PHZ4710 – Introduction to Biological Physics

Spring 2021, course number PHZ4710-3812(17981)

Classes:

Tuesday & Thursday, 11:45 AM – 1:15 PM (Period 5 – 6.5)
Classes will be delivered online synchronously (Zoom ID: 95757848852, passcode: see Canvas)

Instructor:

Prof. BingKan Xue

  • Email: b.xue@ufl.edu (communication about this course should be sent via Canvas)
  • Phone: 2-6973
  • Office: NPB 2328
  • Office Hour: Tuesday & Thursday, 5:10 – 6:00 PM (Zoom ID: 96305881483, passcode: see Canvas)

Objectives:

This course is for advanced undergraduate students looking for basic training in biological physics. The field of biological physics has evolved to encompass diverse topics ranging from molecular to ecological scales. These topics are studied using quantitative methods that have become standard in the field, yet are not part of the traditional curriculum. This course provides an overview of such methods and their applications in biological physics. We will learn to use these methods by doing exercises that involve computation and programming.

Prerequisites:

One year of introductory physics (PHY 2053/2054, PHY 2048/2049, or the equivalent) and one year of calculus (MAC 2311/2312 or the equivalent). Some experience in programming would be helpful (Python, MatLab, etc.); a quick tutorial will be given at the beginning of the course.

Textbooks:

We will not follow a particular textbook. For each part of the course, some references may be provided later. The lecture notes will be made available after class. Lecture notes from a previous version of the course by Prof. Stephen Hagen are recommended for reading.

Description:

The topics covered in this course may include:

  • diffusion vs navigation: from Brownian motion to bacterial chemotaxis
  • probability and statistics: visual sensitivity and bacterial resistance
  • stochastic processes: population growth and gene expression
  • reaction kinetics: protein folding and kinetic proofreading
  • dynamical systems: genetic circuits and ecological systems
  • neural networks: associative memory, perceptron and machine learning
  • data analyses: classification, clustering and dimensional reduction
  • information and strategy: entropy, environmental variation and evolution

The class will meet twice a week, about 1.5h each time. In each class there will be a lecture (~50min) followed by a practice session (~30min), with a short break in between. The practice session is designed to help students with computation and programming related to homework assignments. During that session, the instructor will demonstrate how to implement the methods described in the lecture, and the students can work in groups and ask questions about the assignments.

Assignments:

Homework will be assigned every few classes according to the topics. Each homework will be to apply the method you learned in class to an example problem, and will typically involve some modeling, numerical computation, simulation, or data analysis. You may work in groups during the practice session or consult each other after class (you should credit your collaborators in the submitted homework), but you must write your own answers that are not simple copies of others’ work. The homework will be submitted online on Canvas.

The final exam will be in the form of a take-home exam that is more substantial than a homework. It may involve searching and reading the literature, solving a problem using the methods you learned, drawing conclusions and discussing your results. You should work on it by yourself and submit a written report summarizing your work (~4 pages), including introduction to the problem and discussion of the results.

Grading:

The final grade will consist of both the homework assignments (70%) and the final exam (30%):

  • There will be seven homework assignments, each contributing 10% to the final grade. Late submission will not be graded.
  • The final exam is due on Tuesday April 27 by 9:30AM. It will be evaluated by its scientific content as well as clarity and grammar.

Attendance at all class meetings is expected. A student who seeks a makeup for missed work should contact the instructor as soon as practical and be prepared to document any excuse. See detailed policies below.


Policies:

Requirements for class attendance and make-up exams, assignments, and other work in this course are consistent with university policies that can be found at:
https://catalog.ufl.edu/UGRD/academic-regulations/attendance-policies/

This course counts toward the Writing Requirement (2000 words), and the writing component will be evaluated on grammar, clarity, as well as scientific content.
https://catalog.ufl.edu/UGRD/student-responsibilities/writing-requirement/

Students with disabilities who experience learning barriers and would like to request academic accommodations should connect with the disability Resource Center by visiting:
https://disability.ufl.edu/students/get-started/
It is important for students to share their accommodation letter with their instructor and discuss their access needs, as early as possible in the semester.

Information on current UF grading policies for assigning grade points may be found at:
https://catalog.ufl.edu/UGRD/academic-regulations/grades-grading-policies/

All students must maintain academic honesty and professional behavior according to the Student Honor Code and Student Conduct Code:
https://sccr.dso.ufl.edu/policies/student-honor-code-student-conduct-code/

Evaluation:

Students are expected to provide professional and respectful feedback on the quality of instruction in this course by completing course evaluations online via GatorEvals. Guidance on how to give feedback in a professional and respectful manner is available at:
https://gatorevals.aa.ufl.edu/students/
Students will be notified when the evaluation period opens, and can complete evaluations through the email they receive from GatorEvals, in their Canvas course menu under GatorEvals, or via ufl.bluera.com/ufl/. Summaries of course evaluation results are available to students at:
https://gatorevals.aa.ufl.edu/public-results/