F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

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On correlated reaction sets and coupled reaction sets in metabolic networks

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720015710031 ◽

2015 ◽

Vol 13 (04) ◽

pp. 1571003 ◽

Cited By ~ 2

Author(s):

Sayed-Amir Marashi ◽

Zhaleh Hosseini

Keyword(s):

Metabolic Networks ◽

Pearson Correlation ◽

Correlation Coefficients ◽

Network Evolution ◽

The Other ◽

Flux Coupling ◽

Expression Of Genes ◽

Genome Scale ◽

Nonzero Flux ◽

Coupled Reaction

Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R2 = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un)coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably.

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Optimization of a modeling platform to predict oncogenes from genome‐scale metabolic networks of non‐small‐cell lung cancers

FEBS Open Bio ◽

10.1002/2211-5463.13231 ◽

2021 ◽

Author(s):

You‐Tyun Wang ◽

Min‐Ru Lin ◽

Wei‐Chen Chen ◽

Wu‐Hsiung Wu ◽

Feng‐Sheng Wang

Keyword(s):

Metabolic Networks ◽

Small Cell ◽

Small Cell Lung ◽

Lung Cancers ◽

Genome Scale

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

Molecular Omics ◽

10.1039/d0mo00135j ◽

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...

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Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

BMC Systems Biology ◽

10.1186/1752-0509-4-114 ◽

2010 ◽

Vol 4 (1) ◽

pp. 114 ◽

Cited By ~ 59

Author(s):

Karin Radrich ◽

Yoshimasa Tsuruoka ◽

Paul Dobson ◽

Albert Gevorgyan ◽

Neil Swainston ◽

...

Keyword(s):

Metabolic Networks ◽

Genome Scale

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Model-guided identification of novel gene amplification targets for improving succinate production in Escherichia coli NZN111

Integrative Biology ◽

10.1039/c7ib00077d ◽

2017 ◽

Vol 9 (10) ◽

pp. 830-835 ◽

Cited By ~ 2

Author(s):

Xingxing Jian ◽

Ningchuan Li ◽

Qian Chen ◽

Qiang Hua

Keyword(s):

Escherichia Coli ◽

Metabolic Engineering ◽

Gene Amplification ◽

Metabolic Networks ◽

Computational Analysis ◽

Succinate Production ◽

Novel Gene ◽

Metabolic Models ◽

Genome Scale

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed.

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Analysis of metabolic networks of Streptomyces leeuwenhoekii C34 by means of a genome scale model: Prediction of modifications that enhance the production of specialized metabolites

Biotechnology and Bioengineering ◽

10.1002/bit.26598 ◽

2018 ◽

Vol 115 (7) ◽

pp. 1815-1828 ◽

Cited By ~ 6

Author(s):

Valeria Razmilic ◽

Jean F. Castro ◽

Barbara Andrews ◽

Juan A. Asenjo

Keyword(s):

Model Prediction ◽

Metabolic Networks ◽

Scale Model ◽

Specialized Metabolites ◽

A Genome ◽

Genome Scale ◽

Genome Scale Model

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Genome-scale reconstructions to assess metabolic phylogeny and organism clustering

PLoS ONE ◽

10.1371/journal.pone.0240953 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0240953

Author(s):

Christian Schulz ◽

Eivind Almaas

Keyword(s):

Phylogenetic Trees ◽

Metabolic Networks ◽

Sulfur Metabolism ◽

Phylogenetic Analyses ◽

Tree Of Life ◽

Significant Heterogeneity ◽

Metabolic Reaction ◽

High Quality ◽

Conserved Genes ◽

Genome Scale

Approaches for systematizing information of relatedness between organisms is important in biology. Phylogenetic analyses based on sets of highly conserved genes are currently the basis for the Tree of Life. Genome-scale metabolic reconstructions contain high-quality information regarding the metabolic capability of an organism and are typically restricted to metabolically active enzyme-encoding genes. While there are many tools available to generate draft reconstructions, expert-level knowledge is still required to generate and manually curate high-quality genome-scale metabolic models and to fill gaps in their reaction networks. Here, we use the tool AutoKEGGRec to construct 975 genome-scale metabolic draft reconstructions encoded in the KEGG database without further curation. The organisms are selected across all three domains, and their metabolic networks serve as basis for generating phylogenetic trees. We find that using all reactions encoded, these metabolism-based comparisons give rise to a phylogenetic tree with close similarity to the Tree of Life. While this tree is quite robust to reasonable levels of noise in the metabolic reaction content of an organism, we find a significant heterogeneity in how much noise an organism may tolerate before it is incorrectly placed in the tree. Furthermore, by using the protein sequences for particular metabolic functions and pathway sets, such as central carbon-, nitrogen-, and sulfur-metabolism, as basis for the organism comparisons, we generate highly specific phylogenetic trees. We believe the generation of phylogenetic trees based on metabolic reaction content, in particular when focused on specific functions and pathways, could aid the identification of functionally important metabolic enzymes and be of value for genome-scale metabolic modellers and enzyme-engineers.

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

10.1101/2020.06.06.137554 ◽

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.

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Investigating the chemolithoautotrophic and formate metabolism of Nitrospira moscoviensis by constraint-based metabolic modeling and 13C-tracer analysis

10.1101/2021.02.18.431926 ◽

2021 ◽

Author(s):

Christopher E. Lawson ◽

Aniela B. Mundinger ◽

Hanna Koch ◽

Tyler B. Jacobson ◽

Coty A. Weathersby ◽

...

Keyword(s):

Wastewater Treatment ◽

Metabolic Networks ◽

Metabolic Model ◽

Scale Model ◽

Nitrifying Bacteria ◽

Metabolomic Analysis ◽

A Genome ◽

Nitrite Oxidizing Bacteria ◽

Genome Scale ◽

Nitrospira Moscoviensis

AbstractNitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used constraint-based analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and 13C-tracer experiments with bicarbonate and formate coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings support that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation. We also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.ImportanceNitrospira are globally abundant nitrifying bacteria in soil and aquatic ecosystems and wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of N. moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite- oxidizing bacteria.

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