COnstraint-Based Reconstruction and Analysis (COBRA) Toolbox

Category Metabolomics/Metabonomics>Metabolic Profiling/Analysis Systems/Tools and Cross-Omics>Agent-Based Modeling/Simulation/Tools

Abstract The COnstraint-Based Reconstruction and Analysis (COBRA) Toolbox is a software package running in the MATLAB environment, which allows for the 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 analysis, sampling the range of possible cellular metabolic states and the determination of network modules.

Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in the Systems Biology Markup Language (SBML) format and perform these calculations with just a few lines of code.

The results are predictions of cellular behavior that have been verified as accurate in a growing body of research.

After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.

The COBRA Toolbox includes implementations of many of the commonly used forms of constraint-based analysis such as Flux Balance Analysis (FBA), gene deletions, Flux Variability Analysis (FVA), sampling, and batch simulations, together with tools to read in and manipulate constraint-based models.

COBRA Toolbox features --

1) Reading and writing models in the SBML format.

2) Reading models exported from the SimPheny system from Genomatica.

Genomatica has developed a unique, comprehensive, integrated metabolic engineering and bio-process development platform.

Their platform combines predictive computational modeling (the ‘SimPheny’ collection of proprietary software tools) with process modeling and advanced experimental lab technology.

Genomatica is able to model, design, create and improve multiple types of organisms and processes, rather than being limited to a single organism or set of pathways.

3) Changing model content and parameters: reactions, bounds, objectives, gene associations.

4) Provide Network and Flux distribution output to Cytoscape.

5) Common interfaces to a number of free and commercial linear programming solvers.

6) Flux balance analysis, linear Minimization Of Metabolic Adjustment (MOMA), and standard quadratic MOMA analysis.

Flux balance analysis - Flux balance analysis (FBA), which can be considered the computational specialization of the more general field of Fluxomics, is a mathematical method for analyzing metabolism.

It does Not require knowledge of metabolite concentration or details of the enzyme kinetics of the system.

The assumption is made that the system being studied is homeostatic and the technique then aims to answer the question: given some known available nutrients, which set of metabolic fluxes maximizes the growth rate of an organism while preserving the internal concentration of metabolites?

Flux balance analysis can also be considered a mathematical approach for analyzing the flow of metabolites through a metabolic network.

Fluxomics - Fluxomics deals with the dynamic changes of molecules within a cell over time. Fluxomics is an OMICS term that essentially is a neologism of flux balance analysis with a wider and more systematic framework.

Minimization Of Metabolic Adjustment (MOMA) - MoMA is a flux-based analysis technique similar to FBA and based on the same stoichiometric constraints, but the optimal growth flux for mutants is relaxed.

Instead, MoMA provides an approximate solution for a sub-optimal growth flux state, which is nearest in flux distribution to the unperturbed state.

7) Robustness and double robustness analysis.

8) Single and double gene deletion analysis.

9) Dynamic flux balance analysis (batch culture simulation).

10) Flux variability analysis (FVA).

Flux variability analysis - Flux variability analysis (FVA) is an FBA-based method for characterizing the multiple feasible states of genomic-scale metabolic models and for classifying the model reactions according to their behavior during simulated growth.

The reaction classification is derived from the minimization and maximization of flux through each model reaction while constraining the biomass production in the model to a minimal growth rate.

Reactions with a minimum and a maximum flux of zero are classified as blocked in the simulated conditions; reactions with a negative maximum flux or positive minimum flux are classified as essential in the simulated conditions; and all other reactions are classified as active.

11) Uniform sampling of flux space using artificial centering hit-and run.

12) Tools for statistical analysis of flux samples.

13) Finding correlated reaction sets.

14) Reporter metabolite analysis. (Reporter metabolites are metabolites around which the most significant transcriptional changes occur).

Note: The methods provided in the COBRA Toolbox can be used, in principle, on any metabolic network, but the more computationally intensive calculations may require extensive computer time.

System Requirements

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Manufacturer Web Site COBRA Toolbox

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G6G Abstract Number 20694

G6G Manufacturer Number 104319