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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Dynamic flux balance analysis of batch fermentation: effect of genetic manipulations on ethanol production
K. P. Lisha,Debasis Sarkar +1 more
TL;DR: In silico optimization of bioethanol production from lignocellulosic biomasses is investigated by combining process systems engineering approach and systems biology approach, and the maximization of ethanol productivity is addressed by computing optimal aerobic–anaerobic switching times.
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Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity.
TL;DR: A computational approach was used to identify human metabolites whose metabolism is incomplete on the basis of their detection in humans but exclusion from the human metabolic network reconstruction RECON 1, and it is reported that C9orf103, previously identified as a candidate tumour suppressor gene, encodes a functional human gluconokinase.
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Computational modeling of metabolism in microbial communities on a genome-scale
Analeigha V. Colarusso,Analeigha V. Colarusso,Isabella Goodchild-Michelman,Maya Rayle,Ali R. Zomorrodi +4 more
TL;DR: Computational modeling of microbial communities using GEnome-scale Models (GEMs) of metabolism is a new frontier in systems biology and recent efforts to integrate GEMs and machine learning for predicting inter-species interactions are reviewed.
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A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism.
Oveis Jamialahmadi,Sameereh Hashemi-Najafabadi,Ehsan Motamedian,Stefano Romeo,Stefano Romeo,Stefano Romeo,Fatemeh Bagheri +6 more
TL;DR: This work integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches and developed a method for the reconstruction of context-specific metabolic models.
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Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks
TL;DR: This model quantitatively model the epigenetic landscape using a kind of artificial neural network called the Hopfield network (HN) and identifies genes and TFs that drive cell-fate transitions, and gains insight into the global dynamics of GRNs.
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