scholarly journals Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling

2020 ◽  
Author(s):  
Takeyuki Tamura
Keyword(s):  
Escherichia Coli ◽  
Metabolic Network ◽  
Metabolic Networks ◽  
Search Space ◽  
Anaerobic Conditions ◽  
Aerobic Conditions ◽  
Metabolic Network Analysis ◽  
Balance Analysis ◽  
Target Production ◽  
Genome Scale

Abstract BackgroundMetabolic engineering strategies enabling the production of specific target metabolites by host strains can be identified in silico through the use of metabolic network analysis such as flux balance analysis. This type of metabolic redesign is based on the computation of reactions that should be deleted from the original network representing the metabolism of the host strain to enable the production of the target metabolites while still ensuring its growth (the concept of growth coupling). In this context, it is important to use algorithms that enable this growth-coupled reaction deletions identification for any metabolic network topologies and any potential target metabolites. A recent method using a strong growth coupling assumption has been shown to be able to identify such computational redesign for nearly all metabolites included in the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae when cultivated under aerobic conditions. However, this approach enables the computational redesign of S. cerevisiae for only 3.9% of all metabolites if under anaerobic conditions. Therefore, it is necessary to develop algorithms able to perform for various culture conditions.ResultsThe author developed an algorithm that could calculate the reaction deletions that achieve the coupling of growth and production for 91.3% metabolites in genome-scale models of S. cerevisiae under anaerobic conditions. Computational experiments showed that the proposed algorithm is efficient also for aerobic conditions and Escherichia coli. In these analyses, the least target production rates were evaluated using flux variability analysis when multiple fluxes yield the highest growth rate. To demonstrate the feasibility of the coupling, the author derived appropriate reaction deletions using the new algorithm for target production in which the search space was divided into small cubes (CubeProd).ConclusionsThe author developed a novel algorithm, CubeProd, to demonstrate that growth coupling is possible for most metabolites in S.cerevisiae under anaerobic conditions. This may imply that growth coupling is possible by reaction deletions for most target metabolites in any genome-scale constraint-based metabolic networks. The developed software, CubeProd, implemented in MATLAB, and the obtained reaction deletion strategies are freely available.

scholarly journals Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling

2019 ◽  
Author(s):  
Takeyuki Tamura
Keyword(s):  
Strong Coupling ◽  
Metabolic Networks ◽  
Search Space ◽  
Anaerobic Conditions ◽  
Case Scenario ◽  
Worst Case ◽  
Metabolic Network Analysis ◽  
Network Topologies ◽  
Genome Scale ◽  
Parameter Values

AbstractBackgroundMetabolic network analysis through flux balance is an established method for the computational redesign of production strains in metabolic engineering. The computational redesign is often based on reaction deletions from the original wild type networks. A key principle often used in this method is the production of target metabolites as by-products of cell growth. From a viewpoint of bioinformatics, it is very important to prepare a set of algorithms that can determine reaction deletions that achieve growth coupling whatever network topologies, target metabolites and parameter values will be considered in the future. Recently, the strong coupling-based method was used to demonstrate that the coupling of growth and production is possible for nearly all metabolites through reaction deletions in genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae under aerobic conditions. However, when growing S. cerevisiae under anaerobic conditions, deletion strategies using the strong coupling-based method were possible for only 3.9% of all metabolites. Therefore, it is necessary to develop algorithms that can achieve growth coupling by reaction deletions for the conditions that the strong coupling-based method was not efficient.ResultsWe developed an algorithm that could calculate the reaction deletions that achieve the coupling of growth and production for 91.3% metabolites in genome-scale models of S. cerevisiae under anaerobic conditions. This analysis was conducted for the worst-case-scenario using flux variability analysis. To demonstrate the feasibility of the coupling, we derived appropriate reaction deletions using the new algorithm for target production in which the search space was divided into small cubes (CubeProd).ConclusionsWe developed a novel algorithm, CubeProd, to demonstrate that growth coupling is possible for most metabolites in S.cerevisiae under anaerobic conditions. This may imply that growth coupling is possible by reaction deletions for most target metabolites in any genome-scale constraint-based metabolic networks. The developed software, CubeProd, implemented in MATLAB, and the obtained reaction deletion strategies are freely available.

scholarly journals Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling

2020 ◽  
Author(s):  
Takeyuki Tamura
Keyword(s):  
Strong Coupling ◽  
Metabolic Networks ◽  
Search Space ◽  
Anaerobic Conditions ◽  
Case Scenario ◽  
Worst Case ◽  
Metabolic Network Analysis ◽  
Network Topologies ◽  
Genome Scale ◽  
Parameter Values

Abstract Background: Metabolic network analysis through flux balance is an established method for the computational redesign of production strains in metabolic engineering. The computational redesign is often based on reaction deletions from the original wild type networks. A key principle often used in this method is the production of target metabolites as by-products of cell growth. From a viewpoint of bioinformatics, it is very important to prepare a set of algorithms that can determine reaction deletions that achieve growth coupling whatever network topologies, target metabolites and parameter values will be considered in the future. Recently, the strong coupling-based method was used to demonstrate that the coupling of growth and production is possible for nearly all metabolites through reaction deletions in genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae under aerobic conditions. However, when growing S. cerevisiae under anaerobic conditions, deletion strategies using the strong coupling-based method were possible for only 3.9% of all metabolites. Therefore, it is necessary to develop algorithms that can achieve growth coupling by reaction deletions for the conditions that the strong coupling-based method was not efficient. Results: We developed an algorithm that could calculate the reaction deletions that achieve the coupling of growth and production for 91.3% metabolites in genome-scale models of S. cerevisiae under anaerobic conditions. This analysis was conducted for the worst-case scenario using flux variability analysis. To demonstrate the feasibility of the coupling, we derived appropriate reaction deletions using the new algorithm for target production in which the search space was divided into small cubes (CubeProd). Conclusions: We developed a novel algorithm, CubeProd, to demonstrate that growth coupling is possible for most metabolites in S.cerevisiae under anaerobic conditions. This may imply that growth coupling is possible by reaction deletions for most target metabolites in any genome-scale constraint-based metabolic networks. The developed software, CubeProd, implemented in MATLAB, and the obtained reaction deletion strategies are freely available.

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

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...

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

2017 ◽  
Vol 9 (10) ◽  
pp. 830-835 ◽  
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.

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.

scholarly journals In vivo cycling of the Escherichia coli transcription factor FNR between active and inactive states

Microbiology ◽  
2005 ◽  
Vol 151 (12) ◽  
pp. 4063-4070 ◽  
Author(s):  
David P. Dibden ◽  
Jeffrey Green
Keyword(s):  
Escherichia Coli ◽  
De Novo ◽  
Oxygen Availability ◽  
Anaerobic Conditions ◽  
Aerobic Conditions ◽  
Fnr Protein ◽  
Transcription Regulators ◽  
Aerobic And Anaerobic ◽  
Aerobic And Anaerobic Conditions

FNR proteins are transcription regulators that sense changes in oxygen availability via assembly–disassembly of [4Fe–4S] clusters. The Escherichia coli FNR protein is present in bacteria grown under aerobic and anaerobic conditions. Under aerobic conditions, FNR is isolated as an inactive monomeric apoprotein, whereas under anaerobic conditions, FNR is present as an active dimeric holoprotein containing one [4Fe–4S] cluster per subunit. It has been suggested that the active and inactive forms of FNR are interconverted in vivo, or that iron–sulphur clusters are mostly incorporated into newly synthesized FNR. Here, experiments using a thermo-inducible fnr expression plasmid showed that a model FNR-dependent promoter is activated under anaerobic conditions by FNR that was synthesized under aerobic conditions. Immunoblots suggested that FNR was more prone to degradation under aerobic compared with anaerobic conditions, and that the ClpXP protease contributes to this degradation. Nevertheless, FNR was sufficiently long lived (half-life under aerobic conditions, ∼45 min) to allow cycling between active and inactive forms. Measuring the abundance of the FNR-activated dms transcript when chloramphenicol-treated cultures were switched between aerobic and anaerobic conditions showed that it increased when cultures were switched to anaerobic conditions, and decreased when aerobic conditions were restored. In contrast, measurement of the abundance of the FNR-repressed ndh transcript under the same conditions showed that it decreased upon switching to anaerobic conditions, and then increased when aerobic conditions were restored. The abundance of the FNR- and oxygen-independent tatE transcript was unaffected by changes in oxygen availability. Thus, the simplest explanation for the observations reported here is that the FNR protein can be switched between inactive and active forms in vivo in the absence of de novo protein synthesis.

scholarly journals Metabolic network-guided binning of metagenomic sequence fragments

2015 ◽  
Vol 32 (6) ◽  
pp. 867-874 ◽  
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.

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

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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