Case Studies In Computational Biology (BIOL30003)

Undergraduate level 3Points: 12.5On Campus (Parkville)

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Overview

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Semester 2
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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

Intended learning outcomes

On completion of this subject, students should:



  • Appreciate the broad range of biological topics, types of data, and computational approaches that are used in computational biology
  • Have an appreciation for how different computational approaches are relevant and appropriate for specific types of biological data
  • Describe the measurement technologies and sources of quantitative data in biology
  • Be aware of online databases and repositories for quantitative biological data, and be able to access, download and manipulate biological data from online resources
  • Understand and be able to convert a biological problem into an appropriate computational problem

Generic skills

  • Time-management: the ability to meet regular deadlines while balancing competing commitments.
  • Ability to bring together knowledge from different disciplines to bear on a scientific or technological problem
  • Ability to find and use appropriate resources (including online)
  • Ability to communicate biological and computational knowledge effectively
  • Capacity for lifelong learning and professional development
  • Understanding of plagiarism, respect for honesty and intellectual integrity, and for the ethics of scholarship

Last updated: 3 November 2022