Phytophthora infestans as a first step towards an integrative model with its host. We predict the biochemical reactions in different cellular compartments and investigate the gene–protein–reaction associations in this model to obtain an impression of the biochemical capabilities of P. infestans. Furthermore, we generate life stage‐specific models to place the transcriptomic changes of the genes encoding metabolic enzymes into a functional context. In sporangia and zoospores, there is an overall down‐regulation, most strikingly reflected in the fatty acid biosynthesis pathway. To investigate the robustness of the GEM, we simulate gene deletions to predict which enzymes are essential for in vitro growth. This model is an essential first step towards an understanding of P. infestans and its interactions with plants as a system, which will help to formulate new hypotheses on infection mechanisms and disease prevention.Genome‐scale metabolic models (GEMs) provide a functional view of the complex network of biochemical reactions in the living cell. Initially mainly applied to reconstruct the metabolism of model organisms, the availability of increasingly sophisticated reconstruction methods and more extensive biochemical databases now make it possible to reconstruct GEMs for less well‐characterized organisms, and have the potential to unravel the metabolism in pathogen–host systems. Here, we present a GEM for the oomycete plant pathogen  It is eet al., et al., The foundation of a GEM is the set of biochemical reactions that may occur in a cell, often catalysed by enzymes. Hence, the identification of enzyme encoding genes in the genome of an organism can help to reconstruct an overview of its biochemical capabilities  We identified all putative enzymes encoded in the Phytophthora infestans is able to assimilate a range of nitrogen compounds, preferably amino acids, but also inorganic forms, such as nitrate  and the cofactors Coenzyme‐A, NADH, NADPH and FADH2, which are generally essential for a eukaryotic cell . It has been reported that many eukaryotes have a highly conserved, independent mitochondrial FAB pathway that is crucial for development  cycle typically occurs in the mitochondria . P. infestans was initially unable to simulate growth  because of missing reactions or invalid reaction directionality constraints. This can be the result of an incomplete genome sequence or misannotations. We therefore performed a model gap‐filling optimization to find the minimal set of reactions in KEGG that must be added to the model to correct this . This method proposed 16 additional reactions, and highlighted three reactions that must be reversed to allow for the production of all biomass precursors. Next, eight extra drain reactions were added to the model to satisfy the steady‐state constraint. Notably, no gap‐filling solutions were found for the production of the fatty acids EPA and behenic acid (a.k.a docosanoate), both of which are produced by P. infestans  and 107 associated genes were removed from the model. The reconstructed metabolic model of P. infestans have been studied . However, a more precise examination of the genome sequence revealed an unannotated open reading frame  that probably encodes this enzyme.After all correction steps, 928 of the 2394 (39%) reactions in the model were able to carry flux based on the defined growth medium, 377 of which carried a non‐zero flux when we calculated the optimal fluxes for maximal biomass production . Of the 2685 metabolites in the model, 809 could not be produced based on our defined growth medium, and may require additional nutrient uptake. By iteratively adding uptake reactions to the model for each of these metabolites, we can simulate whether the import of a specific metabolite would allow the production of additional metabolites (Table S2). This reveals unresolved gaps in the model that could have a technical cause, but may also hint at biological properties. For example, episterol is proposed as a compound that would enable the production of four other metabolites. This is striking as P. infestans GEM  with GEMs of other eukaryotic microbes . Although these numbers might be smaller because of the genome annotation quality and the level of model curation, they might also be a result of the loss of primary metabolic pathways, for which these parasites rely on nutrient import from their hosts . The diatom model is presumably of higher quality, as most reactions are manually curated. However, it might also hint at less redundancy of metabolic enzymes.The relation of a gene to an enzyme and its associated reactions is called the gene–protein–reaction (GPR) association , and deployed the iMAT algorithm . For instance, the mycelium model contains two unique reactions of the ‘Vitamin B6 metabolism’ pathway (KEGG R00173 and R00174), which represent the interconversion of pyridoxal (vitamin B6) to pyridoxal phosphate, an important cofactor for a large number of reactions, especially for the synthesis of amino acids  a straightforward procedure. However, gene silencing is feasible and has been applied to study the function of several genes involved in pathogenesis or signal transduction. The first functional study dealing with genes involved in primary metabolism was published only recently. Abrahamian et al. . By building this model, we can identify genes that have an essential role when converting simple nutrients to the building blocks of life. The metabolic model reconstructed here provides a scaffold for future genome‐wide systems biology approaches to characterize the metabolism of P. infestans, and is an essential first step towards an integrative model of P. infestans–host interactions.Life stage‐specific models provide a direct functional context for transcriptome data by predicting the behaviour of getKEGGModelForOrganism method from the RAVEN Toolbox , currently hosted at the National Center for Biotechnology Information . Default parameters were maintained for hmmbuild and hmmsearch, and an E‐value threshold of 10−20 was applied for hmmsearch. Similar to the getKEGGModelForOrganism function of the RAVEN Toolbox log(EbestKO)<0.9, where E represents the E‐value of the respective protein to a KO group, and EbestKO represents the E‐value of the best‐matching KO group for that protein. In other words, protein hits are often removed if they have a better match to another KO group. Second, any protein match to a KO group was removed if log(E)log(EbestProt)<0.5, where E again represents the E‐value of the respective protein match to a KO group, and EbestProt represents the lowest E‐value of any protein to this KO group. This reduces the number of matches per KO group to reduce the number of false positives, as there is clearly a better matching protein. Subsequently, we retrieved all KO annotations of P. infestans from KEGG (organism ID ‘pif’). The combined set of matched KO groups was used to retrieve all associated reactions and metabolites from KEGG. Consequently, each reaction in the model was associated with a number of P. infestans genes. We hypothesized that each of the genes associated with a reaction is able to catalyse it. We did not consider enzyme complexes, which together would fulfil a single enzymatic task. Reactions were removed automatically if their stoichiometry was undefined  + UDP‐D‐glucose <=> 1,3‐beta‐D‐glucan(n + 1) + UDP’), and if the same metabolite ID occurred at both sides of the reaction arrow, which implies a polymer reaction (e.g. ‘UTP + RNA <=> diphosphate + RNA’). In addition, reactions that were associated with metabolites containing the substrings ‘acceptor’, ‘donor’, ‘tRNA’, ‘enzyme’, ‘aglycon’ and ‘fatty acid’ were removed.We re‐implemented the 1b Eddy,  to trainP. infestans proteins using LocTree 3  to solve the (mixed‐integer) linear programs.The initially reconstructed model was exported to SBML and Microsoft Excel format using CobraPy v0.4.1  from the model, and then iteratively adds uptake reactions for these metabolites, to see whether the uptake of these metabolites unblocks metabolites elsewhere in the model.After these correction steps, we used the function et al., unpublished data) to quantify gene expression in four in vitro life stages of P. infestans, i.e. mycelium, sporangia, zoospores and germinating cysts. Gene expression of P. infestans strain T30‐4  incorporated in the COBRA Toolbox , mitochondrion (mito), extracellular space (extr), endoplasmic reticulum (ER), peroxisome (pero), Golgi complex (golg) and vacuole (vacu). The bars at the bottom left show the total numbers of reactions in each subcellular compartment. The connected bullets indicate the compartments that are compared, and the bars in the graph represent the number of reactions  that overlap between the compartments.Click here for additional data file.Fig. S2 Frequencies of gene numbers per reaction (a) and reaction numbers per gene (b) in the Phytophthora infestans model.Click here for additional data file.Fig. S3 Transcriptome data of Phytophthora infestans in relation to stage‐specific metabolic models. (a) Distributions of transcripts per million (TPM) expression values of all P. infestans genes (background) and the genes in the model, combined from four life stages. The red line indicates the TPM threshold set to distinguish expressed/non‐expressed genes in the model. (b) The percentages of all P. infestans genes (background) and the genes in the model for which gene expression in each life stage exceeds the TPM threshold. MY, mycelium; SP, sporangia; ZO, zoospores; GC, germinating cysts.Click here for additional data file.Fig. S4 Numbers of reactions per KEGG pathway that are shared between the Phytophthora infestans life stage‐specific models of mycelium (MY), sporangia (SP), zoospores (ZO) and germinating cysts (GC). The colours of the tiles scale to the relative frequencies of all non‐core reactions (i.e. the reactions that are absent in at least one stage‐specific model). The numbers in the two right‐most columns represent the core set of reactions  and the total set of reactions for the respective pathway (core + non‐core).Click here for additional data file.Table S1 Properties of all reactions in the Phytophthora infestans model.Click here for additional data file.Table S2 Gap‐filling solutions, drain reactions, candidate metabolites and essential reactions in the Phytophthora infestans model.Click here for additional data file.File S1 The Phytophthora infestans model in SBML format.Click here for additional data file.


Constraint-based metabolic flux analysis of knockout strategies is an efficient method to simulate the production of useful metabolites in microbes. Owing to the recent development of technologies for artificial DNA synthesis, it may become important in the near future to mathematically design minimum metabolic networks to simulate metabolite production.We have developed a computational method where parsimonious metabolic flux distribution is computed for designated constraints on growth and production rates which are represented by grids. When the growth rate of this obtained parsimonious metabolic network is maximized, higher production rates compared to those noted using existing methods are observed for many target metabolites. The set of reactions used in this parsimonious flux distribution consists of reactions included in the original genome scale model iAF1260. The computational experiments show that the grid size affects the obtained production rates. Under the conditions that the growth rate is maximized and the minimum cases of flux variability analysis are considered, the developed method produced more than 90% of metabolites, while the existing methods produced less than 50%. Mathematical explanations using examples are provided to demonstrate potential reasons for the ability of the proposed algorithm to identify design strategies that the existing methods could not identify.We developed an efficient method for computing the design of minimum metabolic networks by using constraint-based flux balance analysis to simulate the production of useful metabolites. The source code is freely available, and is implemented in MATLAB and COBRA toolbox.The online version of this article (10.1186/s12859-018-2352-6) contains supplementary material, which is available to authorized users. Finding knockout strategies with minimum sets of genes for the production of valuable metabolites is an important problem in computational biology. Because a significant amount of time and effort is required for knocking out several genes, a smaller number of knockouts is preferred in knockout strategies.However, the technologies for DNA synthesis are being improved . AlthougFBA) is a widely used method for estimating metabolic flux. In FBA, a pseudo-steady sate is assumed where the sum of incoming fluxes is equal to the sum of outgoing fluxes for each internal metabolite  indicates that a and b are the lower and upper bounds of the flux for the corresponding reaction. Suppose that the necessary minimum GR is 1 in this example.The core idea of GridProd is explained using the following examples. Suppose that a toy model of the metabolic network as shown in Fig. In the original state, if GR is maximized, GR becomes 10 by  = . However, PR becomes 0 since the sum of upper bounds of R1 and R2 is 10, and all flow from R1 and R2 goes to R7. If PR is maximized, R6 becomes 8 since R4 =5 and R5 =3 are the bottle necks. Therefore, TMGR and TMPR are 10 and 8, respectively. If PR is maximized for a fixed GR as in IdealKnock, PR becomes max.The optimal design strategy in this network to obtain the maximum PR under the condition that GR is maximized is to knockout R3 where R5 is optional. In this case,  = is obtained. Note that the minimum necessary GR is set to 1 in this example. If R3 is not knocked out,  = is always obtained.GR≤1 is fixed and PR is maximized. Note that setting GR<1 is possible for the first LP, although the necessary minimum GR is 1 for the second LP. Then,  = is obtained, and PR is 8. Since R3 is not knocked out in this case,  = is obtained when GR is maximized. Thus, the ideal flow-based approach that maximizes PR for the fixed values of GR cannot identify the strategy of knocking out R3 and does not obtain PR =4.Suppose we adopt the strategy where a set of reactions not included in the initially obtained flux is knocked out. If GR > 1 is fixed and PR is maximized, R3 must be used since the upper bound of R8 is 1. Therefore, R3 is not knocked out, and then  = is obtained when GR is maximized. Next, suppose that To address this, GridProd applies P to both GR and PR. However, there may be multiple flows that satisfy the given constraints for GR and PR. For example, if  = is given as the constraints, there are multiple flows satisfying these constraints. However, R4 must be used in any flow since the upper bound of R5 is 3. If R4 is 5, then R8 is 1 and R3 =R5 =0 holds. If R3 and R5 are knocked out,  = is achieved. However, if R4 < 5 holds, then R3 and R8 must be used and R5 is optional. Then  = is obtained. Since GridProd minimizes the total sum of absolute values of fluxes,  = is obtained by knocking out R3.R1+R2=6 must hold. R5 and R8 are optional. In every case, the consequent reaction knockout results in  =. Note that the necessary minimum growth is assumed as 1 in this example, however, GR is allowed to be less than 1 if GR ≥ 1 is satisfied in the consequent strategies. When = is given, R4 must be used since the upper bound of R5 is 3. R3 is optional. If R3 is used, then R5 must be used, and R8 is optional. If {R3,R5,R8} is knocked out, then GR becomes 0 and minGrowth cannot be satisfied. If only R8 is knocked out, then  = is obtained. When  = is given, there are multiple flows. If R4 is not used, then R3 and R5 must be 5 and 3, respectively. Consequently, R4 and R8 are knocked out, and then  = is obtained. If R4 is used, R3 must be used since the upper bound of R8 is 1. R5 and R8 are optional. Then,  = is obtained. When  = is given, R4 must be used since the upper bound of R5 is 3. Since the upper bound of R8 is 1, R3 must be used. R5 and R8 are optional. Then,  = is obtained. If  is , , or , then there is no flux satisfying the condition since the lower bound of R2 is 5.To discuss the effects of the size of each grid, we analyze each case where GR ∈{0,1,2} and PR ∈{3,4,5} are given in the following. Suppose that  = or  = is given. Then, R4 must be used since the upper bound of R5 is 3. In addition to R4, R3 also must be used since GR≤2 and 3≤PR≤5. Then, one of the possible obtained flow by the first LP is  = since the sum of absolute values of fluxes are minimized in the first LP of GridProd. Consequently, R3, R5, and R8 are knockedout. Then the second LP is not feasible. However, if the size of each grid is small and the corresponding constraints are 1−ε≤GR≤1+ε and 4−ε≤PR≤4+ε where ε is a small positive constant, then  = is achieved in the second LP. Therefore, the size of each grid affects the resulting PR of the target metabolites. Table P−1 becomes larger, the production ability improves when P−1≤25. However, when P−1>25 holds, the production ability does not improve as P−1 becomes larger. This indicates that the necessary minimum size of ε in the above example is related to the necessary minimum size of P−1.Therefore, when GR ∈{0,1,2} and PR ∈{3,4,5} are given for the first LP, the consequent  obtained by the second LP is represented in Table Table Since finding an optimal subnetwork that achieves the maximum PR is NP-hard problem, it is almost impossible to find it for genome-scale models in realistic time. Threfore, GridProd does not ensure to find the optimal subnetwork. However, it succeeds to find a better subnetwork than other methods for many target metabolites.GridProd computes the design of chemical reaction networks by choosing reactions used in the first LP. Because many reactions in iAF1260 are not associated with genes, it is not directly possible to extend the idea of GridProd for the selection of a set of genes.P−2 small grids, and conducts LP twice for each grid. The area size of each grid is (P×TMGR)×(P×TMPR). TMPR stands for theoretical maximum production rate. The first LP obtains reactions included in the designed DNA, and the second LP calculates the PR of the target metabolite under the condition that the GR is maximized for each grid. The design strategy of the grid whose PR is the best is then adopted as the GridProd solution.In this study, we introduce a novel method of calculating parsimonious metabolic networks for producing metabolites (GridProd) by extending the idea of IdealKnock and pFBA. In contrast to IdealKnock, in the calculation of the ideal points, GridProd applies “P” to PR as well as GR. Furthermore, GridProd divides the solution space of FBA into P−1=25.Computational experiments were conducted to inspect the efficiency of GridProd using a genome-scale model, iAF1260. The production ability of GridProd strategies was compared to those of IdealKnock and FastPros strategies. GridProd achieves higher PR than IdealKnock for many target metabolites. The average computation time for GridProd is within a few minutes for each target metabolite. The effects of the grid sizes were also inspected. When the solution space was divided into 625 small grids, the obtained PRs were the optimal in the computational experiments, which corresponds to The pseudo-code of GridProd is as follows.Si,j is the stoichiometric matrix. LBj and UBj are the lower and upper bounds of vj, respectively, that represents the flux of the jth reaction.In the above pseudo-code, the TMGR and TMPR are calculated first. biomassLB” and “biomassUB” represent the lower and upper bounds of GR, respectively. Similarly, “targetLB” and “targetUB” represent the lower and upper bounds of PR, respectively, which are used as the constraints in the first LP. Each grid is represented by the two constraints, “ biomassLB≤vgrowth≤biomassUB” and “ targetLB≤vtarget≤targetUB”. TMPR×P and TMGR×P represent the horizontal and vertical lengths of the grids, respectively.In each grid, LP is conducted twice. “−5) are represented as biomassLB”, “biomassUB‘”, “targetLB”, and “targetUB” are used, but the fluxes of the reactions included in PR. Otherwise 0 is stored. Finally, the  that yields the maximum value in PR is searched, and the corresponding PR are obtained. The minimum and maximum PRs from FVA for In the solution of the first LP, a set of reactions whose fluxes are almost 0


Kluyveromyces marxianus is a thermotolerant yeast with multiple biotechnological potentials for industrial applications, which can metabolize a broad range of carbon sources, including less conventional sugars like lactose, xylose, arabinose and inulin. These phenotypic traits are sustained even up to 45 °C, what makes it a relevant candidate for industrial biotechnology applications, such as ethanol production. It is therefore of much interest to get more insight into the metabolism of this yeast. Recent studies suggested, that thermotolerance is achieved by reducing the number of growth-determining proteins or suppressing oxidative phosphorylation. Here we aimed to find related factors contributing to the thermotolerance of K. marxianus.Kluyveromyces marxianus, iSM996, using a publicly available Kluyveromyces lactis model as template. The model was manually curated and refined to include the missing species-specific metabolic capabilities. The iSM996 model includes 1913 reactions, associated with 996 genes and 1531 metabolites. It performed well to predict the carbon source utilization and growth rates under different growth conditions. Moreover, the model was coupled with transcriptomics data and used to perform simulations at various growth temperatures.Here, we reported the first genome-scale metabolic model of K. marxianus iSM996 represents a well-annotated metabolic model of thermotolerant yeast, which provides a new insight into theoretical metabolic profiles at different temperatures of K. marxianus. This could accelerate the integrative analysis of multi-omics data, leading to model-driven strain design and improvement. Kluyveromyces marxianus is a prospective microbial host for industrial biotechnology. It is known as a fast growing, Crabtree negative organism, capable of utilizing a broad selection of sugars and having a high secretion capacity for proteins [K. marxianus has been successfully applied in numerous studies for the endogenous enzymes production including inulinase [Kluyveromyces lactis contains 94% of K. marxianus genes [K. marxianus is thermotolerant, growing up to 45–50 °C. Since it has been isolated mainly from dairy products for years, it has GRAS  and QPS  status, therefore making it suitable for applications in the food and pharma industry.The ascomycetous yeast proteins , 2. For nulinase , β-galacnulinase , β-gluconulinase  and β-xynulinase . Even thus genes , only K.K. marxianus intracellular metabolite levels at high temperatures. Such knowledge may be useful for minimal media design and for identifying high temperature metabolic capabilities, in particular for evaluation of the production potential of various compounds.Thermotolerant hosts may positively impact the high temperature industrial bioprocesses in several ways. Firstly, thermotolerance is favourable once the simultaneous saccharification and fermentation (SSF) is considered as many amylases and cellulases are more efficient at high temperatures . SecondlGenome-scale metabolic models (GEMs) are valuable systems biology tools as they combine genome annotation, cultivation and other experimental data for a given organism into a whole-cell metabolic network. Complementary to the general omics analysis, GEMs give an ability to integrate omics data into metabolic networks and predict the condition specific metabolic capabilities. GEMs have been successfully used to design strains and evaluate the cell capabilities upon different conditions , 14.K. marxianus, we reconstructed iSM996, the first genome-scale metabolic model (GEM) for this species. This model was used to predict the growth on various carbon sources and we found a good consistency between the model predictions and the experimental data reported in the literature. We used transcriptomics data to obtain the context specific models at different chemostat conditions, i.e. 30 °C YPD shaking, 30 °C YPD non-shaking and 45 °C YPD shaking. The models revealed the main metabolic bottlenecks associated with growth at high temperatures. To our knowledge, this approach is one of the first attempts to use GEMs to evaluate metabolic profiles at different temperatures.To evaluate the metabolic capabilities of K. marxianus DMKU3–1042 was based on the published genome annotation (NCBI accession PRJDA65233) and other databases including KEGG [Kluyveromyces lactis [getModelFromHomology, which utilises BLASTP [getKEGGModelForOrganism. This function uses HMMER (http://hmmer.org/) in homology search, where the query proteome is queried against KEGG Orthology (KO) specific hidden Markov model (HMM) sets. The default e-value threshold was used, i.e. 1E-50. It was then compartmentalised with the RAVEN function predictLocalization, which uses WoLF PSORT [The genome-scale metabolic network reconstruction for ing KEGG , MetaCycing KEGG , Transpoing KEGG  and BRENing KEGG . Two draing KEGG . The firs lactis . This was BLASTP  for the LF PSORT  protein LF PSORT  as referK. marxianus bibliographic data wherever possible. The cell dry weight composition (g/gDW) for protein, carbohydrate, lipid, RNA and DNA content was incorporated from cultivation study [K. marxianus.The biomass composition was adapted from iOD907 and adjusted with on study . The stoon study  was usedon study . The detfillGaps was used to identify the candidate reactions for the gap filling, which were kept in the model upon no contradictions in the literature. Such pre-processed draft model was then greatly improved with K. marxianus literature knowledge. For instance, we added the missing uptake pathways for several carbon [The semi-automatic gap filling was performed to ensure that the draft model could produce all biomass components in Verduyn medium . This mel carbon  sources l carbon , metaboll carbon , BRENDA l carbon  and Metal carbon , Transpol carbon  as referThe iSM996 model  under the accession ID GSE66600. Three conditions were considered from the downloaded dataset: 30 °C YPD shaking (30D), 30 °C YPD non-shaking (30DS) and 45 °C YPD shaking (45D). TSS-seq dataset also contained the data for D-xylose medium (YPX) in 30 °C (30X). However, it was decided not to include this condition in the analysis, since KmXKS1 gene, responsible for D-xylulose phosphorylation, was inactive therefore preventing D-xylose assimilation. However, all four conditions were utilised to identify the genes which were turned off in at least one condition. Thereafter, three condition specific models were obtained, each having inactivated reactions once their associated genes were inactive. No threshold values for expression abundance were considered, the gene was called as inactive if it had zero expression values in all three replicates per condition.The quantitative transcription start site sequencing (TSS-seq)  data wasoptimizeCbModel function from The COBRA Toolbox for MATLAB [The values for reduced cost were calculated using the r MATLAB .K. marxianus included four steps: i) obtain and curate the draft reconstruction based on template model iOD907; ii) fetch and curate the second draft model from KEGG; iii) merge both draft models; iv) perform the thorough manual curation. The first draft model after curation had 875 genes including 20 manually identified homologs and 29 non-gene-associated spontaneous reactions. Meanwhile, the compartmentalised and curated second draft model had 524 genes. These two models were merged into a new draft model with 987 genes, 1934 reactions and 1678 metabolites. We then added exchange, biomass and ATP maintenance reactions from iOD907 and performed the growth simulation in a minimal medium. As the model could predict growth, we used the draft model as a basis for further model modifications.The model reconstruction of The manual curation started by fixing two inconsistencies inferred from the template GEM. Firstly, in iOD907 all the cytosolic metabolites could be transported to extracellular space and had exchange reactions. Since the cell ability to import or secrete all cytosolic metabolites seemed unspecific, we commenced the manual check by using Yeast 7.6 as reference. Overall, the number of transportable cytosolic metabolites was decreased from 313 to 182 in iSM996. Another iOD907 problem was that the cell could import or secrete several fatty acid-acyl carrier protein (ACP) complexes even though the cell membrane should be impermeable for such large complexes. These complexes were therefore replaced by the corresponding free fatty acids. Correspondingly, the biosynthetic pathways were added for the known products like 2-phenylethanol, phenethyl acetate or ethyl acetate.K. marxianus specific 110 genes, 455 reactions and 298 metabolites. The most represented metabolic pathways are related to transport, exchange reactions and amino acid, lipid, carbohydrate metabolism , 30 °C static (30DS) and 45 °C shaking (45D). The analysis would greatly benefit from additionally considering the metabolomics data, however we reckon that the comparative analysis of condition-specific models based on only transcriptomics data is still relevant to identify the key differences for biomass precursor synthesis between the conditions.The TSS-seq data was used to turn off the gene-associated reactions without transcriptional evidence while leaving the constraints for other reactions intact. These models were also used to predict the maximal production capacity for the main biomass components Fig. . We calcK. marxianus retains the ability to allocate the amino acids necessary for roughly the same protein mass. Amino acid auxotrophies to L-arginine and L-histidine were identified in 30D and 45D. High temperature-specific auxotrophies included the previously reported L-lysine, L-isoleucine [The simulations indicate that across the conditions, oleucine  and prevKmRIB3, which catalyses the final reactions of GTP and D-ribul 5-P (D-ribulose 5-phosphate) linear pathways (r_0939 and r_0940 correspondingly) is inactivated. However, the interconversion ability is possibly retained between riboflavin and the downstream metabolites FMN and FAD. At the 45D condition, the expression of KmRIB4 and KmFMN1 genes are inactivated. The former gene is responsible for 67dm81Drl lumazine) production (r_0938), while the latter gene is involved in FMN conversion from riboflavin (r_0937). Thus, under the high temperature condition, K. marxianus can only interconvert FMN and FAD or hydrolyse it back to riboflavin. These findings suggest that the organism has to find an optimal balance between ATP synthesis (riboflavin) and other important metabolic functions linked with FMN and FAD. While at the 30DS condition, the cell can freely allocate these three compounds. Under the 45D condition, K. marxianus features an irreversible riboflavin production from FMN and FAD, suggesting that ATP shortage is likely the main limitation for the cell at high temperatures, probably further affected by the limited ferroheme availability. The additional supplementation of riboflavin is known to increase the tolerance to high temperatures upon low ATP/ADP ratio [The auxotrophy to riboflavin, found in 30DS and 45D conditions, has the other potential benefits in addition to just conserving the building blocks. Two condition-specific strategies are considered to inactivate de novo riboflavin synthesis. As shown in Fig. DP ratio . Since tKmDGK1 gene, which is a known contributor to the increased heat sensitivity when overexpressed [The lipid pool size remains the same at all three conditions. However, this assumption is based on the arbitrary abundance value for myo-inositol (0.001 mmol/gDW/h), so one may find the different lipid pool sizes once the experimental values for myo-inositol are used. The excessive availability for the other lipid components is suppressed by an inactivation of the xpressed .KmISN1 and KmSDT1 genes, dAMP shortage cannot be compensated by salvaging other purine nucleotides. This suggest that the cell aims to conserve these building blocks. This coincides with the upregulation of the genes responsible for DNA repair [The significant decrease of flux found in deoxynucleotides was the shortage of dAMP. Due to the inactivation of the A repair .K. marxianus. This model is capable of predicting the growth on the majority of the reported carbon and nitrogen sources. The growth simulations hereby indicate the close correspondence between in vivo an in silico growth rates for the numerous carbon sources. In addition, the model contains several unique biosynthetic pathways for aroma compounds like 2-phenylethanol, phenethyl acetate and ethyl acetate. The iSM996 was used to construct three condition specific models in YPD medium while considering the low oxygen and high temperature conditions. The results suggested that at the high temperature the cell turns off more genes thereby introducing new auxotrophies and hereby utilizing as many resources as possible from the medium. These findings may be used to the growth medium design upon the low oxygen availability and high temperature. Finally, the iSM996 model is a solid tool to evaluate K. marxianus metabolic features, allowing the experimental data integration and the model-driven strain design.We hereby present iSM996, the first genome scale-metabolic model for the thermotolerant yeast Additional file 1. Model file in SBML format.Additional file 2. Model file in Excel format.Additional file 3. The full iSM996 report from memote – the genome-scale metabolic model test suite.Additional file 4. Biomass composition details for overall components (Sheet 1), deoxyribonucleotides (Sheet 2), ribonucleotides (Sheet 3), amino acids (Sheet 4). Also included the model performance upon chemostat conditions for the growth rate (Sheet 5), oxygen consumption (Sheet 6) and carbon dioxide production (Sheet 7). The model performance upon different carbon sources in respect of the growth rate is also included (Sheet 8). References are included in the file.Additional file 5. List of inactive genes (Sheet 1) and corresponding reactions (Sheet 2) between 30D, 30DS, 30X and 45D conditions.Additional file 6: Figure S1. A radar chart showing the predicted potential for biomass precursors excessive production in 30D, 30DS and 45D conditions. As the magnitude is different for each metabolite, the relative production values are shown, where 100% indicates the largest production capacity between conditions. The data for the 30D condition is shown as the green polygon bordered with the dashed border while the corresponding data for the 30DS condition is in purple (dotted border) and the data for the 45D condition is in red (dot dash border) color. (a) Abbreviations: G6P , F6P (beta-D-fructose 6-phosphate), E4P (D-erythrose 4-phosphate), R5P (D-ribose 5-phosphate), GAP , 3PG (3-phosphoglycerate), PEP (phosphoenolpyruvate), PYR (pyruvate), OXA , ACA (acetyl-CoA), 2OG (2-oxoglutarate), SCA (succinyl-CoA). (b) Abbreviations: AMP (adenosine monophosphate), CMP (cytidine monophosphate), GMP (guanosine monophosphate), UMP (uridine monophosphate), dAMP (deoxyadenosine monophosphate), dCMP (deoxycytidine monophosphate), dGMP (deoxyguanosine monophosphate), dTMP (thymidine monophosphate).


Fine-tuning the aeration for cultivations when oxygen-limited conditions are demanded  is still a challenge in the area of bioreactor automation and advanced control. In this work, an innovative control strategy based on metabolic fluxes was implemented and evaluated in a case study: micro-aerated ethanol fermentation.Saccharomyces cerevisiae, defined medium, and glucose as carbon source. Simulations of a genome-scale metabolic model for Saccharomyces cerevisiae were used to identify the range of oxygen and substrate fluxes that would maximize ethanol fluxes. Oxygen supply and feed flow rate were manipulated to control oxygen and substrate fluxes, as well as the respiratory quotient (RQ). The performance of the controlled cultivation was compared to two other fermentation strategies: a conventional “Brazilian fuel-ethanol plant” fermentation and a strictly anaerobic fermentation (with ultra-pure nitrogen used as the inlet gas). The cultivation carried out under the proposed control strategy showed the best average volumetric ethanol productivity (7.0 g L−1 h−1), with a final ethanol concentration of 87 g L−1 and yield of 0.46 gethanol gsubstrate−1. The other fermentation strategies showed lower yields (close to 0.40 gethanol gsubstrate−1) and ethanol productivity around 4.0 g L−1 h−1.The experiments were carried out in fed-batch mode, using commercial The control system based on fluxes was successfully implemented. The proposed approach could also be adapted to control several bioprocesses that require restrict aeration. The performance of bioreactors depends on the control of several cultivation conditions , in order to direct cell metabolism towards the improvement in yield, selectivity, and productivity of the target product. For example, the control of the oxygen supply to facultative microorganisms plays a key role in driving metabolic routes towards the production of biomass (favored under aerobic conditions) or the synthesis of some fermentative products (favored by low or zero oxygen availability) .Conventional strategies in aerobic processes are essentially based on measurements of the dissolved oxygen concentration (DOC), which is then controlled by adjusting the stirrer speed and, occasionally, the composition of the gas supplied to the reactor  or the total gas flow rate , 3. SomeH. influenzae, S. pnemoniae, and N. meningitidis [S. cerevisiae [E. coli [P. stipitis [n-butanol [However, the application of these strategies may be not effective under microaerobic conditions, because ordinary probes are not accurate at low dissolved oxygen tension . Under ongitidis –9, and brevisiae , by E. c[E. coli , and by stipitis . The pro-butanol , 2,3-but-butanol  and acet-butanol , importa-butanol  and bioe-butanol , servingS. cerevisiae, the catabolism of sugars is a key factor affecting process performance and is strongly influenced by oxygen availability. If the oxygen supplied is below a threshold value, glycerol will be produced, decreasing the ethanol yield [In the context of bioethanol production using ol yield . On the ol yield , also leol yield  and to mol yield , 21. Furol yield , 22.S. cerevisiae under oxygen limitation was reported in a couple of works [S. cerevisiae cultures [S. cerevisiae and consisted in the manipulation of the inlet gas stream composition to maintain RQ at the desired level and enforce microaerobic conditions [S. cerevisiae respiratory activity [Despite the importance of the control of micro-aerobic conditions in fermentations, there are not many studies addressing this topic in the literature. The use of a constant (sparged or headspace) air flow, without any control, to cultivate of works , 22, 23.cultures , 25, wasnditions . Easily activity , 25. FurSaccharomyces cerevisiae, in particular, has been extensively studied, with at least 12 GSMs published ever since 2003 [S. cerevisiae GSMs have been employed with satisfactory results to improve the understanding of yeast physiology, as well as to predict targets for metabolic engineering in order to overproduce chemicals such as succinic acid [l-tyrosine [In this context, genome-scale metabolic models (GSMs) can also be employed for bioreactor control. They are a source of in silico data reproducing the cell metabolism. With these models, it is possible to estimate intracellular and extracellular fluxes of metabolites at steady-state for different environmental conditions, considering the inlet fluxes of substrate and oxygen—which in fact drive the metabolic reactions occurring within the cell. nce 2003 . S. cerenic acid , fumaricnic acid , bioethanic acid , 2,3-butnic acid  and l-tytyrosine . Still, GSMs are stoichiometric models, which assume steady-state behavior. Usually, this is not the case in real fermentations, especially in industrial scale, but the information concerning metabolic fluxes may delimit regions of operation for the reactor. Following this concept, the GSMs output can be understood as a pseudo-stationary response to the environmental conditions observed during the process. These estimates of substrate/product fluxes may provide useful information for the control system, even though the bioreactor does not operate at steady-state.An innovative bioreactor control system is herein reported, based on metabolic fluxes estimated using a GSM. This new approach is evaluated using the production of bioethanol under microaerobic conditions as a case study. Indeed, an important problem in industrial fermentations is to increase bioethanol production at the expense of biomass and glycerol, and this was the focus of this work. A novel supervisory system is also presented, which combines advanced strategies for bioprocess control, on-line data acquisition, and a flux-oriented control model.S. cerevisiae fermentation operated in fed-batch mode was chosen as a case study to test the flux-based micro-aeration control (FMC).The development of the metabolic flux-oriented control involves several steps including GSM simulations, selection of suitable simulated data, and their integration with model equations to configure the control system. Additionally, it is important to assess the control performance by applying it to a real process. In the present work, ethanol production in S. cerevisiae iND750 [iMM) for different species  were collected and used to generate mathematical correlations (MC), which in turn provided estimations of substrate MM, as well as the mathematical correlations to predict JMC, are presented in the following section.Initially, simulations using the genome-scale metabolic model of e iND750  and the e iND750  were runmentclass2pt{minim2 flow rate, cell mass, temperature, pressure and outlet gas composition), combined with equations derived from mass balances, the selected outputs, inlet O2 mol fraction (air) whereas In the next step, the controller was set up. In brief, from a set of on-line (or at-line) inputs  was compared to other fermentation strategies: a conventional fermentation strategy, used in Brazilian Bioethanol Plants (BBP), and another cultivation carried out under strictly anaerobic conditions (SAC).S. cerevisiae iND750 [2, and glycerol were generated for each simulated condition  of e iND750  were rune iND750 . Differeentclass1pt{minimaDW−1 h−1). A first shift occurs at a minimum critical value of Figure entclass1pt{minimaentclass1pt{minimaS. cerevisiae metabolism that were expected under the influence of different fluxes of oxygen: biomass formation was favored at higher oxygen fluxes, while glycerol production was intensified at low oxygen fluxes, as already reported [The responses of the metabolic model were consistent with the changes in reported . From thmentclass2pt{minimIn order to implement the information mapped through GSM simulations Figs. a, 2a in mentclass2pt{minimmentclass2pt{minimMM and AIR) was updated using Eqs. Equation mentclass2pt{minimmentclass2pt{minimAIR) and fresh medium feed rate (F), respectively  framework, experiments were carried out in fed-batch mode, using defined medium containing glucose as the sole carbon source. Glucose, biomass, ethanol and glycerol profiles for the reactor operating under FMC can be seen in Figure SM3 ; (iii) a final batch phase (from 10.6 to 12.6 h), started when glucose feeding was stopped (at 10.6 h), still carried out under micro-aeration conditions implemented by the proposed control strategy for O2 supply.Using the control logic previously summarized . The micro-aeration control remained active until all glucose was consumed at the end of the 3rd stage of the FMC experiment in order to get a full picture of its performance.After 2.6 h the dissolved oxygen was set to zero   Fig. a followeX) and, consequently, the oxygen uptake flux A reduction of the cell fermentative capability, probably caused by ethanol inhibition or viability loss, as discussed later, may have contributed to decreasing the specific growth rate  values obtained at-line, and the on-line calculated volume (V) . This production is higher than the values predicted by the metabolic model, which would vary from 0 to 0.12 mmol gDW−1 h−1 (data not shown) for the values of Regarding glycerol production, there was an accumulation of 9 g LIn the previous topics, the flux-based micro-aeration control (FMC) was detailed and its implementation was described. However, the embracement of the FMC strategy in real life cultivations will depend on its ethanol productivity and yield, when compared to other fermentation strategies. So, two additional reference cultivations were performed. The first reference fed-batch cultivation represented the conventional fermentation process in a Brazilian Bioethanol Plant (BBP), which is carried out without aeration. The second reference cultivation was conducted under strictly anaerobic conditions (SAC). The same experimental conditions employed in FMC fermentation, including FMC’s fresh medium feeding profile, were reproduced for both BBP and SAC. Thus, after BBP and SAC experiments were carried out, the results from the three cultivations strategies can be compared to further evaluate the performance of the micro-aeration control based on the metabolic fluxes.The experimental data showing the changes in the concentrations of glucose, biomass, ethanol, and glycerol obtained in the FMC, BBP and SAC experiments  and converted mainly to ethanol, with overall yields ranging from 0.46 to 0.40 gDW−1 h−1 throughout the fed-batch phase  in chemostat cultures under anaerobic/micro-aerated conditions using S. cerevisiae CBS 8066 [S. cerevisiae lab strain (maximum specific growth rate of 0.45 h−1 at 30 °C) shows high glucose uptake fluxes (up 20 mmol gDW−1 h−1) under anaerobic conditions [S. cerevisiae strains would certainly lead to larger experimental ethanol fluxes.Regarding ethanol production, the highest experimental ethanol flux achieved was 17 mmol gFMC Fig. a, applyiCBS 8066 . This fanditions . Thus, tIn this case, the control action proved to be sufficiently robust in managing with the limited capability of the commercial baker’s yeast used for ethanol production. Higher inlet fluxes of oxygen led to increased biomass fluxes, resulting in an RQ decrease, which was satisfactorily corrected by the implemented control loop Fig. .−1, most certainly due to product inhibition and viability loss. This increase in glucose concentration was a consequence of misleading biomass information provided by the at-line optical density measurements, which reflected the concentrations of viable and non-viable cells. High ethanol concentrations can trigger viability loss, but the OD600 readings ), ethanol can act as an inhibitor of yeast growth, while high ethanol concentrations (10% (v/v)) greatly reduce cell viability . One of In addition, other assumptions, such as the pseudo-stationary state for substrate balance and the constant value for biomass yield, could also influence the feed flow rate calculated by the expression F Box . NeverthTable ethanol L−1 h−1, respectively) than those obtained in any other reported study, while the product yield (0.46 gethanol gsubstrate−1) was similar to the values achieved in four [The cultivations performed according to the metabolic flux-based control logic showed higher productivity  and others following heterofermentative patterns.Overall, the integration of on-line/at-line data acquisition with the trajectory predicted for the metabolic fluxes from GSM simulations within the control logic provided suitable oxygen fluxes for running a micro-aerated fermentation. The structure of the proposed control system is more flexible and comprehensive than micro-aeration tuning schemes based just on RQ measurements. The control algorithm and the mathematical model can be easily modified to handle different inputs (JS. cerevisiae was successfully implemented. A new supervisory system (SuperSys_Ferm) was developed and was evaluated under real fermentation conditions, with biomass concentrations ranging from 4 to 16 gDW L−1, ethanol concentrations over 70 g L−1, and fed-batch/batch bioreactor operation mode. The efficacy of the control strategy was confirmed by the high ethanol yield  and productivity  that were achieved relative to controls.The proposed control system based on fluxes estimated from a genome-scale metabolic model (GSM) for This work has applied GSM simulations for controlling a bioprocess, manipulating both feeding and inlet gas flow rates to meet cell requirements for the desired product. As already mentioned, tuning of the control action and estimation of experimental fluxes by the supervisory system would greatly benefit from the use of an in-line biomass sensor able to determine the actual active cell concentration, which was not employed in this study. The proposed control approach is rather general and can be adapted to control several bioprocesses that require restricted aeration.Saccharomyces cerevisiae  and minimal medium , with glucose as carbon source (30 g L−1 for batch cultivations and 300 g L−1 for the feeding medium) [Fed-batch cultures were carried out using fresh commercial  medium) .Three cultivation strategies were evaluated: (i) Metabolic Flux oriented based micro-aeration Control (FMC); (ii) “Brazilian Bioethanol Plant” type fermentation (BBP); (iii) Strictly Anaerobic Condition (SAC).The BBP strategy reproduced the current combined fed-batch/batch cultivations used for ethanol production in most of the Brazilian sugarcane mills. Briefly, in the usual Brazilian bioethanol production process, also known as Melle-Boinot  a stream4OH (7%) and H3PO4 (20%). Ammonium solution added during all cultures, doubling as a nitrogen source [2 and an additional Mettler Toledo Inpro 6800 probe for O2. The feed supply was provided by an Ismatec BVP pump. In all the experiments, data acquisition and monitoring/control of the instruments were achieved using SuperSys_Ferm, an in-house computational tool for flux-based control of micro-aerated fermentation processes. SuperSys_Ferm was implemented in LabView, via a Compact FieldPoint , and was based on SuperSys_HCDC, which was developed to assist fed-batch bioreactor operation during high cell density cultivations [The experiments were conducted in a 5-L stirred tank bioreactor (fabricated in-house) with a working volume of 3.5 L. The pH was automatically controlled  at 4.5 by the addition of NHn source , 51. Theivations , 53.−1. After inoculation, the cultivations were split into three stages, as described in the following.For all experiments, 41 g  of fresh commercial baker’s yeast was suspended in 0.6 L of defined medium. This inoculum was transferred to the bioreactor leading to an initial biomass concentration of about 3.5 g LDW L−1). In this stage, the oxygen saturation was kept at 30% by means of a PID controller that adjusted the stirrer speed between 200 and 1000 rpm. The air was supplied to the bioreactor at flow rates ranging from 0.5 to 5 L min−1, adjusted using a mass flow controller . For all experiments, as soon as the biomass concentration reached the desired range, air supply was shut down and industrial nitrogen started to be flushed to the bioreactor to reduce DOC.The first stage was a batch phase used for activation of the yeast cells and increase of the biomass concentration up to the desired range (8–12 g2 (v/v)), supplied by two mass flow controllers at the rates automatically defined by the FMC control strategy. For BBP, no gas stream was supplied, whereas for SAC ultrapure nitrogen (99.999% N2 (v/v)) was provided at a constant flow rate of 1 L min−1.For all experiments, the second and third stages mimic the current combined fed-batch/batch cultivations used for ethanol production in Brazil. The second stage started after the DOC was below 3% and was conducted in fed-batch mode. For FMC, the flow rate of the feeding medium was defined by the control strategy (further detailed in Methods Control Algorithm Design). For both BBP and SAC cultivations, the feeding profiles reproduced the one automatically implemented in the FMC culture by the control action. Since all experiments were performed using the same feeding pattern and the same overall mass of supplied substrate, this approach enabled a direct comparison of the three competing strategies. Concerning the gas supply in this cultivation stage, for FMC the inlet gas stream was a mixture of air and industrial nitrogen  at λ = 600 nm and dry cell weight method [600 measurements into cell mass concentration, CX (gDW L−1), to be used as input to the control action (better detailed in Methods Control Algorithm Design).During the experiments, samples were withdrawn and biomass formation was measured by at-line optical density readings  , using ti component was based on Eq. i is the volumetric production or consumption rate , in mmol of “i” L−1 h−1, and Cx is the biomass concentration, in gDW L−1.Determination of the experimental flux (Box mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimIndex) was defined as the ratio between the concentrations of ethanol and glycerol at the end of the cultivation, as stated in Eq. An additional index, denominated Overall Selectivity Index (Si/s)  Eq.  and volu p,i Eq.  were obtAIR), nitrogen (QN2), and fresh medium feed (F). The nomenclature included at the beginning of the article provides a detailed description of all fluxes used in this work and their symbols.Besides experimental fluxes  were collected for each The simulations focused on mapping the metabolism, given inlet fluxes of oxygen and glucose ranging from 15 (fully aerobic) to 0.001 (severely oxygen-limited) mmol Oectively . SeveralMM, In Fig. mentclass2pt{minim2 and CO2 molar fractions in the exhaust gas as input variables (Box The flux-based micro-aerated control fermentation (FMC) was implemented as an on-line closed-loop type control policy , with RQmentclass2pt{minimmentclass2pt{minim2 equation was based on the combination of the metabolic flux definition and mass balance  during the experiment’s second and third stages (as described in Methods Cultivation Strategies). The respiratory quotient  and the volume (V) were estimated at-line by the supervisory system. The value of CSF was set to the actual glucose concentration in the feeding medium (~ 300 g L−1) measured before the experiment was started.Cell masses min), outlet temperature (Tout), outlet oxygen fraction . The influence of RQCA on Figure S3. FMC cultivation profile: biomass (black square), ethanol (red circle), glycerol (brown inverted triangle), glucose (blue triangle), dissolved oxygen (green line). (a) Beginning of control action; (b) end of fresh medium feeding. Figure S4. BBP cultivation profile: biomass (black square), ethanol (red circle), glycerol (brown inverted triangle), glucose (blue triangle), dissolved oxygen (green line). (a) Beginning of fresh medium feeding (F profile reproduced from FMC); (b) end of fresh medium feeding. Figure S5. SAC cultivation profile: biomass (black square), ethanol (red circle), glycerol (brown inverted triangle), glucose (blue triangle), dissolved oxygen (green line). (a) Beginning of fresh medium feeding (F profile reproduced from FMC); (b) end of fresh medium feeding. Table S1. Results of in silico studies (Step 1) using the IND750 metabolic model . Influence of the oxygen inlet fluxes on the biomass, CO2, and ethanol production fluxes (for an inlet glucose flux of 3 mmol gDW−1 h−1). *Conditions for the maximum ethanol flux production. Table S2. Ethanol fluxes for different oxygen inlet fluxes (Step 2) (considering an inlet glucose flux of 3 mmol gDW−1 h−1). *Inlet O2 flux that led to 90% of the maximum ethanol production flux observed. + Maximum ethanol flux production


Streptococcus pneumoniae R6 model strain (iDS372), comprising 372 genes and 529 reactions, was developed. The construction of this model involved performing a genome-wide reannotation to identify the metabolic capacity of the bacterium. A reaction representing the abstraction of the biomass composition was reconciled from several studies reported in the literature and previous models, and included in the model. The final model comprises two compartments and manifold automatically generated gene rules. The validation was performed with experimental data from recent studies, regarding the usability of carbon sources, the effect of the presence of oxygen, and the requirement of amino acids for growth. This model can be used to better understand the metabolism of this major pathogen, provide clues regarding new drug targets, and eventually design strategies for fighting infections by these bacteria.A high-quality GSM model for  GSM) models  is a Gram-positive, lactic acid bacterium, which not only asymptomatically colonizes the nasopharynx of humans, particularly of young children, but is also a major human pathogen, responsible for diseases such as otitis media, pneumonia, or meningitis. The S. pneumoniae R6 strain is one of the best studied strains in this species. It is a non-capsulated, highly competent derivative of a serotype 2 strain isolated from a child in the early 20th century. There is still a limited amount of biological information and literature available for this microorganism, although relatively recent studies have focused in reducing this gap  which contributes to the formation of H2O2 and acetate from pyruvate and also the expression of flavin-type NADH oxidases that reduce O2 to less toxic forms   . NeverthGSM model for S. pneumoniae strain R6, to perform comprehensive comparative studies between experimentally determined and computationally predicted phenotypes, under different environmental conditions and with various genetic alterations. This model will allow gaining insights into S. pneumoniae physiology and metabolism, beyond what the experimental data has been providing.The purpose of this work was constructing a high-quality Several databases were used throughout this work to aid in all stages of the study. A brief description of the information retrieved from each database is available in The genome sequence with the NCBI assembly accession number ASM704v1 was retrieved from the GenBank repository.GSM was supported by merlin  makes the annotation traceable, as each leaf of the workflow indicates a different decision regarding each gene’s annotation. Moreover, the labels can also be seen as an indicator of the confidence in a particular annotation.New gene functions assignments and corrections may have a significant impact in model performance. Therefore, genome-wide re-annotations should be carried out periodically, to retrieve the most up-to-date genomic information. For this purpose, a re-annotation workflow was developed and implemented, as shown in merlin, candidate metabolic genes were analyzed by verifying if an EC number was also identified in UniProt for that specific protein either in S. pneumoniae R6 or other S. pneumoniae strains. If true, an additional analysis was performed to verify if merlin’s classification matched the one in the UniProt database. Label A was assigned to genes encoding proteins with matching functions in UniProtKB/Swiss-Prot and label B to genes encoding proteins with matching functions in UniProtKB/TrEMBL.The analysis relied on the assignment of EC numbers to each enzyme or putative enzyme encoded in the genome. For the cases in which a complete EC number had been identified by merlin during genome annotation, was evaluated. If only one complete EC number with a classification score >0.2  was available, then the annotation was accepted. In cases where more than one complete EC number was present, priority was given to those that matched the homologous gene of another reference strain (Label D). S. pneumoniae strain D39 was selected as the first reference strain, while Lactococcus lactis strain NZ9000 was the alternative reference if no homologous genes existed in D39. For cases in which no matching gene function was ascertained from the reference organism, additional tasks, such as BLAST searching against proteins in UniProt/Swiss-Prot, were carried out in a final effort to identify a gene function. These annotations are identified with Label G.When there were conflicting EC numbers, priority was given to proteins in UniProt/Swiss-Prot when assigning gene functions (Label C). If no EC number was present in UniProt, then the existence of multiple complete EC numbers, identified by merlin. If available, the re-annotation was carried out in the same manner as in the cases in which no EC number was present on UniProt and therefore the gene was labeled G, F, or discarded, according to the workflow shown in If an incomplete EC number was identified, an alternative complete one was sought in In case of an absent complete EC number, a search across multiple databases such as BRENDA , UniProtAll cases that did not meet the minimum requirements stated in the re-annotation workflow were discarded from the model.Note that the presence of an EC number after annotation does not necessarily mean that the gene will be included in the metabolic model. Such are the cases of genes involved in DNA and RNA processes such as methylation or rRNA modification, as well as pseudo-genes among others.merlin these reactions are retrieved from KEGG. Such reactions should be spontaneous or promoted by enzymes encoded in the organism’s genome. Hence, the genome annotation determines which reactions will be included in the model. The algorithm used by merlin for the assembly of the metabolic network from the annotated genome is described in detail elsewhere (The assembly of the metabolic network involves collecting a set of reactions. In lsewhere .f G′0) and of reaction (Δr G′0), by analyzing manually curated GSM models of closely related organisms or by biochemical studies of the enzymes.The stoichiometry of reactions in the network should be verified to guarantee that all reactions are balanced. Likewise, the reversibility of the reactions should also be confirmed to avoid gaps and mispredictions of the model. The reversibility may be determined from the estimation of the standard Gibbs free energy of formation  is a repository of transport protein encoding genes, reported in the literature. s TRIAGE  for retrmerlin uses information retrieved from KEGG BRITE to implement these rules.The genes–proteins–reactions (GPR) associations are usually determined by searching biological databases and literature. However, determining if the genes encode subunits of a single protein, isoenzymes, or different proteins belonging to a protein complex may not be straightforward. Hence, Merlin searches for the structure of the protein complex modules, including their subunits, and the stoichiometry of every EC number available in the model. Subsequently, this tool parses the data, identifying the orthologs required by each GPR rule and searches for these sequences in the studied organism’s genome, thus identifying the rule and the subunits of the protein in the model  are rendered non-essential.L. lactis (iAO358) model  model  was usedmerlin , the growth ATP requirements are obtained. Although these requirements should be adjusted to different growth conditions, it was assumed, due to lack of additional data, that growth ATP requirements were the same for all genetic and environmental conditions, which is a common practice in GSM models. Therefore, this value was used for all simulations presented in this work. Likewise, 1 mmol.gbio−1 h−1 was used as the initial flux value (FVA) for maintenance ATP. However, this flux was adjusted to experimental data, both for anaerobic and aerobic conditions, as described in the section “Results”.The growth and maintenance ATP requirements were calculated with data retrieved from previous work . In anaeGSM models. Whenever experimental data are available, a comparison between simulations and experiments is easily performed, whereas when such information is not available model accuracy can be impaired. For this work, physiological data detailed in available references were used.The evaluation of the simulation performance under physiologically meaningful environmental conditions is essential for the validation of −1 at 1% to 0.47 h−1 at 3% of glucose), originating also a prolonged lag-phase and lower biomass production. Furthermore, growth and fermentation profiles were measured in semi-aerobic, anaerobic, and aerobic conditions, under pH controlled at 6.5. In semi-aerobic conditions, R6 grows until glucose is depleted from the medium (1% w/v) producing mainly lactate and minor amounts of acetate, formate, and ethanol. Under anaerobic conditions, strain R6 specific growth rate slightly increases (0.69 h−1 to 0.78 h−1) and by-products formation exhibits the same profile observed in semi-aerobic environment. The lower growth rate observed for semi-aerobic conditions can be explained by the higher pyruvate oxidase activity this strain exhibits, when compared with strain D39, whose enzymatic reaction has H2O2 as by-product, which is notably known to arrest growth in S. pneumoniae. Fully aerobic conditions drastically change growth and by-products accumulation in R6. For instance, specific growth rate increases to 1.07 h−1 for a short period of time (2 h), which entails a 16-fold decrease in biomass yield regarding semi-aerobic conditions. Metabolism shifts from lactate to acetate and H2O2 production, denoting also a high activity of pyruvate oxidase. Also, formate is no longer produced, revealing a total inhibition of pyruvate formate lyase (PFL). Upon growth arrest, lactate is consumed to produce acetate and H2O2, with generation of ATP. However, due to the impossibility of determining oxygen consumption rate in semi-aerobic conditions, simulations will only be performed for studies in anaerobic and aerobic conditions, henceforth referred to as study 1 and study 2, respectively.N-acetyl-D-glucosamine, mannose, and galactose. These sugars were chosen for their natural presence in the human nasopharynx. As stated above for strain R6, strain D39 produces mainly lactate in the presence of glucose as sole carbon source and in minor quantities acetate and ethanol. Formate is also formed but in values below the limit of quantification. The same profile of full homolactic fermentation is observed for growth in N-acetyl-D-glucosamine, although for this carbon source, formate and ethanol are produced in higher quantities. Curiously, the specific growth rate in N-acetyl-D-glucosamine decreases to 0.55 h−1 in comparison with 0.9 h−1 obtained in glucose and reaches a 1.3-fold lower optical density (OD 2.29 vs. OD 1.76). Consumption of mannose also denotes homolactic fermentation, although the products of mixed-acid fermentation increase 2.9-fold for acetate, 5.5-fold for ethanol, and 3-fold for formate in comparison with its production in N-acetyl-D-glucosamine. Growth rate and biomass production in this carbon source are similar with fermentation in N-acetyl-D-glucosamine. Lastly, strain D39 shows a mixed-acid fermentation profile on galactose, producing formate (in higher quantities), ethanol, and acetate in a proportion of 2:1:1. Lactate is detected as a minor product of fermentation. The specific growth rate for this carbon source is 0.48 h−1 and maximal OD is 2.16. This study henceforth referred to as study 3 will encompass the assessment of the model with all four carbon sources.Due to lack of information regarding carbon sources other than glucose for strain R6, the results published for parental strain D39 were also considered in this work. S. pneumoniae strain D39 in chemically defined medium supplemented with glucose. Briefly, one of the experiments performed in Härtel’s study aimed at determining which amino acids were essential for growth. S. pneumoniae D39 in CDM in which each amino acid was separately omitted. Analysis showed that pneumococci are auxotrophic for L-arginine, L-cysteine, L-glutamine, glycine, L-histidine, L-leucine, L-isoleucine, and L-valine. de novo biosynthesis of serine and have demonstrated the dual utilization of carbohydrates and amino acids by pneumococci. This work henceforth referred to as study 4 will be used to assess the impact of amino acids on organism growth.S. pneumoniae R6. Additionally, determining the function of genes identified as critical involved assessing information retrieved from several databases   was acceatabases  and litepfl (spr0415), pflF (spr0232), ackA (spr1854), adh (spr1866), pta (spr1007), lctO (spr0627), and activates ldh (spr1100). When galactose is used as the carbon source, the enzymes downstream of pyruvate exhibit alleviation of repression of the genes involved mixed-acid fermentation.For strain D39, it has been described previously  that wheOptflux , an openpfl genes was simulated by limiting the maximum flux through the reactions associated with these genes, to a fraction of the flux in the reference flux distribution (RFD). The RFD can be obtained by performing unconstrained simulations, for different environmental and genetic conditions. To assess anaerobic growth with study 1, two RFDs were determined: the first maximizing biomass with the defined conditions (RFD A.1) and the second by setting the specific growth rate to 0.78 h−1 (RFD A.2). The metabolism in different carbon sources involved determining one RFD for each. In this case, all RFDs were determined by maximizing biomass with the same medium, except for the carbon source. RFDs B.1, B.2, B.3, and B.4 refer to glucose, galactose, N-acetyl-D-glucosamine, and mannose, respectively.The repression of transcription of the pfl genes (spr0415 and spr0232) control the flux between pyruvate and acetyl-CoA, simulations using glucose as carbon source were performed by varying the levels of pfl between 0 and 100% (deactivated to fully active) of the respective RFDs, with 10% intervals. These simulations allowed assessing the behavior of the model, regarding the level of repression of the mixed-acid genes. Moreover, due to inhibition by CcpA (ackA (spr1854), adh (spr1866), and pta (spr1007) were severely restricted under anaerobic conditions, as shown in study 1, and were therefore set to 10% of the RDFs while lctO (spr0627) was reduced to 0%.Hence, for study 1, as  by CcpA  genes acpfl genes (spr0415 and spr0232), adh (spr1866), pta (spr1007), and lctO (spr0627) were restricted to zero, to simulate growth with glucose under aerobic conditions, as described in Likewise, for study 2, the flux of N-acetyl-D-glucosamine were performed by implementing the same limitations as for anaerobic conditions, as these genes are controlled by the regulator CcpA, which is active in the presence of these carbon sources   comparinin silico environmental conditions and by-product secretion. The consumption rates of all components of the environmental conditions for all studies were assumed to be the same as the ones calculated with anaerobic growth experimental data, except for the carbon sources and oxygen.The CDM composition utilized by GSM models simulate steady-state conditions, the experiments considered in this work were performed in batch conditions. Thus, only the exponential growth phase was considered.Although qS) and the specific rate of product formation (qp) are the coefficient of the linear regression of substrate [S] or product [P] versus biomass over specific growth rate X/μ. This relationship is linear when μ and qS are constant  against biomass X divided by the specific growth rate μ, which is approximately qS ≈ In batch cultures, the specific growth rates (μ) can be determined as the coefficient of the log-linear regression of the biomass concentration versus time, whereas the specific rate of substrate consumption (constant . For insqCS) were calculated using the linear regression approach, using data from the study performed by qGlc) for studies 1 and 2, summarized in qGlc was obtained by dividing the growth rate by the biomass yield and converting it to mmol, according to the following expression: qGlc ≈ In study 3, the carbon source consumption rates  were used to calculate the respective rates.2 flux to zero, whereas for aerobic growth (study 2), oxygen was left unbounded. Finally, the carbon sources assessment (study 3) was performed in anaerobic conditions.Anaerobic growth (study 1) was simulated restricting OThe CDM composition previously described  was usedin silico gene essentiality assessment, as genes considered essential in rich media should also be essential in CDM  two assessments were performed. In the first, the specific growth rate was maximized and in the second, it was fixed at 0.78 h to 100% , study 12O2 was simulated. Specific growth rate was fixed at 1.07 h−1, as reported in Regarding study 2, for aerobic growth  and ep is the relative production rate, regarding the product with the largest flux, in either the simulation or experimental data. The best pfl under-expression level was determined by the lowest distance to the experimental data.Where S. pneumoniae R6 exists regarding essential amino acids for growth, study 4, in S. pneumoniae R6 is auxotrophic.As, no experimental data for The comparison of data from study 5 with results obtained from OptFlux simulations was processed as explained below.For all genes considered essential using OptFlux with a match in OGEE, the reason(s) for essentiality was(were) sought in literature and biological data, and the gene was annotated as essential under those experimental conditions. For cases in which a gene identified as essential by OptFlux was considered non-essential by the OGEE, a first analysis that relied on verifying if the products of the associated reaction were required for biomass production and how many reactions were able to synthesize these products was performed. If the products were required for biomass production and all reactions that led to their production were dependent on that single gene then the gene was considered essential.in silico experimental conditions provided the compounds required for biomass production, rendering the reactions that led to their production non-essential. In this case, the simulation constraints were changed  to ascertain the need for the protein encoding gene(s). If under these new conditions, growth was inhibited, then the gene was classified as probably essential under specific conditions, otherwise it was labeled as probably non-essential.For cases in which genes were classified as essential by OGEE but not OptFlux, a first analysis relied on verifying if the reaction products were required for growth. Next, a search for multiple genes assigned the same function was performed. Whenever more than one gene was annotated with the same metabolic capability, a search across several databases and literature was carried out to determine if the annotation was correct. If all genes were correctly annotated, then GPR rules were analyzed. This step relied on analyzing biological data, as well as verifying orthologous genes from KEGG BRITE . If GPR in silico results did not match experimental data, model curation was performed. Merlin’s user-friendly graphical user interfaces allow performing re-annotations, correction of reactions’ directionality, and inclusion/exclusion of reactions from the model, as well as exporting the final model in Systems Biology Markup Language (SBML) (Whenever e (SBML)  format.merlin. This tool analyses the connectivity of all metabolites available in the model and determines which ones are either only products or reactants. Then, merlin identifies reactions in which these metabolites participate and highlights them. These reactions were analyzed against MetaCyc and KEGG pathways. This process involved identifying reactions in the vicinity of blocked reactions containing dead-end metabolites. Then, enzymes promoting the neighboring reactions were sought in merlin’s annotation to identify missing or misannotations. Also, literature on the main pathways in which blocked reactions participate was studied to assess any particularities of the organism, such as auxotrophies.The gap filling process relied on the analysis of the gaps highlighted by a feature available in Streptococcus species were closer to S. pneumoniae R6 than L. lactis and Bacillus subtilis. However, when no information for streptococci was found, specifically regarding reference strain S. pneumoniae D39, searches were first performed using homologous genes from L. lactis and secondly from B. subtilis. In addition to their close relatedness to S. pneumoniae R6, the metabolism of these species has been well described and both have manually curated GSM models available.As stated in the section “Materials and Methods”, a phylogenetic tree  was deveRe-annotation of the genome identified 1242 metabolic genes out of the 2046 candidates, representing 61% of the whole genome. Several reasons, such as the removal of pseudo and truncated genes, blocked reactions and corresponding encoding genes, removal of genes whose function could not be fully determined , manual curation of both annotation and model , decreased this number down to the final 372 (18.2%) genes that exist in the model. Examples of outcomes during the re-annotation phase, using the re-annotation workflow, are shown in merlin was able to classify it as a beta-lactamase encoding gene. The analysis of the protein sequence using CDD suggested that it belongs to the beta-lactamase superfamily, which led to the acceptance of the initial annotation and assignment of the respective EC number.Several annotation scenarios were faced, as seen in merlin’s assignment matched the information retrieved from reviewed genes in UniProt can be regarded with higher confidence. In these cases, information was often replicated throughout the remaining databases, such as the case of gene Spr0021.Gene annotations in which The annotation of genes with incomplete EC number, such as the cases of Spr0022 and Spr0064, required an in-depth analysis. Databases such as KEGG, UniProtKB, and CDD were consulted. In the case of Spr0064, the CDD classified the protein as a sugar isomerase. A search of the protein name assigned by BRENDA returned the EC number 5.3.1.26 which also belonged to the sugar isomerase superfamily. Due to the absence of literature support and information on other databases, the gene was annotated with this EC number and assigned the Label E. Regarding gene Spr0022, KEGG assigned it EC number 3.5.4.33 [tRNA(adenine34) deaminase], UniProtKB annotated the product as a hypothetical protein while BioCyc assigned the EC number 3.5.4.5 (cytidine deaminase) to the gene product. Analysis of the protein sequence on CDD revealed that the gene encoded a protein of the cytidine/deoxycytidilate deaminase superfamily and BLAST on UniProtKB revealed similarity to a gene that encodes a cytidine deaminase protein and search of this protein on BRENDA returned the EC number 3.5.4.5. Thus, this gene was assigned the Label E. Labels A, B, and C account for over 86% of the annotation, with approximately 33, 34, and 20% of the classifications, respectively.merlin increased reliability of the assigned function (uridine phosphorylase in this example).There were cases in which genes assigned with incomplete EC numbers  were ultimately annotated with complete ones. Despite the classification as hypothetical protein by KEGG and UniProtKB, the analysis of the conserved domains as well as the availability of complete EC numbers that matched protein function by merlin’s TRIAGE is available in Finally, a list of genes annotated as transporters by iAO358, is presented in DW) was provided in a personal communication by Mafalda Cavaleiro from Rute Neves lab. The cell wall contents were calculated from previous publications  represent about 0.26 g of cell wall per gram of biomass.The biomass macromolecular composition, mostly adapted from ications , accordiiAO358. As strain R6 lacks capsule, the capsular polysaccharide contents in iAO358 were distributed by the other macromolecules, maintaining the proportions for each component.The remaining macromolecular relative contents were obtained from merlin  in the SBML version 3 format is included in the The validation of this model involved using an abstraction of the CDM, developed with the aim of providing a high pneumococcal growth yield, by in silico environmental conditions, are shown in The results of the calculations performed according to −1), ΔX = 1.27 gBiomass L−1 and Δl–alanine = −0.24 g L−1. Thus, according to the calculations for determining the substrates specific consumption rates performed using linear regressions, the ql–alanine = 1.66 mmoll–alanine gBiomass−1 h−1. The linear regression approach was used to determine the qS for every metabolite in that table, except glucose and oxygen. Whereas the latter was left unbounded in aerobic conditions (and bounded to zero in anaerobic conditions), the former was retrieved from qGlucose = μ ×YX/S−1 × 1000 ≈ 31.71 molglucose−1 . Likewise, the aerobic maintenance ATP flux was set to 4.5 mmolATP−1. The remaining simulations, shown in the present work, were performed considering these estimates.Although using a flux of 1 mmoliDS372 model involved comparing simulation results to the work of S. pneumoniae R6 is depicted in The first validation performed to the pfl) and spr0232 (pflF), acetokinase spr1854 (ackA), alcohol dehydrogenase spr1866 (adh), and phosphate acetyltransferase spr1007 (pta) are repressed, and lactate dehydrogenase spr1100 (ldh) is activated, which confirms the prominent role of PFL in the by-product flux distribution. As discussed before, in anaerobic conditions using glucose as carbon source, S. pneumoniae strain R6 produces mainly lactic acid (93%) together with mixed-acid fermentation products, namely formate, acetate, and ethanol, which account for only 7% for the total of fermentation products as shown in It is well known that PFL is an intrinsic part of the mixed-acid fermentation process. After being synthesized, it has to be activated via a PFL-activating enzyme . Also, PS. pneumoniae R6 model in anaerobic conditions (study 1), FVA was performed for each of the by-products, while simultaneously varying the level of expression of pfl between 0 and 100%. This assessment involved restricting the flux of the reactions promoted by PFL to different ratios of the flux obtained in RFDs A.1 and A.2 for such reactions, while simulating the maximization and minimization of the production of each metabolite, and the results of these simulations are shown in Regarding the simulation using the −1), which was expected as this rate was used to determine the maintenance ATP requirements. However, in these conditions, the model cannot provide the observed by-products profile, as though silencing the pfl genes in the simulation allows generating enough lactate, formate, and acetate cannot be produced in sufficient amounts. When the flux of the reactions promoted by PFL was restricted to 10% of RFD A.1, the growth rate increases to ∼0.83 h−1, and the minimum amount of formate produced by this bacterium was 2.86 mmolformate−1, more than the one attained experimentally (1.9 mmolformate−1), not allowing a recapitulation of the by-products profile. However, in this case, the maximum production of lactate in the simulation is identical (58.27 mmollactate−1) to the one experimentally determined (58.98 mmollactate−1).As shown in the left panel of −1. The assessment of the influence of the genes encoding PFL demonstrates that, like before, when these genes are silenced the organism cannot produce enough formate. Instead, it redirects the metabolism to homolactic fermentation. Increasing PFL activity induces a shift from lactate to mixed acid fermentation products, namely formate that was found in minor amounts in the experimental conditions, which indicates that the pfl was not completely silenced. Restricting growth allows mimicking the experimental results for all levels of expression of genes encoding the pfl, except for the knockout (0%) as this restriction impairs the production of formate and acetate when compared to the experimental data. The experimental by-products profile can be reached in all simulations (except the case described before), when performing FVA analyses. Nevertheless, such profile is more robust when the expression of genes encoding PFL is closer to 0, as the lower the expression results in a lower the production of formate, as depicted in The next set of simulations  used for anaerobic simulations was also tested for aerobic growth.Simulations performed under aerobic environmental conditions (study 2) were rather different from the ones performed under the absence of oxygen, as shown in −1, while deactivating genes encoding the PFL enzyme and maximizing/minimizing acetate, H2O2, and lactate production, as described in the section “Materials and Methods”. Results show that, in these conditions, production of acetate and H2O2 is mandatory, whereas lactate’s FVA shows that only trace amounts of this metabolite can be produced  under anaerobic conditions. As seen before, it is possible to simulate different types of fermentation by varying N-acetyl-D-glucosamine simulation results are the farthest from the experimental results from all tested carbon sources, with a seemingly anomalous production of acetate. This result is explained by the fact that acetate is a by-product of N-acetyl-D-glucosamine degradation. In the model, genes spr0668 and spr1528 encoding phosphoenolpyruvate-dependent phosphotransferase sugar-specific systems transport this compound into the cell converting it to N-acetyl-D-glucosamine-6-phosphate and releasing pyruvate. Then, gene spr1867 encoding an N-acetyl-D-glucosamine-6-phosphate amidohydrolase converts the phosphorylated compound into acetate and D-glucosamine-6-phosphate, which explains the behavior shared between this carbon source and glucose. Therefore, study 3 assessment allowed determining the level of activation of pfl genes in different carbon sources, which until date has not been described.As expected, the results for galactose show the prevalence of mixed acid fermentation, while mainly the homolactic fermentation profile is exhibited for glucose and an increase in the fermentation behavior is seen in mannose, all as described in iDS372 model are shown in The results obtained by simulating the environmental conditions used in in silico using this model yields results showing a high degree of similarity (80%) to the results obtained by S. pneumoniae R6 although auxotrophic for L-arginine, L-cysteine, glycine, L-histidine, and L-valine contains all reactions required for de novo biosynthesis of this amino acids. Likewise, the unconventional pathway for the de novo biosynthesis of serine suggested by their study was also confirmed. The mismatching results were in respect to the amino acids isoleucine, leucine, valine, and glutamine. Härtel suggested that S. pneumoniae D39 was auxotrophic for these amino acids, as this strain did not grow in their absence, but in silico results performed in this study could not confirm this. The annotation of genes involved in the de novo synthesis of these amino acids and the lack of additional data to fine tune the pathways or determine how and when these genes are expressed prevent any attempt to further curate these pathways. Hence, these pathways either have regulatory mechanisms preventing the biosynthesis of these amino acids or some of the enzymes involved cannot sustain the flux required for biomass growth. The study from S. pneumoniae D39, in the absence of glutamate, proline, and methionine, presented a decreased growth rate. The independent omission of these amino acids from the medium, in silico, did not have any impact on the growth rate which was expected as this validation was qualitative and not quantitative. The fact that S. pneumoniae R6 could also explain the differences observed in these results.Overall, mimicking their experiment S. pneumoniae strain R6. Out of the 89 essential genes, 67 were not listed as essential by OGEE. Only 50 genes identified as essential by OGEE were present in our model and from these only 23 matched the results obtained by OptFlux’s gene essentiality tool. From this comparison, another nine genes could be considered essential if certain experimental conditions were met such as the removal of some amino acids , vitamins  from the medium composition or certain GPR rules were adjusted. The remaining 27 genes would never be classified as essential in this model due to the nature of stoichiometric models that do not take in account different catalytic activity of isoenzymes.As previously stated in the section “Materials and Methods”, study 5 involved performing the gene essentiality analysis with OptFlux’s gene essentiality tool using the anaerobic experimental conditions of iDS372 as the “real” results shown in OptFlux’s results were labeled as the “predicted” and the list of genes obtained from OGEE that exist in in vitro or in vivo gene knockout studies should be performed using the defined medium described in Overall, the results obtained clearly demonstrate that the model performs well in the discrimination of essential from non-essential genes with a high level of accuracy (75%). The model performs very well in predicting non-essential genes, shown by the high specificity (∼79%) and negative predictive value (∼90%). The prediction of essential genes is, in comparison to the non-essential, lower. This is shown by the relatively low sensitivity (46%) and precision (26%). A possible explanation for this relies on the fact that the genes identified as essential by OptFlux are directly linked to metabolism and influenced by the medium composition . On the other hand, the essential gene list obtained from OGEE, although generated using a complex medium, includes non-metabolic genes. Hence, differences in the number of essential genes from each approach were expected. For an improved validation of the results, In S. pneumoniae R6 strain, capable of predicting essential genes and simulating phenotypic behavior. This is the first manually curated GSM model to be reconstructed for any strain of S. pneumoniae and establishes the groundwork for a better understanding of the metabolism of this major pathogen.The main objective of this study was to reconstruct a GSM model for the S. pneumoniae proposed by Carvalho and colleagues was confirmed in this study. Also, this model was able to replicate S. pneumoniae’s behavior under different environmental conditions, including different carbon sources and oxygen availability. Considering the overall results obtained, iDS372 can be employed to provide reliable qualitative or quantitative simulations under different experimental conditions.A high level of manual curation, based on literature, experimental data, and biological databases, was performed when constructing the network, which should increase the reliability of the model. The prominent role of the PFL regulation in The medium used by iDS372’s predictions confirmed almost all essential amino acids under the conditions established by S. pneumoniae R6 possesses all the genes required to synthesize the amino acids whose results did not match suggests that other factors may regulate their expression in S. pneumoniae R6. Further studies on this topic can provide additional information on the differences between R6 and D39.iDS372 performed well on all five studies used to validate its phenotypical predictions to different genetic and environmental conditions.Finally, the model provided in the SBML level 3 version 2 was able to score over 97% on all consistency tests on the Memote  test suiThe lack of experimental data of quantitative nature limits the spectrum of application of this model. The elaboration of new studies using CDM and less rich media clearly defined environmental conditions, and quantification of both substrate consumption as well as by-product formation would yield more information, which could be used to further curate the network and enhance and extend its predicting capabilities.S. pneumoniae species, and the fact that iDS372 is the first curated GSM model for this species, it will be a pivotal model to study the impact of that genetic diversity in the metabolic capabilities of specific strains with potentially relevant clinical correlations.Considering the natural genetic diversity within the This manuscript contains previously unpublished data. The name of the repository and accession number are not available.OD and IR designed the research and provided guide throughout the investigation. OD, IR, and FP coordinated the project. JS and OD developed and refined the model. OD, JS, MR, and CF performed the model validation and evaluation. All the authors wrote the manuscript, read, and approved the final version of 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.