Course Title: Computational Biology

Part A: Course Overview

Course Title: Computational Biology

Credit Points: 12.00

Important Information:

Please note that this course may have compulsory in-person attendance requirements for some teaching activities. 

To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption. 

Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT:  https://policies.rmit.edu.au/document/view.php?id=209

Please read the Student website for additional requirements of in-person attendance:  https://www.rmit.edu.au/covid/coming-to-campus 

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 



Course Coordinator: Jessica Holien

Course Coordinator Phone: +61 3 9925 7256

Course Coordinator Email: jessica.holien@rmit.edu.au

Course Coordinator Location: Building 223, Bundoora West

Course Coordinator Availability: By Appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Assumed Knowledge:

Some basic computational knowledge and knowledge of genetics, chemistry and/or biochemistry would be advantageous, such as from the courses Cell Biology & Biochemistry, Genetics & Molecular Biology, and Genomics & Gene Technologies.


Course Description

Computational Biology is an arm of Bioinformatics. It is the computational use of biological information to solve problems. This course will deliver examples in this rapidly evolving field including descriptions of biological databases and relevant tools available to retrieve and analyse the information within these. You will learn how to utilise this data and relate it to protein structure/function and computational drug discovery. This course will enable you to get a taste of the current computational biology techniques used in academia and industry. The skills developed will aid those exploring future careers in multi-disciplinary science, particularly Bioinformatics, Biotechnology, and Drug Discovery.


Objectives/Learning Outcomes/Capability Development

This course contributes to the program learning outcomes for the following programs:

BP350 Bachelor of Science

This course contributes to the following program learning outcomes:

  • PLO 2     Analyse and critically examine scientific evidence using methods, technical skills, tools and emerging technologies in a range of scientific activities.
  • PLO 3     Analyse and apply principles of scientific inquiry and critical evaluation to address real-world scientific challenges and inform evidence based decision making.
  • PLO 4     Communicate, report and reflect on scientific findings, to diverse audiences utilising a variety of formats employing integrity and culturally safe practices.
  • PLO 5     Work independently, with agility, safety, and accountability for own learning and professional future.

For more information on the program learning outcomes for your program, please see the program guide.


This course is optional as part of the following programs:

BP340 Bachelor of Data Science
BP348 Bachelor of Data Science (Professional)
BP094 Bachelor of Computer Science
BP347 Bachelor of Computer Science (Professional)
BP162 Bachelor of Information Technology
BP349 Bachelor of Information Technology (Professional)
BP096 Bachelor of Software Engineering



Course Learning Outcomes

Upon successful completion of this course, you will be able to:

  1. Identify the basic principles that underpin computational biology and bioinformatic analyses, and apply these principles when analysing biological data.
  2. Analyse biological data using a variety of computational bioinformatic tools.
  3. Interpret correctly the outputs from tools used to analyse biological data and make meaningful predictions from these outputs.
  4. Explain how computational biology is used in real world settings and apply this knowledge to deliver solutions to these real world problems.


Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both.

You will be presented with a series of lectorials that illustrate both fundamental principles of Computational Biology and the applications of these principles in particular areas. Experts in such fields will present these lectorials. A set of online computer tutorials will accompany the lectorials.

 Learning will be facilitated by: 

  • your participation in lectorials where syllabus material will be presented and explained, and the subject will be illustrated with demonstrations and examples;
  • completion of computer tutorials designed to give you further practice in the application of theory and procedures, and to give feedback on your progress and understanding;
  • completion of assignments requiring an integrated understanding of the subject matter; and
  • private study, working through the course as presented in classes and learning materials, and gaining practice at solving conceptual problems.

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through Canvas.


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through Canvas and the RMIT Student website.

Lectorial and computer tutorial notes will be provided through Canvas, as will other articles of interest such as research articles and websites. No formal textbook is assigned, however students are directed to the liaison librarian

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the RMIT Student website.


Overview of Assessment

Assessment Tasks

Assessment task 1: Tutorial Reports
Weighting:  40%
This assessment task supports CLO’s 1, 2, 3 and 4 

Assessment task 2: Online Quiz  
Weighting: 20%
This assessment task supports CLO’s 1, and 3 

Assessment task 3: Practical report
Weighting: 40%
This assessment task supports CLO’s 1, 2, 3 and 4 

If you have a long term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.