Systematic investigations of complex pathological cascades during ischemic brain injury help to elucidate novel therapeutic targets against cerebral ischemia. Although some transcription factors (TFs) involved in cerebral ischemia, systematic surveys of their changes during ischemic brain injury have not been reported. Moreover, some multi-target agents effectively protected against ischemic stroke, but their mechanisms, especially the targets of TFs, are still unclear. Therefore, a comprehensive approach by integrating network pharmacology strategy and a new concatenated tandem array of consensus transcription factor response elements method to systematically investigate the target TFs critical in the protection against cerebral ischemia by a medication was first reported, and then applied to a multi-target drug, Danhong injection (DHI). High-throughput nature and depth of coverage, as well as high quantitative accuracy of the developed approach, make it more suitable for analyzing such multi-target agents. Results indicated that pre-B-cell leukemia transcription factor 1 and cyclic AMP-dependent transcription factor 1, along with six other TFs, are putative target TFs for DHI-mediated protection against cerebral ischemia. This study provides, for the first time, a systematic investigation of the target TFs critical to DHI-mediated protection against cerebral ischemia, as well as reveals more potential therapeutic targets for ischemic stroke. Cerebral ischemia or stroke is a serious neurological disease, which can lead to broad cerebral injury and result in high disability and mortality rates in many countries567891011A computational approach, such as a network pharmacology-based1415Large-scale quantitative profiling of TFs during ischemic brain injury can be another good choice to comprehensively investigate their roles in cerebral ischemia. However, due to the low abundance of TFsRadix Salviae miltiorrhizae and Flos Carthami tinctorii, has been shown to be effective in protecting against ischemic stroke, its pharmacological mechanisms, especially the putative multi-targets of TFs, are still unclear. This study provides the first systematic investigation of the TFs critical to DHI-mediated protection against cerebral ischemia, as well as reveals more potential therapeutic targets for ischemic stroke.In this study, an approach by integrating network pharmacology strategy and the catTFREs method to systematically investigate the target TFs critical in the protection against cerebral ischemia by a medication was first reported, and then applied to a multi-target conventional drug for coronary heart disease and cerebral ischemia, Danhong injection (DHI)202122Concatenated tandem array of consensus transcription factor response element (catTFRE) DNA was synthesized by Genscript . Biotinylated catTFRE primers were synthesized by Sigma . Dynabeads (M-280 streptavidin) were purchased from Invitrogen. Sequencing grade porcine trypsin was obtained from Promega . Nuclear extract prep kits were purchased from Thermo Fisher. Danhong injection (DHI) was obtained from Shandong Danhong Pharmaceutical Co., Ltd . All other chemicals were of analytical grade reagent. Deionized water (R > 18.2 MΩ) used for all experiments was purified by using Millipore purification system .Male C57BL/6 mice (Charles River Laboratories), six-eight weeks of age, were treated by intraluminal occlusion using monofilament for preparation of the permanent middle cerebral artery occlusion (MCAO) model24Based on the literatures about the processes after cerebral ischemia2627To evaluate the protection effect of DHI against cerebral ischemia, each MCAO mouse was administered 5 mL/kg/time DHI immediately after MCAO-operated by intraperitoneal injection (i.p.), then twice per day, respectively in the morning and evening, for eight consecutive days. For control, each sham mouse without MCAO was also administered 5 mL/kg/time DHI, and the usage is same as above described.Nuclear extracts of the mouse brain were prepared by using nuclear extract prep kits (Thermo Fisher) according to the protocols of manuals. Biotinylated DNA was pre-immobilized on Dynabeads and then mixed with nuclear extract. The mixture was supplemented with EDTA/EGTA to a final concentration of 1 mM and adjusted with NaCl to 200–250 mM total salt concentration. The solution was then incubated at 4 °C for 2 h. The supernatant was discarded and Dynabeads were washed with NETN  twice followed by PBS solution twice. Beads were digested overnight with trypsin.Tryptic peptides were separated on a C18 column  with a flow rate of 350 nL/min, and were analyzed by LTQ-Orbitrap Velos (Thermo). The MS conditions were as the followings: Nano-spray ion source was used. A spray voltage of 1800 V was applied, with no sheath gas flow and with the ion transfer tube at 350 °C. The mass spectrometer was programmed to acquire in a data dependent mode. The survey scan was from m/z 375 to 1600 with resolution 60,000 at m/z 400. The 50 most intense peaks with charge state 2 and above were acquired with collision induced dissociation with normalized collision energy of 35%, activation time of 5 ms, one microscan and the intensity threshold was set at 500. The MS2 spectra were acquired in the LTQ normal scan mode. Proteins were identified by Proteome Discovery version 1.3 using MASCOT search engine with percolator against the mouse RefSeq protein database (updated on 11-17-2014). The mass tolerance was set to be 20 ppm for precursor. As for the tolerance of product ions, Velos was set as 0.5 Da. Oxidation (Met), Acetyl (N terminus) was chosen as variable modifications; carbamidomethyl (Cys) was chosen as a fixed modification; and one missed cleavage on trypsin was allowed. Both peptide and protein level false discovery rates (FDRs) were controlled lower than 1%. Transcription factors (TFs) were assigned based on TFClassin silico protein digestion with a PERL script, and all fully tryptic peptides between 6 and 30 amino acids were counted while missed cleavages were neglected.Intensity-based absolute quantification (iBAQ)-based protein quantificationshttp://tcm.cmu.edu.tw/, Updated in Jun 28, 2012), which is currently the largest non-commercial TCM database worldwide. Totally, structural information of 101 compounds for Radix Salviae miltiorrhizae and 22 compounds for Flos Carthami tinctorii were collected from this database.Structural information (*.mol or *.sdf files) of the chemical components of each herb in DHI were obtained from TCM Database@Taiwanhttp://www.drugbank.ca/, version: 3.0) and the Online Mendelian Inheritance in Man (OMIM) databasehttp://www.omim.org/, Last updated: October 31, 2013). After deleting redundancy, 62 known therapeutic targets for the treatment of ischemic stroke were collected in this study. Please see detailed information in Known therapeutic targets for ischemic stroke were collected from DrugBank databasehttp://www.reactome.org/, Version 37) and Stringhttp://www.string-db.org/, Version 9.1) were used for the collection of PPI data.Two existing PPI databases, including ReactomeRadix Salviae miltiorrhizae and Flos Carthami tinctorii were predicted by MetaDrug from GeneGo, Inc. MetaDrug predicts the putative targets for certain chemical components using three methods: (1) Based on the MetaBase database, which contains compound-protein interactions; (2) Based on QSAR predictions of protein target affinity from the included models that define a limited number of potential targets for novel molecules and/or their metabolites submitted for analysis; (3) Based on a similarity search for the structure and its major metabolites against the database of existing structures and their targets. Putative targets for certain compounds are inferred through structurally similar compounds in the database .Putative targets of the chemical components contained in Chemical components contained in DHI, the corresponding putative targets, known therapeutic targets for ischemic stroke and the differentially expressed TFs in MCAO models and controls were used to construct two interaction networks: putative targets-known therapeutic targets-TFs network and chemical components-major putative target-major TFs network. Navigator software (Version 2.2.1) and Cytoscape (Version 2.8.1) were utilized to visualize the networks.Four topological features for each node in the network, including ‘Degree’, ‘Betweenness’, ‘Closeness’ and ‘K value’ were calculated to evaluate the topological importance of DHI putative targets and the differentially expressed TFs in MCAO models and controls. For each node “i” in these two networks, “Degree” was defined as the number of links to node i; “Betweenness” was defined as the number of edges running through node i; “Closeness” was defined as the inverse of the sum of node i distances to all other nodes. “Degree”, “Betweenness” and “Closeness” correlate with a protein’s topological importance in the PPI network2, 50 ng poly(dI-dC), 0.05% NP-40, and incubated with 10 fmol of biotin-labeled DNA probe for 15 min at room temperature. DNA probe/protein complexes were then separated by 5% native polyacrylamide gel at 100 V for 60 min, followed by transferred to a nylon membrane , and visualized by chemiluminescence according to the manufacturer’s instructions (GE ImageQuant LAS 4000mini).Nuclear extracts of the mouse brain were prepared by using nuclear extract prep kits (Thermo Fisher) as above mentioned. EMSA was performed by using the LightShift chemiluminescent EMSA kit  according to the protocols of manuals. The sense strand sequences of the oligonucleotides for the EMSAs are as follows: ATF1, 5′-ATGACGTCAATGACGTCAATGACGTCA-3′, Mut-DNA of ATF1, 5′-ACGATGTCAACGATGTCAACGATGTCA-3′; PBX1, 5′-ATCAATCAAATCAATCAAATCAATCAA-3′, Mut-DNA of PBX1, 5′-ACATATAACACATATAACACATATAAC-3′. Nuclear protein (6 μg) was mixed with 10 × binding buffer (Thermo Fisher), 2.5% glycerol, 5 mM MgCl−ΔCt. Then relative differences of gene expression among groups were expressed as relatively changes, setting the values of sham mice as one. The assays were carried out in triplicate and the results were analyzed by one-way analysis of variance, and significant at P < 0.05.qPCR was performed with primers listed in First, we measured the changes in TF activation in the brain after ischemic injury using the catTFREs method and the intensity-based absolute quantification (iBAQ)-based quantification approach, which had been demonstrated to be accurate in protein quantification3038262739Among the 250 identified activated TFs, 47 TFs were expressed only in sham controls, while 19 were expressed only in ischemic brains . Moreove2From the fold changes of the quantified activated TFs in ischemic brains compared with the controls , we also2Next, using network pharmacology strategies, TFs critical to DHI-mediated protection against cerebral ischemia were computationally predicted. First, a total of 660 putative targets were predicted for 57 chemical components contained in DHI. Detailed information regarding the putative targets and corresponding chemical components contained in DHI is listed in The network consists of 553 nodes  and 2204 edges. Four topological features, ‘Degree’, ‘Betweenness’, ‘Closeness’ and ‘K value’ (defined in EXPERIMENTAL SECTION), were chosen to identify major putative targets and major differentially expressed TFs. In total, we identified 175 major nodes, the ‘Degree’, ‘Betweenness’, ‘Closeness’ and ‘K value’ of which are all larger than the corresponding median values. Among these major nodes, there are 140 major putative targets and 14 major differentially expressed TFs. The interaction network of 175 major nodes is shown in To experimentally validate the predicted target TFs crucial to the DHI-mediated protection against cerebral ischemia, first, the pharmacological effects of DHI on MCAO mice was evaluated. As shown in To further investigate the pharmacological effects of DHI on MCAO mice, another group of mice that received DHI were euthanized six hours after MCAO operation and their brains were stained with TTC. As shown in 2223Based on the reliable protective effect of DHI against cerebral ischemia, we further experimentally verified the TFs that were critical to this protection by applying the catTFREs method and the accurate iBAQ-based quantitative approach to comprehensively measure the corresponding change in TF activation in pooled samples from three ischemic brains after DHI treatment. Meanwhile, to further confirm these TFs as critical, we conducted a parallel test by administering the same amount of DHI to sham mice without MCAO, followed by a high-throughput analysis of the corresponding changes in TF activation using the same method mentioned above. Moreover, to attain accurate quantitative analysis, peptide-spectrum match (PSM)From the results of the computational prediction and the experimental verification of TFs critical to DHI-mediated protection against cerebral ischemia, we found that pre-B-cell leukemia transcription factor 1 (PBX1) and cyclic AMP-dependent transcription factor 1 (ATF1) were authenticated by both approaches. PBX1 binds the sequence 5′-ATCAATCAA-3′ and may act as a transcriptional activator of platelet factor 4 (PF4) in complex with homeobox protein Meis1 (MEIS1)To further verify the TF activities of PBX1 and ATF1, the electrophoretic mobility shift assay (EMSA) experiments were conducted. Both PBX1 and ATF1 showed a stronger binding in MCAO mice with DHI treatment than in the ones without DHI treatment. And the same result was also obtained between sham mice with and without DHI treatment a,b. FurtMoreover, from our experimental verification results, we also found out that, in addition to PBX1 and ATF1, six other TFs, nuclear transcription factor Y subunit gamma (NFYC), POU domain, class 3, transcription factor 1 (POU3F1), POU3F2, POU3F3, DNA-binding protein SATB1 (SATB1) and SATB2, may also be critical target TFs for DHI-mediated protection against cerebral ischemia. NFYC is the component of the sequence-specific heterotrimeric TF (NF-Y) that specifically recognizes a 5′-CCAAT-3′ box motif found in the promoters of its target genesTo further clarify the relationships of eight major target TFs with major putative targets of DHI and known therapeutic targets for ischemic stroke, we constructed a network of interactions among these genes as shown in 555657Radix Salviae miltiorrhizae and Flos Carthami tinctorii), 33 chemical components, 8 major TFs, 10 major putative targets of DHI, 2 known therapeutic targets, 27 other differentially expressed TFs and 145 edges, was constructed and shown in Radix Salviae miltiorrhizae and Flos Carthami tinctorii, respectively, had interactions with PBX1, NFYC and ATF1 through their corresponding putative targets, suggesting that these three major TFs might be more closely associated with the therapeutic effects of DHI acting on ischemic stroke.Finally, the chemical components-major putative targets-major TFs network, which contains 74 nodes, including 2 herbs , a conventional drug for coronary heart disease and cerebral ischemia. After changes in TF activation during the ischemic brain injury in mouse MCAO models were measured using the catTFREs method and an accurate quantitative approach, the target TFs that were critical to DHI-mediated protection against cerebral ischemia were computationally predicted by the network pharmacology strategy. Then, by combining the evaluation of the pharmacological effects of DHI on MCAO mice, the predicted target TFs were experimentally validated. The experimental results indicated that PBX1 and ATF1, along with six other TFs, are putative target TFs for DHI-mediated protection against cerebral ischemia. This study provides the first systematic insight into the target TFs critical to DHI-mediated protection against cerebral ischemia and reveals novel potential therapeutic targets for ischemic stroke.How to cite this article: Wei, J. et al. Systematic investigation of transcription factors critical in the protection against cerebral ischemia by Danhong injection. Sci. Rep.6, 29823; doi: 10.1038/srep29823 (2016).


The RNA World hypothesis offers a plausible bridge from no-life to life on prebiotic Earth, by assuming that RNA, the only known molecule type capable of playing genetic and catalytic roles at the same time, could have been the first evolvable entity on the evolutionary path to the first living cell. We have developed the Metabolically Coupled Replicator System (MCRS), a spatially explicit simulation modelling approach to prebiotic RNA-World evolution on mineral surfaces, in which we incorporate the most important experimental facts and theoretical considerations to comply with recent knowledge on RNA and prebiotic evolution. In this paper the MCRS model framework has been extended in order to investigate the dynamical and evolutionary consequences of adding an important physico-chemical detail, namely explicit replicator structure – nucleotide sequence and 2D folding calculated from thermodynamical criteria – and their possible mutational changes, to the assumptions of a previously less detailed toy model.emergent uniformity of metabolic replicator fitnesses imposed on the community by local group selection and attained through replicator trait convergence, i.e., the tendency of replicator lengths, ribozyme activities and population sizes to become similar between the coevolving replicator species that are otherwise both structurally and functionally different.For each mutable nucleotide sequence the corresponding 2D folded structure with minimum free energy is calculated, which in turn is used to determine the fitness components  of the replicator. We show that the community of such replicators providing the monomer supply for their own replication by evolving metabolic enzyme activities features an improved propensity for stable coexistence and structural adaptation. These evolutionary advantages are due to the extra viability of the metabolic replicator system that the present model adds to the MCRS concept of the origin of life. Surface-bound, metabolically coupled RNA replicators tend to evolve different, enzymatically active sites within thermodynamically stable secondary structures, and the system as a whole evolves towards the robust coexistence of a complete set of such ribozymes driving the metabolism producing monomers for their own replication.In the most general terms it is the surprisingly high This article was reviewed by Gáspár Jékely, Anthony Poole and Armen Mulkidjanian The RNA-World scenario of the origin of life is based on the hypothesis that the first evolvable ancestors of all recent life on Earth must have been RNA-like macromolecules ,2. Even dual nature of RNA: it carries genetic information in its nucleotide sequence, and RNA molecules of different sequences are capable of catalysing a seemingly infinite variety of different chemical reactions  range. Simulations lasted for 106 generations, after which the frequency of each replicator type , the average of each enzymatic activity A1, A2 and A3 and the average length of each replicator type  were recorded . The ecological and the evolutionary aspects of the simulations are treated in separate sections below.The mineral surface (the lattice of 300 × 300 sites) was seeded in all simulations with a random population of replicators at an initial frequency of 80%, leaving 20% of the sites empty. The length of the initial replicators was also random with per se in a random sequence pool. Obviously, the emergence of two or more “active centres” within the same random sequence  is much less likely but still occurs in 1-2% of the initial RNA pool. In most of the simulations the frequencies of mono-active replicators increase to their equilibria, while promiscuous enzymes almost completely disappear from the system after a temporary surge upwards  parameter: the smaller the penalty the longer the average replicator, which implies that it is easier to fit more than one active loops into its sequence. At the extremely low b2 = 0.001 the frequency of bi-active enzymes increases to about 10%, and even replicators with all three types of active loop remain in the system at a low abundance.In the random initial replicator population the proportion of enzymatically active replicators was low (slightly above 20%) with mono-active enzymes represented at the highest frequency, suggesting that enzymatically active secondary structures as defined by the rules of the model Figure  are not s Figure A. This bh = 3 × 3 = 9.Extremely small metabolic neighbourhoods are unlikely (or unable) to accommodate all the necessary enzyme activities and thus result in system collapse, because the number of catalytic activities is larger or just slightly smaller than the number of neighbourhood sites. This effect does not show up in our simulations, because system size  is 3, whereas the smallest metabolic neighbourhood size used in the simulations is 6 generations. The frequencies of enzymatically active replicators  parasites tend to be somewhat shorter than cooperating ribozymes, but the difference is not very large, even though length reduction is an efficient way to increase fitness through better replicability  that fosters parasite invasion, which is evident on Figure D the mono-active replicators are at an advantage over bi-active ones, because promiscuous enzymes are less efficient in both catalytic functions than mono-actives (cf. Eq. αi > 1), and bi-actives still need some mixing to avoid forming homogeneous patches on the surface and thus going extinct. Two of the three possible bi-active types  show up and then vanish during the first 3 x 105 generations . This suggests that the initial difficulty of assembling three different mono-active replicators in the same metabolic neighbourhood is circumvented by evolving bi-active ones that are later excluded by their own, more efficient mono-active mutants. Note that {2,3} type bi-active replicators do not show up in large numbers because they need to be longer to be stable, and they are expendable because the two shorter bi-active types can deliver all the three necessary enzyme functions. 2) Tri-active replicators need to be much longer than mono-actives to fit all the three active loops into their secondary structure. This makes them much less probable to replicate than shorter replicators in the same replication neighbourhood, so that rarely emerging tri-active mutants are very likely fast eliminated from the system by their competing neighbours.Figure i Figure A, the avi Figure B and theL Figure C – of thE) of a (folded) sequence defines the probability of the sequence being in the folded state  are traded off: increasing pfold decreases replicability  that is optimized during the evolution of the replicator community.The Gibbs free energy  and metabolically active replicators at the stationary state of the system may be surprising at first, but the explanation is simple. Mono-active replicators easily mutate to parasitic ones, and parasites are in principle free to mutate further towards increasing their direct fitness (by higher replicability and/or lower degradation). However, fast replicating parasites are strongly selected against locally, because they demolish metabolism in their own vicinities and starve to death. That is, parasites are quickly eliminated by local selection, and permanently reintroduced by mutation from metabolically active replicators. Thus the parasites we see in the system are the close sequence relatives of functional metabolic enzymes, representing their parasitic quasispecies  obtained with toy model simulations scanning a wide range of the  parameter space. The earlier study revealed a positive effect of increasing dispersal (D) which prevents the aggregation of conspecific replicators, and a negative effect of increasing h, though approximating the mean-field situation that is known to go extinct. The deleterious effect of larger h can be compensated by increasing D to some extent, but the compensatory effect is limited: at too large metabolic neighbourhood sizes the system collapses anyway . The parameter setting suggested by the Reviewer  is, unfortunately, practically not feasible in the explicit model, because even on a high-capacity grid computer it would take months to run a single simulation. The huge difference between the CPU time demands of the toy models and the present one is due to the “handling time” of sequence folding. However, the results of the toy models are likely to carry over to the explicit case in this respect, too, since in all other respects we experienced qualitative matches.Importantly, the authors should provide their code as an Additional file or deposit it to a public repository for others to reproduce or extend the model calculations.As the code is the result of a long process of development, and it will be further developed for later studies, we do not find it convenient to publish it at this stage. However, we are willing to send the code to the reviewer or to anyone for further studies or for reproducing our results, on an individual basis.Minor comments:pg 19–20 The discussion about the dynamics of parasites is repetitive in this section. I suggest to delete or shorten this part: “Mono-active replicators easily mutate to parasitic ones … their own vicinities and starve to death”.We have shortened this part.Typos:page 11: “we assume that different RNS molecules” change to RNACorrected.page 17: “i.e., larger metabolic neighbourhood) that fosters parasite invasion, which is evident on Figure Corrected.This is a very elegant study which has been explained in very accessible language. I found the results very insightful, and need say little other than that, for those interested in the RNA world, this is a paper well worth reading.We thank the Reviewer for their positive judgment of the study.For my money, the most exciting result is that this model suggests that catalytic promiscuity in early ribozymes may have been extremely short-lived. This bears thinking about, particularly given the view, popular in protein science circles that early enzymes were promiscuous (both in their substrate specificity and enzymatic reactions). It is also noteworthy that group selection appears as a feature of the model. This is broadly consistent with the cooperative networks that Lehman and colleagues observed for fragmented ribozymes (Nature 491:72–77). I would be interested to see a brief discussion of that work and how it relates to the authors’ findings.Lehman and co-workers showed that mixtures of RNA fragments that self-assemble into self-replicating ribozymes can form catalytic cycles and more complex networks. We agree that some aspects of our model show some similarity to some of Lehman’s, even though the two models (Lehman’s and ours) are essentially different in their basic assumptions. Ours assumes cooperation in the evolutionary sense, so that a complete metabolic neighbourhood  is capable of replicating a focal sequence which thus will have two identical copies locally (apart from mutations). The model of Lehman, on the other hand, assumes collective autocatalysis: in which the complex networks arise due to the fact that the members of the set catalyze each other’s formation, rather than replication. Yet, it is true that both models are prone to being parasitized and ultimately exterminated by parasitic replicators in a mean-field framework, both requiring some form of spatial structure as a potential defense: “Longer term evolutionary optimization would have required spatial heterogenity or compartmentalization to provide lasting immunity against parasitic species or short autocatalytic cycles.” ).http://dx.doi.org/10.1371/journal.pcbi.1002752) used a comparative genomic approach to look at whether there are ‘clues’ of the RNA world in modern organisms, so is perhaps more appropriate, given the sentence.Just a minor quibble about this statement in the Background: “recent organisms still carry reliable clues suggesting that RNA had played a central role both in the metabolism and in the genetics of very early forms of life ”. The papers cited here are both excellent, but address the more chemical aspects of the origin of RNA itself and of RNA catalysis. By contrast, the recent paper by Hoeppner et al. (PLoS Comp Biol 8: e1002752. Thanks for drawing our attention to the paper – we have cited it in the corresponding part of the text.Minor comments/typos (can be deleted from the review once addressed):Page 7, the use of the term “catching” - perhaps “binding” is more appropriate here. Enzymologists routinely talk about substrate binding and product release.We rephrased the sentence in a more clear way.Page 11, “hydroxil” should be hydroxylCorrected.P11, RNS should be RNACorrected.P26, “thedesing” should be the designCorrected.Figure Corrected.Könnyű and co-workers have attempted to fill the gap between purely mathematical, "toy" modeling of very early evolution and the physico-chemical realm within which such evolution may have proceeded. Specifically, the model of Könnyű and co-workers explicitly accounts for the primary and secondary structure of replicators. Hopefully, the authors would continue their efforts to model the physics and chemistry of the early evolution. Therefore, the comments below contain certain recommendations which could be realized either upon revising of the given manuscript or in the future work of the authors.Major comment:1) My major concern is the plausibility of the metabolic part of the model. The authors assumed that “the different RNA species cooperate to produce monomers for their own replication, and possibly also to supply other “common goods” for the replicator community”. Thereby, only three catalytic activities were assumed to be sufficient to perform all these functions in the model of the authors. Obviously, the assumption of only three catalytic activities is an oversimplification, which is quite understandable in the given context. In a wider context, however, the source of monomers and “common goods” for the first replicators is one of the open questions in the origin of life research. Apparently much more than three catalytic activities should have been simultaneously needed to produce different monomers and, in addition, the “common goods”. Furthermore, there is no evidence of ribozymes capable of synthesizing nucleotides from scratch; the chemistry of the RNA catalysis, as revealed so far, is not very encouraging in this respect. A possible solution for this conundrum is that monomers and other common goods (e.g. amino acids and sugars) could be initially produced in abiotic reactions [1–5]. Only later, step by step, the first replicating entities may have learned to synthesize nucleotides. Syntheses of nucleobases and amino acids were recently demonstrated in the solutions that contained formamide or urea and in the presence of UV-light, see  for reviews. Environments with high levels of amides/urea could be envisioned on the primordial Earth . The hypothesis of abiotic origin of monomers got a major boost after Sutherland, Powner and their co-workers succeeded in synthesizing nucleotides from scratch in geologically plausible, one-pot settings . In addition, these authors have shown that natural nucleotides were particularly UV-stable, so that their relative fraction selectively increased under UV illumination [10], in support of earlier theoretical predictions [12]. In such a case, the very first replicators would require only catalytic abilities of assembling abiotically formed monomers - a task that should have been feasible for ribozymes. Hence, scenarios of “abiotic syntheses” match the simplified approach of Könnyű and co-workers in that they imply only few catalytic activities. In a framework of such scenarios, however, an essential source of monomers for replication would be the decay of other replicators. Furthermore, the abilities to accelerate this decay by cleaving neighboring sequences (which would correspond to a reversion of the assembly reaction) as well as to use the fragments for building the own “body” would be extremely advantageous. A scenario of “abiotic syntheses”, of course, would require a separate model that would differ from the model in the given manuscript. Still, in the view of anticipated importance of replicator decay/cleavage, an analysis of the present model in relation to the decay processes, namely a consideration of the model outcome as a function of decay parameters (currently absent from the manuscript) might be of use for readers.We absolutely agree with the Reviewer in that even a very simple realistic metabolism requires a lot more different types of catalysts than postulated in our model. However, we are also sure that the only way such a metabolically competent ribozyme set could have evolved is through the retroevolutionary mechanism explained in some detail elsewhere [20–21], which must have started from abiotiocally produced monomers at its very beginning, exactly as the Reviewer suggests. We have inserted a paragraph into the Background section pointing this out explicitly and citing the relevant literature.Minor comments:s must be complemented by all three different enzymatically active molecules in its metabolic neighborhood , the set of h sites concentric on the site of the focal replicator s)” is not quite clear.2) The manuscript would benefit from a graphical presentation of the focal cell with its neighborhood. Without such a figure, the sentence “For a replication event to occur the focal replicator We added a figure that explains metabolic and replicator neighbourhood configurations.3) It is not clear how the model accounts for “the time consumption of “releasing” the product and “catching” the next substrate for catalysis”. The whole section on sub-additive effects is rather incomprehensible and showed be re-written in a more clear way.We rephreased it in a more clear way.4) The statement that "replication is possible only in the unfolded state" should be defined as one of assumptions of the model. Generally, it is possible to imagine that unfolding could proceed concurrently with replication. That is how replication occurs in our cells.Indeed, we cannot exclude the possibility of the simultaneous unfolding and replication of RNA molecules on the basis of first principles, but note that our postulate of the temporal separation of the two processes is in fact a worst-case assumption: the chance of catalytically active, low-energy folds to be replicated is small. Therefore if this assumption has any effect on the results, then it is negative, but we actually think that changing it may not alter the results in the qualitative sense.References[1] A.I. Oparin, The Origin of Life, Moskowskiy rabochiy, Moscow, 1924.[2] J.B.S. Haldane, The Origin of Life, in: C.A. Watts (Ed.) The Rationalist Annual 1929, pp. 3–10.[3] N.H. Horowitz, On the Evolution of Biochemical Syntheses, Proc. Natl. Acad. Sci. USA, 31 (1945) 153–157.[4] S.L. Miller, L.E. Orgel, The Origins of Life, Prentice Hall Englewood Cliffs, NJ, 1973.[5] A. Lazcano, S.L. Miller, On the origin of metabolic pathways, J. Mol. Evol., 49 (1999) 424–431.[6] S.A. Benner, H.J. Kim, M.A. Carrigan, Asphalt, water, and the prebiotic synthesis of ribose, ribonucleosides, and RNA, Acc Chem Res, 45 (2012) 2025–2034.[7] R. Saladino, C. Crestini, S. Pino, G. Costanzo, E. Di Mauro, Formamide and the origin of life, Phys Life Rev, 9 (2012) 84–104.[8] A.Y. Mulkidjanian, A.Y. Bychkov, D.V. Dibrova, M.Y. Galperin, E.V. Koonin, Origin of first cells at terrestrial, anoxic geothermal fields, Proc Natl Acad Sci U S A, 109 (2012) E821-830.[9] D.V. Dibrova, M.Y. Chudetsky, M.Y. Galperin, E.V. Koonin, A.Y. Mulkidjanian, The role of energy in the emergence of biology from chemistry, Orig Life Evol Biosph, 42 (2012) 459–468.[10] M.W. Powner, B. Gerland, J.D. Sutherland, Synthesis of activated pyrimidine ribonucleotides in prebiotically plausible conditions, Nature, 459 (2009) 239–242.[11] M.W. Powner, J.D. Sutherland, J.W. Szostak, Chemoselective multicomponent one-pot assembly of purine precursors in water, J Am Chem Soc, 132 (2010) 16677–16688.[12] A.Y. Mulkidjanian, D.A. Cherepanov, M.Y. Galperin, Survival of the fittest before the beginning of life: selection of the first oligonucleotide-like polymers by UV light, BMC Evol Biol, 3 (2003) 12.


Methanosarcina acetivorans is a model archaeon with renewed interest due to its unique reversible methane production pathways. However, the mechanism and relevant pathways implicated in (co)utilizing novel carbon substrates in this organism are still not fully understood. This paper provides a comprehensive inventory of thermodynamically feasible routes for anaerobic methane oxidation, co-reactant utilization, and maximum carbon yields of major biofuel candidates by M. acetivorans.M. acetivorans is introduced  by integrating information from two previously reconstructed metabolic models , modifying 17 reactions, adding 24 new reactions, and revising 64 gene-protein-reaction associations based on newly available information. The new model establishes improved predictions of growth yields on native substrates and is capable of correctly predicting the knockout outcomes for 27 out of 28 gene deletion mutants. By tracing a bifurcated electron flow mechanism, the iMAC868 model predicts thermodynamically feasible (co)utilization pathway of methane and bicarbonate using various terminal electron acceptors through the reversal of the aceticlastic pathway.Here, an updated genome-scale metabolic model of Archaea.This effort paves the way in informing the search for thermodynamically feasible ways of (co)utilizing novel carbon substrates in the domain The online version of this article (doi:10.1186/s12934-015-0404-4) contains supplementary material, which is available to authorized users. Methane, the second most important greenhouse gas, is regulated primarily by microbial processes . A renew3− [42− [Methanosarcina acetivorans for which trace methane oxidation has been observed [The global methane cycle is predominantly controlled by anaerobic methanotrophic archaea (ANME) in anoxic environments , 12 and 3−  or SO42−3− [42− . Despite3− [42− , 22. Of observed , 24 implobserved , 26.M. acetivorans, a strictly anaerobic marine methanogen possessing one of the largest known archaeal genomes [M. acetivorans remain largely uncharacterized. Two genome-scale metabolic (GSM) models, iVS941 [M. acetivorans cell extracts grown with methane  motivates an update in the existing genome-scale models to incorporate recent findings and to allow for the analysis of methane utilization in silico. genomes , has eme genomes  and vers genomes , 30. Whi genomes –34, rele, iVS941  and iMB7, iVS941 , for thi, iVS941 –42. ReceM. acetivorans to postulate pathways for reversing methanogenesis while maintaining overall thermodynamic feasibility. We first generated an up-to-date GSM model for M. acetivorans by combining information from two earlier models  along with the most recent data from literature and databases. M. acetivorans has transcriptome and proteome profiles that differ depending on growth substrate [3+, NO3−, SO42−, and MnO2 as external electron acceptors. Overall ΔG ≤ 0 is imposed as a constraint to ensure thermodynamic feasibility of methanogenesis reversal in the presence of an external electron acceptor. The interplay between externally supplied electron acceptors and various by-products is analyzed. The feasibility of methanotrophy by resting cells is assessed when all carbons coming from methane and bicarbonate are converted into acetate, formate, CO2, and methyl sulfide, the known byproducts of M. acetivorans’ metabolism [In this paper we make use of a revamped GSM for ubstrate , 34. We tabolism , 43 sometabolism  as end p+/H+ ratio for sodium/proton antiporter (Mrp) and ATP synthase. Among the charge re-balanced reactions, those involving cofactor F420 in the methylotrophic pathway and ATP synthase also required proton rebalancing to accurately account for proton exchange across the cell membrane. The number of Na+ pumped out by ferredoxin-dependent methanophenazine reductase (Rnf) was updated from three Na+ in iMB745 [+ per methanophenazine reduced in accordance with experimental findings [+ and H+ gradients [+/H+ ratio recapitulating the growth yields on native substrates was estimated to be 2:1 for Mrp and 3:1 and 1:2 for ATP synthase, respectively. The two identified solutions for ATP synthase are rendered equivalent by the reversible 2:1 antiport of Na+/H+ across the cell membrane by Mrp, which makes one intracellular H+ equivalent to two extracellular Na+. We also added an F420-dependent NADP reductase to the iMAC868 model , which functions as a source of NADPH for cell biosynthesis.iMAC868 contains 868 genes, 845 reactions, and 718 metabolites  growing with acetate due to the unknown role of the enzymes in acetate-grown cells [mch [420 for NADPH production in acetate-grown cells thereby rendering mch (methenyl-H4SPT cyclohydrolase) essential. Comparing with the iVS941 model, the iMAC868 model correctly predicts the essentiality of rnf, mtr, and the membrane-bound hdr due to the updated GPRs and ion transport stoichiometries included in this model.Upon correcting 64 GPRs based on updated gene annotations  bifurcates yielding a high-potential electron reducing Fe3+ to Fe2+ (Em = +770 mV) and a low-potential electron reducing ferredoxin (Em = −420 mV). Flavin-based electron bifurcation is common among anaerobic microbes including methanogens [3+ on the outer aspect of the cytoplasmic membrane, the bifurcation event occurs either at the cytoplasm or the membrane. Bifurcation in the membrane depends on the Rnf complex, abundant in acetate-grown M. acetivorans, which interacts with ferredoxin and contains two FMN-bound subunits that are possible sites for electron bifurcation [M. acetivorans [3+ takes place. Finally, an electron transfer reaction is included in the model to transfer the electrons from reduced methanophenazine to an externally supplied electron acceptor based on its reported in vivo essentiality [2. The addition of prospective biofuel molecule production pathways for ethanol [In order to allow for methanotrophy, the iMAC868 model was customized to enable three new processes: (1) reversal of methyl-coenzyme M reductase (Mcr) reaction, (2) inclusion of a cytosolic methyltransferase (CmtA), and (3) inclusion of a mechanism enabling electron bifurcation and its subsequent discharge to an external electron acceptor. The methyl-coenzyme M reductase of an anaerobic methanotroph (ANME-MCR), capable of oxidizing methane , was apptivorans , 24, 48,tivorans , and thenotrophy . CmtA [5notrophy  serves aetyl-CoA . Two flahanogens , 53. Alturcation . Oxidatiurcation . Methanotivorans . Oxidatintiality , 20, 55.ntiality  and organtiality . These a ethanol , butanol ethanol , and iso ethanol  to the m2 and biomass as the main products of methanotrophy using all tested electron acceptors. Methane is oxidized to methyl-H4SPT by ANME-MCR and Mtr (or CmtA), a part of which is oxidized via the methylotrophic pathway to produce intracellular CO2. The remaining methyl-H4SPT is used to produce acetyl-CoA, the primary building block for all biomass precursors. Additional carbon fixation occurs via reductive carboxylation by Cdh and Por. Acetyl-CoA is also converted to acetate, generating ATP via substrate-level phosphorylation  of the supplied electron acceptor is less than 50.5 kJ/electron-pair  compared to SO42− reduction (ΔG = 44.53 kJ/electron-pair). This thermodynamic advantage for Fe3+ allows for the co-utilization of bicarbonate up to a maximum HCO3−/CH4 ratio of 0.44 with complete incorporation of all substrate carbons into biomass. However, the endergonic nature of bicarbonate uptake disallows HCO3−/CH4 co-utilization for increasing biomass yield under sulfate-dependent methanotrophy. Methanotrophy using NO3− and MnO2 also allows for co-utilization of bicarbonate as both electron acceptors have ΔGred greater than −20 kJ/electron-pair , constrained only by stoichiometry for both Fe2 production remains non-essential during ferric-dependent methanotrophy as revealed by the model , thereby restricting maximum achievable biofuel yield  as opposed to CO2 reduction by acetyl-CoA synthesis, causing all electrons to be generated by the ANME-MCR. During sulfate-dependent methanotrophy, none of the products could be produced with the complete carbon conversion efficiency due to the fact that coupling biofuel production with SO42− reduction remains thermodynamically infeasible  is necessary to maintain thermodynamic feasibility of the observed solution spaces  to methyl sulfide. Increasing Fe3+ uptake allows more flux through the methylotrophic pathway, thereby generating additional intracellular CO2 for an increased acetate production of up to a maximum of 0.71 mol/mol-methane at an Fe3+ uptake of 2.2 mol/mol-methane  and methane was feasible for certain ratios leading to improved carbon yields for acetate and biofuel molecules. Re-routing of a fraction of carbon towards CO2 was found to be a recurring mechanism for driving growth and production within thermodynamically constrained metabolic states. Finally, the availability in excess of inorganic electron acceptors resulted in a switch between substrate-level and chemiosmotic ATP synthesis. Thermodynamic constraints were often the limiting factor in product yields. Unsurprisingly, the inability to completely reverse the aceticlastic pathway in the absence of an external electron acceptor was confirmed.An updated genome-scale metabolic reconstruction (iMAC868) for the archaeon The theoretical limits of external electron acceptor utilization possibilities to drive the reversal of methanogenesis were thoroughly explored. Key challenges that confound the fidelity of model predictions are still unknown sodium gradient requirements, substrate-dependent regulation and the detailed mechanism of electron transport from internal electron carriers to their external counterparts. Shedding light to these questions will require systematic experimental investigations to confirm or refute electron flow paths guided by the rapidly expanding modeling infrastructure.M. acetivorans, iMAC868, was constructed by appending genes and reactions from iVS941 to iMB745. The stoichiometric coefficients of the ions associated with the membrane-bound electron transport chain were updated based on recent findings. This includes Rnf, ATP synthase and the Na+/H+ antiporter Mrp. All reactions in the model were checked for mass and charge balances and corrected if necessary. Five reactions from amino acid, two from tRNA, one from cofactor biosynthesis pathways, three from methanogenesis, and three metabolite transport reactions required elemental and charge rebalancing. The biomass equation formulation of iMB745 model was adopted in the new iMAC868 model as reported previously [−1. The medium composition was assumed to be a defined high-salt medium [http://www.gnu.org/software/glpk/) and Gurobi (http://www.gurobi.com) solvers in Matlab using COBRA toolbox [The updated genome-scale metabolic model for eviously . It was eviously . The iMAt medium . The modt medium . FBA opt toolbox . Flux va toolbox . Both FBI = {i|i = 1,2,…,M} = Set of metabolites in the stoichiometric modelJ = {j|j = 1,2,…,N} = Set of reactions in the stoichiometric modelJex = {j|j = 1,2,…,Nex} = Set of exchange reactions in the stoichiometric modelSets:vj = Flux of reaction vbiomass = Flux of the biomass formation reactionVariables:Sij = Stoichiometric coefficient of metabolite UBj = Upper bound for the flux of reaction LBj = Lower bound for the flux of reaction Parameters:biomass) is estimated to be 3750 J/gDCW. This value is identified so as the overall stoichiometries for growth on acetate and methanol (see below) remain thermodynamically feasible:All standard transformed ΔG values were calculated at pH of 7, temperature of 25 °C and ionic concentration of 0.25 M  listed ivj separately subject to stoichiometric and thermodynamic constraints.In the above LP problem, the flux of the biomass is maximized subject to the constraints of stoichiometry (1), thermodynamics (2), metabolic network fluxes (3), fixed uptake of methane (4), minimum requirements of maintenance ATP of 2.5 mmol/gDCW-h (5), and fixed ΔG values of input/output metabolites to/from the system (6). Flux ranges for target products were obtained by iteratively solving the above LP problem to minimize and maximize all M. acetivorans grown with acetate and methanol [Step 1k in the total gene set K, the value ck was calculated as the ratio of protein abundance for cells grown on substrate 1 to cells grown on substrate 2 where substrate 1 and 2 can be any of acetate or methanol.Calculate the ratio of protein abundance under different growth substrates. For each gene Step 2ck with a pre-defined cutoff value . If the ratio ck is below or equal to the cutoff value then gene k is added to a candidate list G for which the feasibility of removing the corresponding reactions is evaluated.Compare the ratio Step 3G. The GPR for each reaction is re-evaluated assuming that all genes in list G are eliminated. If re-evaluation of the GPR reveals no associated gene then the reaction is added to the set Jexp that contains the candidate reactions for removal.Re-evaluate GPRs within list Step 4Jexp that can be removed from the model without dropping the biomass yield below the experimental value:The following mathematical formulation is used to identify the maximum number of reactions in the set Gene-protein-reactions (GPRs) associations in the iMAC868 model were thoroughly assessed and 64 GPRs were corrected using a list of 781 newly revised gene annotations Jon = {j|j = 1, 2, …, Non} = Set of reactions for which the evaluation of their GPR indicates active reactionsJoff = {j|j = 1, 2, …, Noff} = Set of reactions for which the evaluation of their GPR indicates inactive reactionsSets:vbiomass = Flux of the reaction producing biomassyj = Binary variable associated with flux of vjVariables:vbiomass,exp = The experimentally measured biomass yield for the given substrateParameters:yj is minimized subject to the limitations on stoichiometry (7), the flux of the reactions for which proteomic data is available but their GPRs are not evaluated because ck is greater than the cutoff value (8), the flux of all other metabolic network reactions which do not belong to set Jexp along with those belong to set Jon (9), and the flux reactions which belong to set Joff (10), and the flux of the reaction producing biomass being equal or greater than the experimentally measured biomass yield for the given substrate (11).Here, the sum of binary variables EAE corresponding to the ΔGred (kJ/electron-pair) of the specific electron acceptor described in Table Electron acceptor reactions are modeled using “electron acceptor equivalents (EAE)”, which serves to drain electron pairs from the model. Each electron pair is drained from reduced methanophenazine. The general form of the electron acceptor reaction used in this model is:+/H+ stoichiometric ratios of ATP synthase and Mrp matching best the observed growth yields, Flux Balance Analysis (FBA) was used as described earlier in this section. FBA optimization problems were solved at varying Na+/H+ ratios of 3:1, 1:1, 1:3, 2:1, and 1:2 for ATP synthase and at varying Na+/H+ ratios of 3:1, 2:1, 1:1, 1:2, and 1:3 for Mrp. These ratios were selected based on recent findings involving archaeal ATP synthase and their dependence on Mrp. Welte and Deppenmeier [+ or Na+) are required to synthesize one molecule of ATP based on the measured electrochemical ion gradients available for a limited number of methanogens. Jasso-Chavez et al. [M. acetivorans. The objective function was the maximization of cellular growth. This procedure was repeated for both acetate and methanol as sole carbon sources. The sum squared error (SSE) between the predicted and observed growth yields were calculated and results are shown in Table To find the optimal Napenmeier  estimatez et al.  proposed


Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology.Constraint-based models of E. coli K–12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc–18.0–GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc–18.0–GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model’s derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc–18.0–GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization.We present EcoCyc–18.0–GEM, a genome-scale model of the Significant advantages can be derived from the combination of model organism databases and flux balance modeling represented by MetaFlux. Interpretation of the EcoCyc database as a flux balance model results in a highly accurate metabolic model and provides a rigorous consistency check for information stored in the database. Escherichia coli K–12 MG1655 metabolic network. A series of E. coli constraint-based models have been published by the group of B. Palsson [E. coli dating back more than twenty years [E. coli modeling, and have seen a range of applications [Constraint-based modeling techniques such as flux balance analysis (FBA) have become central to systems biology ,2, enabl Palsson -6, extenty years -10. Thesications -13 incluE. coli metabolic models, we aimed to illustrate the benefits of integrating metabolic modeling into model organism databases by developing an E. coli model derived directly from the EcoCyc bioinformatics database [Motivated by the widespread use of database . First, Second, we sought to make the model easy to understand and operate. Our goal was a high level of model accessibility and readability through (a) tight web-based integration of the model with extensive model query and visualization tools, and (b) a model representation that captures extensive information that enriches the model and aids its understanding, such as metabolic pathways, chemical structures, and genetic regulatory information. Metabolic models are not just mathematical entities that output predictions; they are also artifacts that scientists interact with in multiple ways. If a model can be quickly and easily understood, scientists are more likely to trust its predictions, and the model is easier to reuse, to modify and extend, to learn from, and to validate through inspection. These aspects of a metabolic model depend strongly on how the model is represented, on the software tools available to interactively inspect the model, and on how tightly integrated the model is with those software tools.E. coli metabolism.Third, we sought to produce a model that is frequently updated to integrate new knowledge of E. coli metabolic model to identify errors in EcoCyc, and open problems in E. coli biology, by performing in-depth investigations of the disagreements between the phenotypic predictions of the model and experimental results.Fourth, we sought to use the EcoCyc-derived E. coli K–12 MG1655 that is directly derived from the EcoCyc model organism database (http://EcoCyc.org) built on the genome sequence of E. coli K–12 MG1655. The model is implemented using the MetaFlux [We present EcoCyc–18.0–GEM, a constraint-based genome-scale metabolic model for MetaFlux  componenE. coli metabolic models. EcoCyc–18.0–GEM is an advance over previous stoichiometric models of E. coli metabolism in four respects: in its size; in its accuracy; in its form, readability and accessibility; and in its update frequency. Here we summarize these results; these points will be expanded in subsequent subsections.The EcoCyc–18.0–GEM generated from EcoCyc 18.0 encompasses 1445 genes, 2286 unique cytosolic and periplasmic reactions, and 1453 unique metabolites. Table The MetaFlux component of Pathway Tools translates Pathway/Genome Database (PGDB) reactions and compounds into constraint-based metabolic models. Our methodology of fusing systems-biology models and bioinformatics databases has several advantages because of strong synergies between these approaches. Databases and models both require extensive literature-based curation and refinement. It is more efficient to perform that curation once in a manner that benefits a database and a model, than to duplicate curation efforts for a database project and a modeling project. Furthermore, the modeling process identifies errors, omissions, and inconsistencies in the description of a metabolic model, and therefore drives correction and further curation of the database if the two efforts are coupled. We made more than 80 EcoCyc updates as a result of comparing model predictions with experimental data and literature for this work. In addition, bioinformatics database curation methods such as the use of evidence codes and citations to provide data provenance, and the incorporation of mini-review summaries that describe enzymes and pathways, benefit systems-biology models, which typically lack data provenance and explanations.Advances in model size. Compared with iJO1366, EcoCyc–18.0–GEM represents a 6% increase in the number of genes, a 23% increase in the number of unique cytosolic and periplasmic reactions, and a 28% increase in the number of unique metabolites. The size of EcoCyc–18.0–GEM is currently exceeded only by the more mathematically complex ME-model of O’Brien et al.[en et al., which iAdvances in model accuracy. We conducted a threephase validation of EcoCyc–18.0–GEM to assess its accuracy . A liteAdvances in model update frequency. Because the MetaFlux component of Pathway Tools generates constraint-based models directly from the EcoCyc PGDBs, as the database is refined through new curation, those refinements are automatically incorporated into newly generated versions of the model. We release new versions of the EcoCyc-based model three times per year; previous models were updated every four years [E. coli.ur years -6. Althoet al., with several revisions stemming from differences in content and functionality between EcoCyc and the iJO1366 model. Gene essentiality in constraint-based models is principally determined by the biomass demands of the cell. Inclusion of a metabolite in the biomass metabolite set forces the genes required for manufacture of that metabolite to become essential in the simulation.Refinement of EcoCyc–18.0–GEM began with the validation of the biomass, nutrient, and secretion metabolite sets, which are detailed at length in Additional file et al. The core biomass metabolite set is a biomass metabolite set that is defined with the aim of maintaining quantitative accuracy with regards to cell performance while predicting the observed experimental essentiality data as accurately as possible. Because much of this work focuses on testing the minimum requirements for cell growth, we frequently employed the core biomass metabolite set in our simulations. We use the term “expanded biomass set” to refer to our version of the wild-type biomass metabolite set described in Orth et al., because we do not wish to imply that the simulated cells always represented the wild-type state.A wild-type biomass metabolite set, which is derived from measurement of the biomolecular composition of healthy, growing cells, is not representative of the minimal set of biomass metabolites required for cell survival. Because biomass metabolites not truly required for cell survival will generate false simulation predictions of essentiality in their biosynthetic pathways, the concept of a core biomass metabolite set was developed by Feist The biomass metabolite sets described here underwent several revisions reflecting differences in scope between EcoCyc–18.0–GEM and iJO1366. Whereas iJO1366 is a purpose-built model developed using the COBRA Toolbox with input from KEGG, EcoCyc, and other databases, EcoCyc is a database with its own schema whose entries are programmatically transformed into an FBA model. The specific metabolites present in iJO1366 therefore cannot always be matched with the specific metabolites generated from EcoCyc by MetaFlux on a one-to-one basis. Several biomass metabolites represented as distinct within iJO1366, such as phosphatidylethanolamines with different chain lengths and saturations, are summed under the heading of a single representative metabolite in the EcoCyc–18.0–GEM biomass set. Additionally, not all processes covered in EcoCyc–18.0–GEM are covered in iJO1366, and the reverse is also true. As a result, the biomass metabolite sets differ slightly. Additional file 2, 2, minerals appropriate to the biomass objective function, and MOPS buffer (usable as a sulfur source) where appropriate. Because of passive diffusion at the high concentration of ammonium used in experimental culture, amtB), whereas all other nutrients are supplied in the periplasmic space.We constructed standard nutrient sets based on culture conditions reflecting experiments in glucose or glycerol minimal media and on the model’s capability to use substrates. Those substrates include glucose or glycerol as appropriate, O E. coli metabolism and dead-end metabolites [E. coli culture media.Finally, we developed a large set of secreted compounds that could be supplied across all growth conditions explored with our model. It contains both plausible products of abolites  within tWe verified the metabolic reachability of each component within the EcoCyc biomass metabolite set by supplying nutrients representing an aerobic glucose minimal medium and setting the production of each individual metabolite in turn as the optimization goal of MetaFlux, and repaired gaps by means of literature-based manual curation of EcoCyc and expansion of the relevant metabolite sets.E. coli FBA.We next confirmed that simulations of aerobic growth on glucose run with maximization of ATP production as their objective made appropriate use of the glycolytic and TCA cycle pathways and agreed with previous work on 2O → ADP + Pi + H + as the objective function to be maximized by MetaFlux. The fluxes resulting from the maximization of ATP production on glucose under aerobic conditions were compared with fluxes from COBRA Toolbox [+ translocated per 2 e -, as proposed by Treberg et al.[+ translocated per 2 e -, as proposed by Wikstrom and Hummer [+ per 2 e - view of translocation stoichiometry, future versions of EcoCyc will be changed to reflect this fact.The maximization of ATP production under aerobic conditions was studied by setting the ATP consumption reaction ATP + H  Toolbox  simulatirg et al.) and thed Hummer ). The exbo oxidase reaction stoichiometry is written in terms of whole molecules of oxygen, while iJO1366 CYTBO3_4pp is written in terms of half-molecules (1 O 2 consumed vs. 0.5 O 2).Further numerical differences are due to a technical consideration: EcoCyc cytochrome After completing our basic validation of biomass production and energy generation, we maximized the rate of EcoCyc–18.0–GEM biomass metabolite set production under several minimal media conditions and ensured that we obtained results comparable to the iJO1366 results for the same conditions obtained using the COBRA Toolbox. Divergences were addressed by literature-based manual curation of EcoCyc and modification of MetaFlux reaction sets. We further compared the extracellular flux distributions resulting from these simulations with the experimental data obtained in carbon-limited chemostat environments under both aerobic and anaerobic conditions.Tables 2 uptake observed experimentally lend support to a lower practical efficiency of proton translation stoichiometry in vivo, perhaps augmented by respiratory inefficiencies such as futile cycling and generation of reactive oxygen species. Both models secrete the expected 1:2:1 mix of acetate, formate, and ethanol during anaerobic growth on glucose that Varma et al.[et al., in which ethanol, then formate, and finally acetate production are eliminated as the cell’s oxygen supply becomes completely sufficient to support aerobic respiration. Figures The behavior of MetaFlux/EcoCyc–18.0–GEM simulations was very similar in most regards to the behavior of COBRA/iJO1366 simulations. Respiration and fermentation rates scaled with nutrient uptake at comparable rates. The generally higher rates of O ma et al. original2 and CO 2 than the simulated fluxes, suggesting a degree of respiratory inefficiency not properly modeled by FBA. Similarly, small quantities of succinate and lactate were produced by experimental fermentation, indicating a degree of divergence from metabolic optimality in vivo. Broader cellular constraints such as regulation, protein crowding, pathway enzyme synthesis requirements, and pathway-throughput limits underlie these differences [et al.[Comparisons between FBA-predicted extracellular fluxes and experimental fluxes show that EcoCyc–18.0–GEM and iJO1366 FBA predictions agree more closely with each other than with experimental flux results, although the correspondence between simulation and experiment was quite close for the experimental fluxes under consideration. This result was expected given the adaptation of the iJO1366 biomass function for use in EcoCyc–18.0–GEM, the use of iJO1366 and preceding reconstructions as benchmarks in the development of EcoCyc–18.0–GEM, and the use of EcoCyc as a reference in the construction of iJO1366 and its predecessors. The experimental measurements generally demonstrate higher fluxes of the respiratory gases O ferences ,26,27. Ss [et al..in silico FBA screens of gene KOs have been applied in a variety of metabolic engineering efforts [E. coli KO library collections with well-characterized growth behavior provide an important tool for flux model validation.One of the most exciting aspects of genome-scale flux modeling is the ability to rapidly test computational gene knockouts (KOs) for their effects on metabolic function. Gene KO simulation is useful both for prediction and for validation:  efforts -31, and FBA gene KO essentiality prediction depends on two types of database associations between genes and chemical reactions: genes whose products catalyze reactions, and genes whose products are reaction substrates . Simulation gene KOs are carried out by identifying all reactions involving the gene, and then identifying all other genes capable of catalyzing the reactions or supplying the substrates thus identified. Reactions for which no isozymes or alternative substrate supplies are found are removed from the FBA stoichiometric network. An FBA solution is then calculated for the new model. If the simulated gene KO has caused the deletion of one or more reactions required for the synthesis of a biomass metabolite, generation of the full biomass metabolite set will be blocked and the FBA simulation returns a no-growth result. Such a result represents a prediction of gene essentiality. If the complete biomass metabolite set can still be produced in spite of the simulated gene knockout, the FBA simulation returns a growth result, indicating a prediction of gene non-essentiality.et al.[et al.[E. coli gene deletion strains for growth on LB rich media and MOPS minimal media with 0.4% glucose. We conducted our glycerol minimal media tests using the gene knockout essentiality data of Joyce et al.[The experimental essentiality data used in our tests consisted of two major datasets. The first, used to study gene essentiality on rich and glucose minimal media, was the deletion study of Baba et al. as updatl.[et al., which tce et al., an expaE. coli gene deletions strongly affect growth on various types of minimal media, but are nonessential to growth on rich media. Because the FBA simulation result is treated as a binary test (growth or no-growth), gene deletions that strongly affect growth on minimal media without producing a completely lethal phenotype must be defined either as experimentally essential or as experimentally nonessential.Several et al. The narrow glucose essentiality criteria treat as essential those Baba et al. and Yamamoto et al. gene deletion mutants with OD600 ≤ 0.005 after 24 and 48 hr growth on glucose minimal media. This requires no perceptible growth over a long period. The broad essentiality criteria was originally defined in relative terms by Joyce et al., as the slowest-growing ninth of all Keio collection deletion mutants.Two representative perspectives on this definition are the narrow essentiality criteria of no observable growth in minimal media and the broad essentiality criteria used by Orth et al. to have OD ≤ 0.091 after 24 hr growth on glucose minimal media, which indicates impaired growth over a shorter period. The practical difference between these two perspectives is illustrated in Figure et al. As the figure illustrates, the broad essentiality criteria include a population of cells with severe growth defects that is not contained in the narrow essentiality data. The comparison between narrow and broad essentiality criteria can be expanded to glycerol minimal media by comparing narrow glycerol essentiality criteria of no observed growth on rich media with the glycerol essentiality criteria of Orth et al., again derived from the criteria of Joyce et al. involving successive division into thirds.In absolute terms, that approach treats as essential those deletion mutants measured by Baba et al. Differences in essentiality predictions between the two data sets illustrated the differences between standard cell composition under nominal conditions and the minimal composition required for cell growth.In order to examine criteria for experimental gene essentiality more deeply and to illustrate the effect of defining a core biomass metabolite set, we conducted essentiality testing using both the expanded and core biomass metabolite sets proposed by Orth et al. and Joyce et al. Gene KO simulations capable of generating any growth at all were scored as nonessential, whereas gene KOs blocking generation of the biomass metabolite set were scored as essential.We simulated single gene KOs on glucose and glycerol minimal media for the 1445 genes in EcoCyc–18.0–GEM to test whether the resulting EcoCyc–18.0–GEM gene deletion mutants were capable of generating core and expanded biomass metabolite sets from sets of nutrients based on the experimental culture media of Baba We compared the results of this simulated essentiality screen with experimental gene essentiality results based on both narrow and broad gene essentiality criteria. Incorrect essentiality predictions were addressed by literature-based manual curation of EcoCyc and modification of MetaFlux metabolite sets. Final essentiality prediction results after curation are summarized in Tables E. coli literature. Our examination covered two types of incorrect gene deletion growth predictions. The first type was a false positive growth prediction. These genes, which are experimentally essential under the conditions tested by Baba et al., were predicted to be nonessential by EcoCyc–18.0–GEM. The second type was a false negative growth prediction. These genes, which are not experimentally essential under the conditions tested by Baba et al., were predicted to be essential by EcoCyc–18.0–GEM.Tables E. coli biology, false predictions resulting from interesting facets of experimental or simulation methods, and other situations of special relevance. Table E. coli metabolism contains several isozymes and opportunities for reversibility, and presents a challenge to FBA essentiality predictions in the absence of complete regulatory modeling. Table et al. experimental growth conditions, insufficiently expressed isozymes, and cases of enzyme inhibition all fall into this category. Table E. coli K–12 literature. Table Tables et al. in E. coli core metabolism with reference to the then-current state of EcoCyc. Constraint-based model improvement and gap-filling based on gene essentiality predictions derived from the work of Baba et al. have been examined for the COBRA family of constraint-based models of E. coli metabolism by Reed et al.[et al.[et al.[Several of the false gene essentiality predictions described within these tables were discussed in the work of Kim and Copley, who examined the essentiality conclusions of Baba ed et al., Kumar el.[et al., Kumar al.[et al., Barua el.[et al., Orth anl.[et al.,144, andl.[et al.. Our revl.[et al. gap-fillAdditional file E. coli respiration for 428 types of media, including 22 conventional types of minimal growth media and 383 Biolog Phenotype Microarray (PM) wells. The 383 Biolog PM media conditions represent a high-throughput method of evaluating metabolic phenotypes in culture based on a tetrazolium dye assay of cellular respiration. Each well in a Biolog 96-well PM plate contains a standard minimal media composition plus a nutrient source that is varied across the PM plate, with the element supplied by the varying nutrient source dependent on the type of Biolog PM plate in use [Observation of culture growth on various nutrient sources is a foundation of microbiology . EcoCyc e in use -149.E. coli growth on the 22 common conventional minimal growth media; (2) consensus estimates of respiration based on four different experimentalists’ measurements of aerobic Biolog 96-well plates PM1–4, representing 313 conflict-free growth observations; and (3) an anaerobic Biolog PM1 plate assay surveying carbon source utilization in the absence of oxygen, representing 96 anaerobic growth observations. Biolog PM data stored in EcoCyc measures utilization of nutrients as sources of carbon (PM1–2), nitrogen (PM3), sulfur (PM4), and phosphorus (PM4).We evaluated the performance of EcoCyc–18.0–GEM in predicting growth for the following available datasets: (1) aerobic et al.[et al.[et al. in our analysis of Biolog PM results because of variation in culture conditions and a high degree of conflict with other datasets under both aerobic and anaerobic conditions [Conventional media compositions and growth results were drawn from the literature. Aerobic Biolog PM nutrient utilization assay results were compiled from four different datasets captured in EcoCyc: (1) from our own experiments; (2) from a dataset obtained from B. Bochner; and from the recent publications of (3) AbuOun et al. and (4) l.[et al.. Anaerobnditions . See the3+ in the medium was replaced with Fe 2+. Anaerobic simulations were prepared identically to those performed for aerobic growth, except for the removal of oxygen from the nutrient set, inclusion of the formate-hydrogen lyase reaction, and the removal of the protoheme and pyridoxal 5’-phosphate synthesis requirement from the biomass.Growth on a given type of media was tested by constructing simulated MetaFlux nutrient sets corresponding to the contents of the media in question and comparing EcoCyc–18.0–GEM growth predictions with experimental growth results. Due to the absence of enterobactin iron uptake modeling in EcoCyc, Fe Literature-based EcoCyc curation and appropriate modifications of MetaFlux metabolite sets were used to address incorrect nutrient utilization predictions. The final results for PM array validation after curation are listed in Table The overall accuracy of nutrient utilization prediction across all aerobic and anaerobic PM and conventional growth media is 348/431 (80.7%). Tables E. coli enzymes are inhibited by ADP?” “What transcriptional regulators affect the expression of the enzymes for reaction X?” Their ability to answer these questions rapidly and accurately is strongly dependent on the model representation, the software tools available for querying and visualizing that representation, the tightness with which those tools are integrated with the model, and the presence of additional enriching information for the model.Scientists naturally need to ask many questions of a metabolic model, such as “What are the chemical structures of all substrates in reaction X, and is X chemically balanced?” “What metabolic pathway(s) is reaction X a member of, and what are the adjacent reactions?” “Which E. coli models are represented as spreadsheet files and as SBML files, making it tedious or impossible for non-programmers to answer the preceding questions directly from those files. Although SBML files can be imported into software tools such as the RAVEN Toolbox [E. coli models do not capture (nor can SBML capture) additional enriching information that, while not required for the mathematical operation of a model, greatly enhances our ability to validate and understand a model, and to answer the preceding questions. Examples of such enriching information present in EcoCyc–18.0–GEM are metabolite chemical structures, arrangements of reactions within metabolic pathways, and gene regulatory information. Note that introducing ad-hoc definitions of these data  in the SBML “Notes” field, or introducing SBML links to external databases, would be considered out of bounds: since pathways are not captured formally in the SBML specification today, there is no guarantee regarding interoperability of software tools with such ad-hoc data.Existing  Toolbox  and rbio Toolbox , in pracE. coli gene regulatory network; constructing structured queries such as: find all reactions of a given metabolite; find all enzymes utilizing a given cofactor; and presentation of text summaries and citations that explain and support aspects of the model. In general, other tools for metabolic model visualization tend to be less comprehensive, and to be less closely coupled to the model; see [EcoCyc–18.0–GEM is highly understandable because it can be interactively queried and visualized through the EcoCyc web site and desktop Pathway Tools software, which supports visualization of metabolic pathways and reaction diagrams; metabolite pages that depict metabolite structures and all reactions a metabolite is involved in; depiction of gene/reaction connections and of genome organization via a genome browser; navigation through the del; see -186 for E. coli models because EcoCyc–18.0–GEM fluxes can be immediately painted onto the EcoCyc Cellular Overview, a zoomable diagram of the complete metabolic map of E. coli that allows immediate visual inspection of flux patterns. Although other software tools exist for visualizing flux patterns on metabolic networks, e.g., the RAVEN Toolbox, they are unlikely to be easily usable with previous E. coli models. For example, RAVEN Toolbox requires that the user manually construct the metabolic network diagram, which could take days or weeks of effort. In contrast, Pathway Tools generates metabolic map diagrams algorithmically from a PGDB.The reaction fluxes computed from EcoCyc–18.0–GEM are more understandable than those from previous E. coli metabolic network, representing gene function with an unprecedented degree of accuracy.EcoCyc–18.0–GEM demonstrates the advantages of literate modeling based on comprehensive organism databases. It provides comprehensive genome-scale coverage of the E. coli, and visualization of pathways and reactions within the model as they change throughout the course of construction. As part of EcoCyc, EcoCyc–18.0–GEM will receive frequent updates to remain abreast of recent research developments.Integration of EcoCyc–18.0–GEM into the EcoCyc database gives investigators working with the model access to the full Pathway Tools bioinformatics and data visualization suite. This allows construction of complex database queries involving the full range of biochemical entities within The process of EcoCyc–18.0–GEM construction and validation resulted in more than 80 updates to EcoCyc. These included expansion and revision of periplasmic phosphatase activities many updates to sugar transport and phosphotransferase system modeling; correction of incorrect compartment assignments; fixes for L-lactate dehydrogenase action; revisions to glutathione hydrolysis; new transport reactions for compounds identified as nutrient sources during Biolog PM testing; addition of MOPS catabolism via the alkanesulfonate pathway; removal of several incorrect reactions and gene-protein relationships; numerous fixes to reaction reversibility and directionality; several compound class reassignments to correct issues with reaction instantiation; mass rebalancing for several reactions; and revisions to ATP synthase proton stoichiometry. These updates are outlined in Additional file E. coli have been introduced as part of this effort. Biomass metabolite sets have been revised to reflect the work of Orth et al., and additional updates have been made on the basis of the validation process in order to create the most accurate final product possible.The MetaFlux software has also been improved as a result of the FBA validation of EcoCyc. These improvements include upgrades to compartmentalization handling, gene deletion code, and electron transfer reaction handling. MetaFlux solution and log files have been updated to contain additional statistical information and provide a more detailed explanation of the metabolic network construction process to the user. Numerous updates and revisions to the MetaFlux model of E. coli modelers and experimentalists were raised in the course of EcoCyc–18.0–GEM development. By highlighting these questions and presenting them within the context of EcoCyc as a reference database, we address the interests of the general metabolic modeling audience and of E. coli experimentalists interested in using models to explore their results and generate new leads for research. We summarize these questions here.Many questions of interest to in vivo deviations from theoretical in silico optimality.Experimental measurements of respiratory fluxes in glucose-fed aerobic chemostat culture are higher than those predicted by simulation, and small quantities of succinate and lactate are generated in experimental anaerobic fermentations, suggesting interesting A small number of metabolic byproducts must be removed directly from the cytosol in the secretion set because of a lack of known salvage or excretion pathways. The fate of these metabolites is of interest.E. coli function in varying environments.Several incorrect essentiality predictions are associated with unclear cellular biomass requirements, pathways with potential alternate routes of catalysis, uncertain determinations of essentiality on glucose minimal media, and ambiguous or missing gene function. Similarly, nutrient utilization predictions have identified a number of compounds that lack clear pathways of entry into metabolism but are capable of supporting respiration and/or growth. Resolution of these uncertainties would improve our understanding of http://brg.ai.sri.com/ptools/. The simulation tests were constructed by using Lisp scripting and the Pathway Tools Lisp API, documented at http://brg.ai.sri.com/ptools/api/. Further details on the construction of EcoCyc–18.0–GEM can be found within Additional file The data used for this project were obtained from EcoCyc version 18.0, and the bioinformatics and flux analysis procedures documented here were performed in either the Web or desktop environment of the Pathway Tools 18.0 software. Pathway Tools can be downloaded, along with documentation and example files, at E. coli BW25113 on glucose is included as Additional file EcoCyc–18.0–GEM development employed the MetaFlux component of Pathway Tools, documented in the Pathway Tools User’s Guide and in . All simWe note that the choice of stoichiometric representation of equations can affect flux balance solutions when minimization of summed flux is used as part of the objective function; see  for furtBriefly, MetaFlux creates a stoichiometric metabolic flux network at run time from the metabolites and reactions contained in a Pathway Tools PGDB. During network construction, MetaFlux removes PGDB reactions that are: ambiguously instantiated (see below); unbalanced or having an undetermined balance state; disconnected from the network; marked as physiologically irrelevant; involved in polymerization; involved in polymer segment or protein modification; lacking substrates on one side; containing substrate entities that are described only by strings; possessed of variable stoichiometry; or have more than 10,000 permutations that must be checked during instantiation. MetaFlux then instantiates reactions containing compound classes, replacing the class reactions with mass-balanced reactions containing instances of the relevant compound classes. The resulting set of metabolites and reactions constitutes the metabolic flux network operated on by MetaFlux. Enzymatic reaction and gene-protein relationship data encoded in the PGDB are used to associate the reactions of the network with enzymes and genes as appropriate.E. coli genome-scale reconstruction were employed in order to validate the EcoCyc FBA simulations and to provide a point of comparison to existing reference models. The iJO1366 simulations were performed using the COBRA Toolbox 5.0.0 within MATLAB R2010a and cobrapy 0.2 within Python 2.7.6. All iJO1366 simulations used taxicab norm minimization of fluxes as described in the documentation for the optimizeCbModel function.COBRA ,188 simuet al. and modified according to our research findings and experimental data on gene essentiality. The ATP turnover requirement for growth-associated maintenance costs (GAM) was set to 53.95 mmol ATP/gCDW, while the ATP turnover requirement for non-growth-associated maintenance costs (NGAM) was set to 3.15 mmol ATP/gCDW, per Orth et al.Biomass metabolite sets were constructed using the wild-type and core biomass sets of Orth μ of 1.0 hr -1 (one new gram of cell dry weight per gram of cell dry weight per hour).All coefficients of the biomass metabolite set represent millimoles (mmol) of each metabolite required per gram of cell dry weight (gCDW). Coefficients of the nutrient and metabolite sets represent specific uptake and output fluxes, in millimoles of each metabolite supplied per gram of cell dry weight per hour (mmol/gCDW/hr). An overall biomass flux of 1.0 thus represents a specific growth rate Aerobic glucose chemostat data for experimental comparisons were obtained from . AnaerobE. coli MG1655 was obtained from the Yale Coli Genetic Stock Center, pre-grown on nutrient agar and used to inoculate the plates following Biolog instructions. The data were collected and analyzed using the OmniLogH PM system, which records the color change every 15 min for each well in the 96 well assay plates. All incubations were performed at 37°C over 48 hr. For the complete details of all PM assay conditions, please refer to the original publications.The protocols followed in our PM experiments are as follows: PM plates 1–4 containing 190 sole carbon sources, 95 sole nitrogen sources, 59 sole phosphate sources and 35 sole sulfur sources were used in this analysis. The PM nutrient-source assay is an assay of respiration based on the generation of NADH by carbon metabolism and the subsequent reduction of a tetrazolium redox dye by NADH. As such, it does not directly measure either the cell growth simulated by FBA biomass objectives or the uptake of noncarbon sources. However, checkpoint linkage of carbon-source catabolism to nitrogen, phosphorus, and sulfur source catabolism enables the tetrazolium redox dye assay to probe the metabolism of non-carbon sources . BochnerBiolog PM results are stored within the EcoCyc database and are accessible via the Pathway Tools API and EcoCyc website. Individual Biolog PM assay scores for each well were compared across experimental datasets to establish a consensus for comparison with EcoCyc–18.0–GEM simulation. Because simulations of nutrient utilization were scored according to growth or no growth, experimental Biolog PM results indicating ‘normal’ and ‘low’ respiration were combined into a ‘positive’ result. The majority of Biolog PM tests (313/383) displayed a consensus of either respiration or no respiration across all four experimental datasets used. In 70 of 383 cases, no clear consensus could be reached. These divergent cases were omitted from the nutrient-utilization assay validation because no reliable conclusion could be reached regarding the results. See Additional file DSW, IMK, and PDK receive fees from SRI International commercial licensing of Pathway Tools.PDK and DSW conceived and designed the experiments. DSW constructed the model and conducted the simulations. DSW analyzed the data. IMK, AM, and DSW curated the database. DSW and PDK wrote the manuscript. AM and ITP performed phenotype microarray experiments. All authors read and approved the final manuscript.Supplementary information text and table descriptions.Click here for fileSupplementary tables (11 tables).Click here for fileMetaFlux .fba file demonstrating simulation of aerobic growth of E. coli BW25113 on glucose.Click here for file