Course syllabus

COMPSCI 369: Computational Biology

Welcome to COMPSCI 369 Computational biology.

Prerequisites: COMPSCI (220 and 225)

Course Description: Computational biology is the development and application of computer algorithms and software to address scientific questions in the biological and life sciences, often using big data. This course includes probabilistic computer modelling, computer-based statistical inference and computer simulation for, and motivated from, the life sciences. It focuses on modeling and analysing real-world biological data with an emphasis on analysing DNA sequence data.

Contacts

Current lecture and tutorial times and venues may be found on Student Services Online.

Learning Outcomes

  • be familiar with the basics of numerical computing
  • Be familiar with how genetic data is collected, processed and stored.
  • be familiar with string matching algorithms
  • understand the principles of dynamic programming
  • be familiar with basic probabilistic modelling techniques, including common probability distributions, simple stochastic processes, Markov chains, and hidden Markov models (HMMs)
  • be able to implement and apply standard dynamic programming algorithms for sequence alignment and hidden Markov models
  • Be able to describe and implement methods for simulating stochastic models
  • understand and apply the maximum likelihood and the least squares framework
  • understand the basic model of genetic sequence evolution
  • be familiar with phylogenetic models of sequence evolution
  • know how to score,construct and interpret phylogenetic trees under neighbour joining, parsimony and likelihood based methods

Lectures

See lectures page.

Assessment

  • 30% assignments (4 @ 7.5% each)
  • 10% written midterm test
  • 60% written final examination
  • All COMPSCI undergraduate courses have a separate pass requirement, i.e., you will need to pass both the theory (test+exam) and the practical (assignments) individually. 
  • We use the standard university grade boundaries. >89.5 for A+, then 5 mark increments down to >49.5 for C-.

Handling illness or absence

  • For problems affecting assignments or tests, see the lecturer, as soon as reasonably possible.
  • For illness during exams (or other problems that affect exam performance) students must contact the University within one week of the last affected examination, to apply for an aegrotat pass (for illness) or compassionate pass (other problems).

Refer to the University information about Aegrotat and Compassionate Considerations: https://www.auckland.ac.nz/en/for/current-students/cs-academic-information/cs-examination-information/cs-aegrotat-and-compassionate-consideration.html

Academic Integrity

The University of Auckland will not tolerate cheating or the assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting his or her learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the world-wide web. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

Please refer to http://www.auckland.ac.nz/uoa/home/about/teaching-learning/honesty.

Harassment

Every member of the University, student or staff, has a right to dignity and respect. Please see the policy on harassment: https://www.auckland.ac.nz/en/for/current-students/cs-student-support-and-services/cs-personal-support/bullying-and-harassment.html

 

Course summary:

Date Details