Web-based metabolic network visualization with a zooming user interface

<p>Abstract</p> <p>Background</p> <p>Displaying complex metabolic-map diagrams, for Web browsers, and allowing users to interact with them for querying and overlaying expression data over them is challenging.</p> <p>Description</p> <p>We present a Web-based metabolic-map diagram, which can be interactively explored by the user, called the <it>Cellular Overview</it>. The main characteristic of this application is the zooming user interface enabling the user to focus on appropriate granularities of the network at will. Various searching commands are available to visually highlight sets of reactions, pathways, enzymes, metabolites, and so on. Expression data from single or multiple experiments can be overlaid on the diagram, which we call the Omics Viewer capability. The application provides Web services to highlight the diagram and to invoke the <it>Omics Viewer</it>. This application is entirely written in JavaScript for the client browsers and connect to a Pathway Tools Web server to retrieve data and diagrams. It uses the OpenLayers library to display tiled diagrams.</p> <p>Conclusions</p> <p>This new online tool is capable of displaying large and complex metabolic-map diagrams in a very interactive manner. This application is available as part of the Pathway Tools software that powers multiple metabolic databases including <monospace>Biocyc.org</monospace>: The Cellular Overview is accessible under the <monospace>Tools</monospace> menu.</p

EcoCyc: fusing model organism databases with systems biology.

EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc

Construction and completion of flux balance models from pathway databases

Motivation: Flux balance analysis (FBA) is a well-known technique for genome-scale modeling of metabolic flux. Typically, an FBA formulation requires the accurate specification of four sets: biochemical reactions, biomass metabolites, nutrients and secreted metabolites. The development of FBA models can be time consuming and tedious because of the difficulty in assembling completely accurate descriptions of these sets, and in identifying errors in the composition of these sets. For example, the presence of a single non-producible metabolite in the biomass will make the entire model infeasible. Other difficulties in FBA modeling are that model distributions, and predicted fluxes, can be cryptic and difficult to understand

Multidimensional annotation of the Escherichia coli K-12 genome

The annotation of the Escherichia coli K-12 genome in the EcoCyc database is one of the most accurate, complete and multidimensional genome annotations. Of the 4460 E. coli genes, EcoCyc assigns biochemical functions to 76%, and 66% of all genes had their functions determined experimentally. EcoCyc assigns E. coli genes to Gene Ontology and to MultiFun. Seventy-five percent of gene products contain reviews authored by the EcoCyc project that summarize the experimental literature about the gene product. EcoCyc information was derived from 15 000 publications. The database contains extensive descriptions of E. coli cellular networks, describing its metabolic, transport and transcriptional regulatory processes. A comparison to genome annotations for other model organisms shows that the E. coli genome contains the most experimentally determined gene functions in both relative and absolute terms: 2941 (66%) for E. coli, 2319 (37%) for Saccharomyces cerevisiae, 1816 (5%) for Arabidopsis thaliana, 1456 (4%) for Mus musculus and 614 (4%) for Drosophila melanogaster. Database queries to EcoCyc survey the global properties of E. coli cellular networks and illuminate the extent of information gaps for E. coli, such as dead-end metabolites. EcoCyc provides a genome browser with novel properties, and a novel interactive display of transcriptional regulatory networks

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism

Fast Symbolic Evaluation of C/C++ Preprocessing Using Conditional Values

C/C++ code relying on preprocessing can be quite complex to analyze. This is often due to free preprocessing variables set at compile time. In general, preprocessing selectively compile parts of the source code based on the values of preprocessing variables which may be free. In this case, the relations between these parts can only be represented by conditional expressions using the free variables. Traditional symbolic evaluation can be used to infer these expressions, but its best case time complexity is exponential. We present a new approach for symbolic evaluation that can efficiently compute these conditions by binding variables to conditional values and avoiding the path feasibility analysis of traditional symbolic evaluation. It infers the exact conditional expressions for which the lines of code are compiled and the (conditional) values of preprocessing variables at each point of the source code. Our prototype shows the approach as practical and scaleable to large C/C++ software

Northrop Grumman IT Technology Advancement Group/FNMOC/U.S. Navy

In reverse engineering, parsing may be partially done to extract lightweight source models. Parsing code containing preprocessing directives, syntactical errors and embedded languages is a difficult task using context-free grammars. Several researchers have proposed some form of lexical analyzer to parse such code. We present a lightweight tool, called RegReg, based on a hierarchy of lexers described by tagged regular expressions. By using tags, the automatically generated parse tree can be easily manipulated. The ability to control the matching rulemechanismforeachregularexpressionincreasesefficiencyand disambiguation choices. RegRegis lightweight asitusesaminimalnumberof featuresandits implementation usesonly deterministic automaton.It has been implemented in Scheme which allows extending the tool in a functional programming style. We demonstrate how RegReg can be used to implement island and fuzzy parsing. RegReg is publicly available under a BSD-like license. 1