Identifying Strong Statistical Bias in the Local Structure of Metabolic Networks - The Metabolic Network of Saccharomyces Cerevisiae as a Test Case

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...

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DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

10.1101/2020.07.17.208918 ◽

2020 ◽

Author(s):

Pablo Rodríguez-Mier ◽

Nathalie Poupin ◽

Carlo de Blasio ◽

Laurent Le Cam ◽

Fabien Jourdan

Keyword(s):

Metabolic Network ◽

Metabolic Networks ◽

Relevant Information ◽

Experimental Information ◽

Correct Identification ◽

Good Representation ◽

Metabolic State ◽

Whole Space ◽

Context Specific ◽

Optimal Networks

AbstractThe correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific organism and condition, containing only the reactions predicted to be active in such context. A major limitation of this approach is that the available information does not generally allow for an unambiguous characterization of the corresponding optimal metabolic sub-network, i.e., there are usually many different sub-network that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic state. In this work, we formalize the problem of enumeration of optimal metabolic networks, we implement a set of techniques that can be used to enumerate optimal networks, and we introduce DEXOM, a novel strategy for diversity-based extraction of optimal metabolic networks. Instead of enumerating the whole space of optimal metabolic networks, which can be computationally intractable, DEXOM samples solutions from the set of optimal metabolic sub-networks maximizing diversity in order to obtain a good representation of the possible metabolic state. We evaluate the solution diversity of the different techniques using simulated and real datasets, and we show how this method can be used to improve in-silico gene essentiality predictions in Saccharomyces Cerevisiae using diversity-based metabolic network ensembles. Both the code and the data used for this research are publicly available on GitHub1.

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Finding Minimum Reaction Cuts of Metabolic Networks Under a Boolean Model Using Integer Programming and Feedback Vertex Sets

Computer Engineering ◽

10.4018/978-1-61350-456-7.ch318 ◽

2012 ◽

pp. 774-791

Author(s):

Takeyuki Tamura ◽

Kazuhiro Takemoto ◽

Tatsuya Akutsu

Keyword(s):

Integer Programming ◽

Metabolic Network ◽

Drug Targets ◽

Metabolic Networks ◽

Boolean Model ◽

Pentose Phosphate ◽

Minimum Size ◽

E Coli ◽

Potential Applications ◽

Vertex Set

In this paper, the authors consider the problem of, given a metabolic network, a set of source compounds and a set of target compounds, finding a minimum size reaction cut, where a Boolean model is used as a model of metabolic networks. The problem has potential applications to measurement of structural robustness of metabolic networks and detection of drug targets. They develop an integer programming-based method for this optimization problem. In order to cope with cycles and reversible reactions, they further develop a novel integer programming (IP) formalization method using a feedback vertex set (FVS). When applied to an E. coli metabolic network consisting of Glycolysis/Glyconeogenesis, Citrate cycle and Pentose phosphate pathway obtained from KEGG database, the FVS-based method can find an optimal set of reactions to be inactivated much faster than a naive IP-based method and several times faster than a flux balance-based method. The authors also confirm that our proposed method works even for large networks and discuss the biological meaning of our results.

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IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks

Nanoscale ◽

10.1039/d0nr07588d ◽

2021 ◽

Author(s):

Bernabé Ortega-Tenezaca ◽

Humberto González-Díaz

Keyword(s):

Machine Learning ◽

Antibacterial Activity ◽

Metabolic Network ◽

Network Structure ◽

Metabolic Networks ◽

Antibacterial Nanoparticles

Machine learning mapping of antibacterial nanoparticles vs. bacteria metabolic network structure.

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Prevalent positive epistasis in Escherichia coli and Saccharomyces cerevisiae metabolic networks

Nature Genetics ◽

10.1038/ng.524 ◽

2010 ◽

Vol 42 (3) ◽

pp. 272-276 ◽

Cited By ~ 104

Author(s):

Xionglei He ◽

Wenfeng Qian ◽

Zhi Wang ◽

Ying Li ◽

Jianzhi Zhang

Keyword(s):

Escherichia Coli ◽

Saccharomyces Cerevisiae ◽

Metabolic Networks

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Graph methods for the investigation of metabolic networks in parasitology

Parasitology ◽

10.1017/s0031182010000363 ◽

2010 ◽

Vol 137 (9) ◽

pp. 1393-1407 ◽

Cited By ~ 14

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.

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Metabolic network-guided binning of metagenomic sequence fragments

Bioinformatics ◽

10.1093/bioinformatics/btv671 ◽

2015 ◽

Vol 32 (6) ◽

pp. 867-874 ◽

Cited By ~ 4

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.

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

Journal of Biological System ◽

10.1142/s021833901150032x ◽

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.

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

Journal of Biological System ◽

10.1142/s0218339018500171 ◽

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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An Unexpected Route to an Essential Cofactor:Escherichia coliRelies on Threonine for Thiamine Biosynthesis

mBio ◽

10.1128/mbio.01840-15 ◽

2016 ◽

Vol 7 (1) ◽

Cited By ~ 14

Author(s):

Jannell V. Bazurto ◽

Kristen R. Farley ◽

Diana M. Downs

Keyword(s):

Escherichia Coli ◽

Metabolic Network ◽

Metabolic Networks ◽

Thiamine Pyrophosphate ◽

Metabolic Potential ◽

E Coli ◽

Regulatory Strategies ◽

Phosphoribosylpyrophosphate Amidotransferase ◽

Pyrimidine Moiety ◽

Thiamine Biosynthesis

ABSTRACTMetabolism consists of biochemical reactions that are combined to generate a robust metabolic network that can respond to perturbations and also adapt to changing environmental conditions.Escherichia coliandSalmonella entericaare closely related enterobacteria that share metabolic components, pathway structures, and regulatory strategies. The synthesis of thiamine inS. entericahas been used to define a node of the metabolic network by analyzing alternative inputs to thiamine synthesis from diverse metabolic pathways. To assess the conservation of metabolic networks in organisms with highly conserved components, metabolic contributions to thiamine synthesis inE. coliwere investigated. Unexpectedly, we found that, unlikeS. enterica,E. colidoes not use the phosphoribosylpyrophosphate (PRPP) amidotransferase (PurF) as the primary enzyme for synthesis of phosphoribosylamine (PRA).In fact, our data showed that up to 50% of the PRA used byE. colito make thiamine requires the activities of threonine dehydratase (IlvA) and anthranilate synthase component II (TrpD). Significantly, the IlvA- and TrpD-dependent pathway to PRA functions inS. entericaonly in the absence of a functionalreactiveintermediatedeaminase (RidA) enzyme, bringing into focus how these closely related bacteria have distinct metabolic networks.IMPORTANCEIn most bacteria, includingSalmonellastrains andEscherichia coli, synthesis of the pyrimidine moiety of the essential coenzyme, thiamine pyrophosphate (TPP), shares enzymes with the purine biosynthetic pathway. Phosphoribosylpyrophosphate amidotransferase, encoded by thepurFgene, generates phosphoribosylamine (PRA) and is considered the first enzyme in the biosynthesis of purines and the pyrimidine moiety of TPP. We show here that, unlikeSalmonella,E. colisynthesizes significant thiamine from PRA derived from threonine using enzymes from the isoleucine and tryptophan biosynthetic pathways. These data show that two closely related organisms can have distinct metabolic network structures despite having similar enzyme components, thus emphasizing caveats associated with predicting metabolic potential from genome content.

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