Aims and Fit of Module

The module aims to equip students with the essential knowledge and skills to apply mathematical and physical modeling techniques to address frontier problems in computational biology.

The curriculum focuses on molecular modeling and molecular dynamics of biological system, and it is divided in 3 major parts:
1. Introduction to the Thermodynamics of biological sytems
2. Molecular modeling and Molecular Dynamics (MD) of biological systems
3. Trajectory analysis and advanced sampling in MD simulations

By covering the topics in these areas, students will gain a comprehensive understanding on how to use Physics based models in computational biology, enabling them to tackle complex problems effectively and become competitive to the cutting-edge job market in the field.

Learning outcomes

A Understand the structure of protein and other biological macromolecules and familiarize with software for molecular visualization.

B Understand the Physics behind Molecular Dynamics (MD) simulations, in particular how MD simulation can reproduce dynamical and thermodynamical properties of biological systems.

C Develop an understanding of fundamental thermodynamic and statistical mechanics concepts, such as: configuration space and ensembles, microstates and macrostates, Internal Energy, Entropy and Free Energy of molecular systems.

D Learn how to build molecular models using standard homology modeling and molecular docking methodologies.

E Understand the foundational concepts and principles underlying molecular dynamics simulations, including force fields, interatomic potentials, barostats, thermostats and periodic boundary conditions in these simulations.

F Conduct MD simulations with standard software. Critically analyze MD trajectories.

G Understand the meaning of Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), running averages and ensemble averages, which are crucial statistical methods in the analysis of molecular dynamics data.

H Apply advanced sampling techniques in the calculation of key thermodynamic observables, critically evaluating their accuracy and reliability.

Method of teaching and learning

This module employs a blend of theoretical instruction and practical application to equip students with the skills and knowledge required to excel in computational biology.

Lectures and Theoretical Instruction:

Core concepts in thermodynamics, molecular modeling, and molecular dynamics will be delivered through lectures. These sessions will provide students with a strong theoretical foundation, emphasizing the mathematical and physical principles underlying biological systems.

Hands-on Computational Labs:

Practical sessions will complement the lectures, allowing students to apply theoretical knowledge through hands-on computational exercises. These labs will focus on building molecular models, setting up and running molecular dynamics simulations, analyzing trajectories, and employing advanced sampling techniques. Students will gain proficiency in using industry-standard software and tools.

Problem Sets and Mathematical Exercises:

By performing simulations and analyzing data, students will engage in problem sets that require mathematical calculations and physical simulations. These exercises will challenge students to apply theoretical concepts to solve real-world biological problems, fostering a deeper understanding of the material.