Visualization Of Metabolic Networks Research Articles

ChiMera: an easy to use pipeline for bacterial genome based metabolic network reconstruction, evaluation and visualization

BackgroundGenome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct eukaryotic and prokaryotic metabolic models, which have contributed to fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills. Moreover, the functionalities required to build models are scattered throughout multiple tools, requiring knowledge and experience for utilizing several tools.ResultsHere we present ChiMera, which combines tools used for model reconstruction, prediction, and visualization. ChiMera uses CarveMe in the reconstruction module, generating a gap-filled draft reconstruction able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., glycolysis, nucleotides and amino acids biosynthesis, fatty acid oxidation and biosynthesis and core-metabolism. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present.ConclusionsOverall, ChiMera uses automation algorithms to combine a variety of tools to automatically perform model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization. ChiMera models readily provide metabolic insights that can aid genetic engineering projects, prediction of phenotypes, and model-driven discoveries.

The BioCyc Metabolic Network Explorer

BackgroundThe Metabolic Network Explorer is a new addition to the BioCyc.org website and the Pathway Tools software suite that supports the interactive exploration of metabolic networks. Any metabolic network visualization tool must by necessity show only a subset of all possible metabolite connections, or the results will be visually overwhelming. Existing tools, even those that purport to show an organism’s full metabolic network, limit the set of displayed connections based on predefined pathways or other preselected criteria. We sought instead to provide a tool that would give the user dynamic control over which connections to follow.ResultsThe Metabolic Network Explorer is an easy-to-use, web-based software tool that allows the user to specify a starting metabolite of interest and interactively explore its immediate metabolic neighborhood in either or both directions to any desired depth, letting the user select from the full set of connected reactions. Although, as for other tools, only a small portion of the metabolic network is visible at a time, that portion is selected by the user, based on the full reaction complement, and it is easy to switch among alternate paths of interest. The display is intuitive, customizable, and provides copious links to more detailed information pages.ConclusionsThe Metabolic Network Explorer fills a gap in the set of metabolic network visualization tools and complements other modes of exploration. Its primary strengths are its ease of use, diagrams that are intuitive to biologists, and its integration with the broader corpus of data provided by a BioCyc Pathway/Genome Database.

Metabolic Atlas – a website for exploration and visualization of metabolic networks

Metabolic Atlas – a website for exploration and visualization of metabolic networks

SAMMI: a semi-automated tool for the visualization of metabolic networks.

Here we present a browser-based Semi-Automated Metabolic Map Illustrator (SAMMI) for the visualization of metabolic networks. While automated features allow for easy network partitioning, navigation, and node positioning, SAMMI also offers a wide array of manual map editing features. This combination allows for fast, context-specific visualization of metabolic networks as well as the development of standardized, large-scale, visually appealing maps. The implementation of SAMMI with popular constraint-based modeling toolboxes also allows for effortless visualization of simulation results of genome-scale metabolic models. SAMMI has been implemented as a standalone web-based tool and as plug-ins for the COBRA and COBRApy toolboxes. SAMMI and its COBRA plugins are available under the GPL 3.0 license and are available along with documentation, tutorials, and source code at www.SammiTool.com. Supplementary data are available at Bioinformatics online.

ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions

BackgroundGenome-scale metabolic network reconstructions are low level chemical representations of biological organisms. These models allow the system-level investigation of metabolic phenotypes using a variety of computational approaches. The link between a metabolic network model and an organisms’ higher-level behaviour is usually found using a constraint-based analysis approach, such as FBA (Flux Balance Analysis). However, the process of model reconstruction rarely proceeds without error. Often, considerable parts of a model cannot carry flux under any condition. This is termed model inconsistency and is caused by faulty topology and/or stoichiometry of the underlying reconstructed network. While there exist several automated gap-filling tools that may solve some of the inconsistencies, much of the work still needs to be carried out manually. The common “linear list” format of writing biochemical reactions makes it difficult to intuit what is at the root of the inconsistent behaviour. Unfortunately, we have frequently observed that model builders do not correct their models past the abilities of automated tools, leaving many widely used models significantly inconsistent.ResultsWe have developed the software ModelExplorer, which main purpose is to fill this gap by providing an intuitive and visual framework that allows the user to explore and correct inconsistencies in genome-scale metabolic models. The software will automatically visualize metabolic networks as graphs with distinct separation and delineation of cellular compartments. ModelExplorer highlights reactions and species that are unable to carry flux (blocked), with several different consistency checking modes available. Our software also allows the automatic identification of neighbours and production pathways of any species or reaction. Additionally, the user may focus on any chosen inconsistent part of the model on its own. This facilitates a rapid and visual identification of reactions and species responsible for model inconsistencies. Finally, ModelExplorer lets the user freely edit, add or delete model elements, allowing straight-forward correction of discovered issues.ConclusionOverall, ModelExplorer is currently the fastest real-time metabolic network visualization program available. It implements several consistency checking algorithms, which in combination with its set of tracking tools, gives an efficient and systematic model-correction process.

Analyzing Metabolic Pathways in Microbiomes.

Understanding the metabolic activity of a microbial community, at both the level of the individual microbe and the whole microbiome, provides fundamental biological, biochemical, and clinical insights into the nature of the microbial community and interactions with their hosts in health and disease. Here, we discuss a method to examine the expression of metabolic pathways in microbial communities using data from metatranscriptomic next-generation sequencing data. The methodology described here encompasses enzyme function annotation, differential enzyme expression and pathway enrichment analyses, and visualization of metabolic networks with differential enzyme expression levels.

MetExploreViz: web component for interactive metabolic network visualization.

SummaryMetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context.Availability and implementationDocumentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/

Knowledge integration strategies for untargeted metabolomics based on MCR-ALS analysis of CE-MS and LC-MS data

In this work, two knowledge integration strategies based on multivariate curve resolution alternating least squares (MCR-ALS) were used for the simultaneous analysis of data from two metabolomic platforms. The benefits and the suitability of these integration strategies were demonstrated in a comparative study of the metabolite profiles from yeast (Saccharomyces cerevisiae) samples grown in non-fermentable (acetate) and fermentable (glucose) carbon source. Untargeted metabolomics data acquired by capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS) were jointly analysed. On the one hand, features obtained by independent MCR-ALS analysis of each dataset were joined to obtain a biological interpretation based on the combined metabolic network visualization. On the other hand, taking advantage of the common spectral mode, a low-level data fusion strategy was proposed merging CE-MS and LC-MS data before the MCR-ALS analysis to extract the most relevant features for further biological interpretation. Then, results obtained by the two presented methods were compared. Overall, the study highlights the ability of MCR-ALS to be used in any of both knowledge integration strategies for untargeted metabolomics. Furthermore, enhanced metabolite identification and differential carbon source response detection were achieved when considering a combination of LC-MS and CE-MS based platforms.

MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data.

The widespread application of next-generation sequencing technologies has revolutionized microbiome research by enabling high-throughput profiling of the genetic contents of microbial communities. How to analyze the resulting large complex datasets remains a key challenge in current microbiome studies. Over the past decade, powerful computational pipelines and robust protocols have been established to enable efficient raw data processing and annotation. The focus has shifted toward downstream statistical analysis and functional interpretation. Here, we introduce MicrobiomeAnalyst, a user-friendly tool that integrates recent progress in statistics and visualization techniques, coupled with novel knowledge bases, to enable comprehensive analysis of common data outputs produced from microbiome studies. MicrobiomeAnalyst contains four modules - the Marker Data Profiling module offers various options for community profiling, comparative analysis and functional prediction based on 16S rRNA marker gene data; the Shotgun Data Profiling module supports exploratory data analysis, functional profiling and metabolic network visualization of shotgun metagenomics or metatranscriptomics data; the Taxon Set Enrichment Analysis module helps interpret taxonomic signatures via enrichment analysis against >300 taxon sets manually curated from literature and public databases; finally, the Projection with Public Data module allows users to visually explore their data with a public reference data for pattern discovery and biological insights. MicrobiomeAnalyst is freely available at http://www.microbiomeanalyst.ca.

Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants.

Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions.

An integrated network visualization framework towards metabolic engineering applications.

BackgroundOver the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential.ResultsIn this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks.ConclusionsThe framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0420-0) contains supplementary material, which is available to authorized users.

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production.

Web-based metabolic network visualization with a zooming user interface

BackgroundDisplaying complex metabolic-map diagrams, for Web browsers, and allowing users to interact with them for querying and overlaying expression data over them is challenging.DescriptionWe present a Web-based metabolic-map diagram, which can be interactively explored by the user, called the Cellular Overview. 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 Omics Viewer. 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.ConclusionsThis 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 Biocyc.org: The Cellular Overview is accessible under the Tools menu.

Comparative Pathway Analyzer—a web server for comparative analysis, clustering and visualization of metabolic networks in multiple organisms

In order to understand the phenotype of any living system, it is essential to not only investigate its genes, but also the specific metabolic pathway variant of the organism of interest, ideally in comparison with other organisms. The Comparative Pathway Analyzer, CPA, calculates and displays the differences in metabolic reaction content between two sets of organisms. Because results are highly dependent on the distribution of organisms into these two sets and the appropriate definition of these sets often is not easy, we provide hierarchical clustering methods for the identification of significant groupings. CPA also visualizes the reaction content of several organisms simultaneously allowing easy comparison. Reaction annotation data and maps for visualizing the results are taken from the KEGG database. Additionally, users can upload their own annotation data. This website is free and open to all users and there is no login requirement. It is available at https://www.cebitec.uni-bielefeld.de/groups/brf/software/cpa/index.html.

Metabolic network visualization eliminating node redundance and preserving metabolic pathways.

BackgroundThe tools that are available to draw and to manipulate the representations of metabolism are usually restricted to metabolic pathways. This limitation becomes problematic when studying processes that span several pathways. The various attempts that have been made to draw genome-scale metabolic networks are confronted with two shortcomings: 1- they do not use contextual information which leads to dense, hard to interpret drawings, 2- they impose to fit to very constrained standards, which implies, in particular, duplicating nodes making topological analysis considerably more difficult.ResultsWe propose a method, called MetaViz, which enables to draw a genome-scale metabolic network and that also takes into account its structuration into pathways. This method consists in two steps: a clustering step which addresses the pathway overlapping problem and a drawing step which consists in drawing the clustered graph and each cluster.ConclusionThe method we propose is original and addresses new drawing issues arising from the no-duplication constraint. We do not propose a single drawing but rather several alternative ways of presenting metabolism depending on the pathway on which one wishes to focus. We believe that this provides a valuable tool to explore the pathway structure of metabolism.