Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species. 13C-MFA can quantify microbial physiology at the level of metabolic reaction rates. To speed up microbial characterizations and fluxomic studies, we hypothesize that genetic and environmental factors generate specific fluxome patterns that can be recognized by machine learning. Aided by constraint programming and quadratic optimization, our platform based on machine learning (ML) can predict meaningful metabolic information about bacterial species in their environments. Further, it can offer constraints to improve the accuracy of flux balance analysis. This study infers that the bacterial metabolic network has a certain degree of rigidity in allocating carbon fluxes, and different microbial species may share common regulatory strategies for balancing carbon and energy metabolisms. As a proof of concept, we demonstrate that the use of data-driven artificial intelligence (AI) approaches, e.g., ML, may assist mechanistic based models to elucidate the topology of microbial fluxomes.Metabolic information is important for disease treatment, bioprocess optimization, environmental remediation, biogeochemical cycle regulation, and our understanding of life’s origin and evolution.  Since the last term We adopted a quadratic programming method similar to minimization of metabolic adjustment (MOMA) , to tuneMaxi and Mini, we used the maximal and minimal values observed in multiple datasets as the default values. Users can manually set desired values for the upper/lower bound of any specific flux in MFlux webpage, or they can opt to not use any boundaries. For instance, users can simply set the boundary of a certain flux as zero if this specific gene is knocked out.For the upper and lower boundaries of each flux, i.e., To ensure user inputs, e.g., growth rates, oxygen usage, and substrate uptake rates, are biologically meaningful, MFlux first checks the satisfiability  of input values . The bioDifferent parts of MFlux mentioned above are put together as illustrated in 13C-MFA to precisely resolve the anaplerotic pathway fluxes [13C-MFA anaerobic metabolisms , because of lacking sufficient samples for machine learning. The omission of those fluxes can also partially explain the high prediction error in some fluxes .The core metabolism of bacteria is summarized into a pathway map in y fluxes . Informa13C-MFA dataset. The average value, the range, and the 95% confidence interval for each flux are shown in 13C-MFA is based on the glucose metabolism). All 29 fluxes are found to have a relatively narrow confidence interval compared to possible flux ranges, suggesting that fluxes of different bacteria species varies in a relatively small range. This is because most 13C-MFA studies are focusing on models species  and glucose based metabolism, while there are much less MFA efforts to study non-model species or metabolism of carbon substrates other than sugars .By statistical analysis, we determined the variation between each flux profile and the average flux profile from our To decide the most suitable ML algorithm, we first performed a grid search in the parameter space, using a dataset of wild type (WT) samples only. The best results of three different algorithms  are presented in v11 (the second step of the oxidative PP pathway) and v24 (the glyoxylate shunt) have relatively large variations. Two factors may contribute to this fact. Both v11 and v24 have relatively narrow ranges  [E. coli. To resolve this problem, the MFlux platform allows the users to manually set the boundaries of central fluxes to improve prediction quality .Better cross validation was expected from the SVM models trained on the WT dataset, rather than on the WP dataset, while sophisticated genetic variations are not included in the WT dataset. However, cross-validation results refuted our initial thought: the models from the WP dataset demonstrated significantly better performance than those trained on the WT dataset (data shown in nges see  and consknockout ) and v24knockout ) signifipathway) . Howeverv24 may have a negative value). To address those issues, we employed quadratic programming for flux correction as described in the Methods section. More rational results with improved accuracy are expected after flux correction. An essential assumption of this step is that ML predictions are relatively close to real values reported in the literature. This hypothesis is backed by our cross-validation results further validated in the following case studies.After parameter optimization, the SVM models of the best parameter sets can predict with relatively small error. However, the flux profile predicted by the ML method does not necessarily satisfy the inherent stoichiometric constraints of metabolic networks because the ML methods do have big enough dataset at this stage to reflect this. The situation could get even worse where specific fluxes predicted by the ML algorithm may go beyond a biologically meaningful range  is illustrated in 13C-MFA flux profiles with Root Mean Squared Error (RMSE) under 5. For ML, the predictions have large variation on specific fluxes . Quadratic programming can further adjust flux profiles and reduce deviations of flux predictions. The corrected flux profiles also meet the basic stoichiometric relationship of the metabolic network. The final prediction from MFlux shows improvement with RMSE reduces to 3.2.In Case 8, v et al. . MicrobiG. thermoglucosidasius strain M10EXG grows under microaerobic conditions. G. thermoglucosidasius is a thermophilic and ethanol tolerant bacterium which can convert both hexose and pentose into ethanol [13C-MFA fluxes with ML-only fluxes and MFlux results. The results are encouraging: ML-only prediction gives an RMSE of 4.0, while MFlux uses both ML and quadratic programming to improve the prediction to an RMSE of only 3.0. Among the 20 case studies, the average flux set has very large variations (RMSE of 33.5) from actual 13C-MFA fluxes , MFlux predictions have an RMSE between 5 and 10, higher than the RMSE for prediction of wild type strains. Since MFlux is currently unable to capture complex regulatory mechanisms of flux reorganization, human-computer interaction can be employed by manually tuning boundary values of certain fluxes to improve flux prediction quality. For example, knocking out ppc on E. coli may activate the glyoxylate shunt [For species with genetic modifications in major pathways  was used [E. coli fluxomes: one case for glucose based 13C-MFA via parallel labeling experiments [13C-MFA results as the control, MFlux results apparently have smaller RMSEs than FBA predictions. In the first case, the FBA has an RMSE of 11.3, while MFlux has an RMSE of 6.5  is an important mechanistic tool to predict unknown cell metabolism . Accuratwas used . Two comeriments  and the E of 6.5 . In the E of 6.5 . To circE of 6.5 . In gene2 generation). Without proper flux constraints and objective functions, it is more challenging for FBA to narrowly determine intracellular fluxomes in suboptimal metabolisms, especially for co-metabolism dual substrates because of the large solution space for the cell metabolism to optimize biomass growth using two substrates. As a complementary tool, MFlux may offer a quick metabolic overview and provide biologically meaningful flux boundaries to reduce FBA solution spaces when proper constraints for FBA are unavailable.FBA alone has given good predictions of growth rate as well as input and output fluxes, but not of intercellular fluxes. It is difficult to obtain actual P/O ratios, the ATP maintenance cost, the oxygen flux, and the transhydrogenase activities . These e“Robustness” was originally defined as the closed-loop process stability under perturbations in the control field. This definition is applicable to biochemical networks. To maintain the physiological output  within a desired range, microorganisms employ sophisticated control disciplines at different architecture levels, from the genome to the phenotype. In contrast to chaotic transcriptional profiles, the microbial fluxome shows robustness so that cells can survive in constantly-altering environments or in response to genetic mutations –58. MetaP. aeruginosa, P. fluorescens, and P. putida have been studied by 13C-MFA in the past decade [Pseudomonas species employ remarkably identical fluxomics types: they employ a highly active ED pathway for glycolytic metabolism and keep a low flux on the PP pathway for biomass synthesis, due to the lack of the pfk gene [Pseudomonas species have slow cell growth rates and their aerobic metabolisms do not yield by-products. They also demonstrate a very active pyruvate shunt (MAL → PYR) and NADPH overproduction flux (a benefit for counteracting oxidative stress). On the other hand, the TCA cycle in Pseudomonas species show plasticity under genetic and environmental variations [Flux pattern recognition enables MFlux to predict metabolism of new species by learning from a small set of fluxome information from the same genus. For example, the metabolisms of t decade –65. The pfk gene . The ED pfk gene , 68. Pseriations , and canriations .E. coli and Bacillus), their fluxomes  can be similar, because central fluxes in catabolism are regulated by energy and building block requirements that show much smaller variations than genome or transcriptional differences. On the other hand, change of carbon substrates may alternate flux distributions. For example, co-utilization of glucose and glycerol in E. coli cause significant re-organization of fluxomes. In a same microbial strain, different fluxome patterns can be employed for metabolizing different substrates . Recognizing these metabolic patterns allows the use of a relatively small training set to perform a decent metabolic prediction of diverse metabolic types. Consequently, these common principles of certain classes of microorganisms can be captured by machine learning for fluxome predictions.For different bacterial species , and thus our current ML cannot predict fluxomes well in certain cases. Such problem (model bias) can be resolved after more 13C-MFA papers for non-model species are included in the database and more constraints are implemented by our platform.There are several major challenges regarding MFlux. First, the 13C-MFA has not widely used by synthetic biology community yet. In future versions of MFlux, new metabolic knowledge and rules should be applied for flux corrections.Second, the predictability of ML is limited to species and pathways that are already included in learning. More information and efforts are required to deal with cases of genetically modified strains with engineered pathways that hijack flux for synthesis of diverse commodity chemicals . CurrentE. coli when both glucose and glycerol are available, E. coli, Bacillus and Corynebacterium).Third, it is still difficult to incorporate regulation mechanisms into the current model. For instance, various catabolite repression mechanisms regulate the cell fluxome in the presence of multiple substrates  to produce complicated overflow metabolites . This meCO2 fixation pathway (such as the reversed TCA cycle). Therefore, our MFlux platform could not make ML predictions but only reports a general description of metabolic features of these species.Fifth, our current dataset is still unable to support ML studies on phototrophic bacterial fluxomes. For phototrophic metabolism, its energy generation  may not be controlled by substrate catabolism. Some phototrophic bacteria  have versatile autotrophic and photomixotrophic metabolism that is highly sensitive to light and substrate availability. Other phototrophs may even have Lastly, ML cannot directly estimate fluxes for carbon sources which are not part of the learning dataset. To predict fluxomes for new substrates, users need to assume that similar entry-points of carbon sources into the central metabolic network may cause similar flux distributions .13C-MFA is a very small field of just hundreds of MFA research papers on microbial species published in the past two decades. In the long term, ML methods may solve this problem: with a large and reliable fluxomics dataset and more information from 13C-MFA and AI scientists, the future MFlux model can make broad-scope metabolism predictions. To sum up, MFlux presents the first platform introducing ML in the field of fluxomics and it will be continuously updated and improved. It will inspire the development of similar computational tools to advance omics and metabolic engineering fields [This proof-of-concept study demonstrates that AI methods can facilitate fluxomics research with reasonable precision. g fields .S1 ProgramPython scripts in a ZIP file.(ZIP)Click here for additional data file.S1 Table13C-MFA flux, the flux profiles predicted by ML, and the flux profiles predicted by MFlux with additional constraints.Detailed information for 20 cases studies using MFlux, including literature references, input conditions, (XLSX)Click here for additional data file.S2 TableThe information of constraints, objective function, and simulation results.(XLSX)Click here for additional data file.


Aquilegia genus has been used as a key model for speciation studies. In this study, we defined the metabolomic profiles of flower samples of two Aquilegia species, A. Canadensis and A. pubescens. We identified a total of 75 metabolites that were classified into six main categories: organic acids, fatty acids, amino acids, esters, sugars, and unknowns. The mean abundances of 25 of these metabolites were significantly different between the two species, providing insights into interspecies variation in floral chemistry. Using the PlantSEED biochemistry database, we found that the majority of these metabolites are involved in biosynthetic pathways. Finally, we explored the annotated genome of A. coerulea, using the PlantSEED pipeline and reconstructed the metabolic network of Aquilegia. This network, which contains the metabolic pathways involved in generating the observed chemical variation, is now publicly available from the DOE Systems Biology Knowledge Base .To date, variation in nectar chemistry of flowering plants has not been studied in detail. Such variation exerts considerable influence on pollinator–plant interactions, as well as on flower traits that play important roles in the selection of a plant for visitation by specific pollinators. Over the past 60 years the  Nectar plays an essential role in the interaction between animal- and insect-pollinated plants and their pollinators . Plant–pAquilegia genus consists of 70 species[Aquilegia canadensis and Aquilegia pubescens, exhibit extensive variation in flower morphology [A. canadensis, which is native to eastern and central North America, has petals that are prolonged backwards into a tubular spur; the petals are primarily yellow, but redden toward the tip of the spur, which is red. Sepals are petal-like. Plants grow from early spring, bloom from March to July, and set fruit in mid-to-late summer. A. pubescens, which is native to the South Sierra mountains in California, grows only in alpine and subalpine climates; both the sepals and spurs of the flowers are yellow. Flower positioning also differs between A. canadensis and A. pubescens: the A. canadensis flower faces downwards, whereas A. pubescens flowers are erect rather than drooping. Flower morphology and positioning may be essential determinants of pollinator visitation . All instruments and the column were obtained from Agilent Technologies. The GC-MS protocol is described in in details in [To isolate the metabolites and for the GC-MS analysis, we followed the methods described by the Metabolomics Standards Initiative. More sptails in . The GC t.test function with the confidence interval set at 0.95. For principal component analysis (PCA), we used the built-in prcomp function with the default settings. For visualization of the principal components, we used the R package ggplot2. We performed Pearson correlation analysis using the cor function.Upon analysis and transformation of analyte peaks to numerical values, we implemented filtering of the data. Specifically, values of metabolites detected in three flower samples from a species were selected for further analysis. All statistical analysis was performed on iPlant’s cloud-computing resource, Atmosphere , using aAquilegia metabolic network was constructed by annotating the reference genome of A. coerulea v1.1; sequence data were produced by the US Department of Energy Joint Genome Institute. Specifically, the annotation originated from the PlantSEED database [Aquilegia sequence. Once the annotation was complete, a metabolic model was generated from the annotation, using the plant template created by the PlantSEED project. The plant template consists of curated reactions involved in plant primary metabolism and an extensive plant biomass. When the template is used, the annotation in the genome is linked to the reactions in the template, which are then used to create a metabolic reconstruction. Finally, the plant biomass is added to the reconstruction to allow it to function as a model of plant primary metabolism in Aquilegia. A gap-filling algorithm was used to find and fill gaps in the reconstruction that would ensure that the metabolic network could generate the same plant biomass using metabolic flux-analysis methods such as flux balance analysis . Each step in the generation of the metabolic network was performed within the DOE Systems Biology Knowledgebase environment and framework . The annotated genome and metabolic reconstruction are available for public use in the KBase workspace named “Acoerulea”, which can be accessed by users via a free KBase account . The same workspace contains results obtained by simulating heterotrophic growth using flux balance analysis, allowing users to browse reaction activity.The database , which cdatabase . All pro–5 for A. canadensis and 5.14•10–5 for A. pubescens; ribitol: 9.35•10–5 for A. canadensis and 9.29•10–5 for A. pubescens). Using t-test, we ended up with a list of 75 metabolites, including 21 organic acids, 1 fatty acid, 5 amino acids, 4 esters, 25 sugars, and 19 chemically undefined compounds  that were present only in A. canadensis, suggesting that the two species exhibit qualitative as well as quantitative differences in sugar metabolites.Sugars constituted over 90% of all metabolites that differed significantly between the two species. Of the 15 different sugar metabolites identified, the mean levels of 10 differed significantly between the two species, including the three most abundant: fructose, glucose and sucrose. Notably, fructose and glucose were more abundant than sucrose in A. canadensis, whereas quinolinic acid was only detectable in A. pubescens. The qualitative and quantitative variation in organic acids, in addition to that of sugars, suggests that nectar composition is a complex trait.The second major category of metabolites we detected was organic acids. Of the 21 organic acids we identified, the levels of only three were significantly different between the two species . SpeciesA. pubescens. Glycine and cytosine levels did not differ significantly. In addition to amino acids, we also found esters; two out of four esters were present only in A. pubescens. Moreover, a sizable fraction of 17 unknown metabolites was detected, only two of which (Unknown #28 and Unknown #29) differed significantly between species; these compounds remain to be identified. The variation in metabolite identities and concentrations prompted us to look for correlations between flower spur length and the levels of metabolites that differed significantly between the two species. With some exceptions, metabolites tended to be positively correlated with spur length in one species and negatively correlated in the other,  differed significantly between the two species, while a third amino acid, serine, was present only in e other, . The mosIn addition, we performed principal component analysis(PCA) to define the metabolites that contributed the most to differentiation between the two species . The PCAWe mapped all of the identified metabolites to known pathways in the PlantSEED database. Of the 75 metabolites, we were able to map 21. Each metabolite mapped to between 4 and 281 pathways . BecauseAquilegia genome resulted in assignment of metabolic annotations to 1,202 different features, and the resulting working metabolic model consisted of 1,573 reactions Click here for additional data file.S2 TableThe loading are reported for each of the metabolites. In column two there is an ID number for each of the metabolites shown in (XLSX)Click here for additional data file.S3 TableMetabolites vary to the number of pathways they belong to. We mapped 22 out of our 79 metabolites(XLSX)Click here for additional data file.S4 TableIn column F we mention an Arabidopsis exemplar that belongs to each of the pathways. Most of the metabolites belong to cytosol some to the rest of the compartments.(XLSX)Click here for additional data file.S5 TableThere is large variation on the number of KEGG pathways each of our identified metabolites belongs to.(XLSX)Click here for additional data file.


Schizochytrium limacinum SR21 is a potential industrial strain for docosahexaenoic acid (DHA) production that contains more than 30–40 % DHA among its total fatty acids.To resolve the DHA biosynthesis mechanism and improve DHA production at a systematic level, a genomescale metabolic model (GSMM), named iCY1170_DHA, which contains 1769 reactions, 1659 metabolites, and 1170 genes, was reconstructed.S. limacinum. Similarly to conventional fatty acid synthesis, the biosynthesis of DHA via PKS requires abundant acetyl-CoA and NADPH. The in silico addition of malate and citrate led to increases of 24.5 % and 37.1 % in DHA production, respectively. Moreover, based on the results predicted by the model, six amino acids were shown to improve DHA production by experiment. Finally, 30 genes were identified as potential targets for DHA over-production using a Minimization of Metabolic Adjustment algorithm.Based on genome annotation results and literature reports, a new DHA synthesis pathway based on a polyketide synthase (PKS) system was detected in iCY1170_DHA, could be used to elucidate the mechanism by which DHA is synthesized in S. limacinum and predict the requirements of abundant acetyl-CoA and NADPH for DHA production as well as the enhanced yields achieved via supplementation with six amino acids, malate, and citrate.The reconstructed GSMM, The online version of this article (doi:10.1186/s12864-015-2042-y) contains supplementary material, which is available to authorized users. Docosahexaenoic acid (DHA), which is an n-3 polyunsaturated fatty acid (PUFA), has been shown to have a positive effect on diseases such as hypertension, arthritis, atherosclerosis, depression, adult-onset diabetes mellitus, myocardial infarction, thrombosis, and some cancers . DHA is Schizochytrium limacinum SR21 (Aurantiochytrium limacinum ATCC MYA-1381) is a marine thraustochytrid that can synthesize lipids with a high content of DHA. In SR21, total fatty acids reportedly constitute more than 50 % of the dry cell weight (DCW) [Schizochytrium sp. in recent years [ht (DCW) . In addint years , 8. For nt years . In addint years , 11.4-pH-auxostat system was developed that appears to be a promising technique for the first stage of production of Schizochytrium sp. biomass as a means of achieving the fastest possible growth rate [The dry cell weight, lipid content, and DHA percentage of the total fatty acids are three important parameters for evaluating fermentations. Many efforts have been directed towards improving the DHA yield. (1) To optimize the culture medium, it was demonstrated that using glucose and glycerol as mixed carbon sources, the DHA productivity was 15.24 % higher than that obtained using glucose as single carbon source . (2) To wth rate . A two-swth rate . (3) To wth rate . AlthougS. limacinum SR21 and corresponding literature reports have made the reconstruction of a GSMM possible.A genome-scale metabolic model (GSMM) represents the microbial metabolic genotype–phenotype relationships of an organism . Such moS. limacinum SR21 was reconstructed. Using this model, we first made a comparison with two oleaginous fungi, Mortierella alpina and Yarrowia lipolytica, that are used for industrial production of arachidonic acid (ARA) and eicosapentaenoic acid (EPA), respectively. Then the pathway of DHA biosynthesis in SR21 was resolved based on genome annotation results and literature mining. Based on the GSMM, biochemical and genetic strategies were applied to improve DHA production.In this study, a GSMM of Schizochytrium limacinum SR21, which contains 1769 reactions and 1659 metabolites, was named iCY1170_DHA , corresponding to 1170 genes of 14,859 ORFs, were incorporated into the model. In iCY1170_DHA, all 1769 reactions (including transport and exchange reactions) were classified into 10 different subsystems, according to the KEGG Pathway Database (http://www.genome.jp/kegg/pathway.html): carbohydrate metabolism, amino acid metabolism (20 common acid acids), other amino acids (D-type amino acids) metabolism, nucleotide metabolism, energy metabolism, lipid metabolism, metabolism of cofactors and vitamins, transport reactions, exchange reactions, and other metabolisms , followed by amino acid metabolism (18.0 %), which agrees with model iCY1106 for Mortierella alpina  and yeast extract (YE) medium conditions using iCY1170_DHA by deleting each gene in turn. The genes were categorized into three classes: essential genes, partially essential genes and non-essential genes. The gene essentiality study revealed that a total of 56 genes were essential in both YE and MG medium. An additional 35 genes were essential only in MG medium  were unique in model iCY1170_DHA, and 61.3 % of these unique reactions were distributed across lipid metabolism, amino acid metabolism, and carbohydrate metabolism. For example, it was reported that S. limacinum could grow with arabinose as the carbon source [Y. lipolytica and M. alpina could not use arabinose [iCY1107_DHA  [in silico result was 16.42 % lower than experimental result [Data from batch cultures of  S Table . When us S Table . Without (8.5 %) . And for (8.5 %) . However (8.5 %) , the in l result . This meiCY1170_DHA to utilize different carbon and nitrogen sources has been verified. According to literature reports, S. limacinum can grow using 15 kinds of carbon sources and 3 kinds of inorganic nitrogen sources, such as NH4+, NO3−, and urea. However, the simulation results show that the model could not grow using 5 carbon sources, including xylose, arabinose, lactose, starch, and trehalose , which can convert lactose into glucose, was not found during genome annotation. After filling this gap, the model could grow with lactose as the carbon source. All of these simulation results, including the growth rate simulations and usage of different carbon and nitrogen sources, indicated that the model iCY1170_DHA is reliable and can be used for further prediction and analysis.Apart from the growth rate simulation, the ability of S. limacinum does not contain delta-4 desaturase, in agreement with literature reports. On the other hand, some ORFs in S. limacinum were predicted to be potential PKS proteins  route and the polyketide synthase (PKS) system . In the ynthesis , 28, 29.M. alpina, which synthesizes arachidonic acid via the FAS route, the DHA synthesis in S. limacinum does not need much more oxygen in the PKS route. DoubleRobustnessAnalysis results showed that, when the growth rate was fixed, a low oxygen uptake rate (below 5 mmol/gDW/h) was beneficial for DHA accumulation , the uptake rate could reach the set maximum values Fig. . For theely Fig. .Fig. 6SiFurthermore, these simulation results were verified by experiment. The experimental results showed that addition of eight of the nine amino acids (except cysteine) could promote cell growth Fig. . The addin silico medium, instead of using the pathway to generate Asn starting from Asp. Via adenylosuccinate synthase (EC: 6.3.4.4) and adenylosuccinate lyase (EC: 4.3.2.2), extra Asp was converted into fumarate, enhancing the TCA cycle. As a result, fluxes of two reactions involving acetyl-CoA were also enhanced significantly. For example, the flux of acetyl-CoA supplied by pyruvate dehydrogenase complex  was increased 28.1 %, and acetyl-CoA flux used for the first step of fatty acid biosynthesis via acetyl-CoA carboxylase  was also increased 20.9 %. In addition to enhancing acetyl-CoA generation, the addition of Asn also increased NADPH flux for producing DHA. After adding Asn, two fluxes in the pentose phosphate pathway catalyzed by G6PD and PGD both increased 122.6 %. This means adding these amino acids could improve DHA production through enhancing the supply of both acetyl-CoA and NADPH.Combined with the FBA results, after adding Asn, SR21 could uptake Asn directly from the MOMA was used to re-calculate the fluxes for the overexpression algorithm. This simulation was carried out based on YE medium. The lower bound of the DHA exchange reaction was set as 0.01 mmol/gDW/h. Then each reaction that had non-zero flux value in the FBA simulation was overexpressed computationally. After finishing the cycle for over-expression, according to equation 1, 32 reactions catalysed by 30 genes were identified as potential targets Fig. .Fig. 8Thin silico, DHA production rose to 0.0307 mmol/gDW/h. As a dehydrase/isomerase (DH) involved in the last step of DHA synthesis in the PKS system, 0.0863 mmol/gDW/h DHA was accumulated by overexpressing this gene. Genes involved in cell growth were distributed across many metabolic subsystems, such as carbohydrate metabolism, amino acid metabolism and nucleotide metabolism. For example, phosphomannomutase  and guanosine diphosphomannose phosphorylase  are two genes involved in the synthesis of L-galactose, which is a precursor of the biomass function. The growth rate decreased by 5.7 %, and DHA production increased 22.0 % by overexpressing these two genes. After overexpressing PRPP synthetase , fluxes used for the synthesis of AMP increased from 0 to 0.0264 mmol/gDW/h, which led to a 52.0 % improvement in DHA production and an 11.5 % decrease in growth rate.These potential targets could be classified into two groups, one of which is directly involved in DHA synthesis while the other is involved in cell growth  were predicted to increase DHA production. According to experimental results, of these 9 amino acids, 6 have been proved to improve DHA production. The addition of Asn could lead a 50.55 % increase in DHA production. Based on MOMA, 30 overexpressed genes, such as those encoding acetyl-CoA synthetase and malic enzyme, were identified as having a positive effect on DHA production.A GSMM of S. limacinum was initially reconstructed based on genome annotation information and the metabolic pathway database. First, the genome sequence of S. limacinum SR21, which contains 181 scaffolds, was downloaded from the JGI database (http://genome.jgi-psf.org/Aurli1/Aurli1.home.html). Three existing models for Mortierella alpina ATCC 32,222 [Cyanothece sp. ATCC 51,142 [Arabidopsis thaliana [The metabolic model of C 32,222 , cyanobaC 51,142 , and Arathaliana  were usethaliana . KEGG Onthaliana  and WoLFthaliana  and BioPthaliana  databasethaliana .in silico flux analysis, especially during the exponential growth phase, where the primary cellular objective is to maximize growth. The cellular composition of S. limacinum SR21 consists of lipids, proteins, carbohydrates, ash, and nucleic acids [S. limacinum [Cyanothece sp. ATCC 51,142 [S. limacinum. The individual weights of nucleotides in the DNA and RNA were calculated based on the genome sequence of S. limacinum, in which the G + C content accounts for 45.17 %. The cell wall composition of S. limacinum was assumed to be the same as S. aggregatum [Knowledge of the cellular biomass composition is an important prerequisite for the ic acids . The lipimacinum . Amino aimacinum . The oveC 51,142  since nogregatum . Detailein silico simulations, and to predict the metabolic characteristics of S. limacinum, constraints-based flux analysis, including flux balance analysis (FBA) [In order to perform is (FBA) , was caris (FBA) , and allis (FBA) . When glis (FBA) . In addiPH value > 1 were identified  We  We PH vabox 2.05  with MATbox 2.05 .Schizochytrium limacinum SR21 was gifted by Prof Xiaobin Yu. The fermentation medium contained 120 g/L glucose, 10 g/L peptone, 5 g/L yeast extract, and 20 g/L crystal sea salt [sea salt . Cells wCell density was calculated from the optical density measured at 660 nm. DCW was determined by transferring a 5-mL cell suspension to a pre-weighed centrifuge tube and then centrifuging at 5000 r/min for 5 min. The cell pellet was then washed twice with distilled water, and dried at 70 °C to constant weight. Fatty acid analysis was accomplished by first harvesting freshly produced cells and freeze drying overnight. The subsequent methods for fatty acid methyl ester (FAME) preparation and gas chromatographic (GC) analysis are the same as reported by Wang .This manuscript does not involve Ethics.


Mortierella alpina is an oleaginous fungus used in the industrial scale production of arachidonic acid (ARA). In order to investigate the metabolic characteristics at a systems level and to explore potential strategies for enhanced lipid production, a genome-scale metabolic model of M. alpina was reconstructed.−1 h−1 was optimal for ARA accumulation. The flux of 53 reactions involving NADPH was significantly altered at different ARA levels. Of these, malic enzyme (ME) was confirmed as a key component in ARA production and NADPH generation. When using minimization of metabolic adjustment, a knock-out of ME led to a 38.28% decrease in ARA production.This model included 1106 genes, 1854 reactions and 1732 metabolites. On minimal growth medium, 86 genes were identified as essential, whereas 49 essential genes were identified on yeast extract medium. A series of sequential desaturase and elongase catalysed steps are involved in the synthesis of polyunsaturated fatty acids (PUFAs) from acetyl-CoA precursors, with concomitant NADPH consumption, and these steps were investigated in this study. Oxygen is known to affect the degree of unsaturation of PUFAs, and robustness analysis determined that an oxygen uptake rate of 2.0 mmol gDWThe simulation results confirmed the model as a useful tool for future research on the metabolism of PUFAs.The online version of this article (doi:10.1186/s12918-014-0137-8) contains supplementary material, which is available to authorized users. Mortierella alpina is an oleaginous zygomycete, that can accumulate lipids to 50% of its dry weight in the form of triacylglycerols [M. alpina is nonpathogenic and nonallergenic, including the spores produced during the industrial production of ARA [N-methyl-N’-nitro-N-nitrosoguanidine (MNNG) [M. alpina strain Y11 which possessed lowered ω-3 desaturation activity, and ARA production was 2.1 fold (2.21 g/L) higher than strain 1S-4. Optimizing the culture medium and fixing the ratio of defatted soybean meal to potassium nitrate at 2:1 gave a fourfold increase (6.0 g/L) in ARA production [lycerols . The implycerols . M. alpin of ARA  which isn of ARA . ARA hase (MNNG) . This wooduction . In anotoduction . Additiooduction .M. alpina for enhanced ARA production remains an attractive proposition and for further research. Early work identified desaturases as key enzymes in PUFA synthesis. Specifically, Δ5 desaturase [Genetic engineering of saturase , which csaturase ,12. NADHsaturase . Despitesaturase , their eM. alpina can accumulate large quantities of lipids, but maximizing lipid production is complicated by the complexity of the regulatory mechanisms associated with lipid metabolism. It is generally difficult to identify key metabolic modules contributing to lipid physiology. Using reconstruction GSMM, we can systematically analyze the function of each gene and metabolic reaction and model the effects using flux balance analysis (FBA). Specific pathways can be understood based on the model of the whole metabolic network, and strain design strategies can also be used to guide metabolic engineering experiments. Two GSMM studies on Yarrowia lipolytica  [M. alpina network model, however, this was a refined network that could only be used to investigate genome annotation and metabolic routes, and not flux distribution or phenotypic behaviors [M. alpina GSMM and used the COBRA Toolbox [A genome-scale metabolic model (GSMM) is an indispensable tool for the study of metabolism that adopts a systems biology approach to integrate data from genomics, transcriptomics, proteomics and metabolomics. It has been widely used in the analysis of the network properties of metabolism , predict (iNL895  and iYL6L619_PCP ) have be. alpina . GSMM st. (2013)  previousehaviors . To syst Toolbox  for subsiCY1106) of M. alpina ATCC 32222 was reconstructed based on sequencing results [de novo synthesis pathways of PUFAs such as ARA and eicosapentaenoic acid (EPA) were subsequently resolved. The roles of acetyl-CoA and NADPH in the regulation of PUFA biosynthesis were probed, and important genes and reactions were systematically analyzed using FBA, flux variability analysis (FVA), and minimization of metabolic adjustment (MOMA).In this study, a genome-scale metabolic model , and catalyzes the transformation of L-asparagine into L-aspartate. When compared with the existing model, many improvements in iCY1106 are apparent. (1) A total of 139 transport proteins were annotated based on TCDB and TransportDB. (2) A total of 216 exchange reactions were added into model iCY1106. (3) Biomass composition of M. alpina was calculated based on literature mining. (4) Additional chemical reactions were also included. (5) The reversibility of some reactions was redefined according to MetaCyc and BioPath databases. For example, in the published model, the reaction catalyzed by aldehyde dehydrogenase (EC: 1.2.1.3) was reversible, whereas this reaction is irreversible according to BioPath. The flux distribution of iCY1106 was normal following this change.The genome-scale metabolic model iCY1106, four compartments  were linked by 141 trans-plasma membrane transport reactions, 98 cytoplasmic-mitochondrial transport reactions and eight cytoplasmic-peroxisome transport reactions  were identified as essential for growth using the FBA method  containing glucose, KHnd NaNO3 , 86 M. aSaccharomyces cerevisiae enzyme  identified in the DEG search. Similarly, nucleoside diphosphate kinase (MA-120-261), which is involved in the synthesis of CTP and GTP, also shared high homology with one of the essential genes identified by DEG . Besides, in MG, there were 46 genes that did not match with DEG  and delta 12 desaturase (MA-334-414) are vital for the synthesis of ARA in M. alpina. In addition, most of these essential genes were distributed in the Amino Acid Metabolism category (28.3%). This was understandable since essential gene identified by DEG were in a rich medium in which genes involving amino acid synthesis may not be essential.The identified genes were compared with the database of essential genes (DEG), and 40 genes were found to share high homology with genes identified by DEG  [M. alpina strains (ATCC 32222 and ME-1) for modeling and experimental components, with ME-1 being the higher ARA producing strain [FBA with constraints was used to investigate the metabolic properties of model DW−1 h-1 , and the [−1 h-1 , and 2.0g strain . FurtheriCY1106 using FBA , and the model could not achieve growth with any individual carbon source. For ethanol, the reaction catalyzed by alcohol dehydrogenase (EC: 1.1.1.1) was irreversible, but should be reversible according to the MetaCyc database. For rhamnose, the lack of L-rhamnulose-1-phosphate lactaldehyde-lyase (EC: 4.1.2.19) meant that rhamnose could not generate glycerone phosphate, which could otherwise be used by the glycolysis pathway. After elimination of initial discrepancies using the continuous gap filling process, a 100% match was acquired. This indicated that model iCY1106 could predict catabolic pathways of various carbon sources including common sugars and alcohols. A similar FBA simulation of nitrogen sources  also generated results that were consistent with experimental fermentation data. Both glutamate and glycine supported M. alpina growth, confirming that the model performed well for predicting the utilization of different carbon and nitrogen sources.The capability of utilizing 14 different carbon sources for cell growth was qualitatively predicted by the model M. alpina  and the synthesis of very long chain fatty acids. During PUFA biosynthesis, all fatty acids should be in the form of acyl-CoA [To further investigate the pathways associated with PUFA biosynthesis and metabolism, genome annotation, literature mining and KEGG database analysis were performed. ARA is a ω-6 PUFA synthesized by a Figure . ARA , fatty acid beta-oxidation, and other acetyl-CoA generation processes   [−1 h−1 and 0.1 mmol gDW−1 h−1, respectively. FBA showed only that leucine and lysine could increase ARA production by 14.06% and 13.28%.Acetyl-CoA is the essential two-carbon donor molecule for fatty acid synthesis. In model ) Figure . However1.8.1.4) . AlthougiCY1106, acetyl-CoA can be consumed by amino acid synthesis, amino sugar metabolism and fatty acid synthesis as well as ARA production . ACC catalyzes the first committal step of the fatty acid biosynthetic pathway, and should be overexpressed to maximize ARA production [M. alpina growth rate in batch culture ranged from 0.06 h−1 to 0.19 h-1 [in silico analysis should be set between 0 and 1.8 mmol gDW−1 h−1. When the glutamate uptake rate was increased, ARA production increased from 0.128 mmol gDW−1 h−1 to 0.355 mmol gDW−1 h−1 , was 0.79 mmol gDWoduction . Glutamaoduction . As the DW−1 h−1 1.0%. ACC0.19 h-1 ,42, the de novo fatty acid synthesis involving desaturation and elongation. In model iCY1106, there were 172 reactions involving NADPH  and 0.128 mmol gDW−1 h−1 (μ = 0.03 h−1), the flux of NADPH reactions were investigated. In total, 53 NADPH reactions exhibited flux changes of > 10−6 mmol gDW−1 h−1  involving accumulation of NADPH were significantly altered. When ARA production was increased, three reactions  underwent flux increases, which corresponded to enhanced enzyme activities due to up-regulation of the genes encoding the enzymes. Of these, malic enzyme , which catalyzes the conversion of malate to pyruvate (R4), is considered a key enzyme in lipogenesis in oduction , and theiCY1106) for Mortierella alpina ATCC 32222 was successfully reconstructed using knowledge from the scientific literature and publicly accessible databases. The model comprised 1106 genes, 1854 reactions and 1732 metabolites, and included 247 transport reactions and 216 exchange reactions. Following a series of simulations and verification by measurement of growth rates and substrate usage, the model was found to agree with published literature. The model was used to investigate the effects of the important precursors acetyl-CoA and NADPH on the biosynthesis of PUFAs such as ARA and EPA. FBA results showed that enhancing the pyruvate dehydrogenase complex increased acetyl-CoA availability for increasing ARA production. Limiting the nitrogen source was an effective method for reducing acetyl-CoA consumption, and malic enzyme was found to be a key node in the regulation of NADPH in ARA biosynthesis. Model iCY1106 could serve as a useful predictive tool for future systems biology studies to guide the genetic engineering of M. alpina to improve the production of industrially important metabolites.In this study, a genome-scale metabolic model  using the Matlab program, where M represents metabolites and N represents reactions . The bas Toolbox . Constra


Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation. Cellular metabolism is defined by a large, intricate network of thousands of components, and plays a fundamental role in many diseases. To study this network in its entirety, metabolic models have been built which encompass all known biochemical reactions in the human metabolism. However, since not all metabolic reactions take place in any given tissue, these generalized models need to be tailored to study specific cell types. Algorithms developed to date to perform this tailoring process have focused on keeping tissue-specific models as concise as possible. This approach, however, can remove essential reactions from the model and hamper subsequent analysis. Here we present CORDA, a tissue-specific building algorithm that yields concise, but not minimalistic, tissue-specific models. CORDA has many advantages over previous methods, including better agreement with experimental data and better model functionality. Using CORDA, we developed a library of 76 healthy and 20 cancer-specific models of metabolism, which we used to identify similarities between healthy and cancerous tissues, as well as metabolic pathways that are unique to cancer. Results of this work provide a broadly applicable tool to model cell- and tissue-specific metabolism, while highlighting potential new pathway targets for cancer therapies. Genome-wide Metabolic Reconstructions (GEMs) computationally model the molecules and reactions responsible for metabolism in any given organism, and have been applied across a variety of fields including metabolic engineering and evolutionary analysis . ComputaA particularly prolific subfield of human GEMs is the development of tissue-specific reconstructions. Different groups of metabolic reactions occur in different cell types. Hence, numerous studies have been dedicated to generating tissue specific or cell specific models of metabolism , 21. TheAutomated tissue-specific reconstruction algorithms developed to date can be broadly categorized into two groups : “flux-dflexible core.The second category of tissue-specific reconstruction methods are pruning algorithms, which include MBA , mCADRE There are two main advantages to defining a core set of reactions before performing the tissue-specific algorithm. The first advantage is the possible inclusion of multiple sources of data and biochemical information , 34. Theparsimonious. These algorithms aim to remove from the tissue-specific model all reactions for which experimental data is unsupportive or unavailable, such as reactions with low levels of gene expression or non-gene associated reactions. While a concise tissue-specific reconstruction is desirable, keeping the reconstruction as parsimonious as possible may lead to the removal of fundamental reactions and physiologically unlikely flux distributions. In Recon 1, for instance, oxygen and H2O exchange reactions can be removed from the reconstruction with no effect on model functionality . CORDA returns a concise, functional tissue-specific reconstruction, and features a flexible reactions core. CORDA does not depend on Flux Variability Analysis  or MixedIn line with previous studies , we appldependency assessment. In the dependency assessment approach, the metabolic network is modified in four ways (associated with the reaction being tested. In order to identify pathways with the same cost (i.e. same number of undesirable reactions), multiple dependency assessment can be performed while adding a small amount of noise to the cost of each reaction.The CORDA algorithm is based on a novel approach to identify the dependency of desirable reactions  on undesirable reactions , a method referred to here as our ways . First, our ways  is perfoour ways . The fludependency assessment, we have developed the CORDA algorithm for the reconstruction of tissue-specific models  amino-acid and ammonium recycling, (2) glucogenic production, and (3) nucleotide production. Briefly, during each test the model was allowed to freely exchange basic metabolites (i.e. water and oxygen), while the remaining exchange reactions were set to mirror the particular test (i.e. uptake of ammonium and release of urea during ammonium recycling test). The model was then forced to produce (1) urea, (2) glucose, or (3) specific nucleotides. If the model was able to do so, the test was considered passed, otherwise, the test was considered failed. If the appropriate exchange reactions were not present to perform the test, the result was considered inconsistent. Further details on how these metabolic tasks were calculated are available in the Materials and Methods section. Test results are summarized in The MBA reconstruction passed all of the amino-acid recycling and glucogenic tests, which is not surprising given the core set of MBA reactions, and therefore the necessary pathways for these tasks, were manually included in the reconstruction. The mCADRE reconstruction passed all eight nucleotide production tests, since these were included in the model building process. Where the tests were not manually included, however, MBA passed only 50% of the nucleotide production tests, and mCADRE passed only 13 of the amino-acid recycling and glucogenic tests, with an additional 17 tests being inconclusive. On the other hand, CORDA passed a total of 43 of the combined 48 metabolic tests (89.6%). This result is significant considering none of these tests were included in the reconstruction process.TRDR, an MC reaction dependent on the NC reaction RNDR1, which was included in some but not all models. This reaction dependency, however, was returned by the CORDA algorithm, and hence the reactions needed to produce all the nucleotides can be easily included upon manual curation.Metabolic tests were also performed for all 108 hepatocyte specific reconstructions generated during the parameter sensitivy analysis section. All reconstructions had the same results for all amino-acid recycling, glucogenic, and five of the nucleotide production tests, demonstrating that task results are not heavily dependent on the CORDA parameters or noise . The inaThese analyses demonstrate that the CORDA algorithm provides a reconstruction with better agreement to experimental data and better metabolic functionality when compared to previous, similar methods. Furthermore, the analysis of the 108 models to perform metabolic tasks highlights the importance of the reaction dependencies returned by CORDA in subsequent manual curation, indicating which NC reaction needs to be added back into the model for the desired MC reaction to carry flux.M. tuberculosis and macrophages ), water (h2o[e]), protons (h[e]), oxygen (o2[e]), phosphate (pi[e]), hydrogen peroxide (h2o2[e]), superoxide anion (o2s[e]), bicarbonate (hco3[e]) and carbon monoxide (co[e]) were unconstrained. All other uptake reactions were blocked unless otherwise specified.During the metabolic tasks validation analysis, the exchange rate of the For each of the 20 amino-acid recycling tests, the uptake rate of the given amino acid and glucose were set to an arbitrary value, so that the amino-acid being tested was the only source or nitrogen. Next, the production of urea was set to a strictly positive value, and FBA was performed while optimizing the production of urea. The same test was also performed for ammonium. For each of the 21 glucogenic tests, the uptake rate of the given metabolite was set to an arbitrary value, and the production of glucose was optimized. For both the amino-acid and glucogenic tests, if the model returned a feasible flux distribution the test was considered passed, otherwise it was considered failed. If the exchange reaction of the given metabolite was not present in the model, the result was considered inconsistent. The generalized Recon1 reconstruction failed two of the glucogenic tests, so the results of the remaining 19 tests are reported in the main text.For the eight nucleotide production tests, a sink consuming the given nucleotide was added to the cytosolic compartment. The model was allowed to uptake glucose and ammonium (as a source of nitrogen), and the flux through the sink was optimized. If the model was able to produce the given nucleotide, the test was considered passed.Following the validation of the CORDA algorithm, we generated a library of 76 healthy and 20 cancer tissue-specific reconstructions using the generalized human reconstruction Recon2  and the HADHB has been analyzed in 12 different samples in the HPA, and was found to be expressed in high levels in 2 of them, medium levels in 8, and in low levels in 2. The protein score associated with HADHB in renal cancer is then calculated as ACAA1 expression was calculated as medium in 5 samples, low in two samples, and not detected in four samples of renal cancer, yielding a score of ACAA2 is present in high levels in one sample, medium level in 5 samples, low levels in one sample and not detected in 3 samples of renal cancer, giving this protein a score of r0634 is calculated as “MIN)”, which is 1.1, putting this reaction in the MC group during the renal cancer reconstruction. Data and reaction distributions used during these calculations can be found in For instance, in renal cancer samples, protein Healthy and cancer specific models were clustered according to reactions present in each model. For that, 4,205 reactions present in at least one, but not all models were obtained. A binary vector was then calculated for each model indicating whether reactions were present (1) or not present (-1). These vectors were then clustered using hierarchical clustering with Hamming distance as the similarity metric, and average linkage. Leaf orders were also calculated in order to maximize the similarity between neighbors in the hierarchical binary cluster tree dendrogram. These results are summarized in Next, in order to divide the clusters according to subsystem expression, a total of 4,751 reactions present in any of the models was obtained. These reactions were then divided by subsystem according to their classification in the Recon2 reconstruction. For each of the clusters of models calculated in the previous step, the average number of reactions from each subsystem included in the cluster’s models was then calculated. Finally, this number was divided by the total number of reactions in that subsystem which were included in any of the models for a final score between zero and one. These values were then clustered using hierarchical clustering with Euclidean distance as the similarity metric, and average linkage. Leaf orders were again organized to maximize similarity between neighbors to yield objective function, defined as a single reaction or group of reactions in the network. This flux distribution is subject to upper- and lower-bound constraints, which include exchange reactions, and a steady state assumption for all model metabolites, so that no metabolite has a net production or consumption rate.Perhaps the most widely used method to analyze GEMs is Flux Balance Analysis (FBA) . FBA preS, where each row defines a metabolite, each column defines a reactions, and each entry the stoichiometric coefficient of that metabolite in that particular reaction. Vectors defining lower (lb) and upper (ub) bounds for each reaction, as well as an objective vector (c) of the same length, are also defined. Given this model, FBA finds a flux vector v through all reactions in the GEM such that:The mathematical formulation of GEMs are defined at the core by a stoichiometric matrix S is altered to reflect the changes described above. Given a reaction j being tested, a group of undesirable reactions Y, and a matrix S of size m by n, let κ. The GEM is modified in the following ways:During the dependency assessment described here, the stoichiometric matrix if →S = −S, lbn+1 = 0, ubn+1 = −lbi and lbi = 0, where S denotes the addition of a column (reaction) to S.Split all reversible reactions: S is the row denoting the cost pseudo-metabolite.Add cost to reactions: S = [0 0 0 … 0 − 1]Add cost consuming reaction: j to desired value: ifϵ > 0 → lbj = ϵ; else → ubj = ϵSet bound of reaction c = [0 0 0…0 1]Set objective to cost consumption: i ∈ Y and vi ≠ 0, the reaction i is deemed associated with j.With these constraints in place, FBA is performed as described above while minimizing the objective function. For each reaction in the reconstruction, if Monte-Carlo sampling was performed in a manner similarly to Bordbar et. al. and LewiThe termination condition imposed on the ACHR algorithm here is the same imposed by Bordbar et. al. and LewiDue to the heterogeneity between tissue-specific models, sampled flux values were evaluated between all cancer and healthy tissue models separately. That is, all sampled flux values for the given reaction were obtained from all cancer models that contain that reaction, and compared to all sampled values from healthy tissue models that contain the reaction. Results of this analysis are presented in S1 Textp sensitivity analysis, comparison between CORDA, MBA and mCADRE, healthy and cancer tissue reconstructions process, and Monte-Carlo sampling analysis.Supplementary text includes details of parameter sensitivity analysis, hypergeometric p-value calculations, parameter (PDF)Click here for additional data file.S2 TextExample of application of CORDA to small sample networks.(PDF)Click here for additional data file.S1 TableTable contains the proteomics data and reaction groups used to calculate the Hepatocyte reconstructions, 32 metabolic tests used throughout the manuscript, reactions used to calculate tissue-specific models, reactions uniquely included in healthy and cancer reconstructions, and essential metabolite results.(XLSX)Click here for additional data file.S1 Fileγ = 105, κ = 10-2, ϵ = 1, p = 2, and n = 5.Reference hepatocyte specific reconstructions calculated using CORDA and Recon1. Two reconstructions are available, one using the same protein dataset used in the mCADRE reconstruction, and one using the most recent protein data in the HPA . The par(ZIP)Click here for additional data file.S2 FileMATLAB function used for CORDA reconstructions.(ZIP)Click here for additional data file.


Applied to the synthesis of ethanol, ethylene, and propane, these in silico transition experiments point to bottlenecks and potential modification targets in cyanobacterial metabolism. Our analysis reveals incompatibilities between biotechnological product synthesis and native host metabolism, such as shifts in ATP/NADPH demand and the requirement to reintegrate metabolic by-products. Similar strategies can be employed for a large class of cyanobacterial products to identify potential stoichiometric bottlenecks.Cyanobacteria are a promising biological chassis for the synthesis of renewable fuels and chemical bulk commodities. Significant efforts have been devoted to improve the yields of cyanobacterial products. However, while the introduction and heterologous expression of product-forming pathways is often feasible, the interactions and incompatibilities of product synthesis with the host metabolism are still insufficiently understood. In this work, we investigate the stoichiometric properties and trade-offs that underlie cyanobacterial product formation using a computational reconstruction of cyanobacterial metabolism. First, we evaluate the synthesis requirements of a selection of cyanobacterial products of potential biotechnological interest. Second, the large-scale metabolic reconstruction allows us to perform  High efforts are currently devoted to expand the range of potential products suitable for production using modified cyanobacteria as a biological chassis. Starting with the synthesis of ethanol in cyanobacteria by Deng and Coleman  and its Synechocystis sp. PCC 6803, for the production of a desired compound is not difficult. However, such an initial modification typically results in low product yields. Strategies to improve yield, genetic stability, and robustness of production strains are therefore urgently needed. As yet, the development of such strategies is often based on trial and error, and is guided only by the experience of researchers. Only few systematic frameworks exist that allow to identify and rank suitable modifications to improve desired properties of a production strain.In principle, the metabolic modification of cyanobacteria, in particular, of the laboratory strain in silico models to rapidly test possible modifications of potential production strains. In particular, the use of large-scale models of microbial metabolism has become a common tool in heterotrophic biotechnology, see Zomorrodi et al. , using 2-oxoglutarate and arginine as substrates and producing succinate, P5C, guanidine, and COLikewise, the alkane propane has a slightly lower maximal stoichiometric yield because of the release of formate by the aldehyde decarbonylase. The maximal energetic yield of propane is significantly below that of 1-butanol, even though both synthesis pathways are almost identical. Otherwise, notwithstanding the overall similarity of maximal energetic yield, small differences in maximal yield can be attributed to pathway length, as well as NADPH and ATP utilization. Ethanol has the highest maximal energetic yield, whereas the value tends to be slightly lower for larger molecules.2 in production pathways. A property of importance is the ATP/NADPH ratio required for the synthesis of a desired product, ranging from approximately 0.92 for propane up to more than 2.0 for ethylene. In contrast, the ATP/NADPH ratio of the photosynthetic linear electron chain (ETC) is approximately 1.29, the computationally estimated ratio for the synthesis of biomass is 2.13. Notwithstanding significant caveats in its estimation , a rather enigmatic enzyme that synthesizes ethylene from 2-oxoglutarate (2OG) and arginine . As yet, synthesis of propane by cyanobacteria is not established, but synthesis feasible in Reaction fluxes that increase during the transition include the synthesis steps itself, as well as the recycling of formate, a by-product of the aldehyde deformylating oxygenase (ADO) reaction. Again, of particular interest are those reactions that increase in flux but are not part of the of the core synthesis pathway. These reactions represent key targets for modification, as they constitute potential bottlenecks for the synthesis of propane. Among the predicted changes observed in the transition from growth to propane synthesis are the aldehyde deformylating oxygenase  yields guanidine as a co-product. The fate of guanidine is unknown and has not been discussed in previous works . Flux-balance analysis is based on knowledge of reaction stoichiometries only. Given a limited input flux, FBA seeks to computationally estimate the maximal possible output flux, usually the synthesis of biomass as specified by a biomass objective function (BOF) that defines the cellular constituents in their correct proportion. We note that FBA is only concerned with stoichiometric yield. Nonetheless, the results are often reported in units of a (growth) rate that is obtained by multiplying the estimated maximal yield with the given input flux rate. The advantages of FBA reside (i) in the fact that the estimation of stoichiometric yield is not computationally expensive and feasible even for large models involving hundreds of reactions; as well as (ii) the fact that only stoichiometric information and no kinetic parameters are required. The latter are usually not available even for small reaction networks. In contrast, stoichiometric information is readily and reliably available for a substantial proportion of annotated enzymes  . +-dependent transport mechanisms. The amount of available light flux was chosen such that the model supports a maximum growth rate of 0.0289 h−1, corresponding to a doubling time of approximately 24 h. For the calculation of stoichiometric properties and trade-off analysis, Rubisco carboxylase and oxygenase (PP0011) were decoupled, resulting in two separate reactions (MO0007 and MO0008). No energy consuming side reactions, such as maintenance, evolution of reactive oxygen species, respiration, or the Mehler-like reaction, were considered for the estimation of maximal stoichiometric product yield, as well as biomass yield, shown in Table −1h−1. The lower heating values (LHVs) at room temperature have been extracted from the NIST-webbook of chemistry  using 10% of the oxygen evolution of photosystem II (PR0043) was assumed to take place. Likewise, the rate of the Mehler-like reaction (PR0033) was fixed to 10% of the initial oxygen evolution of photosystem II (PR0043). The evolution of reactive oxygen species (ROS) from the Mehler-reaction (PR0032) and from PS II (PR0034) were both set to 0.5% of the electrons converted in the respective photosystem . The basal ATP-demand for general cellular maintenance (GE0001) was set to 0.6312 mmol gDW−1h−1. Isoreactions have been excluded in the analysis of flux variability. For the transition experiment, a constant light availability of 18.0 mmol photons gDW−1h−1 was assumed, again corresponding to a maximal doubling time of approximately 24 h when considering the additional constraints. For detailed discussion of metabolic constraints used for flux estimation see Knoop et al. . Reactions with an decreasing flux rate were assigned as “flux decrease,” either “to flux” and “to zero,” depending on whether the mean of the flux rate at 100% product synthesis was larger than 10−4 mmol gDW−1h−1. The label “sign change” indicates that the product of the minimal flux rate for 100% biomass formation and the minimal flux rate for 100% product synthesis, or the maximal flux rate for 100% biomass formation and the maximal flux rate for 100% product synthesis is negative. We emphasize that the predicted flux solutions optimized for high product synthesis are not predictions of actual flux solutions after heterologous expression of product-forming pathways. Rather they must be interpreted as desirable target states that may or may not be attained using further targeted modifications of metabolism. It has been shown that that in practice high carbon partitioning toward product synthesis may also have detrimental effects that are not accounted for by flux-balance analysis .File Results_Transition_Analysis_Supplementary_File2. xls: full results of the transition analysis (xls).File Knoop2015_Simulation_Constraints_Supplementary_File3.xls: additional constraints used in the computational simulations (xls).Click here for additional data file.


Saccharomyces cerevisiae, the representation of biomass in its metabolic model has hardly changed in over a decade, despite major advances in analytical technologies. Here, we use the stoichiometric model of the yeast metabolic network to show that its ability to predict mutant phenotypes is particularly poor for genes encoding enzymes involved in energy generation. We then identify apparently inefficient energy-generating pathways in the model and demonstrate that the network suffers from the high energy burden associated with the generation of biomass. This is tightly connected to the availability of phosphate since this macronutrient links energy generation and structural biomass components. Variations in yeast’s biomass composition, within experimentally-determined bounds, demonstrated that flux distributions are very sensitive to such changes and to the identity of the growth-limiting nutrient. The predictive accuracy of the yeast metabolic model is, therefore, compromised by its failure to represent biomass composition in an accurate and context-dependent manner.Genome-scale stoichiometric models, constrained to optimise biomass production are often used to predict mutant phenotypes. However, for The online version of this article (doi:10.1007/s11306-015-0819-2) contains supplementary material, which is available to authorized users. When the entire metabolic network is viewed as a multi-enzyme system, small changes in enzyme concentrations have been found not to elicit profound changes in the overall metabolic flux, demonstrating the robustness of metabolic pathways  was used to predict metabolic phenotypes under different conditions, such as substrate and oxygen availability, by simply constraining the appropriate fluxes to predict a particular flux distribution using linear optimization  in synthetic defined medium ] as described by the manufacturer.The wild-type S. cerevisiae metabolic network  was used to determine the synthetically lethal gene pairs where no growth was represented as μ < 0.0001 h−1. Only synthetically lethal interactions, which occur at a frequency of ca. 1 % across all pairwise combinations of genes in the S. cerevisiae genome;  and the Generic Gene Ontology (GO) Term Finder was employed to conduct the GO term enrichment analysis using hypergeometric distribution for multiple hypothesis testing  and partial least squares regression (PLSR) were conducted on z-score normalized data centred around 0 (µ) and scaled by 1 (σ). Singular value decomposition was used in the PCA analysis. PLSR was conducted on 1740 predictor variables with 73 observations (SBC + 72 BRs) in each case of macronutrient limitation. Pearson correlation coefficient was used for determining the degree of linear dependence between the fluxes.The flux distribution data were analysed using standard statistical techniques available in Microsoft Excel and MATLAB R2012b with Statistics Toolbox . 2-tailed dependent Student’s S. cerevisiae metabolic network  were associated with reactions with non-zero fluxes. The fluxes, whose absolute values were determined to be less than 1 % of the maximum absolute reaction flux in magnitude, were considered as inconsequential and were eliminated from this analysis. This left a set of fluxes associated with 121 unique enzyme-encoding genes, which we call the “highly-elevated flux sub-network” (HFS). The HFS was enriched for enzyme reactions that have low variability as given by the flux variability analysis and ca. 3 % of yeast’s metabolic reactions were reported to be always active under different simulated growth conditions while the remaining reactions are conditionally active and respond to specific environmental changes, defined as the flux-based plasticity . We further identified a subset of reactions with “extremely elevated fluxes”, for which the magnitude of the computed absolute fluxes was greater than 10 % of the value determined for the maximum absolute reaction flux. These reactions are catalysed by enzymes specified by 31 unique metabolic genes that are significantly associated with ATP synthesis, coupled proton transport , and amino-acid catabolic process to alcohol via the Ehrlich pathway  . It was previously suggested that the overall intracellular flux distribution could be minimized since microorganisms have evolved to maximize enzymatic efficiency in order achieve rapid growth rates  to be tested was that the strains predicted to be inviable in the model-based simulations would indeed prove to be inviable under the conditions for which empirical data were available . The viable/lethal phenotype could correctly be predicted for 92 % of the query enzymes , and ‘programmed cell death’ (p = 5.17 × 10−4); see Table We then proceeded to investigate whether the landscape of interactions between the genes of the HFS were similar to or different from those of the global genetic interaction network. We observed that the incorrect predictions of gene essentiality led to further inaccuracies in the prediction of synthetic lethal (SL) pairs. Only 2 of the 163 predictions of SL pairs in the network were experimentally verified leaving the specificity of the HFS at 8.00 %, which was slightly lower than that of CN (10.51 %). As might have been predicted, although the essential genes themselves were under-represented in the HFS, there was an enrichment of SL interactions among the genes in the HFS network in comparison to that of the global genetic interaction network (0.08 vs. 2.53 %). This is congruent with the idea that the presence of many isozymes in this vital subset of the central metabolic network results in an under-representation of essential enzymes, but a high proportion of synthetically lethal interaction between pairs of genes encoding the enzymes of the HFS. The false-negative interactions were predicted among 46 genes that encode components of the electron transport chain complexes I, III and IV. In contrast to the case of essential genes, the SL pairs were identified to be over-represented in the sub-network. A recent study on the characterization of genetic interaction networks in yeast metabolism also reported a negative correlation between single-gene deletant fitness in FBA predictions and the FBA predicted epistasis  would be to changes in the chemical composition of biomass (as represented in the model) as well as to macronutrient limitation constraints introduced to the model. For this purpose, the stoichiometric coefficients of the 36 biomass constituents were individually varied by an arbitrarily selected factor of two under conditions of nutrient sufficiency as well as under conditions of limitation for one of four major macronutrients required for the growth and proliferation of S. cerevisiae in a chemically-defined nutrient environment: glucose, ammonium, phosphate and sulphate as primary sources of carbon, nitrogen, phosphorus and sulphur, respectively . The empirical data that are available on the biomass composition of S. cerevisiae indicated that the previously reported values for the biomass constituents varied within a 10-fold range of the values that were implemented in Yeast 7.00 model. The only exception to this is the lipids, for which even consensus molecular weights are still unavailable  of the reactions in the metabolic network were affected by changing the biomass content under different conditions of nutrient availability. Growth rates were determined to be similar under different metabolic reconfigurations for the same environmental condition. Sulphate limitation did not impair growth as predicted by the model. However, growth rates were lower for the case of the three remaining limitations; those of ammonium, glucose and phosphorus, with the highest growth impairment observed under phosphorus limitation and the lowest under ammonium limitation Fig. a. A prevd-glucan and glutamate content resulted in variations in a greater number of fluxes under glucose and ammonium limitations, respectively. On the other hand, fewer reactions were affected by variations in trehalose content (non-limiting and glucose-limited environments), cysteine content (ammonium- and phosphate-limited environment) and tryptophan content (sulphate-limited environment) in the metabolic network. More reactions were significantly affected by increasing (as compared to decreasing) the structural/storage carbohydrate content of biomass under non-limiting conditions, glucose, or ammonium limitation ; the dNMP content of biomass under non-limiting conditions, glucose or sulphate limitation ; or the NMP content of biomass under glucose or phosphate limitation .We explored how different biomass reconfigurations affected the in silico distribution of fluxes under non-limiting nutrient conditions and under limited macronutrient availability. More reactions were affected by varying the lipid content of biomass under non-limiting conditions and under sulphate or phosphate limitation. Variations in 1-3 β-d-glucan, 1-6 β-d-glucan, alanine, aspartate, glutamate and glutamine. Consequently, the highest variation, indicated by the flux scores, was observed in the fluxes through transport and other reactions involved in lipid and sphingolipid biosynthetic routes in accordance with our earlier findings.We carried out an orthogonal transformation of the flux distributions as a function of different BR and the first 3 principal components were sufficient to capture more than 91 % of the total variation in the dataset. The weights of structural constituents of biomass such as 1-3-β-glucan and 1-6-β-glucan and the lipid content along with alanine, aspartate, glutamate, and glutamine were the highest among the biomass constituents . Glucose-limitation was used as a template for predicting the distribution of fluxes in response to phosphate-limitation owing to the tightly interwoven roles of these two macronutrients in energy metabolism. The landscape under non-limiting conditions was observed to effectively predict the response observed under glucose, ammonium or sulphate limitation with just the first loading predictor and response loadings explaining more than 80 % of the total variance among the datasets. Similarly, the distribution of fluxes under ammonium-limitation could successfully be utilized as a predictor of the response under sulphate-limitation Fig. a. On theThe residual profiles across all reactions in the yeast metabolic network were observed to display similar trends with the only difference being that the magnitude and residual values in the response fluxes under phosphate limitation were higher than in any of the remaining cases under investigation Fig. c–h. Specd-glucan, 1-6 β-d-glucan and the lipid content (PCC > 0.9). The statistical dependence of the flux distributions calculated for the BR, in which the 1-3 β-d-glucan, 1-6 β-d-glucan, mannan, or lipid content of the biomass was increased and lipid, ALA, ARG, ASN, ASP, GLN, GLN, GLU, PRO or THR content of the biomass was reduced, was low between the nutrient non-limiting and limiting conditions independent of which macronutrient was supplied in growth-rate limiting amounts in the environment  between non-limiting and glucose-, ammonium-, or sulphate-limited conditions; whereas they were uncorrelated (PCC < 0.5) between the non-limiting and the phosphate-limited environments, except for increasing the structural content of biomass through 1-3 β-Although we could not observe any correlation between the impact of reconfiguration on the distribution of the fluxes and either the fraction of biomass that the reconfigured constituent represents or the connectivity of that constituent, a striking feature was observed in the case of the lipids. Although constituting a relatively small fraction of the total biomass , they are involved in a substantial portion of the metabolic network  and it is likely that this high connectivity was the reason behind the large impact of reconfigurations that we observed when changing the lipid content of the biomass. Due to the highly connected structure of metabolic networks, a single perturbation was previously reported to necessitate the adaptation of the network to the new state as a whole  investigated. In only a minority of such pairs, moderate to low correlations were found. Since this analysis was carried out using an arbitrarily selected twofold perturbation of the biomass composition between the selected conditions, the immediate questions arising from this observation were (i) whether the distribution of the fluxes would be affected by a sharp or a gradual perturbation in a component of the biomass and (ii) whether the wiring of the intracellular fluxes would remain unaffected by the magnitude of the change imposed under any of the physiological conditions or components of the biomass investigated. Either of these would indicate how sensitive the changes in the distribution of fluxes are to variations in biomass composition or the physiological status. We therefore performed a sensitivity analysis to evaluate the robustness of the differences and similarities identified in the present findings. Simulation of even very extreme conditions of limited macronutrient availability  did not disturb the high correlation between flux distributions of cells with the SBC grown under non-limiting environmental conditions, or under glucose or sulphate limitation. On the other hand, the predicted flux distributions soon became uncorrelated as the ammonium available for uptake was reduced from 2 to 1 % of its non-limiting value. The decrease observed in the correlation was a sharp response rather than a gradual one Fig. b. Converl-methionine, l-alanine, glycogen, and 1-3 β-d-glucan were investigated for this purpose. Glycogen (10.86 %) and 1-3 β-d-glucan (17.98 %) are among the most abundant constituents of biomass whereas l-methionine (0.24 %) and l-alanine (1.28 %) make markedly small contributions to the total cell mass. Increasing the l-methionine or glycogen content of biomass did not affect the overall flux distribution appreciably, whereas increasing the 1-3 β-d-glucan content of biomass or decreasing the l-alanine content resulted in flux distributions that were non-correlated with those of the flux predictions based on the SBC. A more severe alteration, by further increasing the glycogen content of biomass by 50 %, caused the distribution of fluxes to become non-correlated with that of the SBC  Below is the link to the electronic supplementary material.