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.

Comparison of different approaches for identifying subnetworks in metabolic networks

2017 ◽  
Vol 15 (06) ◽  
pp. 1750025 ◽  
Author(s):  
Abolfazl Rezvan ◽  
Changiz Eslahchi
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Comprehensive Evaluation ◽  
Network Models ◽  
Evaluation Framework ◽  
System Level ◽  
Computational Framework ◽  
Kegg Pathways ◽  
A Cell ◽  
Single Method

A metabolic network model provides a computational framework for studying the metabolism of a cell at the system level. The organization of metabolic networks has been investigated in different studies. One of the organization aspects considered in these studies is the decomposition of a metabolic network. The decompositions produced by different methods are very different and there is no comprehensive evaluation framework to compare the results with each other. In this study, these methods are reviewed and compared in the first place. Then they are applied to six different metabolic network models and the results are evaluated and compared based on two existing and two newly proposed criteria. Results show that no single method can beat others in all criteria but it seems that the methods introduced by Guimera and Amaral and Verwoerd do better on among metabolite-based methods and the method introduced by Sridharan et al. does better among reaction-based ones. Also, the methods are applied to several artificial networks, each constructed from merging a few KEGG pathways. Then, their capability to recover those pathways are compared. Results show that among metabolite-based methods, the method of Guimera and Amaral does better again, however, no notable difference between the performances of reaction-based methods was detected.

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

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Abdelhalim Larhlimi ◽  
Laszlo David ◽  
Joachim Selbig ◽  
Alexander Bockmayr
Keyword(s):  
Metabolic Networks ◽  
Flux Coupling ◽  
Genome Scale

scholarly journals Genome-Scale Metabolic Modeling with Protein Expressions of Normal and Cancerous Colorectal Tissues for Oncogene Inference

Metabolites ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 16 ◽  
Author(s):  
Feng-Sheng Wang ◽  
Wu-Hsiung Wu ◽  
Wei-Shiang Hsiu ◽  
Yan-Jun Liu ◽  
Kuan-Wei Chuang
Keyword(s):  
Cancer Cells ◽  
Cancer Metabolism ◽  
Protein Signaling ◽  
Protein Expressions ◽  
Ras Family ◽  
Average Similarity ◽  
Similarity Ratio ◽  
A Cell ◽  
Genome Scale

Although cancer has historically been regarded as a cell proliferation disorder, it has recently been considered a metabolic disease. The first discovery of metabolic alterations in cancer cells refers to Otto Warburg’s observations. Cancer metabolism results in alterations in metabolic fluxes that are evident in cancer cells compared with most normal tissue cells. This study applied protein expressions of normal and cancer cells to reconstruct two tissue-specific genome-scale metabolic models. Both models were employed in a tri-level optimization framework to infer oncogenes. Moreover, this study also introduced enzyme pseudo-coding numbers in the gene association expression to avoid performing posterior decision-making that is necessary for the reaction-based method. Colorectal cancer (CRC) was the topic of this case study, and 20 top-ranked oncogenes were determined. Notably, these dysregulated genes were involved in various metabolic subsystems and compartments. We found that the average similarity ratio for each dysregulation is higher than 98%, and the extent of similarity for flux changes is higher than 93%. On the basis of surveys of PubMed and GeneCards, these oncogenes were also investigated in various carcinomas and diseases. Most dysregulated genes connect to catalase that acts as a hub and connects protein signaling pathways, such as those involving TP53, mTOR, AKT1, MAPK1, EGFR, MYC, CDK8, and RAS family.

scholarly journals MINREACT: an efficient algorithm for identifying minimal metabolic networks

2020 ◽  
Author(s):  
Gayathri Sambamoorthy ◽  
Karthik Raman
Keyword(s):  
Network Structure ◽  
Efficient Algorithm ◽  
Metabolic Networks ◽  
Design Principles ◽  
Functional Networks ◽  
Original Network ◽  
Compensatory Reactions ◽  
Genome Scale ◽  
Minimal Networks

AbstractGenome-scale metabolic models are widely constructed and studied for understanding various design principles underlying metabolism, predominantly redundancy. Metabolic networks are highly redundant and it is possible to minimise the metabolic networks into smaller networks that retain the functionality of the original network. Here, we establish a new method, MinReact that systematically removes reactions from a given network to identify minimal reactome(s). We show that our method identifies smaller minimal reactomes than existing methods and also scales well to larger metabolic networks. Notably, our method exploits known aspects of network structure and redundancy to identify multiple minimal metabolic networks. We illustrate the utility of MinReact by identifying multiple minimal networks for 74 organisms from the BiGG database. We show that these multiple minimal reactomes arise due to the presence of compensatory reactions/pathways. We further employed MinReact for a case study to identify the minimal reactomes of different organisms in both glucose and xylose minimal environments. Identification of minimal reactomes of these different organisms elucidate that they exhibit varying levels of redundancy. A comparison of the minimal reactomes on glucose and xylose illustrate that the differences in the reactions required to sustain growth on either medium. Overall, our algorithm provides a rapid and reliable way to identify minimal subsets of reactions that are essential for survival, in a systematic manner.Author summaryAn organism’s metabolism is routinely modelled by a metabolic network, which consists of all the enzyme-catalysed reactions that occur in the organism. These reactions are numerous, majorly due to the presence of redundant reactions that perform compensatory functions. Also, not all the reactions are functional in all environments and are unique to the environmental conditions. So, it is possible to minimise such large metabolic networks into smaller functional networks. Such minimal networks help in easier dissection of the capabilities of the network and also further our understanding of the various redundancies and other design principles occurring in these networks. Here, we have developed a new algorithm for identification of such minimal networks, that is efficient and superior to existing algorithms. We show the utility of our algorithm in identifying such minimal sets of reactions for many known metabolic networks. We have also shown a case study, using our algorithm to identify such minimal networks for different organisms in varied nutrient conditions.

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 Evolutionary design principles in metabolism

2019 ◽  
Vol 286 (1898) ◽  
pp. 20190098 ◽  
Author(s):  
Gayathri Sambamoorthy ◽  
Himanshu Sinha ◽  
Karthik Raman
Keyword(s):  
Protein Interactions ◽  
Metabolic Networks ◽  
Design Principles ◽  
Dynamic Environments ◽  
Evolutionary Design ◽  
Protein Protein Interactions ◽  
Flux Coupling ◽  
A Cell ◽  
And Function ◽  
Novel Design

Microorganisms are ubiquitous and adapt to various dynamic environments to sustain growth. These adaptations accumulate, generating new traits forming the basis of evolution. Organisms adapt at various levels, such as gene regulation, signalling, protein–protein interactions and metabolism. Of these, metabolism forms the integral core of an organism for maintaining the growth and function of a cell. Therefore, studying adaptations in metabolic networks is crucial to understand the emergence of novel metabolic capabilities. Metabolic networks, composed of enzyme-catalysed reactions, exhibit certain repeating paradigms or design principles that arise out of different selection pressures. In this review, we discuss the design principles that are known to exist in metabolic networks, such as functional redundancy, modularity, flux coupling and exaptations. We elaborate on the studies that have helped gain insights highlighting the interplay of these design principles and adaptation. Further, we discuss how evolution plays a role in exploiting such paradigms to enhance the robustness of organisms. Looking forward, we predict that with the availability of ever-increasing numbers of bacterial, archaeal and eukaryotic genomic sequences, novel design principles will be identified, expanding our understanding of these paradigms shaped by varied evolutionary processes.

scholarly journals Metabolic network reductions

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.

scholarly journals MinReact: a systematic approach for identifying minimal metabolic networks

2020 ◽  
Vol 36 (15) ◽  
pp. 4309-4315
Author(s):  
Gayathri Sambamoorthy ◽  
Karthik Raman
Keyword(s):  
Network Structure ◽  
Metabolic Networks ◽  
Systematic Approach ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Original Network ◽  
Compensatory Reactions ◽  
Genome Scale ◽  
Minimal Networks

Abstract Motivation Genome-scale metabolic models are widely constructed and studied for understanding various design principles underlying metabolism, predominantly redundancy. Metabolic networks are highly redundant and it is possible to minimize the metabolic networks into smaller networks that retain the functionality of the original network. Results Here, we establish a new method, MinReact that systematically removes reactions from a given network to identify minimal reactome(s). We show that our method identifies smaller minimal reactomes than existing methods and also scales well to larger metabolic networks. Notably, our method exploits known aspects of network structure and redundancy to identify multiple minimal metabolic networks. We illustrate the utility of MinReact by identifying multiple minimal networks for 77 organisms from the BiGG database. We show that these multiple minimal reactomes arise due to the presence of compensatory reactions/pathways. We further employed MinReact for a case study to identify the minimal reactomes of different organisms in both glucose and xylose minimal environments. Identification of minimal reactomes of these different organisms elucidate that they exhibit varying levels of redundancy. A comparison of the minimal reactomes on glucose and xylose illustrates that the differences in the reactions required to sustain growth on either medium. Overall, our algorithm provides a rapid and reliable way to identify minimal subsets of reactions that are essential for survival, in a systematic manner. Availability and implementation Algorithm is available from https://github.com/RamanLab/MinReact. Supplementary information Supplementary data are available at Bioinformatics online.

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 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.

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