COM4508/6508 Introduction to Computational Systems Biology

Lecturer: Prof. Magnus Rattray

Aims

This unit aims to introduce the main concepts and methodology of modern computational systems biology applied to biological systems at different scales, from sub-cellular systems to tissue and organ-level systems. Students will learn how to numerically solve and interpret models, and how to parameterize and assess the validity of models given experimental evidence.

Pre-requisites and Co-requisites

The content is mathematical and knowledge of basic differential calculus is required. Here is an outline of some of the Basic Mathematics required to follow the course.

Objectives

By the end of the unit, a candidate will be able to demonstrate the ability to:

apply basic mathematical concepts to build models of dynamical systems described by differential equations;
solve systems biology models based on differential equations;
relate the behaviour and properties of systems biology models to biological function, using examples of systems at different scales and/or multiple scales;
use computational tools to learn the parameters of a model from experimental data;
understand the limitations and validity of a particular model.

Course Announcements

The mini-project description is now available. The lab sessions will now be used to support the project. The deadline for submitting the project is the end of week 11.
Discussion board available on MOLE. Contact me if you need to be registered on MOLE.

Course Materials

Notes for Lectures 1-6

Richard Clayton Lecture

Systems Biology Toolbox tutorial

Matlab scripts and SB Toolbox files for Lecture 2

Matlab scripts and SB Toolbox files for Lecture 3

Matlab scripts and SB Toolbox files for Lecture 4

Matlab scripts and SB Toolbox files for Lecture 5

Matlab scripts and SB Toolbox files for Lecture 6

Structure and Teaching Method

Lectures (2 hours per week) will initially be used to present new concepts and later will be more in the style or research seminars presenting case studies from Systems Biology research carried out across the University [all learning outcomes].

Examples sheets (homework) will be used to develop mathematical intuitions and practice exam-style questions. Some lecture time will be used to review example sheet solutions and discussion boards will be made available.

Lab exercises (5 weeks, 2 hour lab per week) will be used to introduce basic numerical methods and mathematical concepts relating to differential equation models using the matlab programming environment. Students will investigate the properties of simple models of cellular systems [learning outcomes (1),(2),(3) and (4), 30% of marks]

Mini-project (5 weeks, 2 hour lab per week) students will work individually on a more open-ended task where a model is not fully specified and must be parameterised and assessed using experiment data. Each student will write an individual short report describing their results. [learning outcomes (3), (4) and (5), 40% of marks]

Resources Required

Matlab programming environment. We will use the freely available Systems Biology and SBML toolboxes.

Assessment

Lab work [30%]
Mini-project marked by written report [40%]
Written exam [30%]

How and When Students Will Receive Feedback

Lab work will be marked online during the lab session with verbal feedback. Comments on the written report will be provided via MOLE.

Recommended Texts

Main text: Uri Alon "An Introduction to Systems Biology" (Chapman and Hall/CRC, 2007). Focussing mainly on sub-cellular processes, this textbook provides an excellent description of the function of network motifs in gene regulatory networks. This provides the core text for the course.
Bernhard Palsson "Systems Biology: Simulation of Dynamic Network States" (Cambridge University Press, 2011). A recent textbook focussing more of metabolic processes. A good discussion of modelling on different time-scales which is a topic we will visit during the latter part of the course.
Neil Lawrence, Mark Girolami, Magnus Rattray and Guido Sanguinetti "Learning and Inference in Computational Systems Biology" (MIT Press, 2010). A collection of papers describing recent work on learning Systems Biology models from data. This book deals with more advanced topics in machine learning and Bayesian statistics than covered in the course and is more appropriate for students interested in pursuing an MSc or PhD dissertation in this area.

Department of Computer Science