# Assays

**27**Assays visible to you, out of a total of

**93**

For scRNA-Seq, iSABs were dissociated using the Primary Cardiomyocyte Isolation Kit (Thermo Fisher Scientific) before library preparation was performed using the 10xGenomics system with subsequent sequencing on the HighSeq4000 (Illumina). The mouse-SAN scRNA-Seq protocol is described in Goodyer et al. Preprocessing of raw sequencing data from iSABs relied on tools of the Cell Ranger Software (v.6.1.0) as was the procedure in Goodyer et al. Downstream analyses were conducted similar for both ...

**Submitter**: Anne-Marie Galow

**Biological problem addressed**: Gene Expression

**Investigation:** 1 hidden item

Application of the LoRAS oversampling approach on single-cell/single-nuclei data to annotate/identify specific cell populations in new data based on previously, manually curated data.

**Submitter**: Markus Wolfien

**Biological problem addressed**: Annotation

**Investigation:** 1 hidden item

**Study:** 1 hidden item

Here, we conduct a proof of principle by comparing a 2D and 3D fluorescent image analysis based approach on unlabeled cardiomyocytes. Based on the CellProfiler software, we extracted high-dimensional features of individual cells and nuclei, which are subsequently down-sampled and clustered. These clusters are furthermore benchmarked via different machine learning classifiers (e.g., AdaBoost, Gradient Boosting, Random Forest) as the ground truth for our proposed approach.

**Submitter**: Markus Wolfien

**Biological problem addressed**: Model Analysis Type

**Investigation:** 1 hidden item

**Study:** 1 hidden item

**Submitter**: Anne-Marie Galow

**Biological problem addressed**: Model Analysis Type

**Investigation:** 1 hidden item

**Submitter**: Markus Wolfien

**Biological problem addressed**: Model Analysis Type

**Investigation:** 1 hidden item

**Submitter**: Markus Wolfien

**Biological problem addressed**: Gene Expression

**Investigation:** 1 hidden item

**Study:** Single nuclei comparison

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.

**Submitter**: Niels Zondervan

**Biological problem addressed**: Model Analysis Type

**Investigation:** Modelling of M. pneumoniae metabolism

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 ...

**Submitter**: Niels Zondervan

**Biological problem addressed**: Model Analysis Type

**Investigation:** Modelling of M. pneumoniae metabolism

**Study:** Core Model predictions

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

**Submitter**: Niels Zondervan

**Biological problem addressed**: Model Analysis Type

**Investigation:** Modelling of M. pneumoniae metabolism

**Study:** Core Model training

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

**Submitter**: Niels Zondervan

**Biological problem addressed**: Model Analysis Type

**Investigation:** Modelling of M. pneumoniae metabolism

**Study:** Core Model predictions

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

**Submitter**: Niels Zondervan

**Assay type**: Proteomics

**Technology type**: Technology Type

**Investigation:** Modelling of M. pneumoniae metabolism

**Study:** Proteomics analysis

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.

**Submitter**: Niels Zondervan

**Assay type**: Experimental Assay Type

**Technology type**: Mass Spectrometry

**Investigation:** Modelling of M. pneumoniae metabolism

**Study:** Metabolomics 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

**Submitter**: Niels Zondervan

**Assay type**: Experimental Assay Type

**Technology type**: Technology Type

**Investigation:** Modelling of M. pneumoniae metabolism

**Study:** Metabolomics measurements

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)

**Submitter**: Niels Zondervan

**Biological problem addressed**: Model Analysis Type

**Investigation:** Modelling of M. pneumoniae metabolism

**Study:** Core Model predictions

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.

**Submitter**: Niels Zondervan

**Assay type**: Experimental Assay Type

**Technology type**: Mass Spectrometry

**Investigation:** Modelling of M. pneumoniae metabolism

**Study:** Metabolomics measurements

**Submitter**: Dorothee Houry

**Assay type**: Experimental Assay Type

**Technology type**: Technology Type

**Investigation:** 1 hidden item

**Submitter**: Dorothee Houry

**Assay type**: Experimental Assay Type

**Technology type**: Technology Type

**Investigation:** 1 hidden item

**Submitter**: Dorothee Houry

**Assay type**: Experimental Assay Type

**Technology type**: Technology Type

**Investigation:** 1 hidden item

**Submitter**: Dorothee Houry

**Assay type**: Experimental Assay Type

**Technology type**: Technology Type

**Investigation:** 1 hidden item

**Submitter**: Dorothee Houry

**Assay type**: Experimental Assay Type

**Technology type**: Technology Type

**Investigation:** 1 hidden item

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

**Submitter**: Niels Zondervan

**Assay type**: Enzymatic Assay

**Technology type**: Technology Type

**Investigation:** Modelling of M. pneumoniae metabolism

**Study:** Core Model training

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

**Submitter**: Niels Zondervan

**Biological problem addressed**: Model Analysis Type

**Investigation:** Modelling of M. pneumoniae metabolism

**Submitter**: Niels Zondervan

**Assay type**: Transcriptional Profiling

**Technology type**: Technology Type

**Investigation:** Modelling of M. pneumoniae metabolism

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

**Submitter**: Niels Zondervan

**Biological problem addressed**: Model Analysis Type

**Investigation:** Modelling of M. pneumoniae metabolism

**Study:** Metabolomics measurements

RobOKoD algorithm was, designed then implemented as part of a study in RobOKoD: microbial strain design for (over)production of target compounds. (http://fairdomhub.org/publications/236). It was used to generate a strain of e.coli for producing butanol, that was then compared to an experimental strain. It was shown to perform better than similar methods (OptKnock, and RobustKnock).

**Submitter**: Natalie Stanford

**Biological problem addressed**: Model Analysis Type

**Investigation:** Designing a new way to predict engineering stra...

OptKnock algorithm was used as part of a study in RobOKoD: microbial strain design for (over)production of target compounds. (http://fairdomhub.org/publications/236). It was used to generate a strain of e.coli for producing butanol, that was then compared to an experimental strain.

**Submitter**: Natalie Stanford

**Biological problem addressed**: Model Analysis Type

**Investigation:** Designing a new way to predict engineering stra...

RobustKnock algorithm was used as part of a study in RobOKoD: microbial strain design for (over)production of target compounds. (http://fairdomhub.org/publications/236). It was used to generate a strain of e.coli for producing butanol, that was then compared to an experimental strain.

**Submitter**: Natalie Stanford

**Biological problem addressed**: Model Analysis Type

**Investigation:** Designing a new way to predict engineering stra...