Skip to content

Computational Stem Cell Biology

Learning things
Level up

This is the compantion site for the course

Its main purpose is to ease accessibility to the computational tutorials. All definitive material for the course, as well as links to videos, assined readings, etc, will be posted to Canvas. Please monitor your email and the Canvas page for notifications and changes in the course schedule.

Biomedical Engineering 580.447/647
Computational Stem Cell Biology
Spring, 2024 (3 credits, EQ)

Education Team

Instructor: Patrick Cahan
Teaching Assistants: Christine Miller & Nadine Zureick
Course email:

Office hours

  • Patrick
    • Clark Hall 314B
    • From the end of class to 2:30PM on Tuesdays and Thursdays, and by appointment.
  • Chistine and Nadine
    • Time and location TBA

Class Meetings

  • 12noon to 1:15 PM on Tuesdays and Thursdays
  • Jan 23rd through April 25th
  • No class March 19th or March 21st
  • Shaffer Rm 304
  • Zoom:

Online resources

Please log in to Canvas for all materials related to this course, including reading assignments, lecture slides, lecture videos, Jupyter notebooks, homeworks, final project description, and announcements.

Course Information

This course teaches students about high-throughput, genome-wide single cell measurements, and approaches to appropriately analyze such data. Real world examples from stem cell biology and developmental biology provide the biological context and motivation, but the computational expertise gained will be broadly applicable. Please see the lecture schedule below for specific topics. The class is heavily weighted by computational assignments. The motivation for this strategy is that regularly occurring, moderately-sized computational projects are the most efficient way to impart an understanding of our models of this extraordinary class of cells, and to inspire a sense of excitement and empowerment in the students By the end of this course, the student should - be conversant in the language of sc-omics technologies, both at the level of general principles, and more granular understanding of how these data-generating platforms work - be a confident practioner in state-of-the art computational methods needed to analyze sc-omic data - understand the fundamentals of stem cell biology and to how sc-omics is allowing us to address major obstacles in this field

EN.580.151 – Structural Biology of Cells 🧬, or equivalent, and prior hands on experience coding in Python . Please see some example homeworks from the 2022 course to assess your readiness for the class.

Course Goals

This course will address the following Criterion 3 Student Outcomes - An ability to apply knowledge of mathematics, science and engineering to solve problems related to stem cell engineering

  • An ability to analyze and interpret data using statistical, computational or mathematical methods
  • An ability to function on multidisciplinary teams (Criteria 3(d))
  • An understanding of professional and ethical responsibility (Criteria 3(f))
  • An ability to communicate effectively (writing) (Criteria 3(g))
  • An ability to communicate effectively (oral presentation) (Criteria 3(g))

Course Topics

  • Stem cell biology
  • single cell omics technologies
  • computational tools for the analysis of single cell omics data
  • Cell identity
  • Pluripotency
  • Gene regulatory networks
  • Cell fate engineering


The strength of the university depends on academic and personal integrity. In this course, you must be honest and truthful. Ethical violations include plagiarism, reuse of assignments, improper use of the Internet and electronic devices, unauthorized collaboration, alteration of graded assignments, forgery and falsification, lying, facilitating academic dishonesty, and unfair competition. In addition, the specific ethics guidelines for this course are: (1) The weekly paper summaries and the homeworks are to be completed by each student alone. Do not share your projects with other students or use material from prior years. (2) The use of Large language models (LLMs) such as ChatGPT are allowed for the homeworks and final project, when doing so is consistent with the stated Course goals and Course expected outcomes (see above). The use of LLMs and similar technology is not allowed for the weekly written summaries. As a guideline, please use LLMs to augment your understanding of the material in the course, to help you to explore algorithmic ideas that might otherwise be challenging to implement, and more generally, to be a more creative problem solver. Report any violations you witness to the instructor. You can find more information about university misconduct policies on the web at these sites: • For undergraduates: • For graduate students:


  • In class participation: 5%
    • Asking questions and offering ideas during lecture counts.
    • Other opportunties to garner particpation points will be announced during class.
    • Please email Patrick and the TAs after class with a brief note reminding them of how you participated.
  • Weekly summarization of assigned reading: 10%
    • Each week you will be required to read a primary research paper, review article, or book chatper. Don't worry, this is not as bad as it might seem. Submit a brief write up about it. We expect these to be about one paragraph in length and to demonstrate that you did, in fact, read the assigned reading. The best write ups will discuss one specific aspect of the paper/chapter that the student found particularly interesting. Other good write ups are those that challenge claims made in the paper/chapter.
    • Individual work; no LLMs, chatbots, etc.
  • Homeworks: 10% per homework. 60% total
    • Detailed expectations will be described for each HW
    • Individual work; assistance from LLMs is allowed per guidelines in the Ethics section above.
  • Final project: 25%
    • Detailed expectations will be described for the project
    • Small teams (2-3 students); assistance from LLMs is allowed per guidelines in the Ethics section above.
    • Entails both a written submission and a live presentation
  • There are no quizzes, tests, or exams
  • We have given opportunties for extra credit in past years and may do so this year
  • Policy on grace periods and extensions: TBA