Graph Algorithms for Bioinformatics: Solving the Longest Path Problem

How do scientists compare complex biological sequences like DNA and proteins to find evolutionary relationships? The answer lies in bioinformatics and graph theory, where sequence alignment is modeled as finding paths through directed graphs. This text-based course guides you from the fundamental principles of graph theory to implementing algorithms that solve the longest path problem in directed acyclic graphs (DAGs). You will understand how biological sequence comparison translates into computational pathfinding and how to write clean, modern code to solve these challenges. What you'll learn: - Understand the core concepts of graph theory, including directed graphs, acyclic structures, and path representations - Map biological sequence alignment problems to directed acyclic graphs - Implement dynamic programming algorithms to solve the longest path problem efficiently - Analyze algorithm complexity and optimize pathfinding performance for large-scale biological datasets - Apply modern Python patterns to model graph data structures and write clean, readable code You will start with essential terminology and the foundational mathematics of directed graphs before moving on to practical algorithmic design. Through written explanations and clear code snippets, you will trace how sequence comparison works step-by-step. This course is designed for beginners in bioinformatics, computer science students, or self-taught programmers who want to explore biological graph algorithms. No advanced background in biology or complex mathematics is required. Begin reading today to unlock the algorithmic power behind biological sequence analysis.