BIOSCI 220 Quantitative Biology - Course Outlines

Course Prescription

An introduction to mathematical, statistical and computational literacy as required for contemporary biologists. Topics include fundamentals of experimental design, data exploration and visualisation, model-based inference to process biological data into biological information, comparing statistical models, prediction using mathematical models of biological processes, critical thinking about models and effective communication of findings. Data analysis and generation is taught using the R programming language. Recommended preparation: STATS 101

Course Overview

Living systems are the most complex systems in science, and biology is naturally variable and noisy due to its many internal and external influences. For these reasons, it is difficult to make inferences from and predictions about biological systems. Understanding biology requires computational skills to effectively analyse and interpret data, and multidisciplinary research approaches are becoming more common as a critical key to solving many of the complex problems of studying life and living organisms in today’s world. So, contrary to the popular undergrad biology student beliefs, statistics, mathematics and computational skills are essential in a biologist’s toolkit.

To understand modern biological research and findings, and to participate in this research (and get jobs!), skills in working with and visualising data, learning from data using models, and generating data using simulations of models are crucial. These might be classic statistical models, mathematical models, or inference with process-based models. Biologists also need to be careful and critical thinkers about data and how it is acquired with a lens on Data Sovereignty, as well as think critically about the models that we use to try to simplify, and thereby understand, the incredible complexity of biology.

BIOSCI 220 Quantitative Biology must be taken by all students in the Biological and Biomedical Sciences majors as a stage two core requirement. STATS 101 is strongly recommended prior to taking this course, although it is not a prerequisite. This course will introduce you to the programming language R to develop the aforementioned skills, with no coding experience assumed or expected. The aim is to give beginners the confidence to continue learning R and not be afraid of statistics and mathematics!

Course director: Jane Allison; Course coordinator: Jenn Jury; Course email: biosci220@auckland.ac.nz

Course Requirements

Prerequisite: 30 points from BIOSCI 101-109

Learning Outcomes

By the end of this course, students will be able to:

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 3 and 4)
Communicate statistical concepts and experimental outcomes clearly using language appropriate for both a scientific and non-scientific audience. (Capability 3 and 6)
Create and communicate informative data visualisations using the R programming language. (Capability 5 and 6)
Design an effective experiment adhering to the three fundamental principles: randomisation, replication, and blocking. (Capability 3 and 5)
Perform, interpret, and critique statistical regression using the R programming language. (Capability 3 and 4)
Define and apply data sovereignty principles as a researcher to the collection, handling and analysis of data, with a focus on indigenous data. (Capability 1 and 8)
Analyse and interpret multivariate data using an appropriate method. (Capability 3 and 4)
Explain how a mathematical model can be used to represent a simplified biological system. (Capability 3, 4 and 6)
Communicate the utility of mathematical representation of a complex biological process to a scientific and non-scientific audience. (Capability 5 and 6)
Critically evaluate the assumptions of models, and how models are deployed in science and public policy. (Capability 4 and 8)

Assessments

Assessment Type	Percentage	Classification
Laboratories	40%	Group & Individual Coursework
Quizzes	10%	Individual Coursework
Test	20%	Individual Test
Final Exam	30%	Individual Examination
4 types	100%

Assessment Type	Learning Outcome Addressed
	1	2	3	4	5	6	7	8	9	10
Laboratories
Quizzes
Test
Final Exam

Students must pass the practical (laboratories) and the theory (quizzes, test and exam) independently to pass the course overall.

Tuākana

Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at
https://www.auckland.ac.nz/en/science/study-with-us/pacific-in-our-faculty.html
https://www.auckland.ac.nz/en/science/study-with-us/maori-in-our-faculty.html

This course is included in the Biological Sciences Tuākana programme.

Key Topics

Module I. Data Exploration and Statistical Inference

Introduction to R and RStudio.
Data exploration and visualisation.
Māori data sovereignty.
Experimental design.
Hypothesis testing and interpretation of p-values.
Linear regression models; model critique and comparison.
Multivariate data analysis, introduction to dimensionality reduction.

Module II. Biological modelling

What (forward) modelling is and why we do it.
Mathematical reasoning and vocabulary.
Models for exponential growth, death, and resource-limited growth (logistic model) of a population, and predator-prey interactions.
SIR model (Susceptible-Infectious-Recovered) for the spread of infectious disease.
Assumptions made by models and when and why these may be justified.

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

Model-based inference, parameter inference with Maximum Likelihood.
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

The course assessment includes an evening test; the date and time for the test is published in the BIOSCI 220 Canvas course.

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 two hours of lectures, a three hour laboratory (one week off, two weeks on), three hours of reading and thinking about the content and three hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

A Campus Experience means:

Attendance is expected at scheduled activities including labs to complete components of the course.
Lectures will be available online and as recordings. Other learning activities including labs will not be available as recordings.
The course will not include live online events.
The activities for the course are scheduled as a standard weekly timetable.

Learning 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.

All course material will be delivered on Canvas. The part of the course that many students nd most challenging is working in "R", a widely-used language for statistical computing. If this is your rst time programming (which is common), you should mentally prepare yourself for the fact that (a) everyone nds R challenging at rst, 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 lectures and labs, but we strongly recommend that R newbies make use of some introductory R tutorials. Beginner tutorials for R/RStudio (there are plenty of more general tutorials available as well): 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. This will give you practice learning coding in R/RStudio itself, one of the best ways to learn coding.

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.

We continually review the course and draw upon the collective view of our students and staff, in developing and fine-tuning this course. For instance, we changed the structure of BIOSCI 220 in 2022 based on feedback from students and staff in 2021. We welcome feedback on the course throughout the semester, including the SET evaluations. Please contact your course coordinator or student representative at any time with your feedback.

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 for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

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

Your course coordinator is Jenn Jury (email jenn.jury@auckland.ac.nz). Please let me know how best we can support you in this course.

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 course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

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 students may be asked to submit 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. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Capability 1:	People and Place
Capability 3:	Knowledge and Practice
Capability 4:	Critical Thinking
Capability 5:	Solution Seeking
Capability 6:	Communication
Capability 8:	Ethics and Professionalism

BIOSCI 220 : Quantitative Biology

Science

2024 Semester One (1243) (15 POINTS)