Model_iRV973_Cauris2023
Ontology highlight
ABSTRACT:
SUBMITTER: 
Romeu Viana
PROVIDER: MODEL2402290003 | BioModels | 2024-03-07
REPOSITORIES: BioModels
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| MODEL2402290003?filename=iRV973.xml | Xml |
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Project description:Kim2009 - Genome-scale metabolic network of
Acinetobacter baumannii (AbyMBEL891)
This model is described in the article:
Genome-scale metabolic
network analysis and drug targeting of multi-drug resistant
pathogen Acinetobacter baumannii AYE.
Kim HU, Kim TY, Lee SY.
Mol Biosyst 2010 Feb; 6(2):
339-348
Abstract:
Acinetobacter baumannii has emerged as a new clinical threat
to human health, particularly to ill patients in the hospital
environment. Current lack of effective clinical solutions to
treat this pathogen urges us to carry out systems-level studies
that could contribute to the development of an effective
therapy. Here we report the development of a strategy for
identifying drug targets by combined genome-scale metabolic
network and essentiality analyses. First, a genome-scale
metabolic network of A. baumannii AYE, a drug-resistant strain,
was reconstructed based on its genome annotation data, and
biochemical knowledge from literatures and databases. In order
to evaluate the performance of the in silico model,
constraints-based flux analysis was carried out with
appropriate constraints. Simulations were performed from both
reaction (gene)- and metabolite-centric perspectives, each of
which identifies essential genes/reactions and metabolites
critical to the cell growth. The gene/reaction essentiality
enables validation of the model and its comparative study with
other known organisms' models. The metabolite essentiality
approach was undertaken to predict essential metabolites that
are critical to the cell growth. The EMFilter, a framework that
filters initially predicted essential metabolites to find the
most effective ones as drug targets, was also developed.
EMFilter considers metabolite types, number of total and
consuming reaction linkage with essential metabolites, and
presence of essential metabolites and their relevant enzymes in
human metabolism. Final drug target candidates obtained by this
system framework are presented along with implications of this
approach.
This model is hosted on
BioModels Database
and identified by:
MODEL1507180029.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2015-07-30 | MODEL1507180029 | BioModels
Project description:Cryptococcus neoformans is a ubiquitous environmental fungus that can also cause life-threatening infections in immunocompromised individuals. As a competent pathogen, Cryptococcus needs to reprogram its metabolism to adapt the drastic differences between environmental niches and host niches. A well-curated genome-scale metabolic model (GEM) is a powerful tool to facilitate the investigation of the metabolic resilience of an organism Here we reconstructed and validated iCNG99, a GEM for C. neoformans reference strain H99, and evaluated its predictive performance across 43 growth conditions and gene essentiality benchmarks. The model achieved high confidence essential gene prediction (precision = 0.77) and recapitulated pathways targeted by clinically available antifungals. Integration with transcriptomic and metabolomic data enabled iCNG99 to capture condition-specific metabolic adaptations and to identify candidate vulnerabilities in drug tolerance, revealing metabolic adaptations associated with survival within host conditions and drug susceptibility. Together, iCNG99 provides a systems-level computational platform for investigating C. neoformans metabolism and for prioritizing antifungal vulnerabilities.
2026-04-13 | GSE327336 | GEO
Project description:This is a model of the genome scale reconstruction of the Vibrio vulnificus metabolic network, VvuMBEL943, described in the article:
Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery
Hyun Uk Kim, Soo Young Kim, Haeyoung Jeong, Tae Yong Kim, Jae Jong Kim, Hyon E Choy, Kyu Yang Yi, Joon Haeng Rhee, and Sang Yup Lee. Molecular Systems Biology
7:460 Jan 2011 doi: 10.1038/msb.2010.115
Abstract:
Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.
This metabolic network model has been thoroughly validated by the authors. VvuMBEL943 is a stoichiometric model that contains the metabolic information of the microbial pathogen, Vibrio vulnificus CMCP6, at genome-scale. The SBML version was generated by Hyun Uk Kim using MetaFluxNet.
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2024-09-02 | BIOMD0000001064 | BioModels
Project description:Targeting epigenetic regulators to overcome drug resistance in the emerging human fungal pathogen Candida auris
| PRJNA1245342 | ENA
Project description:A Large-Scale Human Toxicogenomics Resource for Drug-Induced Liver Injury Prediction
| PRJNA1331546 | ENA
Project description:Candida albicans is the most common human fungal pathogen causing mucosal and systemic infections, but human anti-fungal immunity remains poorly defined. Expression profiling of Candida-stimulated human peripheral blood mononuclear cells (PBMCs) provides new insights into Candida-specific host defense mechanisms in humans.
2014-05-01 | GSE42606 | GEO
Project description:Wodke2013 - Genome-scale constraint-based model of M.pneumoniae energy metabolism (iJW145)
A new genome-scale metabolic reconstruction of M.pneumoniae is used in combination with external metabolite measurement and protein abundance measurements to quantitatively explore the energy metabolism of this genome-reduce human pathogen.
This model is described in the article:
Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling.
Wodke JA, Puchałka J, Lluch-Senar M, Marcos J, Yus E, Godinho M, Gutiérrez-Gallego R, dos Santos VA, Serrano L, Klipp E, Maier T.
Mol Syst Biol. 2013;9:653.
Abstract:
Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint-based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time-dependent changes, albeit using a static model. By performing an in silico knock-out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.
This model is hosted on BioModels Database
and identified
by: MODEL1301290000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource
for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to the public
domain worldwide. Please refer to CC0 Public Domain
Dedication
for more information.
2005-01-01 | MODEL1301290000 | BioModels
Project description:This data was generated to compare genome-wide expression differences between a major fungal pathogen of humans, Candida albicans and its less pathogenic relative, Candida dubliniensis, using interspecies hybrids to systematically identify cis-regulatory adaptations.
2021-03-31 | GSE168112 | GEO
Project description:The in silico single gene deletion step was adapted to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance. Based on publicly available and in-house large scale transcriptomic data, metabolic models for melanoma were reconstructed, enabling the prediction of 28 candidate drugs and estimating their respective efficacy.
| EGAS00001006463 | EGA
Project description:Genome-scale models represent the link between an organism's genetic information and experimentally observable biological phenotypes. They facilitate metabolic engineering and the discovery of network properties such as the identification of novel drug targets. Most commonly, metabolite consumption data is used to limit the solution space, sometimes in combination with gene expression data. However, information about gene expression only poorly correlates with the abundance of the respective proteins within the cell. As such, we developed a method to map and integrate the whole-cell proteome into genome-scale models on the example of lactic acid bacteria (LAB). To the best of our knowledge, this work represents the first effort to integrate proteome data into genome-scale models on such a scale.
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