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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A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1
Martin I. Sigurdsson,Neema Jamshidi,Eiríkur Steingrímsson,Ines Thiele,Bernhard O. Palsson,Bernhard O. Palsson +5 more
TL;DR: A high-quality mouse genome-scale metabolic reconstruction, iMM1415 (Mus Musculus, 1415 genes), is created and it is demonstrated that the mouse model can be used to perform phenotype simulations, similar to models of microbe metabolism.
Journal ArticleDOI
Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate.
Allison J. Lopatkin,Allison J. Lopatkin,Allison J. Lopatkin,Jonathan M. Stokes,Jonathan M. Stokes,Erica J. Zheng,Erica J. Zheng,Jason H. Yang,Jason H. Yang,Melissa K. Takahashi,Melissa K. Takahashi,Lingchong You,James J. Collins +12 more
TL;DR: This study provides a cohesive metabolic-dependent basis for antibiotic-mediated cell death, with implications for current treatment strategies and future drug development, and shows that metabolic state and ATP levels are better predictors of antibiotic lethality across diverse bacterial species than growth rate.
Journal ArticleDOI
Parallel Exploitation of Diverse Host Nutrients Enhances Salmonella Virulence
Benjamin Steeb,Beatrice Claudi,Neil A. Burton,Petra Tienz,Alexander Schmidt,Hesso Farhan,Alain Mazé,Dirk Bumann +7 more
TL;DR: A genome-scale computational model of Salmonella in vivo metabolism based on data was fully consistent with independent large-scale experimental data onSalmonella enzyme quantities, and correctly predicted 92% of 738 reported experimental mutant virulence phenotypes, suggesting that the analysis provided a comprehensive overview of host nutrient supply, Salmoneella metabolism, and salmonella growth during infection.
Journal ArticleDOI
Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.
TL;DR: Constrained-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions and explained lactate formation as the result of competition for oxygen by the other flux modes.
Journal ArticleDOI
Towards kinetic modeling of genome‐scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints
Anirikh Chakrabarti,Anirikh Chakrabarti,Ljubisa Miskovic,Ljubisa Miskovic,Keng Cher Soh,Keng Cher Soh,Vassily Hatzimanikatis,Vassily Hatzimanikatis +7 more
TL;DR: This work applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli to investigate the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism.
References
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