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
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.
Available from: Alejandro H. Buschmann
- "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.
- "glycan #2 in Fig. 7A, the frequency was split evenly among all possible structures). Subsequently , flux variability analysis (Becker et al., 2007;Burgard et al., 2001;Gudmundsson and Thiele, 2010) is used to calculate the maximum and minimum possible flux through each reaction, given these constraints on the system's glycan secretion. Reactions that cannot carry any flux under these circumstances are removed, leading to reduction of the network. "
[Show abstract] [Hide abstract]
ABSTRACT: Glycosylation is a critical quality attribute of most recombinant biotherapeutics. Consequently, drug development requires careful control of glycoforms to meet bioactivity and biosafety requirements. However, glycoengineering can be extraordinarily difficult given the complex reaction networks underlying glycosylation and the vast number of different glycans that can be synthesized in a host cell. Computational modeling offers an intriguing option to rationally guide glycoengineering, but the high parametric demands of current modeling approaches pose challenges to their application. Here we present a novel low-parameter approach to describe glycosylation using flux-balance and Markov chain modeling. The model recapitulates the biological complexity of glycosylation, but does not require user-provided kinetic information. We use this method to predict and experimentally validate glycoprofiles on EPO, IgG as well as the endogenous secretome following glycosyltransferase knock-out in different Chinese hamster ovary (CHO) cell lines. Our approach offers a flexible and user-friendly platform that can serve as a basis for powerful computational engineering efforts in mammalian cell factories for biopharmaceutical production.
Available from: Octavio Perez-Garcia
- "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.
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.