Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms

In order to understand how cellular metabolism has taken its modern form, the conservation and variations between metabolic pathways were evaluated by using a genetic algorithm (GA). The GA approach considered information on the complete metabolism of the bacterium Escherichia coli K-12, as deposite...

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Main Authors:	Ortegon, Patricia, Poot-Hernández, Augusto C., Perez-Rueda, Ernesto, Rodriguez-Vazquez, Katya
Format:	Online
Language:	English
Published:	Research Network of Computational and Structural Biotechnology 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423528/

id	pubmed-4423528
recordtype	oai_dc
spelling	pubmed-44235282015-05-13 Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms Ortegon, Patricia Poot-Hernández, Augusto C. Perez-Rueda, Ernesto Rodriguez-Vazquez, Katya Article In order to understand how cellular metabolism has taken its modern form, the conservation and variations between metabolic pathways were evaluated by using a genetic algorithm (GA). The GA approach considered information on the complete metabolism of the bacterium Escherichia coli K-12, as deposited in the KEGG database, and the enzymes belonging to a particular pathway were transformed into enzymatic step sequences by using the breadth-first search algorithm. These sequences represent contiguous enzymes linked to each other, based on their catalytic activities as they are encoded in the Enzyme Commission numbers. In a posterior step, these sequences were compared using a GA in an all-against-all (pairwise comparisons) approach. Individual reactions were chosen based on their measure of fitness to act as parents of offspring, which constitute the new generation. The sequences compared were used to construct a similarity matrix (of fitness values) that was then considered to be clustered by using a k-medoids algorithm. A total of 34 clusters of conserved reactions were obtained, and their sequences were finally aligned with a multiple-sequence alignment GA optimized to align all the reaction sequences included in each group or cluster. From these comparisons, maps associated with the metabolism of similar compounds also contained similar enzymatic step sequences, reinforcing the Patchwork Model for the evolution of metabolism in E. coli K-12, an observation that can be expanded to other organisms, for which there is metabolism information. Finally, our mapping of these reactions is discussed, with illustrations from a particular case. Research Network of Computational and Structural Biotechnology 2015-04-09 /pmc/articles/PMC4423528/ /pubmed/25973143 http://dx.doi.org/10.1016/j.csbj.2015.04.001 Text en © 2015 Ortegon et al. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Ortegon, Patricia Poot-Hernández, Augusto C. Perez-Rueda, Ernesto Rodriguez-Vazquez, Katya
spellingShingle	Ortegon, Patricia Poot-Hernández, Augusto C. Perez-Rueda, Ernesto Rodriguez-Vazquez, Katya Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms
author_facet	Ortegon, Patricia Poot-Hernández, Augusto C. Perez-Rueda, Ernesto Rodriguez-Vazquez, Katya
author_sort	Ortegon, Patricia
title	Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms
title_short	Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms
title_full	Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms
title_fullStr	Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms
title_full_unstemmed	Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms
title_sort	comparison of metabolic pathways in escherichia coli by using genetic algorithms
description	In order to understand how cellular metabolism has taken its modern form, the conservation and variations between metabolic pathways were evaluated by using a genetic algorithm (GA). The GA approach considered information on the complete metabolism of the bacterium Escherichia coli K-12, as deposited in the KEGG database, and the enzymes belonging to a particular pathway were transformed into enzymatic step sequences by using the breadth-first search algorithm. These sequences represent contiguous enzymes linked to each other, based on their catalytic activities as they are encoded in the Enzyme Commission numbers. In a posterior step, these sequences were compared using a GA in an all-against-all (pairwise comparisons) approach. Individual reactions were chosen based on their measure of fitness to act as parents of offspring, which constitute the new generation. The sequences compared were used to construct a similarity matrix (of fitness values) that was then considered to be clustered by using a k-medoids algorithm. A total of 34 clusters of conserved reactions were obtained, and their sequences were finally aligned with a multiple-sequence alignment GA optimized to align all the reaction sequences included in each group or cluster. From these comparisons, maps associated with the metabolism of similar compounds also contained similar enzymatic step sequences, reinforcing the Patchwork Model for the evolution of metabolism in E. coli K-12, an observation that can be expanded to other organisms, for which there is metabolism information. Finally, our mapping of these reactions is discussed, with illustrations from a particular case.
publisher	Research Network of Computational and Structural Biotechnology
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423528/
_version_	1613220659744210944

Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms

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