Graph Algorithms for Bioinformatics: The Manhattan Tourist Problem

Sequence alignment is a cornerstone of computational biology, but mapping complex genomic data requires a solid grasp of underlying graph theory. By conceptualizing sequence comparison as a grid-navigation challenge, you can unlock the core algorithmic logic used in modern bioinformatics tools. This course guides you step-by-step from foundational graph concepts to the practical execution of the Manhattan Tourist Problem. You will read clear explanations, analyze structured code snippets, and learn how to find maximum-weight paths in directed acyclic graphs to align biological sequences efficiently. What you'll learn: Understand the mathematical foundations of directed acyclic graphs and grid-based pathfinding; Model biological sequence alignment as a Manhattan Tourist Problem; Apply dynamic programming techniques to calculate optimal alignment scores; Analyze edge weights and backtrack through grids to reconstruct maximum-weight paths; Explore modern memory-efficiency optimizations for sequence alignment algorithms. The course begins with essential definitions of graphs and sequence comparison before moving into step-by-step dynamic programming transitions and algorithmic walk-throughs. This introductory course is designed for aspiring bioinformaticians, computer science students, and software developers eager to understand the algorithmic side of computational biology, with no advanced prerequisites required. Start reading today to master the algorithmic foundations of sequence alignment.