Network Modeling Module (2019)
As part of the Informatics Approaches for Life Science Omics (InfoOmics) course, I organized and taught the Network Modeling module, which consisted of three lecture sessions. This module introduced students to the core principles of network science in the context of biological systems, focusing on the modeling of molecular interactions and the interpretation of biological networks relevant to biomedical research. Students engaged in both theoretical learning and practical assignments using R. They learned how to construct and analyze networks derived from omics datasets, interpret network topology, and apply network-based reasoning to real biological problems. The module also included guided exercises that helped students work with example datasets and apply network analysis through hands-on coding. The broader InfoOmics course was designed to provide life science students with a comprehensive foundation in modern data analysis techniques. Topics covered included statistical analysis, such as hypothesis testing and model fitting; network modeling for structural and functional understanding of biological systems; data integration across multiple omics platforms and biological databases; and basic machine learning methods for biological data interpretation. By the end of the course, students had acquired practical skills in computational analysis and systems-level thinking, preparing them to navigate and interpret complex omics data with confidence.