This model is a stepping stone to broader applications of fish systems biology, for example by guiding study design through comparison with human metabolism and the integration of multiple data types. MetaFishNet resources, including a pathway enrichment analysis tool, are accessible at  Danio rerio), medaka (Oryzias latipes), stickleback (Gasterosteus aculeatus), European flounder (Platichthys flesus), channel catfish , sheepshead minnow (Cyprinodon variegatus), mummichog (Fundulus heteroclitus), Atlantic salmon , common carp (Cyprinus carpio), rainbow trout (Oncorhynchus mykiss) and swordtail (Xiphophorus hellerii). Each of these fish species has its own niche as a research tool. For example, Xiphophorus is a classic genetic model of melanomas [in vivo imaging [Small fish species are widely used in ecological and pharmaceutical toxicology, developmental biology and genetics, evolutionary biology and as human disease models. Among the species commonly found in scientific literature are zebrafish . We then use MetaFishNet to methodically comparefish and human metabolism to identify the most and least conserved pathways. The last sections of this paper will demonstrate the application of MetaFishNet in analyzing two sets of DNA microarray data: one from zebrafish as liver cancer model in public repository, the other from sheepshead minnow exposed to cadmium in our lab.Our genome-wide fish metabolic network, MetaFishNet, adopts a conventional bipartite network structure, where enzymes and compounds are two types of nodes. The construction strategy of MetaFishNet is shown in Figure D. rerio, O. latipes, T. rubripes, T. nigroviridis and G. aculeatus) to create a list of all fish metabolic genes via gene ontology. From this metabolic gene list, the corresponding enzymes were identified using either orthologous relationships to human genes or similarity to consensus enzyme sequences . This enzyme is associated to a reaction in the EHMN model that converts 1-Phosphatidyl-D-myo-inositol 4,5-bisphosphate to Phosphatidylinositol-3,4,5-trisphosphate. Thus, this same reaction is carried over to the MetaFishNet model. Another sequence ENSORLG00000018911 also has a human homolog, PIP4K2B, which is a phosphatidylinositol-5-phosphate 4-kinase with enzyme commission number 2.7.1.149. Although no reaction for this enzyme is found for any of the reference models, we learn from the KEGG LIGAND database that this enzyme converts 1-Phosphatidyl-1D-myo-inositol 5-phosphate to 1-Phosphatidyl-D-myo-inositol 4,5-bisphosphate. This reaction is added to MetaFishNet as an inferred reaction. Furthermore, because the second reaction produces the substrate for the first reaction, the two reactions are linked together in the 'Phosphatidylinositol phosphate metabolism' pathway.We carefully reconciled the pathway organization during integration of the three reference models by comparing the reactions in each pathway. Thus, the pathway organization in MetaFishNet follows biochemical conventions wherever possible. Yet, over 600 reactions still do not map directly to these reference pathways. Since pathways can be viewed as modules within a metabolic network , we extrTetraodon genome (Ensembl 51). Since these are critical enzymes in a central pathway, these missing enzymes reflect annotation errors. The combined model is thus more comprehensive than using any single species alone  a combined model will be more useful for other under-represented fish species; b) genome annotations are far from perfect - combining five genome sequences will reduce the chance of missing true metabolic genes. For example in the TCA cycle, we did not find ATP citrate synthase in the zebrafish genome, nor succinate-CoA ligase in the  Ensembl 1. Since A MySQL database was set up to host MetaFishNet data. As we elected to use Google App Engine to host the project website , a port It is now widely accepted that teleost fish underwent an extra round of genome duplication after their evolutionary separation from the mammalian line ,37. GenoWhen comparing the fish metabolic genes in MetaFishNet to their human orthologs, we have noticed that the level of ortholog mapping differs between metabolic genes and other genes. As seen in Table Multiple genes may have the same catalytic activity (isozymes), differing only in their sequences or regulatory contexts. We do not distinguish isozymes in this study, but leave them for future refinement. At the enzyme level, we have identified 911 enzymes from fish genomes. They overlap with the human data by 772 enzymes . Given per se - they accumulate them from their diet, algae [Omega-3 fatty acids are deemed essential nutrients, boosting a popular dietary preference for fish and fish oil consumption. But fish, just like humans, do not produce omega-3 fatty acids t, algae . HoweverWe next demonstrate the application of MetaFishNet model to the analysis of gene expression data in a case of zebrafish as a cancer model. Gong and coworkers conducted microarray experiments to examine the similarity between zebrafish and human liver tumors at the level of gene expression . AlthougThe microarray data from  were retThe result, shown in Table Fatty acid metabolism is also well known to be involved in cancer biology ,50. HoweKEGG also offers an expression analysis tool, KegArray , which mUltimately, the quality of pathway data determines the quality of analysis. MetaFishNet, with 3,342 reactions over the 1,031 reactions in KEGG zebrafish model, not only allows applications to other fish species, but also improve the data for zebrafish. A better comparison between the KEGG zebrafish model and MetaFishNet is to use the same enrichment statistics. That is, we use the KEGG pathways in our software instead of MetaFishNet pathways to reanalyze the zebrafish liver cancer data in Section 2.4. The result is shown in Additional file Xenobiotics metabolism is a pathway that is improved from five enzymes in KEGG to eight enzymes in MetaFishNet. Accordingly, the MetaFishNet pathway has three hits while the KEGG pathway has two hits. The Methane metabolism pathway, nonexistent in MetaFishNet, was also identified in KEGG. The KEGG Methane metabolism pathway is rather a bacterial pathway that is mapped to zebrafish with only three reactions. Reaction R06983 is catalyzed by an enzyme (1.1.1.284) that is yet to be confirmed in any fish genome. Reaction R00945 converts 5,10-Methylenetetrahydrofolate to Tetrahydrofolate, thus is assigned to vitamin B9 (folate) metabolism pathway in MetaFishNet. This leaves only one reaction, which does not justify a pathway in MetaFishNet. We think the improved data and pathways in MetaFishNet will benefit downstream studies.C. variegatus) is a common, small estuarine fish that is found along the Atlantic and Gulf coasts of the United States. The US Environmental Protection Agency has adopted C. variegatus as a model organism for studying pollution levels in estuarine waters [Finally, we apply MetaFishNet to a fish species with little functional data. Sheepshead minnow . This is a less stringent selection, but additional statistical strength is gained at the pathway level by incorporating collective pathway information. Sheepshead minnow clones were then mapped to MetaFishNet by sequence comparison via SeaSpider. MetaFishNet pathway enrichment was computed again by Fisher's Exact Test and the result is shown in Table To analyze the data in MetaFishNet, we first selected 325 differentially expressed clones between the treated group and control group by Wilcoxon's rank sum test  was developed in this project to draw pathway maps automatically.Even though visualization tools are abundant, there is a particular challenge to balance automation with the kind of clarity desired in a metabolic map. KEGG, and many other pathway databases, creates graphs manually. Hence, all downstream automatic programs in fact depends on the original manual versions.CellDesigner  is an exWe should emphasize that the knowledge of vertebrate metabolism is still very incomplete. This is already evident when considering the obvious differences between the two human models ,31. WithAs a first generation model, MetaFishNet will need much refinement to fully realize the power of a genome-wide metabolic model. Traditionally, metabolism was studied piecemeal by dissecting enzyme activities and tracking metabolites. Powerful new tools have now been introduced to genome-wide models ,70. For et al. used a E. coli metabolic network [Recent studies have shown that gene expression data, combined with metabolic network models, can successfully predict metabolic flux regulation in specific biological contexts -76. This network  to combi network .et al. combined transcriptomics and metabolomics on Ingenuity's human metabolic pathways http://www.ingenuity.com to identify type two diabetes markers [Connor  markers . With th markers -81, MetaAll cDNA sequences of the five fish species were retrieved from the Ensembl database . IdentifWe first integrated the two high-quality human metabolic models ,31. The The EHMN model has 2,322 genes and 2,824 reactions (excluding transport reactions). The EHMN model included previous metabolic data from all major databases, and streamlined the identities of compounds. Automatic extraction of metabolic models from KEGG has been a challenge. Even though KEGG offers an XML (Extensible Markup Language) distribution  of its pathways, molecular interactions were mixed with visual elements in these KGML les. KEGG API (Application Programming Interface) was also limited by not distinguishing reactants from products. We developed a practical solution by combining KGML files and KEGG API, where KGML defines the scope of reactions and API confirms relationships. Our Python script, leveraging on SBML libraries, successfully parsed out the 101 zebrafish metabolic pathways from KEGG , with 517 ECs and 1,031 reactions.The integration of three models was at both the reaction and pathway levels. Two reactions were considered identical when they have the same enzymes and major compounds. To gain the most compatibility, EC numbers and KEGG compound IDs were used wherever possible. The conventional pathways in MetaFishNet primarily followed the pathway organization in EHMN. Pathways were merged if they shared a significant number of common reactions. Different naming styles were reconciled. For example, the 'Cholesterol Metabolism' pathway in the UCSD model overlaps with the 'Squalene and cholesterol biosynthesis' pathway in the EHMN model by 14 enzymes and 16 reactions. The two pathways were merged during the integration of the two human models. All three reactions in the KEGG zebrafish pathway 'Terpenoid biosynthesis' are included in the human 'Squalene and cholesterol biosynthesis' pathway and were therefore merged with the latter. Nine out of 11 enzymes in the zebrafish 'Biosynthesis of steroids' pathway are included in the human Squalene and cholesterol biosynthesis pathway, and were therefore merged as well. Complete lists of pathway reorganization are given in the Additional file ab initio construction, while a number of modules were organized into existing pathways (Additional file Among the 911fish enzymes identified in this project, 561 could be matched to the reference data. For the remaining 350 enzymes, their associated compounds were retrieved from the KEGG LIGAND database wherever available. These enzyme-compounds interactions formed 260 newly inferred reactions. Since there was no way to distinguish reactants from products in these inferred metabolic data, the directions of these reactions were treated as unknown. These newly inferred reactions, plus the isolated reactions from the reference data, were subjected to a combined approach of module-finding and manual curation. We adopted an algorithm by Mark Newman, which partitions network modules according to the eigenvectors of a characteristic matrix for the network . The modFishEye, our pathway visualization tool, is built on Networkx and PyGraphviz . It exteWe have previously generated Suppressive Subtractive Hybridization libraries for sheepshead minnows, and sequenced over 10,000 clones . Based oExposures and animal sampling were performed as previously described ,89. CadmAPI: application programming interface; EC: enzyme commission; EHMN: Edinburgh human metabolic network; FDR: false discovery rate; GEO: gene expression omnibus; GO: gene ontology; GSEA: gene set enrichment analysis; IUBMB: international union of biochemistry and molecular biology; KEGG: Kyoto encyclopedia of genes and genomes; KGML: KEGG markup language; SAM: significance analysis of microarrays; SBML: systems biology markup language; UCSD: University of California at San Diego; XML: extensible markup language.SL designed and performed most of the computational work. MB designed and supervised the experimental study. AP and MB provided critical guidance of the project and valuable discussions. CSM performed the cadmium exposure of sheepshead minnows. NBP and RR dissected the fish, extracted and labeled RNA. AP coordinated the sheepshead minnow microarray design and experiments. SL and MB wrote the manuscript.Supplemental method [l method -101.Click here for fileSpecies specific statistics of pathways.Click here for fileList of MetaFishNet pathways.Click here for fileMetaFishNet reaction data.Click here for fileSBML distribution of MetaFishNet pathways.Click here for fileFish and human enzymes.Click here for fileAnalysis of zebrafish liver cancer data by KegArray.Click here for fileAnalysis of zebrafish liver cancer data by KEGG pathways and Fisher's exact test.Click here for fileComplete comparison between fish and human metabolic pathways.Click here for file


We selected ammonia as a low cost, abundant, safe, and soluble redox mediator that facilitated energy transfer to biomass. Nitrosomonas europaea, a chemolithoautotroph, was used as the biocatalyst due to its inherent capability to utilize ammonia as its sole energy source for growth. An electrochemical reactor was designed for the regeneration of ammonia from nitrite, and current efficiencies of 100% were achieved. Calculations indicated that overall bioproduction efficiency could approach 2.7±0.2% under optimal electrolysis conditions. The application of chemolithoautotrophy for industrial bioproduction has been largely unexplored, and results suggest that this and related rMFC platforms may enable biofuel and related biochemical production.The storage of renewable electrical energy within chemical bonds of biofuels and other chemicals is a route to decreasing petroleum usage. A critical challenge is the efficient transfer of electrons into a biological host that can covert this energy into high energy organic compounds. In this paper, we describe an approach whereby biomass is grown using energy obtained from a soluble mediator that is regenerated electrochemically. The net result is a separate-stage reverse microbial fuel cell (rMFC) that fixes CO This has been termed microbial electrosynthesis 2) as well as those involved with the nitrogen, iron and sulfur biogeochemical cycles (i.e. ammonia (NH3), nitrite (NO2−), iron (Fe2+), hydrogen sulfide (H2S)) are attractive options for use in this platform since they can facilitate the construction of multi-carbon organics from carbon dioxide using naturally occurring carbon fixation pathways. These inorganic compounds naturally yield sufficient energy to support biomass growth and can be reduced via electrolysis (Table S1).An alternative approach to creating rMFCs is to use soluble electron mediators that can shuttle electrons from the electrode to the cells. The use of a mediator enables the utilization of planktonic cells in the bioreactor and facilitates easy 3-D scale-up of the individual components. Furthermore, the use of mediators can also enable separate-stage designs that afford spatial and temporal decoupling of energy capture and bioproduction. This can allow both processes to be operated and optimized separately. In the mediated approach, electrons are first transferred from an electrode to a soluble mediator and then the mediator would be oxidized by the cell. Inorganic compounds that are linked with chemoautotrophy and can be electrolytically regenerated, like hydrogen  cycle using energy derived solely from ammonia. These results suggest that this approach can be expanded to produce biofuels and other chemicals via genetic modification of the N. europaea cells in the bioreactor without the need for photosynthesis.Successful rMFC operation has recently been demonstrated using electrochemically produced formate coupled with genetically modified europaea Figure 1Successful operation of a rMFC platform requires the selection of soluble mediators that not only support biosynthesis but can be efficiently recycled. In this study, we utilize the ammonia/nitrite couple as the soluble mediator. Consequently, experiments were designed to, 1) further our understanding ammonia regenerated via electrolysis and, 2) demonstrate that electrochemically derived ammonia can support biomass production. In confirming that biomass can be produced using this approach, it is our intention to showcase a flexible bioproduction strategy that could be expanded for the production of biofuels and biochemical via genetic engineering strategies.pH 7.0 = 0.12 V vs. Ag/AgCl). Copper electrodes were found to be highly active for nitrate reduction both in basic and weakly alkaline solutions 2) indicated that the nickel cathode (0.65 V) had the lowest overpotential  followed by copper (0.85 V) and glassy carbon (1.13 V). Even though glassy carbon displayed the highest overpotential among the three cathode materials tested, it was selected for further study due to its biocompatibility, inertness and long term performance Since nitrogen can have oxidation numbers ranging from +5 to −3, the kinetics and selectivity of the nitrite reduction reaction are highly dependent on various criteria including the electrolytes, cathode materials, operating conditions N. europaea cultures. Initial current efficiencies  of 104.5%±9.0 were observed in the presence of high nitrite concentration (50 mM). However, as the nitrite concentration decreased to 22% of the initial concentration (11 mM), the current efficiency decreased to 58.4%±4.7 (inset). These findings demonstrate that electrolysis is an effective approach for converting renewable electrical energy into chemical energy (NH4+/NH3).Further experiments investigated the feasibility of using glassy carbon cathodes to electrochemically produce ammonia from spent growth media obtained from 8.4%±4.7 Figure 3N. europaea culture media. However, an increase in catholyte pH was initially observed. This pH increase was due to production of OH− when nitrite is reduced to ammonia at the cathode and could not be offset by the proton content of the anolyte used in this experiment (phosphate buffer at pH 7.0). To prevent this increase, pH control of the catholyte via addition of hydrochloric acid (5 M) was performed. This resulted in ∼3% dilution of the catholyte. Alternatively, use of an acidic anolyte and a proton exchange membrane would compensate for OH− produced at the cathode and would eliminate catholyte dilution in future systems.No signs of corrosion and electrode passivation at the anode and cathode or precipitation in the catholyte and anolyte were observed in experiments using spent N. europaea  in replicate continuous flow reactors. Ammonia removal efficiency was maintained above 96% during all periods of operation  = 97.2±1.5%; Period 2  = 97.9±1.3%; Period 3  = 95.5±2.7%) while observed biomass yields during these experiments were statistically identical , demonstrating that the free energy efficiency of ammonia utilization for biomass construction . Biokinetic and stoichiometric parameters describing growth , biomass yield coefficient , half saturation constant ) were also statistically unchanged across all culturing periods , and were within ranges previously reported by other researchers N. europaea. These finding are not unexpected since all nutrients  were provided in excess during chemostat cultivation and during short-term assays.Once efficient electrochemical generation of ammonia from nitrite was demonstrated, experiments were performed to verify biomass growth using the regenerated ammonia. Results confirmed that this approach is a viable biosynthesis technology with electrochemically produced ammonia being successfully used to cultivate wild-type 3, NO2−, Fe2+, H2S, H2). From a thermodynamic perspective, the minimum power required for biomass production, which is equal to the Gibbs free energy of formation of the biomass, is equivalent for these five mediators; however, since the energy state of electrons varies per mediator, the rate of reduced mediator consumption and subsequent regeneration is governed by the biocatalyst ability to synthesize biomass using the energy from the mediator. This ability can be quantified by examining biomass growth characteristics such as specific growth rates and yield coefficients for each system. Comparison of biomass growth kinetics and yields associated with the five mediators indicates that H2, H2S and Fe2+ oxidizers can produce biomass more rapidly than ammonia and nitrite oxidizers (Table S3); however, the low water solubility, high flammability and toxic nature of H2 and H2S require complex storage and delivery capabilities, rendering use of H2 and H2S in rMFCs challenging (Table S4). Similarly, Fe2+ is unstable under aerobic conditions and will undergo rapid abiotic oxidation to Fe3+, making storage and sustained use of this mediator potentially difficult. In contrast, NH3 and NO2− are highly water soluble, less toxic/flammable than H2S or H2 and can be stored under aerobic conditions. Additionally, these compounds are ubiquitous as environmental pollutants, rendering them inexpensive and suitable for use in chemolithoautotrophic bioreactors.Coupling electrolysis and biosynthesis in a rMFC using mediated electron transfer requires an electron mediator that is inexpensive, highly soluble, non-flammable, and amenable to long term storage. In addition, this mediator must be compatible with the electrochemical cell with low power requirements for regeneration and it must also be capable of being efficiently utilized by the biocatalyst. Bacteria that have evolved to obtain energy from natural redox mediators will have optimized this process, and are likely ideal for use in rMFC systems. Five inorganic redox active compounds that are capable of supporting lithoautotrophy were considered for this purpose  Using Faraday’s law and assuming 100% current efficiency, this suggests that a charge of 162 Ahr is required for the reduction of 1 mol of nitrite to ammonia, resulting in a power requirement of 0.324 kWhr (162 Ahr×2 V). Using the experimentally measured biomass yield of 0.06±0.01 mg biomass/mg N  will be impacted by the cell overpotential. Using the experimentally determined overpotential Figure 2N. europaea growth. In these experiments, electricity generated from non-renewable sources was used; however, it should be noted that electricity derived from renewable sources can be easily substituted. In such a scenario, if one further assumes that the electrical energy used during electrolysis is derived from solar sources  Continuous operation of this ammonia based rMFC would need to resolve issues associated with nitrogen losses and inorganic carbon and proton supply. In the present study , total nitrogen losses accounted for 5.4 mg N/day  utilizing modified autotrophic medium (designated SynM) comprising (mg/L): MgSO4·7H2O (200), CaCl2·2H2O (20), K2HPO4 (87), KH2PO4 (8820), EPPS (2520) Na2MoO4·2H2O (0.01), MnSO4·H2O (0.017), CoCl2·7H20 (.0004), CuCl2·2H2O (0.17), ZnSO4·7H2O (0.01), chelated iron (1), NaNO2 (690) and (NH4)2SO4  3 (80 g/L). Culture purity was confirmed via direct cell counts and DNA sequencing 3 concentration that was not statistically different at the 95% confidence interval from the three previous time-points; defined as Period 1), the autotrophic media described previously was replaced with spent media (from Period 1) that was electrochemically regenerated once or twice as described below (defined as Periods 2 and 3 respectively). Reactor performance was then monitored for changes in cell counts, nitrogen species , biokinetic and stoichiometric parameters. Statistical comparison of parameter estimates  from Periods 1 through 3 was performed using univariate analysis of variance (ANOVA). The significance level (α) used was 0.05 and the corresponding P-values  are reported where appropriate.Upon achieving steady state removal of ammonia  Nitrogen mass balances were constructed from running averages of ammonia, nitrite and cell count profiles, before during and after experiments to account for the changes in overall nitrogen loading. Carbon mass balances were also constructed by estimating the mass of CON. europaea cells using an YSI model 5331 oxygen electrodes  connected to a dual channel meter  such that DO readings were recorded every 10 seconds using an automated data acquisition system (Labview 8.0). Specific OUR (sOURmax) values were determined by dividing the OUR values by the cell concentration of each sample. Yield coefficients, growth rates and half saturation constants were estimated using extant batch procedures described elsewhere Oxygen uptake rates (OUR) were determined in duplicate for chemostat cultured whereAOB = active oxygen uptake due to ammonia oxidation to nitrite (mg/L)OUmax,AOB = maximum specific growth rate of Nitrosomonas europaea (1/day),µS,AOB = yield coefficient for Nitrosomonas europaea (mg biomass/mg NH3-NOD)fNH3 = ammonia concentration (mg NH3-NOD/L),SAOB = biomass concentration (mg biomass as COD/L),XNH3 = half saturation constant (mg NH3-NOD/L).K3 = 3.43 mg NOD when ammonia oxidation to nitrite is considered)Wet Chemistry and Analytical ChemistryNOD = nitrogenous oxygen demand . Samples for nitrogen analyses were filtered through 0.45 µm filters prior to immediate analyses for nitrite (Griess reagent) and ammonia  3) filtered samples (0.45 µm) using inductive coupled plasma atomic emission spectroscopy .Cell counts Linear sweep voltammetry experiments were conducted using a µAutolab Potentiostat . Glassy carbon rotating disk electrodes (3 mm diameter) were produced in-house as described in Gallaway et al 2 was applied for 325 s using µAutolab Potentiostat .A three electrode cell described above consisting of a Ag/AgCl reference electrode, a platinum counter electrode and a nickel, glassy carbon or copper rotating disk electrode was also used in constant current experiments. The electrode was rotated at 400 rpm and constant current of 2 mA/cm2 was used as the cathode. µAutolab Potentiostat was used to apply constant potentials between 0.9 to 1.6 V vs. Ag/AgCl for 1 hr. At the end of the experiments the samples were collected for ammonia measurements.A three electrode cell similar to the one described above was used in constant applied potential experiments. Instead of a rotating disk electrode a 5 pores per inch (ppi) porous glassy carbon electrode with an estimated area of 13.2 cm2 based on cross sectional area of the flow cell. A peristaltic pump  was used to flow the catholyte and anolyte at a rate of 0.8 ml/s. pH controller purchased from Omega (USA) and was used to keep the pH between 8.4 and 8.6 at the catholyte. Experiments were run until 80% conversion was achieved and samples were collected throughout the experiment for ammonia measurement.The home-made flow-by electrolysis cell described in Sahin et al was used for flow cell experiments Figure S1Ammonia removal efficiency and observed biomass yield for continuous flow cultivated Nitrosomonas europaea.(TIFF)Click here for additional data file.Table S1Half-cell reactions and thermodynamic cell potential for inorganic electron mediators.(DOC)Click here for additional data file.Table S2Summary of biokinetic and stoichiometric parameters describing growth of wild-type Nitrosomonas europaea on electrochemically regenerated media.(DOC)Click here for additional data file.Table S3Thermodynamic and biokinetic properties of common inorganic electron mediator couples.(DOC)Click here for additional data file.Table S4Physical, thermodynamic and biokinetic properties of ammonia, nitrite, iron (II), hydrogen sulfide and hydrogen.(DOC)Click here for additional data file.


Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms.The filamentous fungus  Neurospora crassa. Given the wealth of knowledge available for this filamentous fungus, the effort required to manually curate a high-quality genome-scale metabolic reconstruction would be daunting. To aid the reconstruction process, we developed three optimization-based algorithms. The first algorithm predicts flux while linearly accounting for metabolite dilution; the second algorithm removes blocked reactions with one compact linear program; and the third algorithm reconciles differences between in silico predictions and experimental observations of mutant viability. We have used these algorithms to develop the first genome-scale metabolic model for Neurospora. We have validated the accuracy of our model against an independent test set of more than 300 growth/no-growth phenotypes, and our model displays 93% sensitivity and specificity. Simulating the biochemical genetics experiments originally performed on Neurospora, we comprehensively predicted essential genes, nutrient rescues of auxotroph mutants and synthetic lethal interactions. With these predictions, we provide potential mechanistic insight into known mutant phenotypes, and testable hypotheses for novel mutant phenotypes. The model, the algorithms and the testable hypotheses provide a computational foundation for the study of Neurospora crassa metabolism.Few organisms have been as foundational to the development of modern genetics and cellular metabolism as  Neurospora crassa has become a model organism for eukaryotic biology and the cornerstone of a vibrant research community Neurospora has led to essential discoveries in circadian rhythms Neurospora thus paved the way for modern genetics and molecular biology.First discovered as an orange mold infestation of Paris bakeries in 1843 Neurospora, coupled with its ability to grow on minimal media, simplified the isolation of mutants with additional nutrient requirements. The first such auxotrophic mutants established the universal link among genes, enzymes, and the ordering of reactions in biosynthetic pathways. Work over subsequent decades led to a compilation of hundreds of such mutants, shedding light on most major biosynthetic pathways Neurospora genome Neurospora metabolism could begin to emerge.Of equal consequence, the work by Beadle and Tatum ushered in a new era in the study of biochemistry and cellular metabolism. The genetic facility of Genome-scale metabolic models have been constructed for over 100 organisms spanning bacteria to mammalian cells Neurospora, along with ongoing efforts to knock-out and phenotypically characterize all ∼10,000 genes in the genome Neurospora as a potential biofuels organism The wealth of genetic and metabolic data available for Neurospora crassa. To guide the process of model construction, we developed a novel suite of algorithms called Fast Automated Reconstruction of Metabolism (FARM). We validated the model against an independent gene essentiality test set, and achieved 93% sensitivity and specificity. We applied the validated model to comprehensively predict nutrient rescue of essential genes and synthetic lethal interactions. With these predictions, we provide potential mechanistic insight into known mutant phenotypes, and testable hypotheses for novel mutant phenotypes. More generally, the model provides a framework for integrating and interpreting ongoing experimental efforts that continue extend the rich history of biochemical research on Neurospora.We report here the construction and validation of a high-quality genome-scale metabolic model for Neurospora metabolic network model in a process consisting of four stages, as shown in We reconstructed, validated and performed computational predictions with the Neurospora genome and literature to generate an initial draft of the metabolic network. This process was initiated by computing the probability that each enzyme activity is encoded in the genome sequence EFICAz enzyme function predictor Neurospora-specific literature to identify experimentally determined enzymes, assign Gene Ontology terms to proteins, distinguish isozymes from enzyme complexes, catalog growth observations, and estimate the biomass composition We integrated the We iteratively improved the initial metabolic model with a manually curated training set of experimentally observed viability phenotypes on minimal and supplemented media. We used FARM on this set to suggest reaction additions/removals that would improve prediction accuracy. These changes were manually reviewed and accepted only if consistent with published experimental evidence.To confirm that the final model was not over-fit to a single training set and to ensure that the predictions of the model could generalize to new phenotypes, we validated the model using an independent test set of experimentally observed viability phenotypes.in silico double knockout experiments to predict synthetic lethal interactions. In all three cases, published observations were available that validated the accuracy of the predictions. The metabolic model extends these published observations in a manner that would be difficult experimentally by assaying a comprehensive set of conditions, providing novel testable hypotheses, and providing potential mechanistic insight into these predictions.We applied the final model to generate three sets of predictions. Firstly, we predicted the essentiality of all genes in our model. Secondly, we predicted which nutrient supplements would rescue a manually curated set of inviable mutants, and provided mechanistic explanations for each rescue. Thirdly, we systematically performed A number of significant challenges remain in the reconstruction of high-quality genome-scale metabolic models Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches; and One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program.To facilitate the automation of metabolic network reconstruction, we developed three optimization-based algorithms, which together comprise S. The matrix S and the set of reaction constraints lb and ub define the set of all possible flux configurations v at steady state. By defining a metabolic objective function Tvc that represents all the essential biomass components necessary for growth, linear programming can be used to predict whether the model supports growth under a given nutrient condition. The linear programming problem is:Flux balance analysis is a widely used method for predicting metabolic capabilities using genome-scale metabolic network models active metabolites. Consequently, FBA can fail to require the biosynthesis of known essential compounds. For example, the Saccharomyces cerevisiae model in silico, whereas biologically quinones must be replenished by S. cerevisiae to overcome their growth-associated dilution. To account for growth-associated dilution of active metabolites, we developed limed-FBA.A known shortcoming of FBA is that it does not account for dilution of metabolites involved in active reactions Neurospora is shown in arg-14 that encodes acetylglutamate synthase. This enzyme acts as an input flux to arginine biosynthesis and is required for growth arg-14 is not essential. In contrast, limed-FBA forces dilution of the metabolites in the acetyl cycle, thus preventing these compounds from being produced without an input flux. As a consequence, limed-FBA correctly predicts that arg-14 is essential.The limed-FBA method works by forcing active metabolites to dilute through an additional small dilution flux see . We illuN-acetyl-L-glutamate, to biomass, then FBA would correctly predict the essentiality of arg-14. However, then FBA would also predict that the arg-14 knockout cannot be rescued by arginine. In fact, arginine does rescue Δarg-14 experimentally, as correctly predicted by limed-FBA.A heuristic that is used in FBA to account for metabolite dilution is to add a small “drain” of diluted metabolites to the biomass composition. The issue with this heuristic is that it requires knowing a priori which metabolites are diluted, whereas limed-FBA determines which metabolites are diluted based on the flux. For example if this heuristic was used to add a metabolite in the cycle of Metabolite Dilution FBA (MD-FBA) mixed integer linear program (MILP), which severely limits its practicality Importantly, we designed limed-FBA as a linear program. Linear programs can be solved robustly and quickly, making limed-FBA a practical solution to account for metabolite dilution. An alternative method that has been developed is Neurospora, including enzyme function predictions, thermodynamic estimates, literature references, and pathway information, in a disciplined manner.During stage 2 of our process, we iteratively improved the ability of the metabolic network model to predict gene knockout phenotypes. A number of computational algorithms have been described to maximize consistency between predicted and experimental growth/no-growth phenotypes Consistent Reproduction Of growth/no-growth Phenotype (CROP). CROP solved inconsistencies while accounting for diverse evidence. This evidence included (1) whether we had manually curated a reaction with experimental evidence from the literature, (2) what pathways a reaction was part of, and whether these pathways were predicted to be in NeurosporaTo quickly and accurately reconcile inconsistencies between predicted and experimental growth/no-growth phenotypes, we developed blocked reactions. The removal of blocked reactions is a process known as functional pruninggoal programmingAfter iteratively applying CROP to improve the model, reactions can be present that cannot carry flux under any nutrient condition. These reactions are referred to as N. crassa iJDZ836. N. crassa iJDZ836 is available in the Systems Biology Markup Language (SBML) in Here we describe the results of our genome-scale metabolic reconstruction and its application as a predictive steady-state model. Following the accepted nomenclature Neurospora-specific enzymes. This level of evidence compares favorably with other highly curated models The model contains 836 genes that encode 1027 unique enzymatic activities. Of these enzyme-catalyzed reactions, 694 are supported by experimental evidence from 491 publications addressing Cellular compartments in the model include the cytosol, the extracellular space, and 4 organelles: the glyoxysome, the vacuole, the nucleus, and the mitochondrion. The 299 transport reactions of the model enabled not only uptake and export of 137 metabolites, but also the exchange of metabolites between the cytosol and each organelle.Neurospora its characteristic orange color), so we included the secondary metabolites module in the biomass composition when predicting wild-type fluxes. On the other hand, secondary metabolites are not strictly required for viability, so we removed this module from the biomass composition when predicting viability.The model's growth objective was based on a modular biomass composition The model quantitatively captures the growth rate of Neurospora. To illustrate this, we have plotted a range of glucose uptake rates against the model's predicted doubling times, and several data points extracted from the experimental literature N. crassa iJDZ836 model, we manually curated a collection of mutant viability phenotypes from the literature. We split this collection into a training set, which we used with FARM to construct the model; and an independent test set, which we used to validate the final model. Both of these collections are available in in silico predictions. Accuracy was measured as two quantities: sensitivity and specificity. Sensitivity was defined as the proportion of experimentally viable mutants that were predicted to be viable in silico. Specificity was defined as the proportion of experimentally inviable mutants that were predicted to be inviable in silico.To validate the accuracy of the The final model's predictive accuracy using limed-FBA is shown in The final model's predictive accuracy on the test and training sets using FBA and MD-FBA is shown in pan-2pan-2 is missed by FBA. In contrast, limed-FBA correctly predicts essentiality of pan-2. Similarly, three other genes involved in coenzyme A biosynthesis (NCU08925 and pan-3) and mitochondrial transport (mic-30) were predicted to be essential by limed-FBA, but not by FBA. Surprisingly, these three genes were in our test set of non-essentials. A potential explanation for these inconsistencies is that the function of these genes can be performed by an isozyme that was not captured in our model. In addition to these three genes, there is one more gene from our test set of non-essentials where limed-FBA predicted essentiality, but FBA predicted non-essentiality. This gene is pab-1. Like arg-14  biosynthesis pathway. However, this pathway includes the eukaryotic thiazole synthase, and to the best of our knowledge its reaction has not been characterized. Thus, this pathway was not included in the model. The incorrect prediction of the ace-7 and ace-8 mutants from the training set is likely due to regulatory effects, which our model does not capture. ace-7 encodes a subunit of glucose-6-phosphate dehydrogenase, and it has been experimentally shown that glucose-6-phosphate dehydrogenase tightly controls NADPH regeneration ace-7. The gene ace-8 encodes pyruvate kinase, which is known to partially control glycolysis ace-8 could inhibit glycolysis in vivo, which would be lethal. But since the model predicts that pyruvate kinase's function can be circumvented by other enzyme activities, the model was unable to capture the essentiality of ace-8.There were four inconsistencies where the model predicted viability and experimental data indicated lethality; these were arg-4, which encodes acetylornithine-glutamate transacetylase. Upon closer examination, it turned out this mutant was experimentally observed to have some growth, albeit very little arg-11. Furthermore we do predict that the double knockout Δarg-4Δarg-11 is synthetically lethal  via the TCA cycle. The model also predicts that ace-2,3,4 mutants can be rescued by L-citrulline, L-arginine, L-ornithine, and L-glutamine, because each of these nutrients can produce 2-oxoglutarate via amino acid degradation pathways.Some novel nutrient rescue predictions can be explained by the existence of an alternate pathway from the nutrient to an essential metabolite that could not otherwise be produced. For example, Neurospora. Computational models provide a mechanism to rapidly and comprehensively test all interactions as a way to prioritize subsequent experimental verification. To predict synthetic lethality using the model, we simulated all pairs of knockouts of non-essential genes and predicted viability on minimal media.Pairwise synthetic lethality arises when two mutants with single gene knockouts are viable, but the double knockout mutant is inviable. Synthetic lethality can reveal cross-pathway dependencies that provide valuable insights into metabolism at the genome scale, but an experimental approach to comprehensively perform double knockouts to identify synthetic lethals is currently infeasible for Of the roughly 700,000 double knockouts in the metabolic model, the model predicted 230 to be synthetically lethal on minimal media. The mechanisms underlying these predicted synthetic lethal interactions fall into three classes: those that encode isozymes of a common reaction, those that encode enzymes of a common pathway, and those that encode enzymes of interacting pathways. This list contains 22 isozyme pairs, 4 gene pairs in the same pathway, and 204 gene pairs in interacting pathways. All of these pairs are tabulated in arg-2 mutant, which is known to be “leaky” To validate the ability of the model to predict synthetic lethality, we manually curated a small number of experimentally observed synthetic lethal interactions. Some of these interactions involved the pho-4:pho-5pho-4:pho-5 mutant is viable.The single known interaction not predicted by the model is for the double mutant am and en(am)-2 mutants are viable, because they represent the two alternate routes to synthesize glutamine. However, the double mutant am:en(am)-2 is blocked in both routes, so it is inviable, unless the nutrient media is supplemented with glutamate.pro-3  mutant in proline biosynthesis, and the ota (acetylornithine transaminase) mutant in arginine degradation are viable, because they represent the two alternate routes to synthesize L-proline. However, the double mutant pro-3:ota is blocked in both routes, so it is inviable, unless the nutrient media is supplemented with L-proline.pyr-1 (dihydroorotate dehydrogenase) in uridine-5′-phosphate synthesis and uc-5 (uracil permease) are viable, because they represent the two alternate routes to synthesize UMP. Consequently, the double mutant pyr-1:uc-5 on uracil-containing media is blocked in both routes, so it is inviable. Further, the pyr-1:uc-5 mutant can be rescued by uridine, since uridine can be phosphorylated to UMP.NCU02726 (ethanolamine kinase) and gsl-3 (3-dehydrosphinganine reductase). The products of these two genes catalyze reactions in two different pathways that lead to the production of the essential metabolite phosphoryl-ethanolamine. Ethanolamine kinase converts ethanolamine to phosphoryl-ethanolamine in a single reaction. Without ethanolamine kinase, the model predicts that phosphoryl-ethanolamine must be produced from the sphingolipid metabolism pathway. This requires the activity of gsl-3, which is an upstream member of this pathway.The novel predictions of synthetic lethality provide testable hypotheses for further experimentation. For example, the model predicts synthetic lethality between suc gene (pyruvate carboxylase) and subunits of mitochondrial complex I (NADH:ubiquinone oxidoreductase). Under normal circumstances, NADH produced in the mitochondrial TCA cycle is oxidized to NAD in the electron transport chain via NADH:ubiquinone oxidoreductase. Yet, the loss of complex I is known to be non-lethal in Neurosporasuc. This synthetic interaction is particularly noteworthy, because S. cerevisiae lacks complex I, making Neurospora a prime model for studying its interactions.Neurospora crassa. We assessed the Neurospora metabolic model's ability to predict the impact of gene deletions, nutrient supplements that would rescue essential gene deletions, and synthetic lethal interactions. In each case, computational predictions were validated against a curated dataset of experimentally observed mutant viability phenotypes. Importantly, to ensure that our model was not over-fit, we separated the experimental data into a training and test set. Whereas training data was used in the development of the model, the test set was reserved to assess the accuracy of the final model. The final accuracy assessment was thus independent of any data used during model training.Building on its long history as a genetic model organism for biochemical genetics and cellular metabolism, we report here the first genome-scale metabolic network model for S. cerevisiae and E. coli. Moreover, at 93% sensitivity and specificity on a test set of 303 phenotyped gene knockouts, the Neurospora model displays high absolute accuracy that lends confidence to the ability of the model to make accurate novel predictions.The prediction of the growth phenotype of gene deletions is a canonical test of metabolic model accuracy and a useful benchmark for assessing the quality of different models N. crassa iJDZ836 model also demonstrates high accuracy in predicting the ability of different nutrients to rescue essential gene knockouts and in predicting synthetic lethal interactions. In the former case, the model displays nearly 90% accuracy on an independent test set of nutrient rescue experiments. In the latter case, we were only able to curate a handful of experimentally verified synthetic lethal interactions. Nonetheless, although no synthetic lethal data was used during model training, the model was able to correctly identify four out of five known synthetic lethal interactions.The Genome-scale metabolic models complement experimental investigations, and one role of metabolic modeling is to rapidly generate and prioritize testable predictions that can be used to guide subsequent experimentation. As important as the predictions themselves, metabolic models also provide potential mechanistic explanations for the results. The explanations provide an important check on the overlying predictions. During the validation of models, these explanations ensure that not only are correct answers given, they are given for valid underlying reasons. For novel predictions, mechanistic explanations can provide potential insight into the results as well as tangible avenues to experimental validation. To illustrate the last point, in Neurospora, the extraordinarily rich literature for this well-studied model organism was the foundation that enabled a model to be generated that performed with high accuracy. Nonetheless, certain areas of the model remain less well developed, and one value of model construction is the objective measure it can provide on the relative information available for different aspects of metabolism. This can be used to target areas that are less well understood. For example, the substrates of certain reactions in the thiamin diphosphate and neurosporaxanthin biosynthesis pathways and the fate of the end-product in the histidine degradation pathway cannot be included with confidence in any metabolic model, because they are open biochemical questions As with all previous modeling efforts, errors in predicting known experimental results highlight limitations in either the model itself or the modeling framework. In terms of the model, the quality will only be as good as the information that was used to develop it. In the case of Neurospora model. In particular, our model does not account for regulation of either enzyme expression or activity. These factors sometimes acted in combination. An illustrative example is gln-1 and gln-2, which code for the alpha and beta subunit, respectively, of glutamine synthetase More generally, the constraint-based modeling framework we used here is known to suffer from certain limitations. As with similar models, this accounts for a significant portion of the prediction errors in the erg-14 was performed on a knockout strain originally designated as a homokaryon. Experimental observations of this strain revealed a normal growth phenotype. In contrast, the model predicted that the Δerg-14 mutant was blocked in the production of mevalonate, which is a necessary precursor for the sterol component of biomass. Moreover, previous efforts to phenotype temperature-sensitive mutants of erg-14 revealed severe morphological defects that were expected to be lethal in the full knockout erg-14 knockout revealed that the mutant used was in fact a heterokaryon. This prediction, in effect, served as a blind control that highlighted the predictive value of the model.In one instance, however, a prediction initially thought to be an error provided the means to identify an issue with an experimentally observed knockout. The viability phenotype experiment for ΔFast Automated Reconstruction of Metabolism (FARM).The construction of genome-scale metabolic models remains a daunting task. Even aided by sophisticated tools for the management and visualization of pathway knowledge, a metabolic reconstruction still requires substantial manual review of the corresponding literature Consistent Reproduction Of growth/no-growth Phenotype (CROP) assists in automating the process of adding and subtracting reactions from a model to improve predictive accuracy. CROP integrates diverse evidence for pathways into a probabilistic framework that assigns a weight to each reaction associated with the likelihood that the reaction is present in the network. These weights are then used to guide the selection of reactions to add or remove. Previous methods to achieve in silico growth used mixed integer linear programming (MILP), and thus required substantial compute time Two of the three FARM algorithms specifically facilitate the process of model construction. OnePrune was developed to efficiently solve the problem of removing reactions that are blocked. OnePrune utilizes the goal programming optimization framework to achieve multiple competing objectives. The advantage of this framework is that once an individual objective is achieved, the optimization can pursue other objectives. OnePrune's goals are to send flux through as many reactions as possible, so once a reaction has achieved a nonzero flux, OnePrune is free to pursue flux through other reactions. Thus, OnePrune identified which reactions could carry flux with a single compact linear program.pab-1 was not essential, because pab-1 serves as input to a metabolic cycle; however, limed-FBA predicted that pab-1 was essential. In fact, essentiality of pab-1 was shown by the original experiment of Beadle and Tatum on Neurospora crassaThe third FARM algorithm, limed-FBA, is an enhancement to the FBA method that improves predictive accuracy. limed-FBA accounts for the dilution of active metabolites that is ignored by FBA , so limeNeurospora crassa was performed in accordance with previously described protocols The metabolic reconstruction of Neurospora crassa genome assembly NC10, gene boundaries were predicted using the Calhoun annotation system EFICAz From the Neurospora literature http://bioinformatics.ai.sri.com/CCO/. Neurospora-specific enzyme kinetics, allosteric regulation, biomass composition, and growth media were added during manual curation of the experimental evidence in the literature.We used the Pathway Tools software suite n and created a lumped reaction that was stoichiometrically equivalent to n steps of the polymerization. The NeurosporaCyc Pathway/Genome Database can be downloaded from the PGDB registry at http://biocyc.org/registry.html and is available online at http://neurosporacyc.broadinstitute.org.Before the NeurosporaCyc PGDB could be used to generate a working model, a number of data-cleaning steps were performed. Reactions were mass balanced, and the individual metabolites were protonated to the intracellular pH of 7.2 To curate our growth rate data, we identified several manuscripts that included glucose concentrations for Neurospora grown on glucose minimal medium where doubling times could be inferred from growth curves The Neurospora crassa e-Compendium, curated by Alan Radford. The e-Compendium contains >2,400 citations and >3,000 gene entries. Numerous gene entries have associated mutant phenotypes extracted from the literature. In some cases, these phenotypes also include supplements that rescue no-growth mutants. Because this resource primarily lists phenotypes from mutants that are not knockouts, viability of a mutant could be due to non-essentiality of a mutated gene or to partial efficacy of a mutated enzyme. Thus, this resource did not clearly identify non-essential genes. The second resource was knockouts from the Neurospora Genome Project To curate our viability phenotype data, we primarily relied on two resources. The first was Neurospora Genome Project (http://www.dartmouth.edu/~neurosporagenome/knockouts_completed.html). Genes that were in the intersection became the test set, and the rest remained in the training set.To collect the essential gene sets, we first identified inviable mutants in the e-Compendium, and only retained genes for which we could manually verify their essentiality in the literature. To split the essential genes into a training set and a test set we intersected the list of inviable mutants in the e-Compendium with the list of heterokaryon knockouts from the Neurospora Genome Project Neurospora crassa Database at the Broad Institute (http://www.broadinstitute.org/annotation/genome/neurospora/Phenotypes.html). This subset became our non-essential training set, while the rest of the homokaryon knockouts became our non-essential test set. Homokaryon knockouts that were in the essential gene set were discarded.To collect the non-essential gene sets, we used the homokaryon knockouts from the Neurospora: Contributions of a Model OrganismBoth the supplemental nutrient rescue training and test sets were initially identified from the e-Compendium and from the book http://www.fgsc.net/methods/vogels.html) in silico by adding/removing exchange reactions. Supplemental nutrients were limited to a flux of 3 mmol/(gram Dry Weight * hour).We simulated Vogel's minimal media  with Tomlab v7.9 and CPLEX 12.2. This code was modified for our model: all MD-FBA constraints could be satisfied by wild-type on Vogel's minimal media, so we did not need to apply their addNewExReactions function. This code allowed 400 seconds per optimization.We simulated gene knockouts by removing reactions that require knocked-out genes. Biomass flux was predicted using limed-FBA, except where stated otherwise. www.r-project.org). Optimizations utilized the Rcplex package in R to call IBM ILOG CPLEX Optimization Studio 12.2 . However, code to reproduce all of our predictions has been made available in MATLAB . This code is integrated with the COBRA Toolbox 2.0.5 https://code.google.com/p/fast-automated-recon-metabolism/.FARM was originally written in the R language and environment , v be a vector of (non-negative) steady-state metabolic fluxes, and b represent a vector of the associated metabolite concentrations changes. Thenb as a linear function of v so that bi is positive but small when metabolite i participates in active reactions, and bi is zero otherwise.Let b, we first introduce the binary stoichiometric matrix, Sbinaryi and column j is 1 if and only if the corresponding element of S≠0. Now consider Sbinaryv for a particular metabolite i (this is row i of Sbinary multiplied by v). Because Sbinary and v are both non-negative, element i of Sbinaryv represents the sum of the absolute values of the fluxes that produce and consume metabolite i. This quantity is twice the turnover of metabolite i. Thus, if metabolite i does not participate in active reactions, then element i of Sbinaryv is zero. Otherwise, if metabolite i does participate in active reactions, then element i of Sbinaryv is positive.To construct such a i participates in active reactions, we multiply Sbinaryv by ε, where ε is a diagonal matrix whose number of rows and columns is the number of metabolites, and its elements, εii, are small non-negative constants that correspond to each metabolite i.To ensure that the dilution is small when metabolite v; rather, it only holds for those v that are feasible in limed-FBA. This equation can be simplified toε so that no metabolite's dilution rate can exceed a pre-chosen constant. Here, we set dilution to never exceed 0.1; this choice gives dilution rates on par with those theoretically prescribed by MD-FBA. In general, we assign εii to bevmax is the overall upper bound on the fluxes. We used vmax = 1000.Thus, we setεii = 0.Metabolites in biomass already dilute according to FBA. So that we do not double-dilute biomass metabolites, metabolites whose growth-associated dilution is already captured in the pre-defined biomass  are assigned εii can create numerical difficulties for optimization solvers, such as IBM ILOG CPLEX. We found that these errors were rare, only occurred when growth was impossible, and disappeared when trying an alternate linear programming solution method (e.g. the simplex method instead of a barrier method). An alternative that we didn't try is to use an exact solver or one with more precision The use of small numbers such as Sbinary that correspond to these simple transport reactions to be all zeroes.As a consequence of limed-FBA's linearity, limed-FBA allows more dilution for metabolites with more flux. This is associated with a potential pitfall. Consider a metabolite that is produced as a by-product of growth, but is not connected to an exporter. To maximize growth, this metabolite would need to be depleted. To deplete this metabolite, limed-FBA could potentially “cheat”: it could create a high-flux loop that includes the metabolite, so that the metabolite is allowed to dilute more. If a reaction includes multiple metabolites, then including it in a loop is more likely to have system-wide effects; conversely, reactions with fewer metabolites are less likely to have system-wide effects. In accord with this, we found that limed-FBA used simple transport reactions (e.g. L-glutamine[cytosol]↔L-glutamine[nucleus]) to “cheat.” To address this, we disallow simple transport reactions from contributing towards dilution. We define these simple transport reactions as those that transport metabolites, but involve no chemical change. This disallowance is implemented mathematically by assigning columns in This algorithm is extensively detailed and compared to similar approaches in S is a stoichiometric matrix consisting of n irreversible reactions. This program does not contain upper bounds on the available nutrient sources, so the only factor limiting reaction flux is connectivity. Thus, in the optimal solution, all ti's should be binary: reactions with ti of one can carry flux, while the other reactions are blocked.OnePrune's goal is to send flux through as many reactions as possible given an unlimited amount of the available nutrients. Mathematically, it's defined as a linear goal program:OnePrune requires an irreversible stoichiometric matrix, but any reversible stoichiometric matrix can be transformed into an irreversible one (by splitting reversible reactions into two irreversible reactions of opposite direction) so this method is completely general. This requirement exists because direct inclusion of a reversible stoichiometric matrix would force OnePrune to deal with absolute values of reaction flux in a manner not amenable to linear programming limed as the stoichiometric matrix. This implementation disallows cycles that lack an input flux, so it disallows the cycle of A→B and A ← B. However, limed-FBA does allow some cycles—namely, those not penalized in Slimed and those that have an input flux. Thus, reversible reactions that form such cycles (e.g. A[cytosol]↔A[extracellular]) will not be pruned by OnePrune. However, these reversible reactions can be identified a priori and addressed with Flux Variability Analysis The set of blocked reactions of an irreversible stoichiometric matrix may differ slightly from those of a reversible stoichiometric matrix. For example, consider a reversible reaction between two metabolites disconnected from the metabolic network: A↔B. In an irreversible stoichiometric matrix, this would be modeled as two reactions: A→B and A ← B. Thus, the reversible reaction is blocked, whereas the two irreversible reactions could carry flux as part of a flux cycle according to FBA. To address this, we implemented OnePrune according to limed-FBA by using STo apply OnePrune to achieve our final pruned model, we allowed all extracellular metabolites in our model to be treated as nutrients.http://code.google.com/p/fast-automated-recon-metabolism. SBML Notes fields contain COBRA-compliant gene associations, pathways, EC numbers, Pubmed IDs, chemical formulae, charge, and NeurosporaCyc and KEGG identifiers We exported the model to the COBRA-compatible subset of the Systems Biology Markup Language (SBML) http://neurosporacyc.broadinstitute.org.The metabolic model  has beenDataset S1iJDZ836 SBML.(XML)Click here for additional data file.Figure S1Δarg-4Δarg-11 synthetic lethal prediction is that two alternate routes exist for generating L-ornithine. (A) When arg-4 is blocked, L-ornithine can be generated from arg-11. (B) When arg-11 is blocked, L-ornithine can be generated via arg-4.The mechanistic explanation for the (EPS)Click here for additional data file.Figure S2ΔsucΔcomplex I synthetic lethal prediction is that two alternate routes exist in the mitochondrion for regenerating NAD+ from NADH. (A) The first route utilizes the ubiquinone:NADH oxidoreductase enzyme encoded by complex I. (B) When ubiquinone:NADH oxidoreductase activity is blocked, NAD+ still can be regenerated by driving the malate dehydrogenase reaction backwards via the pyruvate carboxylase activity encoded by suc. When both routes are blocked, the mitochondria unable to regenerate NAD+ from NADH and the resulting double mutant is inviable.The mechanistic explanation for the (EPS)Click here for additional data file.Figure S3Neurospora crassa.Zoomable metabolic map of (EPS)Click here for additional data file.Figure S4Plots glucose uptake rate (in millimoles per gram dry weight per hour) against doubling time (in hours). The black curve depicts the model's predictions using FBA, where doubling time is calculated as ln(2)/growth rate (EPS)Click here for additional data file.Figure S5Simulating the effect of oxygen limitation on xylose fermentation. In the first step, xylose is reduced to xylitol via a xylose reductase that has a strong cofactor preference for for NADPH (XR-NADPH) and a weaker preference for NADH (XR-NADH) (EPS)Click here for additional data file.Table S1Describes our evidence source for our growth/no-growth annotation of mutants, nutrient rescues, and synthetic lethals.(XLSX)Click here for additional data file.Table S2Comprehensive set of our predicted essential genes, supplemental rescues, and synthetic lethal gene pairs.(XLS)Click here for additional data file.Text S1Describes the 19 inconsistencies between the test set of non-essential genes and the essential gene predictions.(DOCX)Click here for additional data file.Text S2Compares the steps we completed to reconstruct this model to those in the Palsson lab's 96 step protocol (DOCX)Click here for additional data file.Text S3Details the CROP algorithm and compares it to GrowMatch and the Model SEED. This has three sections: 1. objective function, 2. resolving incorrect predictions by restoring growth, and 3. resolving incorrect predictions by suppressing growth.(DOCX)Click here for additional data file.Text S4Simulating the effect of oxygen limitation on xylose fermentation.(DOCX)Click here for additional data file.


Saccharomyces cerevisiae by measuring 5636 mRNAs, 50 metabolites, 97 lipids, and 57 13C-reaction fluxes in yeast using a three-factor full-factorial design. Correlation analysis across eight environmental conditions revealed 2279 gene expression level-metabolite/lipid relationships that characterize the extent of transcriptional regulation in lipid metabolism relative to major metabolic hubs within the cell. To query this network, we developed integrative methods for correlation of multi-omics datasets that elucidate global regulatory signatures. Our data highlight many characterized regulators of lipid metabolism and reveal that sterols are regulated more at the transcriptional level than are amino acids. Beyond providing insights into the systems-level organization of lipid metabolism, we anticipate that our dataset and approach can join an emerging number of studies to be widely used for interrogating cellular systems through the combination of mathematical modeling and experimental biology.Lipids play a central role in cellular function as constituents of membranes, as signaling molecules, and as storage materials. Although much is known about the role of lipids in regulating specific steps of metabolism, comprehensive studies integrating genome-wide expression data, metabolite levels, and lipid levels are currently lacking. Here, we map condition-dependent regulation controlling lipid metabolism in  Upc2p and Ecm22p (sterol regulatory element-binding protein in humans) uses a feedback pathway to concomitantly control synthesis and uptake  and into how fluxes toward different lipid species are controlled.In this study, we set out to elucidate global regulatory structure controlling lipid metabolism under different environmental conditions . Toward S. cerevisiae CEN.PK113-7D (MATa) (−1. Aerobic conditions were maintained by sparging the cultures with air (1.0 liter⋅min−1) and the concentration of dissolved oxygen was measured with Mettler Toledo polarographic electrode. Anaerobic conditions were maintained by sparging the medium reservoir and the fermentor with pure nitrogen gas (0.5 liter⋅min−1). Moreover, oxygen diffusion was minimized by using norprene tubing and butyl septa. The bioreactors were fitted with cooled condensers (2°–4°) and the off-gas was led to a gas analyzer (INNOVA and NGA 2000 Rosemount) to measure the content of CO2 and O2. Steady-state was reached when at least five but less than seven residence times had passed since starting the continuous cultivation and carbon dioxide evolution, dry weight measurements, and high-performance liquid chromatography (HPLC) measurements of extracellular metabolites were constant.The reference laboratory strain −1. This was to sustain glucose repression at the same level in all cultivations. To achieve the targeted glucose concentrations in the N-limited cultivations, the glucose feed concentration was 46 g⋅liter−1 for cells grown at 15° and 74 g⋅liter−1 for cells grown at 30°. The mineral medium composition for the N-limited cultivations was (amounts per liter) as follows: 1 g (NH4)2SO4; 3 g KH2PO4; 5.3 g K2SO4; 0.5 g MgSO4⋅7H2O; 1 ml Trace Metal Solution; 0.05 ml antifoaming agent; and 1 ml vitamin solution. The mineral medium composition for the C-limited cultivations was (amounts per liter) as follows: 5 g (NH4)2SO4; 3 g KH2PO4; 0.5 g MgSO4⋅7H2O; 1 ml Trace Metal Solution; 0.05 ml antifoaming agent; and 1 ml vitamin solution. The trace metal solution and vitamin solution are reported in −1 Tween 80 and 10 mg⋅L−1 ergosterol . The filter was washed with distilled water, dried in a microwave oven at 150 W for 15 min, and finally weighed to determine its increase in dry weight.Samples for RNA isolation from aerobic cultivations were taken by rapidly sampling 20 ml of culture into a tube with 35–40 ml crushed ice to decrease the sample temperature to less than 2° in less than 10 sec. Cells were then centrifuged (4500 rpm at 0° for 3 min), instantly frozen in liquid nitrogen, and stored at −80° until further use. Sampling for RNA isolations from anaerobic cultivations was performed as described . Briefly, CEL files were normalized and the probe sets were summarized using the GeneChip RMA (GCRMA) package (http://www.ebi.ac.uk/arrayexpress/) with accession E-MEXP-3704.Raw microarray data (CEL files) were analyzed using R and BioConductor  and the potential interaction between pairs of factors  using an ANOVA approach (multi-way ANOVA). The model for mRNA expression each gene, in silico fluxes for all growth conditions was performed following a flux balance analysis (FBA) approach using the biopt software  from our experiments were directly used as additional constraints in addition to the stoichiometry of the iIN800 model , and pH of 5.0, and gas flow rate was kept at 1 vvm by air and N2 for aerobic growth and anaerobic growth, respectively. Steady-state samples were taken after five volume changes; 100% of the glucose used was labeled in position 1 . 13C-labeled biomass was harvested by centrifugation at 4000 rpm and 0° for 5 min. After centrifugation, the supernatant was poured off, and the cell pellet was frozen instantaneously in liquid nitrogen and stored at –80°. The samples were derivatized with both ECF derivatization and DMFDMA derivatization (g for 4 min in a rotor precooled to −20° to separate the cells from the quenching solution. Chloroform:methanol:buffer (CMB) extraction was performed . GC-MS and HPLC analysis were performed as indicated. Samples were normalized by the biomass weight per sample.trans-methylated using a method combining alkaline methanolysis and BFl3-catalyzed trans-methylation described by Samples for lipid isolation were taken by rapidly centrifuging at 4° and 10,000 rpm. The cell pellets were rinsed twice with DI water, instantly frozen in liquid nitrogen, and stored at −80° until lipid extraction. For quantification of lipid levels, internal standards  were added to each sample before extraction. The lipid extraction was performed using a modified Folch method -reaction; metabolite/lipid-reaction; and lipid distribution edges. Gene (enzyme)-reaction and metabolite/lipid-reaction edges were defined from the stoichiometry of the genome-scale model. Lipid distribution edges mapped acyl-chain composition for different species. For reasonable visualization, linear pathway reactions were merged, for example, the following set of reactions, PE + SAM → PMME + SAH, PMME + SAM → PDME + SAH, and PDME + SAM → PC + SAH was simplified to PE + SAM → PC + SAH for metabolites S-adenosyl-L-methionine (SAM), S-adenosyl-homocysteine (SAH), phosphatidylethanolamine (PE), phosphatidylmonomethylethanolamine (PMME), phosphatidyldimethylethanolamine (PDME), and phosphatidylcholine (PC). The simplified reaction PE + SAM → PC + SAH had two gene reaction associations: CHO2  and OPI3 . To help parse the complexity, we found it useful to visualize smaller networks, such as central metabolism, the TCA cycle and amino acid biosynthesis pathways, sterol biosynthesis, phospholipid biosynthesis, fatty acid biosynthesis, sphingolipid biosynthesis, and lipase degradation.To gain an integrated perspective of environmental growth condition perturbations on gene, metabolite, and lipid expression, these data were visualized in Cytoscape  and the resulting P values were adjusted using the Bonferroni method to account for the number of gene expression level–lipid or gene expression level–metabolite combinations tested.The influence of gene expression on lipid and metabolite levels, or vice versa, was investigated using Pearson product-moment correlation coefficient, iIN800 that included acyl chain information and that contained 1754 reactions, 781 genes, and 897 lipids and metabolites was used to investigate regions of surprising correlation between lipids/metabolites and the mRNA of enzymes that may generate or utilize them  were compared to their correlation coefficients to all non-neighbor gene expression levels using a Mann-Whitney U-test (Wilcoxon rank-sum test). The nonparametric, two-sided hypothesis test provided a P value for each lipid and metabolite regarding how positively or negatively correlated it was to genes directly upstream or downstream in the metabolic network as compared to all other non-neighboring genes. In addition to this “first-neighbor” metabolic network analysis, a similar “second-neighbor” analysis was performed that defined neighbor correlations for all genes within two reaction steps of the lipid/metabolite in question.An extended yeast metabolic network based on Using the pathway definitions in KEGG to group sets of reactions and sets of lipids/metabolites, we performed a Mann-Whitney analysis similar to that applied to individual lipids/metabolites to assess the significance of correlation within a defined pathway represented by the mRNAs encoding the enzymes and the lipids/metabolites they produce. This test was performed to compare the sample of correlation values between lipids/metabolites and gene expression levels within a pathway to all other correlations between lipids/metabolites and gene expression levels not in the tested pathway.e.g., fatty acid desaturation) and sphingolipid metabolism  and data from protein interaction databases with metabolic network topology . We purstabolism . Each fai.e., the fatty acid composition of phospholipid species; C12:0, C16:1, and others). Detailed physiological data, carbon balances, and complete datasets are provided in Table S2 and Table S3.From each chemostat cultivation, we measured mRNA, metabolite, and lipid levels . We wereFigure S1, Figure S2, Figure S3, Figure S4, Figure S5, Figure S6, Figure S7, Figure S8, and Figure S9). We used a multi-factor ANOVA to identify significant changes at the mRNA, metabolite, and lipid levels (Table S4). In this way, the main experimental factors tested  and, in addition, all the possible interaction factors were tested  to identify mRNAs, metabolites, and lipids significantly influenced by combinations of growth conditions, highlighting the power of the full factorial design. At a threshold of P ≤ 0.001 after Bonferroni correction (P ≤ 0.001). By comparison, aerobicity appeared to influence expression of approximately half as many genes (516 at P ≤ 0.001). By contrast, the lipidome data were most influenced by the presence or absence of oxygen. A majority of the 97 lipids measured were significantly changing due to at least one of the tested factors (78 at P \x{2264} 0.001). Aerobicity accounted for changes in two-thirds (65) of the lipids tested. C:N and T:t accounted for 47 and 30 lipid changes, respectively. Two-thirds (33) of the 50 metabolites measured were changing because of at least one factor. C:N and O:A each affected approximately half , whereas T:t only influenced approximately one-third (17) of the 50 metabolites measured. The overlaps among significant genes, lipids, and metabolites are highlighted in the Supporting Information .Despite similar specific growth rates, marked changes were observed at the molecular level. For example, our lipid measurements confirmed that lipid class levels vary based on the three types of environmental conditions evaluated . The cube of factor combinations was tilted and rotated in the first three PCA dimensions, indicating that each of these dimensions was influenced by all three factors but to differing extents. The first principle component (37% of mRNA variance) was primarily attributable to C:N factor; the second dimension (24% of mRNA variance) was an approximately equal balance of all experimental factors, whereas the third dimension (15% of mRNA variance) was primarily attributable to aerobicity (O:A). This indicates that mRNA levels were most influenced by C-limitation and N-limitation, which makes sense when we consider that few cellular process are independent of carbon and nitrogen utilization. The PCA analysis of the metabolite data attributed >80% variance to the first two PCA dimensions (63% to PC 1), with C:N as the primary source of variability. Metabolite PC 1 showed similarity to mRNA PC 2 and, vice versa, PC 2 showed similarity to mRNA PC 1. By contrast, lipidome data displayed the greatest variance along the aerobic–anaerobic split  was used to provide a nonbiased means of assessing the influence of growth factors  on mRNA, metabolite, and lipid levels . Becauseic split . Becausenditions .iIN800  cycle, amino acid biosynthesis, sterol biosynthesis, steryl ester biosynthesis, triacylglyceride (TAG), biosynthesis, FFA biosynthesis, phospholipid biosynthesis, and sphingolipid biosynthesis. In total, our visual network map consisted of 368 gene, 174 metabolite/lipid, and 131 reaction nodes connected by 849 gene (enzyme)-reaction and metabolite/lipid-reaction edges (Table S6). Nine distribution nodes were also included for mapping information on lipid acyl-chain composition. A distribution node is connected to all of the acyl-chain species involving that particular lipid or fatty acid type. For the reaction nodes, we performed flux balance analysis to estimate values of reaction fluxes in silico under the constraints of maximized biomass production, a steady-state metabolic network, and fixed protein composition (Table S7) (vs. anaerobic (A) conditions]. Although the interplay between different metabolic pathways could be missed in our visualization approach, we have previously shown that such a model provides a useful framework for exploring the connectivity of mRNAs, metabolites, lipids, and fluxes (Data integration through hypothesis testing across multiple cellular levels section).To determine the conditional dependence of mRNA, metabolite, and lipid data, we first constructed a visual map based on the genome-scale metabolic model, Figure S11 and Figure S12). These networks provided a direct route for querying cellular changes one at a time and validated the high quality of our data . For example, we observed the downregulation of acetyl-CoA carboxylase (ACC1), which catalyzes the carboxylation of acetyl-CoA to form malonyl-CoA as the first committed step of de novo fatty acid biosynthesis when comparing growth at 30° to 15° . This was positively correlated to a reduction in levels of pyruvate, alpha-ketoglutarate, malate, and citrate . Citrate is known to allosterically activate Acc1p . Under nitrogen-limited  and aerobic conditions, levels of TAG increased from 0.7 ± 0.2 μmol/g DCW to 3.8 ± 0.2 μmol/g DCW, and levels of SE increased from 0.8 ± 0.6 μmol/g DCW to 4.9 ± 0.4 μmol/g DCW. When compared to all other conditions (N-limited aerobic vs. others), only 26 genes, 11 lipids, and 3 metabolites were significantly changed (P ≤ 0.01 following Bonferroni correction) (Table S8). Within this small subset, it is striking that none of the biosynthetic genes encoding enzymes that directly produce diacylglycerol (DAG), TAG, ergosterol, or SEs is significantly upregulated. Rather, we observed a confluence of changes that contribute to the observed phenotype, including high levels of pyruvate and alpha-ketoglutarate , more reducing power (flux through the pentose phosphate pathway increases), downregulation of regulatory proteins  (de novo phospholipids synthesis) . We also observed an inverse regulatory relationship between the expression of ERG6 and ERG7 and TAG and SE levels   , and a lnthesis) . ConsistE levels . Erg6p aarticles  interactFigure S3, Figure S4, Figure S5, and Figure S7). Uniquely, the distribution nodes of our model enabled mapping of quantitative acyl-chain composition information . To our knowledge, this fatty acid species has not previously been reported as a major component inside the cell. The presence of di-substituted medium acyl-chain fatty acids, which we denote with an “S” , under anaerobic growth suggested redox constraints. Because the fatty acid synthase uses two reduced NADPH for each 2C-atom added to a growing fatty acid, we hypothesized that a decrease in reducing power would hamper fatty acid elongation and cause a shift toward production of medium-chain fatty acids. The flux through the pentose phosphate pathway (PPP) is relatively low under anaerobic conditions, causing cells to be redox-constrained. Previously, for example, it was shown that NADPH is balanced under fermentative growth conditions, whereas excess NADPH is produced under aerobic conditions  revealed a 10-fold average decrease in the PPP flux when comparing anaerobic to aerobic conditions. To experimentally validate this hypothesis, we performed follow-up 13C-flux analysis experiments. To experimentally determine fluxes, we cultivated cells on 1-13C-glucose and experimentally measured isotopic labeling patterns with GC-MS. We observed that measured flux through the PPP is reduced 3.5-fold in anaerobic conditions (P ≤ 0.01) , which is consistent with our in silico fluxes and the previous work of Our visual metabolic model also highlighted changes in phospholipid metabolism. Phospholipid levels varied significantly with temperature and oxygen availability. Globally, this trend is a result of increased phosphotidylinositol (PINS) levels, as well as increased PC and PE levels at lower temperatures under anaerobic conditions  target sets (hypergeometric test at P ≤ 0.01) . For example, 14 of 17 known Upc2p regulatory targets . Although experimentally determined or computed fluxes were not directly integrated into this analysis, fluxes are indirectly considered because the lipid levels in the biomass were directly correlated to the fluxes when we used chemostat cultures and lipid levels, which are given per unit biomass. Our analysis revealed 831 significant correlations among gene expression levels and metabolites, 245 significant correlations among gene expression levels and lipids, and 1203 significant interactions among gene expression levels and lipid species characterized by length. Relationships were then separated into positive and negative correlations and filtered based on ANOVA significance main effects  for visualization in Cytoscape. Gene expression level-metabolite and gene expression level–lipid interactions were combined into a single network, leaving gene expression level–lipid by acyl-chain composition correlations separate. The organization of the network contained mainly single-connection, chain, and bi-fan motifs . There were 2279 significant correlations.Beyond interrogating data types one component at a time, we sought to address the pluralism of causes and effects in biological networks by developing integration strategies for observing regulatory signatures. This is one of the grand challenges of 21 biology , we testErg1p, for example, encodes for squalene synthase, an enzyme in the ergosterol biosynthesis pathway that requires oxygen  levels were observed to increase when IZH2 and AUS1 , only 5 of 2279 correlations were between first neighbors and direct substrates or products (Table S9). At a quantitative systems level, this suggests that biosynthetic enzymes that either use metabolites and/or lipids as reactants or catalyze their formation were not directly connected. In other words, transcriptional regulation is not tightly controlled at the one-step level.Our correlation analysis enabled exploration of two key features that would not have otherwise been observed by mapping data onto our visual metabolic model alone . First, and AUS1  and phosand AUS1 , an obseic model  could ber > 0) or anti-correlated (r < 0).Because lipids/metabolites and gene expression levels were not co-regulated at the one-step level, we next sought to refine the correlation analysis using the following three parallel and independent approaches: co-regulated gene neighborhoods ; co-reguU-test). However, 22 metabolites and lipid species had a significant bias when considering both first and second neighbors  (Table S10). Given that amino acids are chiefly directed into proteins, this suggests that the cell has developed more complex methods to regulate proteins post-transcriptionally than sterols and the entry point into phospholipid biosynthesis. Furthermore, biosynthesis of amino acids involves far more branched pathways than the biosynthesis of lipids; to ensure a balanced supply of the 20 different amino acids required for protein biosynthesis, cells have evolved complex regulation of flux at the enzyme level. This finding is consistent with our recent work  , with erP ≤ 0.01, Benjamini Hochberg P value adjustment) (Table S11), validating the utility of using a systems perspective to integrate omics information.Another way to refine the correlation analysis is to look for transcriptional regulation across genes in pathways , rather P ≤ 0.01) TF gene expression level–lipid and TF gene expression level–metabolite enrichments capture a global regulatory model of lipid metabolism. The Hap family of TFs and, particularly, Hap4p and Hap1p were found associated with genes negatively correlated to a number of lipid levels. Interestingly, Upc2p and Rox1p targets were enriched for genes positively correlated to PINS, PINSS, PC, and PCS, and were negatively correlated to ergosterol. These regulators are known players in sterol regulation  and mapped 102 genes linked to ergosterol in the genome scale metabolic model. By taking the intersection of these two groups, we identified nine metabolites that were both linked as a first and second gene neighbors to ergosterol in the metabolic map and were significantly correlated . This topological map was then integrated with the TF-regulated modules implicated in the enrichment analysis to provide a global regulatory picture , which is responsible for the synthesis of phosphatidic acid (PA). PA is the central precursor for glycerophospholipids, DAG, and TAG, and it is also a signaling lipid and key transcriptional regulator of lipid biosynthesis  and PINS utilization . We also analyzed the storage depots of the cell, noting that steryl esters (se161) and triacylglycerol (tag100 and tag140) were significantly correlated and linked in the metabolic network to PLB2, FAA2, FAT1, LRO1, POT1, and CAT2 .For phosphatidylinositol species identified in the co-regulated gene neighborhood analysis , we observed that  to PINS . These de.g., known network topology or results from the analysis of other omics datasets such as transcription factor interactions). Specifically, we leveraged a full factorial design to build a visual metabolic model of the regulatory architecture controlling lipid metabolism in S. cerevisiae. Simple analysis led to the identification of genetic and metabolic changes involved in TAG and SE accumulation under carbon excess. From an adaptive perspective, yeast and, more broadly, eukaryotes make use of TAG as the major storage unit for energy and fatty acids used in membrane biosynthesis. Our results from growth conditions with high levels of residual glucose are entirely consistent with this phenomena and much is known about the synthesis, turnover, and regulation of nonpolar lipids in yeast (13C-reaction flux data to demonstrate this was attributable to redox constraints.In summary, we provide a new integrative method for mapping condition-dependent regulation in cells through the integrated analysis of multi-omics datasets and genome-scale metabolic models given previous knowledge (Upc2p as the key regulator (We also developed integrative methods for data analysis that use correlation analysis, metabolic topology, and transcription factor enrichment to interrogate and characterize the complex relationships that arise between gene expression levels, metabolite levels, lipid levels, and reaction fluxes across multiple conditions. Our approach quantitatively revealed that transcriptional regulation is not tightly controlled at the one-step level. Strikingly, only 22 metabolites and lipids were tightly correlated to their first and second gene neighbors. In the case of ergosterol, our analysis identified egulator . Our sys