The convergence of artificial intelligence, machine learning, robotics, bioinformatics, and synthetic biology is transforming modern life sciences into an integrated engineering discipline, where biological systems are treated as programmable, data-driven architectures that can be modeled and optimized across molecular, cellular, and systemic scales through computational frameworks integrating experimental biology with predictive modeling, enabling the reconstruction of living processes as multilayered informational systems governed by molecular interactions and regulatory dynamics. Advances in machine learning algorithms and deep neural network architectures have enabled unprecedented capabilities in biological data interpretation, allowing researchers to identify regulatory patterns within genomic, transcriptomic, epigenomic, and proteomic datasets while improving predictive accuracy in complex biological systems governed by nonlinear interactions and multilayered regulatory ne...