Course Pathway

Computational Biology

Using code to find patterns in living things

Living systems generate enormous amounts of data: DNA sequences, protein structures, medical images, populations of cells. Far too much for any person to read by hand. This pathway is about using code to make sense of that data — to count, compare, transform, and reveal patterns that would otherwise stay hidden.

You do not need to be a biology expert to start. You need curiosity about how living things work, and a willingness to treat a strand of DNA or a microscope image as something a program can read.

The big idea

Biology is full of hidden patterns. Code is very good at finding patterns in large amounts of data. This pathway sits exactly where those two things meet.

Is this pathway for you?

Every pathway has a version that looks easy and a version that is genuinely interesting. Here is an honest read on who tends to enjoy this one.

A good fit if you...

are curious about how living systems work
like the idea that data can reveal something you cannot see directly
enjoy spotting patterns and testing whether they hold
find it satisfying when a small program explains something real

Think twice if you...

want fast visual feedback like graphics or gameplay
would rather design an experience than analyse data
dislike working carefully with text, sequences, and detail
prefer building tools for people over studying systems

None of these are permanent. They just tell you where this pathway will feel like a pull rather than a push.

What you would actually work on

You will not do all of these — they are examples of the kind of problem this pathway contains, so you can picture the work. Each one starts simple and can go as deep as you want to take it.

Read patterns in DNA DNA is a long string of four letters — A, C, G, and T. Your first programs count and measure these patterns: how often each letter appears, where certain sequences repeat, what a stretch of DNA is "made of."

Compare two sequences to find what they share Given two genetic sequences, how much do they have in common? Programs that answer this help biologists trace shared ancestry between species. It is also a first taste of real algorithmic thinking.

Simulate how a cell builds a protein Cells follow a precise set of rules to turn DNA into RNA and then into proteins. You can write a program that carries out those exact steps — a small, working model of one of the most important processes in biology.

Predict the shape of a protein A protein's sequence influences how it folds, and its shape decides what it does. Using simple rules, you can write a program that makes a first guess at a protein's structure from its sequence alone.

Teach a computer to read biological images Counting cells in a microscope image or spotting a marker in a medical scan is slow and error-prone by hand. Image-recognition programs can do it automatically — a real technique used in labs and hospitals.

Work with real medical and genetic datasets Public databases hold genuine biological and medical data. Learning to load, filter, and analyse a large dataset — carefully and responsibly — is a skill that carries far beyond this course.

The tools you would use

Everything here is done in Python, the same language you are already learning. Most real computational biology work happens inside Jupyter notebooks — documents that mix code, results, and explanation in one place — so you will learn to read and build those too. No specialised biology software is required to begin.

Grade 9 and Grade 10 in this pathway

The pathway is the same in both years. What changes is the difficulty of the problems you take on.

Grade 9

You focus on reading, counting, transforming, and visualising patterns in biological data — building understanding of how a program can model something real, one clear step at a time.

Grade 10

You take on more complex problems: real algorithms, larger datasets, and systems where several parts work together. The goal shifts from "can I model this?" to "can I design a solution to a genuinely hard problem?"

What makes a strong project here

A good computational biology project connects a real biological question to a computational method. As you think about ideas, these questions help:

What biological data could benefit from being analysed by code — and how would you process it?
Could you simulate a biological process, such as a step in how cells work?
Could a program help with a health or diagnosis problem?
How would you handle a large dataset efficiently, without it becoming unmanageable?

One responsibility to keep in mind

Biological and medical data is often personal and sensitive. If your project touches real health or genetic data, privacy is part of the work — not an afterthought. Handling data ethically is one of the things that makes a project in this pathway genuinely serious.

If the idea of turning something living into something a program can read and reason about excites you, this is your pathway.

Computational biology: an introduction