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MIRAGE / MetabolIc Reconstruction via functionAl GEnomics
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.
COBRA Toolbox / COnstraints Based Reconstruction and Analysis
A software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules.
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%.
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.
DEF / Dead-End Filler
A webserver aimed at aiding the gap filling step by finding the most biological meaningful candidate reactions in the metabolic network reconstruction. DEF constructs an endosymbiosis-resembling model to maximize the flux of certain dead-end metabolites deriving the wisdom from evolution of metabolic systems, thus to get the most probable candidate reactions. This method can find indirectly dead-end-related reactions with biological importance for the target organism and is applicable to any given metabolic model.
OMNI / Optimal Metabolic Network Identification
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.
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).
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