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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Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions.
Xihui Xu,Raphy Zarecki,Shlomit Medina,Shany Ofaim,Shany Ofaim,Xiaowei Liu,Chen Chen,Shunli Hu,Dan Brom,Daniella Gat,Seema Porob,Hanan Eizenberg,Zeev Ronen,Jiandong Jiang,Shiri Freilich +14 more
TL;DR: By modeling community function, this analysis demonstrates that understanding community function in its wider context, beyond the single direct degrader perspective, promotes the design of biostimulation strategies.
Journal ArticleDOI
Co-regulation of metabolic genes is better explained by flux coupling than by network distance
TL;DR: It is shown that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon, and it is demonstrated that network distance per se has relatively minor influence on gene co-regulation.
Journal ArticleDOI
Metabolic engineering of a tyrosine-overproducing yeast platform using targeted metabolomics
Nicholas D. Gold,Christopher M. Gowen,Francois-Xavier Lussier,Sarat C. Cautha,Radhakrishnan Mahadevan,Vincent J. J. Martin +5 more
TL;DR: The genome-scale metabolic model identified design strategies that have the potential to improve availability of erythrose 4-phosphate for DAHP synthase and cofactor availability for prephenate dehydrogenase and provide recommendations for further improvement of aromatic amino acid biosynthesis in S. cerevisiae.
Journal ArticleDOI
DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis.
TL;DR: Lexicographic optimization is used to determine unique exchange fluxes which are necessary for a well-defined dynamic system and DFBAlab does not fail during numerical integration due to infeasible LPs.
Journal ArticleDOI
Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation
Nicolas A. L. Flahaut,Anne Wiersma,Bert van de Bunt,Dirk E. Martens,Peter J. Schaap,Lolke Sijtsma,Vitor A. P. Martins dos Santos,Willem M. de Vos,Willem M. de Vos +8 more
TL;DR: The genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.
References
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Global Mapping of the Yeast Genetic Interaction Network
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TL;DR: Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.