I’ve taught several times a graduate course on biophysics, the first instance of which I blogged about here. I love teaching this class, and students seem fond of it as well. I’m teaching it again next Fall (2023) but with a twist: it will now be a combined undergraduate / graduate course. The motivations are twofold: At the University of Oregon, as at many universities, there isn’t sufficient demand to sustain distinct undergraduate and graduate biological physics classes, and the unfamiliarity of most physics students with the wonders of biophysics means that an introduction to the topic can, with minimal modification, serve both undergraduate and graduate students. Recently, several students have asked me for a course description, and rather than just sending them the draft syllabus (which is here), I thought I’d create this blog post to be easily accessible and searchable. This post includes the material in the draft syllabus, minus some boring administrative pieces, and plus a section on “Comments about the prerequisites, especially programming,” and “Comments about scheduling.”
If you’re a potentially interested student, I encourage you to contact me. Please be sure to see the sections on prerequisites and scheduling.
From the (draft) syllabus
Instructor Information
Professor Raghuveer Parthasarathy
Office: Willamette 362
Office hours: To be determined
Time and Place
Tuesday and Thursday 12:00 – 1:50 pm, Willamette Hall Room 350
Description and Learning Goals
The living world exhibits a dazzling variety of form and function, yet also shows remarkable universality in its components and mechanisms. Every cell, for example, uses a stiff polymer (DNA) to carry genetic information; every cellular membrane is a two-dimensional liquid; every protein adopts a particular three-dimensional shape while being buffeted by random forces. More subtly, phenomena as different as vision and evolution make use of the statistical variation of physical processes. The field of Biological Physics / Biophysics aims to understand how universal physical principles govern the structure and activity of the living world. This course introduces students to a vibrant area of contemporary science, exploring the mechanical properties of biomaterials, the sensory abilities of cells and organisms, the dynamical properties of information processing networks, and more.
By the end of the course, students will be able to:
- Understand the physical principles that govern ubiquitous biological phenomena such as DNA packaging, bacterial navigation, membrane deformation, and gene regulation.
- Apply statistical and statistical-mechanical ideas to a wide variety of complex systems.
- Read contemporary papers in biophysics and follow the aims and approaches.
- Model and computationally simulate complex dynamical systems.
4xx/5xx Differences
This course is a combined graduate and undergraduate course. As required by university guidelines, the graduate (510) course will require roughly one-third more work than the undergraduate (410) course, which will take the form of additional readings, additional problems on homework assignments, and a presentation in addition to a written document for the final project.
Prerequisites
Undergraduate (410 students) should have completed a course in statistical mechanics (such as Physics 352/353) or the equivalent (such as Physical Chemistry) and should have a solid grasp of lower-division physics and calculus. Graduate students should have a good knowledge of undergraduate physics, especially statistical mechanics , and a corresponding adeptness with math. No prior knowledge of biology is required, though I’ll expect everyone to pick up basic biological facts, through readings, early in the term.
Being comfortable with computer programming is a valuable skill in any science. We will write programs throughout the term, and a sizeable part of the homework assignments will involve writing computer simulations. I recommend using MATLAB or Python; both are easy to learn and powerful, and Python is free. (UO has a site license for MATLAB, so it’s effectively free here.) I can provide guidance and tutorials for those new to programming.
Textbook and other readings
We’ll use Biological Physics, by Philip Nelson (Student Edition, Chiliagon Science, 2020) for much of the course, and you should consider this book required. It’s an excellent textbook, written for both undergraduates and graduate students. Feel free to find used copies, share with your friends, etc. Since its initial publication, the author got the rights to the book back from the publisher, since he was annoyed by the high price they were charging! For details and information on how to get the book ($10 eBook, $27 paper), please see: https://www.physics.upenn.edu/biophys/BPse/
We’ll read parts of two other excellent textbooks; I’ll supply excerpts. These are: (i) Biophysics: Searching for Principles by W. Bialek (Princeton University Press, 2012); (ii) Physical Biology of the Cell by R. Phillips, J. Kondev, J. Theriot, and H. Garcia (Garland Science, 2nd edition, 2012); (iii) Physical Models of Living Systems, Second Edition: Probability, Simulation, Dynamics, by Philip Nelson (Chiliagon Science, 2020). See https://eighteenthelephant.wordpress.com/2013/10/31/readings-in-biophysics-part-i/ for some comments about these books. I’ve written a biophysics book intended for the general public that gives non-technical descriptions of most of the topics of this course (and much more); you may find it interesting: So Simple a Beginning: How Four Physical Principles Shape Our Living World, by Raghuveer Parthasarathy (Princeton University Press, 2022).
I’ll also assign a variety of other readings including contemporary research articles. There’s no shortage of recent papers that are accessible and that illuminate fundamental concepts.
Topics
Introduction; Physics, statistics, and sight. What are the fundamental limits on vision, and how close does biology come to reaching them? (A brief look.)
Components of biological systems. What are the components of biological systems? What are the length, time, and energy scales that we’ll care about? How can we organize a large list of “parts?”
Probability and heredity (a quick look). We’ll review concepts in probability and statistics. We’ll discuss a classic example of how a quantitative understanding of probability revealed how inheritance and mutation are related.
Random Walks. We can make sense of a remarkable array of biophysical processes, from the diffusion of molecules to the swimming strategies of bacteria to the conformations of biomolecules, by understanding the properties of random walks.
Life at Low Reynolds Number. We’ll figure out why bacteria swim, and why they don’t swim like whales.
Entropy, Energy, and Electrostatics. We’ll see how entropy governs electrostatics in water, the “melting” of DNA, phase transitions in membranes, and more.
Mechanics in the Cell. We’ll look more at the mechanical properties of DNA, membranes, and other cellular components, and also learn how we can measure them.
Circuits in the Cell. Cells sense their environment and perform computations using data they collect. How can cells build switches, memory elements, and oscillators? What physical principles govern these circuits?
Cool things everyone should be aware of. We live in an age in which we can shine a laser at neurons in a live animal to stimulate it, paste genes into any organism we wish, and read the genetic information in a single cell. It would be tragic to be ignorant of these almost magical things, and they contain nice physics as well!
Course structure and grade components
- In class. I’ll lecture, but not exhaustively. We’ll spend quite a bit of time in class on discussions and problem-solving. To have fruitful discussions, it is important for people to have read the pre-class readings. I will ask students questions; it is fine to not know answers, or to respond with more questions.
- Classroom behavior. I expect active participation in class: asking and answering questions, working with others on problem-solving activities, and engaging with the material. Given the level of the course and its nature as an elective, and based on consistently enjoyable past experiences, I expect that students will be active and engaged without the necessity of assigning a grade to participation; I can revisit this if need be.
- Contemporary papers. We’ll discuss recent articles, and I may assign students to be “in charge” of them.
- Homework. We’ll have homework assignments roughly every week. Many of these will involve computer simulations. Students are encouraged to talk to each other about how to approach the problems, and to compare answers, but I recommend (i) first staring at the assignment alone, and (ii) making sure that the final output is your own. With programming especially, it is useful to talk to classmates. Write clearly and indicate key points and conclusions. Assignments will be graded based on correctness of the results and clarity of explanations. The homework assignments will be challenging and completing them will be the crucial for learning the material.
- ChatGPT policy. [This is tentative, and may change] It’s fine to use ChatGPT to help you write code for homework assignments. The key goal of the programming assignments is to learn to model biophysical phenomena and to gain insights into the models, not to learn coding for its own sake. That said, you will find it impossible to assess or trust ChatGPT’s code or, more importantly, to know what to ask it for without a basic grasp of the logic and syntax of programming.
- Quizzes. There be approximately six quizzes during the term. We’ll use these to assess understanding of key points without the heavy weight of exams. The quizzes will also revisit homework problems. Each student’s lowest quiz score will be dropped from the overall total. There won’t be any make-up quizzes; if you miss one, this will be the quiz dropped from your overall grade calculation.
- Exams. There will not be any exams.
- Final Project. We’ll have a final project that involves reading a few related research papers and either writing a summary (undergraduates) or giving a presentation to the class (graduate students) that includes a proposal for future experiments to be done.
- Grade weights. Homework: 65%, Quizzes: 20%, Final project: 15%.
- Grading scale and criteria. Scale: A = [89,100%], B = [79,89), C = [69,79), D = [59,69), F = [0,59%). An “A” indicates demonstration of an excellent understanding of course material and the ability to mathematically calculate, computationally simulate, and explain biophysical processes. A “B” indicates good understanding, perhaps lacking in one aspect of calculation, simulation, or explanation. A “C” indicates satisfactory understanding, and likely correlates with incomplete or inadequate submission of course assignments. A “D” indicates significant lack of understanding of many course topics. An “F” indicates consistent lack of understanding of course topics. An A+ may be awarded, based on exemplary final project work involving original exploration of biophysical topics.
University Policies on absences and other things, the use of Canvas, and non-specific advice on doing well
See the PDF syllabus.
Comments about the prerequisites, especially programming
Programming
As noted in the syllabus, programming is an immensely useful skill, and a very transferrable one as well. This course is not the place to learn programming starting from zero. If you’re new to programming, I recommend learning Python before the start of the term (or fairly intensively once the term starts). There are a lot of excellent self-learning tools out there; this book is also great.
To help a student prepare for my image analysis course in 2022, I made this set of Python exercises — you might like to use it to assess your abilities and to help you learn. (Skip the parts about images.) If you want to take a quick look: see if you can do Exercises 2.5 and 7.1. If “yes,” you’re likely in good shape.
If you’re reading this before Fall 2023, I’m happy to meet with you a few times during the summer, go over some of the exercises, and offer advice!
Statistical Mechanics
If you’re a UO undergrad and you’ve passed my Physics 353 class, you’re definitely prepared to take this course! (More so than many grad students, in fact…)
If you’ve taken a physical chemistry class, even if it didn’t span statistical mechanics per se, you’re very likely fine — you’ve encountered terms like “free energy” and “entropy” before.
Math
We won’t need anything beyond basic calculus, with which everyone who is a physics major or graduate student will be familiar. For example, you should be able to, within a few minutes, take a function like
p = a2/ L2 + kT / (6 π n L v a),
sketch its behavior as a function of a, and determine the a at which any maxima or minima occur. (This equation comes from a homework exercise on “diffusion to capture,” which governs among other things how bacteria catch food!)
Students from other fields
Past iterations of the (graduate) course have had a few students from Chemistry and Biology, which has been great! If you’re in this category, it is very likely that you’ll have insights into biological systems and techniques that I’ll encourage you to share, and that the time you’ll save by knowing more about biology can put to use brushing up on physics or other subjects.
Comments about scheduling
The course is currently scheduled for TuTh 12:00-1:50 pm in Fall 2023. If this conflicts with other courses please let me know — if several people email me, I can look into changing the day/time, though I don’t know if this is actually possible. (If the course moves, it will probably be to something early in the morning.)
Questions!
I’m happy to answer any questions about the course!
Today’s illustration
A lily. There’s nothing especially biophysical about it, but it’s the only thing I’ve painted in the last two weeks. I didn’t take much care to stay in the lines.
— Raghuveer Parthasarathy. May 28, 2023
The Eighteenth Elephant 