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The EcoCyc database is an online scientific database which provides an integrated view of the metabolic and regulatory network of the bacterium Escherichia coli K-12 and facilitates computational exploration of this important model organism. We have analysed the occurrence of dead end metabolites within the database--these are metabolites which lack the requisite reactions (either metabolic or transport) that would account for their production or consumption within the metabolic network. 127 dead end metabolites were identified from the 995 compounds that are contained within the EcoCyc metabolic network. Their presence reflects either a deficit in our representation of the network or in our knowledge of E. coli metabolism. Extensive literature searches resulted in the addition of 38 transport reactions and 3 metabolic reactions to the database and led to an improved representation of the pathway for Vitamin B12 salvage. 39 dead end metabolites were identified as components of reactions that are not physiologically relevant to E. coli K-12--these reactions are properties of purified enzymes in vitro that would not be expected to occur in vivo. Our analysis led to improvements in the software that underpins the database and to the program that finds dead end metabolites within EcoCyc. The remaining dead end metabolites in the EcoCyc database likely represent deficiencies in our knowledge of E. coli metabolism.

Can a bacterial cell model vet large datasets from disparate sources? Macklin et al. explored whether a comprehensive mathematical model can be used to verify or find conflicts in massive amounts of data that have been reported for the bacterium Escherichia coli , produced in thousands of papers from hundreds of labs. Although most data were consistent, there were data that could not accommodate known biological results, such as insufficient output of RNA polymerases and ribosomes to produce measured cell-doubling times. Other analyses showed that for some essential proteins, no RNA may be transcribed or translated in a cell's lifetime, but viability can be maintained without certain enzymes through a pool of stable metabolites produced earlier. Science , this issue p. eaav3751 Construction of a large-scale mechanistic model of Escherichia coli brings models and large datasets together to enhance knowledge in biology. The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

Background Constraint-based models of 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. Results We present EcoCyc–18.0–GEM, a genome-scale model of the 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. Conclusion 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.

Background As more complete genome sequences become available, bioinformatics challenges arise in how to exploit genome sequences to make phenotypic predictions. One type of phenotypic prediction is to determine sets of compounds that will support the growth of a bacterium from the metabolic network inferred from the genome sequence of that organism. Results We present a method for computationally determining alternative growth media for an organism based on its metabolic network and transporter complement. Our method predicted 787 alternative anaerobic minimal nutrient sets for Escherichia coli K-12 MG1655 from the EcoCyc database. The program automatically partitioned the nutrients within these sets into 21 equivalence classes, most of which correspond to compounds serving as sources of carbon, nitrogen, phosphorous, and sulfur, or combinations of these essential elements. The nutrient sets were predicted with 72.5% accuracy as evaluated by comparison with 91 growth experiments. Novel aspects of our approach include (a) exhaustive consideration of all combinations of nutrients rather than assuming that all element sources can substitute for one another(an assumption that can be invalid in general) (b) leveraging the notion of a machinery-duplicating constraint, namely, that all intermediate metabolites used in active reactions must be produced in increasing concentrations to prevent successive dilution from cell division, (c) the use of Satisfiability Modulo Theory solvers rather than Linear Programming solvers, because our approach cannot be formulated as linear programming, (d) the use of Binary Decision Diagrams to produce an efficient implementation. Conclusions Our method for generating minimal nutrient sets from the metabolic network and transporters of an organism combines linear constraint solving with binary decision diagrams to efficiently produce solution sets to provided growth problems.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model ofEscherichia colienabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

The rpoN gene encoding the transcription factor σ 54 in Myxococcus xanthus has been cloned using a heterologous rpoN probe. The sequence of the cross-hybridizing DNA confirmed the existence of an ORF 1518 bp long that encodes a well conserved member of the σ 54 family of σ factors. Low- as well as high-stringency hybridizations detected only a single rpoN gene in the M. xanthus chromosome. In other bacteria, σ 54 is an alternative σ, and null mutants are viable. However, all attempts to construct a strain containing a null mutation in the M. xanthus rpoN have been unsuccessful. Partial diploids of rpoN + / rpoN null are viable. Recombination experiments with such partial diploids showed the impossibility of constructing, either by segregation or by transduction, a viable null haploid under any of a wide range of growth conditions. The product of the rpoN gene, σ 54 , therefore appears to be essential for growth in M. xanthus . transcription σ factor RNA polymerase promoter activator proteins

The EcoCyc database is an online scientific database which provides an integrated view of the metabolic and regulatory network of the bacterium Escherichia coli K-12 and facilitates computational exploration of this important model organism. We have analysed the occurrence of dead end metabolites within the database - these are metabolites which lack the requisite reactions (either metabolic or transport) that would account for their production or consumption within the metabolic network. 127 dead end metabolites were identified from the 995 compounds that are contained within the EcoCyc metabolic network. Their presence reflects either a deficit in our representation of the network or in our knowledge of E. coli metabolism. Extensive literature searches resulted in the addition of 38 transport reactions and 3 metabolic reactions to the database and led to an improved representation of the pathway for Vitamin B12 salvage. 39 dead end metabolites were identified as components of reactions that are not physiologically relevant to E. coli K-12 - these reactions are properties of purified enzymes in vitro that would not be expected to occur in vivo. Our analysis led to improvements in the software that underpins the database and to the program that finds dead end metabolites within EcoCyc. The remaining dead end metabolites in the EcoCyc database likely represent deficiencies in our knowledge of E. coli metabolism.

The rpoN gene encoding the transcription factor σ 54 in Myxococcus xanthus has been cloned using a heterologous rpoN probe. The sequence of the crosshybridizing DNA confirmed the existence of an ORF 1518 bp long that encodes a well conserved member of the σ 54 family of σ factors. Low- as well as high-stringency hybridizations detected only a single rpoN gene in the M. xanthus chromosome. In other bacteria, σ 54 is an alternative σ , and null mutants are viable. However, all attempts to construct a strain containing a null mutation in the M. xanthus rpoN have been unsuccessful. Partial diploids of rpoN+/rpoN null are viable. Recombination experiments with such partial diploids showed the impossibility of constructing, either by segregation or by transduction, a viable null haploid under any of a wide range of growth conditions. The product of the rpoN gene, σ 54, therefore appears to be essential for growth in M. xanthus.

The rpoN gene encoding the transcription factor sigma54 in Myxococcus xanthus has been cloned using a heterologous rpoN probe. The sequence of the cross-hybridizing DNA confirmed the existence of an ORF 1518 bp long that encodes a well conserved member of the sigma54 family of sigma factors. Low- as well as high-stringency hybridizations detected only a single rpoN gene in the M. xanthus chromosome. In other bacteria, sigma54 is an alternative sigma, and null mutants are viable. However, all attempts to construct a strain containing a null mutation in the M. xanthus rpoN have been unsuccessful. Partial diploids of rpoN+/rpoN null are viable. Recombination experiments with such partial diploids showed the impossibility of constructing, either by segregation or by transduction, a viable null haploid under any of a wide range of growth conditions. The product of the rpoN gene, sigma54, therefore appears to be essential for growth in M. xanthus.