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.
"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.
"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) . 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. "
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.