Offers a framework for prediction of cellular behavior. COBRA Toolbox gathers numerous algorithms intending to be applied to any biochemical system with prior mechanistic information, including those with incomplete one. The application proposes more than 30 functions divided into five sections: (i) analysis, that includes features for coupling or deletion; (ii) data integration; (iii) design; (iv) reconstruction and (v) base that includes solvers and utilities.
An optimization procedure that proceeds to fill gaps identified by GapFind by making the minimal number of modifications to the metabolic model. GapFill attempts to correct them using one or more of the following strategies: adding reactions from external multi-organism databases such as KEGG or MetaCyc to the model; allowing for reactions to also operate in their reverse directions, and adding transport reactions (both intra and extracellular in the case of multi-compartment models).
An optimization-based framework for reconciling in silico/in vivo growth predictions. GrowMatch reconciles GNG predictions (by suppressing functionalities in the model) and NGG predictions (by adding functionalities to the model). We use GrowMatch to resolve inconsistencies between the predictions of the latest in silico Escherichia coli (iAF1260) model and the in vivo data available in the Keio collection and improved the consistency of in silico with in vivo predictions from 90.6% to 96.7%.
Allows users to proceed topological gap-filling of genome-scale draft metabolic networks. Meneco uses an Answer Set Programming (ASP) to considerer gap-filling as a qualitative combinatorial optimization problem. The software is a flexible tool for hypothesis generation for a large scale of questions and types of data.
A scalable algorithm capable of detecting and filling network gaps in compartmentalized genome-scale models. fastGapFill draws on, and extends, fastcore, an algorithm to approximate the cardinality function to identify a compact flux consistent model, in which all reactions carry a non-zero flux in at least one flux distribution. fastGapFill allows integrating all three notions of model consistency, namely, gap-filling, flux consistency and stoichiometric consistency in a single tool.
Identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. To reconstruct a metabolic network model for an organism of interest, MIRAGE starts from a core set of reactions, whose presence is established via strong genomic evidence, and identifies missing reactions from other species that are required to activate the latter core reactions, whose presence is further supported by phylogenetic and gene expression data.
Determines the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. OMNI allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.