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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Microbial carbon use efficiency predicted from genome-scale metabolic models.
TL;DR: The authors estimate bacterial carbon use efficiency in soils for over 200 species using constraint-based modeling, incorporate the values into an ecosystem model, and find that shifts in community composition may impact carbon storage.
Journal ArticleDOI
Genome-wide enzyme annotation with precision control: catalytic families (CatFam) databases.
TL;DR: Comparisons of CatFam databases against other established profile‐based methods for the functional annotation of 13 bacterial genomes indicate thatCatFam consistently achieves higher precision and (in most cases) higher recall, and that the proposed method provides a valuable contribution to the automated prediction of protein catalytic functions.
Journal ArticleDOI
Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism
TL;DR: A multi-scale, spatiotemporal dynamic flux-balance analysis model is proposed to study the emergence of metabolic diversity in a spatial gut-like, tubular environment and is a helpful tool in systems biology to predict and explain the controlling factors of microbial diversity in the gut.
Journal ArticleDOI
Systematic overexpression study to find target enzymes enhancing production of terpenes in Synechocystis PCC 6803, using isoprene as a model compound.
TL;DR: A study on the overexpression of each enzyme in the MEP pathway in the unicellular cyanobacterium Synechocystis sp.
Journal ArticleDOI
Metabolic Adaptation after Whole Genome Duplication
TL;DR: The model confirms the hypothesis that W GD has been important in the adaptation of yeast to the new, glucose-rich environment that arose after the appearance of angiosperms and shows that WGD leads to better adaptation than small-scale duplications, in environments for which duplication of a whole pathway instead of single reactions is needed to increase fitness.
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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.