Biological Modelling and Simulation (MAST30032)

Undergraduate level 3Points: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1
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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.

Intended learning outcomes

On completion of this subject, students should:




 

  • Appreciate how abstraction and simplification of biological systems through modelling can provide new insight into biological phenomena
  • Be able to distinguish between different approaches to modelling (deterministic, stochastic, agent-based, statistical) and critically evaluate the suitability of these alternative approaches for particular biological problems
  • Be able to develop computer programs that implement and solve simple models of biological phenomena
  • Be familiar with the concept of statistical simulation and its role in testing hypotheses and understanding model behaviour
  • Use models and their application to data to formally evaluate biological hypotheses
  • Understand how to interpret and critique the biological modelling literature

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. In particular

  • modelling skills: the ability to abstract and generalise from observations of a complex system, providing an alternative perspective on the problem
  • numerical and computer simulation skills: the ability to design simple computer programs to solve models and test hypotheses
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments.

Last updated: 31 January 2024

Eligibility and requirements

Prerequisites

Undergraduate students:

Undergraduate students must complete either one of the following pathways:

1. Normal Pathway

Code Name Teaching period Credit Points
COMP10002 Foundations of Algorithms
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5

AND

One of

Code Name Teaching period Credit Points
BIOL10010 Introductory Biology: Life's Complexity
Semester 2 (On Campus - Parkville)
 12.5
BIOL10011 Biology: Life's Complexity
Semester 2 (On Campus - Parkville)
 12.5

BIOL10005 Genetics and The Evolution of Life

AND

Two of

Code Name Teaching period Credit Points
BCMB20002 Biochemistry and Molecular Biology
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
CEDB20003 Fundamentals of Cell Biology
Semester 1 (On Campus - Parkville)
 12.5
GENE20001 Foundations of Genetics and Genomics
Semester 1 (On Campus - Parkville)
 12.5
GENE20004 Applications of Genetics and Genomics
Semester 2 (On Campus - Parkville)
 12.5
MIIM20001 Principles of Microbiology & Immunology
Semester 1 (On Campus - Parkville)
 12.5

GENE20002 Genes and Genomes (BSc pre-2020)

AND

One of the following subject sets (A, B, or C):

Set A:

One of

Code Name Teaching period Credit Points
MAST10006 Calculus 2
Summer Term (Dual-Delivery - Parkville)
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
MAST10021 Calculus 2: Advanced
Semester 2 (On Campus - Parkville)
 12.5

AND

One of

Code Name Teaching period Credit Points
MAST10007 Linear Algebra
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
Summer Term (Dual-Delivery - Parkville)
 12.5
MAST10022 Linear Algebra: Advanced
Semester 1 (On Campus - Parkville)
 12.5

AND

Code Name Teaching period Credit Points
MAST20031 Analysis of Biological Data
Semester 1 (On Campus - Parkville)
 12.5

Set B:

One of

Code Name Teaching period Credit Points
MAST10006 Calculus 2
Summer Term (Dual-Delivery - Parkville)
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
MAST10021 Calculus 2: Advanced
Semester 2 (On Campus - Parkville)
 12.5

AND

One of

Code Name Teaching period Credit Points
MAST10007 Linear Algebra
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
Summer Term (Dual-Delivery - Parkville)
 12.5
MAST10022 Linear Algebra: Advanced
Semester 1 (On Campus - Parkville)
 12.5

AND

Code Name Teaching period Credit Points
MAST10010 Data Analysis 1
Semester 2 (On Campus - Parkville)
 12.5

Set C:

Code Name Teaching period Credit Points
MAST20005 Statistics
Semester 2 (On Campus - Parkville)
Summer Term (Dual-Delivery - Parkville)
 12.5

2. Advanced Mathematical Pathway

Code Name Teaching period Credit Points
MAST20030 Differential Equations
Semester 2 (On Campus - Parkville)
 12.5

One of

Code Name Teaching period Credit Points
COMP10001 Foundations of Computing
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Summer Term (Dual-Delivery - Parkville)
 12.5
COMP20005 Intro. to Numerical Computation in C
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
PHYC20013 Laboratory and Computational Physics 2
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5

Other evidence of competence in computer programming can also be provided to meet this pre-requisite, including passing a relevant Year 12 school subject, or a statement of achievement

from a relevant MOOC, or passing a programming competency test administered by

another University of Melbourne School.

Graduate students:

Admission into the MC-COMPBIO Master of Computational Biology

Corequisites

None

Non-allowed subjects

None

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 31 January 2024

Assessment

DescriptionTimingPercentage

One written assignment

  • 1000 words
Week 820%

Four laboratory exercises completed during practice classes, held at regular intervals due in weeks 4, 6, 10, 12 (10% for each exercise, requiring approximately 44 hours of work in total)*

  • 44 hours (of work required)
During the teaching period40%

A written examination

  • 2 hours
During the examination period40%

Last updated: 31 January 2024

Dates & times

  • Semester 1
    CoordinatorAlex Zarebski
    Mode of deliveryOn Campus (Parkville)
    Contact hours48 hours: 24 x one-hour lectures (2 per week), 12 x two-hour practice classes (1 per week)
    Total time commitment170 hours
    Teaching period27 February 2023 to 28 May 2023
    Last self-enrol date10 March 2023
    Census date31 March 2023
    Last date to withdraw without fail 5 May 2023
    Assessment period ends23 June 2023

    Semester 1 contact information

    azarebski@unimelb.edu.au

Time commitment details

170 hours

What do these dates mean

Visit this webpage to find out about these key dates, including how they impact on:

  • Your tuition fees, academic transcript and statements.
  • And for Commonwealth Supported students, your:
       -  Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
     

    Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.

Last updated: 31 January 2024

Further information

  • Texts

    Prescribed texts

    There are no specifically prescribed or recommended texts for this subject.

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseBachelor of Science
    Informal specialisationScience Discipline subjects - new generation B-SCI
    MajorComputational Biology
  • Available through the Community Access Program

    About the Community Access Program (CAP)

    This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

    Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants

    Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

  • Available to Study Abroad and/or Study Exchange Students

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 31 January 2024