About this course
The course consists of three parts. The first two weeks are devoted to the theoretical background. We discuss the use of statistical mechanics to describe biophysical processes at the molecular scale. This includes a brief introduction of the basic biological concepts, a discussion on the role of equilibrium and non-equilibrium processes in biophysics, and the statistical mechanical background of the simulation methods we will use. The rest of the course is split over two practical assignments in which the student acquires hands-on experience with Molecular Dynamics and Monte Carlo simulations of biophysical models for membrane formation and biopolymer mechanics.
Prior experience with coding in Python or Mathematica is useful but not essential to be able to take this course.
In the Molecular Dynamics assignment, the student will use LAMMPS and Python to simulate the dynamics of a simplified model for phospholipids to study the formation of biological membranes, and learn how LAMMPS deals with basic MD concepts such as integration of the equations of motion, boundary conditions, thermostats, neighbor lists, and reduced unit systems.
In the Monte Carlo assignment the student will obtain the equilibrium statistical properties of biopolymers using the Metropolis MC method, and learn how to set up such a simulation from scratch in Mathematica or Python.
- the student can explain the use of statistical mechanical models for the physics of biomolecular systems;
- given a description of a biomolecular process, the student can assess what aspects of it are governed by equilibrium statistical mechanics and what aspects are inherently of a non-equilibrium character;
- the student can explain the basic properties of phospholipid membranes, including their composition, self-assembly and mechanics;
- the student can explain how thermal fluctuations affect the mechanical properties of biopolymers;
- the student can explain the concept of persistence length and how it affects the mechanical properties of biopolymers;
- given a (bio)physical problem, the student can formulate a Metropolis Monte Carlo scheme to simulate its behaviour at constant temperature;
- given a Monte Carlo scheme, the student can identify mistakes that would lead to incorrect or inefficient sampling;
- the student can implement a Monte Carlo simulation in Mathematica or Python and analyse its results;
- the student can explain the basics of molecular dynamics simulations: numerical integration of Newton’s equations, periodic boundaries, the idea behind thermostatting;
- given a particle-scale modeling question, the student can set up, execute and analyse a molecular dynamics simulation in LAMMPS.
Required prior knowledge
You must meet the following requirements
- Registered for a degree programme other than
- HBO-TOP Applied Physics