23877 genome annotation was manually refined by using RAST  were used as reference in a comparative genomics workflow for identifying potentially missing reactions. BLAST  metabolic model , where m is the number of metabolites and n the number of reactions. FBA is based on the assumption of the cellular pseudo-steady state, according to which the net sum of all the production and consumption rates of each internal metabolite within a cell is considered to be zero. Under this assumption, the system can be described by the set of linear equations:The FBA method was employed to simulate flux distribution in different conditions. Briefly, FBA is a constraint-based method relying on the representation of the biochemical system under investigation in the form of a stoichiometric matrix S .where l®R2009b (Mathworks Inc.). Gurobi 5.61 and GLPK 4.322 solvers were used for computational simulations presented herein.The reconstructed model was analyzed using COBRAToolbox-2.0  in MATLAWe calculated the growth dependencies between biomass and antibiotics biosynthesis using Pareto optimality as described in ∗h-1g-1. Next, the objective function was set to spiramycin production and the theoretical maximum product formation rate was calculated. Afterward, FSEOF was performed under the objective function of maximizing cell growth while the spiramycin production rate is gradually enforced from the flux value used in the first simulation to the theoretical maximum production rate obtained with the second simulation . Candidate target genes were then collected identifying those fluxes that increased upon the application of the enforced objective flux without changing the reaction’s direction. More specifically, we selected those reactions (and corresponding coding genes) showing an increased flux in at least one of the 10 simulations, without changing the reaction’s direction. Please refer to The FSEOF algorithm for flux scanning based on enforced objective flux was implemented for the selection of gene targets to be amplified as described in S. ambofaciens ATCC 23877 growth in shake flask batch cultivation using complex YS medium . The analysis of growth kinetics also showed that during RG1 specific growth rates (μmax) were not constant over time but exhibited three distinct peaks at 12, 18, and 24 h, respectively . Spiramycin production started at 18 h and reached the highest titre in the medium at 48 h . Then its concentration decreased progressively till 78 h and afterward it remained nearly constant till 117 h . The evaluation of specific production rates (pq) led us to distinguish three distinct phases of spiramycin production  for RNA extraction. Two biological replicates were analyzed for each time point.The maSigPro bioconductor package  was adopFigure 2). Clusters 1, 4, 6, and 8 consist of genes whose expression levels increase during the time course, although with distinct patterns and basal expression levels. In particular the expression levels of genes in cluster 8 has high basal expression at T1 (48 h), rapidly increased from T1 (48 h) to T2 (72 h) and then remained roughly constant throughout T3 (96 h) and T4 (120 h). Expression levels of genes in cluster 4 also exhibited high basal expression at T1 (48 h), progressively increased from T1 to T3 and remained high at T4. These two clusters are significantly enriched in functional categories “cell division and cell cycle” (cluster 4), “cell wall and capsule” (clusters 4 and 8), “DNA metabolism” (clusters 4 and 8), and “sulfur metabolism” (cluster 8) .The 1186 variable genes were finally subdivided into nine clusters according to their gene expression profiles  and median profile was then inferred for each cluster (1 and 6 showed low expression levels at T1 (48 h) and gradually reached their highest expression level during T4, although with different kinetics. Cluster 1 is enriched in the functional category “secondary metabolism” , clusters 2 and 3 contains many genes coding for ribosomal proteins and translation factors, and genes involved in central carbon (and energy) metabolism. Indeed, all these genes are expected to be strongly down-regulated beyond the RG1. Cluster 2 is enriched in the functional categories “membrane transport,” “virulence, disease and defense,” while cluster 3 is enriched in genes involved in “secondary metabolism” . Cluster 7 exhibits an enrichment in the category “fatty acid, lipid, isoprenoids,” while clusters 5 and 9 exhibit no significant enrichment in any considered functional categories.Such a competition is possibly alleviated by differential temporal expression patterns of the genes involved in spiramycin and stambomycin biosynthesis  and the inclusion of gap-filling reactions  to reconcile S. ambofaciens growth on defined minimum media (see below). The obtained reconstruction was named iMF1244 according to the standard nomenclature and its main features are reported in Table 1. The SBML-formatted version of iMF1244 is available as Supplementary Data Sheet A S. ambofaciens model was inferred by comparing the predicted growth rates on a set of different minimal media against a set of experimentally determined ones available from previous studies . More in detail, lower bounds of exchange reactions accounting for all the salts present in the experimental medium were set to -10 mmol g-1 × h-1, in order to mimic non-limiting conditions. The carbon source of each available minimum growth medium was then chosen as the unique carbon source of this in silico medium and its uptake flux was set to the corresponding experimentally determined value. The compositions of all the simulated in silico media are reported in Supplementary Data Sheet in silico simulation were then compared to those experimentally determined, revealing an overall good agreement . This indicates that the model should be able to reproduce a realistic picture of the S. ambofaciens ATCC 23877 metabolic landscape.The reliability of the reconstructed  studies . AccordiS. ambofaciens has been extensively analyzed by max) of 0.15 h-1 was soon established after setting up the culture. The specific growth rate then decreased sharply to reach zero value at 24 h and rose up again when a second growth phase with a μmax of 0.012 h-1 was observed. During this second growth phase, spiramycin biosynthesis was observed, with a specific production rate (qp) of 1.46 mg-1h-1g-1 (corresponding to 0.0017 mmol∗h-1g-1). The growth on YE of S. ambofaciens was characterized by the consumption of all the amino acids available in the medium .Afterward, we tested the response of the model in a simulated complex medium. The assimilation of yeast extract (YE) amino acids and its effect on the production of spiramycin in culture of e medium . AccordiIn a typical scenario, the metabolic pathways for the production of secondary products (such as antibiotics) should compete for the pool of available metabolic intermediates with all the other cellular biosynthetic processes . This is typically accounted for by the so-called Pareto front between the two objectives . Pareto ∗h-1g-1 for spiramycin, antimycin, stambomycin and congocidine, respectively. Afterward, the model (correctly) predicts a decrease in the production rates of each antibiotic as a consequence of an increase of the growth rate. These results, together with the consistence of growth rates prediction, support the iMF1244 as being a reliable reconstruction of the metabolism of this S. ambofaciens ATCC 23877.Accordingly, we computed  Pareto optimality for each of the four antibiotics that were included in the reconstruction. As shown in Supplementary Data Sheet S. ambofaciens metabolism in the analyzed time points, by integrating gene expression data with constraint-based metabolic modeling. In general, many methods to achieve this task have been developed to date . Accordingly, around 1000 reactions are predicted to be inactive in each time point . This is in agreement with the fact that the bacterium is growing in a complex medium and thus can find in the surrounding environment most of its cellular building blocks. Interestingly, the second time point is the one displaying the highest number of flux-carrying reactions (475), in agreement with the fact that, unlike the other time points, S. ambofaciens is actively producing biomass in this moment of the growth phase. After this time point, S. ambofaciens experiences a decreasing trend in the number of flux-carrying reactions  reflecting with both the reduced growth rate and lower spiramycin productivity observed in vivo in last phases of the growth.Our model predicts that, on average, 392 reactions are active across all the time points . Indeed, a similar scheme of reactions is apparently carrying flux across all the time points, including the main central routes of S. ambofaciens metabolism . This suggests the absence of a massive metabolic reprogramming in the four time points analyzed, i.e., the final part of S. ambofaciens growth. The most evident exception to this general trend is represented by the second time point, the one also showing the highest number of active reactions. Our functional annotation of reactions predicted to be carrying flux suggests that this difference is due to the activation of four main pathways i.e., amino-sugar metabolism, steroid biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, riboflavin metabolism . Thus, according to our simulations, these pathways (or part thereof) play a role when the cell is trying to achieve two objective functions simultaneously, i.e., biomass and antibiotic synthesis. The activation of riboflavin metabolism and a consequent increase in FAD cofactor availability, for example, is consistent with an increased cellular demand of acetyl-CoA (a substrate for the synthesis of many spiramycin intermediates) that, in turn, can be obtained through the FAD-dependent degradation of fatty acids. Similarly, the use of amino sugar metabolism might be propaedeutic to the recycling of nucleotide hexoses , valuable precursors for the sugar moieties of the spiramycin molecule.From a functional viewpoint, our modeling predicts that the set of active pathways is largely shared by the four time points . The hypothetical scenario depicted here indicates both FAD production and nucleotide sugars recycling as key factors in allowing the cell to achieve the imposed double optimization.S. ambofaciens. To this purpose we used the FSEOF approach described in We next exploited the iMF1244 metabolic reconstruction for the genome-wide identification of gene amplification targets potentially allowing spiramycin overproduction in D-glucose 1-phosphate 1,6-phosphomutase responsible for the conversion of D-glucose 1-phosphate into D-glucose 6-phosphate) were also identified.A large fraction of this set of reactions is involved in the synthesis of precursors of the macrolactone backbone of spiramycin. These include, for example, glycerone phosphate (produced by sn-glycerol-3-phosphate: NAD+ 2-oxidoreductase) involved in the biosynthesis of methoxymalonyl-ACP, crotonoyl-CoA (the product of butanoyl-CoA: oxygen 2-oxidoreductase) precursor of ethylmalonyl-CoA and acetyl-CoA, the substrate of 3-oxopropanoate: NAD+ oxidoreductase for the generation of malonyl-CoA. Reactions leading to the production of intermediates necessary for the synthesis of the deoxyhexose sugars embedded in the spiramycin molecule  that is then transformed to -β-methylmalyl-CoA by hydration and is finally cleaved to glyoxylate and propionyl-CoA. The key enzyme of the pathway is CCR, which simultaneously carboxylates and reduces the C4-compound crotonyl-CoA, forming the C5-compound (2S)-ethylmalonyl-CoA . As illustrated in Supplementary Data Sheet Although there is more than one pathway to ethylmalonyl-CoA, the pathway involving the crotonyl-CoA carboxylase/reductase (CCR)  commonl. This paS. ambofaciens the ethylmalonyl-CoA is used as a building block to synthesize the polyketide backbone of spiramycin and antimycin, and the full set of genes necessary for a functioning ethylmalonyl-CoA pathway is clustered together in the S. ambofaciens chromosome not very distant from the spiramycin biosynthetic cluster. The cluster comprises the ORFs: SAM23877_RS28800 coding for methylsuccinyl-CoA dehydrogenase (msd gene); SAM23877_RS28805 coding for mesaconyl-CoA hydratase (mcd gene); SAM23877_RS28810 coding for L-malyl-CoA/beta-methylmalyl-CoA lyase (mclA gene); SAM23877_RS28815 encoding ethylmalonyl-CoA mutase (ecm gene); SAM23877_RS28820 encoding the CCR (ccr gene); SAM23877_RS28825 coding for a TetR family transcriptional regulator; SAM23877_RS28830 coding for 3-hydroxybutyryl-CoA dehydrogenase (Supplementary Data Sheet In srm4) and antimycin (SAM23877_RS01780) biosynthetic gene clusters. RNAseq data showed that under tested experimental conditions all three CCR-encoding genes were poorly expressed, the gene RS26790 has an expression level higher than the gene RS28820 which is more expressed than the gene RS01780 (RS26790 > RS28820 > RS01780), among them only the gene SAM23877_RS26790 (srm4) shows an expression level above the baseline threshold (RPKM > 10) in all the time points analyzed, thus allowing us to consider it effectively expressed along the growth curve (Table 2). Although the CCR enzymatic activity of the ccr paralog gene products has not been proven so far, their role is likely to synchronize ethylmalonyl-CoA synthesis from acetoacetyl-CoA with polyketide assembly, and/or to increase intracellular ethylmalonyl-CoA levels in order to ensure adequate levels of the precursor during polyketide biosynthesis. Therefore, the carbon flow through the ethylmalonyl-CoA pathway was consistently associated with spiramycin productivity by our metabolic model.As well as in other polyketides gene clusters, a secondary copy of the CCR-encoding gene is located in both spiramycin  protein was overproduced in E. coli BL21(DE3) strain and purified (Supplementary Data Sheet ccr (srm4), the gene was placed under the control of the PgroES promoter , spiramycin production  ccr (srm4) mRNA levels , and CCR enzymatic activity in crude cell extracts  were analyzed in the trans-conjugant strains grown in YS medium. With respect to growth kinetics, compared to control strain (harboring pNGEM/OriT/P) the ccr-over-expressing strain exhibited major differences during the RG1 with about 14.6% lower final biomass values but similar growth rates (0.0697 vs. 0.0622 between 24 and 48 h)  mRNA expression levels of the strain over-expressing ccr (pNGEM/OriT/P/CCR), were compared at the same time both with the expression levels of the wild type and of the strain with control plasmid (pNGEM/OriT/P). As shown in Figure 5C the strain over-expressing ccr (srm4) exhibited an average maximum increase in the mRNA levels of 2–3-fold when compared respectively with wild type or with the control plasmid strain, thus indicating a moderate gene overexpression at each time point . It is noteworthy that during the time course, in all the three strains ccr (srm4) mRNA levels reached the highest values of expression at 48 h. So in the light of these results we can infer that the percentage of the overexpressed Srm4 protein in the total soluble S. ambofaciens proteome is therefore almost negligible.Strain manipulation did not alter significantly either soluble protein content (expressed as mg protein/g DCW) or electrophoretic pattern as determined, respectively, by Bio-Rad colorimetric assays and SDS-PAGE (Supplementary Data Sheet ccr-over-expressing strain spiramycin productivity was enhanced during the RG1 and T phases. Compared to control strain spiramycin specific production rates (pq) were increased by a factor of 3.63 at 24 h (due to early triggering of antibiotic biosynthesis) and 1.52 at 48 h , when spiramycin titres reached the highest values during the time course. Antibiotic titres were instead significantly increased by a factor of 4.336 at 72 h   strains overexpressing, respectively, the ribulose 5-phosphate 3-epimerase and the NADP-dependent malic enzyme  production could be achieved, respectively, by overexpression of zwf2 (coding for glucose-6-phosphate dehydrogenase), accA2 (coding for acetyl-CoA carboxylase) or dahp (coding for 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase). However, the combined effect of gdhA-deletion and dahp-, accA2-, zwf2-overexpression enhanced the concentration of FK506 up to 1.47-fold in fed-batch fermentations . In particular, at 48 h of growth, the enzyme activity in the ccr-over-expressing strain was about two-fold higher than in control strain (harboring pNGEM/OriT/P). It should be noted also that the order of magnitude of CCR activity that was determined in the protein extracts from S. ambofaciens is similar to those assessed in protein extracts from Rhodobacter sphaeroides (0.7 U mg-1), Methylobacterium extorquens (0.8 U mg-1) and Streptomyces coelicolor A3(2) (0.4 U mg-1)  . AltogetStreptomyces ambofaciens with the aim to provide a system-level understanding of its metabolic features, and to identify a list of potential metabolic engineering targets for the overproduction of the secondary metabolites in this microorganism .In this study we have applied an integrated approach to explore the metabolic landscape of ccr paralog srm4 in an ad hoc engineered plasmid. In the future this strategy will be valuable in reconstructing the genetic and metabolic profiles of other streptomycetes, and of great benefit to the development of industrial overproducers by genetic engineering.Our work culminated in the construction of a metabolic model that enabled us to identify key molecular targets for strain improvement. In particular, we proposed a set of genes/reactions that might be preferential targets for metabolic engineering experiments aimed at the overproduction of spiramycin. Notably, we selected one of these targets and we validated the model predictions by providing evidence that spiramycin productivity may be considerably increased by enhancing the carbon flow through the recently described ethylmalonyl-CoA metabolic pathway. This goal was achieved by over-expressing the PA, GDB, MF, and CP conceived the experimental design; CC, CP, and EP produced RNA-seq data and performed the relative analysis; MF and BM constructed the metabolic model; MF performed all the simulations with the model; PA, FD, DF, GEDB, AT, and MT performed the experimental validation and provided the material for RNA-seq library preparation. PA, MF, CP, EP, and AT wrote the draft of the work. All the authors critically revised and finally approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k Kinetic models of microbial metabolism have great potential to aid metabolic engineering efforts, but the challenge of parameterization has so far limited them to core metabolism. Here, the authors introduce a genome-scale metabolic model of E. coli metabolism that satisfies fluxomic data for a wild-type and 25 mutant strains in various growth conditions. Escherichia coli15Rapid and pervasive advances in genome editing457810E. coli metabolism by seamlessly integrating flux data for a wild-type and seven mutant strains under aerobic conditions with glucose as the carbon substrate. The model was accurate in recapitulating genetic perturbations as long as growth conditions remained aerobic and the list of deleted genes was in the neighbourhood of the ones used during model parameterization11Recent approaches first deconstruct complex kinetic expressions into their elementary stepsE. coli metabolism was parameterized by combining a machine-learning algorithm202122Km and 49 kcat values) extracted from BRENDAKm and kcat values, respectively, are within the experimentally reported ranges. This level of agreement of k-ecoli457 with experimental data exceeded the metrics reached by the core modelE. coli phenotypes under different growth conditions with a substantially higher accuracy than any other earlier modelling effort.The k-ecoli457 genome-scale kinetic model of E. coliiAF1260 model  upon imposing the flux data of the reference (wild-type) strain or blocked The k-ecoli457 model contains 457 reactions and 337 metabolites and includes all reactions from the largest previously published mechanistic kinetic model of Km and vmax) using the experimentally measured flux data for the nineteen mutant strains grown aerobically with glucose. This is achieved by identifying the best combination of the sampled kinetic parameters in the ensemble (see Km) were fixed and the levels of enzymes  were estimated under the other three growth conditions , separately, by solving the second step of the optimization procedure  strain was constructed see . A two-sdure see . In genedure see . This isdure see .pgi by the previously published core modelpfkA, ΔpfkB and ΔfbaB) that filled in information lost by removing the Δpgi data set. Unsurprisingly, model parameterization was not as robust for the two mutant strains under anaerobic conditions  as alluded by higher prediction deviations from experimental data upon removal of one of the two data sets . Because fluxes of the fermentative products  are significantly different between the two data sets, both data sets are needed as they cannot complement information upon the loss of one of the two. These discrepancies propagate to some extent in other parts of the network, however, model prediction is still adequate for the rest of reactions .We also performed leave-one-out and leave-two-out cross-validation analyses to assess the robustness of the estimated parameters in k-ecoli457 compared with that of the core model  of the predictions within the confidence ranges in the core model were predicted again within ranges by k-ecoli457. Notably, the computed Michaelis–Menten constants in k-ecoli457 for amino acid and pyruvate metabolism exhibited significantly higher agreement with experimental data compared with the values in the core modelKm and 14 kcat parameters. Comparisons revealed that for this set 36% of the estimated Km and 29% of the estimated kcat values are within the experimentally reported ranges. Overall, these analyses demonstrate the importance of integrating pathways distant to central metabolism into the model as they can affect the quality of parameterization through the pools of shared metabolites such as cofactors. This motivates the development of flux data sets in response to genetic perturbations distal from central metabolism pathways to improve overall model parameterization. It is also important to note that the large magnitude in the confidence ranges for both measured and computed kcat values detracts from the confidence for some of the estimated kcat values.To assess the accuracy of the estimated elementary parameters, we compared the corresponding Michaelis–Menten constants with experimental values from BRENDA20nges see . For thepgi, Δgnd, Δzwf, ΔrpiA, ΔrpiB and wild-type strain grown with acetate . The target products included 24 different chemicals under both aerobic (184 mutants) and anaerobic (136 mutants) conditions spanning biofuels , pharmaceuticals and nutraceuticals (artemisinin and naringenin), polymer precursors  and commodity and specialty chemicals  . A comparison also revealed that the product yield in 129 out of 320 designed strains are predicted within 20% of the experimental measurements by k-ecoli457, whereas FBA, MOMA and maximization of product yield predicted the product yield for only 16, 18 and 65 of the designed strains, respectively, with the same accuracy. In general, under aerobic conditions k-ecoli457 closely reproduces the yields for products that branch out from the PP pathway , the TCA cycle  and pyruvate metabolism . Both FBA and MOMA tend to underestimate their yields as more carbon is diverted towards biomass  were available for model parameterization implying insufficient training data as affirmed in the cross-validation analysis (see −1), while product yield data were often reported in higher dilution rates. These observations identify the limits of prediction and pinpoint needed flux measurements under different dilution rates, growth phases, alternate substrates and anaerobic conditions using both batchk-ecoli457 often underestimates the yield of acetate under aerobic conditions as well as ethanol and formate under anaerobic conditions see  due to iE. coli metabolism that approaches genome-scale coverage (457 reactions and 337 metabolites). Comparisons of k-ecoli457 with a previously constructed core modela posteriori correction may not be a fair representation of the proposed model and methodology. These limitations are likely to be ameliorated as expanded metabolomic  uptake rates to become an output of the kinetic model. In k-ecoli457 all mutant fluxes in the training data sets were scaled with the corresponding substrate uptake rate. Given substrate uptake rates data sets24Looking beyond substrate-level regulation, the increasing availability of data sets that provide genome-wide collections of interactions between mRNAs4E. coli metabolism composed of 457 reactions and 337 metabolites was constructed . Enzyme fraction is defined as the level of unbound enzyme e normalized by the total pool of the specific enzyme  in the iAF1260 model using FBA while imposing the experimental flux measurements (for 43 reactions)17=131,072 models, as no improvement in model predictions and convergence to the optimal solution was achieved for a larger ensemble size.The reference flux distribution was obtained by first maximizing biomass yield ldh) with glucose as the carbon substratezwf and Δgnd)E. coli biomass reactionModel parameterization was carried out using steady-state flux data for 25 mutant strains of Km and kcat) with multiple reported values in BRENDA162833575859iAF1260 model of E. coliConfidence intervals were provided only for the measured fluxes of the wild-type strain in the training data setsModel parameterization was performed in two stages: (a) estimation of the elementary kinetic parameters and isozyme activity using the experimentally measured flux data for the single knockout mutants growing under aerobic minimal glucose conditions ; and (b) estimation of enzyme levels under minimal glucose fermentative (anaerobic) conditions and alternate carbon substrates pyruvate and acetate .Km and vmax values in Michaelis–Menten description by permuting parameterizations for different reactions across models in the ensemble. The consequence of an enzyme knockout is captured by fixing the total pool of the normalized enzyme level pfkA, ΔpfkB, ΔfbaB, ΔgpmA, ΔgpmB, Δpgl, ΔrpiA, ΔrpiB, ΔtalA, ΔtalB, ΔtktA and ΔtktB under aerobic conditions with glucose as the carbon substrate) during step one , we next estimated vmax The objective function of the parameterization problem y is defined as the rate of reaction that produces the product per rate of uptake reaction (product mol per glucose mol).Product yield yexp and predicted ypre values.For comparisons, the majority of experimental studies reported only the measured product yield with no confidence range. Consequently, for each product yield the error was calculated based on relative deviation between the measured http://www.maranasgroup.com as a MAT-file (MathWorks Inc.).All parameterization problems were solved using a GA implementation. All calculations, including the ensemble construction and the GA problems, were implemented in MATLAB (MathWorks Inc.) and solved in parallel on three nodes of Intel Xeon E5-2670 v2 with 2.5 GHz (20 cores per node and 256 GB RAM) on the Penn State Lion-X clusters. The k-ecoli457 parameterization took ∼58,800 CPU-hour. Integrating k-ecoli457  for a given metabolic condition also takes up to a few minutes. The estimated elementary kinetic parameters as well as stoichiometry matrix of the model are available in http://www.maranasgroup.com as a MAT-file (MathWorks Inc.).The authors declare that the code supporting the findings of this study is available within the article's All data generated or analysed during this study are included in this published article and its How to cite this article: Khodayari, A. & Maranas, C. D. A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains. Nat. Commun.7, 13806 doi: 10.1038/ncomms13806 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary methods, supplementary tables, supplementary figures, supplementary references.List of metabolites, reactions and regulatory interactionsEstimated kinetic parameters and predicted metabolic fluxes, metabolite concentrations and product yieldsMATLAB version of the model


Escherichia coli, a particularly popular cellular factory due to its high growth rate and well-characterized physiology. For this, the systems biology approach such as modeling is powerful for the analysis of the metabolism and for the design of microbial cellular factories.Microbial production of biofuels and biochemicals from renewable feedstocks has received considerable recent attention from environmental protection and energy production perspectives. Many biofuels and biochemicals are produced by fermentation under oxygen-limited conditions following initiation of aerobic cultivation to enhance the cell growth rate. Thus, it is of significant interest to investigate the effect of dissolved oxygen concentration on redox regulation in E. coli strain. The model reasonably predicted the metabolic characteristics and molecular mechanisms of fnr and arcA gene-knockout mutants. Moreover, an aerobic–microaerobic dual-phase cultivation method for lactate production in a pfl-knockout mutant exhibited promising yield and productivity.Here, we developed a kinetic model that describes the dynamics of fermentation by taking into account transcription factors such as ArcA/B and Fnr, respiratory chain reactions and fermentative pathways, and catabolite regulation. The hallmark of the kinetic model is its ability to predict the dynamics of metabolism at different dissolved oxygen levels and facilitate the rational design of cultivation methods. The kinetic model was verified based on the experimental data for a wild-type It is quite important to understand metabolic regulation mechanisms from both scientific and engineering points of view. In particular, redox regulation in response to oxygen limitation is critically important in the practical production of biofuel and biochemical compounds. The developed model can thus be used as a platform for designing microbial factories to produce a variety of biofuels and biochemicals.The online version of this article (doi:10.1186/s13068-017-0867-0) contains supplementary material, which is available to authorized users. Escherichia coli is probably the most widely used cellular factory due to its high growth rate and well-characterized physiology  and oxygenated quinone  concentration [Q], respectively. The activities of Fnr and ArcA can be expressed as Hill equation  was defined by2] is the dissolved oxygen concentration in the culture medium and O2] is lower than [DO2]. Figure npts denotes the gene that codes for glucose transporters other than glucose-PTS, and L_emp denotes a hypothetical gene that codes for the lumped reactions through glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglucokinase (Pgk), phosphoglucomutase (Pgm), and enolase (Eno). The DO concentration in the culture medium [DO2] was scaled from 0 to 100% and was defined as follows:2]* represents the saturated DO concentration at 37 °C. Thus, 0 and 100% DO levels represent the absence of oxygen (anaerobic condition) and DO saturation at 37 °C, respectively.In the redox regulation, Fnr and ArcA play important oxygen-dependent roles, with the activities of such TFs governed by cytoplasmic oxygen concentration [equation  as folloiosensor , we assumentclass2pt{minimModel parameters were adjusted so that the model can reproduce the experimental behavior of WT strain in the batch cultures under both micro-aerobic and aerobic conditions , 42, whep < 0.05), as shown in Additional file To verify the appropriateness of the kinetic model, the simulated extracellular concentrations of fermentation products were compared with the experimental data obtained from batch cultures under micro-aerobic (DO level = 1%) and aerobic (DO level = 40%) conditions , 42, as 2 production at a high growth rate in E. coli [Figure  E. coli , 46, as  E. coli . As the  E. coli , 47.Fig.Fnr and TFArcA in Fig. As shown in Fig. The specific oxygen uptake rate (qOUR), which indicates the rate of oxygen consumption via Cyo and Cyd reactions, was simulated to be high under condition IV, whereas it decreased under conditions III, II, and I Fig. b. As theaceE/F genes that encode PDH , and LDH, play critical roles in determining the metabolite formation pattern. As the DO level decreased, the Pfl flux was simulated to increase under condition III because ArcA activated the Pfl reaction Fig. . The PflPDH Fig. . The Pfl+ ratio increased steeply with decreasing DO level under conditions I and II because NADH is hard to be consumed by the NADH dehydrogenases in the respiratory chain. This simulation result was consistent with the experimental data [+ ratio promoted the ADH reaction, which resulted in enhanced ethanol production under conditions I and II. Since Fnr activates the Frd flux , as experimentally observed [To obtain a better understanding of the mechanisms by which ATP and NADH are produced or consumed under the categorized DO conditions examined, the specific production/consumption rates of ATP and NADH were simulated, as illustrated in Fig. observed , 42, 47.+ ratio  in the WT strain. The metabolic modes changed significantly depending on DO level. Most of glucose was converted to biomass, CO2, and acetate under condition IV. On the other hand, biomass and CO2 production were decreased under condition I.Additional file fnr or arcA gene knockout on the primary metabolism. Figure fnr-knockout mutant, in which the concentrations of acetate, lactate, formate, ethanol, and succinate were simulated at the time point at which 10 g/l of glucose was depleted. As compared to the WT strain , whereas the NADH consumption rate through ADH was lower than that of the WT strain. As compared with the WT strain, the NADH consumption rate by the NADH dehydrogenases in the respiratory chain increased under condition II because the lack of Fnr de-repressed the NADH dehydrogenase reactions.The simulated specific production/consumption rates of ATP and NADH are shown in Fig. ain Fig. c, whereaain Fig. d, 4d. Th2 and biomass in the fnr-knockout mutant at different DO levels. The carbon balances differed between the WT strain and the fnr-knockout mutant under conditions I and II , and isocitrate dehydrogenase (ICDH) enzymes are allosterically inhibited by NADH to suppress an excess production of NADH [+ ratios of the WT strain and the fnr-knockout mutant because their NADH level was not so high as that of the arcA-knockout mutant, but would overestimate the NADH/NAD+ ratio of the arcA-knockout mutant. As the DO level decreased, the Frd flux steeply increased and then declined  and productivity (g/l of product concentration/h of cultivation time) was simulated for the rce Fig. b. The DOrce Fig. b.E. coli under both aerobic and anaerobic conditions using the kinetic model-based k-OptForce method with ensemble modeling approach and parameterization based on the data obtained from multiple mutant strains. Their model was able to predict the metabolism that improves the succinate yield under aerobic condition but failed to predict it under anaerobic condition. It is essential to predict the dynamics of the cell growth and metabolite production over a broad range of DO levels and to understand the metabolic regulation mechanisms for the rational design of useful metabolite production. To meet these requirements, we have developed a kinetic model that implements redox regulation by Fnr and ArcA into central carbon metabolism [There are several modeling approaches to simulate the fermentation characteristics. Khodayari et al.  simulatetabolism , 20. An fnr-knockout mutant [arcA-knockout mutant [pfl-knockout mutant [The model was constructed and verified using available experimental data for the WT strain –51, 54. t mutant , 55, 60,t mutant .pfl-knockout mutant. This investigation revealed the importance of the optimal switching time from aerobic to micro-aerobic conditions to maximize the productivity  pathway, and Zymomonas has the Entner–Doudoroff (ED) pathway. Since the detailed metabolic pathways depend on the microbes, it is essential to take into account their differences to construct their kinetic models, while using the E. coli kinetic model as a reference model.On the other hand, another characteristic of microbes is their metabolic variety due to evolution under various growth conditions on earth. For example, yeast produces ethanol via pyruvate decarboxylase (PDC) and ADH, fnr- and arcA-knockout mutants were simulated to validate the model. Using this kinetic model, a rational operation strategy for the pfl-knockout mutant was designed to enhance lactate production. A dual-phase strategy was considered that involves initial cultivation under aerobic condition to enhance the cell growth rate, with subsequent cultivation under anaerobic or micro-aerobic condition to enhance the lactate production.It is quite important to understand metabolic regulation mechanisms from both scientific and engineering points of view. In particular, redox regulation in response to oxygen limitation is critically important in the practical production of biofuel and biochemical compounds. Therefore, we developed a kinetic model with enzymatic and transcriptional regulations to predict the dynamics of metabolism at different DO levels. Transcription factor activities, metabolite concentrations, and fluxes of the WT strain and EI enzyme I, EIIA enzyme IIA, ED pathway, Entner–Doudoroff pathway, HPr histidine-phosphorylatable protein, PP pathway, pentose phosphate pathway, PTS phosphotransferase system, TCA cycle tricarboxylic acid cycle.ACAL acetaldehyde, AcCoA acetyl-CoA, CIT citrate, DHAP dihydroxy acetone phosphate, E4P erythrose-4-phosphate, ETH ethanol, FBP fructose-1,6-bisphosphate, FOR formate, F6P fructose-6-phosphate, FUM fumarate, G6P glucose-6-phosphate, GAP glyceraldehyde-3-phosphate, GLC glucose, GOX glyoxylate, ICI isocitrate, αKG α-ketoglutarate, LAC lactate, MAL malate, OAA oxaloacetate, PEP phosphoenol pyruvate, 6PG 6-phosphogluconate, 6PGL 6-phosphogluconolactone, PYR pyruvate, Q quinone, QH2 quinol, R5P ribose-5-phosphate, RU5P ribulose-5-phosphate, S7P sedoheptulose-7-phosphate, SUC succinate.Ack acetate kinase, Acs acetyl coenzyme A synthetase, ADH alcohol dehydrogenase, ALDH acetaldehyde dehydrogenase, Cya adenylate cyclase, Cyd cytochrome bd, Cyo cytochrome bo, CS citrate synthase, Eno enolase, Fba fructose-1,6-bisphosphate aldolase, Fbp fructose bisphosphatase, Frd fumarate reductase, Fum fumarase, G6PDH glucose-6-phosphate dehydrogenase, GAPDH glyceraldehyde-3-phosphate dehydrogenase, Glk glucokinase, ICDH isocitrate dehydrogenase, Icl isocitrate lyase, αKGDH α-ketoglutarate dehydrogenase, LDH lactate dehydrogenase, MDH malate dehydrogenase, Mez malic enzyme, MS malate synthase, Ndh NADH dehydrogenase-II, Nuo NADH dehydrogenase-I, Pck phosphoenolpyruvate carboxykinase, PDH pyruvate dehydrogenase, Pfk phosphofructokinase, Pfl pyruvate formate-lyase, PGDH 6-phosphogluconate dehydrogenase, Pgk phosphoglucokinase, Pgm phosphoglucomutase, Ppc phosphoenolpyruvate carboxylase, Pps phosphoenolpyruvate synthase, Pta phosphotransacetylase, Pyk pyruvate kinase, Rpe ribulose phosphate epimerase, Rpi ribose phosphate isomerase, SDH succinate dehydrogenase, Tal transaldolase, TktA transketolase I, TktB transketolase II.Additional file 1. Detailed model equations.Additional file 2. Model parameters.Additional file 3. Additional results; including additional figures S1–S5.