Saccharomyces cerevisiae by combining published kinetic models and calibrating the model parameters to published proteomics and fluxomics datasets. Using this approach, we were able to predict specific proteomes at different dilution rates under chemostat conditions. Interestingly, the approach suggests that the occurrence of aerobic fermentation (Crabtree effect) in S. cerevisiae is not caused by space limitation in the total proteome but rather an effect of constraints on the mitochondria. When exposing the approach to repetitive, dynamic substrate conditions, the proteome space was allocated differently. Less space was predicted to be available for non-essential enzymes (reserve space). This could indicate that the perceived “overcapacity” present in experimentally measured proteomes may very likely serve a purpose in increasing the robustness of a cell to dynamic conditions, especially an increase of proteome space for the growth reaction as well as of the trehalose cycle that was shown to be essential in providing robustness upon stronger substrate perturbations. The model predictions of proteome adaptation to dynamic conditions were additionally evaluated against respective experimentally measured proteomes, which highlighted the model’s ability to accurately predict major proteome adaptation trends. This proof of principle for the approach can be extended to production organisms and applied for both understanding metabolic adaptation and improving industrial process design.Exposed to changes in their environment, microorganisms will adapt their phenotype, including metabolism, to ensure survival. To understand the adaptation principles, resource allocation-based approaches were successfully applied to predict an optimal proteome allocation under (quasi) steady-state conditions. Nevertheless, for a general, dynamic environment, enzyme kinetics will have to be taken into account which was not included in the linear resource allocation models. To this end, a resource-dependent kinetic model was developed and applied to the model organism  Saccharomyces cerevisiae (S. cerevisiae) when shifting from growth under minimal to rich medium conditions; cells grown under rich nutrient conditions require a significantly smaller proteome fraction for biosynthesis genes  metabolic networks is flux balance analysis (FBA) . This meCombining approaches from both dFBA and RBA leads to conditional FBA (cFBA) , which cS. cerevisiae cultures have a different metabolic response to substrate perturbations depending on the cultivation condition, especially cells cultured under repetitive dynamic substrate conditions, the so-called “feast/famine” regime showed a different response compared to cultures grown under steady-state limitations  alloRecent studies have shown that the amount of protein in a cell is limited due to macromolecular crowding, the kinetics of protein synthesis, and degradation . When alWe were curious to study the impact of short-term vs. long-term adaptations to substrate perturbations encountered in natural and laboratory environments. Therefore, we developed a resource-dependent kinetic model and exposed this to different dynamic environments to evaluate the impact of the allocation of proteins in the cellular proteome on the metabolic fitness of a yeast cell under short-term extracellular substrate dynamics.The proteome-dependent kinetic yeast model is based on a system of ordinary differential equations (ODEs) that describe the mass balances of all intra- and extra-cellular metabolites. This system of ODEs is solved with the ode15s function in MATLAB 2020b, for which the absolute tolerance is set to 1e-4, and all variables are constrained to be higher than zero with the “nonnegative” setting. A detailed description of the final proteome-dependent kinetic yeast model used is given in To predict which proteome composition is the most competitive for defined environmental conditions, a Monte Carlo approach is used. The metabolic behavior of 1,000 random proteomes, generated around a seed proteome, is compared based on an objective function. Under steady-state conditions, the minimization of the residual substrate concentration was used as an objective function. Under dynamic conditions, the minimization of a time-weighted average substrate concentration was used, to promote fast consumption of the available substrate, therefore selecting for competitive proteomes:Subsequently, it is determined whether the solution is balanced. If the objective function is optimized and the solution is balanced, the objective function and the seed proteome are updated. In the next iteration, the proteome is then generated around this new seed proteome, with a maximum deviation of 25% per sector.All proteins from experimental datasets are sorted in the same nine protein sectors that are used in the model, to allow for a direct comparison of the experimental proteomes and the optimized proteomes. The proteins are categorized per sector based on either the protein name or the description in the KEGG database  Supplem. The whocat parameters, the difference between the experimental and simulated fluxes is minimized. Additional weight in the objective function was applied for the growth rate, as kcat parameters have to be rejected if the maximum growth rate is not reached.The proteome cost parameters are estimated by optimization with the MATLAB 2020b function fmincon. For all parameter optimizations, a multi-start approach is used. This approach minimizes the risk of reaching a local minimum in the solution space by starting the optimization from different initial guesses. The tolerance of the function is set to 1e-12 for all optimizations. For the estimation of the kThe amount of overcapacity in the yeast proteome is determined by introducing a 10th protein sector. This new protein sector does not have a function for the cells, and hence, only takes up space in the proteome. Therefore, the fraction of the proteome that can be allocated into the extra sector without altering the metabolic fluxes is defined as overcapacity. The overcapacity is estimated for each sector separately, to minimize the changes in each step. The sectors are sorted in a decreasing order and then optimized for overcapacity in this order. The amount of overcapacity in each sector is determined in a step-wise approach. Per iteration, one percent of the specific protein sector is removed and allocated into the extra sector. Subsequently, the fluxes of the adapted proteome are compared to the reference fluxes, and only if the change in the fluxes remains within the boundaries, the seed proteome is updated. This new seed proteome is then used for the next iteration, in which the sector size is again decreased by 1%. By decreasing the sector size by 1% of the current size, the step size is reduced with each iteration. If the flux profile deviates more than the threshold value, the adapted proteome allocation is rejected. The fluxes are evaluated based on the following criterion: the average value of the uptake and growth fluxes should not deviate more than 1% from the reference flux, to ensure that the same substrate uptake and growth rates are achieved.S. cerevisiae. Furthermore, it should be calibrated with available experimental data. We constructed the model based on the kinetic model of yeast glycolysis  with three intermediates: G6P, FBP, and pyruvate. The stoichiometry of the growth reaction was based on NDE, and vTCA, respectively) have been implemented using general Michaelis–Menten kinetics. However, the two reactions are interdependent—both connect to the electron transport chain—and consequently, the rate is determined by the same proteome fraction. A maximum value for the rate of the two reactions combined is defined, reflecting the capacity in the electron transport chain, limited by the provided proteome sector size  were estimated from experimental omics datasets. In the proposed model, the kcat,i for each reaction i is defined as the maximum reaction rate per fraction of proteome (mol/Cmolx/h), where 100% proteome reflects 500 mg protein per gX . At the critical dilution rate (D = 0.28 h−1), the overcapacity sector still has a significant fraction (21%). As briefly discussed earlier, A major difference between this model and earlier studies  is that The proteome compositions, especially at low dilution rates were characterized by a significant overcapacity sector. The kinetic proteome allocation approach could not yet answer why the cells maintained such an excess proteome. As discussed earlier, the hypothesis for a proteome overcapacity is competitiveness and robustness including dynamic environmental conditions. Overcapacity could enable faster substrate uptake rates and enable a competitive advantage and outcompete slower consuming microbes . Further−1), leading to the average dilution rate of D = 0.1 h−1 over the complete cycle  under dynamic conditions compared to the enzyme saturation under chemostat conditions  growth rate. First, we studied the maximum, minimum, and average enzyme saturation  and consequently high flux dynamics. The growth reaction seemed to act as an efficient and fast sink for substrate and ATP. However, in reality, the growth sector does not consist of a single reaction and may not be able to provide a rapid response upon glucose influx. For this reason, two other scenarios were additionally evaluated: 1) the regulation of the trehalose cycle upon repeated substrate pulses and 2) the regulation of the ratio between upper and lower glycolysis see .The trehalose cycle has been described to function as a “safety valve” upon large changes in the glycolytic flux . Under dTo evaluate the prediction accuracy and trends of the predicted proteomes under dynamic conditions, the simulated proteome adaptation from chemostat to feast/famine conditions was compared with the experimentally measured proteome fold changes between chemostat and feast/famine conditions  Figure . ProteinThe model predicted the experimentally observed changes in upper and lower glycolysis . The enzcat’s of enzymes in the trehalose cycle  the synthesis of trehalose has additional functions in the cell which are not represented in the model—it is described that trehalose plays an important role in different stress responses, including severe substrate limitation at low dilution rates see also . 2) The se cycle , nor chaFurthermore, there could also be a bias from the experimental setup–the differences in the trehalose sector, combined with the observed increase of the lower glycolysis sector, suggest that the experimental chemostat proteome is potentially already primed for dynamic environments, compared to experimental conditions, and as such is more robust than the predicted optimized chemostat proteomes.In this work, we developed a proteome-dependent kinetic modeling framework that predicts the optimal proteome composition for defined extracellular dynamic conditions. The approach could reproduce observed complex metabolic phenomena, such as the Crabtree effect, including long-term adjustments under chemostat conditions.Analysis of the predicted proteomes showed that under substrate-limiting conditions  with close to constant extracellular concentrations, a significant part of the optimized proteome is not required (thus a lot of overcapacity). With increasing substrate availability and/or concentration fluctuations, this overcapacity is shown to decrease. Cells optimized for steady-state conditions were not able to survive these substrate perturbations. This suggests that in reality, when conditions are never as ideal and “optimal” as presented in the model simulations, cells already possess proteome adjustments to create a more robust metabolism, allowing them to cope effectively with external perturbations such as substrate gradients.Such adjustments to perturbations were found when comparing steady-state and feast/famine condition predictions. The approach generated a stable phenotype and the predicted changes in proteome allocation, i.e., downregulation of uptake and upper glycolysis sectors and upregulation of the lower glycolysis sector were also found experimentally. This complex and strongly kinetics-dependent prediction highlights the relevance of kinetic properties also for the regulation of protein expression. Nevertheless, to achieve this prediction, some constraints, which had to be derived from experimental observations, had to be included: the maximum mitochondrial fraction and the glucose repression on fermentation. These boundaries seemed to be only stretched after very long-term evolution, as observed by


This type presented high nucleotide interconversion (N+) and low purine recycling (P−). N+P−-type exhibited a significantly better outcome (log-rank p = 4.7 × 10−7) than that of N−P+. GBM patients with the N+P−-type had a median survival time of 19.6 months and lived 65% longer than other GBM patients. Our results highlighted a novel molecular type of GBM, which showed relatively high frequency (26%) in GBM patients lacking the IDH1 mutation, and therefore exhibits potential in GBM prognostic assessment and personalized therapy.Glioblastoma (GBM) is one of the most aggressive forms of cancer. Although IDH1 mutation indicates a good prognosis and a potential target for treatment, most GBMs are IDH1 wild-type. Identifying additional molecular markers would help to generate personalized therapies and improve patient outcomes. Here, we used our recently developed metabolic modeling method  to investigate the metabolic profiles of GBM, aiming to identify additional novel molecular markers for this disease. We systematically analyzed the metabolic reaction profiles of 149 GBM samples lacking IDH1 mutation. Forty-eight reactions showing significant association with prognosis were identified. Further analysis indicated that the purine recycling, nucleotide interconversion, and folate metabolism pathways were the most robust modules related to prognosis. Considering the three pathways, we then identified the most significant GBM type for a better prognosis, namely N Glioblastoma (GBM) is the most common and devastating primary brain cancer . This diGBM patients can be classified into different types based on the mutation profiles of different molecular markers . As an iAn effective way to identify new molecular markers and generate new treatments for malignancies is by targeting cancer metabolic dysfunction, an emerging cancer hallmark . Metabol+P−, which exhibited a significantly better prognosis than other GBM patients. This “good” prognosis type could potentially be used to personalize cancer therapy and design anti-GBM drugs.Here, we used GPMM to perform genome-wide metabolic modeling of GBMs lacking IDH1 mutation and investigated their metabolic profiles. We found a new GBM type independent of the IDH1 mutant, named Nhttps://github.com/GonghuaLi/Code_for_publications/tree/master/GPMM_Glioblastoma (accessed on 17 July 2020). The clinical dataset and mutation information were downloaded from the TCGA cbioportal website . Of these 174 samples, 149 lacked IDH1 mutations and remained for further analysis.The GBM gene expression data were downloaded from the Genomic Data Commons (GDC) web server using the R package in The Cancer Genome Atlas (TCGA) biolinks. As the GPMM only supports FPKM input, we only downloaded FPKM values from the GDC. We obtained 174 samples that have the FPKM values. The source code for downloading the expression data can be found at: https://github.com/GonghuaLi/GPMM). The metabolic uptake rates of the brain were obtained from previous literature and are shown in https://github.com/GonghuaLi/Code_for_publications/tree/master/GPMM_Glioblastoma). Briefly, the human metabolic map, metabolic flux unit, and gene expression type parameters were set to curated Recon3D [2 of 0.86 between the predicted and experimental measurements over the performance of existing methods .https://www.bioconductor.org). The GBM patients were classified using a median flux cutoff, with samples divided into two subtypes: “high flux” (above median) and “low flux” (below median) subtypes. Log-rank survival p-values and visualizations were performed using the R survminer package. A log-rank p-value < 0.01 considered that the flux is significantly associated with GBM prognosis.The prognosis profiles of metabolic fluxes were analyzed using the R package from Bioconductor  in Bioconductor.We first calculated the log2-transformed fluxes and then used the eBayes method in the Limma R package in Bioconductor for differential flux analysis. The p < 0.01); (2) reaction not singular, i.e., correlated with more than one significant reaction (R > 0.8). After applying these strict criteria, we obtained 12 metabolic modules.To obtain the robust metabolic reactions related to prognosis, we filtered the reactions using the following criteria: (1) reaction flux significantly associated with prognosis (WDA) score to analyze pathway abundance in GBM, which was calculated as follows:For flux pathway analysis, we used a similar method to the differential abundance (DA) score to identify the overall flux changes in a pathway . We usedTo investigate which metabolic pathways are associated with the prognosis of GBM, we have analyzed the expression profile of metabolic-related genes of the 149 GBM patients  based on their transcriptomic data see . The metWe then used our recently developed metabolic modeling method GPMM to estimate the metabolic profiles of GBM patients. The GPMM uses a state-of-the-art human metabolic map and analysis toolbox (Cobra 3.0) . We obtaResults showed that 48 metabolic reactions were linked with overall survival time in GBM , with 25After applying two strict criteria see , we obtap = 0.001, 0.0044, and 0.0012, respectively; The above three modules were all independent of IDH1 mutation, as all GBM samples with this mutation were previously disregarded. To define a new GBM type with a better prognosis, we further performed the survival analysis of reactions in these three modules. Since the fluxes in each module are highly correlated  but lower purine recycling activity (P−) and the patients with higher nucleotide interconversion activity (N−) but lower purine recycling activity (P+). We found that the combination of nucleotide interconversion and purine recycling (NP) has a hazard ratio (HR) of 0.32  but lower purine recycling activity (P−). Patients with N+P− lived significantly longer than those with N−P+ (log-rank p = 4.7 × 10−7). In addition, N+P− patients exhibited a median overall survival time of 19.6 months and lived 116% (10.3 months) longer than N−P+ patients  and purine recycling (representative reaction: EX_prpp(e)) D, we com0 × 10−4 F. This s0 × 10−4 D–F. Thispatients D, 34% , which exhibits higher nucleotide interconversion (N+) and lower purine recycling (P−). Given that our metabolic modeling was constrained by the objective function of biomass reaction and ATP production using flux balance analysis (FBA), it is possible that the identified reaction modules might be related to biomass reaction and ATP production. We performed a large-scale Markov chain Monte Carlo (MCMC) sampling after the FBA analysis to address this issue. This strategy enables the GPMM to be more robust and, thus, obtains more reactions that are less related to the biomass equation and ATP production itself. For example, we used typical FBA analysis to find that 258 reactions are essential for biomass reaction and ATP production  or higher nucleotide interconversion (N+) showing anticancer effects. Specifically, purine is an essential substrate for nucleotide synthesis [Interestingly, the molecular characteristics of the newly defined GBM type, namely, purine recycling and nucleotide interconversion, have already been documented to play important roles in carcinogenesis, with either lower purine recycling , likely because metabolic characteristics are the main factor in our definition of GBM subtypes. A more comprehensive investigation of metabolism and fluxes would be indispensable to address this issue.We also investigated the protein abundance of nucleotide interconversion-associated genes (HPRT1) and purine recycling-associated genes (CMPK1) and analyzed the relationship between the abundance of these proteins and GBM prognosis by using the GBM proteome data reported recently . Unfortup = 0.0012). To the best of our knowledge, this is the first study to link nucleotide interconversion with cancer prognosis.As to nucleotide interconversion, by which extracellular ATP is produced from ADP, AMP, GTP, and UTP , extrace+P− type, we performed gene ontology (GO) enrichment analysis. The KEGG pathway analyses of GBM samples belonging to the N+P− type revealed that the gene expression profiles were significantly enriched in the AMPK signaling pathway. Recent GBM research demonstrated that AMPK rewires GBM stem cell metabolism and promotes tumor growth [+P−.To obtain the gene expression profile of the Nr growth , supportAlthough we have identified a novel GBM subtype using the GPMM approach, using metabolic reaction fluxes as clinical biomarkers is still challenging, partly due to technical limitations in measuring them robustly and at scale. Therefore, finding other easily measurable value(s) closely related to flux biomarkers, such as metabolites, protein abundance, etc., collecting more high-quality data, and further experimental validation will be our next step in the future.+P−) independent of the IDH1 mutant type, exhibiting a significantly better prognosis than other GBM patients. This type displayed a relatively high frequency (26%) in GBM patients lacking the IDH1 mutation, indicative of its considerable potential in GBM prognostic assessment. The characteristics of the N+P− type suggest the possibility of personalized therapies for IDH1 wild-type GBM by improving nucleotide interconversion and inhibiting purine recycling.Using our newly developed GPMM approach, we investigated the metabolic profiles of GBM patients lacking IDH1 mutation and successfully identified 48 metabolic reactions significantly associated with prognosis. Importantly, we defined a novel GBM type (N


Ustilago maydis is an important plant pathogen that causes corn smut disease and serves as an effective biotechnological production host. The lack of a comprehensive metabolic overview hinders a full understanding of the organism’s environmental adaptation and a full use of its metabolic potential. Here, we report the first genome-scale metabolic model (GSMM) of Ustilago maydis (iUma22) for the simulation of metabolic activities. iUma22 was reconstructed from sequencing and annotation using PathwayTools, and the biomass equation was derived from literature values and from the codon composition. The final model contains over 25% annotated genes (6909) in the sequenced genome. Substrate utilization was corrected by BIOLOG phenotype arrays, and exponential batch cultivations were used to test growth predictions. The growth data revealed a decrease in glucose uptake rate with rising glucose concentration. A pangenome of four different U. maydis strains highlighted missing metabolic pathways in iUma22. The new model allows for studies of metabolic adaptations to different environmental niches as well as for biotechnological applications. Ustilago maydis is a model organism and economically important fungus from the division of Basidiomycota. The associated corn smut disease affects maize harvest, but the tumors are also used as food [U. maydis grows into the plant tissue to extract substrates for its own metabolic activity. Ustilaginaceae show a versatile product spectrum, such as organic acids , polyols , and extracellular glycolipids, which are considered value-added chemicals with potential applications in the pharmaceutical, food, and chemical industries. U. maydis has developed an effective native production of itaconic acid, an important platform chemical. Indeed, the itaconic acid production in U. maydis was improved to surpass the current biotechnological route of Aspergillus terreus-based production. The advantages are yeast-like growth; high productivities, yields, and titer; and reduced byproduct formation [ as food . As a paormation ,3,4, andormation .Ustilago maydis strain 512 enabled a deeper understanding of its pathogenic mechanisms as well as metabolic competencies [The annotated genome sequence of etencies . Annotatetencies . GSMM caetencies . The peretencies ,10,11.U. maydis, called iUma22. Growth phenotype assays based on BIOLOG with 190 substrates were conducted to reveal the metabolic versatility of U. maydis for more realistic model predictions in native habitats. Moreover, growth kinetics across a range of high glucose concentrations were performed, which allowed for improved metabolic characterization during biotechnological fermentations. To judge the metabolic completeness of iUma22, as well as metabolic capacities in comparison to the Ustilago genus, a pangenome of annotated enzymes of different U. maydis strains was constructed. The model is freely available online via the Biomodels database ID: MODEL2203250001 and GitHub: https://github.com/iAMB-RWTH-Aachen/Ustilago_maydis-GEM (accessed on 24 March 2022). The quality of the model was assessed using the Memote evaluation tool, as well as FROG reports for reproducibility  [U. maydis via pBlast. Protein sequences were queried on PEDANT, MUMDB, MetaCyc or KEGG [U. maydis can be accessed via EnsemblFungi: https://fungi.ensembl.org/Ustilago_maydis/Info/Index (accessed 24 March 2022) [https://mycocosm.jgi.doe.gov/Ustma2_2/Ustma2_2.home.html (accessed on 24 March 2022), NCBI (genome assembly ID: 225285), and Uniprot (Proteome ID: UP000000561).The genomic DNA sequence of SC 9021)  was obtaSC 9021) . A correSC 9021) . Using tSC 9021) , the seq or KEGG ,16 and mch 2022) , MycoCosU. maydis strain family, a pangenome consisting of five Ustilago maydis strains was assembled, including strains 198, 482, 485, and 512 [To identify metabolic differences within the  and 512 . The Nan and 512 , contigs and 512  for ten  and 512  and Bowt and 512  were useU. maydis strains was calculated by means of EDGAR 3.0 [Genes were predicted using GeneMark-ES 4.6.2.  and funcDGAR 3.0 . The KEGDGAR 3.0  was usedDGAR 3.0 . U. maydis is not available; however, the specific elemental composition [AA/gProt), first, the AA-protein molarity (MPAA in gAA/molProt) was calculated by multiplying the AA codon frequency with the AA molar mass (minus the molar mass of water released during polymerization) (Equation (1)). Normalizing each AA-protein molarity by the overall sum yields the weight fraction of each AA (WPAA) (Equation (2)). Division of the AA weight fraction (WPAA) by its molar mass and multiplication with the weight fraction of protein to the dry weight (X in gProt/gCDW) and conversion from mol to mmol (factor 1000) provides the stoichiometric factor (SFAA) (Equation (3)). To calculate the stoichiometric factor of an AA (AASF), the molar percentage (MP) is multiplied with the fractional protein mass per biomass (X) (Equation (2)).The composition of proteins, RNA, and DNA was estimated based on the respective protein and genome sequences, whereas the composition of lipids and the cell wall were results-mined from scientific articles ,35. The position  (and theposition ) was useposition . For exaX) is unknown and was determined by linear optimization. The average elemental composition of each macromolecule  was determined by summing up the products of the absolute amount of each element, and the corresponding C-mole content was calculated [Sacharomyces cerevisiae. The optimization followed the formula:The fraction of protein on total biomass . Vector b represents the measured elemental biomass, C, H, O, and N, supplemented by the elemental content of the P and S of S. cerevisiae. The equation was solved for vector X, the biomass fractions of each of the macromolecules . The inoculation fluid was prepared with IFY-0 (1.2×), cell suspension, and sterile water to obtain a starting turbidity of 62% T, with 100 μL for each well. The inoculated plates were shaken at 200 rpm with a shaking diameter of 50 mm at 30 °C and with a humidity of 70% for up to 168 h . Microbial growth was measured with the SynergyMX  with an optical density of 600 nm. The BIOLOG raw data is available in the For the substrate utilization experiments, the BIOLOG Phenotype Microplates™ PM1 and PM2A were used with CDW/L using the empirical relation from yeast of 0.62 gCDW/L /OD  histograms for each plate. A normal distribution at low OD values represents the OD range below positive growth see . The fin 111182, ). The gr0), the max biomass capacity (C), the growth rate (m), and time (t). The substrate uptake rate was estimated with a linear equation [Which calculates the biomass from the initial biomass  [The genome-scale model for rain 521 . Table 2rain 521 , version. (2022) , as wellS. cerevisiae community model  while others, such as lactate (PM1:B9), malate (PM1:G12), and succinamate (PM2A:F10), were positive . The degU. maydis MB215 control strains [2 = 0.99, with a realistic growth-associated yield (slope) of 0.47 gCDW/gglc. While experiments ‘130v1’ and ‘130v2’ displayed disparate substrate uptake and growth rates despite similar initial glucose concentrations, their yield was comparable, indicating a similar metabolic state. The maintenance parameter of the high-yield experiments was calculated as the x-axis interception, i.e., glucose uptake in the absence of growth, and resulted in 0.2 mmol/gCDW/h, comparable to S. cerevisiae . The grU. maydis strains [U. maydis pangenome of E.C.-annotated genes . ed genes B. Table ed genes . Particued genes C, not onU. maydis, which correctly simulates a large number of substrate phenotypes as well as glucose-based growth rates. The decrease in glucose uptake at high concentrations indicates potential for biotechnological optimization. The model can be further used to identify the biotechnological potential of metabolite overproduction and to optimize metabolic engineering strategies. It can also be used to study metabolic shifts in different life cycles of the fungus during plant infection. While the reconstruction was performed based on model strain 521, the genome sequencing of additional U. maydis strains provided insight into additional metabolic pathways, which could be used to generate a pangenome-scale metabolic model of U. maydis.Here, we present iUma22, a genome-scale metabolic model of


Introduction: The fungal priority pathogen Cryptococcus neoformans causes cryptococcal meningoencephalitis in immunocompromised individuals and leads to hundreds of thousands of deaths per year. The undesirable side effects of existing treatments, the need for long application times to prevent the disease from recurring, the lack of resources for these treatment methods to spread over all continents necessitate the search for new treatment methods.Methods: Genome-scale models have been shown to be valuable in studying the metabolism of many organisms. Here we present the first genome-scale metabolic model for C. neoformans, iCryptococcus. This comprehensive model consists of 1,270 reactions, 1,143 metabolites, 649 genes, and eight compartments. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources and growth rate in comparison to experimental data.Results and Discussion: The compatibility of the in silico Cryptococcus metabolism under infection conditions was assessed. The steroid and amino acid metabolisms found in the essentiality analyses have the potential to be drug targets for the therapeutic strategies to be developed against Cryptococcus species. iCryptococcus model can be applied to explore new targets for antifungal drugs along with essential gene, metabolite and reaction analyses and provides a promising platform for elucidation of pathogen metabolism. Cryptococcus neoformans is a fungal pathogen that causes cryptococcosis in humans. In healthy people, C. neoformans is easily eliminated and rarely causes disease. However, it brings about cryptococcal meningoencephalitis, which causes 6,00,000 deaths annually in immunocompromised individuals . The ATP demand from glucose was found not to exceed this threshold value in the iCryptococcus model, indicating no other reaction mechanism besides the oxidative phosphorylation is involved in ATP production.n represents the reactions. The values corresponding to a reaction column give the stoichiometric coefficients of the metabolites in that reaction. A negative stoichiometric coefficient is used for each metabolite consumed in a reaction, while a positive coefficient is used for metabolites produced. Fluxes are further subjected to capacity constraints, i.e., adding an upper bound and a lower bound for each reaction. In this study, the lower bounds of irreversible reactions were set to zero and the upper bound to +1,000. The lower limit of reversible reactions (430 reactions) was set to −1,000 and the upper limit to +1,000. These limits allow reactions to carry positive or negative flows under steady-state conditions in which there is no accumulation of intracellular metabolites.Constraint-based modeling is a widely used strategy to interpret the responses of organisms with complex metabolic networks under different conditions. This method, which is applied in genome-scale metabolic models and works under the assumption of a steady state, contributes to the elucidation of the genotype-phenotype relationship by using some features specific to the metabolic network of the organism as constraints. Flux balance analysis (FBA) examines the flow of metabolites in a metabolic network by means of network-specific constraints, and enables to predict the growth of the organism or the production of a particular metabolite according to the intended use of the organism. In FBA, the mass balance equation for each reaction and reaction reversibility are used as constraints. For the mathematical representation of the reactions in the metabolic network, a stoichiometric matrix (S) with the dimension (m x n) containing the stoichiometric coefficients of the metabolites in each reaction is used where m represents the metabolites and In the optimization step, an objective function that satisfies all the constraints must be defined to find a single solution space. The objective function may be the growth of the organism, or it may be the flux through a reaction related to the production of a metabolite of interest. Linear programming was used to determine maximal theoretical flux with constraint . Due to Robustness analysis is applied to find the sensitivity of objective function of the model to a specific reaction . In thisThe shadow price of a metabolite in the model shows the sensitivity of the biomass objective function to the change in the amount of this metabolite . In otheC. neoformans var. neoformans JEC21. For this purpose, singleGeneDeletion and doubleGeneDeletion functions in the COBRA Toolbox , these genes were determined as an essential gene pair. The essential genes obtained in the single-gene deletion analysis were not used in this analysis, since they significantly reduce or stop the biomass production irrespective of the genes with which they were paired.In the double-gene deletion analysis, the same procedure was applied with the deletion of two separate genes together. If the biomass production obtained as a result of deletion of these genes was less than the threshold value  that regulate(s) each reaction, and the related enzyme numbers (EC number) were determined. There were 8 compartments in the model: cytosol, mitochondria, peroxisome, nucleus, endoplasmic reticulum, vacuole, extracellular space and golgi apparatus. Most of the reactions found in the model take place in the cytosol (660) and mitochondria (196). This is followed by peroxisome (28), nucleus (12), endoplasmic reticulum (6) vacuole (4), extracellular space (3) and Golgi apparatus (2), respectively.In the KEGG database, each metabolic pathway is represented by a metabolic map, which includes reaction and gene information obtained from the genome of the organism. In this way, 911 enzymatic reactions were collected from 57 pathways of in vitro and in vivo experiments. Therefore, in addition to the KEGG database, reactions for capsule, cell wall formation and sphingolipid metabolism were added to the model by using the evidence in the literature. Although the downstream steps of capsule synthesis have been elucidated in detail, the reactions involved in the final production phase from monomers have not yet been elucidated. Since the capsule is an important target component in drug studies, these reactions  were included into the iCryptococcus model to complete the pathway as also done in many literature studies  was obtained .ogenesis . Structutionally . In our In the iCryptococcus model obtained, all reactions in the glycolysis/gluconeogenesis pathway took place in the cytosol with the product pyruvate which was then transported to the mitochondria where it was converted to acetyl-CoA. In the KEGG pathway map, this reaction was represented by the sum of four different reactions, R00014, R03270, R02569, and R07618. Instead of these four reactions, pyruvate dehydrogenase (PDHm) catalyzed reaction in the BiGG Model was taken as a single reaction during the metabolic model reconstruction. Similarly, the conversion of 2-oxoglutarate to succinyl-CoA in the TCA cycle was represented by the reactions R00621, R03316, R02570, R07618 in the KEGG database. Instead of these four reactions, 2-oxoglutarate dehydrogenase (AKGDm) catalyzed reaction in the BiGG Model was taken during the model reconstruction. In the starch and sucrose metabolic pathway, the conversion reactions between sucrose (sucr), D-fructose (fru), maltose , D-glucose (glc D), D-glucose 6- phosphate (g6p), D-fructose 6-phosphate (f6p), trehalose (tre) and trehalose 6-phosphate (tre6p) take place. Glycogen (glycogen) and 1–3 beta D-glucan (13BDglcn) involved in the biomass reaction, were also produced in this pathway, and all these reactions took place in the cytosol.Cryptococcus neoformans infectivity. Reduced virulence has been observed in non-encapsulated organisms in several studies  is converted to UDP-glucose (udpg). In addition, some of the UDP-glucose is converted to UDP-glucuronate. While UDP-glucose is an important donor component for glucan in the cell wall structure, UDP-glucuronate acts as a donor for capsule structure components. All reactions in this pathway were included in the model and they took place in the cytosol. The amino sugar and nucleotide sugar metabolism pathway is of great importance as it enables crucial sugar donor production for capsule and cell wall production. The cell wall is composed of mainly glucan, and the other minor components are chitin and chitosan. UDP-glucose nucleotide sugar donor is needed for the synthesis of alpha and beta glucan structures. The sugar removed from the donor by various synthesis enzymes is added to the glucan structure. Chitin and chitosan are important components for growth and pathogenicity. Although chitin and chitosan have a minor contribution to the cell wall structure, their deficiency caused a decrease in virulence and a slowdown in the growth of the organism . Therefo studies . NucleotS. cerevisiae was used in the metabolic model reconstruction. The fatty acid degradation pathway takes place in the peroxisome wherein the fatty acids are broken down. Reactions starting from Palmitoyl-CoA (pmtcoa) continue until Acetyl-CoA (accoa) production. A lumped reaction (FAO80p in the BiGG Model) between octanoyl-CoA (occoa) and acetyl-CoA (accoa) was used during the GSMM model reconstruction.The map of oxidative phosphorylation for Cryptococcus species was not provided in literature, including the KEGG database. Therefore, the oxidative phosphorylation pathway of the iMM904 model  developeRiboflavin (ribflv) is an important metabolite for cell growth as it is involved in the biomass reaction of the Cryptococcus model. In the riboflavin metabolic pathway, 3,4- dihydroxy-2-butanone 4-phosphate (db4p) is produced from D-ribulose 5-phosphate (ru5p_D). After the production of this metabolite, riboflavin (ribflv) is produced in the presence of 4- (1-D-Ribitylamino)-5-aminouracil (4r5au). Since there is no reaction for the production of 4- (1-D-Ribitylamino)-5-aminouracil (4r5au) in the KEGG database, this metabolite was added to the model as a demand reaction (DM 4r5au c).The purine metabolic pathway is one of the pathways with the highest number of reactions in the model. Several currency metabolites are produced in this pathway and transported to different pathways. In terms of the metabolites produced and/or consumed, it is generally linked to the pyrimidine and thiamine pathways. In addition to the reactions in the KEGG map, the sink reaction for 5-amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxylate (5aizc) and the demand reaction for (S)-2-[5-amino-1- (5-phospho-D-ribosyl)imidazole-4-carboxamido] succinate (25aics) were added to the model. These reactions generally take place in the cytosol.In the terpenoid backbone biosynthetic pathway, metabolites having isoprene units are produced. Depending on the presence of (R)-5-phosphomevalonate (5pmev); (R)-5 diphosphomevalonate (5dpmev), isopentenyl diphosphate (ipdp), dimethylallyl diphosphate (dmpp), geranyl diphosphate (grdp) and farnesyl diphosphate (frdp) are produced. Farnesyl diphosphate, produced in this pathway, participates in the production of ergosterol in the steroid metabolic pathway. Since there is no reaction for the production of (R)-5-phosphomevalonate in the KEGG database, this metabolite was added to the model as a demand reaction (DM 5pmev c). The reactions in steroid biosynthesis pathway generally take place in the cytosol or endoplasmic reticulum. The organelles in which the reactions took place were determined using gene localization and the relevant transport reactions were added to the model. The steroid biosynthesis pathway is very important because of the variety of compounds produced, such as ergosterol. Besides being in the biomass reaction, ergosterol is also critical for the regulation of membrane fluidity . R00702 C. neoformans genome, the database of Clusters of Orthologous Groups of proteins (COG) (In order to identify the categories of proteins encoded in the ns (COG)  providinns (COG) .Cryptococcus neoformans var. neoformans JEC21 in the literature was used to simulate, evaluate and validate the iCryptococcus model. First, the growth rate obtained under MEMOTE default medium conditions was compared with experimental observations to validate the model. The doubling time of C. neoformans is between 80 min and 160 min, depending on the nutrient medium  catalyzed reaction had no flux when the carbon source was glucose. However, the flux through PPCK catalyzed reaction increased when infection conditions  were simulated  reaction increases under the condition of infection. The deletion of this reaction inhibits growth on acetate, ethanol and glycerol, in agreement with experimental results.rophages . The introphages . The envrophages , the flu studies . Aconita studies . In the imulated . The fluimulated . Acetatenfection . In the The growth rate calculated by the iCryptococcus model was 0.36/h when 4 mmol/gDW/h glucose was used as the carbon source (the amount of oxygen uptake was unlimited). As the amount of glucose increased, the growth rate increased linearly since the oxygen uptake was unlimited . HoweverThe sensitivity of biomass production to carbon source and oxygen availability was also evaluated through shadow price analysis, i.e., we determined how the addition of metabolites, glucose and oxygen, affects the biomass objective function. C. neoformans , there was a flux through the reaction responsible for sn-glycero-3-phosphocholine production, implying active reaction rate. Phospholipases affect membrane stabilization and lead to infection by suppressing the immune system .All genes that contribute to the increase of the organism’s biomass production can also be detected by gene deletion analysis. In the iCryptococcus model, 153 of 649 genes were found as essential genes by single gene deletion analysis. Moreover, 202 of 649 genes, including 153 essential genes, were also found to affect biomass production. Of the 153 essential genes involved in various pathways, 58 of them do not have orthologs in the human model .C. neoformans var. neoformans JEC21. The essential gene pairs and reactions regulated by these genes are shown in Further on this issue, the double-gene deletion analysis and comparison with the human genes in the Recon3D model revealed eight essential gene pairs for The number of metabolites involved in at least three reactions in the iCryptococcus model is 379. As a result of the metabolite deletion analysis, 185 of 379 metabolites were found to be essential by the iCryptococcus model. 69 of these essential metabolites are currency metabolites. When the remaining 116 metabolites were compared with the human model (Recon3D), thirteen of these metabolites were found to be absent in the human model .The iCryptococcus model has 71 essentials (58 genes and 13 metabolites) not found in the human model. In DrugBank database , there aIn the reaction essentiality analysis, 198 reactions were found to be essential out of a total of 1,270 reactions in the iCryptococcus model, irrespective of their involvement in the production of essential metabolites. Metabolites and reactions essential for the vital activity of the pathogen, but not found in humans, are very important as potential drug targets. An inhibition in the production of essential metabolites and reactions in the pathogen metabolism will inhibit its proliferation and growth. Since these metabolites and reactions were not found in humans, the risk of any side effects to humans is eliminated. In this sense, 13 metabolites and 198 reactions  found in the essentiality analysis have the potential to be drug targets for the therapeutic strategies to be developed.in silico genome-scale models provide information about the complex processes occurring in metabolism and are important tools for many metabolic engineering methodologies. In this study, a genome-scale metabolic model specific to C. neoformans, which is responsible for more than hundreds of thousands of deaths annually worldwide was created in order to conduct detailed research on the metabolic processes of the pathogen by using computational systems biology approaches. The iCryptococcus model created includes the biochemical reactions involved in the synthesis of the main macromolecule components of the organism, along with the known virulence factors. Genome-scale metabolic models have a great advantage over traditional methods, with their stoichiometric consistency and system-level integration ability, discovering vital elements for the organism under different environmental conditions  analysis, 58 genes and 8 gene pairs of the 649 genes and 13 metabolites of the 1,143 total in the iCryptococcus model were found to be essential, and they do not have an ortholog in the human model. The pathways related to the metabolisms of starch and sucrose, amino acid, steroid etc. Appear to include these essential genes, metabolites and reactions, and can thus be considered as potential drug targets.CNH02650, CNF03410, CNI00560, and CNM00820) that regulate chorismate-producing and consuming reactions in the iCryptococcus model were also found to be essential genes in gene essentiality analysis. Fortunately, these genes were not found in the human model. In this sense, chorismate can be a potential drug target, consistent with the study of Ziebart and colleagues (Chorismate (chor) is one of the 13 essential metabolites in the iCryptococcus model. It is an important biochemical intermediate found in plants and microorganisms. It is at the center of the biosynthesis of carboxylic aromatic compounds such as aromatic amino acid, vitamins E and K, ubiquinone. It is synthesized by the pathway called the shikimate pathway, which is involved in many bacteria and various fungal species. The presence of the pathway in many parasites and pathogens, but not in metazoa, makes it a prime drug target . The genlleagues  reportinCND05120 and CNL06640 regulating the reaction were found as a result of essentiality analysis. These results are consistent with those of Ducati and colleagues (2-dehydro-3-deoxy-D-arabino-heptonate 7-phosphate (2dda7p) is an essential metabolite in the iCryptococcus model. The first step of the biosynthesis of aromatic rings from carbohydrate precursors in microorganisms and plants begins with 3-deoxy-D-arabino-heptulosonate-7-phosphate. It is produced from phosphoenolpyruvate and D-erythrose-4-phosphate by an enzyme catalyzed by DAHP synthase. In the model, both this reaction and the genes lleagues , reportiCNA02450) that regulates this reaction is an essential gene in the iCryptococcus model. L-Aspartic-4-semialdehyde is an a-amino acid derivative of aspartate. It is an important intermediate of the aspartate pathway found in bacteria and plants in general. In this pathway, the biosynthesis of amino acids such as lysine, methionine and threonine from aspartate takes place. These results show that L-aspartate 4-semialdehyde can be a potential drug target, consistent with the study of Dahal and colleagues  is a metabolite among the 13 essential metabolites. The reaction (reaction abbreviation: ASAD) producing the L-aspartate 4-semialdehyde is an essential reaction, and the gene . Alpha and beta-glucan structures constitute nearly 90% of the cell wall biomass of C. neoformans. The cell wall structure has an important effect on the survival of the organism. In addition, a deformation in the structure of the cell wall causes a decrease in virulence.iCryptococcus model was able to find the targets of the main drug groups  used for up drugs . Ergosteup drugs . FecosteSeventy one key components (58 genes and 13 metabolites) unique to the iCryptococcus model were also searched in DrugBank, and 16 of these were found to be targeted by several drugs for different diseases. Thus, a drug repurposing procedure can be used with authorized or experimental drugs. This strategy has a lot of advantages over developing an entire unique drug specifically for a given need.C. neoformans can be improved upon using laboratory and biochemical studies, omics data, and other sources of information. This model will open the door to a better understanding of the metabolism of this human pathogen, its interactions, and its prominence. In order to narrow the range of viable flux phenotypes, the integration of omics data, including transcriptomics and metabolomics, may be used as additional constraints. One may also incorporate thermodynamic limitations. Such a technique allows for modeling condition-specific metabolism combining data from reaction thermodynamics and transcriptomics/metabolomics, and provides a thermodynamically feasible metabolic model. Despite some errors due to limited coverage of the whole genome of an organism, genome-scale metabolic reconstructions have shown to be quite effective at identifying new drug targets, and if such a model is reconstructed, drug targets can be forecast, that is followed by the discovery of effective medications and the experimental validation of these targets.We were able to confirm the expected virulence factors and growth patterns as well as identify new drug targets in this fungal pathogen using our high-quality model, iCryptococcus. This first-of-its-kind genome-scale metabolic model of


C1-molecules, such as methanol or formate are considered as attractive alternative feedstock for biotechnological processes due to their sustainable production using only CO2, water and renewable energy. Native methylotrophic microorganisms can utilize these feedstock for the production of value-added compounds. Currently, constraints exist regarding the understanding of methylotrophic metabolism and the available genetic engineering tools are limited. For this reason, the development of synthetic methylotrophic cell factories based on the integration of natural or artificial methanol assimilation pathways in biotechnologically relevant microorganisms is receiving special attention. Yeasts like Saccharomyces cerevisiae and Yarrowia lipolytica are capable of producing important products from sugar-based feedstock and the switch to produce these in the future from methanol is important in order to realize a CO2-based economy that is independent from land use. Here, we review historical biotechnological applications, the metabolism and the characteristics of methylotrophic yeasts. Various studies demonstrated the production of a broad set of promising products from fine chemicals to bulk chemicals by applying methylotrophic yeasts. Regarding synthetic methylotrophy, the deep understanding of the methylotrophic metabolism serves as the basis for microbial strain engineering and paves the way towards a CO2-based circular bioeconomy. We highlight design aspects of synthetic methylotrophy and discuss the resulting chances and challenges using non-conventional yeasts as host organisms. We conclude that the road towards synthetic methylotrophic yeasts can only be achieved through a combination of methods . Furthermore, we presume that the installation of metabolic regeneration cycles such as supporting carbon re-entry towards the pentose phosphate pathway from C1-metabolism is a pivotal target for synthetic methylotrophy.Global energy-related emissions, in particular carbon dioxide, are rapidly increasing. Without immediate and strong reductions across all sectors, limiting global warming to 1.5 °C and thus mitigating climate change is beyond reach. In addition to the expansion of renewable energies and the increase in energy efficiency, the so-called Carbon Capture and Utilization technologies represent an innovative approach for closing the carbon cycle and establishing a circular economy. One option is to combine CO The use of conventional energy sources  represents by far the largest source of greenhouse gas emissions from human activities and thus contributes significantly to global warming. The depletion of fossil fuels and historical and on-going geopolitical conflicts are further reasons to commit to renewable energy sources.Encountering climate change and mitigating its impact on the environment, on our economies and on the society is the defining challenge of our time. Due to the rapid growth of the world's population, the demand for energy is increasing dramatically every year and CO2 from an  process or even directly from the air and its subsequent utilization  is one option to reduce industrial emissions and realizing a circular economy, provided that the energy used in capturing and converting the CO2 is zero carbon [2 is used, which can be of various origins  or directly from the atmosphere.In this regard, the capture of COo carbon . In gene2 source influences CCU costs dramatically when comparing “pure”  or “diluted” streams  [2 into C1 compounds like methanol or formic acid with subsequent application in microbial methylotrophic fermentation.CCU is currently the most cost-effective alternative for reducing emissions from the production of bulk chemicals . The preeration) . One inn2/CO2 and a mixture of H2 and CO in advanced microbial gas-fermentation aroused attention [1 substrates  support striking advantages in comparison to gaseous C1 substrates . In detail, storage or handling of liquids is convenient and easy compared to gases. Even more, from the view point of the bioprocess, the feeding of gaseous substrates in the fermentation broth comes along with specific drawbacks, in terms of mass transport and gas water solubility [Methylotrophic microbes are a divergent group of microorganisms, such as bacteria or yeasts, which can harness reduced one-carbon compounds for growth, energy generation and consequently the production of value-added chemicals, materials or food and feed ingredients. In general, methanol or formate are the carbon sources of choice in terms of methylotrophy. In the context of one-carbon substrates, also the valorization of Httention . Howeverlubility . Therefo2 reduction, respectively [2-dependent methanol production is climate-friendly and independent of fossil resource usage and consequently increases the environmental benefit while reducing CO2 emissions. When considering future trends in energy supply and demand, it is important to acknowledge that compelling market factors will continue to strongly influence the price of energy. Fossil fuels are not only the cause of environmental pollution and climate crisis, but also of historical and on-going conflicts and it is anticipated that future prices will increase [2 as an indirect fermentation substrate.Recently, methanol and formate received also attention due to their easy and efficient production via heterogeneous chemical catalysis or electrochemical COectively . A CO2-dincrease . The pro2-based bioeconomy using tailor-made methylotrophic cell factories.Here, we first briefly review uses and characteristics of methylotrophic yeasts in bioprocessing and their metabolism. Towards the exploitation of (methylotrophic) yeasts for the production of biofuels and other bioproducts, an overview of demonstration examples is given. In addition, the growing field of synthetic biology leads to new opportunities like the installation of synthetic methylotrophy in established microbial hosts. Hence, a deep understanding of the methylotrophic metabolism serves as the basis for synthetic methylotrophy, which can be applied to establish a sustainable COEscherichia coli and Corynebacterium glutamicum, which have been to date harnessed for the production of various relevant chemicals. Nonetheless, yeast species like S. cerevisiae or non-conventional yeasts like Y. lipolytica also have potential as hosts for engineering synthetic methylotrophy as they provide distinct advantages over organisms such as E. coli for use in industrial fermentation. It was shown that protein expression is superior in terms of gene expression, protein folding, and post-translational modifications of numerous eukaryotic proteins [Recent approaches have focused on engineering synthetic methylotrophy in bacteria, such as proteins , 9. One proteins . Furtherproteins . Moreoveproteins . This reCandida, Pichia, Ogataea, Komagataella and Kuraishia. A fundamental discovery was the identification of a methanol-utilizing pathway common for all methylotrophic yeasts. While bacteria conduct the initial step either using a pyrroloquinoline quinone (PQQ)- or nicotinamide adenine dinucleotide (NAD+)-dependent dehydrogenase, methylotrophic yeasts harness an unspecific alcohol oxidase (AOX) using molecular oxygen as the electron acceptor [Bacterial methylotrophs belong to diverse phylae, whereas eukaryotic methylotrophs are restricted to a limited number of yeast genera, including acceptor . Since tacceptor , 15, we Pichia pastoris, and corresponding promoters from other methylotrophic yeasts, are used for recombinant protein production [P. pastoris GAP, FLD1, PEX8, and YPT1 promoters are used [Hansenula polymorpha the formate dehydrogenase (FMD) promoter is commonly harnessed [In the 1980s and 1990s, numerous genetic tools to engineer methylotrophic yeasts became available, which were refined ever since. In particular, the exploration of various transformation methods , 17 and oduction . Alternaare used , whereasarnessed .S. cerevisiae derived CYC1-TT are utilized. Prielhofer et al. assessed the efficiency of different transcription promoters and terminators of strongly expressed P. pastoris genes. The promoter and terminator strength potential was characterized by expressing the intracellular reporter eGFP. In total, 10 terminators were tested with the GAP promoter and normalized to termination with ScCYC1-TT. Seven transcription terminator sequences resulted in a slightly higher eGFP expression compared to the widely used ScCYC1-TT [Besides engineering the transcription initiation, less effort was laid on transcription termination. Genetic switches like transcription terminators (TT) are additionally used to adjust gene expression. Usually, the AOX1-TT and the cCYC1-TT .H. polymorpha was explored. The authors monitored GFP expression in batch cultivations on glucose, glycerol, and methanol or mixtures of these. Through terminator variation, a sixfold difference in gene expression was accomplished with the methanol oxidase (MOX) terminator. Using the MOX terminator resulted in around 50% higher gene expression on all carbon sources compared to the second-strongest terminator [Lately, the effect of six promoters and 15 terminators on fine-tuning gene expression in rminator . Since trminator .P. pastoris [H. polymorpha [P. pastoris by genome mining, various efficient episomal expression plasmids could be constructed [H. polymorpha, plasmids containing an autonomously replicating sequence (HARS) derived from subtelomeric regions exist [A remarkable knowledge gain was achieved in 2009 and 2013, respectively, by publication of the genome sequences of pastoris  and H. plymorpha . Recombistructed –30. Alsons exist .P. pastoris. The latter enables genetic engineering via nonhomologous end joining (NHEJ) at an outstanding high efficiency [P. pastoris was developed, which enabled single-locus (~ 100%), two-loci (~ 93%), and three-loci (~ 75%) integration at high efficiencies [H. polymorpha [Another recent groundbreaking event was the development of a CRISPR/Cas9 genome editing system for ficiency . In a suciencies . In addilymorpha . Recentllymorpha .P. pastoris was engineered for the production of various protein-based polymers such as (IV) collagen [P. pastoris for polymer production [The availability of functional genetic tools led to numerous biotechnological applications of methylotrophic yeasts. Several products were obtained using methanol or mixtures of methanol with renewable feedstock. In particular, the production of (I) human serum albumin (HSA) , (II) thcollagen , 40, (V)collagen , (VI) sicollagen , 43 and collagen , 45. Nevoduction .P. pastoris was constructed for malic acid production solely from methanol by redistribution of metabolic fluxes and deletion of genes related to by-product formation. To achieve this, various malic acid accumulation modules were systematically evaluated and optimized. Additionally, glucose-6-phosphate isomerase, a key enzyme in the xylulose monophosphate (XuMP) pathway, was knocked out to release metabolic fluxes trapped in this cycle. The latter approach resulted in the accumulation of 2.79 g L−1 malic acid when using methanol as feedstock together with optimizing the nitrogen source [In a recent study, recombinant n source .H. polymorpha-based processes. For example, the production of various recombinant proteins such as Hepatitis E virus-like particles [H. polymorpha [Likewise, the mentioned advances in genomic-editing tools have led to the exploitation of articles  or ferriarticles  from metlymorpha .The presented examples demonstrate the versatility of biotechnological applications using methylotrophic yeasts and demonstrate the potential to produce such and similar products using pure methanol as the substrate.1 substrates, sustainably produced from CO2, used for the production of bulk chemicals via fermentation can pinpoint the direction towards a cyclic bioeconomy to reduce mankind’s greenhouse gas emission footprint while providing economic benefits. Already in the early 2000s, the application of methylotrophic yeasts in the agricultural sector as biofertilizers and for the treatment of the methanol and formaldehyde containing wastewater was shown [During the last years, there has also been an interest in methylotrophy and its application in white biotechnology as a potential silver bullet against climate change . Variousas shown , 55.P. pastoris for the production of the platform chemical and potential biofuel isopentanol. Here, the authors heterologously expressed the keto-acid degradation pathway to convert 2-ketoisocaproate to isopentanol and reduced the production of the side-product ethanol via using the CRISPR/Cas9 system to delete PDC1. Consequently, 191 mg L−1 of isopentanol were produced, so far the highest reported titer in a non-conventional yeast [Furthermore, the biotechnological production of high-energy fuels by economically feasible processes has emerged as an attractive alternative to the traditional production . One proal yeast .P. pastoris was engineered towards CO2 consumption via the Calvin–Benson–Bassham cycle, the primary natural CO2-fixation pathway of photosynthetic organisms. By introduction of eight heterologous genes P. pastoris was converted into an autotroph capable to use CO2 as its single carbon source. To separate the foreign fixation machinery of CO2 from energy generation, the first steps of the XuMP pathway  were deleted. Following laboratory evolution, the engineered strain achieved a maximum growth rate of 0.018 h−1 [2 in the future.Lately, .018 h−1 . Example1-metabolism of methylotrophic yeasts compared to bacteria differs primarily in the recruited enzymes, energy generation and carbon assimilation pathways. In general, methanol is oxidized to formaldehyde, which then can be diverted to either assimilatory (product biomass) or dissimilatory (product CO2) pathways  [rG = − 127.5 kJ mol−1) compared to PQQ-dependent MDHs (ΔrG = 59.1 kJ mol−1) or NAD+-dependent MDHs (ΔrG = − 0.4 kJ mol−1), calculated under physiological conditions  [The Cc yeasts , 60; andc yeasts , 62. Thec yeasts . For assse (AOX) . In detase (AOX) . The oxise (AOX) . In detase (AOX) . HowevermM MeOH) .Fig. 2TrA detailed understanding of formaldehyde assimilation and dissimilation pathways in methylotrophic microbes is crucial for the establishment of synthetic methylotrophic modules in yeasts.3 compounds are formed in this reaction, namely dihydroxyacetone (DHA) and glyceraldehyde 3-phosphate (G3P) to fuel gluconeogenic reactions [P. pastoris. It was shown by omics-level investigations of the metabolism that this yeast orchestrates all assimilation steps within the peroxisome [1-metabolism with the common central carbon metabolism on the level of glycolysis by formation of fructose 1,6-bisphosphate  from DHAP and G3P. Subsequently, fructose 6-phosphate (F6P) is formed by dephosphorylating F1,6BP, connecting the former C1-assimilation to gluconeogenesis and the pentose phosphate pathway [4) into sedoheptulose 1,7-bisphosphate (C7) using DHAP (C3) by application of an aldolase. Finally, sedoheptulose 1,7-bisphosphate is dephosphorylated to sedoheptulose 7-phosphate, which is in turn converted by a transketolase and G3P into two Xu5P units [In methylotrophic yeasts, formaldehyde is assimilated using the dihydroxyacetone (DHA) pathway, also known as the XuMP pathway . A compaeactions . Subsequeactions . Recentlroxisome . Next, troxisome . The lat pathway . In part5P units .In consequence, the molecule sedoheptulose 1,7-bisphosphate and sedoheptulose 7-phosphate are central intermediates for Xu5P regeneration in comparison to the traditional non-oxidative pentose phosphate branch. The fact that Xu5P is directly regenerated within the peroxisomes makes its import unnecessary and thus improving the efficiency of formaldehyde assimilation. Previously, it was thought that the import of Xu5P into the peroxisome is strictly necessary , 72.The pentose regeneration is strictly required to maintain efficient methanol assimilation  by regenDuring growth on methanol, the peroxisomes of methylotrophic yeasts massively proliferate accompanied by high AOX and DAS expression . Juxtapo2 takes place. The dissimilation is closely related to redox power generation. Particularly, the dissimilation functions as a valve to cope with toxic intracellular formaldehyde concentrations while covering NAD(P)H demand. The most frequent pathway for formaldehyde detoxification is the cytosolic thiol-dependent pathway, which employs reactive thiols as the initial formaldehyde acceptor [2 in a subsequent cytosolic GSH-dependent oxidation cascade. In detail, the S-HMG is released from the peroxisomes into the cytosol and is oxidized to S-formylglutathione (S-FG) via a NAD+-linked and GSH-dependent formaldehyde dehydrogenase (FLD), which is shown to be essential for growth of C. boidinii on methanol [2 accompanied by NADH formation. In turn, the role of FDH is not only the formaldehyde detoxification but also retaining the redox-state and the regulation of the glutathione level in cells. However, it was demonstrated, that FDH is not essential for growth on methanol in C. boidinii. Nonetheless, as the complete genome is not yet sequenced, the existence of other FDHs cannot be excluded that supported growth during the study [In order to keep the intracellular formaldehyde levels low, not only assimilation into biomass but also the dissimilation towards COacceptor . This paacceptor . The proacceptor , which imethanol . Subsequhe study . The lathe study .It is still not completely understood how the efficient and dynamic distribution of formaldehyde between assimilatory and dissimilatory metabolism without toxic accumulation is conducted. However, it can be stated that compartmentalization of peroxisomal methylotrophy is highly beneficial for methylotrophic yeasts. Juxtaposed, for bacteria the formaldehyde distribution is a challenge in regard of balancing metabolic fluxes into the dissimilatory and assimilatory branch, to avoid formaldehyde accumulation.4F synthesis in M. extorquens AM1 was assessed. It was indicated, that during laboratory growth conditions, methylene H4F is originally formed from formaldehyde via the H4MPT and H4F interconversion pathway. The latter suggests that indeed formate and not formaldehyde represents the primary metabolic branch point between assimilation and dissimilation of C1 units in this microbe [4F does not occur in vivo, which was confirmed recently [While in the XuMP pathway formaldehyde represents the central intermediate, some studies concluded that in methylotrophic bacteria formate is the branch point between assimilatory and dissimilatory pathways . Here, t microbe . In turnrecently , 83.2 or the recruitment by central carbon metabolism using the RuMP pathway. Here, formaldehyde is primarily diverted to biomass formation [2 for NADH generation as well as incorporation into the serine cycle. Moreover, it is also described that formaldehyde oxidation to formate in bacteria has a much greater capacity than methanol oxidation to avoid toxic formaldehyde accumulation [Other studies consider both formaldehyde and formate as key intermediates of the bacterial methylotrophic metabolism . Specifiormation . Moreoveormation . An accuormation . This fomulation .When designing synthetic methylotrophic hosts, not only pathway kinetics and accumulation of toxic intermediates but also the stoichiometry of carbon and energy conservation have to be considered. While the serine cycle achieves the highest yield of the metabolic precursor pyruvate, it also has the highest metabolic costs in terms of ATP usage Table . The RuMAll these considerations should be taken into account when designing synthetic methylotrophic engineering projects, but they also demonstrate that there is still a need for research in the field of methylotrophy in general.1 feedstock which supports increased biomass and product yields. In addition, the oxidative combustion of methanol provides more energy (ΔG°′ = − 4276.6 kJ mol−1) compared to glucose oxidation (ΔG°′ = 2870 kJ mol−1) [But why is the installation of methylotrophy in non-methylotrophic microorganisms a current trend? When comparing glucose and methanol, the latter is a promising non-food CJ mol−1) . TherefoJ mol−1) .Although notable progress has been made regarding the availability of genetic tools for native methylotrophic organisms, many of them are still not adequate characterized or their intrinsic capabilities to efficiently produce high value-added chemicals are limited. Besides that, a fundamental knowledge about the physiology, the genome and the metabolism is crucial for successful metabolic engineering of such microbes. Many of these aspects lack a robust basis when considering methylotrophic yeasts for engineering efficient microbial cell factories.1 substrates. Due to the long tradition of investigating such microbes, the knowledge base and available engineering tools are fundamentally broad and established to realize synthetic methylotrophy [S. cerevisiae or the oleaginous yeast Y. lipolytica have the ability or were engineered to produce industrially relevant products such as bulk chemicals  or biofuels and precursors of biofuel molecules  with increased yield and titer  is the shortest, ATP-independent, carbon-conserving and oxygen-insensitive pathway for acetyl-CoA biosynthesis from a C1 feedstock. The latter opens enormous possibilities for producing acetyl-CoA-derived chemicals from renewable one-carbon resources [E. coli was engineered towards growth on one-carbon compounds using the reductive glycine pathway. Integration of the synthetic pathway coupled to laboratory evolution enabled growth on formate and CO2 with a doubling time of ~ 8 h and growth yield of ~ 50 mg cell dry weight (CDW) g formate−1. Furthermore, growth on methanol and CO2 was achieved by expressing a methanol dehydrogenase, resulting in a further increased doubling time (54 ± 5.5 h), due to the slow methanol oxidation rate [Even though incorporation of labeled carbon from n source , 113. Toin cycle . In detaesources . Furtherion rate . This stThe reviewed advances in the bacterial phyla are a promising blueprint for adaptation into conventional yeasts to establish synthetic methylotrophy in eukaryotic hosts, opening the door for additional applications, products and processes. However, it has to be respected that the eukaryotic compartmentalization is also challenging when considering prokaryotic engineering strategies. Furthermore, several factors can affect the expression yield of recombinant enzymes in yeasts. When expressing bacterial genes in yeasts often codon optimization is required to achieve faster translation rates and high accuracy .S. cerevisiae or other industrially relevant yeast, which were shown to produce various products with high titers, exhibit a vast potential as hosts for synthetic methylotrophy [Beside bacterial hosts, well established model organisms like lotrophy .S. cerevisiae was carried out [Recently, the installation of synthetic methylotrophic modules in ried out . In deta+ dependent MDH, or a bacterial RuMP pathway was conducted. Subsequently, methanol toxicity assays and 13C-methanol labeling demonstrated basic functionality of the bacterial RuMP pathway. In addition, this variant seemed to be the most promising synthetic pathway, indicated by the growth profile and the increased 13C-CO2 production levels.The latter strategy resulted in a subtle growth increase on agar plates containing YNB and 1% methanol compared to the empty vector control. In subsequent steps, engineering of a ‘hybrid’ XuMP pathway including a NAD1-assimilation was proven by 13C-ethanol production from 13C-methanol. The latter suggests that S. cerevisiae possesses native capacities for methanol oxidation and assimilation. Such findings offer new opportunities to advance microbial strain development of both, native and synthetic, one-carbon assimilation pathways in this organism [Surprisingly, at higher substrate concentrations striking methanol assimilation was observed in the wild-type strain. This Corganism . In partS. cerevisiae and tested in vivo methanol assimilation. The strain engineering relied on genomic integration of AOX, catalase (CAT), dihydroxyacetone synthase (DAS) and dihydroxyacetone kinase (DAK) derived from P. pastoris. In subsequent growth experiments, the engineered strain consumed 1.04 g L−1 methanol applying shake-flask conditions with synthetic medium. The yeast produced 0.26 g L−1 pyruvate and exhibited a 3.13% improvement of biomass formation in methanol minimal medium compared to the wild-type strain. Consistent with previous findings, the supplementation of yeast extract improved methanol consumption even further to 2.35 g L−1 and cell growth by 11.7%, respectively [Following the modular approach, another study demonstrated implementation of synthetic methylotrophy in ectively . This grectively , 120.S. cerevisiae, the model strains S288C and CEN.PK were investigated in terms of growth and transcriptomic responses to methanol. The strain CEN.PK showed improved growth and the upregulation of genes linked to mitochondrial and peroxisomal metabolism, alcohol and formate oxidation and the mig3 gene. The rational overexpression of the mig3 gene improved furthermore the methanol-dependent growth in CEN.PK, generating a superior strain for future synthetic methylotrophic applications [To further enhance synthetic methylotrophic capabilities of ications .S. cerevisiae has a native capacity for methylotrophy. Native methanol assimilation was confirmed through 13C-tracer analysis and further improved by applying ALE. It was demonstrated that global rearrangements in central carbon metabolism and a truncation of the transcriptional regulator Ygr067cp improved growth on methanol. Nevertheless, also in this study the requirement for yeast extract in liquid methanol medium still remains a challenge [Lately, it was verified that hallenge . Recent hallenge .Beside the oxidative pentose phosphate pathway branch, yielding mainly NADPH for assimilatory reactions, the non-oxidative pentose phosphate pathway branch has also a central role in methanol assimilation during methylotrophy . In partP. pastoris (and presumably other methylotrophic yeasts) the Xu5P regeneration reactions of the XuMP cycle are located in the peroxisomes. This was recently shown by analyzing the systems-level organization of the P. pastoris metabolism [With regard to the latter aspects, it is remarkable that in tabolism .P. pastoris are methanol-inducible and possess a peroxisomal targeting signal (PTS1). It is speculated that the non-oxidative pentose phosphate pathway involving Tal2p functions in a complementary manner in the cytosol [In detail, the pentose phosphate pathway gene–protein pairs of the enzymes transaldolase  and ribose-5-phosphate isomerase (Rki1-2) were upregulated. Furthermore, it was shown that the Fructose-1,6-bisphosphate aldolase 2 (Fba2p) and the Transaldolase 2  from  cytosol . In cont cytosol . All proIn particular, it is interesting that the interconversion of Xu5P and Ru5P is of minor importance due to the same differential Rpe1-2 expression profiles. This fits the assumption that replenishment of Ru5P within the XuMP cycle via these reactions of the non-oxidative pentose phosphate branch is in general not necessary due to the central role of Xu5P . Taken tP. pastoris, replenishing Xu5P by hydrolysis of sedoheptulose 1,7-bisphosphate to sedoheptulose 7-phosphate, which finally fuels the pentose pool [P. pastoris cells in contrast to the lack of this molecule in glucose-grown cells. Taken together, many aspects of the methylotrophic yeast metabolism were elucidated by studying P. pastoris, but further investigations are needed on fluxome level with solely methanol-grown cells to fully analyze and describe the flux routing of the pentose phosphate interconversions to fuel Xu5P replenishment. Such investigations, and in consequence the understanding of the exact flux distribution, can yield a blueprint for fine-tuning synthetic methylotrophic metabolism when installing the XuMP  cycle.However, another remarkable finding is that a second peroxisomal metabolic module relying on non-oxidative pentose phosphate pathway exists in ose pool . The latP. pastoris is associated with the overproduction of vitamins and cofactors [In addition to pentose regeneration, the importance of the non-oxidative pentose phosphate branch is even deeper intertwined with the metabolism. It was shown that methanol utilization in ofactors . These mofactors . Such fiE. coli strain was optimized in the absence of yeast extract in a laboratory evolution approach [frmA), NAD(H) homeostasis/biosynthesis (nadR), phosphopentomutase (deoB), and gluconate metabolism (gntR) [deoB induced a genetic loss of function. This is remarkable since the associated enzyme represents a branch point in the RuMP cycle. It catalyzes the transfer of a phosphate group between the C1 and C5 carbon atoms of ribose or deoxyribose, respectively [In this regard the specific role of yeast extract and associated compounds that stimulate cell growth during synthetic methylotrophy is not fully known to date. Yeast extract is a complex hydrolysate of yeast biomass, which provides carbon, sulfur, trace nutrients, vitamin B complex and other important growth factors . To releapproach . Initialm (gntR) . The ideectively . Even thP. pastoris on systems-level scale.Due to the complex interconnection of the pentose regeneration and the synthesis of vitamins and cofactors (or even other unknown aspects) a combination of metabolic engineering and systems-level analysis can support successful installation of synthetic methylotrophy in yeast. This approach was impressively demonstrated by elucidation of methylotrophic traits in Y. lipolytica offer potential advantages over S. cerevisiae in terms of general substrate scope, metabolic pathway requirements, and physiological stress responses. It has a higher solvent tolerance in general and was shown to easily tolerate 4% methanol as a co-substrate [Non-conventional yeast species like ubstrate .S. cerevisiae also for the ascomycetous yeast Y. lipolytica a native capacity for methylotrophy in form of a non-specific alcohol dehydrogenase was proposed. A recent approach suggests using crude glycerol, which is contaminated with methanol, as a feedstock for engineered Y. lipolytica. In order to develop microbes, which use methanol as a co-substrate, the formaldehyde dehydrogenase (FLD) gene was identified and deleted. This prevents methanol dissimilation to CO2 via formaldehyde and formate. The generated deletion strain oxidized methanol to formaldehyde without the expression of a heterologous methanol dehydrogenase. To complement the Δfld1 strain, either HPS or DHAS were expressed and these designs enabled restoring the formaldehyde tolerance upon FLD deletion [Similar to deletion .Y. lipolytica, engineering enhanced precursor supply, and ALE, improved methanol assimilation up to 1.1 g L−1 after 72 h was achieved. Here, a chimeric pathway, consisting of BsMDH, BmHPS, and BmPHI (RuMP pathway) and PpDAS1 and PpDAK2 (XuMP pathway), facilitated the most efficient methanol assimilation in Y. lipolytica. Furthermore, fine-tuning of methanol assimilation and enhancing formate dehydrogenation and serine pathways were exploited. In addition, upregulation of ribulose monophosphate/xylulose monophosphate (RuMP/XuMP) regeneration genes and subjecting the resulting strain to ALE were key towards improved methanol assimilation [Another approach combined metabolic engineering with ALE. By rationally constructing a chimeric assimilation pathway in milation .1 compounds as a feedstock for synthetic methylotrophic eukaryotes can be reached (Fig. The most recent findings suggest, that at this stage, ALE plays a more important role than rational metabolic engineering in constructing synthetic methylotrophy. By combining both strategies, exciting advances for using Ched Fig. .Fig. 4Th1-utilization, the substrate-of-interest should be coupled with cellular growth or survival. A combination of advanced metabolic engineering, in silico modeling, and automation to maximize evolutionary efficiency should be considered. Finally, the subsequent omics-analysis of the evolved strains can lead to new insights into the mode of action and further genetic targets to improve efficiency of synthetic methylotrophy in eukaryotes even more [In conjunction with next-generation sequencing and omics-technologies, ALE can reveal relationships between genotypes and phenotypes, as well as the molecular mechanisms underlying the desired complex phenotypes. However, in order for ALE to be successful in generating strains with improved Cven more .1 compound, which can be generated in a renewable manner from electrochemical reduction of CO2 and used as a soluble feedstock [S. cerevisiae natively harbors NAD+-dependent FDH as well as all the enzymatic components needed for the reductive glycine pathway and could therefore serve as an ideal host. Via overexpression of these endogenous enzymes, glycine biosynthesis from formate and CO2 was achieved. Interestingly, growth rates of this engineered strain remained more or less constant for formate concentrations between 1 and 500 mM, indicating high tolerance as well as high affinity towards this substrate [Y. lipolytica strain. However, an innovative fermentation process which cofeeds glucose and formate was developed. It was shown that co-feeding formate and glucose, up to a molar ratio of ~ 5:1, linearly increased the biomass yield of Y. lipolytica. Consistent with previous observations in other yeasts, it is hypothesized that consumption of formate under these conditions has a positive net ATP yield and therefore promotes growth [Besides methanol, formate is another attractive Ceedstock . In termeedstock . S. cereubstrate . These fubstrate . Althougs growth . While bTo wrap it up, the on-going research shows that vast steps were made to unlock synthetic methylotrophy in various species including yeasts. However, many challenges and unknown aspects of metabolic traits, or effects of metabolic interventions when installing synthetic methylotrophy, remain unsolved. Especially the role of the pentose phosphate pathway and its interconnection with biosynthesis of complex metabolites or biomass associated molecules is of interest. Finally, the finding that methylotrophy is present in common glycolytic yeast strains has a striking potential to elucidate new engineering strategies for establishing efficient microbial cell factories for methanol-based production.2-derived C1 feedstock received great attention. One reason is that the key feedstock CO2, is virtually unlimited [1 substrates might be one possibility in the solution space to face climate change and obtain sustainable commercial production processes for bulk- and fine-chemicals as well as biofuels. In addition, CCU approaches to fix CO2 into methanol or formate followed by fermentation is promising for future directions into a sustainable and cyclic bioeconomy.Due to the depletion of fossil fuels and concerns about environmental pollution, there is an urgent need to develop sustainable and climate neutral products and chemicals. In this regard, the application of COnlimited . Therefo1 feedstock, depicting them as attractive biotechnological platform strains for industrial strain development. In particular, from the viewpoint of the bioprocess, these strains provide key features to establish sustainable bioprocesses. Nevertheless, challenges remain and limit their broad use on large and commercially feasible scales so far, in terms of applying gaseous or liquid substrates. In particular, remaining challenges are low conversion and growth rates using gaseous substrates, low biomass yields and a lack of reliable genetic engineering tools when considering native methylotrophs. From the viewpoint of synthetic methylotrophy, implementation of functional genetic methylotrophic modules in established industrial host organism were so far introduced but are still limited. To date, the literature indicates that the installation of metabolic regeneration cycles such as supporting carbon re-entry towards the pentose phosphate pathway from C1 fueled central carbon metabolism is a crucial target for synthetic methylotrophy. Moreover, the recent engineering of industrially relevant microbes, such as E. coli or S. cerevisiae, towards utilization of methanol or formate as the sole carbon source succeeded. Despite this, the future for both approaches, native and synthetic methylotrophy, seems promising, as the tools and technologies are now emerging to push the frontier towards efficient C1-utilization in a modern bioeconomy.The nature of methylotrophs empowers these microbes to utilize renewable derived C


Escherichia coli. Compared with CubeProd, DynCubeProd significantly accelerated the calculation of the reaction deletion strategy for each target metabolite production. In addition, under the anaerobic condition of iJO1366, DynCubeProd could obtain the reaction deletion strategies for almost 40% of the target metabolites that the elementary flux vector-based method, which is one of the most effective methods in existence, could not. The developed software is available on https://github.com/Ma-Yier/DynCubeProd.Flux balance analysis (FBA) is a crucial method to analyze large-scale constraint-based metabolic networks and computing design strategies for strain production in metabolic engineering. However, as it is often non-straightforward to obtain such design strategies to produce valuable metabolites, many tools have been proposed based on FBA. Among them, GridProd, which divides the solution space into small squares by focusing on the cell growth rate and the target metabolite production rate to efficiently find the reaction deletion strategies, was extended to CubeProd, which divides the solution space into small cubes. However, as GridProd and CubeProd naively divide the solution space into equal sizes, even places where solutions are unlikely to exist are examined. To address this issue, we introduce dynamic solution space division methods based on CubeProd for faster computing by avoiding searching in places where the solutions do not exist. We applied the proposed method DynCubeProd to iJO1366, which is a genome-scale constraint-based model of  Metabolic engineering is a DNA recombination-based technology proposed in 1991 to improve the designated substance production and the cell properties by manipulating and introducing specific biochemical reactions . In manyConstraint-based modeling is a mathematical method to identify the best solution within a set of possible choices subject to pre-specified constraints . ConstraIn the constraint-based models of metabolic networks, the cell growth reaction and the target metabolite production reactions are of particular interest. The cell growth reaction has been virtually designed to simulate the efficient conversion of uptake resources into cellular energy and chemical components, which support cell growth in response to selection pressure to construct the system in the most plausible physiological state . The tarGrowth coupling is a fundamental design principle in metabolic engineering and computational strain design. The purpose of growth coupling is to make the target metabolite a mandatory by-product of the cell growth reaction. We say that growth coupling is achieved if the target metabolite is produced when cell growth is maximized as shown in In this study, the core and basic problems are to find a growth coupling method for the target metabolite production through reaction deletions. The method should produce as much target metabolite as possible by modifying the metabolic network when GR is maximized as the objective function. The relationship between the original network and the designed network is shown in The most basic and pioneer algorithm for this purpose is OptKnock, which is a bilevel optimization-based method that identifies knockout strategies that result in the maximum PR when GR is maximized. The inner optimization performs the flux (reaction speed) allocation with regard to the optimization of cellular objectives  . The outEscherichia coli under anaerobic conditions, the reaction deletion strategies could not be obtained for many target compounds. In particular, for GridProd and CubeProd, the bottleneck was the computing speed. Therefore, it was expected to extend GridProd or CubeProd to shorten the computation time.Considering that finding the optimal strategy is NP-hard, it is reasonable to only find out the strategy that meets the expected requirements. For example, the elementary flux vector (EFV)-based method determines reaction deletion strategies in which cell growth forces the production of the target metabolite, and the success ratio of this method was very high under both anaerobic and aerobic conditions for several microbial models . GridProIn this study, we developed DynCubeProd that improves the computation speed of CubeProd. DynCubeProd employs a dynamic strategy for the cube sizes to obtain the same results as CubeProd; however its computation speed is much faster. The reaction deletion strategies obtained by DynCubeProd also supplement those of the EFV-based method under certain conditions. Under anaerobic conditions, we obtained the reaction deletion strategies for close to 40% of the target metabolites for which the EFV-based method could not determine strategies.The general formalization of constraint-based modeling is as follows :minimize (or maximize):   subject to:         The general form of the FBA is as follow:maximize   subject to:      Our goal was to find reaction deletion strategies for growth coupling of target metabolite production. Let GivenFindsuch That  maximize  subject to:               When A toy example of the constraint-based model with 11 nodes is shown in Suppose that We developed an algorithm DynCubeProd for calculating reaction deletion strategies that achieve growth coupling of designated target metabolite production in constraint-based models of metabolic networks.Because DynCubeProd is a method obtained by improving CubeProd , in thisIdea: CubeProd considers the three-dimensional solution space whose axes represent GR, PR and sum of absolute values of fluxes (SF). Let TMGR, TMPR, and TMSF be the theoretical maximum values of the above, respectively. Then the whole constraint space is a rectangle formed by , , and The value of p = 1 and doubles DynCubeProd starts with Because the intervals on each of the three axes are equally subdivided into In each of those The number of sub-spaces to be computed is A dynamic strategy is adopted by DynCubeProd to save time. Starting with Example:As shown in p = 2 is applied. The results are summarized in Therefore, DynCubeProd with Lemma 1:For a positive integer, the solution obtained by DynCubeProd foris also a solution for, and the constraint sub-spaces skipped by DynCubeProd do not include a solution.Proof:Define The pseudo code of DynCubeProd is as follow:Algorithm 1:whiledofordo fordo  fordo   end for     end for   end forfordofordofordoifhenelseIfthen                     elseifthenreturn        end if        end if       end if     end for    end for  end for ifhen   end ifend whileDynCubeProd Escherichia coli K-12 MG1655 from the BiGG database with 1805 metabolites and 2583 reactions  While CubeProd divides the solution space based on pre-specified parameters, DynCubeProd gradually divides the solution space into smaller and smaller pieces. 2) CubeProd mechanically explores even the subspace where no solution is expected to exist, while DynCubeProd stops dividing the solution space when no solution exists. 3) While CubeProd searches the entire solution space, DynCubeProd stops as soon as it finds a solution that meets the conditions. The results of computer experiments using iJO1366 confirmed that DynCubeProd reduces the computation time more than 10 times than CubeProd. The reduction in computation time enabled finer solution space partitioning, and reaction deletion strategies could be calculated for about 40% of the target metabolites for which reaction deletion strategies could not be obtained by the EFV-based method. In this study, we developed DynCubeProd, by improving the computation speed of CubeProd, which enabled us to calculate reaction deletion strategies in anaerobic conditions for many target compounds that could not be calculated before.