Cell lines
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1286941 | ENA |
REPOSITORIES: ENA
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Project description:Mus musculus strain:cell lines Genome sequencing
| PRJNA226622 | ENA
Project description:<p><strong>Background:</strong> Cell line specific, genome-scale metabolic models enable rigorous and systematic in silico investigation of cellular metabolism. Such models have recently become available for Chinese hamster ovary (CHO) too. However, a key ingredient, namely an experimentally validated biomass function that summarizes the cellular composition, was so far missing in all genome-scale metabolic models of CHO cell lines. Here, we close this gap by providing extensive experimental data on the biomass composition of 13 parental and producer CHO cell lines under various conditions.</p><p><strong>Results:</strong> We report protein, lipid, DNA, RNA and carbohydrate content, cell dry mass, and protein and lipid composition. Furthermore, we present meticulous data on exchange rates between cells and environment and provide detailed experimental protocols on how to determine all of the above. The biomass composition is converted into cell line and condition specific biomass functions for usage in cell line specific, genome-scale metabolic models of CHO. Finally, flux-balance analysis (FBA) is used to demonstrate consistency between in silico predictions and experimental analysis.</p><p><strong>Conclusions:</strong> Our study reveals a strong variability of the total protein content and cell dry mass across cell lines. Yet, the relative amino acid composition is independent of the cell lines and conditions and thus needs not be explicitly measured for each new cell line. In contrast, the lipid composition is strongly influenced by the growth media and needs to be determined for each case. These cell line specific variations in the biomass composition have a small impact on growth rate predictions with FBA, as inaccuracies in the predictions are typically dominated by inaccuracies in the exchange rate spectra. Cell specific biomass variations become only important if the experimental errors in the exchange rate spectra drop below twenty percent.</p><p><br></p><p><strong>Links:</strong></p><p>All data and code for data processing are available at <a href='http://dx.doi.org/10.17632/g3wz7spyc9.1' rel='noopener noreferrer' target='_blank'>Data Mendeley</a>.</p><p>Cell line specific metabolic models were deposited in <a href='https://www.ebi.ac.uk/biomodels/(Chelliah et al, 2014)' rel='noopener noreferrer' target='_blank'>BioModels</a>, identifiers are listed in the study sample table.</p>
2020-07-13 | MTBLS1210 | MetaboLights
Project description:Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling processes. While thousands of mutations have been identified, mostly by genome-wide sequencing, systematic interpretation of their role in cancer and impact on cellular information processing is presently missing. Here, we propose a computational approach (ReKINect) to identify mutations attacking signaling networks. We demonstrate six types of network-attacking mutations (NAMs) including changes in kinase modulation, network rewiring as well as the genesis and extinction of specific phosphorylation sites. Through global, quantitative analysis of the exomes and (phospho-)proteomes of five ovarian cancer cell lines we identify and validate numerous NAMs. Finally, we explore the entire cancer genome repertoire and predict hundreds of NAMs affecting kinase and SH2 driven signaling. Our approach is scalable with the complexity of cancer genomes and cell signaling, and can be readily applied in personalized precision medicine.
2016-07-08 | PXD000901 | Pride