scholarly journals Metage2Metabo: metabolic complementarity applied to genomes of large-scale microbiotas for the identification of keystone species

2019 ◽  
Author(s):  
Arnaud Belcour ◽  
Clémence Frioux ◽  
Méziane Aite ◽  
Anthony Bretaudeau ◽  
Anne Siegel
Keyword(s):  
Large Scale ◽  
Metabolic Networks ◽  
Keystone Species ◽  
Large Family ◽  
Functional Screening ◽  
Metabolic Cooperation ◽  
Metabolic Functions ◽  
Genome Scale ◽  
Equivalent Properties ◽  
Processing Solution

AbstractCapturing the functional diversity of microbiotas entails identifying metabolic functions and species of interest within hundreds or thousands. Starting from genomes, a way to functionally analyse genetic information is to build metabolic networks. Yet, no method enables a functional screening of such a large number of metabolic networks nor the identification of critical species with respect to metabolic cooperation.Metage2Metabo (M2M) addresses scalability issues raised by metagenomics datasets to identify keystone, essential and alternative symbionts in large microbiotas communities with respect to individual metabolism and collective metabolic complementarity. Genome-scale metabolic networks for the community can be either provided by the user or very efficiently reconstructed from a large family of genomes thanks to a multi-processing solution to run the Pathway Tools software. The pipeline was applied to 1,520 genomes from the gut microbiota and 913 metagenome-assembled genomes of the rumen microbiota. Reconstruction of metabolic networks and subsequent metabolic analyses were performed in a reasonable time.M2M identifies keystone, essential and alternative organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.

scholarly journals Metage2Metabo, microbiota-scale metabolic complementarity for the identication of key species

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Arnaud Belcour ◽  
Clémence Frioux ◽  
Méziane Aite ◽  
Anthony Bretaudeau ◽  
Falk Hildebrand ◽  
...  
Keyword(s):  
Functional Diversity ◽  
Large Scale ◽  
Metabolic Networks ◽  
De Novo ◽  
Functional Screening ◽  
Metabolic Cooperation ◽  
Metabolic Functions ◽  
Key Species ◽  
Genome Scale ◽  
Equivalent Properties

To capture the functional diversity of microbiota, one must identify metabolic functions and species of interest within hundreds or thousands of microorganisms. We present Metage2Metabo (M2M) a resource that meets the need for de-novo functional screening of genome-scale metabolic networks (GSMNs) at the scale of a metagenome, and the identification of critical species with respect to metabolic cooperation. M2M comprises a flexible pipeline for the characterisation of individual metabolisms and collective metabolic complementarity. In addition, M2M identifies key species, that are meaningful members of the community for functions of interest. We demonstrate that M2M is applicable to collections of genomes as well as metagenome-assembled genomes, permits an efficient GSMN reconstruction with Pathway Tools, and assesses the cooperation potential between species. M2M identifies key organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.

scholarly journals What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data

2020 ◽  
Author(s):  
Anne Richelle ◽  
Benjamin P. Kellman ◽  
Alexander T. Wenzel ◽  
Austin W.T. Chiang ◽  
Tyler Reagan ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Large Scale ◽  
Metabolic Networks ◽  
Omics Data ◽  
Web Based ◽  
Cell Functions ◽  
Metabolic Functions ◽  
Biological Studies ◽  
Biological Entities ◽  
Genome Scale

AbstractLarge-scale omics experiments have become standard in biological studies, leading to a deluge of data. However, researchers still face the challenge of connecting changes in the omics data to changes in cell functions, due to the complex interdependencies between genes, proteins and metabolites. Here we present a novel framework that begins to overcome this problem by allowing users to infer how metabolic functions change, based on omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. We then used genome-scale metabolic networks to define gene modules responsible for each specific metabolic task. We further developed a framework to overlay omics data on these modules to predict pathway usage for each metabolic task. The proposed approach allows one to directly predict how changes in omics experiments change cell or tissue function. We further demonstrated how this new approach can be used to leverage the metabolic functions of biological entities from the single cell to their organization in tissues and organs using multiple transcriptomic datasets (human and mouse). Finally, we created a web-based CellFie module that has been integrated into the list of tools available in GenePattern (www.genepattern.org) to enable adoption of the approach.

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.

FCDECOMP: Decomposition of metabolic networks based on flux coupling relations

2014 ◽  
Vol 12 (05) ◽  
pp. 1450028 ◽  
Author(s):  
Abolfazl Rezvan ◽  
Sayed-Amir Marashi ◽  
Changiz Eslahchi
Keyword(s):  
Metabolic Networks ◽  
System Level ◽  
Computational Framework ◽  
Flux Coupling ◽  
Metabolic Functions ◽  
Original Genome ◽  
A Cell ◽  
Scale Network ◽  
Genome Scale

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.

scholarly journals Nature lessons: the whitefly bacterial endosymbiont is a minimal amino acid factory with unusual energetics

2016 ◽  
Author(s):  
Jorge Calle-Espinosa ◽  
Miguel Ponce-de-Leon ◽  
Diego Santos-Garcia ◽  
Francisco J. Silva ◽  
Francisco Montero ◽  
...  
Keyword(s):  
Amino Acid ◽  
Energy Production ◽  
Metabolic Networks ◽  
Metabolic Model ◽  
Atp Production ◽  
Symbiotic Associations ◽  
Bacterial Endosymbiont ◽  
Metabolic Functions ◽  
A Genome ◽  
Genome Scale

Bacterial lineages that establish obligate symbiotic associations with insect hosts are known to possess highly reduced genomes with streamlined metabolic functions that are commonly focused on amino acid and vitamin synthesis. We constructed a genome-scale metabolic model of the whitefly bacterial endosymbiont Candidatus Portiera aleyrodidarum to study the energy production capabilities using stoichiometric analysis. Strikingly, the results suggest that the energetic metabolism of the bacterial endosymbiont relies on the use of pathways related to the synthesis of amino acids and carotenoids. A deeper insight showed that the ATP production via carotenoid synthesis may also have a potential role in the regulation of amino acid production. The coupling of energy production to anabolism suggest that minimization of metabolic networks as a consequence of genome size reduction does not necessarily limit the biosynthetic potential of obligate endosymbionts.

scholarly journals Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

2020 ◽  
Vol 71 (1) ◽  
pp. 303-326 ◽  
Author(s):  
Teresa J. Clark ◽  
Longyun Guo ◽  
John Morgan ◽  
Jorg Schwender
Keyword(s):  
Large Scale ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Isotope Labeling ◽  
Flux Analysis ◽  
Plant Metabolism ◽  
Metabolic Pathway Analysis ◽  
Metabolic Functions ◽  
Constraint Based Modeling ◽  
The Many

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For 13C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

scholarly journals Scalable metabolic pathway analysis

2020 ◽  
Author(s):  
Ove Øyås ◽  
Jörg Stelling
Keyword(s):  
Pathway Analysis ◽  
Metabolic Pathway ◽  
Large Scale ◽  
Metabolic Networks ◽  
Central Carbon Metabolism ◽  
Graph Representation ◽  
Metabolic Pathway Analysis ◽  
A Genome ◽  
Scope Of Application ◽  
Genome Scale

The scope of application of genome-scale constraint-based models (CBMs) of metabolic networks rapidly expands toward multicellular systems. However, comprehensive analysis of CBMs through metabolic pathway analysis remains a major computational challenge because pathway numbers grow combinatorially with model sizes. Here, we define the minimal pathways (MPs) of a metabolic (sub)network as a subset of its elementary flux vectors. We enumerate or sample them efficiently using iterative minimization and a simple graph representation of MPs. These methods outperform the state of the art and they allow scalable pathway analysis for microbial and mammalian CBMs. Sampling random MPs from Escherichia coli’s central carbon metabolism in the context of a genome-scale CBM improves predictions of gene importance, and enumerating all minimal exchanges in a host-microbe model of the human gut predicts exchanges of metabolites associated with host-microbiota homeostasis and human health. MPs thereby open up new possibilities for the detailed analysis of large-scale metabolic networks.

scholarly journals Cellular determinants of metabolite concentration ranges

2018 ◽  
Author(s):  
Jeanne M. O. Eloundou-Mbebi ◽  
Anika Küken ◽  
Georg Basler ◽  
Zoran Nikoloski
Keyword(s):  
Escherichia Coli ◽  
Large Scale ◽  
Metabolic Networks ◽  
Reaction Rates ◽  
Mass Action ◽  
Cellular Mechanisms ◽  
Real World Data ◽  
A Genome ◽  
Metabolic Models ◽  
Genome Scale

AbstractCellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component’s concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.Author SummaryWe present a computational approach for inferring concentration ranges from genome-scale metabolic models. The approach specifies a determinant and molecular mechanism underling facile control of concentration ranges for components in large-scale cellular networks. Most importantly, the predictions about concentration ranges do not require knowledge of kinetic parameters (which are difficult to specify at a genome scale), provided measurements of concentrations in a reference state. The approach assumes that reaction rates follow the mass action law used in the derivations of other types of kinetics. We apply the approach with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life to show that we can identify a proportion of metabolites to which our approach is applicable. By challenging the predictions of concentration ranges in the genome-scale metabolic network of E. coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach.

scholarly journals Dynamic Solution Space Division-Based Methods for Calculating Reaction Deletion Strategies for Constraint-Based Metabolic Networks for Substance Production: DynCubeProd

2021 ◽  
Vol 1 ◽  
Author(s):  
Yier Ma ◽  
Takeyuki Tamura
Keyword(s):  
Large Scale ◽  
Metabolic Networks ◽  
Solution Space ◽  
Design Strategies ◽  
Metabolite Production ◽  
Dynamic Solution ◽  
Space Division ◽  
A Genome ◽  
Balance Analysis ◽  
Genome Scale

Flux balance analysis (FBA) is a crucial method to analyze large-scale constraint-based metabolic networks and computing design strategies for strain production in metabolic engineering. However, as it is often non-straightforward to obtain such design strategies to produce valuable metabolites, many tools have been proposed based on FBA. Among them, GridProd, which divides the solution space into small squares by focusing on the cell growth rate and the target metabolite production rate to efficiently find the reaction deletion strategies, was extended to CubeProd, which divides the solution space into small cubes. However, as GridProd and CubeProd naively divide the solution space into equal sizes, even places where solutions are unlikely to exist are examined. To address this issue, we introduce dynamic solution space division methods based on CubeProd for faster computing by avoiding searching in places where the solutions do not exist. We applied the proposed method DynCubeProd to iJO1366, which is a genome-scale constraint-based model of Escherichia coli. Compared with CubeProd, DynCubeProd significantly accelerated the calculation of the reaction deletion strategy for each target metabolite production. In addition, under the anaerobic condition of iJO1366, DynCubeProd could obtain the reaction deletion strategies for almost 40% of the target metabolites that the elementary flux vector-based method, which is one of the most effective methods in existence, could not. The developed software is available on https://github.com/Ma-Yier/DynCubeProd.

scholarly journals SAMMI: a semi-automated tool for the visualization of metabolic networks

2019 ◽  
Vol 36 (8) ◽  
pp. 2616-2617
Author(s):  
Andre Schultz ◽  
Rehan Akbani
Keyword(s):  
Large Scale ◽  
Metabolic Networks ◽  
Source Code ◽  
Supplementary Information ◽  
Network Partitioning ◽  
Web Based ◽  
Constraint Based Modeling ◽  
Context Specific ◽  
Genome Scale ◽  
Automated Tool

Abstract Summary Here we present a browser-based Semi-Automated Metabolic Map Illustrator (SAMMI) for the visualization of metabolic networks. While automated features allow for easy network partitioning, navigation, and node positioning, SAMMI also offers a wide array of manual map editing features. This combination allows for fast, context-specific visualization of metabolic networks as well as the development of standardized, large-scale, visually appealing maps. The implementation of SAMMI with popular constraint-based modeling toolboxes also allows for effortless visualization of simulation results of genome-scale metabolic models. Availability and implementation SAMMI has been implemented as a standalone web-based tool and as plug-ins for the COBRA and COBRApy toolboxes. SAMMI and its COBRA plugins are available under the GPL 3.0 license and are available along with documentation, tutorials, and source code at www.SammiTool.com. Supplementary information Supplementary data are available at Bioinformatics online.

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