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
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ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.
TL;DR: The ReacKnock algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.
Journal ArticleDOI
Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis
Daniel C. Zielinski,Fabian V. Filipp,Aarash Bordbar,Kasper Lynge Jensen,Jeffrey W. Smith,Markus J. Herrgård,Monica L. Mo,Bernhard O. Palsson +7 more
TL;DR: It is found that non-pharmacokinetic metabolic pathways dysregulated by drugs are linked to the development of side effects, and it is suggested that elucidating the relationships between the cellular response to drugs, genetic variation of patients and cell metabolism may help managing side effects by personalizing drug prescriptions and nutritional intervention strategies.
Journal ArticleDOI
Kinetic modeling of the Calvin cycle identifies flux control and stable metabolomes in Synechocystis carbon fixation
TL;DR: An improved kinetic model of the cyanobacterial Calvin cycle employing random sampling identifies probabilistic conditions for metabolome stability and metabolic flux control, agreeing with experimental data and aiding engineering efforts.
Journal ArticleDOI
The post-transcriptional regulatory system CSR controls the balance of metabolic pools in upper glycolysis of Escherichia coli.
Manon Morin,Delphine Ropers,Fabien Létisse,Fabien Létisse,Fabien Létisse,Sandrine Laguerre,Sandrine Laguerre,Sandrine Laguerre,Jean-Charles Portais,Jean-Charles Portais,Jean-Charles Portais,Muriel Cocaign-Bousquet,Muriel Cocaign-Bousquet,Muriel Cocaign-Bousquet,Brice Enjalbert,Brice Enjalbert,Brice Enjalbert +16 more
TL;DR: The carbon storage regulator system is essential for the effective functioning of the upper glycolysis mainly through its control of PfkA, demonstrating the pivotal role of post‐transcriptional regulation to shape the carbon metabolism.
Journal ArticleDOI
A curated C. difficile strain 630 metabolic network: prediction of essential targets and inhibitors
TL;DR: i MLTC806cdf is the most complete and accurate metabolic reconstruction of a gram-positive anaerobic bacteria to date and is used to predict essential genes as potential new therapeutic targets in the fight against Clostridium difficile infections.
References
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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.