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Contains the raw output of the global sensitivity analysis, can be used as input for plotting using the R script "plotLocalGlobalSensitivity1.5.R" associated to the same asset
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Created: 10th Jan 2019 at 13:25
Last updated: 14th Jan 2019 at 16:00
Last used: 21st Feb 2021 at 18:16

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Projects: MycoSynVac - Engineering Mycoplasma pneumoniae as a broad-spectrum animal vaccine, WURSynBio
Institutions: Wageningen University & Research

Roles: PhD Student
Expertise: Bioinformatics, Systems Biology, Agent-based modelling, Dynamic modelling, Python, Java, R, pathogen host interaction, Molecular Biology
Tools: Copasi, libRoadrunner, Python, R, semantic web
I am a researcher (PhD student) working at Wageningen University & Research as bioinformatician and modeller. I am working as part of the MycoSynVac (http://www.mycosynvac.eu/) project on dynamic modelling of central carbon metabolism in M. pneumoniae, to be extended to full dynamic modelling of metabolism to be implemented in a whole cell model.
I am also looking into possibilities to improve standards in model generation using semantic technologies, improving automatic generation, annotation
...
The MycoSynVac project AIMS at using cutting-edge synthetic biology methodologies to engineer Mycoplasma pneumoniae as a universal chassis for vaccination.
Designing a universal Mycoplasma chassis that can be deployed as single- or multi-vaccine in a range of animal hosts. Annually, infections caused by Mycoplasma species in poultry, cows, and pigs result in multimillion Euro losses in the USA and Europe.
There is no effective vaccination against many Mycoplasmas that infect pets, humans and farm
...
Programme: Independent Projects
Public web page: http://www.mycosynvac.eu/
Organisms: Mycoplasma pneumoniae
1. To develop a whole-cell dynamic model framework of the metabolism of M. pneumoniae
2. To build upon M. pneumoniae models to develop a genome-scale, constraint-based model of M.
hyopneumoniae for vaccine optimization
3. To deploy the metabolic model(s) to: 1) the rational design and optimization of the vaccine chassis; 2)
aid the development of a higher-growth rate chassis; 3) assist the development of a nutrient optimized a
serum-free growth medium and; 4) assess, at genome scale, the metabolic
...
Snapshots: Snapshot 1, Snapshot 2, Snapshot 3
Studies: Core Model predictions, Core Model training, Core model predicting combined mutations and perturbations, Genome-scale, constraint-based metabolic modeling of M. hyopneumonia, Metabolomics measurements, Proteomics analysis, Transcriptomics of M. pneumoniae at different times of growth
Assays: 40 samples data analysis - metabolite correlation, 40 samples, OE mutants of glycolysis and pyruvate metabolism enzymes com..., All samples data, Comparison of Kcat values from the model and values from literature, Construction and training of the core model, Construction of a Genome Scale Metabolitic model of M. hyopneumoniae, Dynamic model simmulation pipeline, Metabolic control analysis (local and global), Metabolomics external metabolites measurements, Metabolomics internal metabolites, time series measurements, Proteomics assay, Transcriptomics assay of M. pneumoniae at diferent times of growth, Validation by simulating independent mutant and perturbation samples
Validation of the core model of glycolysis, pyruvate metabolism and ATPase reaction using OE, KO mutant samples and perturbation samples
Person responsible: Niels Zondervan
Snapshots: No snapshots
Metabolic control analysis:
Local control coefficients for 40 independent samples based on 100x sampling from the measurement distribution
Global control analysis based on 100.000 Latin Hypercube sampling from the parameter search range (0.01-100 for Km values and 0.001-1000 for Vmax values)
Submitter: Niels Zondervan
Biological problem addressed: Model Analysis Type
Snapshots: No snapshots
Investigation: Modelling of M. pneumoniae metabolism
Study: Core Model predictions
Organisms: No organisms
Models: Dynamic model of glycolysis, pyruvate metabolis...
SOPs: No SOPs
Data files: Dynamic modelling pipeline, Global sensitivity analysis, Global sensitivity analysis - correlation betwe..., Global sensitivity analysis - tab seperated file, Local sensitivity analysis based on 40 samples, Local sensitivity analysis based on 40 samples ... and 1 hidden item