Module Catalogues

Modeling for Computational Biology

Module Title Modeling for Computational Biology
Module Level Level 3
Module Credits 5.00
Academic Year 2024/25
Semester SEM2

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 encompasses a diverse range of topics, including:
1. Molecular dynamics (MD) simulations for biological systems
2. Modeling techniques in genomics and systems biology
By covering the advanced topics in these areas, students will gain a comprehensive understanding of the various mathematical and statistical modeling methods used 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 Physics behind MD simulations, in particular how MD simulation can reproduce dynamical and thermodynamical properties of biological systems.
B Analyze the foundational concepts and principles underlying molecular dynamics simulations, including force fields, interatomic potentials, barostats and thermostats and the significance of periodic boundary conditions in these simulations.
C Conduct Molecular Dynamics (MD) simulations and critically analyze the resulting data paths, including understanding measurements like Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF).
D Execute and critically interpret running averages and ensemble averages, which are crucial statistical methods in the analysis of molecular dynamics data.
E Develop an understanding of fundamental thermodynamic concepts, including Entropy and Free Energy.
F Apply advanced sampling techniques in the calculation of key thermodynamic observables, critically evaluating their accuracy and reliability.
G Understand the foundational principles and techniques of hierarchical modeling in genomics and systems biology, including the learning and inference methods in a Bayesian network.
H Evaluate the important paradigm of statistical modeling in genomics by fitting finite mixture models with EM algorithm.

Method of teaching and learning

This module is designed to deliver its content through a mathematically grounded approach. Students will gain a thorough understanding of the mathematical and physical foundations on which biological modeling is based, with a strong emphasis on the formal aspects behind each concept.
To ensure the effective assimilation of knowledge, students will engage in problem sets and exercises that involve mathematical calculations of physical simulations. These hands-on activities will allow them to directly apply the theory learned in class, enhancing their practical understanding of the material.
For instance, in the Molecular Dynamics part of the course, students will learn how to set up the simulation of a biological system, and analyze the results of the simulation.
In the systems biology component of the course, students will gain hands-on experience in calculating posterior inferences from Bayesian networks. Through this approach, the module ensures that students develop both the theoretical and applicational expertise required to apply mathematics in the field of computational biology.