Quantitative Prediction of Cellular Metabolism with Constraint-based Models: The COBRA Toolbox

Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, California 92093-0412, USA.
Nature Protocol (Impact Factor: 9.67). 02/2007; 2(3):727-38. DOI: 10.1038/nprot.2007.99
Source: PubMed


The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, 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. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language 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.

93 Reads
  • Source
    • "and C x the biomass concentration . It was assumed that the rates remain constant during each 0 . 5 h integration step . The solution to this equation was fitted to the experimental data . All simulations were per - formed using MATLAB and the COBRA Toolbox software packages with Gurobi TM Optimizer ( Gurobi Optimization , Inc . , Houston , TX ) ( Becker et al . , 2007 ; Schellenberger et al . , 2011 ) ."
    [Show abstract] [Hide abstract]
    ABSTRACT: Macroalgae have high potential to be an efficient, and sustainable feedstock for the production of biofuels and other more valuable chemicals. Attempts have been made to enable the co-fermentation of alginate and mannitol by Saccharomyces cerevisiae to unlock the full potential of this marine biomass. However, the efficient use of the sugars derived from macroalgae depends on the equilibrium of cofactors derived from the alginate and mannitol catabolic pathways. There are a number of strong metabolic limitations that have to be tackled before this bioconversion can be carried out efficiently by engineered yeast cells.
  • Source
    • "oxygen and ammonium) as model input. The unknown rates (or fluxes) were found by optimizing with linear programing an objective function (Z) subject to the specified substrate uptake rates (Becker et al., 2007; Orth et al., 2010; Varma and Palsson, 1994). The obtained metabolic rates were used to infer the physiological mechanisms responsible for the modulation of pathways leading the production of these gases. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Metabolic network modelling and metabolomics are computational and analytical techniques used to characterize the flow of compounds and energy within metabolic pathways of microbes. This paper illustrates the application of such techniques to explain how different environmental conditions of biological nitrogen removal (BNR) processes trigger the production and emission of nitrous oxide (N 2 O)-a greenhouse gas and ozone depletion substance-by nitrifying and denitrifying microbes. The research approach is exemplified by analysing N 2 O production in laboratory scale BNR systems by: (i) pure nitrifying species and (ii) mixed nitrifying cultures. The pure cultures (Nitrosomonas europaea) simulations shows that N 2 O is produced due to electron flow imbalances in nitrifying cells, and that electron carriers play a key role by distributing electron equivalents to N 2 O and NO formation reactions. The mixed culture simulations reveal two key aspects of N 2 O formation in nitrifying microbial communities: (i) microbes can lower N 2 O emissions by dissipating NO (a N 2 O precursor molecule); and (ii) the structure (i.e. the richness and abundance of species) of the microbial community influences the amount of N 2 O produced and emitted. This study concludes that operational conditions that promote imbalances between the cell's electron donors and electron acceptors cause N 2 O formation. Specifically, in nitrification processes, a build-up of electron donors leads to N 2 O formation. This paper demonstrates the unique features of metabolic modelling procedures and metabolomics by applying these to obtain insight into microbial functioning in wastewater treatment processes.
    NZ Water Conference, Hamilton, New Zealand; 09/2015
    • "Recon2 and MPS models were done by COnstraints Based Reconstruction and Analysis (COBRA) [39]. For the analysis we focused on the cell compartments present in Recon2 (cytosol, extracellular, nucleus, mitochondria, Golgi apparatus, endoplasmic reticulum, peroxisome, lysosome), used all the 100 pathways of metabolism and no additional restrictions were applied to the model. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS. Copyright © 2015. Published by Elsevier Inc.
    Molecular Genetics and Metabolism 08/2015; DOI:10.1016/j.ymgme.2015.08.001 · 2.63 Impact Factor
Show more