Mycobacterium tuberculosis confronts a generally hostile and nutrient-poor in vivo host environment. Existing models and analyses of M. tuberculosis metabolic networks are able to reproduce experimentally measured cellular growth rates and identify genes required for growth in a range of different in vitro media. However, these models, under in vitro conditions, do not provide an adequate description of the metabolic processes required by the pathogen to infect and persist in a host.During infection, M. tuberculosis in the host environment, we developed a set of procedures to systematically modify an existing in vitro metabolic network by enhancing the agreement between calculated and in vivo-measured gene essentiality data. After our modifications, the new in vivo network contained 663 genes, 838 metabolites, and 1,049 reactions and had a significantly increased sensitivity (0.81) in predicted gene essentiality than the in vitro network (0.31). We verified the modifications generated from the purely computational analysis through a review of the literature and found, for example, that, as the analysis suggested, lipids are used as the main source for carbon metabolism and oxygen must be available for the pathogen under in vivo conditions. Moreover, we used the developed in vivo network to predict the effects of double-gene deletions on M. tuberculosis growth in the host environment, explore metabolic adaptations to life in an acidic environment, highlight the importance of different enzymes in the tricarboxylic acid-cycle under different limiting nutrient conditions, investigate the effects of inhibiting multiple reactions, and look at the importance of both aerobic and anaerobic cellular respiration during infection.To better account for the metabolic activity of M. tuberculosis during host infection compared with in vitro growth. Likewise, the double-gene deletion calculations highlight the importance of specific metabolic pathways used by the pathogen in the host environment. The newly constructed network provides a quantitative model to study the metabolism and associated drug targets of M. tuberculosis under in vivo conditions.The network modifications we implemented suggest a distinctive set of metabolic conditions and requirements faced by  Mycobacterium tuberculosis, the causative agent of TB [Tuberculosis (TB) continues to be a major health threat, with 9.2 million new cases and 1.7 million deaths reported worldwide in 2006 [nt of TB . Worldwint of TB ,4.1) metabolism is required for the bacterium to sustain itself and 2) many bacterial metabolic targets are absent in humans. Novel efforts in developing drugs that target the intracellular metabolism of M. tuberculosis often focus on metabolic pathways that are specific to M. tuberculosis [in vivo and in vitro conditions [M. tuberculosis inside a host.In general, bacterial metabolism is an attractive drug target for two main reasons: rculosis ,6. Howevrculosis . This, cnditions -10, creaEscherichia coli [Helicobacter pylori [M. tuberculosis [Salmonella typhimurium. Moreover, metabolic network analyses can then be used to identify organism-specific essential genes by predicting the attenuation of microbial growth of specific deletion mutants [Recently, genome-scale metabolic network reconstructions for different organisms have enabled systematic analyses of metabolic functions and predictions of metabolism-related phenotypes ,12. By chia coli , Helicobr pylori , and M. rculosis ,16), eukrculosis -19, micerculosis , and everculosis . The Webrculosis . Analysirculosis ,15-17,23rculosis  used an  mutants -17,19. MM. tuberculosis [M. tuberculosis in select media [in vitro network to model the drug-induced growth inhibition of M. tuberculosis when grown on defined media [M. tuberculosis growth in an in vivo host environment based on an in vitro model is hindered by a lack of understanding of the pathogen's metabolism in the often poorly defined in vivo environment [M. tuberculosis in murine macrophage cells [M. tuberculosis, to predict the production of mycolic acid [M. tuberculosis, including individual [M. tuberculosis growth in an in vitro medium [in vivo infection [Using the sequenced genome of rculosis  togetherrculosis ,16. Analct media ,16. We hed media . Howeverironment ,27. To ege cells  and cellge cells . Gene exlic acid . Gene dedividual -33 and hdividual , have ido medium  and genenfection . This asper se are typically used to verify that a genome-scale metabolic network reconstruction is accurate; however, these data can also be used in the refinement process itself, either for specific pathways [ad hoc corrections, Kumar and Maranas [in silico/in vivo growth predictions in large-scale metabolic networks. Here, we build on and extend these refinement methodologies to develop an enhanced set of systematic procedures to modify the in vitro metabolic network model of M. tuberculosis iNJ661 [in vivo metabolism during the initial eight-week post-infection period. Importantly, the resultant network modifications provide indirect insights into the nutrient availability and metabolic states of M. tuberculosis in the mouse host environment. Furthermore, we used the newly developed in vivo network to predict the growth of double-deletion mutants to identify drug targets that are either specific for the host environment or common to both in vivo and in vitro conditions.For modeling, gene essentiality data pathways  or for tpathways -39, in wpathways -39. Movis iNJ661  and deveM. tuberculosis metabolic network model commensurate with an in vivo cellular environment, we modified an existing network in two separate steps. First, we corrected an existing in vitro network model to account for missing or inconsistent chemical reactions and metabolites, and then, through a systematic set of procedures, we modified this network to be compatible with gene essentiality data generated under in vivo conditions.To develop a iNJ661 metabolic network model of M. tuberculosis H37Rv [in vitro experimentally observed growth rates in different media, as the starting point for our work. Our modified in vitro network, iNJ661 m, models cellular growth in Middlebrook 7H9 medium supplemented with glucose and glycerol. We then used the GSMN-TB metabolic network of M. tuberculosis [iNJ661m network model contained 663 genes, 838 metabolites, and 1,049 reactions. The developed network is provided in the Additional files in both Systems Biology Markup Language , we labeled the gene as essential; otherwise, it was deemed as non-essential. Since the growth rate ratios obtained from FBA were either <10-4 or >0.2, the chosen threshold value of 0.2 differentiated growth rate ratios close to zero from those significantly higher than zero.We used FBA of the metabolic networks to predict the essentiality of single genes. Using linear programming, FBA can maximize the cellular growth rate subject to the steady-state mass balance of all the intracellular metabolites and the stoichiometric constraints defined by the reactions -42. For in vivo essentiality in mice [We compared the predicted gene essentiality with experimentally determined  in mice ,36 and dM. tuberculosis by calculating the growth rates of all possible double-gene deletion mutants in the metabolic network models. Two genes were classified as synthetically essential if each single individual gene deletion did not affect the growth of the organism, whereas the double-gene deletion impaired growth. Similar to the predictions for single-gene deletion mutants, we classified a deleted gene pair as synthetically essential if the ratio of the growth rate of the double-gene deletion mutant to that of the wild-type bacterium was smaller than the threshold (0.2) and the ratios for the two single-gene deletions were greater than the threshold.We predicted the synthetic essentiality of gene pairs in in vitro network, iNJ661 m, to optimally reproduce gene essentiality under an in vivo condition and create an in vivo network, iNJ661v. Figure Step I, we compared the gene essentiality of iNJ661m with experimental in vivo data and identified the set of FP and FN predictions. In Step II, for each incorrect prediction, we attempted to obtain a set of possible modifications to correct the predictions, commensurate with a minimum number of adjustments to iNJ661 m. In Step III, we combined all obtained modifications for each of the incorrect predictions and screened the sets of combined modifications to identify rational and consistent sets of metabolic modifications. In Step IV, we analyzed the availability and blockage of nutrient uptakes based on the original iNJ661m network and attempted to use small, chemically uncomplicated molecules as nutrient sources and to reduce the number of uptakes. Finally, in Step V, we reviewed the relevant literature to verify the biochemical and biological veracity of the introduced modifications. This set of procedure generated a number of different resultant networks, each representing a "minimal" adjustment that can optimally reproduce the given gene essentiality data. Because it is desirable to create a single unique network representation suitable for modeling and computational analysis, and because we do not have enough information to a priori discriminate against any particular network representation, we combined all networks as long as the combination did not generate any new incorrect predictions. Thus, the resultant network description iNJ661v corresponds to an unbiased assembly of minimal adjustments compatible with the experimental data.We developed a systematic set of procedures to optimally modify an existing metabolic network based on discrepancies in gene essentiality between computational predictions and experimental data. We applied the procedures to modify the original The wrongly predicted FP and FN genes were collated as identified from the gene essentiality predictions described above.iNJ661m network. In the original iNJ661 network, 16 vitamins and cofactors were included as part of the biomass based on in vitro gene essentiality data but without any experimental verification [M. tuberculosis grows in a different nutritional environment. Therefore, we systematically investigated the removal of one or more of these 16 metabolites. Initially, we removed one metabolite at a time from the biomass objective function and recorded the removals that corrected the FP prediction and caused no original TP prediction to become FN. If the single-metabolite removal did not correct the FP prediction, we expanded the removal to include all pairwise removals, and so on, until exhaustion or until the prediction was corrected. The second set of attempts to correct FP predictions introduced new nutrient uptakes and/or changed irreversible reactions to reversible. This procedure is based on the optimization model that Kumar and Maranas [E. coli metabolic network by adding a minimum number of reactions from a pool of reactions collected from multi-organism databases (MetaCyc [1) all uptake reactions blocked in the original iNJ661m network and 2) all irreversible reactions in iNJ661m with their directions reversed. We used this model to obtain the modification(s) that changed the minimum number of irreversible reactions, and accepted the modification(s) if the changed irreversible reaction(s) was (were) reported as thermodynamically reversible in either the metabolic network of E.coli [Bacillus subtilis [iNJ661m network as the starting point. The set of identified modifications was then analyzed together in Step III: analysis of combined modifications.Figure fication , suggest Maranas  develope(MetaCyc  and KEGG(MetaCyc ). We usef E.coli  or that subtilis . These tiNJ66 m, we restored the uptake. If a metabolite could not be consumed, we added this metabolite to the biomass objective function with a coefficient of 10-6 mmol/gDW, that is, mmol per gram dry weight of M. tuberculosis. This coefficient was used in the biomass objective function of the original iNJ661 network to include metabolites for which quantitative biomass composition data were not available [iNJ661 m. For this procedure, we used the optimization model developed by Kumar and Maranas [Figure vailable . Finally Maranas  for resoStep II procedures, we needed to assess whether a modification was adequate. Figure iNJ661 m, the following three criteria were satisfied: 1) the calculated wild-type growth rate was greater than the minimal rate  the FP or FN prediction was corrected, and 3) no TP(TN) prediction became FN(FP).Note that, to correct for FP and FN predictions in the rophages ), 2) theStep II, there might be more than one modification used to correct for each of the FP or FN predictions, and we needed to analyze the consequences of combining these modifications into a single network description. Figure gene A, three possible modifications that correct for the prediction of gene B, etc., then there would be 2*3*... possible network realizations, where each realization includes one modification for each incorrect prediction. Next, for each network realization, we checked whether the network contained contradictory modifications, e.g., one modification in the network blocked a reaction flux, whereas another modification allowed for the same reaction flux, or one modification added a metabolite to the biomass objective function, whereas another modification removed it. If contradictory modifications were not detected, then we applied the modifications to iNJ661m and verified that 1) the wild-type growth rate  was greater than the minimal rate, 2) incorrect predictions were corrected, and 3) correct predictions (TP or TN) were still correct. If true, we recorded the network as plausible. Finally, from all plausible networks, we selected the ones that were associated with the smallest number of adjustments .After the completion of Step II, we allowed for the addition and removal of a fixed set of nutrient uptakes, leaving other nutrient uptakes (blocked or unblocked) of iNJ661m unexamined. Here, we re-examined these uptakes in light of the altered nutritional environment under in vivo conditions. The harsh nutritional environment that M. tuberculosis confronts when the bacterium infects a mouse suggests that nutrient uptake is limited and restricted to generally available small molecules as possible metabolite sources. Thus, we implemented a nutrient uptake-based analysis procedure to eliminate uptakes of complex nutrients as much as possible while minimizing the number of additional small-molecule uptakes and maintaining the compatibility with the in vivo gene essentiality data. The primary location of M. tuberculosis in a recently infected animal is the phagosomal compartments of macrophages. In this environment, large molecules are typically broken down into smaller ones. For example, macrophages decompose proteins into amino acids [in vivo nutrient environment did not contain a significant abundance of all large molecules, such as amino acids, but rather was primarily composed of small molecules, such as nitrite, nitrate, ammonia, phosphate, and so on. However, as some large molecules may not be broken down, we still kept the uptakes for these molecules in two cases: 1) if their uptakes was added during the correction of a false gene essentiality prediction ; and 2) if deletions of these uptakes caused a growth rate lower than the defined threshold or generated new false gene essentiality predictions.In no acids , which ino acids . Therefo1) contained the necessary atomic elements found among the biomass molecules, i.e., carbon, nitrogen, oxygen, phosphorus, sulphur, iron, potassium, and sodium; 2) had a recorded uptake reaction in iNJ661m; and 3) contained a minimum number of other elements. Based on the elements, these small-molecule metabolites were then grouped accordingly: carbon monoxide (CO) and carbon dioxide (CO2) for carbon; ammonium (NH4+), nitrite (NO2-), and nitrate (NO3-) for nitrogen; O2 for oxygen; phosphate (HPO42-) for phosphorus; sulphate (SO42-) for sulphur; ferrous (Fe2+) and ferric (Fe3+) ions for iron; K+ for potassium; and Na+ for sodium. Note that nitric oxide (NO) is a small nitrogen-containing molecule that was not included in the analysis as it is generated as part of the host defense system rather than acting as a nitrogen source [c oxidase because experiments have suggested that NO prevents the function of this enzyme [Step V.Before doing any calculation, we first assembled a set of available small-molecular-weight metabolites that n source . Commenss enzyme . Note thStep III, we divided all uptake reactions into the following two sets: 1) a "minimal" uptake set comprising the uptakes of H2O and H+, all uptakes added in Step II, and the uptakes of the small molecules defined above ; and 2) an "extended" uptake set comprising all other defined uptake reactions in iNJ661m (extended set). Initially, we allowed uptake reactions from the minimal set and blocked all reactions from the extended set. As expected, this resulted in a non-biological network displaying an overall growth rate of zero. We then used the minimization procedure adopted from Kumar and Maranas [For each network realization from  Maranas  to deter1) removing both the CO and CO2 uptakes, 2) removing only the CO uptake, and 3) removing only the CO2 uptake. When investigating the next element group, all other small-molecule metabolite uptakes were restored. Finally, we selected the minimal number of small-molecule uptakes for each element that was compatible with maintaining wild-type growth and preserving the true gene essentiality predictions.Next, we fixed this set of uptake reactions and investigated which small-molecule uptake reactions from the minimal set were dispensable, i.e., we tried to find a minimal set of uptakes that was still compatible with growth. To do this, we allowed metabolite uptakes for all members of the small-molecule set and investigated removal of specific uptakes based on each element group as defined above. We did this by systematically removing, for each element group, all combinations of small-molecule uptakes and recording which combinations resulted in a wild-type growth rate that was greater than the minimal rate and where each of the TP and TN predictions were preserved. For example, for the carbon group, we investigated the possibilities of Steps II-IV) were fully automated and attempted to capture all feasible minimal modifications to the network that optimized compatibility with the gene essentiality data. Supplemental Section S2 in Additional file in vivo network iNJ661v, we included all feasible modifications, as long as no new incorrect predictions or contradictory nutrient uptakes were generated.All the computational correction procedures above  to predict experimental in vivo gene essentiality [We used statistical methods to compare the ability of the three metabolic network models  that was compatible with pathogen growth under in vivo conditions.Sassetti et al.  experimend Rubin  adapted  in mice . Althougpathogen . In addipathogen  showed teriments ,36 as thiNJ661 [iNJ661 m, and GSMN-TB [in vitro experimental datasets [iNJ661 yielded 153 TP genes and 84 FN genes using a growth rate ratio threshold of 1.0. Due to minor numerical differences in cutoffs and constraint values in the FBA, these numbers were slightly different from the published results (154 TP and 83 FN) [iNJ661 gave MCC values ranging from 0.39 to 0.42 depending on the specific threshold. We also performed the same calculations for iNJ661m and GSMN-TB. We obtained very similar results for iNJ661 m, suggesting that iNJ661m had the same ability to predict in vitro gene essentiality. The agreement between the GSMN-TB-predicted essentiality and the in vitro experimental data was slightly better than that for the other two networks, with MCC values for GSMN-TB ranging from 0.49-0.52.Table iNJ661 , iNJ661  GSMN-TB  as well datasets . To veridatasets  using thin vitro results, the in vivo-predicted essentiality of iNJ661, iNJ661 m, and GSMN-TB was less satisfactory. Using the designated in vivo biomass composition formulation in the GSMN-TB network (indicated as GSMN-TBv) provided a slightly larger sensitivity in the essentiality prediction, but the improvement was only modest. The sensitivity for the predicted essentiality of these networks ranged from 0.23 to 0.44 depending on the threshold used. MCC values also decreased to 0.03-0.11 when we used these networks to predict in vivo essentiality. The relatively poor match between these predictions and the in vivo experimental data suggests that iNJ661, iNJ661 m, and GSMN-TB(v) are inappropriate to describe the metabolic activity of M. tuberculosis under in vivo conditions.In contrast to the M. tuberculosis. Among the two available networks [iNJ661 as the starting point because it is based on the H37Rv strain of M. tuberculosis used in the in vivo gene essentiality experiments [M. tuberculosis metabolism. iNJ661 also successfully predicts the growth rate of M. tuberculosis H37Rv in two different media: Youmans and the "chemically defined rich culture media," while the growth rates predicted from GSMN-TB are only compared with experimental data for M. bovis BCG. Since the key aim of our work is to mimic as faithfully as possible the H37Rv strain, we choose not to use the GSMN-TB network as our starting point, although we used reactions relevant to the H37Rv strain from the GSMN-TB network to augment our construction.Therefore, we attempted to obtain the new network by modifying an existing metabolic network of networks ,16, we seriments ,36. In ciNJ661 m, the slightly improved version of iNJ661, as a starting point and performed the systematic procedures shown in Figure iNJ661v) to better describe the in vivo metabolic activity of M. tuberculosis. Table in vivo experimental essentiality and the predicted essentiality of iNJ661m at the end of Step I yielded 76 FP and 25 FN predictions with a threshold of ≤0.2. All 25 FN and 76 FP genes were taken as input to Step II. The automated FP and FN correction procedures shown in Figures We used Step III. As shown in Additional file 30 × 52 × 23 = 200 possible network realizations. Next, we examined each one of these using the criteria shown in Figure 31 × 24 = 16 plausible network realizations.Since there were multiple ways in which we could combine the different modifications, it became necessary to try to reduce the number of possible network realizations, as outlined in Step IV, we re-examined the assigned nutrient uptakes in the networks as outlined in the Methods Section. This analysis was performed for each of the 16 network realizations from Step III, each yielding the same set of uptakes. Table 2O and H+ , a minimum number of uptakes of small molecules, uptakes added based on the Step II analysis, and glycerol. We further decreased the upper limit of glycerol input to 0.06 mmol·h-1·gram dry weight-1 until lower values of the glycerol input caused the fum gene to be incorrectly predicted as essential under in vivo conditions. Each of the resultant 16 networks contained the same set of uptakes and yielded the same number of correct predictions. Since we had no prior information to discriminate among these networks, we applied all modifications to iNJ661 m. Because this combination did not generate any new incorrect gene essentiality predictions, we designated this network as the "optimal" unbiased construction of the in vivo network iNJ661v.In iNJ661v network contained 663 genes, 838 metabolites, and 1,049 reactions and is provided in the Additional files in both Systems Biology Markup Language  compared with using the in vitro networks . Thus, a significant correlation between experimental gene essentiality in mice and calculated mutant growth rates was only present in iNJ661v.The iNJ661, iNJ661 m, and iNJ661v to predict experimental gene essentiality under in vivo conditions. Figure iNJ661: 0.57 ± 0.09, iNJ661m: 0.57 ± 0.09, and iNJ661v: 0.84 ± 0.06). The AUCs of the in vitro models iNJ661m and iNJ661 were not significantly larger than those of random predictions (0.50), indicating the inability of these networks to predict in vivo essentiality. Conversely, the AUC of iNJ661v was significantly larger than those of the other two networks, demonstrating that iNJ661v was better able to predict experimental gene essentiality in mice.We further applied threshold-independent statistical tests to compare the abilities of Step IV, the iNJ661v network still contained several incorrect gene essentiality predictions. Table atpB, nirA, proV, accD1, and cobL genes because each one of them is required together with one or more TN genes. For example, the FN gene proV is required together with the TN genes proW, proX, and proZ for the transport of choline, carnitine, glycine betaine, and proline into the cell [proV would change the TN predictions for proW, proX, and proZ into FP. It is likely that the gene products of proV have other essential functions that may or may not be related to metabolic functions and were not accounted for in the current network description. Conversely, we failed to correct the prediction for the Rv3534c gene because it belongs to a pathway containing a metabolite without a source. Thus, further studies are necessary to discover how this metabolite is synthesized and how this information can be incorporated into the network.After the cell . Any "coin silico/in vivo growth inconsistencies generated by the original iNJ661m metabolic network created a network (iNJ661v) that was more compatible with in vivo growth. The metabolic modifications reflected how the pathogen adjusts its metabolism to adapt to the environment that M. tuberculosis confronts during infection in the mouse. Here, we discuss the performed modifications vis-a-vis the available relevant literature within the context of the affected metabolic pathways. Table By design, our automated and systematic analysis of the Rv1099c and ppgK genes. This modification suggested that the host environment may lack glucose and, thus, forces M. tuberculosis to generate glucose through gluconeogenesis (the pathway to synthesize glucose from the citric acid cycle). In the "fatty acid metabolism" pathways, to correct the FP prediction of the fabG2, inhA, and pks15 genes and the FN prediction of the desA3 gene, we added the uptakes of several fatty acids from the host environment and blocked the synthesis of a fatty acid, hexadecenoate. This modification suggested that fatty acids were available in the host environment and that M. tuberculosis stopped synthesizing them under in vivo conditions. In summary, the modifications in these two groups of pathways, along with the retention of glycerol uptake (see Table M. tuberculosis during infection in the mouse required lipids (composed of fatty acids and glycerol) instead of glucose as its primary source of carbon. The important role of lipids as carbon sources is evident from the observed up-regulation of genes involved in fatty acid catabolism during M. tuberculosis in vivo growth [M. tuberculosis growth in dipalmitoyl phosphatidylcholine  medium [In the "glycolysis/gluconeogenesis" pathways, we deleted the uptake of glucose from the environment and blocked the synthesis of glucose from maltose and trehalose to correct the FN predictions of the o growth ,28,52-55) medium , the abi) medium , and the) medium .ribH, ribC, and ribD genes. The removal suggested that riboflavin was not required for the survival of M. tuberculosis under in vivo conditions, which is supported by the observation that riboflavin is used for glycolysis (the pathway of glucose catabolism) in M. tuberculosis [bioA, bioF, and bioB genes by adapting the biomass objective function to include biotinyl-5'-adenosine monophosphate (AMP) and blocked the ability of the gene product of the bioF2 gene to catalyze the synthesis of the precursor of biotin. Because biotinyl-5'-AMP is the activated form of biotin [M. tuberculosis under in vivo conditions. This observation is compatible with biotin playing a role in gluconeogenesis [M. tuberculosis obtains glucose through gluconeogenesis under in vivo conditions [in vivo conditions, which is commensurate with the observed down-regulation of bioF2 in the presence of hydrogen peroxide (H2O2) [In the pathways associated with "vitamin and cofactor metabolism," we removed riboflavin from the biomass objective function to correct the FP prediction of the rculosis  and thatrculosis . In the f biotin , the incogenesis  and thatnditions . The bloe (H2O2) , a reactilvC and ilvN genes, suggesting that M. tuberculosis might be able to absorb these amino acids from the host environment. Although we could not directly verify these uptakes in M. tuberculosis, experiments have shown that a M. tuberculosis strain that lacks the ability to synthesize three amino acids  could persist in mice for four weeks [In the "amino acid metabolism" pathways, we added uptakes of isoleucine and valine to correct the FP predictions of the ur weeks . This sulppX gene. The biomass objective function of iNJ661 and the in vivo biomass objective function of GSMN-TB include intracellular phthiocerol dimycocerosate A. In fact, these lipids are known to be secreted by M. tuberculosis into the environment and subsequently are associated with the pathogen envelope, where they aid in avoiding host immune attacks [3-, O2, and Fe3+  have shown that 49% of the predicted synthetically lethal double-deletion mutants are correct [In silico studies of the growth of double-deletion mutants of metabolic genes have also been performed for E. coli [Helicobacter pylori [Leishmania major [Double-gene deletion experiments provide insights into redundant pathways, non-obvious coupling of metabolite flows, and potential new drug targets. For example, although neither the g target . Large-s correct -70. In s E. coli , Helicobr pylori , and Leiia major , althougiNJ661m and iNJ661v to model the growth of two experimentally examined double-gene deletion mutants. In the first case, experimental work showed that the growth of the ΔpanCD mutant (deletion of Rv3602c and Rv3601c) is highly attenuated in mice [iNJ661m and iNJ661v predicted that ΔpanCD mutants have a growth rate of zero, consistent with the experimental observation. Although not confirmed experimentally, our analysis of iNJ661m indicated that the growth attenuation may also take place within an in vitro medium. In the second case, experimental work has indicated that the mutant Δicl1Δicl2 (deletion of Rv0467 and Rv1915) of M. tuberculosis cannot survive in mice but can grow under certain in vitro conditions [iNJ661m predicted that the growth rate of Δicl1Δicl2 was equal to that of wild-type M. tuberculosis, whereas analysis of iNJ661v predicted that the growth rate of Δicl1Δicl2 was only 26% of the wild-type rate, suggesting that iNJ661v was a better predictor for in vivo growth.We initially examined the capability of  in mice . FBA of nditions . FBA of iNJ661m and iNJ661v to simulate the growth of M. tuberculosis double-gene deletion mutants under an in vitro condition and an in vivo condition, respectively, and predicted synthetic lethality. Additional file iNJ661v to carbon metabolism-related pathways. Figure iNJ661m and iNJ661v). The iNJ661v network predicted a substantially larger number of synthetically lethal genes under in vivo conditions than the iNJ661m network under in vitro conditions. This was partly due to the more constrained nutritional environment faced by iNJ661v with respect to carbon metabolism. Although drugs effective under in vitro conditions may not be effective under in vivo conditions, the converse may also be true [iNJ661v indicate novel potential drug targets under in vivo conditions. Likewise, the 35 gene pairs that were predicted to be essential under both conditions may represent more robust drug targets based on their insensitivity to environmental conditions.We then performed a comprehensive FBA of  be true . The addiNJ661v were related to carbon and energy metabolism. This reflects the modifications that we implemented in iNJ661v to reproduce the in vivo gene essentiality data in these parts of the metabolic network. Synthetic lethality in carbon metabolism was primarily located in the two different pathways that can be used to synthesize glucose precursors from glycerol and fatty acids -cycle under different limiting nutrient conditions, the effects of inhibiting multiple reactions, and the modes of cellular respiration during infection.The gene essentiality data used to help construct the M. tuberculosis adapts to this acidic condition has not been fully elucidated [+ entry [+ and urea into NH4+ and CO2 [4+ also contributes to the M. tuberculosis survival by preventing the maturation of the phagosome [iNJ661v network to explore additional acid adaptation mechanisms based on the metabolic flow of H+. We used flux variability analysis (FVA) to estimate the ranges of reaction fluxes in the metabolic network at the optimal growth rate. This allowed us to estimate the range of the overall H+ exchange between the environment and the M. tuberculosis cells at the optimal growth rate for wild type iNJ661v. The protonation state of the metabolites is chosen to correspond to pH 7.2 [+ flux in both in vitro and in vivo metabolic network models, the protonation states of the metabolites were not changed in this calculation. Table + exchange was within a narrow range of negative values, suggesting that the in vivo metabolism consumed H+ as a whole, contributing to the relief of acidic stress. The primary reaction important for this H+ consumption was the nitrite reductase (NR) reaction that uses H+ and reduces NO2- into NH4+. Table iNJ661v were positive but did not vary, suggesting that there must be flux through this reaction at the optimal growth of wild type cells. Conversely, for mutants whose NR was removed  network does not include a complete urea synthesis pathway, we added a urea uptake in order to create a flux through the urease reaction. Table + exchange and the reaction fluxes for iNJ661v with urea uptake. Overall, H+ is consumed to relieve acid stress. In the wild type strain, the flux through the NR reaction was greater than zero, suggesting that NR still contributes to H+ consumption in the presence of urease. Removal of the NR reaction (ΔNR mutant) diminished H+ consumption, but did not abolish the overall H+ exchange. Importantly, we performed the same analysis for the in vitro iNJ661m network and found that the NR reaction was always inactive, suggesting that NR does not play a role in acid adaptation in the in vitro medium  and fast  growth. We simulated the slow in vivo growth by constraining fatty acid uptake of iNJ661v until the growth rate was one third of its original value [Persistence and slow growth are clinically important states of the  pathway . Considerculosis , here weal value , and fasiNJ661v included the 2-oxoglutarate decarboxylase (OXGDC) and succinate-semialdehyde dehydrogenase (SSAL) reactions, suggesting that these reactions were likely to be important in fatty-acid-limited slow growth. The previously suggested increased flux and importance of isocitrate lyase (ICL) under slow-growth conditions is a direct consequence of the glycerol limitation [in vivo metabolic state, the OXGDC and SSAL reactions took on a heightened function and importance in slow-growth maintenance. The suggested metabolic responses to fatty-acid-limited growth rate could be experimentally tested by measuring the activities of the enzymes in the TCA cycle of M. tuberculosis during slow and normal growth in host environment or in an in vitro condition infused with fatty acids as carbon sources.Figure mitation . In fattin vivo growth-reduction strategies. Drug combinations that achieve optimal therapeutic response and avoid side effects caused by high doses of single drugs [in vivo nutrients are lipids, we focused on two reactions that are required to process these metabolites, i.e., the glycerol-3-phosphate dehydrogenase (G3PD) reaction, which is necessary for the utilization of glycerol, and the ICL reaction, which is a known potential drug target and is required for the conversion of fatty acids into other metabolites, such as pyruvate [in vivo iNJ661v (panel A) and in vitro iNJ661m (panel B) networks.Given the ability of the metabolic network to provide different reaction fluxes under different metabolic conditions, we can explore combinations of mechanisms to inhibit multiple reactions to derive optimal le drugs ,80 can rpyruvate . To stud-3 mmol/h/gDW. Figure in vivo results, shows that these flux limits effectively blocked growth of the organism as indicated by the blue color designating strongly retarded growth rates. One can also see that whereas the ICL reaction is essential, i.e., the organism cannot effectively grow if this reaction is sufficiently inhibited, single blockage of the G3PD reaction would not prevent growth if the ICL reaction was left unconstrained  and the nitrate reductase (narG-narJ) enzymes were synthetically essential. Because these two enzymatic groups are associated with aerobic and anaerobic respiration, respectively, synthetic essentiality indicates that therapy targeting cellular respiration needs to simultaneously inhibit both aerobic and anaerobic respiration. This conclusion is not evident from gene expression data, which instead indicates that seven weeks post-infection the pathogen only employs nitrate reductase enzymes for anaerobic respiration [narG-narJ) are non-essential in mice, even after seven weeks [2 is available to the bacterium in the host environment [M. tuberculosis under in vivo and in vivo conditions.Finally, we examined the results of the piration . Howeveren weeks , indicatironment . TherefoiNJ661v network to explore metabolism-based M. tuberculosis acid adaptation mechanisms, study metabolic responses to in vivo slow growth, estimate the effects of different levels of inhibitions of multiple reactions, and gain insights into respiration-targeting therapy.In summary, we used the developed M. tuberculosis iNJ661 [in vitro conditions, it lacks predictive power for in vivo gene essentiality. We developed a set of automated procedures that systematically examined possible metabolic modifications to the original iNJ661 network and optimized predictions of experimental in vivo essentiality. By design, the newly developed in vivo network iNJ661v provided significantly better agreement  and modifications to better model experimental in vivo gene essentiality. These conditions capture part of the pathogen's metabolic processes, but large knowledge gaps still exist. An analysis of the M. tuberculosis genome indicated that while 1,286 genes are directly associated with metabolic processes , only 663 genes were explicitly included in the developed network. This highlights the need for further systematic theoretical analyses to improve the network description and, more importantly, the need for experimental data under a variety of different in vivo and in vitro growth conditions that could be used to guide and validate model development.The existence of inaccurate essentiality predictions based on in silico modeling of organisms provides the foundation for the eventual integration of metabolic information with gene regulation and signaling networks to model biological phenomena. Ultimately, the model development presented here can be extended to answer additional questions as they relate to the metabolic status of the pathogen population before, during, and after infection: What nutrients are present in different in vivo compartments, how do they change as infection progresses, and how do they relate to cellular growth rates and population sizes? What is the appropriate objective function to use for the persistent dormant phase of M. tuberculosis infection? What processes does M. tuberculosis use to handle nutrient deficiencies and antagonistic conditions found in macrophages? These are the questions that we can now begin to address using the developed iNJ661v as a more sophisticated in vivo representation of the metabolic network for M. tuberculosis.Higher fidelity M. tuberculosis, the causative agent of TB, continues to pose a major health threat worldwide, with nearly two million deaths annually. Modeling of and accounting for the varying metabolic requirements of M. tuberculosis during host infection can help identify the metabolic enzymes suitable for therapeutic intervention. To this end, we developed procedures to construct an in vivo metabolic network model of M. tuberculosis that maximizes the agreement between predicted and measured gene essentiality determined from infection experiments in the mouse. We verified the modifications obtained computationally by reviewing the available relevant literature. For example, lipids are major carbon sources for M. tuberculosis in the host environment. The network provided a metabolic description of the pathogen consistent with the generally hostile and nutrient-poor in vivo conditions in the host that can be exploited in evaluation, selection, and modeling of novel potential drug targets.All authors contributed to the design and coordination of the study. XF performed the computational implementations, and XF and AW prepared the original draft, which was revised by JR. All authors read and approved the final manuscript.Supplementary materials. It provides the detailed steps for constructing the iNJ661m and iNJ661v networks, and all supplemental tables and figure.Click here for fileiNJ661m metabolic networkThe . It describes the iNJ661m metabolic network, using the Systems Biology Markup Language format.Click here for fileiNJ661m metabolic networkThe . It describes the iNJ661m metabolic network, using Microsoft Excel 2003.Click here for fileiNJ661v metabolic networkThe . It describes the iNJ661v metabolic network, using the Systems Biology Markup Language format.Click here for fileiNJ661v metabolic networkThe . It describes the iNJ661v metabolic network, using Microsoft Excel 2003.Click here for file


Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation , stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail .MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones.The standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries). MetRxn is hosted on a web-based platform that uses relational database models (MySQL). The ever accelerating pace of DNA sequencing and annotation information generation  is spearA major impediment is the presence of metabolites with multiple names across databases and models, and in some cases within the same resource, which significantly slows down the pooling of information from multiple sources. Therefore, the almost unavoidable inclusion of multiple replicates of the same metabolite can lead to missed opportunities to reveal (synthetic) lethal gene deletions, repair network gaps and quantify metabolic flows. Moreover, most data sources inadvertently include some reactions that may be stoichiometrically inconsistent  and/or eE. coli genome scale model iAF1260 with EcoCyc [Arabidopsis thaliana database and KEGG for generating genome-scale models [There have already been a number of efforts aimed at addressing some of these limitations. The Rhea database, hosted by the European Bioinformatics Institute, aggregates reaction data primarily from IntEnz  and ENZYh EcoCyc  and the e models  in a seme models . Also, me models ,36. An ie models . All of http://metrxn.che.psu.edu.In this paper, we describe the development and highlight applications of the web-based resource MetRxn that integrates, using internally consistent descriptions, metabolite and reaction information from 8 databases and 44 metabolic models. The MetRxn knowledgebase (as of October 2011) contains over 76,000 metabolites and 72,000 reactions (including unresolved entries) that are charge and elementally balanced. By conforming to standardized metabolite and reaction descriptions, MetRxn enables users to efficiently perform queries and comparisons across models and/or databases. For example, common metabolites and/or reactions between models and databases can rapidly be generated along with connected paths that link source to target metabolites. MetRxn supports export of models in SBML format. New models are being added as they are published or made available to us. It is available as a web-based resource at The construction of MetRxn largely followed the following steps, as illustrated in Figure Metabolite and reaction data was downloaded from BRENDA, KEGG, BioCyc, BKM-react and other databases using a variety of methods based on protocols such as SOAP, FTP and HTTP. We preprocessed the data into flat files that were subsequently imported into the knowledgebase. All original information pertaining to metabolite name, abbreviations, metabolite geometry, related reactions, catalyzing enzyme and organism name, gene-protein-reaction associations, and compartmentalization was retained. For all 44 initial genome-scale models listed, the online information from the corresponding publications was also imported. The source codes for all parsers used in Step 1 are available on the MetRxn website.The "raw data" from both databases and models was unified using standard SQL scripts on a MySQL server. The description schema for metabolites includes source, name, abbreviations used in the source, chemical formula, and geometry. The schema for reactions accounts for source, name, reaction string (reactants and products), organism designation, associated enzymes and genes, EC number, compartment, reversibility/direction, and pathway information. Once a source has been imported into the MySQL server, a data source-specific dictionary is created to map metabolite abbreviations onto names/synonyms and structures and metabolites to reactions.We used Marvin (Chemaxon) to analyze all 218,122 raw metabolite entries containing structural information . Inconsistencies were found in 12,965 entries typically due to wrong atom connectivity, valence, bond length or stereo chemical information, which were corrected using APIs available in Marvin. A final corrected version of the metabolite geometries was calculated at a fixed pH of 7.2 and converted into standard Isomeric SMILES format. The structure/formula used corresponded to the major microspecies found during the charge calculation, which effectively rounds the charge to an integer value in accordance with previous model construction conventions. This format includes both chiral and stereo information, as it allows specification of molecular configuration -40. MetaRaw metabolite entries were assigned to Isomeric SMILES representations whenever possible. If insufficient structural information was available for a downloaded raw metabolite then it was assigned temporarily with the Canonical SMILES and revisited during the reaction reconciliation. Canonical SMILES retain atom connectivity but not stereo-specificity and are used as the basic metabolite topology descriptors as many metabolic models lack stereo-specificity information. After generating the initial metabolite associations, we identified reaction overlaps using the reaction synonyms and reaction strings along with the metabolite SMILES representations. Directionality and cofactor usage were temporarily ignored. During this step, reactions were flagged as single-compartment or two-compartment . MetRxn internally retains the original compartment designations, but currently only displays these simplified compartment designations. In analogy to metabolites, reactions were grouped into families that shared participants but in the source data sets occurred in different compartments or differed only in protonation.Once metabolites were assigned correct elemental composition and protonation states, reactions were charge and elementally balanced. To this end, for charge balancing we relied on a linear programming representation that minimizes the difference in the sum of the charge of the reactants and the sum of the charge on the products. The complete formulation is provided in the documentation at MetRxn.cis, trans, L-, D-, alpha, beta, gamma, and numerical entries because they change the phonetic signature of the synonym under investigation. In addition, the algorithm ignores non-chemistry related words  that are found in some metabolite names. Certain tokens such as "-ic acid" and "-ate" are treated as equivalent. PubChem and Chemspider sources were accessed through the GUI so that the curator gets as much information as possible to identify the data correctly. Phonetic matches provided clues for resolving over 159 metabolites. The iterative application of string and phonetic comparison algorithms resolved as many as 8,879 metabolites after 18 rounds of reconciliation.Reactions with one (or more) unresolved reactants and/or products were string compared against the entire resolved collection of reactions. This step was successively executed as newly resolved metabolites and reactions could enable the resolution of previously unresolved ones. After the first pass 164 metabolites were resolved, while subsequent passes (up to 18 for some models) helped resolved a total of 8,720 entries. Reactions with significant (but not complete) overlapping sets of reactants/products are additionally sent to the curator GUI including phonetic information. Briefly, the phonetic tokens of synonyms with known structures were compared against the ones without any associated structure. The algorithm suppresses keywords/tokens depicting stereo information such as Upon completion of this workflow, all genome-scale models are reformatted into a computations-ready form and Flux Balance Analysis  is perfoMetRxn supports a number of export capabilities. In general, any list that is displayed contains live links to the metabolite or reaction entities. These lists can consist of an entire model, data from a comparison, or query results. All items can be exported to SMBL format. In addition, the public MySQL database will be made available upon request. Because of licensing limitations, the BRENDA database cannot be exported and is not part of the public MySQL database. However, we plan to provide Java source code that allows for the integration of a local copy of the public MySQL database with the BRENDA database (provided upon request).In addition to listing the content  of the selected data source(s), MetRxn contains tools for comparing two or more models and visualizing the results. These associations can be for metabolites or reactions. During these comparisons compartment information and reversibility are suppressed. Comparison tables are generated by comparing the associations between the selected data source(s) using the canonical structures.An initial repository of reaction  and metabolite  entries were downloaded from 8 databases and 44 genome-scale metabolic models. We compiled a non-redundant list of 42,540 metabolites and 35,474 reactions (after consolidating duplicate entries) containing full atomistic and bond connectivity detail. Another 6,100 metabolites and 11,401 reactions have partial atomistic detail typically containing generic side-chains (R) and/or an unspecified number of polymer repeat units. Finally, 5,436 metabolites in metabolic models and 8,000 metabolites in databases are retained with no atomistic detail. In some cases lack of atomistic detail reflects complete lack of identity specificity  whereas in other cases even though the chemical species is fully defined, atomistic level description is not warranted ). Figure E. coli iAF1260 [Corynebacterium glutamicum model [E. coli iAF1260 model.The workflow followed in the creation of the MetRxn knowledgebase identified a number of inconsistencies. For instance, the same metabolite name may map to molecules with different numbers of repeat units  or completely different structures . Notably, even for the most well-curated metabolic model,  iAF1260 , we founum model , 7,8-amiAcinetobacter baylyi model [Bacillus subtilis model [Aspergillus nidulans model [which implies that 7,8-aminopelargonic acid (DAPA) is identical to 7,8-Diaminononanoate (dann). Examination of pelargonic acid and nonanoate reveals that they were indeed known synonyms. In many cases, we were also able to assign stereo-specific information to metabolite entries in models . We made use of an iterative approach that allowed us to map structures from models with explicit links to structures (e.g. to KEGG or CAS numbers) to models that only provided metabolite names. Furthermore, by using a phonetic algorithm that uses tokens for equivalent strings in metabolite names  we were able to resolve more than an additional 159 metabolites. For example, phonetic searches flagged cis-4-coumarate and COUMARATE in the yi model  as potenis model  and 1- on both the published and MetRxn-based rebalanced version of the isted in  to asses2 transport) were directly recognized as common across those 34 models using a simple string match comparison. Even when examining models for only a few bacterial organisms  simple text searches recognized only 40 common reactions . The reason for this glaring inconsistency is that differing metabolite naming conventions, compartment designations, stoichiometric ratios, reversibility, and water/proton balancing issues prevents the automated recognition of genuinely shared reactions across models. Using the glucose-6-phosphate dehydrogenase reaction as a representative example, Table B. subtilis, E. coli and Salmonella Typhimurium.At the onset of creating MetRxn, we conducted a brief preliminary study to quantify the extent/severity of naming inconsistencies by contrasting the reaction information contained in an initial collection of 34 of the most popular genome-scale models spanning 21 bacterial, 10 eukaryotic and three archaeal organisms. Across all branches of life, most metabolic processes are largely conserved  therefore we expected to uncover a large core of common reactions shared by all models. Surprisingly, we found that only three reactions . However, we found many differences that appear to reflect different conventions adopted when the two models were generated rather than genuine differences in metabolism. In particular, in the C. thermocellum model [C. acetobutylicum model [Bacillus subtilis iBsu1103 model [C. thermocellum and B. subtilis. Most of the reaction overlaps are in the amino acids biosynthesis pathways, carbohydrate metabolism, and nucleoside metabolism. It is important to note that 48 reactions in C. acetobutylicum, 67 reactions in C. thermocellum, and 120 reactions in B. subtilis lack full atomistic information  into useful products  and bio-fuels . Selected production routes must exhibit high yields, avoid thermodynamic barriers, bypass toxic intermediates and circumvent existing intellectual property restrictions. Historically, the incorporation of heterologous pathways relied largely on human intuition and literature review followed by experimentation ,68. Curret. al.  introduc[E. coli . Based o[E. coli  was intr[E. coli , PathCom[E. coli , Pathway[E. coli ,78, Meta[E. coli , PathFin[E. coli  and UM-B[E. coli  have beeE. coli for the production of 1-butanol (shown in orange). The first pathway involves the fermentative transformation of pyruvate and acetyl-CoA to 1-butanol using enzymes from C. acetobutylicum [We recently published  a graph-utylicum . The secutylicum . This exMetRxn enables the standardization, correction and utilization of rapidly growing metabolic information for over 76,000 metabolites participating in 72,000 reactions (including unresolved entries). The library of standardized and balanced reactions streamlines the process of reconstructing organism-specific metabolism and opens the way for identifying new paths for metabolic flux redirection. Moreover, the standardization of published genome-scale models enables the rapidly growing community of researchers who make use of metabolic information to understand metabolism at an organism-level and re-deploy it for various biotechnological objectives. By removing standardization and data heterogeneity bottlenecks the pace of knowledge creation and discovery from users of this resource will be accelerated. MetRxn is constructed in a way that allows for quick updating and tracking of changes that occur in the primary databases, as well as available parsing tools that allow for rapid import of new genome-scale metabolic models as they become available. By having exports in SBML, MetRxn's output can be directly interfaced with software packages such as the COBRA toolbox.During the construction of the initial release of MetRxn, we managed to associate structures for over 8,800 metabolites and re-balanced more than 2,400 reaction instances across 44 metabolic models. This enables the genuine comparison of metabolic content between metabolic models. Preliminary results reinforce that that discrepancies between metabolic models echo not only genuine differences in metabolism but also assumptions and workflow followed by the model creator(s). Going forward, we will continue to expand MetRxn to include more genome-scale metabolic models and add additional tools to aid in their analysis. Because we anticipate that the scope and number of models will rapidly expand, we plan to invite and encourage the community to offer comments about metabolite and reaction information as well as provide feedback on MetRxn itself.http://metrxn.che.psu.edu. Its use is freely available for all non-commercial activity.MetRxn is available at AK generated the software and tools for MetRxn and assisted in drafting the manuscript. PFS participated in the design of the database, performed database curation and FBA analysis, and drafted the manuscript. CDM conceived the study, participated in the design of the database and edited the manuscript. All authors read and approved the final manuscript.


More recently, isolated liver perfusion systems have been increasingly used to characterize intrinsic metabolic changes in the liver caused by various perturbations, including systemic injury, hepatotoxin exposure and warm ischemia. Metabolic engineering tools have been widely applied to these systems to identify metabolic flux distributions using metabolic flux analysis or flux balance analysis and to characterize the topology of the networks using metabolic pathway analysis. In this context, hepatic metabolic models, together with experimental methodologies where hepatocytes or perfused livers are mainly investigated, are described in detail in this review. The challenges and opportunities are also discussed extensively.: The liver has many complex physiological functions, including lipid, protein and carbohydrate metabolism, as well as bile and urea production. It detoxifies toxic substances and medicinal products. It also plays a key role in the onset and maintenance of abnormal metabolic patterns associated with various disease states, such as burns, infections and major traumas. Liver cells have been commonly used in  These experiments performed at cell and/or organ levels are important for functional characterization of the mammalian systems or to improve the functions of medicinally important tools.The liver has many complex physiological functions. It plays an important role during the hyper-metabolic state associated with various diseases, including burns, infections and major trauma that are characterized by an accelerated breakdown of skeletal muscle protein, increased resting energy expenditure and a negative nitrogen balance. Isolated perfused livers have been commonly used to characterize disease-related metabolic alterations at the organ level ,4,5,6,7.There is a significant number of studies in literature where metabolic properties of the liver have been investigated. Herein, we mainly focus on the studies where metabolic network of hepatocytes or the organ itself have been investigated, using metabolic engineering tools such as metabolic flux analysis, flux balance analysis and pathway analysis. After briefly summarizing the functions of the liver, this review paper explains the experimental procedures and the stoichiometric models where perfused livers or hepatocytes have been utilized. Major outcomes of these studies and current challenges are also comprehensively discussed.The liver is an extremely important organ that has a variety of roles in mammalian function, including blood protein synthesis, metabolism of nutrients, removal of toxic substances and the synthesis of bile. It is unique amongst mammalian organs due to its ability to regenerate . It is aAs a regulator of metabolism, the liver is heavily involved in the synthesis of glycogen, fatty acid production and consumption, and the production of glucose. Glucose production is performed through a series of metabolic reactions that convert pyruvate to glucose, known as gluconeogenesis. The glucose that is produced by the liver is exported out to other cells in the body, and is utilized either through aerobic or anaerobic respiration. The liver plays a crucial role in the Cori cycle. This is the process whereby muscles produce lactate via glycolysis, which is then taken up by the liver and converted back into glucose via gluconeogenesis. Glucose is then transported back to the muscle cells, thereby replenishing their energy stores . GlycogeAnother major role that the liver has in mammalian organ function is that it detoxifies a variety of foreign objects in the blood stream through a process called drug metabolism, which makes up a variety of chemical modifications that can be applied to foreign substances to make them prone to further metabolism . One of Liver cells can exhibit different functions based on their location (hepatic zonation). This phenomenon is caused by heterogeneity within the liver that arises from differences in blood supply: hepatocytes that are located in the upstream zone of the liver contain different enzymes and receptors than their downstream counterparts, leading to highly specialized metabolic capabilities . The hepin vitro metabolic studies. This process is not unique to rats, and has been performed on many other mammalian species, making it ideal for assessing liver function in a broad variety of mammalian systems [in vivo, and without it they cannot form the extracellular structures necessary for both viability and function [et al. were able to create a more in vivo-like environment for hepatocytes to grow in, using an adipogel generated from the differentiation of preadipocytes [in vivo-like environment. Borel Rinkes et al. used sandwiched collagen layers in order to create a three-dimensional hepatocyte culture, where layers of hepatocytes were maintained between two layers of collagen, preserving long-term cell function in in vitro cultures [in vivo structure, and improving the diffusion of substrates to all hepatocytes simultaneously. This is important, as many metabolic studies aim to characterize the effects of specific substrates on hepatocyte function [in vivo systems, and the impact of individual substrates can easily be measured. However, care must be taken in the isolation, cell purity and medium quality of the culture in order to improve viability, and the collagen base should be optimized to create as in vivo-like conditions as possible.Using primary hepatocytes for the analysis of hepatic metabolism has been a widely used practice that allows investigators to observe liver cell function and metabolism in a highly controlled environment. In order to prepare this model system, it is necessary to first isolate hepatocytes from the liver, which has been well characterized by perfusion with collagenase . In this systems . Assessi systems . Another systems . The int systems . Once th systems . Collagefunction . Traditifunction . Howeveripocytes . The difcultures . This hafunction . The othfunction . Specififunction . Overallin vivo environment and prevents their exposure to a variety of extracellular processes. Because the dynamics of many of the diseases studied in vitro are not completely understood, it is difficult for investigators to completely mimic the in vivo conditions of these pathogenic processes. Isolated perfusion of rat livers is one method that has been extensively explored in recent years for assessing the metabolic function of the liver in vivo following an insult or injury [insitu at a constant flow rate by a peristaltic pump. The perfusion system also includes a heat exchanger , an oxygenator (to oxygenate the perfusate) and a bubble trap (to remove the bubbles in the perfusate which might block the blood vessels in the liver). One important consideration in this procedure is the flow rate at which the medium is passed through the liver: If the pressure is too high, liver trauma can occur, which significantly impacts liver viability and will compromise the effectiveness of the study [in vivo is at 37 °C, it is important to maintain that temperature throughout the liver during the perfusion. A variation in the temperature of more than 1 °C can cause a distortion of results, and impact liver function [et al. have shown that even a slight difference in mitochondrial oxygen concentration can significantly impact cellular function [Though primary hepatocytes isolated from the liver can provide important metabolic data, the culturing process removes the cells from their r injury . The isor injury . IPRL ishe study . Anotherfunction . The durfunction . One of function . Althougfunction . One metfunction ,48. Anotfunction . Similarfunction . Beyond function , it is ifunction . For radfunction . IPRL rein vivo-like conditions, and better exposure to substrate. For example, Yamada et al. were able to use a size exclusion micro fluidic device in order to separate hepatocytes from other cell types that are native to the liver [in vivo conditions by forming bile cannuli, which are an important part of liver morphology that has not been captured in cell culture studies [et al. have been able to create a micro fluidic system that can assess intracellular metabolite concentrations at a single cell level, which could significantly improve the computational accuracy of metabolic models in the future [in vivo-like conditions by the incorporation of fibroblasts and other mammalian cell types into the system [et al. have created an artificial barrier in a micro fluidic device with properties similar to endothelial cells in order to assess the effects of the endothelial barrier on hepatocyte function [Another emerging field in the study of the metabolism of hepatocytes is the use of micro fluidic devices to create liver-like physical structures for hepatocytes, or to assess hepatocyte function on an individual level, although a stoichiometric-based analysis for hepatocytes in micro fluidic devices has not been reported yet. The advantages of micro fluidic studies are the greatly increased control over cell position, cell neighbors, and the amount of medium that a cell is exposed to . These phe liver , allowinhe liver . Cell cu studies . One of e future . Though e system . Normalle system . One of function . Overalli.e., liver-specific biochemical reactions and pathways, should be first well described. However, constructing a metabolic network requires collection of a variety of genomic, biochemical, and physiological data from the primary literature and databases. The liver metabolic network was originally developed for perfused livers and hepatocyte cultures [et al. modified the previously published network to simultaneously include both glycolytic and gluconeogenic pathways, fatty acid synthesis and oxidation, as well as glycogenesis and glycogenolysis [Metabolic network analysis integrating multiple reaction rates to analyze the network and cell behaviors have been increasingly applied for liver systems and these studies are becoming important for physiological and topological characterization of the liver at cellular or organ level. Therefore, the structure of the liver network, cultures ,5,6,59. cultures ,3,4,5,6.cultures . In somecultures . Since aenolysis .et al. [i.e., flux balance analysis. Jerby et al. [The hepatic model discussed above is a medium scale network. Very recently, genome scale hepatic models have been developed for the analysis of liver physiology. Gille et al.  construcy et al.  also devGenerally, metabolic flux analysis (MFA) has been used to characterize the hepatic intracellular reactions by using a set of measured extracellular fluxes and the hepatic metabolic network that has been constructed ,4,5,6,59v is the flux distribution vector, and S is the stoichiometric matrix where rows correspond to the metabolites and columns represent the reaction rates. Using a set of measured fluxes (mv), Equation 1 is rewritten as below in order to calculate the unknown fluxes (uv): where i.e., the number of linearly independent equations should be equal to number of unknown fluxes. In the studies previously published [In MFA analysis, the system described by Equation 2 should be determined in order to identify unknown fluxes uniquely, ublished ,4,5,6,59i.e., the number of experimental measurements is not enough to determine the unknown fluxes uniquely. Flux balance analysis (FBA) is one of the metabolic engineering tools widely applied to solve this problem. FBA is based on an optimization problem with an objective function depending on the physiological or topological properties of the systems, and with the well-known constraints, including mass balance equations, reaction reversibility or thermodynamic constraints. The steady state solution space defined by linear equations and inequalities is called the flux cone. The optimum point within this cone, which is the solution of the problem, is described by the objective function. However, proposing an appropriate objective function for liver systems is a major challenge, since liver cells exhibit various phenotypic properties depending on the environmental conditions. Although some objective functions such as maximization of cell growth, or maximization of ATP production rates, have been commonly used for microbial systems, these phenotypes might not be associated with liver systems. A number of studies identified the intracellular hepatic fluxes by minimizing the error between the observed and calculated external fluxes [, where jv and jobsv are the predicted and observed external flux values, respectively). Another objective function that has also been used for hepatocytes is maximizing the summation of weighted fluxes . Therefore, a weight can be attributed for each flux, which shows the importance of that flux. Yang et al. [et al. [in vitro.In some instances, the system described by Equation 2 might be underdetermined, l fluxes ,64,65. Tg et al.  and Nola [et al.  applied et al. [i.e., urea production, by designing a rational medium composition. Due to the possibility of alternative flux distributions resulting in the same optimum value, they applied mixed integer programming to calculate all solutions. In fact, hepatocytes exhibit multiple functions that need to be considered simultaneously. Yang and co-workers further investigated the effects of amino acid supplementation on the objectives of urea production and fatty acid oxidation in hepatocytes [i.e., urea production, while converting the other objective (fatty acid oxidation) into a constraint. Actually, choosing different values for the constraint describing the rate of fatty acid oxidation determined the Pareto optimal solutions where urea flux and fatty acid oxidation rate are best compromised. Sharma et al. [There are other objective functions that have been used for liver systems. These objective functions are generally based on the physiological properties of the liver. Calik and Akbay  calculatet al.  used a Fatocytes . The moda et al. , on the a et al. ,72 also FluxAnalyzer [, where w denotes a vector involving the weight for each elementary mode; and P is the matrix of elementary modes). However, the decomposition of a steady state flux vector into pathways is not always unique for large networks because the number of pathways is not usually equal to the dimension of the null space of the stoichiometric matrix [et al. further introduced other optimization algorithms, including maximization of activity of short pathways and maximization of activity of liver-specific pathways, including urea and glucose production, to analyze the hepatic metabolism with the data sets obtained from perfused livers of fasted rats receiving burn injury [Metabolic pathway analysis (MPA), mainly consisting of elementary modes and extreme pathways, has been recently used to characterize the liver networks. Elementary modes consist of the minimum number of reactions that exist as a functional unit. Extreme pathways that correspond to extreme rays of flux cone are the independent subset of elementary modes. However, analyzing a biological system through a set of extreme pathways can result in the exclusion of possibly important modes ,74. PathAnalyzer . Pathwayc matrix . To overc matrix ,79,80,81n injury ., where  and iw are respectively Gibbs free energy and weight value of the pathway i [i can be active (wi ≥ 0) if its Gibbs free energy is less than zero , otherwise it is not active (wi = 0).Thermodynamic-based metabolic flux analysis has been profoundly analyzed in microbial systems ,83. Sincathway i . This coetc., if they are associated with cyclic reactions. However, the latter requires a complete metabolic network. In general, reciprocal control between the enzymes forming a futile cycle inhibits its formation. This is reasonable, since futile cycles result in energy waste. We recently proposed a mathematical formulation by introducing binary variables to prevent futile cycles in liver systems [The hepatic metabolic network includes both glycolytic and gluconeogenic pathways, fatty acid synthesis and oxidation, as well as glycogenesis and glycogenolysis . Therefo systems :A and VB which form a futile cycle) to inhibit each other. y is a binary variable, and β is a very large number which forces y to be 0 (thus VB is 0) if the reaction VA is active. If the reaction VA is zero, then VB can take any value.This constraint described by the Equation 3 allows the two reactions  cycle fluxes and gluconeogenic fluxes were up-regulated by both hormone and amino acid supplementation.Liver cells are significantly used in research programs to characterize not only the hepatic metabolic functions but also hepatic signaling and genetic functions. They are also used for medicinally important devices such as the bioartificial liver, which is one of the promising technologies for the treatment of liver failure. However, hepatocytes exhibit poor liver specific functions in ecretion . Similarentation ,68,69. Tentation ,89. Channtations ,89. Theyet al. [2), hyperoxic perfusate (95% O2), and hyperoxic perfusate with oxygen carriers (95% O2 + 10% hematocrit using bovine red blood cells). They found that perfused livers consumed oxygen up to the rate of 400 μmole/g liver/h when the perfusate contained red blood cells (RBCs). On the other hand, using 95% O2, in the absence of oxygen carriers, oxygen uptake was significantly reduced; urea and ketone body production were significantly decreased, and metabolic pathway analysis elucidated that significant anaerobic glycolysis occurred.Isolated liver perfusion systems have been commonly used to characterize intrinsic metabolic changes in liver under various conditions. In order to improve the perfusion system, Orman et al.  used a fet al. showed that D-Galactosamine inhibited hepatic glucose synthesis, due to the reduction in amino acid entry into TCA cycle [Liver is a critical organ in mammalian physiology, serving to synthesize proteins both constitutively and in the acute phase, as well as regulate whole body energy levels through metabolic pathways and signaling molecules. The liver’s roles in the Cori cycle and the urea cycle are also well known functions. Therefore, it is necessary to understand the changes that the liver undergoes in response to injury, and the underlying regulations that govern those changes, in order to fully understand the impact of the injury. Many studies have shown that significant metabolic alterations take place in the liver in response to various injuries, including sepsis and burns. The liver perfusion system creates an important portal to characterize these alterations. Lee and co-workers used metabolic flux analysis to determine the effects of burn injury on the flux distribution of liver metabolism ,6. They CA cycle . YokoyamCA cycle . They shet al. characterized the hepatic carcinoma cells cultured under various levels of glucose and insulin up to five days [Iyer ive days . InteresAs emphasized previously, it is necessary to understand the underpinning dynamics of the liver response (or liver failure) during the disease conditions. This also provides insights for better treatments in clinical settings. Using pathway analysis, Orman and co-workers characterized the hepatic metabolism following the burn injury . They idet al. [et al. [Subtoxic effects of drugs and other medical products were also tested on the hepatocytes, the primary cells having a function of detoxification. Niklas et al.  analyzed [et al.  studied Liver metabolism is very complex, and constructing a hepatic metabolic network requires intensive experimental evidence. Most of the hepatic metabolic network studies for perfused livers and hepatocytes cultures have utilized a medium scale network, including the major liver specific reactions or pathways. It has been already shown that there is a good consistency between the network and measured fluxes ,6. Althoetc. Most of the studies have been performed in vitro without exposing the liver to the hormones or signaling proteins that might drastically affect these metabolic switches. Inherent regulatory mechanisms that might also result in changes in hepatic fluxes were also ignored in published studies. The condition for the pseudo-steady-state approximation for metabolic flux balance is that the rate of change of  metabolite concentration should be small compared to its rate of turnover (flux). It has been shown that the fluxes are considerably higher than the actual changes in the intracellular metabolite concentration in HepG2-C3A cells, which supports the pseudo-steady-state approximation [Although the aforementioned methods provide a comprehensive map of stationary flux distribution in the metabolic network, they clearly fall short of giving insight into metabolic regulation. Moreover, the steady state assumption for the liver might not be valid since it permanently switches between glucose consumption and glucose production, triglyceride formation and triglyceride degradations, ximation .i.e., the isolated liver is exposed to a defined media (perfusate) for about 1 h. The perfused livers are not exposed to circulating factors  or substrate concentrations changes that occur in vivo. It is likely that switches between the major pathways in perfused livers do not occur during the course of experiment. Since the concentrations of metabolites in the perfusate reservoir were found to change linearly as a function of time  [in vitro, flux measurements have been performed every 24 h [Liver perfusion experiments are performed in a very short period of time, n rates) ,4,5,6,7,ery 24 h ,68,69,84Despite the significant advances that have been made in recent years with regards to metabolic flux analysis in the liver, there is still significant room for improvement in many areas of the field. Although mathematical models are becoming better structured and optimized with the inclusion of multiple optimization parameters and added physiological and thermodynamic constraints, investigators are still left with the difficult task of predicting the overall goal of the liver. Because of the difficulty involved in obtaining intracellular metabolite concentrations, the resulting underdetermined system can only be solved through an educated guess at the objectives that result in the measured fluxes . Thus, tex vivo environment that may potentially induce aberrant metabolic patterns. In particular, in perfusion systems it is quite challenging to mimic the physiological environment  of the liver that is isolated from the body. Among these parameters mentioned above, inadequate oxygenation could by itself alter gene expression level and promote anaerobic pathways [et al. have recently showed that hyperoxic oxygenation without the use of oxygen carriers was not sufficient to support perfused rat liver function [in vivo conditions. The challenge therein is threefold: Firstly, the baseline dynamics of serum proteins in the absence of injury must be characterized. The presence of the circadian rhythm, which is a time-based gene regulation system that modulates metabolism based on feeding and sleeping cycles [in vivo conditions. This also has the added benefit of increasing understanding of liver metabolism, both how it changes during circadian rhythms, and which proteins are responsible for its dynamics. Since circadian rhythms have been associated with immune regulation, and their disruption with pathophysiology [Although experimental procedures developed for liver cell cultures or perfused livers have provided important information regarding the liver metabolic properties, an obvious drawback is the artificial pathways . Howeverfunction . The usefunction . Howeverfunction , howeverg cycles , impliesg cycles , and so g cycles , therefoysiology , these sin vitro studies encounter when performing experiments on primary hepatocytes is the phenomenon of hepatocyte zonation. Although other cell types that reside within the liver also manifest functional differences based on their location [in vitro metabolic studies by utilizing metabolic pathways that may not be consistent with the rest of the liver’s behavior. The concepts of metabolic zonation are critical to the design of in vitro primary hepatocyte studies that aim to assess metabolic function, as an imbalance between metabolic states within the cell culture may skew the results toward non-physiological behavior. The use of oxygen gradients in hepatocyte cell cultures can produce hepatocyte zonation, similar to what has been observed in in vivo studies [in vitro experiments to observe metabolic changes in each zone independently. A more in vivo approach to differentiating the functions of the perivenous and periportal zones are to use ortho- and retrograde liver perfusions that contain labeled substrates [in vitro studies, it is possible for investigators to gain further insight into physiological behaviors of the whole liver, while also potentially generating additional constraints for metabolic models. One other challenge that location , the zon studies . A viabl studies . Primary studies , which fbstrates , and folin vivo conditions. Unlike the IPRL systems, there is a significant amount of control over the environment of hepatocytes in culture, and thus improvements on the system would be of immense benefit to the collective understanding of liver metabolism. Efforts have already been made to create culture systems in a plated environment that mimic in vivo conditions [in vivo conditions, it will likely require a combination of effects  to truly be able to mimic the behavior of in vivo hepatocytes. Thus, the challenge that remains for investigators working with hepatocyte cell cultures is to incorporate and consolidate the various new methods being developed that mimic in vivo conditions. There is a significant technical challenge involved in reconciling techniques that has been applied to cell cultures to a micro fluidic device, and vice versa. However, just as the liver does not contain just an epithelial barrier or bile cannaliculi, but rather both simultaneously, an appropriate in vitro model must also contain both of these elements. This challenge, combined with the need for better medium control in IPRL systems, and the development of better techniques for intracellular flux determination and modeling, are necessary for the development and enhancement of the understanding of metabolic flux and liver function, both on a computational and biological level. Finally, there is still room for improvement in the area of cultured hepatocytes, and the methods by which investigators are attempting to generate nditions ,38, as wnditions ,102. HowThe liver is an important organ, having a very complex metabolic network. Metabolic engineering tools and advanced experimental methods have been applied to this complex system to characterize the underlying mechanisms of various conditions leading to metabolic alterations in the liver. With new advanced methodologies, more insight will be gained regarding the liver metabolic network properties and liver associated diseases. In this review, we mainly focused on the published studies where stoichiometric models were utilized for liver systems. The experimental and computational methods as well as current challenges have been extensively discussed.


Danio rerio (zebrafish) on a global scale. This reconstruction involves extraction of known biochemical reactions in D. rerio for both primary and secondary metabolism and the implementation of methods for determining subcellular localisation and assignment of enzymes. The reconstructed model (ZebraGEM) is amenable for constraint-based modelling analysis, and accounts for 4,988 genes coding for 2,406 gene-associated reactions and only 418 non-gene-associated reactions. A set of computational validations  strongly testifies to the predictive ability of the model. Overall, the reconstructed model is expected to lay down the foundations for computational-based rational design of fish metabolic engineering in aquaculture.Plant and microbial metabolic engineering is commonly used in the production of functional foods and quality trait improvement. Computational model-based approaches have been used in this important endeavour. However, to date, fish metabolic models have only been scarcely and partially developed, in marked contrast to their prominent success in metabolic engineering. In this study we present the reconstruction of fully compartmentalised models of the  High blood levels of long-chain omega-3 fatty acids (n-3 FAs), eicosapentaenoic acid  and docosahexaenoic acid , decrease risk of human cardiovascular disease events e.g., mass-balance constraints), steady-state assumptions Current challenges in using fish as factories for public health nutrition and nutraceuticals require predesigned and efficient strategies for metabolic engineering D. rerio (ZebraGEM), which relied on genomic and biochemical data extracted from various databases and literature. The validity of the model reconstruction steps is demonstrated via computational tests .A significant step forward in fish metabolic modelling was made with the publication of large-scale MetaFishNet model analysing high throughput expression data, as well as a framework for future systems studies D. rerio metabolic map. ZebraGEM is a comprehensive literature-based genome-scale metabolic reconstruction using manual curation, including extensive gap-assessment and filling. ZebraGEM has 9 compartments , 18 interfaces (surface orientations) and accounts for the functions of 4,988 proteins, 1,553 metabolites, and 2,824 metabolic and transport reactions. This network reconstruction was transformed into an in silico model and validated through the simulation of known metabolic functions found in a several tissue types.In this study, we present the reconstruction of the global D. rerio metabolism, where each metabolic gene is assigned to one or more reactions. We used Enzyme Commission numbers D. reria metabolic model (ZebraGEM) was then reconstructed based on biochemical and physiological knowledge by surveying literature and mining databases. We evaluated more than 15 years of biological evidence from more than 1,800 primary literature articles, reviews, and biochemical textbooks. The reconstruction was almost entirely constructed from zebrafish-specific data and includes many reactions directly extracted from the literature that are not described in any chart or database. Furthermore, it represents carefully formulated metabolites and reactions, which account for known reaction stoichiometry, substrate/cofactor specificity, and directionality, as well as overall conservation of mass and metabolite ionisation states at pH 7.2. Unambiguous metabolite names were verified using Chemical Identifier Resolver We adopted a reaction-centered view of D. rerio model reconstructed in the above step and to account for subcellular compartmentalisation of metabolic processes, we considered the known sub-localisation of the reactions taken from literature articles and reviews. Manual literature-based reconstruction ensured that the network components and their interactions were based on direct biochemical and subcellular evidence and reflected the current knowledge of D. rerio metabolism (see Methods).To extend the genome-scale e.g., endoplasmic reticulum membrane, cytosolic side) was identified.The analysis resulted in a compartmentalised model , with eaFirst, we decided to test whether the model was able to produce biomass by optimising the corresponding reaction accounting for all known biomass precursors required for cell replication. The non-compartmentalised draft model was able to produce biomass without any modifications. In contrast, the compartmentalised draft model was unable to produce biomass. Therefore, we sought to gap fill the draft compartmentalised to identify transporters needed to be added to the model for production of all biomass components. We searched for the minimal number of transporters necessary to add to the model in order to enable the production of biomass. The results of the algorithm were checked manually for two criteria: (i) the result did not suggest a reversible reaction for a known irreversible reaction and (ii) the added reaction transports a metabolite from a compartment where it was produced to a compartment where it was used as reactant.e.g., one cytosolic reaction can occur both on the cytosolic side of the Golgi apparatus and the endoplasmic reticulum membranes). However, even if some reactions occur in more than one compartment, enzymes are reaction- and compartment-specific; No enzyme was described in more than one compartment. A detailed list of the transporters is provided in D. rerio ZebraGEM is freely available Systems Biology Markup Language format Following previous model reconstructions de novo biosynthesis of taurine, our model suggests that the only path able to synthesis the taurine is controled by cysteine sulfinic acid decarboxylase. This finding is in accordance with the recently reported critical role of cysteine sulfinic acid decarboxylase in D. rerio taurine homeostasis and cardiac development Examination of experimentally characterised pathway activity was used to validate the global metabolism model. For example, the System biology approaches to evolution are attractive as they bring the promise of quantitative analyses of trait evolution and behaviour, integrating phenomena from the molecular to the ecosystem level. As a result, they allow us to describe precise hypotheses as to how traits can be successively used for metabolic engineering.D. rerio metabolic functions, and thus cannot accurately predict some of the organism’s functions. It should be noted, however, that further experimental validation should be performed to reaffirm the predictions.Several limitations should be noted: (i) As the approach hinges upon different molecular data, its accuracy depends on the quality of the latter. (ii) Several assumptions are used in the reconstruction process, such as the assumption of minimal addition of reactions and directionality relaxation to resolve network gaps, and the assumption of minimal number of metabolite transmembrane transport used in the localisation assignment. These assumptions, although commonly used for similar purposes D. rerio, open up future opportunities for metabolic engineering of increased production rates of various nutraceuticals important for human and fish health.A number of future refinements and extensions will improve the quality of the resulting network models. Such reconstructed models, in addition to those presented here for D. reria, a model fish and present a new feature in metabolic modelling: surface orientation. Despite its evolutionary divergence to farm fish, the genetic make-up of the D. reria model fish is the first step toward fish metabolic engineering, making its study highly useful to other fish, as well as to the fish industry, where metabolic engineering is commonly used for quality-trait improvement.In this work we present the reconstruction of a fully compartmentalised model of D. rerio assembly Zv9, and scientific literature. Extra reactions were collected from MetaFishNET release 1.9 D. rerio ZebraGEM, with the references used to describe each reaction, is freely available Systems Biology Markup Language format An initial component list was assembled as described in the text (see Results and Discussion). Putative gene assignments were collected for KEGG build 39 Each mapped reaction is defined as a node in our network. Edges between these nodes are defined by shared metabolites between the reactions. The network is directed: for irreversible reactions if the product of one reaction is a reactant in the second, we defined a directed edge. Reversible reactions are treated similarly, except that both directions of the reaction were allowed and handled independently. The networks were visualised with Gephi v0.8 beta +, H2O, ATP, ADP, Pi, PPi, Na+, coenzyme A, O2, NAD+, NADH, NADP+, and NADPH) were removed from all analyses  in whichever compartments they occurred D. rerio in a freshwater fish and the model allows the synthesis of eicosapentaenoic and docosahexaenoic acid, only linolenic and linoleic acid were included. Oxygen, water and carbon dioxide can freely exchange with the environment. Metabolic network biomass is composed of all amino acids (protein), nucleic acids  and lipids, including triacylglycerol and cholesterol  and for being reported in at least one other species  before been included. We searched for the minimal number of transporters necessary to add to the model in order to enable the production of biomass.We used the Systems Biology Research Tool v2.0.0 i.e., reaction directions, model inputs).Functional validation was performed by using flux balance analysis. The test simulations validates that the targeted metabolite could be synthetised from a particular source metabolite without violating the other model constraints (Table S1Detailed list of the transporters.(CSV)Click here for additional data file.Table S2Detailed list of the pathways by compartment and the number of reaction involved.(CSV)Click here for additional data file.Table S3Detailed list the uptakes and biomass reactions.(CSV)Click here for additional data file.Table S4Simulations of known metabolic functions. Complete list of the 160 simulations used to validate ZebraGEM.(CSV)Click here for additional data file.Table S5Exchange reactions added to enable uptake and secretion of extracellular metabolites for the purpose of simulations.(CSV)Click here for additional data file.