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

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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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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Prediction of metabolic pathways from genome-scale metabolic networks

Biosystems ◽

10.1016/j.biosystems.2011.05.004 ◽

2011 ◽

Vol 105 (2) ◽

pp. 109-121 ◽

Cited By ~ 21

Author(s):

Karoline Faust ◽

Didier Croes ◽

Jacques van Helden

Keyword(s):

Metabolic Pathways ◽

Metabolic Networks ◽

Genome Scale

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Sequence- and Structure-Based Functional Annotation and Assessment of Metabolic Transporters inAspergillus oryzae: A Representative Case Study

BioMed Research International ◽

10.1155/2016/8124636 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Nachon Raethong ◽

Jirasak Wong-ekkabut ◽

Kobkul Laoteng ◽

Wanwipa Vongsangnak

Keyword(s):

Metabolic Networks ◽

Dynamics Simulation ◽

Sequence Structure ◽

Metabolic Network Reconstruction ◽

Representative Case ◽

Manual Curation ◽

And Function ◽

The Relationship ◽

Annotation Strategy

Aspergillus oryzaeis widely used for the industrial production of enzymes. InA. oryzaemetabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While theA. oryzaegenome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation ofA. oryzaemetabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in theA. oryzaegenome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism ofA. oryzae, the H+-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction.

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Metabolic network reductions

10.1101/499251 ◽

2018 ◽

Author(s):

Mojtaba Tefagh ◽

Stephen P. Boyd

Keyword(s):

Metabolic Network ◽

Metabolic Pathways ◽

Degrees Of Freedom ◽

Metabolic Networks ◽

System Level ◽

Network Reduction ◽

Computational Aspects ◽

Sheer Size ◽

Problem Instances ◽

Genome Scale

AbstractGenome-scale metabolic networks are exceptionally huge and even efficient algorithms can take a while to run because of the sheer size of the problem instances. To address this problem, metabolic network reductions can substantially reduce the overwhelming size of the problem instances at hand. We begin by formulating some reasonable axioms defining what it means for a metabolic network reduction to be “canonical” which conceptually enforces reversibility without loss of any information on the feasible flux distributions. Then, we start to search for an efficient way to deduce some of the attributes of the original network from the reduced one in order to improve the performance. As the next step, we will demonstrate how to reduce a metabolic network repeatedly until no more reductions are possible. In the end, we sum up by pointing out some of the biological implications of this study apart from the computational aspects discussed earlier.Author summaryMetabolic networks appear at first sight to be nothing more than an enormous body of reactions. The dynamics of each reaction obey the same fundamental laws and a metabolic network as a whole is the melange of its reactions. The oversight in this kind of reductionist thinking is that although the behavior of a metabolic network is determined by the states of its reactions in theory, nevertheless it cannot be inferred directly from them in practice. Apart from the infeasibility of this viewpoint, metabolic pathways are what explain the biological functions of the organism and thus also what we are frequently concerned about at the system level.Canonical metabolic network reductions decrease the number of reactions substantially despite leaving the metabolic pathways intact. In other words, the reduced metabolic networks are smaller in size while retaining the same metabolic pathways. The possibility of such operations is rooted in the fact that the total degrees of freedom of a metabolic network in the steady-state conditions are significantly lower than the number of its reactions because of some emergent redundancies. Strangely enough, these redundancies turn out to be very well-studied in the literature.

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Fluxer: a web application to compute, analyze and visualize genome-scale metabolic flux networks

Nucleic Acids Research ◽

10.1093/nar/gkaa409 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W427-W435 ◽

Cited By ~ 1

Author(s):

Archana Hari ◽

Daniel Lobo

Keyword(s):

Metabolic Pathways ◽

Web Application ◽

Metabolic Networks ◽

Metabolic Flux ◽

Scale Model ◽

Balance Analysis ◽

Systems Biology Markup Language ◽

User Friendly ◽

Genome Scale ◽

Genome Scale Model

Abstract Next-generation sequencing has paved the way for the reconstruction of genome-scale metabolic networks as a powerful tool for understanding metabolic circuits in any organism. However, the visualization and extraction of knowledge from these large networks comprising thousands of reactions and metabolites is a current challenge in need of user-friendly tools. Here we present Fluxer (https://fluxer.umbc.edu), a free and open-access novel web application for the computation and visualization of genome-scale metabolic flux networks. Any genome-scale model based on the Systems Biology Markup Language can be uploaded to the tool, which automatically performs Flux Balance Analysis and computes different flux graphs for visualization and analysis. The major metabolic pathways for biomass growth or for biosynthesis of any metabolite can be interactively knocked-out, analyzed and visualized as a spanning tree, dendrogram or complete graph using different layouts. In addition, Fluxer can compute and visualize the k-shortest metabolic paths between any two metabolites or reactions to identify the main metabolic routes between two compounds of interest. The web application includes >80 whole-genome metabolic reconstructions of diverse organisms from bacteria to human, readily available for exploration. Fluxer enables the efficient analysis and visualization of genome-scale metabolic models toward the discovery of key metabolic pathways.

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Knowledge-based generalization of metabolic networks: A practical study

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720014410017 ◽

2014 ◽

Vol 12 (02) ◽

pp. 1441001 ◽

Cited By ~ 1

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.

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The Major Transitions in Evolution

10.1093/oso/9780198502944.001.0001 ◽

1997 ◽

Cited By ~ 27

Author(s):

John Maynard Smith ◽

Eors Szathmary

Keyword(s):

Full Range ◽

Evolution Of Cooperation ◽

Major Transitions ◽

Insect Societies ◽

Wide Range ◽

Major Transition ◽

Life Itself ◽

History Of ◽

Emergence Of Cooperation ◽

Multicellular Organisms

Over the history of life there have been several major changes in the way genetic information is organized and transmitted from one generation to the next. These transitions include the origin of life itself, the first eukaryotic cells, reproduction by sexual means, the appearance of multicellular plants and animals, the emergence of cooperation and of animal societies, and the unique language ability of humans. This ambitious book provides the first unified discussion of the full range of these transitions. The authors highlight the similarities between different transitions--between the union of replicating molecules to form chromosomes and of cells to form multicellular organisms, for example--and show how understanding one transition sheds light on others. They trace a common theme throughout the history of evolution: after a major transition some entities lose the ability to replicate independently, becoming able to reproduce only as part of a larger whole. The authors investigate this pattern and why selection between entities at a lower level does not disrupt selection at more complex levels. Their explanation encompasses a compelling theory of the evolution of cooperation at all levels of complexity. Engagingly written and filled with numerous illustrations, this book can be read with enjoyment by anyone with an undergraduate training in biology. It is ideal for advanced discussion groups on evolution and includes accessible discussions of a wide range of topics, from molecular biology and linguistics to insect societies.

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Dynamic histone acetylation in floral volatile synthesis and emission in petunia flowers

Journal of Experimental Botany ◽

10.1093/jxb/erab072 ◽

2021 ◽

Author(s):

Ryan M Patrick ◽

Xing-Qi Huang ◽

Natalia Dudareva ◽

Ying Li

Keyword(s):

Transcriptional Activation ◽

Metabolic Pathways ◽

Transcriptional Repression ◽

Metabolic Networks ◽

Underlying Mechanism ◽

Expression Of Genes ◽

Genome Wide ◽

Chemical Inhibitors ◽

Volatile Organic ◽

Floral Volatile

Abstract Biosynthesis of secondary metabolites relies on primary metabolic pathways to provide precursors, energy, and cofactors, thus requiring coordinated regulation of primary and secondary metabolic networks. However, to date, it remains largely unknown how this coordination is achieved. Using Petunia hybrida flowers, which emit high levels of phenylpropanoid/benzenoid volatile organic compounds (VOCs), we uncovered genome-wide dynamic deposition of histone H3 lysine 9 acetylation (H3K9ac) during anthesis as an underlying mechanism to coordinate primary and secondary metabolic networks. The observed epigenome reprogramming is accompanied by transcriptional activation at gene loci involved in primary metabolic pathways that provide precursor phenylalanine, as well as secondary metabolic pathways to produce volatile compounds. We also observed transcriptional repression among genes involved in alternative phenylpropanoid branches that compete for metabolic precursors. We show that GNAT family histone acetyltransferase(s) (HATs) are required for the expression of genes involved in VOC biosynthesis and emission, by using chemical inhibitors of HATs, and by knocking down a specific HAT gene, ELP3, through transient RNAi. Together, our study supports that regulatory mechanisms at chromatin level may play an essential role in activating primary and secondary metabolic pathways to regulate VOC synthesis in petunia flowers.

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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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Effects of Dysbiosis and Dietary Manipulation on the Digestive Microbiota of a Detritivorous Arthropod

Microorganisms ◽

10.3390/microorganisms9010148 ◽

2021 ◽

Vol 9 (1) ◽

pp. 148

Author(s):

Marius Bredon ◽

Elisabeth Depuydt ◽

Lucas Brisson ◽

Laurent Moulin ◽

Ciriac Charles ◽

...

Keyword(s):

Crucial Role ◽

Structure And Function ◽

Ecological Interactions ◽

Dietary Manipulation ◽

Biotic Factors ◽

Abiotic And Biotic Factors ◽

And Function ◽

Multicellular Organisms ◽

Over Time

The crucial role of microbes in the evolution, development, health, and ecological interactions of multicellular organisms is now widely recognized in the holobiont concept. However, the structure and stability of microbiota are highly dependent on abiotic and biotic factors, especially in the gut, which can be colonized by transient bacteria depending on the host’s diet. We studied these impacts by manipulating the digestive microbiota of the detritivore Armadillidium vulgare and analyzing the consequences on its structure and function. Hosts were exposed to initial starvation and then were fed diets that varied the different components of lignocellulose. A total of 72 digestive microbiota were analyzed according to the type of the diet (standard or enriched in cellulose, lignin, or hemicellulose) and the period following dysbiosis. The results showed that microbiota from the hepatopancreas were very stable and resilient, while the most diverse and labile over time were found in the hindgut. Dysbiosis and selective diets may have affected the host fitness by altering the structure of the microbiota and its predicted functions. Overall, these modifications can therefore have effects not only on the holobiont, but also on the “eco-holobiont” conceptualization of macroorganisms.

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