This report studies State-Space Models for sequence modeling, focusing on S4 and Mamba. It explains how SSMs can model long-range dependencies through recurrent and structured formulations, and how Mamba introduces input-dependent dynamics for content-aware memory selection. The project applies these ideas to biological sequences, especially protein data from Pfam, comparing different representations and sequence orderings.