CBIT: Constraintbased isotope tracing ConstraintBased Isotope Tracing (CBIT) is a method for efficient inference of metabolic flux directly from isotope tracing data, overcoming major limitations of the current MFA methodology. The method is based on local inference of bounds on relative fluxes through converging reactions and bounds on the abundance of isotopomers (i.e. distinct labeling patterns of metabolites), and the propagation of these bounds throughout the network. The running time of CBIT is about two orders of magnitude faster than that of stateoftheart MFA methods. We demonstrate the ability of CBIT to perform optimal design of isotope tracing experiments, involving numerous repeated flux estimations. [implementation] Constraintbased Isotope Tracing (CBIT): Inferring flux constraints from isotopic tracing data

Tandemers (Metabolic Flux Analysis via MS/MS data)
MS/MS enables the measurement of a metabolite tandem massisotopomer distribution, representing the abundance in which certain parent and product fragments of a metabolite have different number of labeled atoms. The tandemer approach efficiently computes metabolite tandem massisotopomer distributions in a metabolic network, given some estimation of metabolic fluxes, facilitating efficient usage of MS/MS data for metabolic flux analysis. [implementation]
Metabolic Flux Analysis via MS/MS data: An efficient method for modeling tandem massisotopomer distributions
MFA/UF (Metabolic Flux Analysis/Unknown Fragments)
We present a novel method, MFA/UF (Metabolic Flux Analysis/Unknown Fragments), that can utilize tandemMS data for flux inference even when the positional origin of collisional fragments is unknown. This is demonstrated by extending the current MFA framework to jointly search for the most likely metabolic flux rates together with the most plausible position of collisional fragments that would optimally match measured tandemMS data. [implementation]
An integrated computational approach for metabolic flux analysis coupled with inference of tandemMS collisional fragment positions.
MIRAGE (MetabolIc Reconstruction via functionAl GEnomics) We present a novel gapfilling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. Specifically, 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. [Cyanobacteria models (SBML)] [implementation] Functional genomicsbased approach for metabolic network model reconstruction: Application for cyanobacteria networks.

Arabidopsis metabolic network models We present a computational pipeline for the reconstruction of genomescale, subcellular 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 constraintbased modeling analysis, provided in standard SBML format. [SBML models] Multilevel reconstruction of Arabidopsis metabolic network models: From subcellular compartmentalization to tissuespecificity.

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 highthroughput detection and quantification of chemicals of interest, enabling combinatorial metabolic engineering experiments aiming to overproduce them. [implementation] Computational design of microbial biosensors for combinatorial metabolic engineering experiments.

Human Liver Metabolic Network Model MBA is an algorithm for the rapid reconstruction of tissuespecific genomescale models of human metabolism. The algorithm starts from the generic human model and generates a reduced tissuespecific model by integrating a variety of tissuespecific molecular data sources, including literaturebased knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying this algorithm, we construct the first genomescale stoichiometric model of hepatic metabolism. [Cytoscape Visualization] Computational Reconstruction of Tissuespecific Metabolic Models: Application to Human Liver Metabolism.

IOMA (Integrative Omics Metabolic Analysis) IOMA quantitatively integrates proteomic and metabolomic data with genomescale metabolic models to predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steadystate flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kineticallyderived flux estimations. [implementation] Integrating Quantitative Proteomics and Metabolomics with a Genomescale Metabolic Network Model.

MDFBA (Metabolite Dilution Flux Balance Analysis) MDFBA 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

RobustKnock RobustKnock is a constraintbased method that predicts gene deletion strategies that lead to the overproduction 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.

iMAT (Integrative Metabolic Analysis Tool) A computational method for integrating transcriptomic and proteomic data with genomescale metabolic network models to predict enzymesâ€™ metabolic flux. [implementation] Networkbased Prediction of Human Tissuespecific Metabolism.
iMAT: Integrative Metabolic Analysis Tool.

SRFBA (SteadyState Regulatory Flux Balance Analysis) SRFBA is a method for predicting the steadystate metabolic and regulatory behaviors in largescale integrated metabolicregulatory models. [implementation] A GenomeScale Computational Study of the Interplay between Transcriptional Regulation and Metabolism.

ROOM (Regulatory On/Off Minimization) Regulatory onoff minimization (ROOM) is a constraintbased 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.

Last updated at April, 2010