Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
Jan Schellenberger,Richard Que,Ronan M. T. Fleming,Ines Thiele,Jeffrey D. Orth,Adam M. Feist,Daniel C. Zielinski,Aarash Bordbar,Nathan E. Lewis,Sorena Rahmanian,Joseph Kang,Daniel R. Hyduke,Bernhard O. Palsson +12 more
TLDR
The constraint-based reconstruction and analysis toolbox as discussed by the authors is a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraintbased approach and 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.Abstract:
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.read more
Citations
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A systems biology approach to drug targets in Pseudomonas aeruginosa biofilm.
TL;DR: This study demonstrates that metabolic modeling of human pathogens can be used to identify oxygen-sensitive drug targets and thus, that this systems biology approach represents a powerful tool to identify novel candidate antibiotic targets.
Journal ArticleDOI
Comparison of 432 Pseudomonas strains through integration of genomic, functional, metabolic and expression data
Jasper J. Koehorst,Jesse C. J. van Dam,Ruben G. A. van Heck,Edoardo Saccenti,Vitor A. P. Martins dos Santos,Maria Suarez-Diez,Peter J. Schaap +6 more
TL;DR: The pan-genome of Pseudomonas is closed indicating a limited role of horizontal gene transfer in the evolutionary history of this genus and the power of integrating large scale comparative functional genomics with heterogeneous data for exploring bacterial diversity and versatility.
Journal ArticleDOI
Counting and correcting thermodynamically infeasible flux cycles in genome-scale metabolic networks.
TL;DR: In this paper, a relaxation algorithm and a Monte Carlo procedure are combined to detect loops, with ad hoc rules (discussed in detail) to eliminate them, and the method is compared with alternative methods to retrieve thermodynamically viable flux patterns based on minimizing specific global quantities.
Journal ArticleDOI
Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris.
Cristal Zuñiga,Jennifer Levering,Maciek R. Antoniewicz,Michael T. Guarnieri,Michael J. Betenbaugh,Karsten Zengler +5 more
TL;DR: Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth, and predicted reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism.
Journal ArticleDOI
Metabolic reconstruction identifies strain-specific regulation of virulence in Toxoplasma gondii.
Carl Song,Melissa A. Chiasson,Nirvana Nursimulu,Stacy S. Hung,James D. Wasmuth,Michael E. Grigg,John Parkinson +6 more
TL;DR: A constraint‐based metabolic model of the opportunistic parasite, Toxoplasma gondii, reveals that observed strain‐specific differences in growth rates are driven by altered capacities for energy production, and proposes that these observed differences reflect an evolutionary strategy that allows the parasite to extend its host range.
References
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