Computational Biophysics
A.Y. 2021/2022
Learning objectives
The course will teach the techniques and the algorithms to study kinetic and equilibrium properties of classical models of molecules and polymers, mainly of biological interest.
Expected learning outcomes
The student at the end of the course will have these abilities:
1. Understand the theory that controls moleclar dynamics simulations
2. To perform molecular dynamics simulations
3. To understand the algorithms to calculate free energies in biomolecular systems
4. To be able to deal with data analysis through simple scripts
1. Understand the theory that controls moleclar dynamics simulations
2. To perform molecular dynamics simulations
3. To understand the algorithms to calculate free energies in biomolecular systems
4. To be able to deal with data analysis through simple scripts
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
1) Molecular dynamics of polymers in the microcanonical ensemble, integrators, ergodicity and integrability, temperature
2) molecular dynamics of model proteins at fixed temperature, Langevin dynamics, calculation of thermodynamic properties, phase transitions, thermostats and barostats, algorithms to optimise molecular dynamics
3) Thermodynamic sampling, Metropolis algorithm, glassy transitions, tempering and multicanonical methods.
4) Molecular dynamics of proteins in explicit solvent, the electrostatic problem, semi empirical potentials, reaction coordinate, umbrella sampling and meta dynamics.
2) molecular dynamics of model proteins at fixed temperature, Langevin dynamics, calculation of thermodynamic properties, phase transitions, thermostats and barostats, algorithms to optimise molecular dynamics
3) Thermodynamic sampling, Metropolis algorithm, glassy transitions, tempering and multicanonical methods.
4) Molecular dynamics of proteins in explicit solvent, the electrostatic problem, semi empirical potentials, reaction coordinate, umbrella sampling and meta dynamics.
Prerequisites for admission
Basic knowledge of Linux and of the C language
Teaching methods
Lectures and exercises on computers
Teaching Resources
Notes that can be downloaded from the Ariel site.
Assessment methods and Criteria
Oral exams of approximately 1/2 hour to assess the degree of comprehension of the theoretical aspects of protein physics, of the ability to reproduce the calculations discussed during the lectures, to implement on a computer the algorithms, of critical thinking and to connect to the subjects learn in other courses.
BIO/10 - BIOCHEMISTRY
FIS/03 - PHYSICS OF MATTER
INF/01 - INFORMATICS
FIS/03 - PHYSICS OF MATTER
INF/01 - INFORMATICS
Lessons: 42 hours
Professor:
Tiana Guido
Professor(s)