MAST10016: Mathematics for Biomedicine

Ten weekly written assignments (25%), an oral presentation (5%) and a final written examination (70%)

Yes, with screen capture.

James Osborne

One past paper is available, but no solutions were available (so frustrating).

3/5

None. Everything you need to know is in the lectures.

3x one hour lectures per week, 1x one hour practical class per week

2020

H1

Having just completed VCE mathematical methods and under the belief that I could kiss maths goodbye forever, MAST10016 came and obliterated my fantasy of a maths-free uni experience. As a first-year core subject, if you are good at mathematics, this subject will not be hard to get a H1 in whatsoever, as long as you keep up to date with lectures and weekly exercise sheets. The nature of the maths is VERY different to what I experienced in VCE, with little to no calculus or complex problem-solving involved. The first week in our tutorial we did a little high-school maths revision exercise, as we begun practicing drawing simple hyperbolas and quadratics and solved some basic derivatives. Other than that, the only other cross-over from VCE is probability, which the entire subject is essentially centred around.

Three topics were explored: population genetics (finding the probability of an 'A' or 'a' allele), enzyme kinetics (investigating the rate of cellular reactions and the role of enzymes and inhibitors in these) and disease modelling (investigating how different types of diseases can be spread throughout the population). Whilst these topics sound quite interesting to a Biomedical student at a glance, as we delve deeper into each topic, personally I found them to be quite mundane and tedious. Whilst James is a knowledgeable, friendly and funny guy, I found that even by the second week, his explanations and derivations went in one ear and out the other - they were simply too hard to follow. If he had simply gotten to the point a lot quicker and emphasised that point, I would have found this subject much easier to follow. There were lectures, particularly towards the end of the course, that I could finish watching and simply have no clue what just happened.

I cannot emphasise enough how useful exercise sheets are. They are what I solely relied on to achieve a H1 in this subject. Because they are harder than tutorial sheets and exam questions, completing these sheets will give you an understanding of the subject that will allow you to complete the exam and assignments with little to no troubles. Sometimes a very similar question would appear on the exercise sheet and the weekly assignment and so you could just go through the workings from the exercise sheet to understand how to do the assignment question. For exam revision, I simply went back and redid all of the exercise sheets and identified all of my areas of weakness, which I found to be a useful method of studying, since there is only one practice exam with NO solutions

All in all, this subject will be pretty uninteresting and often painstaking unless you have a great fondness for maths. However, the actual content itself is not that hard so a nice WAM booster

Weekly assignments 20%, Oral presentation 5%, 3 hour exam at end of semester 75%

I have always had a terrible relationship with mathematics, and have only had very fleeting moments when I have enjoyed it. This subject, however, completely convinced me that mathematics can be fun and enjoyable to study.

The other review for this subject speaks of it being exceedingly difficult and the lectures too quick to follow. The maths and stats department heard a lot of these complaints from the students who studied it last semester, and all credit to the Maths and Stats faculty, listened to their advice. The experiences of this subject relayed to me by people who took this subject last semester were not all consistent with my experiences of this subject.

First of all, I want to start by saying that Anthony Morphett is a sensational lecturer. Lecturing in mathematics is one of the most challenging things to do at a university. Mathematics is very much an area that requires a high level of interaction between the teachers and their students. It requires trial and error and actually getting down to doing things. Thus, as all maths lecturers bemoan, lectures for maths is a poor way to teach it. I tend to agree, though Anthony Morphett is the exception to that rule. He always explained concepts really well and in a way that was digestible for maths morons like me.

There are three main areas that make the focus of this subject. I'll give a quick run down of the three areas.

Population genetics: we start off with the base model of population genetics (Hardy-Weinberg). This essentially describes how allele frequencies will behave over time if any intervening influence, including random effects, are removed from the equation. We then build on this model, including parameters that allow us to observe the influence of mutation (this leads us to a new model called the Fisher-Haldane-Wright model). Also looking at the inheritance of X-linked traits and male and female gene pools. In order to observe these things, we learn the basic of difference equations. Which a models for change over discrete time. They're pretty simple to work with and there's nothing too difficult about them at all! The techniques involved such as cobwebbing and using the linear stability criterion don't involve difficult maths whatsoever and are very easily worked with. After we've dealt with these things, we need to start looking at what occurs in small populations. This is truly fascinating and shows the major influence that population size can have on allele frequencies. These models (the Wright-Fisher Model and the Moran Model) allow us to demonstrate how particular traits can disappear from a population simply by chance and indeed how new mutations become fixed in populations without the effect of selection. These require different mathematical techniques, in particular, Markov chains and using the Monte-Carlo simulation to track these over time. It probably all sounds a bit onerous, but it is really quite easy and a lot of fun. It shows the true force of genetics and how it all occurs on a large scale.

Biochemistry: After six weeks of genetics, this is generally a welcome change for most students (though not me—I quite liked genetics!). The mathematical models used are a bit different as we no longer look at discrete time, but rather, continuous time. This introduces us to differential equations, which are quite similar to difference equations. The basics of both are actually quite similar, and this makes the concepts quite easy to grasp. We start off with something called mass action kinetics and use difference equations to model the way that reactions proceed over time. This leads us into some weird and wonderful tricks for working with difference equations and a hell of a lot of graphing. This can become quite tricky, but at the end isn't too bad. We then finish off by looking at Michaelis-Menten kinetics, which is a little bit different. This is very much a section that confuses people and you spend the first two weeks or so not really appreciating what is going on. Once everything has been taught though, things click very quickly. It wasn't as interesting as genetics for me, though my marks did climb up for this section a bit. The maths itself was more enjoyable and there was a little of work with computers for this section.

Infectious disease: This particular section is a godsend. This is three weeks of using maths that we've already learned in the biochemistry section. Again, we look at how epidemics are formed over continuous time. Thus, all of the techniques involving difference equations are equally applicable and there is a lot of revision of those techniques. This is very much applied maths, with the only difficulties coming from understanding the various parameters and their effect on disease and under what conditions an epidemic will arise or under what conditions the disease will become endemic. There are a number of different models, which essentially give rise to different difference equations, including the SI, SIR and SIS models. When thought about logically, they are very easy to understand and everything just becomes a matter of interpreting the mathematics of the model. Personally, I stopped paying attention during these three weeks and was lucky that I chose them, because it wasn't particularly detrimental at all.

Overall, this was a great subject. I'm not a maths person at all and yet I found this subject fantastic, I also found that during the semester I was doing quite well. The sticky point was probably the exam. It was a lot harder than the previous semester's and will see a great number of people fall well below their expectations, I think I will be included in that. It was also a lot harder than the practice exam that we were given, which was a bit disappointing. There are a lot of resources available to students, including tutorials (which are worth going to) and weekly exercise sheets with answers. The exercise sheets are quite challenging, though being able to complete them will set you up very nicely for the exam. The exam is intended to be written to be easier than the exercise sheets.

There is an oral presentation component of the assessment. This is essentially free marks, so it's good for that. Most students found it to be a waste of time, though I don't agree with them. Perhaps I'm biased—I love public speaking—but I thought that it actually did function quite well as a way to help us discuss and better appreciate the mathematics that we were using. This was an imperative for everyone studying the course. The key to doing well was to be able to appreciate what the maths meant, with a whole section of the course devoted to just that. The maths in the course isn't particularly intense (and believe me when I say that's something coming from me) and was made a hell of a lot easier by actually understanding the maths. A bit of logic and a basic understanding of the maths would easily lead to a reasonable answer. This is definitely a subject wherein it is more important to understand the importance of the maths and what it represents rather than being able to use the skills themselves. In other words, you could sort of bullshit through the maths a little bit by just using your head, something that suited me very well.

Biomedicine throws out some terrible core subjects. This is certainly not one of them if you get Anthony Morphett as your lecturer.

Yes, with screen capture (though we had microphone troubles all semester)

Dr Anthony Morphett

None, there was one sample exam without answers. Though we had a Facebook group and made our own answers document.

4 Out of 5

None, the lecture slides are brilliant though.

3x1 hour lectures per week and 1x1hour tutorial per week

2013, Semester 2

82 H1