Evolutionary algorithm for metabolic pathways synthesis

被引:2
作者
Gerard, Matias F. [1 ]
Stegmayer, Georgina [1 ]
Milone, Diego H. [1 ]
机构
[1] FICH UNL CONICET, Res Inst Signals Syst & Computat Intelligence Sin, Santa Fe, Argentina
关键词
Evolutionary algorithms; Search strategies; De novo pathway building; Reactions network; Sets of compounds; GENETIC ALGORITHM; ESCHERICHIA-COLI; NETWORKS; ORGANIZATION; COMPUTATION; DATABASE; TOOL;
D O I
10.1016/j.biosystems.2016.04.002
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Metabolic pathway building is an active field of research, necessary to understand and manipulate the metabolism of organisms. There are different approaches, mainly based on classical search methods, to find linear sequences of reactions linking two compounds. However, an important limitation of these methods is the exponential increase of search trees when a large number of compounds and reactions is considered. Besides, such models do not take into account all substrates for each reaction during the search, leading to solutions that lack biological feasibility in many cases. This work proposes a new evolutionary algorithm that allows searching not only linear, but also branched metabolic pathways, formed by feasible reactions that relate multiple compounds simultaneously. Tests performed using several sets of reactions show that this algorithm is able to find feasible linear and branched metabolic pathways. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
引用
收藏
页码:55 / 67
页数:13
相关论文
共 33 条
[1]  
Affenzeller M, 2009, NUMER INSIGHT, pXXV
[2]   A systematic comparison of the MetaCyc and KEGG pathway databases [J].
Altman, Tomer ;
Travers, Michael ;
Kothari, Anamika ;
Caspi, Ron ;
Karp, Peter D. .
BMC BIOINFORMATICS, 2013, 14
[3]  
[Anonymous], 2010, Articial intelligence: A modern approach
[4]  
Arita M, 2012, METHODS MOL BIOL, V804, P93, DOI 10.1007/978-1-61779-361-5_6
[5]  
Back T., 2000, EVOLUTIONARY COMPUTA
[6]   Constraint-based models predict metabolic and associated cellular functions [J].
Bordbar, Aarash ;
Monk, Jonathan M. ;
King, Zachary A. ;
Palsson, Bernhard O. .
NATURE REVIEWS GENETICS, 2014, 15 (02) :107-120
[7]   A survey on optimization metaheuristics [J].
Boussaid, Ilhern ;
Lepagnot, Julien ;
Siarry, Patrick .
INFORMATION SCIENCES, 2013, 237 :82-117
[8]   Data-intensive applications, challenges, techniques and technologies: A survey on Big Data [J].
Chen, C. L. Philip ;
Zhang, Chun-Yang .
INFORMATION SCIENCES, 2014, 275 :314-347
[9]   Metabolic PathFinding: inferring relevant pathways in biochemical networks [J].
Croes, D ;
Couche, F ;
Wodak, SJ ;
van Helden, J .
NUCLEIC ACIDS RESEARCH, 2005, 33 :W326-W330
[10]   A dynamic niching genetic algorithm strategy for docking highly flexible ligands [J].
de Magalhaes, Camila Silva ;
Almeida, Diogo Marinho ;
Correa Barbosa, Helio Jose ;
Dardenne, Laurent Emmanuel .
INFORMATION SCIENCES, 2014, 289 :206-224