cell culture
Definition
Cell culture is a fundamental laboratory technique involving the growth and maintenance of cells outside their natural environment under controlled conditions. Cells are isolated from tissues or organisms and cultivated in specialized media containing nutrients, growth factors, and optimal pH and temperature conditions. This methodology enables researchers to study cellular behavior, molecular mechanisms, drug responses, and disease processes in a simplified, reproducible system. Cell cultures can be primary (directly isolated from tissue) or immortalized cell lines (capable of indefinite division). The technique is essential for drug screening, vaccine production, cancer research, and understanding cellular signaling pathways, providing a bridge between in silico predictions and in vivo validation.
Visualize cell culture in Nodes Bio
Researchers can map cell culture experimental data onto network graphs to visualize how treatments affect cellular pathways. By integrating gene expression profiles, protein interactions, or metabolomic data from cultured cells, users can identify activated signaling cascades, perturbed regulatory networks, or drug-target interactions. Nodes Bio enables comparison of network states across different culture conditions, cell lines, or treatment groups to reveal mechanistic insights.
Visualization Ideas:
- Gene expression networks comparing treated vs. untreated cell cultures
- Protein-protein interaction networks affected by culture conditions
- Time-series pathway activation maps during cell differentiation in culture
Example Use Case
A pharmaceutical team investigating a novel kinase inhibitor uses HeLa cell cultures to assess drug efficacy. They perform RNA-seq at multiple time points post-treatment and integrate the expression data with known protein-protein interaction networks. Using network visualization, they identify that the drug not only inhibits the target kinase but also unexpectedly modulates the MAPK/ERK pathway, revealing potential off-target effects. This network-level understanding guides optimization of the compound's selectivity and predicts potential side effects before animal studies.