Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that lack manual refinement and result in inaccurate models or manual curation, which is a time-consuming process that limits the continuous update of reliable GEMs. Here, we present a novel algorithm-aided protocol that overcomes these limitations and facilitates the continuous updating of highly curated GEMs. The algorithm enables the automatic curation and/or expansion of existing GEMs or generates a highly curated metabolic network based on current information retrieved from multiple databases in real time. This tool was applied to the latest reconstruction of human metabolism (Human1), generating a series of the human GEMs that improve and expand the reference model and generating the most extensive and comprehensive general reconstruction of human metabolism to date. The tool presented here goes beyond the current state of the art and paves the way for the automatic reconstruction of a highly curated, up-to-date GEM with high potential in computational biology as well as in multiple fields of biological science where metabolism is relevant. Human metabolism is an integral part of cellular function, and many health conditions may be associated with abnormal metabolic states ,2. SeverDespite the importance of metabolism and advances allowing for the simultaneous measurement of thousands of metabolites , understGEMs encompass the metabolic reactions encoded by an organism’s genome . These sGEM development has been initiated for bacteria  and has The process of constructing GEMs, either from scratch or based on existing reconstructions, is described by Thiele et al. (2013) and Ma et al. (2007) ,27. An iAs the manual reconstruction of genome-scale networks is laborious and time consuming, many automated network reconstruction tools have been developed to accelerate the reconstruction process, including ModelSeed , CarveMehttps://github.com/biosustain/THG, accessed on 25 February 2023) and can be used either to curate and expand existing reference GEMs or to generate metabolic network models from scratch based on the data available in multiple databases in real time.In order to overcome this difficulty, here, we present an algorithm-aided protocol for the automatic construction of highly curated GEMs. The tool is available in a GitHub repository (β1 (β2 (β2 and the “Human Database” (One of the most recent reconstructions of human metabolism (Human1) was used as reference model a to applβ1   and a moIn addition, since the overall method relies on the information stored in many databases at the moment the model is built, this tool can readily be used to update already existing GEMs as well as provide an excellent platform to generate highly customized GEMs without relying on third-party repositories and when or how these are updated. The tool developed in this work represents a significant advancement in large-scale metabolic network model building and curation. It offers a highly specific and curated approach to automatically reconstruct up-to-date GEMs, which has the potential to have a significant impact not only in the computational biology field but also in all fields of biological science where metabolism plays a critical role.Reference model curation, correction and enrichment a at the Database construction from all common online sources of molecular information concerning human metabolism   step 2;Final model generation from merging human database and curated reference model  or generate a new model from scratch by gathering relevant information from multiple online databases. The resulting model is provided in the widely used Systems Biology Markup Language (SBML) format, ensuring compatibility with the COBRA toolbox ,36. Our Firstly, the reference model is curated at different levels by enriching/correcting the annotation, as well as ensuring mass balance. This enriched model enables a broader and more precise integration of high-throughput omics data, while a fully mass-balanced model provides more reliable representation of metabolic flux profiles. Below, this process is explained in more detail.Metabolites: Metabolites are first identified based on a similarity analysis (“identify_metabolites function”) comparing metabolite names and formula in the model with entries in the PubChem database ,38. For ith metabolite in the reference model and each metabolite in the database with values between 0 and 1. The match with the highest score is selected to identify the ith metabolite if it is above a given threshold; otherwise, the metabolite remains unidentified. Using LCS as a method to identify metabolites enables the identification of metabolites by name even while minimizing the error due to typos in metabolite names or if the model uses a non-official name or synonym. In this study, different thresholds are tested to determine the robustness of this method.The LCS analysis provides a general score between the Next, the same process is carried out for the molecular formula. Here, to account for possible discrepancies due to differences in the metabolite charge between the reference model and databases, the molecular formula is changed to its neutral form for the analysis. Thus, the LCS analysis enables metabolite identification by molecular formula even when radicals, functional groups or molecular structure descriptions differ between the reference model and database.Once the metabolites have been identified based on the name and formula, the metabolites’ external database IDs are either corrected in the case of incorrect annotation, enriched with a new ID from non-annotated databases and/or added in cases where the metabolite had no external database ID in the reference model.Reaction: Reactions are curated at different levels. i.JI) metrics. In this task, KEGG, ChEBI, HMDB and LipidMaps identifiers, as well as the names and formula of the metabolites involved in the reaction, are used as a unique fingerprint that identifies the reaction in the reference model with the information retrieved from online databases.First, reactions are identified by their unique combination of substrates and products. This task is performed using Jaccard index (n groups of substrates (S) and products (P) for the first set of reactions and g groups of substrates (S) and products (P) for the second set of reactions, where n and g are the number of reactions in the first and second set of reactions, respectively . The reversibility of each reaction is determined based on information retrieved from online databases. Then, the algorithm compares each ith group of substrates and products (from the reference model) with each jth group of substrates and products (from the database) and seeks to identify the groups with the highest similarities between sets of reactions (Equations (1)–(3)). Equations (4)–(7) define the JI between the groups of substrates and products of the two sets of reactions.Here, the algorithm defines In this study, only perfect matches between reactions and databases are considered. This represents a strong constraint that can, however, be relaxed to accept non-perfect matches. Since metabolite charge may vary between different databases and the reference model, protons are not considered in this analysis;ii.1.First, the reaction is analyzed to detect mass balance inconsistencies. This is calculated by considering the molecular formula and the stoichiometry of the species. If the reaction is not mass balanced, a second method is applied;2.Here, the metabolic reaction is transformed to a matrix form (M) representing the number of each atom that each metabolite in the reaction has, where rows and columns represent metabolites (substrates and products) and atoms, respectively. The coefficients in the M matrix corresponding to products are multiplied by −1, and the system is solved;3.If the previous step provides multiple solutions or no solution, the reduced row echelon form of M matrix is solved. If multiple solutions are found, the one describing the lowest overall stoichiometric coefficients is selected;4.Finally, if no solution exists for integer numbers using the previous approaches, the reaction mass balance is formalized as a linear programming problem. Here, the stoichiometric coefficients are the variables to determine, and the difference of atoms between the right and left side of the metabolic reaction is minimized (objective function) while constraining the results to values higher than 0.Finally, the metabolic reactions are mass balanced using the “mass_balance” function. This function sequentially applies four different analyses of the molecular formulas of substrates and products until the balance is achieved.+, H2O, K+, Na+, Ca2+, Fe and radicals (R).In the case where the above-mentioned methods are not able to mass balance the reaction, the algorithm adds a new metabolite as substrate and/or product, and the methods for mass balancing is run again in the same order. The added metabolites can be HThe algorithm also enables the mass balance of reactions involving glycans. Here, glycan building blocks are treated as atoms in a regular molecular formula, which enables the use of the above-mentioned pipeline to mass balance reactions involving glycans.Genes and proteins: Once the metabolic reactions are correctly identified by applying the JI-based algorithm, the associated genes and proteins IDs are corrected or added if necessary.Gene-protein-reaction associations: Once the metabolic reactions are identified following the above-mentioned approach, the Enzyme Commission (EC) number is corrected if necessary. The EC number is a numerical code consisting of four digits that describes the enzymatic activity of a given protein that catalyzes one or several reactions. Since enzymatic activity is directly linked to specific proteins, the EC number enables identification of the gene/s encoding for the specific protein catalyzing a given reaction. Furthermore, it is possible to define how these genes are associated with the corresponding metabolic reactions. This association can be either formulated using logic “and”/”or” to describe genes encoding for either isoenzymes or complexes, respectively (GPRs), or further enriched with the number of protomers required to generate a catalytically active unit or enzyme (S-GPRs). To incorporate both GPRs and S-GPRs into the GEM reconstruction, an algorithm developed by Marin et al. in 2019  is incorNext, this information is combined and interpreted by using Levenshtein automaton formalism  to generCell location: EC number annotated in the metabolic reactions in the model defines the activity of a catalytic unit (enzyme or complex) catalyzing a biochemical reaction. These catalytic units can be located in different cell compartments. The algorithm can identify if the enzyme catalyzing a given reaction is in one or more cell compartments, or, in the case of a complex composed of several subunits, the location is identified as the common location/s of all the subunits. This information is determined by combining information from the KEGG , Biocyc In case a given reaction can be present in more than one subcellular location , as many copies of the reaction as compartments where the reaction is present are generated, including the corresponding cellular-location-specific substrates and products and the gene-protein-reaction associations.To expand the already improved reference model, a database collecting all the online available molecular information concerning human metabolism  is built d. Data fThe database is constructed to retrieve the same type of information that is stored in the reference model in order to optimize the merging step.The process starts by defining a list of human metabolic pathways. In this work, the human-specific KEGG PATHWAY reference database pathway maps stored as KEGG Markup Language (KGML), along with the human KEGG pathway map, are collected. All the reactions in each pathway are added to the database along with the associated compounds (substrates and products) and EC number/s. Next, IDs from a variety of databases are added to each metabolite  pipeline  step 5; https://github.com/SysBioChalmers/Human-GEM/, accessed on 24 February 2023). The analysis is performed as described by Henriksen et al. in 2022 [Task analysis  step 5:  in 2022  and inteTo evaluate the quality of the models constructed, two methods were used:A JI-based algorithm was developed to identify metabolic reactions. This method relies on the unique combination of substrates and products as a fingerprint to identify metabolic reactions without the need for annotation. This approach is especially useful for identifying model reactions with no annotation.A set of 635 unique reactions with EC numbers, including substrates and products from the “Human Database” metabolic network, was used as a testing dataset to evaluate the capability of the algorithm to identify metabolic reactions. Here, the input of the algorithm was the substrate and products that the algorithm uses to predict the EC number that was compared with the reference. Based on Equations (1)–(7), the JI can have values between 0 and 2, where 2 represents a perfect match. In this analysis, only perfect matches were considered (JI = 2).The algorithm demonstrated a high predictive capability, able to identify up to 84.1% of the metabolic reactions providing the correct EC number a. See SuIncorrectly annotated metabolites were identified by applying a novel method that compares metabolite name and molecular formula with the metabolite names (and synonyms) and formulas retrieved from the PubChem database. If the similarity was above a given threshold (between 0 and 1), the metabolites were considered identical, and the PubChem ID was used to identify the metabolite.Here, a list of 1418 metabolites with the corresponding names, formulas and PubChem IDs extracted from the human database c were usReaction mass balance was performed by the “mass_balance” function. This function uses three different methods that combine linear algebra, matrix calculation and linear programming to ensure that the metabolic reactions are mass balanced. Briefly, the function assesses the mass balance of the reaction, and, if it is imbalanced, the three methods are applied until the reaction is balanced. If the reaction cannot be mass balanced, potential missing species such as protons or water are added, and the different methods are applied again until the reaction is mass balanced. To assess the mass_balance function, a total of 9295 unique reactions from the reference model and the “Human Database” were used as a test dataset. The algorithm was able to mass balance 82.1% of the reactions in the Human1 model. The remaining 17.9% of imbalanced reactions were as follows: i. 77.42% were exchange reactions, which are imbalanced by definition as they only have substrates, ii. 4.75% were a mix of a molecular and glycan formula, iii. 1.32% of the reactions had at least one compound that had no formula and iv. 16.51% of the reactions described coupled reactions that could not be mass balanced unless the reactions were decoupled and mass balanced separately .c. More GPR building was carried out by the “get_gpr” function. Here, we evaluate the capability of this algorithm to extract information from different databases and build the corresponding S-GPRs. This algorithm uses as input the EC number to identify the associated genes and infers the corresponding S-GPRs and GPRs . A listOne of the functionalities of the developed protocol is to enrich and curate existing GEMs. The protocol was applied on the reference model by using the “build_model” function. This function incorporates the above-assessed algorithms to evaluate, curate and enrich existing GEMs based on the information currently available in multiple online databases. The databases and identifiers used in the process are KEGG , HumanCyβ1, β1  were generated, the “merge_metabolic_networks” function was applied (Once the database of the human metabolism (Human Database) and the refined and expanded reference model  between the two metabolic networks.Overlapping metabolites were identified using the developed text similarity function to compare metabolite IDs between the THGThe merged model accounted for 20,677 reactions, 13,840 metabolites, 3392 genes and 15,651 gene-protein-reaction associations, representing an increase of 58.2%, 65.4%, 10.6% and 95.1% in the number of reactions, metabolites, genes and gene-protein-reaction associations, respectively, compared with the reference model .β1 achieved a score of 82, and THGβ2 and THG achieved 81, while the reference model was scored as 81 (out of 100)  . β2 showed a higher percentage of mass- and charge-balanced reactions. This was due to the isoenzyme-based expansion of the model, which duplicated a higher proportion of mass-balanced reactions in the corresponding subcellular locations, increasing the overall percentage of balanced reactions in the model. The TGH model presented a lower percentage of imbalanced reactions, since all the metabolic reactions added from the “Human Database” were mass balanced, thus, diluting the effect of the imbalanced reaction in the reference model. Since the SBML format does not accept glycans formulation using building blocks, this information is stored in the annotations attributed for each metabolite, so it was not considered during the mass balance assessment in MEMOTE.All the models showed a similar performance in the consistency analysis; however, THGIn the metabolite annotation section, the TGH series showed a clear improvement (score of 78) compared with the reference model (score of 73). Regarding the reactions and gene annotations, all the models showed similar scores . It is worth noting that MEMOTE analysis only evaluated the number of entities with annotation, so all the annotations that were corrected in the process were not considered here. Additionally, relevant IDs that were improved by our protocol, such as LipidMaps for metabolites or Ensembl for genes, were not considered in the analysis.β1 ; this was due to the duplicate genes that were eliminated in THGβ1 , showing a more complete coverage of the relation between metabolism and gene regulatory mechanisms. A metabolic task analysis was also carried out on the models to determine if they were able to perform the essential metabolic functions necessary for cell viability [The “metabolic coverage”, defined as the number of reactions divided by the number of genes, provides a score to evaluate the degree of detail in a model. Here, the score in the reference model was higher than in THGin THGβ1 . Howeveriability ,23. The β1 accounted for the same number of entities as the reference model and improved, corrected and enriched the annotation of metabolites, genes and reactions and corrected the unbalanced reactions, ii. THGβ2 added isoenzyme reactions to other compartments, iii. the Human Database gathered information for metabolites, reactions, genes and S-GPRs of all the human-specific pathways using the same quality criteria as in THGβ1 and THGβ2 and iv. THG was the result of combining the THGβ2 and Human Database network models and represents the most extensive and comprehensive reconstruction of human metabolism to date . The number of metabolites with lipid annotation in the reference model was increased by 50% by adding the LipidMap ID to metabolites missing this identifier. This is especially relevant for integrating the increasing generation of lipidomics datasets [The tool incorporates a novel machine-learning method based on text similarity that was developed to identify incorrectly annotated metabolites in a reference model. This novel method identifies incorrectly annotated metabolites based on their name and molecular formula, as well as corrects and improves annotations for already annotated metabolites. The algorithm was able to reduce the number of incorrectly annotated metabolites by half  accounted for 20,677 reactions, 13,840 metabolites, 3394 genes and 15,651 gene protein reactions  compared to a score of 81 for the reference model. The differences were mainly due metabolite and reaction annotation, as well as network consistency and connectivity . All theThus, the presented protocol substantially expanded and improved one of the latest reconstructions of human metabolism (Human1) i. from an annotation point of view, which increases the model’s omics integration capabilities and ii. in terms of the number of moieties  while keeping the mass balance and network consistency. The model generated by the presented protocol enables a broader analysis of human metabolism using high-quality and consistent models with improved annotation that allows for more and better integration of omics data, enhancing the model’s predictive capabilities.The current work presented a computational tool combining algorithms based on machine learning, data mining and metabolic modeling to robustly curate, correct and expand GEMs. In addition, the tool developed here has the potential to construct highly curated GEMs without the need for a reference model. These novel methodologies were applied to generate an improved and expanded version of one of the latest reconstructions of human cell metabolism (Human1): THG. The MEMOTE pipeline was used to evaluate the new models, and all showed a better or similar performance compared to the reference model. This indicates that the developed protocol is not only able to generate larger models but also high-quality, consistent and robust metabolic network models.This protocol gathers all the known information about human metabolism retrieved from many databases into a comprehensive model in COBRA format, which bridges the gap between the knowledge stored in databases and COBRA tools for the analysis of metabolic models.In addition, the computational tool developed in this work has the potential to change the paradigm of how GEMs are built since it establishes the foundations for the automatic generation of high-quality and robust up-to-date GEMs. This enables the fast generation of curated GEMs, gathering the most recently updated molecular information of cell metabolism while providing a standardized pipeline to construct a GEM.


Saccharomyces kudriavzevii is a cold-tolerant species identified as a good alternative for industrial winemaking. Although S. kudriavzevii has never been found in winemaking, its co-occurrence with Saccharomyces cerevisiae in Mediterranean oaks is well documented. This sympatric association is believed to be possible due to the different growth temperatures of the two yeast species. However, the mechanisms behind the cold tolerance of S. kudriavzevii are not well understood. In this work, we propose the use of a dynamic genome-scale model to compare the metabolic routes used by S. kudriavzevii at two temperatures, 25°C and 12°C, to decipher pathways relevant to cold tolerance. The model successfully recovered the dynamics of biomass and external metabolites and allowed us to link the observed phenotype with exact intracellular pathways. The model predicted fluxes that are consistent with previous findings, but it also led to novel results which we further confirmed with intracellular metabolomics and transcriptomic data. The proposed model  provides a comprehensive picture of the mechanisms of cold tolerance that occur within S. kudriavzevii. The proposed strategy offers a systematic approach to explore microbial diversity from extracellular fermentation data at low temperatures.IMPORTANCE Nonconventional yeasts promise to provide new metabolic pathways for producing industrially relevant compounds and tolerating specific stressors such as cold temperatures. The mechanisms behind the cold tolerance of S. kudriavzevii or its sympatric relationship with S. cerevisiae in Mediterranean oaks are not well understood. This study proposes a dynamic genome-scale model to investigate metabolic pathways relevant to cold tolerance. The predictions of the model would indicate the ability of S. kudriavzevii to produce assimilable nitrogen sources from extracellular proteins present in its natural niche. These predictions were further confirmed with metabolomics and transcriptomic data. This finding suggests that not only the different growth temperature preferences but also this proteolytic activity may contribute to the sympatric association with S. cerevisiae. Further exploration of these natural adaptations could lead to novel engineering targets for the biotechnological industry. Saccharomyces genus, play an important role in human activity , and thus eliminates competition. Furthermore, S. cerevisiae is remarkably tolerant to high sugar concentrations and produces aromatic and volatile compounds during fermentation. The last characteristic is particularly important in the fermentation of foods and beverages.Yeast species, particularly those belonging to the activity . Dozens S. cerevisiae in fermentation  and at a cold temperature (12°C). The model, adapted from the one recently proposed by several groups  shunt to produce almost all succinate. This route appears to provide the NADPH required for the mevalonate pathway, initiated with acetate uptake in the stationary phase. Particularly interesting is the fact that S. kudriavzevii showed a high protein turnover, further enhanced at lower temperatures. This may indicate the ability of S. kudriavzevii to produce assimilable nitrogen sources from extracellular proteins present in its natural niche. This finding suggests that not only the different preferences for growth temperature but also this proteolytic activity may contribute to the sympatric association with S. cerevisiae.The model successfully recovered the dynamics of biomass and external metabolites and allowed us to link the observed phenotype to exact intracellular pathways at two different temperatures. The predicted fluxes are consistent with previous findings but also led to novel results which we further confirmed with intracellular metabolomics and transcriptomic data. In particular, S. cerevisiae S288c and S. kudriavzevii CR85 revealed very small differences in terms of metabolic genes . From the list of metabolic genes that differ between S. kudriavzevii and S. cerevisiae, a large proportion corresponds to redundant genes, that is, genes that catalyze reactions that can be catalyzed by alternative genes. Another fraction is not associated with the reactions present in the S. cerevisiae reconstruction. As a result, only seven reactions needed to be removed from the wine reconstruction of S. cerevisiae (Table S1).A comparative analysis of the genome sequences of Yarrowia lipolytica ). About half of this amount can be attributed to polymerization costs, and the rest can be attributed to processes required to maintain cellular structural integrity against constant leakage of molecules over membranes  and degradation of cellular polymers.F) that regulates the fraction of GAM that is not associated with the polymerization costs. Using parameter estimation, we found that GAM corresponded to approximately 137 and 99.8 mmol of ATP/(gDW·h) at 12 and 25°C, respectively (Table S2). These values were substantially higher than the total GAM of S. cerevisiae, implying a lower biomass yield for a given hexose uptake in S. kudriavzevii.Our model includes a parameter  data from intracellular metabolites from previous experiments carried out in a chemostat at a dilution rate (D) of 0.04 h−1 . Furthermore, in light of our modeling results, a large group of dipeptides  were found in S. kudriavzevii at concentrations up to 40 times higher than in S. cerevisiae (Table S4). Together, these results suggest that during growth, S. kudriavzevii has a high protein turnover rate compared to that of S. cerevisiae or S. uvarum, which is explained by the predicted expenditure of ATP (for protein synthesis) and the significant increase in the concentration of dipeptides (from protein degradation).To understand this apparent higher energy expenditure in 0.04 h−1 . The aut2 fold increases in the number of transcripts associated with protein synthesis during the exponential phase and growth in limited nitrogen, respectively. We also explored the significance of this result with the paired t, two-sample t, and Kolmogorov-Smirnov tests. All tests rejected the null hypothesis (both times), indicating that ribosomal gene transcription increases in response to cold temperature. The former results support the role of increased protein synthesis in response to cold temperatures. This situation was reversed during the stationary phase, during which greater transcription was observed at 25°C.To further explore whether an increase in protein synthesis could be associated with cold temperature, we compared the number of transcripts associated with translation at 12 and 25°C. The cold condition resulted in 1.44- and 0.76-logS. kudriavzevii, we calculated the dynamic flux ratios obtained at 12 and 25°C. We observed that there was an overlap in the production of succinate and higher alcohols, as well as in the consumption of acetate during the stationary phase. Thus, we focused our study on this particular phase. The flux ratios for all reactions during this phase are given in Table S5. Fig. 1i,DS at 25°C and i,DS at 12°C was >0.1 and their log2 ratio was >1).To investigate the role of temperature in the metabolism of 2 fold higher than at 12°C) through the reaction r_0558, corresponding to the activation of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, the rate-controlling enzyme of the mevalonate pathway. This would indicate that lipids are the main sink for exogenous acetate consumed by S. kudriavzevii at 25°C. Another important difference appears in the activation of glutamate decarboxylase , indicating a higher activity of the GABA shunt at 25°C. Significant differences also appear in the production of isoamyl alcohol and isobutyl alcohol. Cells use aldehyde dehydrogenase, although they activate different reactions at 12°C and at 25°C. Reactions r_0181 and r_0184, with NADPH uptake, are selected at 12°C, while reactions r_0179 and r_0180, which consume NADH, are the preferred routes at 25°C. At 12°C, cells use r_0171 to contribute to produce 2-phenylethanol (PEA), while at 25°C, this route is not selected. These results exemplify the effect of temperature on secondary metabolism.Saccharomyces yeasts mainly at the beginning of fermentation  by acetyl-CoA synthetase consumes ATP and produces AMP as a by-product. Interestingly, the flux through the cytoplasmic adenylate kinase (r_0148), a major AMP:ATP phosphotransferase in y charge , was caly charge . ConsequS. cerevisiae, the reductive branch is the main route of succinate production during wine fermentation . This reaction is catalyzed by ADH3-encoded mitochondrial alcohol dehydrogenase in Saccharomyces .S. kudriavzevii, succinate production during the stationary phase would be derived exclusively from the GABA shunt pathway at both temperatures , but with higher flux at 25°C  . In addition, the highest predicted flux toward malate at 25°C through the cytoplasmic reductive branch of the tricarboxylic acid (TCA)  indirectly sustains the exchange of malate and 2-oxoglutarate through mitochondrial membranes  at the level of the 2-oxodicarboxylate carrier (As shown in d r_0123 ). This h1; 12°C)  showing  carrier , r_2132,Although the most relevant differences were detected in terms of NADPH production in the GABA shunt, the pentose phosphate pathway (PPP) also played a crucial role in providing NADPH equivalents . In thisde novo synthesis of amino acids and important fermentative aromas . An interesting result related to this is that a higher flux of cytosolic pyruvate was incorporated into mitochondria at 12°C .As shown in de novo synthesis of isoamyl alcohol and the aldehyde 3-methylbutanal. In addition to a higher de novo synthesis of isoamyl alcohol at the low temperature, the model predicted a higher flux from IPM toward 3-methylbutanal at 12°C  and isoamyl alcohol .Once inside the mitochondria, a higher fraction of pyruvate was converted to 3-methyl-2-oxobutanoate (3-m-2-o) at 12°C , while the opposite was observed for the flux from pyruvate to acetyl-CoA  . Acetyl-The production of isobutanol from its aldehyde precursor  was carried out primarily in the cytoplasm  at the cold temperature, while the opposite pattern was observed at the standard temperature . Although the effect was smaller in relative terms, the same was also true for isoamyl alcohol, with a higher cytoplasmic contribution at the cold temperature  and a higher mitochondrial contribution at the standard temperature .Regarding the aromatic higher alcohol 2-phenylethanol, its production was similar at both temperatures . However, at 12°C, it was expected that most of the phenylacetaldehyde (PEA) would convert to higher alcohol using NADH (0.08 mmol/mmol of H), while at 25°C, NADPH was the main redox cofactor. Together, these results suggest that greater availability of cytoplasmic NADPH at 25°C is obtained at the cost of increased mitochondrial activity and redox shuttles.S. kudriavzevii at different temperatures. The implementation of the dynamic FBA (dFBA) approach with Yeast8 metabolic reconstruction was modified, taking into account the species genome, the production of erythritol, and the qualitatively different dynamics of l-arginine consumption . The model was fitted to time series data for S. kudriavzevii fermentations at cold (12°C) and standard (25°C) temperatures. At the extracellular level, our dynamic genome-scale metabolic model was able to reproduce the data satisfactorily; many of the observations we had made for the cold-tolerant strain S. uvarum . A striking example of this was the consumption of acetate during the stationary phase.In this work, we have adapted the model presented by several groups  to expla. uvarum  appear tS. cerevisiae [60 mmol of ATP/(gDW·h)] was needed to explain the low glucose-to-biomass yield of S. kudriavzevii, with a substantially higher estimated GAM at 12°C [137 mmol of ATP/(gDW·h)] than at 25°C [99.8 mmol of ATP/(gDW·h)]. The former is consistent with the results of Tronchoni et al.  and degradation of cellular polymers at the cost of cellular ATP , 28. To i et al. , in whichemostat . These dhemostat  could behemostat , this raoak bark , it woulS. uvarum  and appeared to correlate with succinate production. Because S. kudriavzevii is a natural isolate, well adapted to cold temperatures, the observed differences in acetate consumption at 12 and 25°C could be related to the variations in the diurnal temperature to which it is subjected in its natural environment.Similar to what we have previously reported for the other cold-tolerant species , 17, 23,S. uvarum accumulates lipids or polyesters, downstream of mevalonate and GABA , transcriptomic data gathered confirmed that at cold temperatures, S. kudriavzevii upregulates genes associated with protein synthesis. These results are in line with the data previously published by Tronchoni et al. , a natural isolate of oak bark from Agudo, Ciudad Real, Spain. Fermentation assays and samplings were carried out in Merseguera white grapes using the same methodology as that used by several groups previously  of 0.100. The dynamics of fermentation was monitored using different probes and detectors to control and measure temperature, pH, dissolved oxygen , and the percentage of carbon dioxide in the gas phase leaving the fermentor . Fermentations were stopped when a constant sugar content was reached, as measured by high-performance liquid chromatography (HPLC).All fermentations were carried out in three independent biological replicates in 500-mL controlled bioreactors  filled with 470 mL of natural grape must. Each bioreactor was inoculated using an overnight starter culture cultivated in Erlenmeyer flasks containing 25 mL of YPD medium  at 25°C and 12°C and at 120 rpm in an agitated incubator . The yeast culture was inoculated at an optical density at 600 nm . The analysis conditions were as follows: 1.5 mM H2SO4 as the eluent, 0.6-mL/min flux, and an oven temperature of 50°C. For sucrose determination, the same HPLC was equipped with a Hi-Plex Pb column , the eluent was Milli-Q water at 0.6 mL/min, and the oven temperature was 50°C. The retention times of the eluted peaks were compared with those of commercial analytical standards . Metabolite concentrations were quantified using calibration graphs  of the standards previously obtained from a linear curve fit of the peak areas using 10 standard mixtures. The determination of yeast assimilable nitrogen in the form of amino acids and ammonia was carried out following the same protocol as described previously (18 (Thermo Scientific) LC column with acetonitrile and acetate buffer as mobile phases.Residual sugars (glucose and fructose), organic acids  and the main fermentative by-products  were quantified using HPLC  coupled with the refraction index and UV-visible (UV-Vis) detectors (210 nm). Metabolites were separated through a HyperREZ XP carbohydrate Heviously  using a The quantification of volatile compounds was performed following the protocol defined previously . ExtractPRJNA473087. The complete set of transcriptomis data is reported in Table S4. Sequence reads were trimmed and quality filtered using Sickle , at the end of the growth phase , and early stationary phase . The broth volume was rapidly collected from the reactor, transferred to a polypropylene tube, and centrifuged  to pellet cells. The supernatant was discarded, and the tube was flash-frozen in liquid nitrogen and stored at −80°C until total RNA extraction. Total RNA was extracted using the High Pure RNA isolation kit  following the manufacturer’s protocol. These samples were sequenced using the Illumina HiSeq 2000, paired-end reads 75 bases long, and sequences were deposited under BioProject number g Sickle    . As bothae S288C  v.8.3.2 ae S288C .The modeling framework is based on knowledge of reaction stoichiometry and mass/charge balances. The model describes the dynamics of internal metabolites as a function of metabolic fluxes. To ensure that a unique solution was obtained, we used a dynamic flux balance analysis (dFBA)  approachLT, ET, ST, and DT. Each phase is characterized by a cellular objective and a set of constraints.The model accounts for the dynamic nature of batch fermentation and divides the process into five phases: the lag phase, exponential growth, growth under nitrogen limitation, stationary phase, and decay. Their duration is imposed by the estimated parameters For the lag phase, the objective is to maximize ATPase expenditure .For the exponential growth phase (up to the time the nitrogen sources were nearly exhausted), the objective is to maximize the growth rate.For growth under nitrogen limitation, the objective is to maximize the growth rate with a higher accumulation of carbohydrates.For the stationary and decay phases, the objective is to maximize the production of both ATP and protein.The cellular objectives for the phases are as follows.Constraints on the external fluxes were modeled using kinetic models in ordinary differential equations. The transport of hexoses was described using Michaelis-Menten-type kinetics subject to noncompetitive inhibition of ethanol . The propJ is the function to maximize in each phase p, S is the stoichiometric matrix, v is the vector of the fluxes in millimoles per gram (DW) per hour, vGlx and Fv are the glucose and fructose fluxes, v2O is the flux of O2 present only at the beginning of fermentation, v4NH is the flux of ammonium, viAA is the exchange rate of the amino acid i (covering 19 amino acids), and vArg corresponds to the flux of arginine. Note that here, we modified the equation to describe the dynamics of l-arginine to account for the inability of S. kudriavzevii to consume this compound completely. Approximately 100 and 80 mg were not consumed at 12 and 25°C, respectively, which corresponds to a relevant fraction of YAN . jPv are the constraints associated with the j=1,…,22 fermentation products considered. Glx, F, NH4, O2, AAi , Arg, and jP  correspond to the concentrations of glucose, fructose, ammonium, O2, amino acids, and products , all expressed in millimoles per liter. Interested readers can find a detailed description of the constraints for the different phases in reference The general formulation of the FBA problem results is as follows:F is a species- or strain-dependent parameter estimated from the data, and the rest are the polymerization costs of the different biomass precursors  was also updated to account for the polymerization costs of the different macromolecules :https://sites.google.com/site/amigo2toolbox/examples).All scripts necessary to reproduce the results are available , stn is the number of sampling times for each measured variable, jy is the corresponding model prediction, and i,jq is a nonnegative definite symmetric weighting matrix. In our case, we used the mean value of the observed readout to normalize the problem.We formulated the parameter estimation problem as a nonlinear optimization problem to compute the unknown model parameters—growth-related constants and kinetic parameters—that minimize the squared sum of the distance between the model and the data:i,GS, i,SS, and i,DS correspond to a given flux ratio i during the growth, stationary, and decay phases, respectively, iv(t) (in millimoles per gram [DW] per hour) corresponds to a given flux , vGlx(t) (in millimoles per gram [DW] per hour) is the flux of glucose, vFr(t) (in millimoles per gram [DW] per hour) is the fructose flux, and DW is the predicted dry weight biomass (in grams). The results correspond to the millimoles of compound produced per millimole of hexose consumed times 100 (represented as millimoles per millimole of H). Flux ratios indicate the overall impact of each reaction on the net oxidation or reduction of electron carriers during the given phase of fermentation.We computed the integral of each flux multiplied by the biomass (millimoles per hour) over time and normalized its value with the accumulated flux of consumed hexoses (glucose and fructose):ode113 implementation of the method provided in MATLAB. At each time step used by ode113, the FBA problem was solved using the COBRA Toolbox   method atrapz in MATLAB).Integrals required for the dynamic flux analysis were evaluated using the standard trapezoidal method (function https://sites.google.com/site/amigo2toolbox/examples.All scripts necessary to reproduce the results are available in


Drosophila metabolic model, iDrosophila1, provided a powerful platform for studying metabolism with systems medicine perspective.The reconstruction of a comprehensive and literature-curated  Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene–reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resulting Drosophila model , iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson’s disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.High conservation of the disease-associated genes between flies and humans facilitates the common use of  Drosophila melanogaster is a well-known model organism with highly tractable genetics for gaining insight into human metabolism. It contains the counterparts of various essential human systems such as the central nervous system, gastrointestinal system, kidney , adipose tissue (fly fat body), and liver (fly oenocytes)  . In addiosophila . Highly an genes .D. melanogaster metabolism. This model called Fruitfly1  (Drosophila model (BMID000000141998) was developed in 2013 without any manual curations  models facilitate the mathematical representation of biological knowledge about cellular metabolism through the inclusion of all known chemical reactions, metabolites, and genes for an organism. The genes are linked to the associated reactions via conditional statements in Boolean logic, which are known as gene–protein-reaction (GPR) rules . Metabol9 genes)  was deri9 genes)  based on9 genes) . Anotherurations . It consurations . In addiosophila . The fliosophila . More reosophila . The corD. melanogaster using a curated version of the Human Metabolic Reaction 2 (HMR2) model . Overall, we believe that iDrosophila1 can enable extensive characterization of fly metabolism and the study of the molecular basis of complex human diseases.Comprehensive metabolic network models can be developed considering genetic similarities between organisms. This semiautomatic reconstruction approach begins with the creation of a draft model based on a template model. The template model should have a high genetic similarity with the organism of interest. The genes of this reference network are replaced by their orthologous counterparts in the target organism. In this way, existing information about gene associations is transferred to reconstruct new GMN models . To date2) model , as the 2) model . This HMD. melanogaster using available metabolic information. In the reconstruction process, extensive model curations associated with metabolic redundancy, gene/compound name standardization, and missing/incomplete components were performed. These reaction-centric, metabolite-centric, and gene-centric curation steps were commonly applied for both the draft metabolic network and the KEGG-MetaCyc metabolic network as explained in the Materials and Methods section. Thus, we aimed to avoid any inconsistencies and redundancies in the reconstructed networks by revising each metabolic component . Additional curations were also applied, if necessary. Overall, the model reconstruction process is summarized in Here, we developed a comprehensive GMN model for D. melanogaster model based on a recent template human model using an orthology-based approach. The template model, an improved version of the HMR2, incorporates several improvements based on the experimental results and the available sources  for accurate phenotype predictions and appropriate contextualization . Only one Drosophila ortholog was assigned to ∼90% of the human genes, whereas the remaining genes were matched with multiple orthologs. The presence of the same Drosophila orthologs for some human genes led to redundancy in the GPR rules in the draft network. These genes were revised to remove redundancy in the model. Gene-associated human model reactions with no Drosophila orthologs were not included in the draft model. We also kept the nonenzymatic reactions in the model. This approach enabled the generation of an HMR2-based draft Drosophila model through the transfer of information about gene associations. Importantly, the HMR2-based template human model contains eight intracellular compartments  along with their Gene Ontology IDs, and this information was also transferred to the draft Drosophila model. The reconstructed draft model includes 6,873 reactions, 4,856 metabolites, and 1,321 genes.As the starting point, we reconstructed a draft lization . The HMRlization , derivedlization , Recon1 lization , and extlization . The modlization . We furtlization . Thus, oDrosophila melanogaster.Orthology-based gene mapping between human and Table S1. D. melanogaster. We encountered two major issues with this network including (1) a need for the modification of metabolite names and (2) the lack of compartmentalization. As highlighted before, the main contribution of the KEGG–MetaCyc network is to expand gene coverage and available metabolic information in the HMR2-based draft model. Therefore, metabolite names in the KEGG–MetaCyc network should be compatible with the draft model for proper merging. To do so, we updated compound names in the KEGG–MetaCyc network by replacing them with the counterparts in the HMR2-based draft model .Table S2. Drosophila model, we compartmentalized the KEGG–MetaCyc-specific metabolic network because it did not include compartment information. To do so, we first identified the compartments of Drosophila gene products in the KEGG–MetaCyc network via available resources . The subcellular localization information was mapped to each gene product in the network. Missing compartments were predicted by the CELLO2GO web server for the cutoff of 10−5 and so we generated a gene–compartment pair list. Using this approach, multiple compartments were assigned to many gene products (Table S3). This compartment dictionary allowed the assignment of subcellular localization(s) to each reaction based on the GPR associations. In this step, if a gene product has multiple compartments, we assumed that the related reaction should be repeated in the model for each compartment. Thus, new reactions and metabolites were added to the network according to the subcellular locations of the corresponding gene product, if necessary. In addition, if the genes catalyzing a reaction have different compartments, multiple compartments were assigned to this reaction and the corresponding genes were distributed based on their localizations. A general framework of the network compartmentalization process is illustrated in Considering the Gene Ontology IDs in the draft Compartmentalized Drosophila genes in the KEGG–MetaCyc network.Table S3. Drosophila model was merged with the compartmentalized KEGG-MetaCyc network. Thus, the number of genes in the draft model was elevated to 2,398. The ability of the merged model to produce biomass was assessed. We determined that many cofactors and vitamins could not be produced. A gap-filling algorithm was therefore applied to ensure the production of all biomass precursors. It allowed the addition of 29 new reactions (Table S4) to the merged model. The newly added reactions enabled filling in the gaps related to cofactor and vitamin metabolism. In the next step, the gap-filled model was curated in terms of cholesterol and apocytochrome-C metabolism, GPR rules, metabolic leaks, and the commonly applied curation steps described in the Materials and Methods section.The HMR2-based draft Reactions added to the merged Drosophila metabolic network model using gap-filling algorithm (fillGaps) in RAVEN toolbox.Table S4. Drosophila membrane and the precursor of steroid hormones. Steroid production is crucial in the regulation of developmental processes, which are required to generate an adult organism  with Drosophila orthologs, whereas there are many non-orthologous human genes  Drosophil pathway . In mamm pathway . The far pathway  Fig 3C)Drosophilmponents . Zhang ed DHCR7)  Drosophila model that correspond to the inactive cholesterol branch. For these reactions, we first identified the corresponding Drosophila genes that were incorrectly defined as human orthologs. Accordingly, two Drosophila genes were found to be assigned as the orthologs of NSDHL gene-encoding sterol-4-alpha-carboxylate 3-dehydrogenase (decarboxylating) and DHCR7 gene-encoding 7-dehydrocholesterol reductase in human. Of these Drosophila genes, CG7724 (FBgn0036698), associated with steroid biosynthesis, was matched with NSDHL (ENSG00000147383) at the maximum DIOPT score of 3. Because Drosophila was reported to lack NSDHL ortholog -encoding lamin B receptor, was matched with DHCR7 (ENSG00000172893) at the maximum DIOPT score of 5. Because DHCR7 was also defined as a non-ortholog, we excluded the related reactions (HMR_1519 and HMR_1565) from the model . Given that a relatively flexible compartmentation procedure was applied in the KEGG–MetaCyc network, compartment information was excluded for the comparisons between KEGG–MetaCyc-derived reactions and template human model-derived reactions in the draft Drosophila model. In this way, we eliminated incorrectly compartmentalized KEGG–MetaCyc reactions from the Drosophila model by carefully examining each reaction match. This also enabled the curation of the leaking ATP problem in the model by removing the incorrectly compartmentalized uridine 5′-monophosphate phosphohydrolase, succinate dehydrogenase (ubiquinone), and NADH-dehydrogenase reactions. We searched for the presence of other leaking energy metabolites in the draft model and identified four additional leaking metabolites including NADH, NADPH, FADH2, and H+. Double-reaction deletion knockouts were performed using the flux balance analysis (FBA) to identify the reactions associated with metabolic leaks. These reactions were manually examined and they were classified as incorrectly compartmentalized reactions or redundant reactions. The reason for this metabolic redundancy was the undetected duplicated reactions derived from the KEGG–MetaCyc model and the template human model because of small differences in metabolite names. Therefore, we removed one of each duplicated reaction to prevent metabolite leakage. The final model, called iDrosophila1, includes 8,230 reactions , 6,990 metabolites, and 2,388 genes. The compatibility of the model with recommended standards was assessed using the MEMOTE test suite. Norsigian et al reported the MEMOTE scores of 108 reconstruction models in the BiGG Models database, most of which are prokaryotic models . One significant modification is the removal of duplicated reactions. The duplicated reactions were identified via reaction comparisons by ignoring Hc models . The iDrc models .Drosophila fat body cells (The iDrosophila1 reactions with missing subsystem (pathway) information were investigated through the KEGG database. The pathway information of any reactions which could be accessed was included in the model. The pathways derived from the KEGG–MetaCyc network were denoted as “KM pathways.” Analysis of the pathway information in the model pointed to the prevalence of major biological processes  and cholesterol ester metabolism . Especiady cells . These ddy cells . Xenobiody cells . In addidy cells  and the dy cells . Cytosoldy cells .Drosophila feeds on fermenting fruits containing high amounts of ethanol and organic acids. On the other hand, a simple diet of sucrose, lyophilized yeast, and weak organic acids was reported as sufficient to raise this organism in laboratory conditions. To mimic this dietary restriction, Piper and colleagues established the holidic diet (HD) with essential and nonessential amino acids, vitamins, cholesterol, sucrose, several metal ions, and lipid precursors. This diet composition was reported to be sufficient for the development of fruit flies (−1 via the FlySilico model by employing a set of constraints for the uptake rates (D. melanogaster with the same uptake constraints (Table S5). For compatibility with the FlySilico model, we fixed the flux of the non-growth-associated maintenance reaction to the estimated value (8.55 mmol ATP g−1 h−1)  . FBA simh−1) .Table S5. Drosophila, we introduced several modifications in iDrosophila1 . In this condition, the optimal growth rate was predicted as ∼ 0.774 h−1. We subsequently analyzed the growth profile across varying cholesterol levels from zero to ∼1/10 of the sucrose uptake rate. As expected, we did not observe biomass formation for the cholesterol deficiency in the diet. Increasing level of cholesterol positively induced biomass formation until reaching the optimal growth rate . We performed GO and pathway enrichment analyses to provide an insight into these genes in terms of corresponding biological processes (Table S6B) and pathways (Table S6C). Unsurprisingly, these genes were found to be predominantly associated with biosynthetic processes of crucial cellular substances such as nucleotides, aminoacyl-tRNAs, amino acids, cofactors, and lipids. Enriched KEGG pathways were also identified to be consistent with these biological processes.Essential genes (iDrosophila1)—list of the essential iDrosophila1 genes identified under expanded holidic diet (cutoff: 30% of the maximum WT growth rate)—enriched biological processes and KEGG pathways of the essential genes in the iDrosophila1 model (FDR at 0.05 level).Table S6. Drosophila  and NTP polymerization (MAR07161) reactions, whereas only PNPase-encoding Drosophila gene (FBgn0039846/CG11337) is responsible for the catalysis of NDP polymerization (HMR_7162) in the iDrosophila1 model. To support this, Pajak and colleagues reported that the “CG11337” gene is the fly ortholog of human PNPase (MAR07162) before in silico gene deletions by replacing the RNAPs with the PNPase. In addition, we modified the reversibility of this reaction to represent its reversible characteristics. The reaction reversibility was also curated in the iDrosophila1 model.We further evaluated the performance of the iDrosophila1 model through the comparison of the essentiality predictions with those obtained by other curated generic fly models. In this process, we introduced two main modifications to the Fruitfly1 model before gene essentiality analysis. The first modification is related to RNA metabolism. Two diverse cytosolic RNA synthesis reactions are present in both iDrosophila1 (HMR_7161 and HMR_7162) and Fruitfly1 (MAR07161 and MAR07162) models. They use nucleoside triphosphates (NTPs) and nucleoside diphosphates (NDPs) as substrates, respectively. Because both molecules contain phosphoanhydride bonds, they are significant energy sources to drive biochemical reactions. NTPs  also serve as substrates for nucleic acid biosynthesis by DNA-directed RNA polymerase (RNAP) enzymes . Each RNosophila . PNPasesorolysis . On the n PNPase . The PNPn PNPase  and the n PNPase . Taken tMAR08639), where nuclear pore complex genes were assigned. Nuclear pore complexes comprise several copies of ∼30 different proteins known as nucleoporins that mediate macromolecular trafficking  between nucleus and cytoplasm and the free diffusion of water, sugars, and ions  showing dynamic chromatin binding behavior in the nucleoplasm, they only mediate transcriptional regulations  and nonessential gene sets in the expanded HD medium for the Fruitfly1 and FlySilico models. Of the predicted 128 essential genes, iDrosophila1-specific results (n = 64) were predominantly found to be related to the metabolism of nucleotides, cofactors, lipids, amino acids, and aminoacyl-tRNAs. For Fruitfly1-specific essential genes (n = 33), metabolic processes associated with vitamins, cofactors, amino acids, aminoacyl-tRNAs, and nucleotide sugars were shown to be significantly enriched. On the other hand, the FlySilico model predicted only six essential genes involved in carbohydrate and amino acid metabolisms. The essential gene sets predicted by each generic fly model were also compared with the gene essentiality dataset retrieved from the Online GEne Essentiality (OGEE) database . We quanEssential genes (Old Models)—lists of the essential FruitFly1 and FlySilico genes identified under expanded holidic diet (cutoff: 30% of the maximum WT growth rate).Table S7. The sensitivity of iDrosophila1 predictions was found to be considerably higher than the other fly models. Unsurprisingly, we revealed that the FlySilico model had the worst sensitivity score because of the lack of protein complex information. In contrast to the low sensitivity values, the specificity results demonstrated that the fly models could correctly classify up to ∼96–97% of the nonessential genes. Based on the precision values, the iDrosophila1 (TP: 90 and FP: 32) and Fruitfly1 (TP: 64 and FP: 25) models were shown to predict the higher ratio of correct essential genes within the predicted essential gene sets in comparison with FlySilico (TP: 3 and FP: 3). Note that six essential genes predicted by iDrosophila1 could not be included in any confusion matrix categories because of the lack of evidence, whereas 10 unclassified essential genes were identified by Fruitfly1 simulation. Because binary metrics only consider two categories, we used additional metrics based on at least three confusion matrix categories. The iDrosophila1 model was demonstrated to exhibit superior performance than the other models for these adopted metrics . We found an extremely low F1 score for FlySilico predictions. It was shown to be the highest for iDrosophila1 predictions, meaning the best compromise between sensitivity and precision. One drawback of the F1 score is the tendency of this value to converge into smaller values for low sensitivity or precision. Another issue is the misclassification potential in evaluating predictions under an imbalanced prevalence . We idenDrosophila ,” which provides disease–gene association information. Neurogenerative disorders, muscular diseases, and mitochondrial diseases were found to be overrepresented with the genes we identified, further confirming the accuracy of transcriptome-guided iDrosophila1 predictions , whole body, microbial communities, and host–microbial group interactions. Considering the dramatic impact of the microbiota on human health and the low microbial diversity of Drosophila, our metabolic network model can also be useful to elucidate metabolic interactions between Drosophila and gut microbiota. Overall, iDrosophila1 may provide avenues to advance fly metabolic modeling and better understand more complicated human metabolism.In conclusion, there is still a gap in the use of this organism for comprehensive condition-specific metabolic modeling although D. melanogaster model using a flux-consistent version of the HMR2 model  as a template  (Drosophila genes (FlyBase gene IDs), their human orthologs, and associated disease phenotypes. The DIOPT score refers to the number of tools supporting a given orthology prediction  with maximum DIOPT scores were considered; (2) when a human gene has over nine Drosophila orthologs, this gene association was ignored  . The lisediction . It was  ignored  because D. melanogaster using the RAVEN toolbox (Drosophila from the FlyBase database (version number: r6.37) against the latest pre-trained hidden Markov model (euk100_kegg94). The same parameters  were used in the reconstruction of both KEGG-based networks. Incomplete information and undefined stoichiometries were excluded from the network. In addition to the combined KEGG-based metabolic network, we generated a MetaCyc-based metabolic network using another automatic reconstruction function in the RAVEN toolbox. In the reconstruction process, default parameters (bit-score ≥ 100 and positives ≥ 45%) were chosen, and unbalanced/undetermined reactions were excluded from the network.To enhance gene coverage of the draft model, we created another metabolic network for  toolbox  by consi toolbox  and Meta toolbox  databaseThe KEGG- and MetaCyc-based metabolic networks were subsequently merged (hereafter referred to as the KEGG–MetaCyc network). The genes that were not shared with the HMR2-based draft model were identified. These KEGG–MetaCyc-specific genes were functionally characterized by identifying significantly enriched KEGG pathways using the g:Profiler web server  with a fDrosophila model was merged with the compartmentalized KEGG–MetaCyc-specific model by adding each KEGG–MetaCyc reaction to the draft model using the addReaction function in the COBRA toolbox . This growth condition was termed an “expanded holidic diet (HD)” because of the addition of several vitamin derivatives to the HD medium (see Table S5) (The HMR2-based draft  toolbox . The bioable S5) . SubsequDrosophila (Drosophila protein complexes reported by Guruharsha and colleagues (2011)   considered in the reconstruction steps of the Drosophila model. The details of these steps are given in the next section.After the gap-filling step, cholesterol metabolism was revised to represent cholesterol auxotrophy in osophila . In addis (2011) . Additios (2011) . We also and H+) . To thisDrosophila model was called iDrosophila1, and it is available in our GitHub repository (https://github.com/SysBioGTU/iDrosophila). The metabolites, reactions, and genes in the model were annotated using a variety of sources . The moReaction-centric, metabolite-centric, and gene-centric curations were commonly applied to the metabolic networks in the reconstruction steps as detailed below. This covers the curation of metabolic redundancy, name standardization, and the removal of missing/incomplete model components.+, H2O, and Pi) and compartment information. If there is any compartmental inconsistency between the duplicated reactions with the same gene content, the compartment information in the template human model was accepted to be correct. This step was performed in a tightly coupled manner with the curation of GPR rules for all models given in the current study. If the duplicated reactions are derived from different metabolic networks, the GPR rules of these reactions were manually examined and combined before the removal of one reaction by avoiding any gene loss. In addition, trivial or incomplete reactions with missing stoichiometric coefficients were removed from the models.Reaction-centric curation included the detection and removal of duplicated reactions, which arose because of the merging of two metabolic models; KEGG–MetaCyc-specific model and template human model . The dupDrosophila model was reconstructed using FlyBase gene IDs. On the other hand, KEGG- and MetaCyc-based networks include FlyBase protein and annotation IDs. Therefore, all genes were denoted based on the current versions of FlyBase gene IDs for compatibility between the models. However, this led to the emergence of redundant genes in a given GPR association. Similarly, the conversion of Ensembl gene IDs into the FlyBase gene IDs in the HMR2-based reconstruction caused the emergence of redundant genes in a given GPR association because of the presence of multiple Drosophila orthologs for some human genes. The redundant genes/gene associations in each GPR rule were manually curated.Gene-centric curations allowed both standardization of gene names and the elimination of gene-based redundancy in models. HMR2-based draft Metabolite-centric curations are based on the elimination of trivial and synonymous metabolites. The trivial metabolites, which were not associated with any reactions (no assigned stoichiometric coefficients), were investigated in all models, and removed. In addition, synonymous metabolites were identified to eliminate metabolic redundancy.Drosophila model, and KEGG–MetaCyc-specific network.First, synonymous metabolites within the model were interrogated based on compound names (metabolite name and BioCyc name) and IDs . Here, we allowed the flexible intake of 47 dietary compounds by limiting the maximum sucrose uptake rate to ∼ 2.212 mmol g−1h−1 . The maxsingleGeneDeletion function in the COBRA toolbox  through the identification of enriched biological processes (Table S6B) and KEGG pathways (Table S6C) using the g:Profiler web server for FDR at the 0.05 level. In the next step, we assessed the predictive capability of the model based on the experimental gene essentiality dataset, which was stored in the OGEE database  from ArrayExpress (accession number: E-MTAB-1406) (pink1B9 (pink1) and park25 (parkin) mutations at different age groups: young flies (3-d-old) and middle-aged flies (21-d-old [parkin] and 30-d-old [pink1]). For each age group, there are three and six biological replicates for pink1 and parkin mutant flies, respectively. The Limma package . To ensure the consistency of the genes with the iDrosophila1 model, all gene annotation IDs were converted to the current versions of FlyBase gene IDs using the FlyBase ID Validator tool  were selected for further analysis. Based on the fold change cutoff values, we applied another filtering process for the genes with multiple probe measurements using the following criteria: (1) if one or more probe(s) of a gene have fold changes ≥1.5, the maximum of fold change values of its probes was considered by assuming the up-regulation of this gene; (2) when the fold change values of any probe(s) ≤ 0.67 (∼1/1.5) for a gene, the minimum fold change value was assigned by assuming the down-regulation of this gene (3) if all probes of a gene had moderate (0.67 < fold change < 1.5) or ambiguous fold change values , the average fold change was assigned to this gene.We further evaluated the capacity of the iDrosophila1 model in phenotypic predictions through the investigation of age- and PD-dependent differential pathways in AB-1406) . It incl package  for R vetor tool . In this−1h−1). Then, the filtered fold change values were mapped to the reactions in the iDrosophila1 model using the COBRA function mapExpressionToReactions. GPR associations were taken into consideration in the mapping process. The minimum fold change value was assigned to the reactions whose corresponding genes are linked with “AND” operator whereas the maximum fold change was used for the genes that are linked with “OR” operator. Differential reaction expression levels were then used to elucidate PD-induced altered metabolism via a recent approach, ΔFBA with default parameters (i| > 0.1% of the largest flux bound). GPR rules were used to determine the corresponding genes involved in the regulated reactions. These genes were characterized in terms of significantly enriched KEGG pathways (FDR < 0.05) and diseases  using the g:Profiler (In the metabolic network analysis step, the maximum uptake rates of all exchange metabolites were set to unrestricted flux (i.e., 1,000 mmol grameters . Note thProfiler  and FlyEProfiler  web servhttps://github.com/SysBioGTU/iDrosophila.The final iDrosophila1 model has been deposited in MAT, SBML, and JSON formats in GitHub:


Finally, we speculate on the future of cofactor engineering applications in cell factories.Cofactors are crucial chemicals that maintain cellular redox balance and drive the cell to do synthetic and catabolic reactions. They are involved in practically all enzymatic activities that occur in live cells. It has been a hot research topic in recent years to manage their concentrations and forms in microbial cells by using appropriate techniques to obtain more high-quality target products. In this review, we first summarize the physiological functions of common cofactors, and give a brief overview of common cofactors acetyl coenzyme A, NAD(P)H/NAD(P) Traditional petrochemical manufacturing has several disadvantages, the most evident of which is irreversible environmental contamination, which is a significant breach of the original aim of harmonious cohabitation between humans and nature. Therefore, bioconversion, which allows the production of chemical products from renewable carbon sources, has become an ideal technology . With th+, and ATP/ADP, of NAD(P)H/NAD(P)+ and ATP/ADP-dependent metabolic reactions involving up to 1,610 enzymes such as transferases, oxidoreductases, lyases, ligases, isomerase, and hydrolases. Acetyl-CoA, NAD(P)H/NAD(P)+, and ATP/ADP play direct roles in the metabolism and transport of sugars, proteins, and lipids H/NAD(P)to NADPH . NAD(P)Hentation . Under art chain . Under art chain .Aspergillus niger (A. niger) strain DS03043 is capable of producing glucoamylase, and 62% of the total ATP produced in its cells is used for biomass formation, while the remaining 27% may be used for non-growth maintenance . The lipid content was then raised to 25.7% by overexpression of acetyl-CoA carboxylase (ACC) and fatty acid synthase (FAS) in Y. lipolytica.Acetyl-CoA and acetic acid can be converted to each other, acetic acid is catalyzed by acetyl-CoA synthase (ACS) to generate acetyl-CoA, while acetyl-CoA is catalyzed by phosphoryl acetyltransferase and acetic acid kinase to produce acetic acid. By overexpressing ACS in aceBAK operon, which encodes these three enzymes. The iclR gene was knocked out, which activated the glyoxylate branch and enhanced the acetyl-CoA synthesis metabolic flux, resulting in a 1.9-fold increase in the yield of 3-hydroxypropionate with acetyl-CoA as a precursor (Corynebacterium glutamicum (C. glutamicum) by alleviating the competition between glyoxylate and TCA cycle and knocking out the gene encoding isocitrate dehydrogenase, with a maximum yield of 5.6 g/L of 5-aminolevulinic acid after optimization of fermentation conditions.Isocitrate lyase, isocitrate dehydrogenase, and malate synthase are key enzymes in the glyoxylate cycle. IclR can inhibit the expression of the recursor . Ge et aAcetyl-CoA, as an important hub of biosynthesis, can be regulated in a variety of ways such as overexpression of related pathway genes, modification of related pathway enzymes, the introduction of exogenous metabolic pathways, and redistribution of metabolic fluxes in order to obtain more target products.pK) and the pyruvate decarboxylase encoding gene (pdC) in a glucose-suppressed mutant and fermented it for 168 h in a 10 L bioreactor, generating 55.38 g/L of liamocin and 25.10 g/L of cell dry weight from 117.27 g/L of glucose. Saccharomyces cerevisiae (S. cerevisiae) by overexpressing a dysregulated pantothenic acid kinase gene and engineering the coenzyme A biosynthetic pathway. E. coli BL21 by modifying the enzyme of the coenzyme factor pyridoxal 5′-phosphate regeneration pathway.pckA (encoding phosphoenolpyruvate carboxykinase) and redistributing flow between naringenin biosynthesis and cell growth at the isocitrate node, By fine-tuning the expression of + and its application in biosynthesis. Some of the NAD(P)H/NAD(P)+ regulatory strategies and their applications are listed in Many carbon metabolic pathways in microorganisms can synthesize NAD(P)H. Among them, the pentose phosphate pathway is the main metabolic pathway for the synthesis of NADPH, and glycolysis is the main metabolic pathway for the synthesis of NADH. Under aerobic conditions, pyruvate is changed to acetyl-CoA and NADH catalyzed by the pyruvate dehydrogenase complex, and the tricarboxylic acid cycle can generate NADH and NADPH. Non-metabolic coupled expression NADH oxidoreductase may lead to disruption of cellular metabolism, which in turn affects microbial growth and the synthesis of metabolites. Reconstructing the cofactor production pathway can effectively avoid the resulting disruption of cellular metabolism. This section focuses on the metabolic regulatory strategy of NAD(P)H/NAD(P)Cgl2680 is an important regulator of NADPH levels and L-lysine biosynthesis in C. glutamicum. By blocking the glycolytic pathway and overexpressing the PPP in lysine-producing strains and knocking out the gene Cgl2680, Cgl2680-deficient strains of C. glutamicum showed increased production of L-lysine and L-leucine as well as increased H2O2 tolerance  and maeA gene  in the PPP, which increased intracellular NADPH by 45% and 66%, respectively, while glucoamylase yields increased by 65% and 30%, respectively. The regeneration of NADPH was promoted by the non-oxidative step of PPP, which facilitated the synthesis of phenolic acid, and the NADPH regeneration strategy increased phenolic acid by 45% compared to the initial strain (The pentose phosphate pathway(PPP) is the main metabolic pathway for NADPH synthesis, and the key enzymes are glucose-6-phosphate dehydrogenase (G6PDH) and 6-phosphogluconate dehydrogenase (6PGDH), so overexpression of G6PDH and 6PGDH can effectively increase the flux of PPP and intracellular NADPH levels. The transcriptional regulator olerance . Insuffil strain . Wernig + can achieve a balance of cofactors while ensuring normal cell growth compared to endogenous enzymes, the introduction of metabolic synthetic pathways of other strains has become an effective means to address the low yield of target products H/NAD(P)products .Streptococcus mutans and overexpressed it in Lactobacillus casei (L. casei) LC2W under the control of the constitutive promoter P23, and the NADH oxidase activity of the recombinant strain LC-nox was 0.854 U/mL, which was nearly 20-fold higher compared to the wild type. The overexpression of NADH oxidase resulted in a 22% reduction in lactate production in the recombinant strain. It was proposed that more carbon sources could be saved and used for extracellular polysaccharide (ESP) biosynthesis with a yield of 219.4 mg/L, a 46% increase over the wild-type strain. Actinobacillus succinogenes. Transient adaptation to glucose reduced the lag phase of the strain and increased its tolerance to high glucose concentrations. The resulting strain produced 7.8 ± 0.0 g/L succinate in shake flasks without pH control with a glucose yield of 0.105 g/g. E. coli and could achieve a xylitol productivity of 21.2 g/L/h by in vitro biotransformation. The enzyme activity reached 1,533 U/L after optimization of induction conditions. It was possible to completely convert 200 g/L xylose to xylitol, and the highest xylitol productivity of 6.37 g/L/h was obtained under optimal conversion conditions. + regenerating enzyme (NADH oxidase) from Streptococcus mutans ATCC 25175 together with xylitol-4-dehydrogenase from Pantoea ananatis into E. coli, co-expression resulted in an increase in L-xylulose concentration and productivity from xylitol as well as intracellular NAD+ concentration. In a 1 L bioconversion system, the final concentration and productivity of L-xylulose from 50 g/L xylitol reached 48.45 g/L and 2.42 g/L·h, respectively.yahK(encoding the NADPH-depleting enzyme), and fecE(encoding the ATP-depleting enzyme) increased the yield of 4-hydroxyphenylacetic acid from 6.32 to 7.76 g/L. E. coli strain could efficiently produce (R)-1,3-butanediol at a yield of 0.6 mol/mol glucose, equivalent to 60% of the theoretical yield. pntAB encoding pyridine nucleotide transhydrogenase in E. coli was selected from five NADPH regeneration genes to supplement redox cofactor NADPH for the conversion of p-coumaric acid to caffeic acid during ferulic acid biosynthesis. E. coli GAPDH by amino acid substitutions at positions 34, 188 and 189, several mutant enzymes with dual NAD+/NADP+ cofactor specificity were obtained and their kinetic parameters were determined. Overexpression of genes encoding mutant GAPDHs with dual cofactor specificity produce d in E. coli cells producing L-lysine, L-threonine and L-proline resulted in a significant increase in the accumulation of the corresponding amino acids in the culture medium. E. coli to obtain a self-sufficient NADPH cofactor regeneration system. In addition, by modifying the ribosome binding site, improved variants with better substrate tolerance and higher catalytic activity for ε-caprolactone were created. The best mutant strain produced 0.80 mol/mol of ε-caprolactone using 60 mM cyclohexanol as a substrate. In four sequential batches, the designed whole-cell biocatalyst produced 126 mM ε-caprolactone with a high molar yield of 0.78 mol/mol.In addition, screening and modification of enzymes for application in the synthesis of compounds has become a means of enhancing the target product. Bacillus subtilis is low, and The introduction of new reactions/pathways, or the reconstruction of metabolic pathways followed by the modified key enzymes has also been shown by many researchers to be an effective means of enhancing the target product. serA and thrA to increase the precursor supply of serine, resulting in the accumulation of 1.380 g/L tryptophan. Finally, to maintain cofactor balance, the genes sthA and pntAB encoding transhydrogenases were overexpressed. With sufficient amounts of precursors and balanced cofactors, the engineered strain produced 1.710 g/L tryptophan after 48 h of shake flask fermentation, which was 2.76-fold higher than the yield of the parental strain.The balance of cofactor concentrations is an important condition for achieving enzyme catalytic efficiency; simply increasing cofactor concentrations alone does not necessarily improve metabolism, but may instead cause an imbalance in the intracellular environment. When the concentration of NADH is too high, the oxygen consumption of cellular respiration increases, substrate consumption is accelerated, and economic efficiency is reduced. The anabolic metabolism of N-acetylglucosamine leads to an excess of NADH, which is utilized for the synthesis of substances such as the NADH-dependent by-product 2,3-butanediol. Bacillus megaterium through co-expression of the precursor (phbRBC) and NADPH cofactor regeneration (zwf) genes resulted in a 2.67-fold increase in PHB accumulation.Insufficient supply of cofactors can limit the production of metabolites, and enhancing the cofactor synthesis pathway can address this limitation well. Currently, there is a better understanding of both material and energy metabolism, and ATP regulation strategies combined with synthetic biology are increasingly being applied in microbial cells to meet the need to enhance target products, etc. . This seE. coli strains with impaired fermentation ability during respiration. Nitrate functions as an external terminal electron acceptor, and enforced ATP hydrolysis causes the recombinant to consume significantly more glucose while maintaining substrate level conversion of the target product. Pyruvate is generated from glucose in yields of 1.77–1.78 mol/mol under anaerobic nitrate respiration and enforced ATP hydrolysis, and the recombinant is almost depleted of substrate with no or minimal by-product formation. por1 encoding mitochondrial pore protein in Candida utilis, which increased intracellular NADH and ATP concentrations. The co-production of S-adenosylmethionine (SAM) and glutathione in the recombinant strain increased by 34.9% and 25.1%, respectively. Monascus via by altering intracellular ROS and ATP levels  gene and the native flavohemoglobin (FHb) gene. During a 10-liter fermentation, the engineered strains were 42.1%, 15.6%, and 40.0% higher than the parental strain in terms of pullulan, yield and productivity, respectively.Furthermore, ATP regulation in microorganisms can be performed by introducing related substrates or by overexpressing/knocking out genes encoding related substrates. S. cerevisiae and overexpression of acetaldehyde dehydrogenase increased glycerol production H/NAD(P)+). The key enzyme in this system is a calculated glucose dehydrogenase, which can be used to support a variety of redox chemistry in vitro. This work proves the effective use of noncanonical cofactors in biocatalysis and metabolic pathway design. Recent findings reignite the interest in engineering enzymes using noncanonical cofactors, which exhibit excellent industrial properties in vitro and can achieve specific electron transport in vivo , the National Natural, Science Foundation of China (31922070 and U2106228), the Jiangsu Synergetic Innovation Center for Advanced Bio-Manufacture (XTC2205), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (22KJB550008), and Scientific Research Start-up Fund for Introduced Talents of Anhui Polytechnic University (2022YOO068).The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Saccharomyces yeasts in wine and beer brewing has gained more attention the recent years. This is both due to the desire to obtain a wider variety of flavours in the product and to reduce the final alcohol content. Given the metabolic differences between the yeast species, we wanted to account for some of the differences by using in silico models. Use of alternative non-Saccharomyces species using an automated processes. These were: Metschnikowia pulcherrima, Lachancea thermotolerans, Hanseniaspora osmophila, Torulaspora delbrueckii and Kluyveromyces lactis. Using the models, we predicted that M. pulcherrima, when compared to the other species, conducts more respiration and thus produces less fermentation products, a finding which agrees with experimental data. Complex I of the electron transport chain was to be present in M. pulcherrima, but absent in the others. The predicted importance of Complex I was diminished when we incorporated constraints on the amount of enzymatic protein, as this shifts the metabolism towards fermentation. We created and studied genome-scale metabolic models of five different non-Metschnikowia pulcherrima and the other yeasts considered. Yet, more annotations and experimental data have the potential to improve model quality in order to increase fidelity and confidence in these results. Further experiments should be conducted to confirm the in vivo effect of Complex I in M. pulcherrima and its respiratory metabolism. Our results suggest that Complex I in the electron transport chain is a key differentiator between The online version contains supplementary material available at 10.1186/s12859-023-05506-7. Saccharomyces yeasts for beer and wine brewing [Saccharomyces fermentation strains: First, wine producers aim to decrease the resultant alcohol content in their products. Second, some brewers seek to enhance the complexity of aroma compounds, thereby emulating the rich flavor profile of spontaneously fermented beverages. In this study, our primary focus will be on the pursuit of reduced alcohol content.In recent years, there has been increased interest in using alternative non- brewing –7. In geClimate change has resulted in warmer and sunnier summers in wine producing regions, leading to higher sugar content in ripe grapes. When the must of high sugar grapes are fermented, this leads to higher alcohol content in the product. As a consequence, the alcohol content of wine has risen by approximately 1% alcohol by volume each decade since the 1980s in some wine producing regions . AdditioSaccharomyces cerevisiae has proven to be challenging. First of all, the most common strains of S. cerevisiae are Crabtree positive, meaning that glucose predominantly gets fermented to ethanol even when oxygen is available [Saccharomyces yeasts produce less acetate and are Crabtree negative [In order to create wines with reduced alcohol content without losing the rich flavours, aeration during the fermentation process has been proposed as a solution . Unfortuvailable –15. Furtvailable . On the negative , 17, 18.Saccharomyces yeasts alone is usually not a good option due to production of bad-tasting compounds and their low tolerance to ethanol. The latter short-coming leads to stuck fermentations and poor wine quality [S. cerevisiae and non-Saccharomyces strains have revealed that exposure of S. cerevisiae to oxygen causes unacceptable amounts of acetate production, even after aeration is turned off [Saccharomyces yeast with air sparging for two to three days before S. cerevisiae is added in order to complete the fermentation. This has proven to be a more viable approach for production of wine with reduced alcohol content, as the production of acetic acid remains low [Using non- quality , 16. Expains low –22.S. cerevisiae, well curated models exist [In order to explain and predict such metabolic properties of yeast, genome-scale metabolic models (GEMs) have become a widely used tool –29. For ls exist , 31 whicls exist , 26, 32.S. cerevisiae, GEMs are not readily available for most non-Saccharomyces yeasts. Nevertheless, the development of tools that enable automatic generation of these models from genomic data presents a possible solution to address this limitation. A promising approach, known as ”carving” as described by Machado and coworkers [In contrast to oworkers , involveoworkers , 35 by aSaccharomyces yeast strains attempted in wine brewing [Hanseniaspora osmophila, Kluveromyces lactis, Metschnikowia pulcherrima, Torulaspora delbrueckii, and Lachancea thermotolerans. The models were automatically constructed from genome data and carved form a curated universal yeast model [in silico.In this article, we constructed GEMs for five of the most commonly applied non- brewing , 7, 36. st model , 38. UsiSaccharomyces yeast strains were created by using CarveFungi [GEMs of the five non-rveFungi , 38. FrorveFungi    simulatil iND750 . Even thae exist , we chosae exist , 38. We M. pulcherrima are quite different from the other yeasts, since less fermentation (production of ethanol and acetate) was undertaken compared to the other yeasts, and the growth dynamics resulted in higher biomass yield. We also initiated the simulations with M. pulcherrima to produce less fermentation products and attain higher biomass, were evident.We observed that simulation results for mentclass2pt{minimIn addition to the glucose concentration, we simulated the effect of the sugar composition of the must. This was conducted by replacing the mentclass2pt{minimK. lactis and L. thermotolerans there already existed published GEMS; the models iOD907 [K. lactis , we found that the modelled growth rates at infinite protein pool were generally somewhat lower than the experimentally determined ones, but still within the experimental variability  performed similarly, we chose to pick K. lactis as a representative for these four strains. This is because, to our knowledge, K. lactis is the one strain for which the most research has been conducted. To address the metabolic phenotypes, we conducted a comparative analysis between the metabolic models of M. pulcherrima and K. lactis, excluding enzymatic constraints from consideration. This approach aimed to identify possible causal factors stemming from variations in metabolic network connectivity.Having discovered that the GEMs predicted distinct metabolic phenotypes for M. pulcherrima model were not present in the K. lactis model. To probe the functional consequence of these reactions, we sequentially (and cumulatively) removed each of these M. pulcherrima reactions and optimized for biomass production. The nutrient environment used for this assessment was identical to the one used for initiating the dFBA simulations. Some of the reactions were essential and therefore reinserted into the model before continuing. Of the considered reactions which were not essential, we observed two reactions which altered the growth rate: Complex I in the respiratory electron transport chain (NADH dehydrogenase), and mitochondrial Methylenetetrahydrofolate dehydrogenase (NAD+). Note that, removal of Complex I alone was sufficient to produce the same growth as in K. lactis. Conversely, adding the Complex I reaction to the K. lactis model yielded the same growth rate as of M. pulcherrima. Not surprisingly, removal of mitochondrial Methylenetetrahydrofolate dehydrogenase (NAD+) from M. pulcherrima did not have any effect on its own, nor did addition of the same reaction into the model of K. lactis: Since Complex I pumps protons and the mitochondrial Methylenetetrahydrofolate dehydrogenase does not, Complex I is beneficial when growth is APT dependent. Therefore, we chose to focus on Complex I when further comparing the models.Directly comparing their reaction content, we found that 191 of the reactions in the M. pulcherrima was annotated with Complex I, whereas none of the other yeast strains contain this reaction. Saccharomyces, Kluveromyces, Torulaspora, Lachancea, and many other yeasts do not have the canonical Complex I of the electron transport chain, but instead feature an alternative Type II NADH dehydrogenases which does not pump protons across the mitochondrial membrane [M. pulcherrima is able to create a larger proton-motive force (PMF) per mole of NADH being oxidized, which in turn increases the efficiency in generation of ATP per mole of glucose.According to the reconstructions, membrane –48. AccoM. pulcherrima, we conducted a BLAST [M. pulcherrima against proteins annotated with Complex I functionality (EC number 7.1.1.2). This search was conducted as a blastp search through UniProt’s web portal [Neurospora crassa with evidence on transcript level: NADH-ubiquinone oxidoreductase 19.3 kDa subunit, mitochondrial; NADH-ubiquinone oxidoreductase 23 kDa subunit, mitochondrial; and NADH-ubiquinone oxidoreductase 24 kDa subunit, mitochondrial. The similarity to these sequences were : In order to obtain further evidence that Complex I was indeed present in  a BLAST  search wb portal  using stb portal , all forM. pulcherrima would become more similar to that of the other yeast strains. We therefore artificially changed the stoichiometry of the reaction to two or zero protons being pumped for each molecule of NADH consumed. We conducted a new set of dFBA simulations without enzyme constraints and with the same starting conditions as earlier, using K. lactis (lacking Complex I) as a baseline  compared to M. pulcherrima even in the presence of inactive Complex I. This observation suggested the involvement of supplementary reactions. To explore this hypothesis, we deactivated Complex I in M. pulcherrima and performed cumulative knockouts of the reactions exclusive to this organism. We then assessed the combined production of acetate and ethanol following each knockout event to elucidate the potential role of these unique reactions in the observed differences. By this strategy, we found two reactions accounting for the difference in fermentation products. These two reactions were mitochondrial L-glutamate:NADP+ oxidoreductase  [When simulating the effect of Complex I stoichiometry of (decFBA) , 51 , while the BRENDA data was downloaded from the BRENDA [https://www.brenda-enzymes.org/download.php, July 2023). Before providing the models to AutoPACMEN, the models are augmented by Uniprot identifiers using Uniprot’s API. AutoPACMEN retrieved sMOMENT models with enzyme constraints were generated by feeding the GEMs into AutoPACMEN  version e BRENDA  website SABIO-RK , 87 and SABIO-RK  using itSaccharomyces strains and the iND750 S. cerevisiae model [in silico with dynamic FBA (dFBA) [The models of the five non-ae model  were simA (dFBA) , 40. TheA (dFBA)  was usedGlucose was the sole carbon source available with a maximum uptake flux determined by Michaelis-Menten kinetics: mentclass2pt{minimThe biomass concentration was as initiated to We monitored several entities, including biomass, glucose, acetate, ethanol, and glycerol. The latter three components were incorporated to track the accumulation of fermentation products generated by the yeast. However, under the tested conditions, none of the models produced glycerol; hence, we omitted its depiction for clarity. Furthermore, we sought to highlight the combined production of ethanol and acetate. Consequently, the graphical representations included a panel displaying the sum of ethanol and acetate concentrations in the medium, and another panel illustrating the acetate concentration separately. To preclude physiologically implausible metabolic exports, we blocked the export reactions for lactate (both stereoisomers), dihydroxyacetone, D-ribulose, arabinitol, and ribose. Additionally, we inhibited a wasteful mitochondrial membrane proton leakage reaction, which would have led to physiologically unreasonable outcomes if not removed from the model.Maximize production of biomassMinimize consumption of glucoseMaximize excretion of ethanolMaximize excretion of acetateMaximize excretion of glycerolThe models were simulated using the static optimization approach and SciPy’s solve_ivp function [To obtain consistent and physiologically plausible results, we applied lexicographic objectives when performing FBA on the models prioritized in the following order: function . For thefunction  was useddFBA was performed both for the original models generated with CarveFungi and the sMOMENT models processed through AutoPACMEN. Upon running decFBA with the sMOMENT models, the level of the enzyme pool was adjusted. Three different levels of the enzymatic protein pool ) were chosen.Additional file 1. contains five figures showing supplementary computer simulations and comparisons.