scholarly journals Medusa: software to build and analyze ensembles of genome-scale metabolic network reconstructions

2019 ◽  
Author(s):  
Gregory L. Medlock ◽  
Jason A. Papin
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Computational Biology ◽  
Metabolic Network ◽  
Metabolic Networks ◽  
Ensemble Simulations ◽  
Link Type ◽  
Genome Scale ◽  
Python Package

AbstractUncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles, perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org/) and from github (https://github.com/gregmedlock/Medusa/), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest/.

scholarly journals BOFdat: generating biomass objective function stoichiometric coefficients from experimental data

2018 ◽  
Author(s):  
Jean-Christophe Lachance ◽  
Jonathan M. Monk ◽  
Colton J. Lloyd ◽  
Yara Seif ◽  
Bernhard O. Palsson ◽  
...  
Keyword(s):  
Experimental Data ◽  
Objective Function ◽  
Source Code ◽  
Cell Composition ◽  
Link Type ◽  
Scale Models ◽  
Relative Abundances ◽  
Genome Scale ◽  
Python Package

AbstractGenome-scale models (GEMs) rely on a biomass objective function (BOF) to predict phenotype from genotype. Here we present BOFdat, a Python package that offers functions to generate biomass objective function stoichiometric coefficients (BOFsc) from macromolecular cell composition and relative abundances of macromolecules obtained from omic datasets. Growth-associated and non-growth associated maintenance (GAM and NGAM) costs can also be calculated by BOFdat.BOFdat is freely available on the Python Package Index (pip install BOFdat). The source code and an example usage (Jupyter Notebook and example files) are available on GitHub (https://github.com/jclachance/BOFdat). The documentation and API are available through ReadTheDocs (https://bofdat.readthedocs.io)[email protected], [email protected], [email protected]

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...

scholarly journals Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Javad Aminian-Dehkordi ◽  
Seyyed Mohammad Mousavi ◽  
Arezou Jafari ◽  
Ivan Mijakovic ◽  
Sayed-Amir Marashi
Keyword(s):  
Experimental Data ◽  
Metabolic Network ◽  
Bacillus Megaterium ◽  
In Silico ◽  
Metabolic Model ◽  
Growth Behavior ◽  
Industrial Biotechnology ◽  
Flux Analysis ◽  
A Genome ◽  
Genome Scale

AbstractBacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks

Nanoscale ◽  
2021 ◽  
Author(s):  
Bernabé Ortega-Tenezaca ◽  
Humberto González-Díaz
Keyword(s):  
Machine Learning ◽  
Antibacterial Activity ◽  
Metabolic Network ◽  
Network Structure ◽  
Metabolic Networks ◽  
Antibacterial Nanoparticles

Machine learning mapping of antibacterial nanoparticles vs. bacteria metabolic network structure.

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 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.

scholarly journals Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions

2021 ◽  
Author(s):  
Thomas James Moutinho ◽  
Benjamin C Neubert ◽  
Matthew L Jenior ◽  
Jason A. Papin
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Biological Data ◽  
Supporting Evidence ◽  
Phenotypic Data ◽  
E Coli ◽  
Simultaneous Integration ◽  
Improve Model ◽  
K 12 ◽  
Genome Scale

Genome-scale metabolic network reconstructions (GENREs) are valuable tools for understanding microbial community metabolism. The process of automatically generating GENREs includes identifying metabolic reactions supported by sufficient genomic evidence to generate a draft metabolic network. The draft GENRE is then gapfilled with additional reactions in order to recapitulate specific growth phenotypes as indicated with associated experimental data. Previous methods have implemented absolute mapping thresholds for the reactions automatically included in draft GENREs; however, there is growing evidence that integrating annotation evidence in a continuous form can improve model accuracy. There is a need for flexibility in the structure of GENREs to better account for uncertainty in biological data, unknown regulatory mechanisms, and context specificity associated with data inputs. To address this issue, we present a novel method that provides a framework for quantifying combined genomic, biochemical, and phenotypic evidence for each biochemical reaction during automated GENRE construction. Our method, Constraint-based Analysis Yielding reaction Usage across metabolic Networks (CANYUNs), generates accurate GENREs with a quantitative metric for the cumulative evidence for each reaction included in the network. The structure of a CANYUN GENRE allows for the simultaneous integration of three data inputs while maintaining all supporting evidence for biochemical reactions that may be active in an organism. CANYUNs is designed to maximize the utility of experimental and annotation datasets and to ultimately assist in the curation of the reference datasets used for the automatic reconstruction of metabolic networks. We validated CANYUNs by generating an E. coli K-12 model and compared it to the manually curated reconstruction iML1515. Finally, we demonstrated the use of CANYUNs to build a model by generating an E. coli Nissle CANYUN GENRE using novel phenotypic data that we collected. This method may address key challenges for the procedural construction of metabolic networks by leveraging uncertainty and redundancy in biological data.

scholarly journals AGEpy: a Python package for computational biology

2018 ◽  
Author(s):  
Franziska Metge ◽  
Robert Sehlke ◽  
Jorge Boucas
Keyword(s):  
Computational Biology ◽  
Open Source ◽  
High Throughput ◽  
Biological Data ◽  
Command Line ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Link Type ◽  
Biological Meaning ◽  
Python Package

AbstractSummary:AGEpy is a Python package focused on the transformation of interpretable data into biological meaning. It is designed to support high-throughput analysis of pre-processed biological data using either local Python based processing or Python based API calls to local or remote servers. In this application note we describe its different Python modules as well as its command line accessible toolsaDiff,abed,blasto,david, andobo2tsv.Availability:The open source AGEpy Python package is freely available at:https://github.com/mpg-age-bioinformatics/AGEpy.Contact:[email protected]

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