Graph methods for the investigation of metabolic networks in parasitology

Parasitology ◽  
2010 ◽  
Vol 137 (9) ◽  
pp. 1393-1407 ◽  
Author(s):  
LUDOVIC COTTRET ◽  
FABIEN JOURDAN
Keyword(s):  
Metabolic Network ◽  
Large Scale ◽  
Metabolic Networks ◽  
Graph Model ◽  
New Drugs ◽  
Mathematical Methods ◽  
Graph Models ◽  
Metabolic Network Reconstruction ◽  
Network Analyses ◽  
Genome Scale

SUMMARYRecently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

A new approach to obtaining EFMs using graph methods based on the shortest path between end nodes

2016 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
José Francisco Hidalgo ◽  
Francisco Guil ◽  
José Manuel García
Keyword(s):  
Metabolic Network ◽  
Shortest Path ◽  
Metabolic Networks ◽  
New Drugs ◽  
New Approach ◽  
Two Phases ◽  
Living Organisms ◽  
Pathway Search ◽  
Genome Scale

Genome-scale metabolic networks let us understand the behaviour of the metabolism in the cells of living organisms. The availability of great amounts of such data gives the scientific community the opportunity to infer in silico new metabolic knowledge. Elementary Flux Modes (EFM) are minimal contained pathways or subsets of a metabolic network that are very useful to achieving the comprehension of a very specific metabolic function (as well as dysfunctions), and to get the knowledge to develop new drugs. Metabolic networks can have large connectivity and, therefore, EFMs resolution faces a combinational explosion challenge to be solved. In this paper we propose a new approach to obtain EFMs based on graph theory, the balanced graph concept and the shortest path between end nodes. Our proposal uses the shortest path between end nodes (input and output nodes) that finds all the pathways in the metabolic network and is able to prioritise the pathway search accounting the biological mean pursued. Our technique has two phases, the exploration phase and the characterisation one, and we show how it works in a well-known case study. We also demonstrate the relevance of the concept of balanced graph to achieve to the full list of EFMs.

scholarly journals Knowledge-based generalization of metabolic networks: A practical study

2014 ◽  
Vol 12 (02) ◽  
pp. 1441001 ◽  
Author(s):  
Anna Zhukova ◽  
David J. Sherman
Keyword(s):  
Fatty Acid ◽  
Metabolic Network ◽  
Fatty Acid Metabolism ◽  
Metabolic Networks ◽  
Acid Metabolism ◽  
Metabolic Network Reconstruction ◽  
Knowledge Based ◽  
Making Sense ◽  
Complex Process ◽  
Genome Scale

The complex process of genome-scale metabolic network reconstruction involves semi-automatic reaction inference, analysis, and refinement through curation by human experts. Unfortunately, decisions by experts are hampered by the complexity of the network, which can mask errors in the inferred network. In order to aid an expert in making sense out of the thousands of reactions in the organism's metabolism, we developed a method for knowledge-based generalization that provides a higher-level view of the network, highlighting the particularities and essential structure, while hiding the details. In this study, we show the application of this generalization method to 1,286 metabolic networks of organisms in Path2Models that describe fatty acid metabolism. We compare the generalised networks and show that we successfully highlight the aspects that are important for their curation and comparison.

Systematic Investigation of Mouse Models of Parkinson’s Disease by Transcriptome Mapping on a Brain-Specific Genome-Scale Metabolic Network

2021 ◽  
Author(s):  
Ecehan Abdik ◽  
Tunahan Cakir
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Metabolic Network ◽  
Mouse Models ◽  
Mus Musculus ◽  
Metabolic Networks ◽  
Systematic Investigation ◽  
Omics Data ◽  
Metabolic Alterations ◽  
Genome Scale

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model...

scholarly journals A genome‐scale metabolic network reconstruction of tomato ( Solanum lycopersicum L.) and its application to photorespiratory metabolism

2016 ◽  
Vol 85 (2) ◽  
pp. 289-304 ◽  
Author(s):  
Huili Yuan ◽  
C.Y. Maurice Cheung ◽  
Mark G. Poolman ◽  
Peter A. J. Hilbers ◽  
Natal A. W. Riel
Keyword(s):  
Metabolic Network ◽  
Solanum Lycopersicum ◽  
Network Reconstruction ◽  
Metabolic Network Reconstruction ◽  
Solanum Lycopersicum L ◽  
A Genome ◽  
Genome Scale

scholarly journals Unlocking Elementary Conversion Modes: ecmtool unveils all capabilities of metabolic networks

2020 ◽  
Author(s):  
Tom J. Clement ◽  
Erik B. Baalhuis ◽  
Bas Teusink ◽  
Frank J. Bruggeman ◽  
Robert Planqué ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Metabolic Networks ◽  
Full Range ◽  
Biotechnological Potential ◽  
Metabolic Network Reconstruction ◽  
And Function ◽  
Multicellular Organisms ◽  
Genome Scale ◽  
Threat Levels ◽  
Experimental Characterisation

AbstractThe metabolic capabilities of cells determine their biotechnological potential, fitness in ecosystems, pathogenic threat levels, and function in multicellular organisms. Their comprehensive experimental characterisation is generally not feasible, particularly for unculturable organisms. In principle, the full range of metabolic capabilities can be computed from an organism’s annotated genome using metabolic network reconstruction. However, current computational methods cannot deal with genome-scale metabolic networks. Part of the problem is that these methods aim to enumerate all metabolic pathways, while computation of all (elementally balanced) conversions between nutrients and products would suffice. Indeed, the elementary conversion modes (ECMs, defined by Urbanczik and Wagner) capture the full metabolic capabilities of a network, but the use of ECMs has not been accessible, until now. We extend and explain the theory of ECMs, implement their enumeration in ecmtool, and illustrate their applicability. This work contributes to the elucidation of the full metabolic footprint of any cell.

scholarly journals Metabolic network-guided binning of metagenomic sequence fragments

2015 ◽  
Vol 32 (6) ◽  
pp. 867-874 ◽  
Author(s):  
Matthew B. Biggs ◽  
Jason A. Papin
Keyword(s):  
Metabolic Network ◽  
Dna Sequences ◽  
Metabolic Networks ◽  
Human Microbiome ◽  
Environmental Dna ◽  
Human Microbiome Project ◽  
Supplementary Information ◽  
Metagenomic Sequence ◽  
Connectivity Score ◽  
Genome Scale

Abstract Motivation: Most microbes on Earth have never been grown in a laboratory, and can only be studied through DNA sequences. Environmental DNA sequence samples are complex mixtures of fragments from many different species, often unknown. There is a pressing need for methods that can reliably reconstruct genomes from complex metagenomic samples in order to address questions in ecology, bioremediation, and human health. Results: We present the SOrting by NEtwork Completion (SONEC) approach for assigning reactions to incomplete metabolic networks based on a metabolite connectivity score. We successfully demonstrate proof of concept in a set of 100 genome-scale metabolic network reconstructions, and delineate the variables that impact reaction assignment accuracy. We further demonstrate the integration of SONEC with existing approaches (such as cross-sample scaffold abundance profile clustering) on a set of 94 metagenomic samples from the Human Microbiome Project. We show that not only does SONEC aid in reconstructing species-level genomes, but it also improves functional predictions made with the resulting metabolic networks. Availability and implementation: The datasets and code presented in this work are available at: https://bitbucket.org/mattbiggs/sorting_by_network_completion/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

REVISITING MODULES (AND CENTERS) IN STAPHYLOCOCCUS AUREUS METABOLIC NETWORK WITH LINK CLUSTERING

2012 ◽  
Vol 20 (01) ◽  
pp. 57-66
Author(s):  
DE-WU DING ◽  
LONG YING
Keyword(s):  
Staphylococcus Aureus ◽  
Community Structure ◽  
Metabolic Network ◽  
Structure Analysis ◽  
Large Scale ◽  
Metabolic Networks ◽  
Clustering Algorithm ◽  
Functional Modules ◽  
Analysis Methods

Community structure analysis methods are important tools in modeling and analyzing large-scale metabolic networks. However, traditional community structure methods are mainly solved by clustering nodes, which results in each metabolite belonging to only a single community, which limits their usefulness in the study of metabolic networks. In the present paper, we analyze the community structure and functional modules in the Staphylococcus aureus (S. aureus) metabolic network, using a link clustering algorithm, and we obtain 10 functional modules with better biological insights, which give better results than our previous study. We also evaluate the essentiality of nodes in S. aureus metabolic networks. We suggest that link clustering could identify functional modules and key metabolites in metabolic networks.

IN SILICO GENOME-SCALE RECONSTRUCTION AND ANALYSIS OF THE SHEWANELLA LOIHICA PV-4 METABOLIC NETWORK

2018 ◽  
Vol 26 (03) ◽  
pp. 373-397
Author(s):  
ZIXIANG XU ◽  
JING GUO ◽  
YUNXIA YUE ◽  
JING MENG ◽  
XIAO SUN
Keyword(s):  
Metabolic Network ◽  
Network Model ◽  
Microbial Fuel Cells ◽  
Metabolic Networks ◽  
Acid Metabolism ◽  
Electricity Production ◽  
Nucleic Acid Metabolism ◽  
Exchange Reactions ◽  
A Genome ◽  
Genome Scale

Microbial Fuel Cells (MFCs) are devices that generate electricity directly from organic compounds with microbes (electricigens) serving as anodic catalysts. As a novel environment-friendly energy source, MFCs have extensive practical value. Since the biological features and metabolic mechanism of electricigens have a great effect on the electricity production of MFCs, it is a big deal to screen strains with high electricity productivity for improving the power output of MFC. Reconstructions and simulations of metabolic networks are of significant help in studying the metabolism of microorganisms so as to guide gene engineering and metabolic engineering to improve their power-generating efficiency. Herein, we reconstructed a genome-scale constraint-based metabolic network model of Shewanella loihica PV-4, an important electricigen, based on its genomic functional annotations, reaction databases and published metabolic network models of seven microorganisms. The resulting network model iGX790 consists of 902 reactions (including 71 exchange reactions), 798 metabolites and 790 genes, covering the main pathways such as carbon metabolism, energy metabolism, amino acid metabolism, nucleic acid metabolism and lipid metabolism. Using the model, we simulated the growth rate, the maximal synthetic rate of ATP, the flux variability analysis of metabolic network, gene deletion and so on to examine the metabolism of S. loihica PV-4.

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