Modeling of Complex Biological Systems

Master’s course 1000-719bMSB

We examine modern challenges in modeling and understanding complex biological systems through data. High-throughput molecular measurements have necessitated development and application of statistics and machine learning, giving rise to computational biology. Microarray and sequencing technologies enable us to quantify how complex systems are responding to and influenced by experimental and external conditions. It may lead to better understanding fundamental organizational principles and functionalities of molecules and cells. Lately, there have been interesting developments in single cell analyses, spatial genomics, imaging and others that involve higher resolutions, scales, and complexities. In this course, we study exploratory data analysis, statistical learning, and neural networks that are specifically designed for such biological studies. Good understanding of statistics and programming are prerequisites. Students will program in R and Python, read primary literature weekly, and complete data analysis projects.


Computer Labs

Homework Assignments

Homeworks are given throughout the semester. They are embedded in the Lab Notebooks, which are partially presented during the lab. You must read through that Markdown file to see the assignments. Write your own code.

When you have completed all the homework problems, upload your R or Python script (preferably markdown, jupyter notebook, or others) and PDF/PNG files to your Github account. Make sure your figures are named “yourlastname_problem1.pdf” and so on. When done uploading, add your instructor as your collaborator.


Students will be assigned to write 2-page summaries of course materials, for the upcoming week. These classnotes must be in your own words - do not copy or plagiarize from any source. These notes will be shared with all students. Email a class note by Saturday night 23:00.

Textbooks and Resources

R materials

Required Readings