FogLight: an efficient matrix-based approach to construct metabolic pathways by search space reduction

Khosraviani, Mehrshad; Zamani, Morteza Saheb; Bidkhori, Gholamreza

doi:10.1093/bioinformatics/btv578

Cited by 14 publications

(10 citation statements)

References 36 publications

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“…Regarding the Bidkhori et al method (Bidkhori et al, 2018 ), we generated directed Metabolite-Metabolite network (MMN), directed Reaction-Reaction network (RRN), and directed Gene-Gene network from GEMs (GGN). The currency metabolites (Khosraviani et al, 2016 ) such as cofactors, coenzymes and H 2 O were removed before generating RRN and GGN. The importance of the nodes (metabolites in MMN, reactions in RRN and genes in GGN) was scored by computing centrality topological parameters i.e., betweenness, eccentricity, closeness, and degree (Bidkhori et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling

et al. 2018

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Genome-scale metabolic models (GEMs) are comprehensive descriptions of cell metabolism and have been extensively used to understand biological responses in health and disease. One such application is in determining metabolic adaptation to the absence of a gene or reaction, i.e., essentiality analysis. However, current methods do not permit efficiently and accurately quantifying reaction/gene essentiality. Here, we present Essentiality Score Simulator (ESS), a tool for quantification of gene/reaction essentialities in GEMs. ESS quantifies and scores essentiality of each reaction/gene and their combinations based on the stoichiometric balance using synthetic lethal analysis. This method provides an option to weight metabolic models which currently rely mostly on topologic parameters, and is potentially useful to investigate the metabolic pathway differences between different organisms, cells, tissues, and/or diseases. We benchmarked the proposed method against multiple network topology parameters, and observed that our method displayed higher accuracy based on experimental evidence. In addition, we demonstrated its application in the wild-type and ldh knock-out E. coli core model, as well as two human cell lines, and revealed the changes of essentiality in metabolic pathways based on the reactions essentiality score. ESS is available without any limitation at https://sourceforge.net/projects/essentiality-score-simulator.

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Section: Methodsmentioning

confidence: 99%

ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling

et al. 2018

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“…Khosraviani et al . have proposed an AND/OR boolean representation of the graph using matrix notation 19 . It allowed search for pathways between source and target compounds over a reduced search space by applying boolean operations.…”

Section: Introductionmentioning

confidence: 99%

Metabolic pathways synthesis based on ant colony optimization

Gérard

Stegmayer

Milone

2018

Sci Rep

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One of the current challenges in bioinformatics is to discover new ways to transform a set of compounds into specific products. The usual approach is finding the reactions to synthesize a particular product, from a given substrate, by means of classical searching algorithms. However, they have three main limitations: difficulty in handling large amounts of reactions and compounds; absence of a step that verifies the availability of substrates; and inability to find branched pathways. We present here a novel bio-inspired algorithm for synthesizing linear and branched metabolic pathways. It allows relating several compounds simultaneously, ensuring the availability of substrates for every reaction in the solution. Comparisons with classical searching algorithms and other recent metaheuristic approaches show clear advantages of this proposal, fully recovering well-known pathways. Furthermore, solutions found can be analyzed in a simple way through graphical representations on the web.

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“…The efforts on studying metabolic pathways can be divided into two complementary types [5], namely stoichiometric methods and graph-based pathfinding methods. Stoichiometric methods build stoichiometry-balanced optimization models based on integer linear programming (ILP) to search for the metabolic pathway that transforms a source metabolite to a target metabolite with high yield.…”

Section: Introductionmentioning

confidence: 99%

“…Some commonly used graph-based methods search metabolic pathways based on machine learning [15, 16], evolutionary algorithms [17, 18], tailored heuristic search strategy [5, 19, 20], retrosynthetic model [21–24], minimized pathway switching [25], and subgraph extraction technique [26]. Graph-based pathfinding methods complement stoichiometric methods as they focus on different aspects of modeling and understanding metabolism [2, 27–29].…”

Section: Introductionmentioning

confidence: 99%

A Method for Finding Metabolic Pathways Using Atomic Group Tracking

et al. 2017

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A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways.

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FogLight: an efficient matrix-based approach to construct metabolic pathways by search space reduction

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 14 publications

References 36 publications

ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling

ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling

Metabolic pathways synthesis based on ant colony optimization

A Method for Finding Metabolic Pathways Using Atomic Group Tracking

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