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Computational biology involves the application of computational and statistical techniques to analyze and simulate biological systems, making it a natural fit under Systems Biology in our hierarchy. It draws from principles of mathematics, computer science, and information theory to model, interpret, and make predictions about genetic data and other complex molecular processes. In this context, it serves as a bridge between the purely theoretical aspects of genetics research and the more experimentally-driven approaches found in fields like molecular biology or biochemistry. By leveraging powerful algorithms and high-performance computing resources, computational biologists can uncover hidden patterns and trends within vast datasets, providing valuable insights into genome function, gene regulation, and evolutionary dynamics. This subfield thus plays a crucial role in driving innovation across the life sciences, offering new ways to understand and tackle some of the most pressing challenges in medicine, agriculture, and environmental conservation.