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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MC3: a steady-state model and constraint consistency checker for biochemical networks.
TL;DR: MC3, Model and Constraint Consistency Checker is presented, a computational tool that can be used for two purposes: identifying potential connectivity and topological issues for a given stoichiometric matrix, S, and flags issues that arise during constraint-based optimization.
Journal ArticleDOI
Carbon 13-Metabolic Flux Analysis derived constraint-based metabolic modelling of Clostridium acetobutylicum in stressed chemostat conditions.
TL;DR: The metabolism of butanol producing bacteria Clostridium acetobutylicum was studied in chemostat with glucose limited conditions, butanol stimulus, and as a reference cultivation using COnstraint-Based Reconstruction and Analysis (COBRA) using additional constraints from (13)C Metabolic Flux Analysis and experimental measurement results.
Journal ArticleDOI
An insight to flux-balance analysis for biochemical networks.
TL;DR: This review article gives an insight into FBA, from the extension of flux balancing to mathematical representation followed by a discussion about the formulation of flux-balance analysis problems, defining constraints for the stoichiometry of the pathways and the tools that can be used in FBA such as FASIMA, COBRA toolbox, and OptFlux.
Journal ArticleDOI
Modelling the metabolism of protein secretion through the Tat route in Streptomyces lividans
TL;DR: This work provides a detailed look to metabolic changes associated to Tat-dependent protein secretion reproducing experimental observations and identifying changes that are specific to each secretory route, opening the way for enhanced metabolic engineering of protein overproduction in S. lividans.
Journal ArticleDOI
Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation
Catherine Bjerre Collin,Tom Gebhardt,Martin Golebiewski,Tugce Karaderi,Maximilian Hillemanns,Faiz M. Khan,Ali Salehzadeh-Yazdi,Marc Kirschner,Sylvia Krobitsch,Eu-Stands Pm Consortium,Lars Kuepfer +10 more
TL;DR: The most relevant computational models for personalized medicine in detail are discussed in detail that can be considered as best-practice guidelines for application in clinical care and provide applicable guidelines and recommendations for study design, data acquisition, and operation.
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