This course will train students to apply or develop computational network, modeling, and machine learning concepts to probe into the systems biology of disease. The aim of this course is to cover general concepts in biological computing that provide the foundation of thinking computationally about anomalous behavior in biological systems that cause diseases. The course also aims to teach students to work in teams and develop the skills to plan and coordinate a scientific project. The course will cover computational frameworks, such as biological networks (including metabolic, regulatory, and signal transduction networks), micro array analysis, proteomic analysis, next generation sequencing, imaging, machine learning, probabilistic inference, genetics, pathway analysis, network and graph theory, and other technologies to medical diseases initially focusing on clincal problems such as cancer, diabetes, inflammation, and aging. The course is aimed at seniors and graduate students in biomedical engineering or bioinformatics; however, students from other disciplines ranging from medicine to physics or computer science can attend the class with some prerequisites. 4 cr.