Protein copy number at 6h, 12h, 24h, 48h, 72h, 96h, average values and SD for the measurements
SEEK ID: https://fairdomhub.org/assays/514
Investigation: Modelling of M. pneumoniae metabolism
Study: Proteomics analysis
Assay type: Proteomics
Technology type: Technology Type
Organisms: No organisms
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 ...
Projects that do not fall under current programmes.
Projects: Manchester Institute for Biotechnology, ICYSB 2015 - International Practical Course in Systems Biology, iRhythmics, INBioPharm, EmPowerPutida, Systo models, MycoSynVac - Engineering Mycoplasma pneumoniae as a broad-spectrum animal vaccine, Multiscale modelling of state transitions in the host-microbiome-brain network, Extremophiles metabolsim, NAD COMPARTMENTATION, Agro-ecological modelling, Bergen(Ziegler lab) project AF-NADase, NAMPT affinity, Stress granules, Modelling COVID-19 epidemics, Bio-crop, ORHIZON-FAIR
Web page: Not specified
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 ...
- To develop a whole-cell dynamic model framework of the metabolism of M. pneumoniae
- To build upon M. pneumoniae models to develop a genome-scale, constraint-based model of M. hyopneumoniae for vaccine optimization
- 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 ...
Submitter: Niels Zondervan
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
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
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