BIOSCI 220 : Quantitative Biology

Science

2021 Semester Two (1215) (15 POINTS)

Course Prescription

Almost every biological discipline will require computational and analytical skills beyond using point-and-click software to enable the processing of biological data into biological information. Students will learn fundamentals of experimental design, data management, and data visualisation. Additionally, students will gain the skills required to critically analyse and interpret biological experiments, understanding how statistics can be both used and misused in the scientific literature. Recommended preparation: STATS 101

Course Overview

Living systems are the most complex things in the Universe. The science of biology is therefore the science of the complex. Other sciences, like physics and chemistry, have simpler study subjects. This may surprise students who think of other sciences as difficult and maths-heavy, and think of biology as science but with less maths.

However, biological research has actually been heavily quantitative for 100+ years. Much of the development of the field of statistics was driven by and for biologists and their research problems, which usually have a large amount of natural variability. In recent decades, the computational revolution has spread to every part of biology, and all biological fields now rely heavily on analyses that would be impossible without computers and computer programming: "big data" studies and complex models of biological phenomena.

Therefore, in order to understand modern biological research and findings, and to participate in this research (and get jobs!), it is now essential for biology students to acquire skills in working with and visualising data, learning from data using models, and generating data using simulations of models. These might be classic statistical models, simulation models, or inference with process-based models. Most importantly, students need to gain the ability to be careful and critical thinkers about data and how it is acquired, as well as the ability to think critically about the models that we use to try to simplify, and thereby understand, the incredible complexity of biology.

For more details, please see the Course Guide.

Course Requirements

Prerequisite: BIOSCI 101, and 30 points from BIOSCI 106-109

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 6: Social and Environmental Responsibilities
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Explain how models are used across the biological sciences to allow: inference in the face of variability and uncertainty; to predict outcomes given starting assumptions; and to test hypotheses about biological processes. (Capability 1, 2 and 3)
  2. Communicate statistical concepts and experimental outcomes clearly using language appropriate for both a scientific and nonscientific audience. (Capability 4 and 6)
  3. Create and communicate informative data visualisations using the R programming language. (Capability 3, 4 and 6)
  4. Design an effective experiment adhering to the three fundamental principles: randomisation, replication, blocking. (Capability 3)
  5. Perform, interpret, and critique statistical regression using the R programming language. (Capability 1 and 2)
  6. Analyse and interpret multivariate data using an appropriate method (e.g., Principal Component Analysis, Cluster Analysis). (Capability 1 and 2)
  7. Critically evaluate published literature for sampling, methods, graphs, and/or interpretations. (Capability 2 and 6)
  8. Communicate the utility of mathematical representation of a complex biological process to a scientific and non-scientific audience. (Capability 3 and 4)
  9. Critically evaluate the assumptions of models, and how models are deployed in science and public policy. (Capability 2 and 6)

Assessments

Assessment Type Percentage Classification
Laboratories 60% Individual Coursework
Quizzes 10% Individual Coursework
Final Exam 30% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7 8 9
Laboratories
Quizzes
Final Exam
The Practical Component of the course is the computer lab exercises (60% of the course marks). The Theory Component of the course consists of the Quizzes and Final Exam (40% of the course marks total).

A pass must be obtained in BOTH the practical and the theory sections to pass any biological sciences paper as a whole. Marks cannot be transferred from one section to another. Most students obtain good practical marks but some, although appearing to pass given their overall mark, will be given a failing grade because they have not passed the theory component.

Key Topics

Module I. Data Exploration and Statistical Inference
  • Data wrangling and visualization. Introduction to R, importing and plotting data, R packages
  • Experimental design and introduction to linear models
  • Linear models with multiple variables; interpretation; ANOVA
  • Interpretation of p-values; model critique and model comparison
  • Large data, exploratory data analysis, introduction to clustering and dimensionality reduction

Module II. Biological modelling

  • Forward modelling in general; what we can learn from models of biological processes; introduction to growth models
  • The SIR model (Susceptible-Infectious-Recovered) as an example model highly relevant to medicine (epidemiology, COVID-19), similar to ecology population models, relevant to evolution (birth-death processes)
  • Modelling the evolution of virulence

Module III. Model-based inference, and critical thinking about models

  • Model-based inference, parameter inference with Maximum Likelihood, fitting curvilinear models
  • Statistical model comparison, using the principle of parsimony to penalise more complex models (with AIC). Fitting SIR models to real-world SARS-CoV-2 data.
  • The crucial role of models in science & society; critical thinking about models, inference, and public policy.

Special Requirements

No special requirements

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.


For this course, you can expect 1 hour of lectures, a 3 hour lab/tutorial, 3 hours of reading and thinking about the content and 3 hours of work on assignments/preparation.

Delivery Mode

Campus Experience

A remote version of the course can be made available to students located overseas because of border restrictions, or those with an exemption to study remotely.

Otherwise, this course is designated a "campus experience," meaning:

  •  Attendance is expected at scheduled activities including labs/tutorials to complete components of the course.
  •  Lectures will be available as recordings. Other learning activities including labs will not be available as recordings, unless the entire campus is forced online due an increase in the COVID-19 Alert Level (see Learning Continuity section for more details).
  •  The course will not include live online events, unless the entire campus is forced online due to an increase in the COVID-19 Alert Level (see Learning Continuity section for more details).
  •  A remote version of the course can be made available to students located overseas because of border restrictions, or those with an exemption to study remotely.
  •  Attendance on campus is required for the exam (except for remote/otherwise exempt students).
  •  The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

All course material will be delivered on Canvas. The part of the course that many students find most challenging is working in "R", a widely-used language for statistical computing. If this is your first time programming (which is common), you should mentally prepare yourself for the fact that (a) everyone finds R challenging at first, but (b) everyone can succeed and make R work for them, and it really is worth it in the end.

You will be given some introduction to R in lab, but we strongly recommend that R newbies make use of some introductory R tutorials, for example:

Introductory tutorial by Nick Matzke. This contains a "quick and dirty," nonsystematic, introductory tutorial to get students functional in some very basic R skills. http://phylo.wikidot.com/biosci220:quantitative-biology

R/RStudio learning resources, including a number of general tutorials: https://education.rstudio.com/learn/beginner/

Swirl tutorials. For a large series of introductory tutorials, open any version of R/Rstudio, and in the command prompt, type:

install.packages("swirl")
library(swirl)
swirl()

...then follow the instructions that swirl gives.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Enter additional content here if appropriate.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Academic Integrity

The University of Auckland will not tolerate cheating, or 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 their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Copyright

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

The delivery modes under COVID-19 Alert Levels are as follows:

Level 1: Delivered normally as specified in delivery mode.

Level 2: You will not be required to attend in person. All teaching and assessment will have a remote option. The computer labs/tutorials will also have an on campus / in person option.

Level 3 / 4: All teaching activities and assessments are delivered remotely.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 16/12/2020 01:49 p.m.