How Can Computational Models Help in Studying Gene Regulatory Networks in Cancer?
Computational models and bioinformatics tools are invaluable for studying the complexity of GRNs. These models can integrate various types of data, including genomics, transcriptomics, and proteomics, to reconstruct GRNs and predict their behavior under different conditions. Machine learning algorithms can also identify patterns and key regulatory nodes that may serve as therapeutic targets. For example, the use of CRISPR-Cas9 screens combined with computational analysis has identified essential genes and pathways in cancer cells.
