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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Metabolic network reconstruction of Chlamydomonas offers insight into light‐driven algal metabolism
Roger L. Chang,Lila Ghamsari,Ani Manichaikul,Erik F. Y. Hom,S. Balaji,Weiqi Fu,Yun Shen,Tong Hao,Bernhard O. Palsson,Kourosh Salehi-Ashtiani,Jason A. Papin +10 more
TL;DR: A genome‐scale metabolic network is reconstructed for this alga and a novel light‐modeling approach is devised that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux.
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Restoring the sense of touch with a prosthetic hand through a brain interface.
Gregg A. Tabot,John F. Dammann,J. A. Berg,Francesco Tenore,Jessica L Boback,R. Jacob Vogelstein,Sliman J. Bensmaia +6 more
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A linked organ-on-chip model of the human neurovascular unit reveals the metabolic coupling of endothelial and neuronal cells.
Ben M. Maoz,Anna Herland,Edward A. FitzGerald,Thomas Grevesse,Thomas Grevesse,Charles Vidoudez,Alan R. Pacheco,Alan R. Pacheco,Sean P. Sheehy,Sean P. Sheehy,Tae-Eun Park,Stephanie Dauth,Stephanie Dauth,Robert Mannix,Robert Mannix,Nikita Budnik,Kevin L. Shores,Kevin L. Shores,Alexander Cho,Alexander Cho,Janna Nawroth,Janna Nawroth,Daniel Segrè,Bogdan Budnik,Donald E. Ingber,Donald E. Ingber,Donald E. Ingber,Kevin Kit Parker,Kevin Kit Parker +28 more
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Microbial laboratory evolution in the era of genome‐scale science
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Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis
TL;DR: A high-quality genome-scale metabolic network for Synechocystis sp.
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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.