Eco Cyc: encyclopedia of Escherichia coli genes and metabolism.

The EcoCyc database describes the genome and gene products of Escherichia coli, its metabolic and signal-transduction pathways, and its tRNAs. The database describes 4391 genes of E.coli, 695 enzymes encoded by a subset of these genes, 904 metabolic reactions that occur in E.coli, and the organization of these reactions into 129 metabolic pathways. The EcoCyc graphical user interface allows scientists to query and explore the EcoCyc database using visualization tools such as genomic-map browsers and automatic layouts of metabolic pathways. EcoCyc has many references to the primary literature, and is a (qualitative) computational model of E. coli metabolism. EcoCyc is available at URL http://ecocyc. PangeaSystems.com/ecocyc/     

EcoCyc: an encyclopedia of Escherichia coli genes and metabolism.

The encyclopedia of Escherichia coli genes and metabolism (EcoCyc) is a database that combines information about the genome and the intermediary metabolism of E.coli. It describes 2034 genes, 306 enzymes encoded by these genes, 580 metabolic reactions that occur in E.coli and the organization of these reactions into 100 metabolic pathways. The EcoCyc graphical user interface allows query and exploration of the EcoCyc database using visualization tools such as genomic map browsers and automatic layouts of metabolic pathways. EcoCyc spans the space from sequence to function to allow investigation of an unusually broad range of questions. EcoCyc can be thought of as both an electronic review article, because of its copious references to the primary literature, and as an in silico model of E.coli that can be probed and analyzed through computational means.     

EcoCyc: Enyclopedia of Escherichia coli Genes and Metabolism.

The Encyclopedia of Genes and Metabolism (EcoCyc) is a database that combines information about the genome and the intermediary metabolism of Escherichia coli. It describes 2970 genes of E.coli, 547 enzymes encoded by these genes, 702 metabolic reactions that occur in E.coli and the organization of these reactions into 107 metabolic pathways. The EcoCyc graphical user interface allows scientists to query and explore the EcoCyc database using visualization tools such as genomic-map browsers and automatic layouts of metabolic pathways. EcoCyc spans the space from sequence to function to allow scientists to investigate an unusually broad range of questions. EcoCyc can be thought of as both an electronic review article because of its copious references to the primary literature, and as an in silicio model of E.coli metabolism that can be probed and analyzed through computational means.     

The EcoCyc and MetaCyc databases

EcoCyc is an organism-specific Pathway/Genome Database that describes the metabolic and signal-transduction pathways of Escherichia coli, its enzymes, and-a new addition-its transport proteins. MetaCyc is a new metabolic-pathway database that describes pathways and enzymes of many different organisms, with a microbial focus. Both databases are queried using the Pathway Tools graphical user interface, which provides a wide variety of query operations and visualization tools. EcoCyc and MetaCyc are available at http://ecocyc.PangeaSystems.com/ecocyc/     

Adapting EcoCyc for use on the World Wide Web

The World Wide Web (WWW) offers the potential to deliver specialized information to an audience of unprecedented size. Along with this exciting new opportunity comes a challenge for software developers: instead of rewriting our software applications to operate over the WWW, how can we maximize software reuse by retrofitting existing applications? We have developed a Web server tool. written in Common Lisp, that allows existing graphical user interface applications written using the Common Lisp Interface Manager (CLIM) to hook easily into the WWW. This tool — CWEST (CLIM-WEb Server Tool, pronounced “quest”) — was developed to operate with EcoCyc, an electronic encylopedia of the genes and metabolism of the bacterium E. coli. EcoCyc consists of a database of objects relevant to E. coli biochemistry and a user interface, implemented in CLIM, that runs on the X-window system and generates graphical displays appropriate to biological objects. Each query to the EcoCyc WWW server is treated as a command to the EcoCyc program, which dynamically generates an appropriate CLIM drawing. CWEST translates that drawing, which can be a mixture of text and graphics, into the HyperText Markup Language (HTML) and/or the Graphics Interchange Format (GIF), which are returned to the client. Sensitive regions embedded in the CLIM drawing are converted to hyperlinks with Universal Resource Locators (URLs) that generate further EcoCyc queries. This tight coupling of CLIM output with Web output makes CLIM a powerful high-level programming tool for Web applications. The flexibility of Common Lisp and CLIM made implementation of the server tool surprisingly easy, requiring few changes to the existing EcoCyc program. The results can be seen at URL http://www.ai.sri.com/ecocyc/browser.html. We have made CWEST available to the CLIM community at large, with the hope that it will spur other software developers to make their CLIM applications available over the WWW.     

Evaluation of computational metabolic-pathway predictions for Helicobacter pylori

MOTIVATION: We seek to determine the accuracy of computational methods for predicting metabolic pathways in sequenced genomes, and to understand the contributions of both the prediction algorithms, and the reference pathway databases used by those algorithms, to the prediction accuracy. RESULTS: The comparisons we performed were as follows. (1) We compared two predictions of the pathway complements of Helicobacter pylori that were computed by an early version of our pathway-prediction algorithm: prediction A used the EcoCyc E. coli pathway DB as the reference database (DB) for prediction, and prediction B used the MetaCyc pathway DB (a superset of EcoCyc) as the reference pathway DB. The MetaCyc-based prediction contained 75% more pathway predictions, but we believe a significant number of those predictions were false positives. (2) We compared two predictions of the pathway complement of H. pylori that used MetaCyc as the reference pathway DB, but that used different algorithms: the original PathoLogic algorithm, and an enhanced version of the algorithm designed to eliminate false-positive pathway predictions. The improved algorithm predicted 30\% fewer metabolic pathways than the original algorithm; all of the eliminated pathways are believed to be false-positive predictions. (3) We compared the 98 pathways predicted by the enhanced algorithm with the results of a manual analysis of the pathways of H. pylori. Results: 40 of the computationally predicted pathways were consistent with the manual analysis, 13 pathways are considered false-positive predictions, and four pathways had partially overlapping topologies. Twenty-six predicted pathways were not mentioned in the manual analysis; we believe these are correct predictions by PathoLogic that were not found by the manual analysis. Five pathways from the manual analysis were not found computationally. Agreement between the computational and manual predictions was good overall, with the computational analysis inferring many pathways that the manual analysis did not identify. Ultimately the manual analysis is also partially speculative, and therefore is not an absolute measure of correctness. The algorithm is designed to err on the side of more false positives to bring more potential pathways to the user's attention. The resulting H. pylori pathway DB is freely available at http://ecocyc.org:1555/HPY/organism-summary?object=HPY. AVAILABILITY: The Pathway Tools software is freely available to academic users, and is available to commercial users for a fee. Contact pkarp@ai.sri.com for information on obtaining the software.     

Addition of Escherichia coli K-12 Growth Observation and Gene Essentiality Data to the EcoCyc Database

The sets of compounds that can support growth of an organism are defined by the presence of transporters and metabolic pathways that convert nutrient sources into cellular components and energy for growth. A collection of known nutrient sources can therefore serve both as an impetus for investigating new metabolic pathways and transporters and as a reference for computational modeling of known metabolic pathways. To establish such a collection for Escherichia coli K-12, we have integrated data on the growth or nongrowth of E. coli K-12 obtained from published observations using a variety of individual media and from high-throughput phenotype microarrays into the EcoCyc database. The assembled collection revealed a substantial number of discrepancies between the high-throughput data sets, which we investigated where possible using low-throughput growth assays on soft agar and in liquid culture. We also integrated six data sets describing 16,119 observations of the growth of single-gene knockout mutants of E. coli K-12 into EcoCyc, which are relevant to antimicrobial drug design, provide clues regarding the roles of genes of unknown function, and are useful for validating metabolic models. To make this information easily accessible to EcoCyc users, we developed software for capturing, querying, and visualizing cellular growth assays and gene essentiality data.     

AraCyc: a biochemical pathway database for Arabidopsis

AraCyc is a database containing biochemical pathways of Arabidopsis, developed at The Arabidopsis Information Resource (http://www.arabidopsis.org). The aim of AraCyc is to represent Arabidopsis metabolism as completely as possible with a user-friendly Web-based interface. It presently features more than 170 pathways that include information on compounds, intermediates, cofactors, reactions, genes, proteins, and protein subcellular locations. The database uses Pathway Tools software, which allows the users to visualize a bird's eye view of all pathways in the database down to the individual chemical structures of the compounds. The database was built using Pathway Tools' Pathologic module with MetaCyc, a collection of pathways from more than 150 species, as a reference database. This initial build was manually refined and annotated. More than 20 plant-specific pathways, including carotenoid, brassinosteroid, and gibberellin biosyntheses have been added from the literature. A list of more than 40 plant pathways will be added in the coming months. The quality of the initial, automatic build of the database was compared with the manually improved version, and with EcoCyc, an Escherichia coli database using the same software system that has been manually annotated for many years. In addition, a Perl interface, PerlCyc, was developed that allows programmers to access Pathway Tools databases from the popular Perl language. AraCyc is available at the tools section of The Arabidopsis Information Resource Web site (http://www.arabidopsis.org/tools/aracyc).     

The Pathway Tools software

MOTIVATION: Bioinformatics requires reusable software tools for creating model-organism databases (MODs). RESULTS: The Pathway Tools is a reusable, production-quality software environment for creating a type of MOD called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc (see http://ecocyc.org) integrates our evolving understanding of the genes, proteins, metabolic network, and genetic network of an organism. This paper provides an overview of the four main components of the Pathway Tools: The PathoLogic component supports creation of new PGDBs from the annotated genome of an organism. The Pathway/Genome Navigator provides query, visualization, and Web-publishing services for PGDBs. The Pathway/Genome Editors support interactive updating of PGDBs. The Pathway Tools ontology defines the schema of PGDBs. The Pathway Tools makes use of the Ocelot object database system for data management services for PGDBs. The Pathway Tools has been used to build PGDBs for 13 organisms within SRI and by external users.     

BioSilico: an integrated metabolic database system

BioSilico is a web-based database system that facilitates the search and analysis of metabolic pathways. Heterogeneous metabolic databases including LIGAND, ENZYME, EcoCyc and MetaCyc are integrated in a systematic way, thereby allowing users to efficiently retrieve the relevant information on enzymes, biochemical compounds and reactions. In addition, it provides well-designed view pages for more detailed summary information. BioSilico is developed as an extensible system with a robust systematic architecture.     

The MetaCyc Database

MetaCyc is a metabolic-pathway database that describes 445 pathways and 1115 enzymes occurring in 158 organisms. MetaCyc is a review-level database in that a given entry in MetaCyc often integrates information from multiple literature sources. The pathways in MetaCyc were determined experimentally, and are labeled with the species in which they are known to occur based on literature references examined to date. MetaCyc contains extensive commentary and literature citations. Applications of MetaCyc include pathway analysis of genomes, metabolic engineering and biochemistry education. MetaCyc is queried using the Pathway Tools graphical user interface, which provides a wide variety of query operations and visualization tools. MetaCyc is available via the World Wide Web at http://ecocyc.org/ecocyc/metacyc.html, and is available for local installation as a binary program for the PC and the Sun workstation, and as a set of flatfiles. Contact metacyc-info@ai.sri.com for information on obtaining a local copy of MetaCyc.     

Elucidating and reprogramming Escherichia coli metabolisms for obligate anaerobic n-butanol and isobutanol production

Elementary mode (EM) analysis based on the constraint-based metabolic network modeling was applied to elucidate and compare complex fermentative metabolisms of Escherichia coli for obligate anaerobic production of n-butanol and isobutanol. The result shows that the n-butanol fermentative metabolism was NADH-deficient, while the isobutanol fermentative metabolism was NADH redundant. E. coli could grow and produce n-butanol anaerobically as the sole fermentative product but not achieve the maximum theoretical n-butanol yield. In contrast, for the isobutanol fermentative metabolism, E. coli was required to couple with either ethanol- or succinate-producing pathway to recycle NADH. To overcome these "defective" metabolisms, EM analysis was implemented to reprogram the native fermentative metabolism of E. coli for optimized anaerobic production of n-butanol and isobutanol through multiple gene deletion (~8-9 genes), addition (~6-7 genes), up- and downexpression (~6-7 genes), and cofactor engineering (e.g., NADH, NADPH). The designed strains were forced to couple both growth and anaerobic production of n-butanol and isobutanol, which is a useful characteristic to enhance biofuel production and tolerance through metabolic pathway evolution. Even though the n-butanol and isobutanol fermentative metabolisms were quite different, the designed strains could be engineered to have identical metabolic flux distribution in "core" metabolic pathways mainly supporting cell growth and maintenance. Finally, the model prediction in elucidating and reprogramming the native fermentative metabolism of E. coli for obligate anaerobic production of n-butanol and isobutanol was validated with published experimental data.     

Hierarchical analysis of dependency in metabolic networks

MOTIVATION: Elucidation of metabolic networks for an increasing number of organisms reveals that even small networks can contain thousands of reactions and chemical species. The intimate connectivity between components complicates their decomposition into biologically meaningful sub-networks. Moreover, traditional higher-order representations of metabolic networks as metabolic pathways, suffers from the lack of rigorous definition, yielding pathways of disparate content and size. RESULTS: We introduce a hierarchical representation that emphasizes the gross organization of metabolic networks in largely independent pathways and sub-systems at several levels of independence. The approach highlights the coupling of different pathways and the shared compounds responsible for those couplings. By assessing our results on Escherichia coli (E.coli metabolic reactions, Genetic Circuits Research Group, University of California, San Diego, http://gcrg.ucsd.edu/organisms/ecoli.html, 'model v 1.01. reactions') against accepted biochemical annotations, we provide the first systematic synopsis of an organism's metabolism. Comparison with operons of E.coli shows that low-level clusters are reflected in genome organization and gene regulation. AVAILABILITY: Source code, data sets and supplementary information are available at http://www.mas.ecp.fr/labo/equipe/gagneur/hierarchy/hierarchy.html     

Why metabolic enzymes are essential or nonessential for growth of Escherichia coli K12 on glucose

The genes encoding metabolic enzymes involved in glucose metabolism, the TCA cycle, and biosynthesis of amino acids, purines, pyrimidines, and cofactors would be expected to be essential for growth of Escherichia coli on glucose because the cells must synthesize all of the building blocks for cellular macromolecules. Surprisingly, 80 of 227 of these genes are not essential. Analysis of why these genes are not essential provides insights into the metabolic sophistication of E. coli and into the evolutionary pressures that have shaped its physiology. Alternative routes enabled by interconnecting pathways can allow a defective step to be bypassed. Isozymes, alternative enzymes, broad-specificity enzymes, and multifunctional enzymes can often substitute for a missing enzyme. We expect that the apparent redundancy in these metabolic pathways has arisen due to the need for E. coli to survive in a variety of habitats and therefore to have a metabolism that allows optimal exploitation of varying environmental resources and synthesis of small molecules when they cannot be obtained from the environment.     

Evolution of biosynthetic pathways: a common ancestor for threonine synthase, threonine dehydratase and D-serine dehydratase.

The Bacillus subtilis genes encoding threonine synthase (thrC) and homoserine kinase (thrB) have been cloned via complementation of Escherichia coli thr mutants. Determination of their nucleotide sequences indicates that the thrC stop codon overlaps the thrB start codon; this genetic organization suggests that the two genes belong to the same operon, as in E. coli. However, the gene order is thrC-thrB in B. subtilis whereas it is thrB-thrC in the thr operon of E. coli. This inversion of the thrC and thrB genes between E. coli and B. subtilis is indicative of a possible independent construction of the thr operon in these two organisms. In other respects, comparison of the predicted amino acid sequences of the B. subtilis and E. coli threonine synthases with that of Saccharomyces cerevisiae threonine dehydratase and that of E. coli D-serine dehydratase revealed extensive homologies between these pyridoxal phosphate-dependent enzymes. This sequence homology, which correlates with similarities in the catalytic mechanisms of these enzymes, indicates that these proteins, catalyzing different reactions in different metabolic pathways, may have evolved from a common ancestor.     

Evaluating microarrays using a semiparametric approach: application to the central carbon metabolism of Escherichia coli BL21 and JM109

Escherichia coli K (JM109) and E. coli B (BL21) are strains used routinely for recombinant protein production. These two strains grow and respond differently to environmental factors such as glucose and oxygen concentration. The differences have been attributed to differential expression of individual genes that constitute certain metabolic pathways that are part of the central carbon metabolism. By implementing a semiparametric algorithm, which is based on a density ratio model, it was possible to compare and quantify the expression patterns of groups of genes involved in several central carbon metabolic pathways. The groups comprising the glyoxylate shunt, TCA cycle, fatty acid, and gluconeogenesis and anaplerotic pathways were expressed differently between the two strains, whereas no differences were apparent for the groups comprising either glycolysis or the pentose phosphate pathway. These results further characterized differences between the two E. coli strains and illustrated the potency of the semiparametric algorithm.