Proteomics Average and SD data for time series data, 6h, 12h, 24h, 48h,72, 96h per protein
SEEK ID: https://fairdomhub.org/studies/236
Experimentalists: Not specified
Created: 18th Apr 2017 at 18:14
Last updated: 14th Jan 2019 at 16:00
Institutions: Wageningen University & Researchhttps://orcid.org/0000-0001-7049-5334
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
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
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
Protein copy number at 6h, 12h, 24h, 48h, 72h, 96h, average values and SD for the measurements
Contributor: Niels Zondervan
Assay type: Proteomics
Technology type: Technology Type
Snapshots: No snapshots
Investigation: Modelling of M. pneumoniae metabolism
Study: Proteomics analysis
Organisms: No organisms
SOPs: No SOPs
Protein copy number estimates, Mean and SD based on multiple proteomics experiments.
Compatible with internal and external metabolite measurements for Growth curve A.
Used as training data for the model
Master file, aggregates metabolite concentrations inside and outside the cell, protein copy number and flux estimates for metabolites in the core model. Based on all internal metabolite concentrations, external metabolite concentrations from growth curve data, flux of glucose, lactate and acetate based on growth curve data and protein copy number data for enzyme concentrations. Combines absolute and relative measurements and metabolomics measurements from different experiment to get an as complete
Investigations: Modelling of M. pneumoniae metabolism