Biological systems utilize multi-layered information transmission networks to sense and adapt to their internal and external environments. The inherent nonlinearity, stochasticity, and multi-scale coupling characteristics of these systems pose great challenges to traditional analytical methods. Information theory, as a powerful theoretical framework for quantifying system uncertainty and correlations, employs core metrics such as entropy, mutual
information, and channel capacity to overcome the limitations of conventional analyses, providing a novel perspective on the flow of information in biological systems. This paper systematically reviews the applications of information theory in network regulation and signal transmission research, detailing its fundamental principles, computational methods, and key application scenarios. We emphasize the unique advantages of mutual information in addressing
biological noise, revealing nonlinear regulatory relationships, and integrating multi-omics data while analyzing current challenges and future directions for development.
