Dynamic modelling pipeline

Version 2

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 ...

Predictions made using the core model for combinatorial perturbations to the model simulating combined effects from OE, KO mutants, perturbations and time series concentrations.

Validation of the core model of glycolysis, pyruvate metabolism and ATPase reaction using OE, KO mutant samples and perturbation samples

Validation by simulating independent OE, KO mutant and perturbation samples, using sampling of the gausian distribution based on the mean and SD of measurements per sample. A 1000 samples of the gausian distribution of the mean and SD was performed per sample to show error in the measurements and how it propegates in predicted metabolite concentration in SS

Simulation of OE mutants targetting enzymes in the model, combined with metabolite concentrations and enzyme fold change of from the 40 samples. For each second mutant the enzyme concentrations in case of OE and KO mutants in updated and the metabolite concentrations of the second sample are loaded in the model. Using this approach the model approximately predicts combinatorial effects of OE mutations with other mutations, perturbations and time series concentrations.

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)

The associated zip files contains all input files and a Jupyter notebook to rerun sampled simmulations, combined simmulations, parameter scan for the model with addition of an oxygin inhibiton of LDH, local- and global-sensitivity analysis and plot simmulation output in various formats. In addition the zip file contains the py36.yaml file that can be used to recreate the model simmulation environment using Anaconda making all simmulations completely reproducable. All information on how to use ...