1 - 29 of 29 results

Pathway Tools

Allows management, analysis, simulation and visualization of integrated collections of genome, pathway and regulatory data. Pathway Tools is a bioinformatics software environment around a type of model-organism database called Pathway/Genome Database (PGDB). The software can manipulate genome data, metabolic networks and regulatory networks. For each datatype, it provides query, visualization, editing and analysis functions. It also provides visual tools for analysis of omics data sets, and tools for the analysis of biological networks.


A comprehensive curated map of human metabolism presented utilizing the Google Maps application program interface (API) for highly responsive interactive navigation within a platform that facilitates queries and custom data visualization. Omics data and flux distributions resulting from simulations can be visualized in in a network context via an extension to the COBRA Toolbox. All users may post suggestions for refinement. Each suggestion is forwarded to virtual metabolic human (VMH) curators for consideration when planning further curation effort.

RAVEN Toolbox / Reconstruction Analysis and Visualization of Metabolic Networks

Allows users to reconstruct, analyze, simulate and visualize genome-scale metabolic models (GEMs). The RAVEN Toolbox permits users to input GEM(s) for one or more template organisms, their corresponding protein sequences, and protein sequences of the target organism. A GEM for target organism is then constructed based on orthology between the protein sequences of target organism and the organisms of template models. This tool also contains a functionality that matches proteins to KEGG Orthology (KO) categories.


Facilitates exploration of highly-connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. Recon2Neo4j provides two major components: i) a graph database for the human metabolism data and ii) a parser for translating the JSON representation into the SBML and SIF formats. Recon2Neo4j has been designed to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. A powerful feature of the Recon2Neo4j is facilitating querying and exploration of integrated metabolic data, which adds to the functionality provided by other systems biology software, such as e.g. cySBML.


Reconstructs, curates and validates genome-scale metabolic models. Pantograph is a template base method and a toolbox developed to help users with reconstruction model. This software exploits existing well-curated metabolic models and carefully rewrites complex gene associations. Finally, it doesn’t require a good-quality annotation for the target organism in order to build a metabolic model. The orthology between template and target genomes is enough to produce a draft model.


Combines ModelSEED’s ability to automatically reconstruct metabolic models with COBRApy’s advanced analysis capabilities to bridge the differences between the two frameworks and to facilitate the study of the metabolic potential of microorganisms. Mackinac allows the comprehensive storage of all the information associated with the models in the COBRA model object, and provides direct access to many of the functions available from this web service, such as functions to reconstruct, gap fill, and optimize genome-scale metabolic models (GEMs).

ASBIG / Autocatalytic Set-Based Identification of Gaps

Detects incomplete parts of network reconstructions based on the following approach: identifying elements (compounds) of catalytic cycles. ASBIG recognizes inconsistencies in existing genome-scale metabolic network reconstructions. This method facilitates network validation and automated gap detection in primary metabolism contributing considerably to the quality improvement of metabolic models. This tool combines conceptual approaches of autocatalytic metabolites and scope analysis.

IdentiCS / Identification of Coding Sequences from Unfinished Genome Sequences

Associates the identification of coding data sequences (CDSs) with reconstruction, comparison and visualization of metabolic networks. IdentiCS permits users to realize reconstruction of the potential metabolic network from low coverage genome sequences of bacteria and prediction protein coding sequences. This method provides a solution to accelerate the utilization of genomic data for studying cellular metabolism.