Physics 434, 2016: Syllabus
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Back to Physics 434, 2016: Physical Biology.
- Course Title
- Physical Biology
- Course Numbers
- Physics 434/Biology 434/Physics 534, Fall 2016, Emory College
- Ilya Nemenman firstname.lastname@example.org (best way to contact me)
- Office Hours
- Wednesday, 3:30-4:45, subject to change, and by appointment
- Class Web Page
About the class
This class is aimed to teach you to think physically about biological systems. Thinking physically means, in the context of this class, creating simple predictive mathematical models of biological processes that provide insight. If you are still confused, we will talk a lot more during the lectures what it means to think physically. The class relies a lot on computer simulations as a tool to verify our understanding -- and you will learn Matlab or Python (your choice) during this class (no prior programming experience necessary, but PHYS 212 / BIOL 212 recommended). The main ideas that we will explore are the ideas of dynamics, randomness, control, inference, and information -- all applied to biology in two different ways: first, how we model and learn biology, and, second, how biological organisms model and learn the world around them. These ideas will be explored in a variety of biological systems, from viruses and bacteria, to neural systems, and to entire populations.
- Main Textbook: P Nelson, Physical Modeling of Living Systems, WH Freeman, Dec 2014.
We will additionally rely on the following books, and I suggest you borrow them from the library or from me, and read before bedtime.
- Introduction to Probability, by CM Grinstead and JL Snell. Available at http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/pdf.html
- Physical Biology of the Cell by R Phillips, J Kondev, J Theriot (Garland Science, 2008)
- Biological Physics: Energy, Information, Life by P Nelson (W.H. Freeman, 2003)
- Biophysics: Searching for Principles, by W Bialek, available at http://www.princeton.edu/~wbialek/PHY562.html
Additional recommended reading includes:
- Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by P Dayan and L Abbott (MIT Press, 2005)
- Random Walks in Biology, by H Berg (Princeton UP, 1993)
- An Introduction to Systems Biology: Design Principles of Biological Circuits by U Alon (Chapman and Hall, 2006)
- Spikes: Exploring the Neural Code by F Rieke, Dd Warland, R de Ruyter van Steveninck, W Bialek (MIT Press, 1999)
- E. coli in motion by H Berg (Springer, 2003)
- Mathworks Matlab Student Edition (free on Emory computers).
- Any version or Octave v 3.4 or greater (Open Source). See Installing Octave on your PC and Mac.
- Anaconda Python distribution with installation instructions.
Pre-requisites are in flux due to restructuring of the Physics major and the QSS major. In general, you should have the following under your belt:
- Calculus (Math 111/112 or Math 115/116 or AP equivalent)
- Intro Physics (Phys 141/142 or Phys 151/152 or AP equivalent), or another natural/social science class that introduces the concept of mathematical modeling of natural phenomena.
- Physics 220 and Physics/Biology 212 -- mathematical methods and computational modeling.
Recommended (but not required) prior classes:
- Intro Biology (Biol 141/142)
- Intro CS (CS110 or CS170)
- Physics 220 and Physics/Biology 212 -- mathematical methods and computational modeling.
- It is recommended, but not required, that the students have some exposure to differential equations, probability, and statistics.
This class is an upper division physics class, and it will involve advanced mathematical and physical concepts comparable to other upper division physics classes. However, no specific prerequisites beyond those indicated above are required, and the necessary concepts will be introduced within class as needed.
This class differs from most other classes in that we will use mathematical modeling and computer simulations as a language that glues physicists and biologists within the class. Computer simulations will help us to achieve deep, physics-style understanding of certain biological phenomena. Therefore, it is important that you come ready with the needed computational background (or with the commitment to get it quickly by studying the appropriate Matlab guide, in addition to math. I don’t expect you to be professional programmers, but it will be very useful if you can write a simple computer program in your favorite computer language that would output a “Hello world!” sentence on a screen. We will develop our math and programming skills as the class progresses.
I expect that, working in groups, those of you with biology backgrounds will learn ideas of computing / modeling from your physics / mathCS peers, and the physicist will learn basic biology facts from the biologists.
I request a lot from my students, but also provide them with all the necessary resources to succeed in the class. As a result, students will work hard, but will likely learn more than in a typical class. In practice, this philosophy is implemented as follows.
- The class will be delivered in a traditional lecture form. However, many lecture will start with a set of questions that will test your understanding of the previous material, or will prepare you to the new topic. We will answer the questions collectively, and I expect that everyone will participate in the ensuing discussion. The questions will not be graded – so don’t be afraid to answer incorrectly. Participation is key here.
- We will have homework assignments weekly. The assignments will be revealed typically online on a Wednesday. They will be due on a Friday of the following week (you will have 10 days or so to solve them; and there will be periods when to assignments are available simultaneously). The assignments will involve calculations and problems, like in physics and math classes, and numerical simulations, like in computational physics, biology, and chemistry classes. I expect that, to earn a high grade on your homework assignments, most of you will need to commit about 6-10 hours a week to them if you choose to work in groups and to attend office hours. You may need to work a lot more if you choose to do it alone. If you start working on an assignment the night before it’s due, you won’t have time to finish. Don’t be discouraged if you cannot figure out a solution to a homework problem immediately: talk to your peers or see me. The problems are meant to be challenging, but, as prior years have shown, they are all solvable if you expend a sufficient effort.
- Some of the problems will be open-ended, and may result in research projects if studied deeply (see below). The open-ended sections will be marked clearly and will be graded for extra credit only. The course will be structured to accommodate both undergraduate and graduate students by providing two sets of homeworks with the appropriate levels of difficulty. Graduate students are additionally expected to study the T2 track of the textbook (largely on their own).
- Review Sessions and office hours
- I will have a regular office hour every week. In addition, we will have review sessions before the exams, or when it becomes clear that many of you have similar concerns.
- We will have an in-class midterm exam, with a structure similar to other upper division physics classes. There will be no final exam per se; instead every student will need to complete and present a project and write a report (see below).
- Midway through the class, I will reveal a set of open-ended projects. Working in groups of 3-4 people, you will choose one of such projects, and you will work on it for the rest of the semester. These will be open-ended problems, with multiple steps of increasing difficulty, progressing from simple review of class material, to research questions (though those parts of the projects where I don't know if a solution exists will not be graded, clearly). During one of the last class meetings, the groups will present the project work to the rest of the class. Each student will be responsible then for an individual written report on the project, to be submitted during/before the exam week. For this report, I will assign you additional questions/problems related to it, and this part of the report will serve as a take-home exam of sorts. I hope that some of these projects may result, in due time, in research papers -- but this has only happened twice in many years of teaching this class. On the other hand, an optimist would say that this is known to have happened!
- Project Presentations
- We will schedule a double class (likely instead of the last class of the semester) -- pending a survey of your availability -- where all students groups will present their projects on one day.
- Rescheduled classes
- I will need to travel professionally during the semester. As a result, a few classes will need to be rescheduled, or conducted by guest lecturers. These will be named as the dates become known.
- Homework assignments – 50%
- Midterm exam -- 15%
- Project work and project presentation -- 20%
- Project writeup / take-home questions -- 15%
In-class questions will not be graded. There may be extra-credit problems available on occasion, which you can use to boost your grade. Your scores will convert to a letter grade as follows:
- 93.0 - 100 A
- 90.0 – 92.9 A-
- 87.0 – 89.9 B+
- 83.0 – 86.9 B
- 80.0 – 82.9 B-
with the pattern repeating for C and D grades; 59.9 or less is a failing grade.
The Emory College Honor Code applies to all homework assignments.
Topics to be covered
This class is aimed to teach you to think physically about biological systems. Thinking physically means, in the context of this class, creating simple predictive mathematical models of biological processes that provide insight. If you are still confused, we will talk a lot more during the lectures what it means to think physically. The class relies a lot on computer simulations as a tool to verify our understanding -- and you will learn Matlab during this class (no prior programming experience necessary). The main ideas that we will explore are the ideas of dynamics, randomness, control, inference, and information -- all applied to biology in two different ways: first, how we model and learn biology, and, second, how biological organisms model and learn the world around them. These ideas will be explored in a variety of biological systems, from viruses and bacteria, to neural systems, and to entire populations.
The first part of the course will largely follow Nelson's textbook. In the second part, we will focus on modeling information processing phenomena specifically, based on the mathematical and computational background we will have by then. This part will emphasize that all living systems have evolved to perform information processing tasks (variously known as learning from observations, signal transduction, regulation, sensing, adaptation, etc.) well. Here we will answer questions like: How can organisms deal with noise, whether extrinsic, or generated by intrinsic fluctuations within them? How can organisms ensure that the information is processed fast enough for the formed response to stay relevant in the ever-changing world? How should the information processing strategies change when the properties of the environment surrounding the organism change? Again, we will study these questions focusing on specific biological examples taking from different branches of biology, from cell biology and neuroscience to population biology.
Tentative class schedule
The schedule of topics covered during each lecture is subject to change. I will revise it periodically to reflect the pace of the class. The current schedule can be found on the class web site.
- Week 1, 2
- Nelson chapters 0-2: Why modeling in biology?
- What is a good model?
- Week 2-6
- Randomness in biology
- Nelson's chapter 3-7
- Additional topics
- Central limit theorem and the beauty of Gaussian random variables.
- Random walks and diffusion in different dimensions and the search for transcription factor binding sites.
- Random walks and diffusion in development – the diffusion equation approach.
- Projects will be assigned around week 7.
- Week 7-9
- Cellular circuits
- Nelson's chapters 8-11
- Midterm will follow this block of the class, tentatively October 31.
- Week 11-13
- Quantifying information processing in biology
- Introduction to information theory.
- Is one bit a lot? What are the limits on information processing imposed by structure of biological systems, temporal structure of signals, and their intrinsic randomness?
- Does biology care about bits? Can noise be useful? Information theory and bet hedging in population biology.
- When noise hurts: Strategies for noise suppression.
- Adaptation and other methods for enhancing information transmission.
- Methods for quantifying information transmission from empirical data
- Week 14
- In-class project presentations
- We will schedule a double class in early December, based on your availability, so that all groups can present during the same day.