Modelling of M. pneumoniae metabolism

  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 capabilities of a series of Mycoplasmas with the purpose of designing a tailored vaccine portfolio

DOI: 10.15490/fairdomhub.1.investigation.133.2

Zenodo URL: None

Created at: 14th Jan 2019 at 16:02

Contents

Metabolomics measurements

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

Metabolomics internal metabolites, time series 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.

Internal metabolite concentrations time series

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.

  • mM_cell_summary3.xlsx

Master file, metabolite concentration, protein copy number and flux estimates to train the model

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
...

  • MasterParMetData_1.4.xlsx

Internal metabolite concentraitons for mutants, perturbation and time series samples including mutant enzyme fold change

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
...

  • 160622_MPN_All_Metabolomics_Data_Merged_WithDiffAnalysis-1-LS NZ4.2.xlsx

Metabolites all experiments, relative measurements

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

  • Metabolites all experiments3.xlsx

40 samples internal metabolite concentrations FC values used for model simmulations

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
...

  • 40_Independend_samples.xlsx

Metabolomics perturbation samples preparation

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

  • Sira_metabolomics_GC_setup.xls

Metabolomics external metabolites measurements

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

External metabolite concentration times series

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

  • Todos datos growth_curve(with uptake rates).xls

Master file, metabolite concentration, protein copy number and flux estimates to train the model

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
...

  • MasterParMetData_1.4.xlsx

40 samples data analysis - metabolite correlation

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

40 samples internal metabolite concentrations FC values used for model simmulations

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
...

  • 40_Independend_samples.xlsx

40 samples metabolite correlation analysis - heatmaps, PCA, correlation network

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

  • MetaboliteCorrelationAnalysis.zip

Proteomics analysis

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

Proteomics assay

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

Master file, metabolite concentration, protein copy number and flux estimates to train the model

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
...

  • MasterParMetData_1.4.xlsx

Proteomics, protein copy number measured over time

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

  • protein copy number average_whole_growth_curve.xlsx

Core Model training

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

Construction and training of the core model

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

Model training, parameter estimation

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.

  • Model training.zip

Master file, metabolite concentration, protein copy number and flux estimates to train the model

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
...

  • MasterParMetData_1.4.xlsx

Parameter estmimation for model with addition of NoxE

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

  • NoxE_training_data.zip

Parameter scan for the model with addition of oxygen inhibition of LDH

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

  • 2B_Oxygen_inhibition_of_Lactate_Dehydrogenase.zip

Dynamic model of glycolysis, pyruvate metabolism and ATPase in M. pneumonaie with innitial parameter values and annotation

Dynamic model of glycolysis, pyruvate metabolism and ATPase. Initial parameters values for Monod and allosteric control constants were taken from the L. lactis model from Costa et al. since no MPN specific measurements were available. Vmax values are based on MPN growth curve data. The model is annotated with MIRIAM compliant annotation and SBO numbers.

  • All code and input to run analyses and simmulations.zip
  • ModelBeforeParameterEstimation_Annotated_SBML_L2V4.xml

Core model of glycolysis, pyruvate metabolism ATPase + NoxE reaction

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.

  • Model_update_names1.9.08_24h_G6P_fixed_NoxE3.cps

SOP for dynamic core model construction and parameter estimation

Explains the different steps involved in building the core dynamic model of glycolysis, pyruvate metabolism and ATPase in M. pneumoniae
-Construction of the model
-Parameter estimation, selection of best parameter set

  • SOP dynamic model training.txt

Comparison of Kcat values from the model and values from literature

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

Comparison of Kcat values model and values from literature

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
...

  • 2016-11-07_KineticParametersOverview1.9.xlsx

Core Model predictions

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

Validation by simulating independent mutant 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

Internal metabolite concentraitons for mutants, perturbation and time series samples including mutant enzyme fold change

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
...

  • 160622_MPN_All_Metabolomics_Data_Merged_WithDiffAnalysis-1-LS NZ4.2.xlsx

Comparison of model SS metabolite concentrations with measured values using sampling

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.

  • Supplement 2B_SS_Metabolite_Concentration_mutants.pdf

Mean Absolute Percentage Error between measured and simulated metabolite concentrations using 1000x sampling

Mean Absolute Percentage Error between measured and simulated metabolite concentrations using 1000x sampling from the Gausian distribution of the measured values based on multiple replicates per measured conditions. SS simulations was performed.
Graphs showing the Mean Absolute Percentage Error 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.

  • Supplement 2A_SS_sMAPE_mutants.pdf

Symmetric mean absolute percentage error per sample

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

  • sMAPE_Overview.png

40 samples internal metabolite concentrations FC values used for model simmulations

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
...

  • 40_Independend_samples.xlsx

Dynamic modelling pipeline

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.

  • All code and input to run analyses and simmulations.zip

Dynamic model of glycolysis, pyruvate metabolism and ATPase in M. pneumonaie with innitial parameter values and annotation

Dynamic model of glycolysis, pyruvate metabolism and ATPase. Initial parameters values for Monod and allosteric control constants were taken from the L. lactis model from Costa et al. since no MPN specific measurements were available. Vmax values are based on MPN growth curve data. The model is annotated with MIRIAM compliant annotation and SBO numbers.

  • All code and input to run analyses and simmulations.zip
  • ModelBeforeParameterEstimation_Annotated_SBML_L2V4.xml

Metabolic control analysis (local and global)

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)

Global sensitivity analysis

Violin plot of the metabolic control of model parameters on the flux through PFK (as a proxy for flux through glycolysis) based on a 100.000 Latin Hypercube samples from the parameter space (range 0.001-100 for Km values and 0.001-1000 for Vmax values).

  • global_sensitivity.pdf

Global sensitivity analysis - correlation between parameters

Shows the correlation in metabolic control between parameters.
The plot shows central carbon metabolis basically consist of a few control hubs of reactions of which the parameters are correlated. In other words, just the reaction network combined with allosteric control and equilibrium constants impose some constrains on possible parameter value combinations that lead to certain behaviour (such as the flux and concentrations measured in vivo/vitro).

  • GlobalSensitivityParameterControlCorrelation.png

Local sensitivity analysis based on 40 samples

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.

  • MasterFile_AllTrainingData.xlsx

Global sensitivity analysis - tab seperated file

Contains the raw output of the global sensitivity analysis, can be used as input for plotting using the R script "plotLocalGlobalSensitivity1.5.R" associated to the same asset

  • globalSensitivity.tsv

Local sensitivity analysis based on 40 samples - tab seperated file

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.

  • localSensitivity.tsv

Dynamic modelling pipeline

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.

  • All code and input to run analyses and simmulations.zip

Dynamic model of glycolysis, pyruvate metabolism and ATPase in M. pneumonaie with innitial parameter values and annotation

Dynamic model of glycolysis, pyruvate metabolism and ATPase. Initial parameters values for Monod and allosteric control constants were taken from the L. lactis model from Costa et al. since no MPN specific measurements were available. Vmax values are based on MPN growth curve data. The model is annotated with MIRIAM compliant annotation and SBO numbers.

  • All code and input to run analyses and simmulations.zip
  • ModelBeforeParameterEstimation_Annotated_SBML_L2V4.xml

Dynamic model simmulation pipeline

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
...

Dynamic modelling pipeline

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.

  • All code and input to run analyses and simmulations.zip

Dynamic model of glycolysis, pyruvate metabolism and ATPase in M. pneumonaie with innitial parameter values and annotation

Dynamic model of glycolysis, pyruvate metabolism and ATPase. Initial parameters values for Monod and allosteric control constants were taken from the L. lactis model from Costa et al. since no MPN specific measurements were available. Vmax values are based on MPN growth curve data. The model is annotated with MIRIAM compliant annotation and SBO numbers.

  • All code and input to run analyses and simmulations.zip
  • ModelBeforeParameterEstimation_Annotated_SBML_L2V4.xml

Core model predicting combined mutations and perturbations

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

40 samples, OE mutants of glycolysis and pyruvate metabolism enzymes combined with metabolite levels of all 40 samples

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.

Double mutants and perturbations

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.

  • Combined mutant simulations, PFK flux fold change.png

Dynamic modelling pipeline

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.

  • All code and input to run analyses and simmulations.zip

Dynamic model of glycolysis, pyruvate metabolism and ATPase in M. pneumonaie with innitial parameter values and annotation

Dynamic model of glycolysis, pyruvate metabolism and ATPase. Initial parameters values for Monod and allosteric control constants were taken from the L. lactis model from Costa et al. since no MPN specific measurements were available. Vmax values are based on MPN growth curve data. The model is annotated with MIRIAM compliant annotation and SBO numbers.

  • All code and input to run analyses and simmulations.zip
  • ModelBeforeParameterEstimation_Annotated_SBML_L2V4.xml

Genome-scale, constraint-based metabolic modeling of M. hyopneumonia

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

Construction of a Genome Scale Metabolitic model of M. hyopneumoniae

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

Genome Scale Metabolic model of M. hyopneumoniae

No description specified

  • TK284-MHyo11.xml

SOP for generating a Genome Scale Metabolic model of M. hyopneumoniae

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

  • SOP Construction of Genome Scale Metabolic model M. hyopneumoniae.docx

Transcriptomics of M. pneumoniae at different times of growth

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.

Transcriptomics assay of M. pneumoniae at diferent times of growth

No description specified

Absolute copy number per locus tag, M. pneumoniae at different times of growth

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

  • GC in absolute copy numbers.xlsx
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Citation
Zondervan, N. (2019). Modelling of M. pneumoniae metabolism. FAIRDOMHub. https://doi.org/10.15490/FAIRDOMHUB.1.INVESTIGATION.133.2
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Created: 14th Jan 2019 at 16:02

Last updated: 14th Jan 2019 at 16:06

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