Class Information

Class meetings:
LSB 231, Tues. & Thurs., 1:30–2:45 pm

Website:
Github, Canvas

Instructor:
Brian Kissmer

Email:
brian.kissmer@usu.edu

Office:
TBD

Office Hours:
Mon. 12:30-3:30 pm

Description of the Course and Learning Objectives

Computers and computational methods are essential tools in many areas of modern biological research. This course teaches the theory and practice of programming for life sciences applications. The course will introduce students to key concepts in programming using R. Applications covered include computational statistics and data analysis, simulation of biological systems, genomics and bioinformatics, and machine learning. The course will emphasize thoughtful and meaningful use of computational methods as tools for life scientists. By the end of the course students will be able to:

  1. Identify and explain major applications of computational methods in biology.
  2. Write their own computer scripts and programs to explore and analyze biological data.

These course-specific learning objectives align with the following IDEA learning objectives and USU Biology Department learning outcomes.

IDEA Center Learning Objectives

  • Learning appropriate methods for collecting, analyzing, and interpreting numerical information.
  • Gaining a basic understanding of the subject (e.g., factual knowledge, methods, principles, generalizations, theories).

Biology Department Learning Outcomes

  • Graduates will be able to use quantitative reasoning, modeling, and simulation to solve problems in biology.
  • Graduates will be able to practice the process of science.
  • Distinguish scientific integrity and the ethical practice of science from deceitful and unethical scientific practices.

Course Materials

Course fees: The fee for this course is $150. This covers part of the cost of a laptop computer.

  • Canvas: Use the course Canvas site to receive announcements, submit assignments, take quizzes and exams, and view grades. Make sure you receive daily notifications for announcements.
  • Course Website: Use the course website to download course material and to access the schedule and syllabus.
  • Textbook: A textbook is not required for this course. Good programming references for R can be found online. Nonetheless, I recommend gaining access to the following book:
    • R. Cotton. 2013. Learning R: A Step-by-Step Function Guide to Data Analysis. 1st edition. O’Reilly Media.
  • Laptop computer: A computer capable of running R is required for this course. Such a computer will be provided for you to use for the semester (your course fees cover part of the computer cost). You may use your own computer if you prefer, but you are responsible for ensuring the required software is installed and runs properly. If you check out a class laptop, you are required to return it before the end of the course. Failure to do so will result in an incomplete for the class until the computer is returned.

Attendance and Participation

Computational biology is a hands-on class. Most weeks will include some lecture to introduce concepts, but we will spend a considerable amount of time in class with live-coding exercises and programming in small groups. Coming to class regularly, with a computer, and prepared to work is thus critical for your success. With that said, I realize that you will sometimes need to miss class (especially if you are sick). That is fine. I will try to record the lecture each class and will provide handouts and slides that should help you catch up on material you miss. Regardless of attendance and participation in class, your success likely depends on regularly reviewing the course material and practicing your programming skills outside of class time too.

Meeting Course Objectives: Assignments & Assessment

Three types of graded assignments will be used to evaluate student performance in the context of the course objectives: coding quizzes, programming projects, and exams. Brief descriptions of each assignment type follow. See our website for more information. Due dates for all assignments can be found on Canvas. Unless arrangements are made with me, late work will not be accepted. In other words, contact me in a timely manner if you have a planned, excused absence or fall ill so that we can work out a plan for late work.

Coding and Pre-Lab Quizzes (10%)

Short (8-10 questions) coding quizzes will be given once every few weeks to assess programming skills (7 total). You will take these quizzes via Canvas. Most quizzes will open for about seven days. You will be able to take the quiz at any time during that time interval.

Pre-Lab quizzes will be due before every lecture. These are very simple, easy quizzes that will be designed to help you understand the main goals and methods for each class. They are due 30 minutes before the start of lecture with no make-ups. The lowest pre-lab quiz will be dropped.

Programming Projects (50%)

Hands-on experience is essential to mastering computational biology. Thus, a key focus of this course will be regular programming projects, 5-7 total. This will involve applying coding skills we have covered to problems in the biological sciences. Each project will culminate in some or all of the following:

  1. Annotated computer code
  2. A write-up/interpretation along with embedded computer code and visualizations
  3. A group presentation on a method and its results.

Midterm Exam (18%) and Final Exam (22%)

Two exams will be given, a midterm and a final exam. These will cover programming skills and knowledge of key application concepts covered in the course, and thus build on the quizzes and projects. The exams will be administered through Canvas.

Summary and Grading Policy

Grading Policy: Final course grades are based on the following scale and will not be rounded up to the nearest whole number:

  • A (93 to 100%)
  • A- (90 to <93%)
  • B+ (87 to <90%)
  • B (83 to <87%)
  • B- (80 to <83%)
  • C+ (77 to <80%)
  • C (73 to <77%)
  • C- (70 to <73%)
  • D+ (67 to <70%)
  • D (60 to <67%)
  • F (0% to <60%)

Breakdown:

  • Quizzes: 10%
  • Programming projects: 50%
  • Midterm exam: 18%
  • Final exam: 22%

Use of Large Language Models (LLMs) and Generative AI

Large language models (a component of some generative AI), such as ChatGPT, can be useful tools in computational biology but can also hinder meaningful learning and mastery of programming for biologists. I will cover some best practices for using such AI software as “programming assistants” during the course. With this in mind, we will have two distinct policies for the use of such software in this class.

Use of LLMs and Generative AI on Programming Projects

You are permitted to use ChatGPT and other AI tools to assist you in these projects. However, you are expected to include a disclosure statement at the end of your assignment describing which AI tool you used and how you used it. For example, “ChatGPT was used to draft about 50 percent of the code and text in this assignment.”

Use of LLMs and Generative AI on Quizzes and Exams

ChatGPT and similar Artificial Intelligence tools, though useful in many contexts, are inappropriate for use when taking quizzes and exams in this course. An important outcome of this course is to strengthen your own abilities as a thinker and programmer. You need to know when AI produces code that does versus doesn’t do what you want, and you need to understand coding concepts on your own. For me to assess your abilities to do these things, it is important for you to take quizzes and exams without the use of AI. Thus, you may not use AI when taking quizzes or exams. If your submission appears to have been written using AI, you may either receive a failing grade or be asked to resubmit. Using AI to study for quizzes and exams is absolutely fine and encouraged.

Schedule of Topics

This course provides a survey and introduction to various applications within computational biology. The general schedule of topics is as follows:

  • Weeks 1–2: Introduction to computational biology
  • Weeks 3–7: Mathematical models and simulations
  • Weeks 8–12: Computational statistics, algorithms, and genomics
  • Weeks 13–15: Bioinformatics and machine learning

Programming concepts will also be introduced each week. See the course home page on Canvas for the schedule of topics, along with links to required readings, due dates for assignments, and other course resources (e.g., slides/notes).

Additional Items

  • This classroom is a safe and welcoming environment for individuals of all backgrounds, identities, and experiences. We value and respect diversity in all its forms, including but not limited to race, ethnicity, gender identity, sexual orientation, religion, age, ability, and socio-economic background. Discrimination, harassment, or exclusion of any kind will not be tolerated. Our goal is to foster a supportive community where everyone can learn, grow, and succeed together.
  • The schedule of topics, assignments, and all other details in this syllabus are subject to change with fair warning.
  • USU welcomes students with disabilities. If you have, or suspect you may have, a physical, mental health, or learning disability that may require accommodations in this course, please contact the Disability Resource Center (DRC) as early in the semester as possible (University Inn #101, 435-797-2444, drc@usu.edu). All disability-related accommodations must be approved by the DRC. Once approved, the DRC will coordinate with faculty to provide accommodations.
  • Policies protecting free speech at USU
    • USU Student Code: “Students can reasonably expect the following: the right, subject to time, place, and manner restrictions, to express personal opinions on campus, to support or oppose causes, to arrange public assemblies, and to hold rallies, demon- strations, and pickets, which do not materially and substantially interfere with normal university activities or the rights of others. Institutional control of facilities shall not be used as a censorship device. Any institutional regulation regarding time, place, 4and manner of expression must be content-neutral, must be narrowly tailored to serve a significant University interest, and must leave open ample alternative channels of communication.” (Student Code Section II-2, D)
    • Utah System of Higher Education: “All members of the institutional community shall be protected from censorship in their exercise of freedom of speech and assembly and, at the same time, protected from interference with a speaker’s presentation of his ideas through acts of disruption. In addition to protection of their own freedom of speech, members of the academic community shall be free to invite and hear any person of their own choosing, in accordance with the principles set forth in the preceding paragraphs and consequent institutional procedures . . .” (Campus Speakers R251)
    • Academic Freedom: “The university is a community dedicated, through promulgation of thought, truth, and understanding, to teaching, research, and service. It must be a place where innovative ideas, original experiments, creative activities, and indepen- dence of thought are not just tolerated but actively encouraged. Faculty members are entitled to full freedom in teaching, research, and creative activities, subject to the limitations imposed by professional responsibility.” (USU Policy 403: Academic Freedom and Professional Responsibility)
  • Utah State University is committed to creating and maintaining an environment free from acts of sexual misconduct and discrimination and to fostering respect and dignity for all members of the USU community. Title IX and USU Policy 339 address sexual harassment in the workplace and academic setting.
  • Mental health is critically important for the success of USU students. As a student, you may experience a range of issues that can cause barriers to learning. Utah State University provides free services for students to assist them with addressing these and other concerns. You can learn more about the broad range of confidential mental health services available on campus at Counseling and Psychological Services (CAPS). Students are also encouraged to download the “SafeUT App” to their smartphones.
  • Students whose religious activities conflict with the class schedule should contact me at the beginning of the semester to make alternative arrangements.
  • Cheating and other forms of academic dishonesty are listed in The Code of Policies and Procedures for Students at Utah State University (revised September 2009), Article VI, Section 1. If you are found to be engaged in academic misconduct, at a minimum, you will receive no credit for that exam or assignment. Repeat or serious offenders can expect more serious consequences.