Data files

Gene Ontology (GO) enrichment is performed using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). The “Biological Process (non-redundant)” GO subset is chosen to minimize annotation overlap. Network nodes denote enriched GO terms, and edges are weighted by the count of common genes, enabling the identification of tightly ...

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Biochemical Reaction Networks: Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations: Kinetic descriptions of biochemical reactions related to each renal cell ...

Enriched pathway landscapes are generated for each cell type using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). Reactome is selected as the pathway ontology. In the resulting graph, nodes correspond to significantly enriched pathways, and edges width is proportional to the number of shared genes between pathway pairs, ...

Protein–protein interaction networks are constructed for each renal cell type to visualize DKD-related molecular connectivity. Nodes represent proteins implicated in diabetic kidney disease (DKD) based on comprehensive literature curation, proteomic profiling, mRNA microarray analyses, and GWAS hits. Edges denote experimentally validated interactions retrieved from the SIGNOR 3.0 database; only interactions for which both source and target belong to the DKD-associated set are retained. Network ...

The pdf-file shows simulations of a hypothetical model of sigma factor competition. It simulates the dynamics that we can expect from the experiments and prepares for the analysis of the experimental data. Analysis of sigma factor competition is based on a Lineweaver-Burk representation of RNApolymerase and competing sigma factors.

B. subtilis was grown in minimal media in a chemostat at different growth rates (µ= max, µ=0.1, µ=0.4) and in the presence of 1.2M NaCl (µ=0.1) with or without glycinebetaine. Here you'll find cell titers for every sample.

B. subtilis was grown in minimal media in a chemostat at different growth rates (µ= max, µ=0.1, µ=0.4) and in the presence of 1.2M NaCl (µ=0.1) with or without glycinebetaine. Here you'll find cell sizes for every sample.

B. subtilis was grown in minimal media in a chemostat at growth rate µ=0.1, with 1.2M NaCl, without glycine betaine. Relative quantification for the proteome was done using metabolic labeling.

B. subtilis was grown in minimal media in a chemostat at growth rate µ=0.1, with 1.2M NaCl and glycine betaine. Relative quantification for the proteome was done using metabolic labeling.

Batch sample publishing

Batch sample publishing

Lists of the created bacterial strains and vectors

Lists of the created bacterial strains and vectors

batch fermatation - The transition from growing to non-growing Bacillus subtilis cells

Array hybridisation was done at Roche Nimblegen Inc. It cannot be displayed over here.

batch fermatation - The transition from growing to non-growing Bacillus subtilis cells

This code uses the PEtab problem (written in the yaml file) to perform MCMC sampling with pyPESTO. Afterwards an ensemble is created, that allows to compute the posterior predictive distribution and therefore the credibility intervals for the model output.

The sampling.py scipt of pyPESTO visulize was adjusted for an improved visualization. The used code was also uploaded.

A Conda environment file (.yml) containing the specific version of Python and installed packages with the according versions, ...

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The ground truth collection for the BeeProject. Contains 3819 png scans of hanwritten tables in the folder 'LHI_PNG', followed by digital json and excel files in 'LHI_GT'

Previously identified gene targets were used as input (see miRNA gene target file).

Literature-curated transcription factor (TF) - miRNA pairs of deregulated miRNAs were extracted from TransmiR (Tong, Cui and Wang 2019 TransmiR). miRNA, target gene, and TF Interaction pairs were visualized in Cytoscape v3.8.2 (Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research 13, 2498-2504, doi:Doi 10.1101/Gr.1239303 (2003). The missing ...

Previously identified gene targets were used as input (see miRNA gene target file).

Literature-curated transcription factor (TF) - miRNA pairs of deregulated miRNAs were extracted from TransmiR (Tong, Cui and Wang 2019 TransmiR). miRNA, target gene, and TF Interaction pairs were visualized in Cytoscape v3.8.2 (Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research 13, 2498-2504, doi:Doi 10.1101/Gr.1239303 (2003). The missing ...

BC7 NLM-Chem-track data and materials

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format

Original BioDare metadata, converted to json format