Student blogs

Before starting this programme there were certain areas which I felt like I had very little experience in – one of these was bioinformatics. So I completely understand that the unknown can be daunting, especially when there’s maths and programming involved. Here’s why you shouldn’t be intimidated by the Bioinformatics from a Physiological and Pharmacological Perspective course that’s part of the MSc in Translational Physiology and Pharmacology programme.

“I know nothing about bioinformatics”

If you’ve had minimal bioinformatics experience before starting this course, then you might feel like you are jumping head first into the unknown. However, there’s no need to worry! It’s highly likely that at one point you’ve encountered some of the topics you’d be learning about. Or if not, then you’ll have some time to read up on these, if you want to feel more prepared. During this course we broadly covered 5 topics:

• Pharmacokinetic modelling
• High-throughput screening
• Pharmacogenomics
• Protein modelling and ligand docking
• Machine learning and artificial intelligence

First, we had the theory on each topic, followed by seminar-type of practical classes where we could actually put the knowledge into action on a specific problem. For example, for the protein modelling part we were working in pairs on a particular receptor to visualise its different configurations, identify agonists and antagonists, and run ligand docking calculations using an open-source software. Personally I really enjoyed diving deeper into the nitty-gritty of these computational methods and the practical classes consolidated some of the more technical aspects. Overall, I found it very useful to add the bioinformatics dimension to my studies after our more theoretical physiology and pharmacology based courses so far in the programme. The topics covered felt particularly relevant for those who are looking to continue in the molecular biology, drug discovery or pharmacogenomics fields.

“I can’t code”

There’s no need to worry about coding! In this course we focused more on how computational methods are useful in physiology and pharmacology research both in academia and the industry. For pharmacokinetic modelling, we had to write some short lines of code, but honestly that was more about defining variables and the relationship between them, so no need to know Python, R or any other programming language.

On the other hand if you are looking to learn coding, in the third semester there’s an elective course called Omics in Science, where we’ll use R to visualise and analyse large datasets.

“How will I pass this course?”

As for most of the other courses in KI, the grading scale is fail/pass/pass with distinction based on assignments and examinations. For this course the examination was divided into two parts: individual multiple choice exam and a small group project focused on integrating 2 to 3 of the topics discussed during the course in a cohesive way. Personally I found the group assignment very interesting as we got to use our creativity to put together a holistic picture of a drug development workflow, or in our case a clinical diagnosis workflow idea to personalise drug selection and dosing in cancer patients.

All in all, this course expanded my knowledge of what can be done with computational methods as well as the currently available tools for it. And any fears I had around this course or thoughts like “This is too complicated” or “I know nothing about informatics” had no basis in hindsight. So if you are thinking of applying to Translational Physiology and Pharmacology, then don’t be scared off by the idea of a little bioinformatics!