1. Genomic Analysis: Computational methods are used to analyze genomic data from cancer patients, identifying mutations, copy number variations, and other genetic alterations. This helps in understanding the genetic basis of cancer and identifying potential biomarkers for diagnosis and treatment.
2. Drug Discovery and Development: Computational biology contributes to the identification of new drug targets and the design of potential therapeutics. In silico modeling and simulations can predict how cancer cells will respond to different compounds, speeding up the drug discovery process.
3. Personalized Medicine: By integrating various types of biological data, computational models can help tailor treatments to individual patients based on their unique genetic makeup and disease profile. This approach aims to improve the efficacy of treatments while minimizing side effects.
4. Tumor Microenvironment: Computational tools are used to study the complex interactions between cancer cells and their microenvironment, including immune cells, stromal cells, and extracellular matrix components. Understanding these interactions can lead to the development of new therapeutic strategies.
