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Prospectus

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Physics of Life (from Motors to Nerve Pulses)

Course
2019-2020

Prerequisites

You should know basic concepts from statistical physics (such as: the laws of thermodynamics, random walks, equipartition theorem, Stokes-Einstein equation, entropy, free energy, Boltzmann factor, partition function) and the main structure of a cell (plasma membrane, cytoskeleton, nucleus). All of this is covered in the course ”Fysica van leven” in the first semester. If you are missing this background, you can study chapters 1-7 in the book “Biological physics: energy, information, life” by Philip Nelson. This course will comprise some programming exercises in Python. If you followed the class “Programmeermethoden” or work through this tutorial (https://docs.python.org/3/tutorial/), you will have sufficient Python programming skills.

Note: This class is taught in English.

Description

This class explores how the dazzling complexity of life at the nanoscale is governed by key concepts of physics, in particular entropy. We will discover how tiny molecular machines can carry out diverse tasks in the cell and begin to understand how brain cells communicate through electrical impulses.

Course objectives

At the end of the course you will be able to:

  1. analyze biological processes to identify where (statistical) physics comes into play.

Specifically, these 5 processes will be treated over 10 classes:

i. Self-assembly of lipids and proteins; chemical reactions => chemical potential, grand canonical ensemble
ii. DNA elasticity, helix-coil transition => Ising model, freely jointed chain
iii. Allostery, cooperative binding => phase transitions
iv. Molecular motors => Brownian ratchet, Michaelis-Menten kinetics
v. Action potentials in neurons => electrodiffusion

  1. dissect a scientific paper, form an opinion on the paper’s strengths and weaknesses and summarize it in a presentation

  2. distinguish different kinds of modeling approaches and apply some of these models to simple problems. Specifically, we will compare these modeling approaches with each other:

  • Kinetic model vs equilibrium approach

  • Microscopic model vs continuum model with phenomenological parameters

  • Linear vs non-linear behavior

Mode of instruction

Each topic is discussed over two classes (one class per week). In total, there will be 5 pairs of classes.

Class 1 – course objective 1
For this class you will prepare by studying a chapter from the mandatory textbook “Biological physics: energy, information, life” by Philip Nelson. Some guiding questions will help you discover the role of physics in biological processes. During the class you will be asked to discuss the guiding questions in groups of 2-5 students and explain the answers to the other students. You will also solve 3-5 problems from the textbook and the solutions to these problems will be discussed.

Class 2 – course objectives 2 and 3

For this class you will prepare by reading an original research paper that is closely related to the process discussed in class 1. You will make at least 3 comments on this paper, using the website perusal.com. During the class, one group of students will give a presentation of the paper followed by a discussion of its strengths and weaknesses. In the second half of the class you will work on programming exercises that highlight different modeling approaches. These exercises will be carried out in groups, using the computers in the studio classroom.

Timetable

Rooster

Assessment method

It is mandatory to leave 3 comments on each of the 5 papers that are discussed during the course. 30% of the grade will depend on the presentation, 70% on a written, closed-book exam, which will comprise problems similar to the problems in the textbook (no programming).

Blackboard

More information will be available on Blackboard UL

Reading list

Philip Nelson, "Biological Physics: Energy, Information, Life", Palgrave Macmillan (Aug 31, 2007) ISBN-13: 978-0716798972 EUR 57,95

The purchase of this book is mandatory.

Contact

Contact details lecturer: Dr. S.Semrau(Stefan)