Contains the estimated oxygen concentration and metabolite concentrations as wel as the model with addition of an oxygen inhibition parameter.
Results: Addition of the oxygen inhibition term does not improve the modell with the current parameter set
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
Training of the core model, parameter estimation using Evolutionary Programming using metabolomics, proteomics and some flux data.
The core model contains reactions in glycolysis, pyruvate metabolism and ATPase
Person responsible: Niels Zondervan
Snapshots: No snapshots
Training of the model, parameter estimation using Evolutionary Programming using metabolomics, proteomics and some flux data.
Contributor: Niels Zondervan
Biological problem addressed: Model Analysis Type
Snapshots: No snapshots
Investigation: Modelling of M. pneumoniae metabolism
Study: Core Model training
Organisms: No organisms
SOPs: No SOPs
Data files: Master file, metabolite concentration, protein ..., Model training, parameter estimation, Parameter estmimation for model with addition o..., Parameter scan for the model with addition of o...