Tomer Shlomi's Lab
Departments of Computer Science and Biology,
Technion
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CBIT: Constraint-based isotope tracing

Constraint-Based 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 state-of-the-art MFA methods. We demonstrate the ability of CBIT to perform optimal design of isotope tracing experiments, involving numerous repeated flux estimations. [implementation]

Constraint-based Isotope Tracing (CBIT): Inferring flux constraints from isotopic tracing data
M. Balber, T. Lavy, and T. Shlomi (Submitted)



Tandemers (Metabolic Flux Analysis via MS/MS data)

MS/MS enables the measurement of a metabolite tandem mass-isotopomer 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 mass-isotopomer 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 mass-isotopomer distributions
N. Tepper and T. Shlomi (Submitted)



MFA/UF (Metabolic Flux Analysis/Unknown Fragments)

We present a novel method, MFA/UF (Metabolic Flux Analysis/Unknown Fragments), that can utilize tandem-MS 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 tandem-MS data. [implementation]

An integrated computational approach for metabolic flux analysis coupled with inference of tandem-MS collisional fragment positions.
N. Tepper and T. Shlomi



MIRAGE (MetabolIc Reconstruction via functionAl GEnomics)

We present a novel gap-filling 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 genomics-based approach for metabolic network model reconstruction: Application for cyanobacteria networks.
E. Vitkin and T. Shlomi



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.
S. Mintz-Oron, E. Ruppin, A. Aharoni, T. Shlomi



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.
N. Tepper and T. Shlomi



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.
L. Jerby, T. Shlomi*, E. Ruppin* (*Equal contribution)



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.
K. Yizhak, T. Benyamini, W. Liebermeister, E. Ruppin, T. Shlomi



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
T. Benyamini, O. Folger, E. Ruppin, T. Shlomi



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.
N. Tepper, T. Shlomi



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.
T. Shlomi*, M. Cabili*, M. Herrgard, B.Ø. Palsson , E. Ruppin. (*Equal contribution).

iMAT: Integrative Metabolic Analysis Tool.
H. Zur, E. Ruppin, T. Shlomi



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.
T. Shlomi, Y. Eisenberg, R. Sharan, E. Ruppin.



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.
T. Shlomi, O. Berkman and E. Ruppin.







Last updated at April, 2010