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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Journal ArticleDOI
The neural stem cell fate determinant TRIM32 regulates complex behavioral traits
Anna-Lena Hillje,Anna-Lena Hillje,Elisabeth Beckmann,Maria Angeliki S. Pavlou,Maria Angeliki S. Pavlou,Christian Jaeger,Maria Pires Pacheco,Thomas Sauter,Jens Christian Schwamborn,Jens Christian Schwamborn,Lars Lewejohann +10 more
TL;DR: This study provides comprehensive data on how the impairment of neurogenesis caused by the loss of the cell fate determinant TRIM32 causes a decrease of olfactory performance as well as a deregulation of metabolomic pathways that are linked to mood disorders.
Journal ArticleDOI
Improvement of constraint-based flux estimation during L-phenylalanine production with Escherichia coli using targeted knock-out mutants.
TL;DR: Uncertainties of intracellular flux estimations by constraint‐based analyses during fed‐batch production of L‐phenylalanine were drastically reduced by application of the malic enzyme knock‐out mutants.
Posted ContentDOI
Systems assessment of transcriptional regulation on central carbon metabolism by Cra and CRP
Donghyuk Kim,Sang Woo Seo,Sang Woo Seo,Hojung Nam,Gabriela I. Guzman,Ye Gao,Bernhard O. Palsson +6 more
TL;DR: An integrated metabolic-regulatory network was formed by including experimentally-derived regulatory information and a genome-scale metabolic network reconstruction and showed that Cra enables the optimal bacterial growth on poor carbon sources by redirecting and repressing the glycolysis flux, by activating the glyoxylate shunt pathway, and by activates the respiratory pathway.
Journal ArticleDOI
Influence of the Crc global regulator on substrate uptake rates and the distribution of metabolic fluxes in Pseudomonas putida KT2440 growing in a complete medium
TL;DR: The present work examines the changes that occur in metabolic fluxes when the crc gene is inactivated and cells grow exponentially in LB complete medium, finding that the lack of the Crc/Hfq regulatory system led to unbalanced metabolism with poorly optimized metabolic fluxe.
Book ChapterDOI
Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives
TL;DR: A review of the principal methods used for constraint-based modelling in systems biology, and how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models is explored.
References
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Global Mapping of the Yeast Genetic Interaction Network
Amy Hin Yan Tong,Guillaume Lesage,Gary D. Bader,Huiming Ding,Hong Xu,Xiaofeng Xin,James D. Young,Gabriel F. Berriz,Renee L. Brost,Michael Chang,Yiqun Chen,Xin Cheng,Gordon Chua,Helena Friesen,Debra S. Goldberg,Jennifer Haynes,Christine Humphries,Grace He,Shamiza Hussein,Lizhu Ke,Nevan J. Krogan,Zhijian Li,Joshua N. Levinson,Hong Lu,Patrice Menard,Christella Munyana,Ainslie B. Parsons,Owen Ryan,Raffi Tonikian,Tania Michelle Roberts,Anne-Marie Sdicu,Jesse Shapiro,Bilal N. Sheikh,Bernhard Suter,Sharyl L. Wong,Lan V. Zhang,Hongwei Zhu,Christopher G. Burd,Sean Munro,Chris Sander,Jasper Rine,Jack Greenblatt,Matthias Peter,Anthony Bretscher,Graham Bell,Frederick P. Roth,Grant W. Brown,Brenda J. Andrews,Howard Bussey,Charles Boone +49 more
TL;DR: Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.