MycoSynVac - Engineering Mycoplasma pneumoniae as a broad-spectrum animal vaccine

Projects that do not fall under current programmes.

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.

Internal metabolites concentrations for time series data (not pulse experiments) and for mutant OE, KO mutants and perturbations External metabolite concentrations for time series data (not pulse experiments) and for mutant OE, KO mutants and perturbations Mutant (OE, KO, perturbation) metabolite measurements

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

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

Construction of a Genome scale constrained-based metabolic modeling of M. hyopneumonia

Proteomics Average and SD data for time series data, 6h, 12h, 24h, 48h,72, 96h per protein

Contains copy number per locus tag at different times of Growth between 0.25h and 96 hours. M. pneumoniae was grown in Batch, cells attached to the bottom of the flask (single cell layer), non stirred, non aerated.

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

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

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

Protein copy number at 6h, 12h, 24h, 48h, 72h, 96h, average values and SD for the measurements

Metabolomics time series measurements for internal metabolites for 6h, 24h and 48h for multiple experiments. Largely based on MAss spectrometry, bioluminescence kits to measure NAD, NADH at 24h, other time points are infered from relative measurements times the absolute measurements at 24h.

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.

Measurements of external metabolites based on growth curve data. Flux estimates for uptake of external metabolites such as glucose and production rates for external metabolites lactate and acetate

Construction and manual curated Genome Scale Metabolitic model of M. hyopneumoniae. Dynamic flux balance analysis was performed for glucose uptake

Contains the analysis of the internal metabolite concentrations of the 40 independend samples Pearson correlation was used to generate heatmaps Pearson correlation with p-value cutof of 0.001 was used and as input for a correlation network (grouping using H-clust) Principal component analysis was performed on samples, F-ion and H-ion data combined and seperately Zip files contains the data (FC.txt), PCA and heatmap plots and the script to re-generate these plots

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 ...

Simple overview of all samples used for training, internal validation by copasi en external validation. Overview of samples metadata, mean metabolite concentration and enzyme concentrations used in the model. Only metabolites present in the model are shown.

Contains time series metabolomics measurements in mM from different experiments. Measurements of these internal metabolites should be combined with Growth curve A measurements forthe external metabolites. Contains mean and standard deviation for a few but not all mutants based on multiple time series experiments carried out over the years. Daniel 3rd experiment data is the most complete and is together with Wodke and Tobias measurements used as training data for the model.

Contains for all samples, mean metabolite concentration and shows enzyme concentration used in model fitting and simmulations. Only metabolite present in the model are shown.

Simulation of double mutants and perturbations and time series samples using for Sample 1 only OE mutants of which we update the enzyme concentrations. 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.

Comparison of model SS metabolite concentrations with measured values using 1000x sampling from the Gausian distribution of the measured values based on multiple replicates per measured conditions. Graphs showing the distribution of measured and simulated metabolite concentration for 95 mutand (KO, OE), perturbation and time series measurements. Model simulations performed using 24h proteomics with modification of enzyme parameters for KO and OE mutants.

Contains: -Relative metabolite measurements at different time points from all experiments -Absolute metabolite measurements for amino-acid analysis of the proteome and the cytosol -Effect on adding CaCl2, KCl or NaCl to the medium on growth -Effect of spiking of growth medium with additional amino acids

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.

Contains a Jupyter notebook file that uses libroadrunner and tellurium to run all simmulations and analysis based on the 40 independent samples. The Readme.txt file contains information on how to recreate the complete modelling environment used for all simmulations and analysis using Anaconda.

Local sensitivity analysis based on 40 samples using 1000x sampling from measurement distribution. The control shown is the control over flux through glycolysis represented by flux through PRK. Th plot summarized the control for each parameter over all observed metabolite concentrations encountered for the 40 samples. As such the metabolic control analysis is local but shows the distribution taking into account measurement error as well as biological variation over the 40 samples.

Tab seperated file containing the raw output of the local sensitivity analysis based on 40 samples (based on 1000x sampled metabolite values) from the MEAN and SD of the metabolite measurements. Sensitivity analysis is based on the flux through PFK as objective and as proxy for flux through glycolysis. Data can be plotted using the R script "plotLocalGlobalSensitivity1.5.R" associated to the same assay.

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 ...

Contains relative mutant (OE, KO) perturbation and time series samples metabolite concentrations and enzyme fold change of targeted enzymes used for model validation. Measured are the relative fold change, Mean and SD of log2 fold change values are based on multiple measurements per sample (minimum of three). Contains input data for Automated Model simulations pipeline to load and update the models metabolite concentrations and enzyme parameters to simulate all sample using a custom python script ...

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 growth curve data such as Glucose uptake rate, lactate and acetate production at different time points. Growth curve A was used train the model with external glucose concentration as well as external lactate, acetate concentration and estimated glucose acetate and lactate flux

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

Contains the absolute copy number per locus tag during growth between 0.25 and 96hours of growth Growth in batch, cells attached to the bottom of the flask, non-aerated, non-stirred

Symmetric mean absolute percentage error per sample graph for the 40 independent samples

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 relative metabolite concentrations for 40 samples based on technical triplicates. Medium and SD values were calculated and used for 1000 sampled simmulations (sampling from the measurement distribution per metabolite) per sample. Also contains annotion to link metabolite concentrations and protein fold change measurements for OE and KO mutants to the model as well as external glucose, acetate and lactate concentrations. A SBtab like format was used to easily load the MEAN and SD metabolite ...

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

Contains the FC metabolite concentration values data and a R script to perform PCA, generate hetamaps and a correlation network.

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.

Metabolomics perturbation sample preparation and description of how the exact details of the perturbations. Perturbations: -Glucose starvation -Amino acid starvation -Fe2+ depletion -Oxidative stress via H2O2 -Glycerol addition to the medium

Standard Operating Procedure describing the process and software used in generating a Genome Scale Metabolic model of M. hyopneumoniae. Used software: Pathway tools PathLogic Cobrapy the Cobra Toolbox libSBML