Core Model training

Projects that do not fall under current programmes.

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 ...

Training of the model, parameter estimation using Evolutionary Programming using metabolomics, proteomics and some flux data.

Comparison of Kcat values from the model and values from literature.

Contains all 10 parameter sets, loaded with proteomics measurements for three time points (6h,24h, 48h). Contains all parameter sets exported from COPASI, an overview of the parameter sets in the three conditions and how well they perform as well as scripts to load parameter sets as well as an R script to generate an overview of the model error in predicting for all 10 parameter sets.

Comparison of Kcat values model and values from literature. Model values are based on Vmax enzyme parameters (maximum activity per enzyme molecule). Literature values are largely based on whole cell enzyme extract assays and do not take into account allosteric control. In addition activity is measured at varying time points and varying conditions. The error based on differences in enzyme concentrations at different time points and the error in protein copy number measurements is taken into account ...

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 ...

Contains training data and model with addition of the NoxE reaction 6h, 24h and 48h metabolite concentration data as well as calculated oxygen concentrations assuming no diffusion limit through the biofilm layer

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

Dynamic model of glycolysis, pyruvate metabolism and NoxE. The model is parameterized by selecting the best out of 100 parameter set using Copasi's Genetic algorithm with 1000 itterations and 500 simmulatanious models.

Core model with the addition of a NoxE reaction to regenerate NAD using O2. COPASI’s build in Evolutionary programming algorithm was used to estimate parameters using a maximum of 2000 generations with a population size of 100 models with value scaling as weights to train the 5 parameters of the NoxE reaction.