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Arabidopsis metabolic network models We present a computational pipe-line for the reconstruction of genome-scale, sub-cellular compartmentalized metabolic network models for multiple Arabidopsis tissues, including leaves, flowers, roots, siliques, seeds, and cell cultures. The models are fully functional and amenable for constraint-based modeling analysis, provided in standard SBML format. [SBML models] Multi-level reconstruction of Arabidopsis metabolic network models: From subcellular compartmentalization to tissue-specificity.
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Computational Design of Microbial Biosensors We present a novel computational method to rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy. The designed biosensors facilitate high-throughput detection and quantification of chemicals of interest, enabling combinatorial metabolic engineering experiments aiming to over-produce them. [implementation] Computational design of microbial biosensors for combinatorial metabolic engineering experiments.
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Human Liver Metabolic Network Model MBA is an algorithm for the rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm starts from the generic human model and generates a reduced tissue-specific model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying this algorithm, we construct the first genome-scale stoichiometric model of hepatic metabolism. [Cytoscape Visualization] Computational Reconstruction of Tissue-specific Metabolic Models: Application to Human Liver Metabolism.
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IOMA (Integrative Omics Metabolic Analysis) IOMA quantitatively integrates proteomic and metabolomic data with genome-scale metabolic models to predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steady-state flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kinetically-derived flux estimations. [implementation] Integrating Quantitative Proteomics and Metabolomics with a Genome-scale Metabolic Network Model.
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MD-FBA (Metabolite Dilution Flux Balance Analysis) MD-FBA is a variant of FBA which aims to predict metabolic flux distributions by accounting for the dilution of all intermediate metabolites that are synthesized under a given condition. The method predicts feasible flux distributions maximizing the production rate of a predefined biomass while accounting for the dilution of all intermediate metabolites. [implementation] Flux balance analysis accounting for metabolite dilution
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RobustKnock RobustKnock is a constraint-based method that predicts gene deletion strategies that lead to the over-production of chemicals of interest, by accounting for the presence of competing pathways in the network. [implementation] Predicting Metabolic Engineering Knockout Strategies for Chemical Production: Accounting for Competing Pathways.
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iMAT (Integrative Metabolic Analysis Tool) A computational method for integrating transcriptomic and proteomic data with genome-scale metabolic network models to predict enzymes’ metabolic flux. [implementation] Network-based Prediction of Human Tissue-specific Metabolism.
iMAT: Integrative Metabolic Analysis Tool.
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SR-FBA (Steady-State Regulatory Flux Balance Analysis) SR-FBA is a method for predicting the steady-state metabolic and regulatory behaviors in large-scale integrated metabolic-regulatory models. [implementation] A Genome-Scale Computational Study of the Interplay between Transcriptional Regulation and Metabolism.
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ROOM (Regulatory On/Off Minimization) Regulatory onoff minimization (ROOM) is a constraint-based algorithm for predicting the metabolic steady state after gene knockouts. It aims to minimize the number of significant flux changes (hence onoff) with respect to the wild type. Regulatory On/Off Minimization Of Metabolic Flux Changes After Genetic Perturbations.
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Last updated at April, 2010