Literature DB >> 21144882

A computational tool for the simulation and optimization of microbial strains accounting integrated metabolic/regulatory information.

Paulo Vilaça1, Isabel Rocha, Miguel Rocha.   

Abstract

BACKGROUND AND SCOPE: Recently, a number of methods and tools have been proposed to allow the use of genome-scale metabolic models for the phenotype simulation and optimization of microbial strains, within the field of Metabolic Engineering (ME). One of the limitations of most of these algorithms and tools is the fact that only metabolic information is taken into account, disregarding knowledge on regulatory events. IMPLEMENTATION AND PERFORMANCES: This work proposes a novel software tool that implements methods for the phenotype simulation and optimization of microbial strains using integrated models, encompassing both metabolic and regulatory information. This tool is developed as a plug-in that runs over OptFlux, a computational platform that aims to be a reference tool for the ME community. AVAILABILITY: The plug-in is made available in the OptFlux web site (www.optflux.org) together with examples and documentation.
Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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Year:  2010        PMID: 21144882     DOI: 10.1016/j.biosystems.2010.11.012

Source DB:  PubMed          Journal:  Biosystems        ISSN: 0303-2647            Impact factor:   1.973


  4 in total

Review 1.  In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories.

Authors:  Paulo Maia; Miguel Rocha; Isabel Rocha
Journal:  Microbiol Mol Biol Rev       Date:  2015-11-25       Impact factor: 11.056

2.  Gene knockout identification using an extension of Bees Hill Flux Balance Analysis.

Authors:  Yee Wen Choon; Mohd Saberi Mohamad; Safaai Deris; Chuii Khim Chong; Sigeru Omatu; Juan Manuel Corchado
Journal:  Biomed Res Int       Date:  2015-03-22       Impact factor: 3.411

3.  OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling.

Authors:  Fangzhou Shen; Renliang Sun; Jie Yao; Jian Li; Qian Liu; Nathan D Price; Chenguang Liu; Zhuo Wang
Journal:  PLoS Comput Biol       Date:  2019-03-08       Impact factor: 4.475

4.  In silico model-guided identification of transcriptional regulator targets for efficient strain design.

Authors:  Lokanand Koduru; Meiyappan Lakshmanan; Dong-Yup Lee
Journal:  Microb Cell Fact       Date:  2018-10-25       Impact factor: 5.328

  4 in total

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