Cell Pattern formation and cell-cell communication are critical processes that drive the organization and functionality of biological systems. This talk will introduce the investigations of these phenomena and their underlying mechanisms through the lens of multi-scale modeling and computational inference. The first part highlights a reaction-diffusion-based modeling approach, demonstrating how individual cells self-organize into structured colonies and how synthetic gene circuits can drive robust pattern formation. The second part introduces novel computational methods for inferring cell-cell communications using single-cell omics data, enabling the identification of critical communication signals and revealing mechanisms in intercellular interactions and disease progression. By integrating mathematical modeling with advanced computational techniques, a comprehensive multi-scale framework can be developed to understand these biological processes, optimize the treatment, and help drug development.