When Math Meets Life: Unraveling the Secrets of Biology Through Computation

The study explores the integration of mathematics and computer science with biology to address complex biological challenges. Key methodologies include stochastic models for capturing randomness in biological processes and machine learning techniques—particularly convolutional neural networks (CNNs) and perceptrons—for analyzing large biological datasets. Noteworthy findings include the effectiveness of the AlphaFold model in predicting protein folding using deep learning, which enhances our understanding of protein structures that are crucial for protein functions and insights into diseases. Limitations include the reliance on computational resources for data-driven modeling. The synergy between these disciplines is emphasized as essential for advancing biomedical research and our understanding of the life sciences.