Literature DB >> 26092635

EvoMS: An evolutionary tool to find de novo metabolic pathways.

Matias F Gerard1, Georgina Stegmayer2, Diego H Milone3.   

Abstract

The evolutionary metabolic synthesizer (EvoMS) is an evolutionary tool capable of finding novel metabolic pathways linking several compounds through feasible reactions. It allows system biologists to explore different alternatives for relating specific metabolites, offering the possibility of indicating the initial compound or allowing the algorithm to automatically select it. Searching process can be followed graphically through several plots of the evolutionary process. Metabolic pathways found are displayed in a web browser as directed graphs. In all cases, solutions are networks of reactions that produce linear or branched metabolic pathways which are feasible from the specified set of available compounds. Source code of EvoMS is available at http://sourceforge.net/projects/sourcesinc/files/evoms/. Subsets of reactions are provided, as well as four examples for searching metabolic pathways among several compounds. Available as a web service at http://fich.unl.edu.ar/sinc/web-demo/evoms/.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Evolutionary algorithms; Metabolic network representation; Metabolic pathway searching; Pathway synthesis

Mesh:

Year:  2015        PMID: 26092635     DOI: 10.1016/j.biosystems.2015.04.006

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


  3 in total

1.  Network design and analysis for multi-enzyme biocatalysis.

Authors:  Lisa Katharina Blaß; Christian Weyler; Elmar Heinzle
Journal:  BMC Bioinformatics       Date:  2017-08-10       Impact factor: 3.169

2.  A Method for Finding Metabolic Pathways Using Atomic Group Tracking.

Authors:  Yiran Huang; Cheng Zhong; Hai Xiang Lin; Jianyi Wang
Journal:  PLoS One       Date:  2017-01-09       Impact factor: 3.240

3.  Metabolic pathways synthesis based on ant colony optimization.

Authors:  Matias F Gerard; Georgina Stegmayer; Diego H Milone
Journal:  Sci Rep       Date:  2018-11-06       Impact factor: 4.379

  3 in total

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