Major
Computational Biology
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What will I study?
Overview
After building your knowledge in biology, mathematics and computer science, you’ll learn how to integrate what you’ve learned to solve problems in fields such as genomics, evolution, ecology, epidemiology and systems biology.
In addition, you’ll also have the opportunity to focus on one of computational biology’s core disciplines – biological science, mathematics and statistics, or computer science.
Your major structure
You’ll complete this major as part of a Bachelor of Science degree.
In your first and second years you will complete subjects that are prerequisites for your major, including mathematics and statistics, computer science, genetics and biology subjects.
In your third year, you will complete 50 points (four subjects) of deep and specialised study in computational biology.
Throughout your degree you will also take science elective subjects and breadth (non-science) subjects.
Read more about studying mathematics and statistics at the University of Melbourne.
Sample course plan
View some sample course plans to help you select subjects that will meet the requirements for this major.
If you did not achieve a study score of at least 29 in VCE Specialist Mathematics 3/4, you may need to enrol in MAST10005 Calculus 1 in your first semester. If you achieved a study score of at least 36 in VCE Specialist Mathematics 3/4 or equivalent, you can enrol in MAST10021 Calculus 2: Advanced and MAST10022 Linear Algebra: Advanced instead of MAST10006 Calculus 2 and MAST10007 Linear Algebra. If you did not achieve a study score of at least 25 in year 12 Biology, you will need to enrol in the relevant introductory first year biology subjects: BIOL10008 Introductory Biology: Life’s Machinery and BIOL10010 Introductory Biology: Life’s Complexity instead of BIOL10009 Biology: Life’s Machinery and BIOL10011 Biology: Life’s Complexity.
Year 1
100 pts
- Semester 1 50 pts
- Semester 2 50 pts
Year 2
100 pts
- Semester 1 50 pts
- Semester 2 50 pts
science elective
12.5 pts
science elective
12.5 pts
breadth
12.5 pts
Year 3
100 pts
- Semester 1 50 pts
- Semester 2 50 pts
If you did not achieve a study score of at least 29 in VCE Specialist Mathematics 3/4, you need to enrol in MAST10005 Calculus 1 in your first semester. If you achieved a study score of at least 36 in VCE Specialist Mathematics 3/4 or equivalent, you can enrol in MAST10021 Calculus 2: Advanced and MAST10022 Linear Algebra: Advanced instead of MAST10006 Calculus 2 and MAST10007 Linear Algebra. If you did not achieve a study score of at least 25 in year 12 Biology, you will need to enrol in the relevant introductory first year biology subjects: BIOL10008 Introductory Biology: Life’s Machinery and BIOL10010 Introductory Biology: Life’s Complexity instead of BIOL10009 Biology: Life’s Machinery and BIOL10011 Biology: Life’s Complexity. Mid-year entry for this major may not suit international students. At least one of the semesters has a part-time load.
Year 1
100 pts
- Semester 2 50 pts
- Semester 1 50 pts
Year 2
100 pts
- Semester 2 50 pts
- Semester 1 50 pts
Year 3
75 pts
If you did not achieve a study score of at least 29 in VCE Specialist Mathematics 3/4, you need to enrol in MAST10005 Calculus 1 in your first semester. If you did not achieve a study score of at least 25 in year 12 Biology, you will need to enrol in the relevant introductory first year biology subjects: BIOL10008 Introductory Biology: Life’s Machinery and BIOL10010 Introductory Biology: Life’s Complexity instead of BIOL10009 Biology: Life’s Machinery and BIOL10011 Biology: Life’s Complexity.
Year 1
100 pts
- Semester 1 50 pts
- Semester 2 50 pts
Year 2
100 pts
- Semester 1 50 pts
- Semester 2 50 pts
science elective
12.5 pts
breadth
12.5 pts
breadth
12.5 pts
Year 3
100 pts
- Semester 1 50 pts
- Semester 2 50 pts
Explore this major
Explore the subjects you could choose as part of this major.
- 12.5 pts
This subject introduces the concepts of mathematical and computational modelling of biological systems, and how they are applied to data in order to study the underlying drivers of observed behaviour. The subject emphasises the role of abstraction and simplification of biological systems and requires an understanding of the underlying biological mechanisms. Combined with an introduction to sampling-based methods for statistical inference, students will learn how to identify common patterns in the rich and diverse nature of biological phenomena and appreciate how the modelling process leads to new insight into biological phenomena.
- Modelling: Deterministic and stochastic population-level dynamic models; agent-based computational models; and geospatial statistical models will be introduced and studied. Indicative examples will be drawn from health (e.g. infectious diseases, cell tumour growth, developmental biology), ecology (e.g. predator-prey systems, sustainable harvesting, environmental decision making) and biotechnology (e.g. biochemical and metabolic models).
- Simulation: Sampling based methods (e.g Monte Carlo simulation, Approximate Bayesian Computation) for parameter estimation and hypothesis testing will be introduced, and their importance in modern computational biology discussed.
- 12.5 pts
This subject will introduce current topics in computational biology, focusing on case studies in a number of different biological areas, and applying a range of different mathematical and computational data handling approaches to solve or interrogate biological problems. Each topic will be developed through a series of lectures introducing the biological topic (relying on a fundamental knowledge of the molecular basis of life gained in second year level genetics and biochemistry subjects), the types and sources of biological data, and the relevant computational approaches, based around case studies. A series of assignments in each of these topic areas, supported by tutorial classes, will illustrate the computational methodologies as they are applied to specific biological data.
Indicative biological topics include applications of computational biology in:
- Phylogenetics, population genetics and evolution
- Ecological and environmental modeling (including geospatial and environmental decision making)
- Bio-imaging and cell tracking in cell biology
- Pathogenesis and immunology
- Structural biology
- Metabolic engineering and biotechnology
- 12.5 pts
This subject introduces the biology and technology underlying modern genomics data, features of the resulting data types including the frequency and patterns of error and missingness, and the statistical methods used to analyse them. It will include hands-on data analysis using R software. The material covered will evolve as genomics technology and practice change, and will span the following four areas: introduction to genomics technology and the resulting data, population genetics, association analysis including tests of association and major sources of confounding, heritability and prediction both in human genetics and for animal and plant breeding, and analysis of expression quantitative trait loci.