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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Book Review: Recent Advances in Yeast Metabolic Engineering
Journal ArticleDOI
Reconstructed Metabolic Network Models Predict Flux-Level Metabolic Reprogramming in Glioblastoma
Emrah Özcan,Tunahan Çakır +1 more
TL;DR: Correct predictions of flux distributions in glycolysis, glutaminolytic, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM.
Journal ArticleDOI
Genome-scale resources for Thermoanaerobacterium saccharolyticum
Devin H Currie,Babu Raman,Babu Raman,Christopher M. Gowen,Christopher M. Gowen,Timothy J. Tschaplinski,Miriam Land,Steven D. Brown,Sean F. Covalla,Dawn M. Klingeman,Zamin K. Yang,Nancy L. Engle,Courtney M Johnson,Miguel Rodriguez,A. Joe Shaw,William R. Kenealy,Lee R. Lynd,Lee R. Lynd,Stephen S. Fong,Jonathan R. Mielenz,Brian H. Davison,David A. Hogsett,Christopher D. Herring,Christopher D. Herring +23 more
TL;DR: A set of genome-scale resources are presented to enable the systems level investigation and development of this potentially important industrial organism and may be useful on a comparative basis for development of other lignocellulose degrading microbes, such as Clostridium thermocellum.
Journal ArticleDOI
Elucidating Xylose Metabolism of Scheffersomyces stipitis for Lignocellulosic Ethanol Production
TL;DR: A constraint-based metabolic network model for the central carbon metabolism of S. stipitis was reconstructed by integrating genomic, biochemical, and physiological information available for this microorganism and other related yeast, and the results provide important insights on cofactor engineering of xylose metabolism.
Journal ArticleDOI
Plasticity and epistasis strongly affect bacterial fitness after losing multiple metabolic genes
Glen D'Souza,Silvio Waschina,Silvio Waschina,Christoph Kaleta,Christoph Kaleta,Christian Kost +5 more
TL;DR: It is demonstrated that both the genetic background and environmental conditions determine the adaptive value of a loss‐of‐biochemical‐function mutation and that fitness gains decelerate, as more biochemical functions are lost.
References
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