Course Title: Computational Biology

Part A: Course Overview

Course Title: Computational Biology

Credit Points: 12.00


Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

BIOL2119

City Campus

Postgraduate

135H Applied Sciences

Face-to-Face

Sem 2 2006,
Sem 2 2007,
Sem 2 2009,
Sem 2 2013

Course Coordinator: Dr Andrew Hung

Course Coordinator Phone: +61 3 9925 1974

Course Coordinator Email: andrew.hung@rmit.edu.au

Course Coordinator Location: Building 14Level 6 City

Course Coordinator Availability: By Appointment (City campus meetings available)


Pre-requisite Courses and Assumed Knowledge and Capabilities

COSC1381 Computing Fundamentals or equivalent experience.


Course Description

As the rate of acquisition of biological data increases exponentially, the management, interrogation and manipulation of this data becomes a complex process that requires novel software solutions. Additionally, advancements in computational algorithms and massively parallel computing technology has enabled the application of chemical and physical modelling of increasingly complex biomolecular systems. The management of biological data, and the simulation and modelling of biological processes, form the foundations of the emerging field of Computational Biology, lying at the intersection between the biological and IT fields. There is a requirement for methodology to facilitate the acquisition, storage and retrieval of data, the analysis of this data, the accurate modelling of biophysical phenomena, and computationally complex tasks such as the prediction of macromolecular structure. This course will introduce these concepts and demonstrate some of the computational techniques currently available.


Objectives/Learning Outcomes/Capability Development

The objectives of this course are as follows:

Introduce the aims and uses of computational biology.

Describe the sources of data, in particular from the characterisation of genomes and proteomes.

Describe how biological information is stored and accurately retrieved.

Introduce computational algorithms that can be used for querying and manipulating biological data.

Describe the chemical and physical concepts involved in biomolecular science, and computational methods for simulating biophysical processes.

Describe some concepts in drug design.

Study some of the practical uses of these algorithms.



Overview of Learning Activities

Students attend a formal program of lectures and computer workshops. There will also be independent learning.

Students are recommended to attend and participate in all scheduled teaching sessions and complete formal items of assessment to achieve satisfactory completion of the course. Formal teaching sessions are available only at the times specified and cannot be repeated. Students are expected to spend an appropriate amount of time out of classes reviewing theoretical and practical material in textbooks, journals and on the Internet, preparing self directed leaning exercises and writing reports.

Oral and written student evaluation of the course will be formally solicited and considered annually by the Program Team in course and program review.


Overview of Learning Resources

There is no textbook formally prescribed for this course. Most of the literature and documentation will be accessible via the internet or made available during classes. For a general introduction, the following texts are recommended:

Gibas, C & Jambeck P. Developing Bioinformatics Computer Skills. O’Reilly, 2001

Lesk, AM. An Introduction to Bioinformatics. Second Ed. Oxford University Press. 2005.

Leach, A.R., Molecular Modelling: Principles and Applications, Pearson Education EMA, 2001

Several suitable introductory texts are available in the University library.


Overview of Assessment

Assessment will be by a combination of assignments and a final examination.

Assignments 3 x 20% each = 60%
These are due at the start of the weekly class in weeks 7, 10 and 13.
Exam (2 hour) 40%