Biochemistry Honours 2024

BCH714: Computational Systems Biology Minicourse

18 March – 16 April 2024

Profs Johann Rohwer and Jacky Snoep, Dr Dawie van Niekerk
Laboratory for Molecular Systems Biology, Dept. of Biochemistry, Stellenbosch University

Course schedule

Face-to-face lectures will take place in the Biochemistry Seminar Room (A111).
Face-to-face computer practicals and data analysis will take place either in the Biochemistry Seminar Room (using own laptop) or in the computer lab (JC Smuts B215, departmental computers).
Practical enzyme kinetics experiments will take place in the molecular systems biology lab (JC Smuts B203A).

Course material

MSB book (PDF)
Biochem 323 Notes (PDF)

Lectures (slides and supplementary materials)

Lecture 1 (PDF)

Data
Lecture 1: Answers to problems (PDF)

Lecture 2 (PDF)

Lecture 2: Answers to problems (PDF)

Lecture 3 (PDF)

Lecture 3: Answers to problems (PDF)

Lecture 4 (PDF)
Lecture 5 (PDF)
Research Lecture (Rohwer) (PDF)

Tutorials and Computer sessions

Tutorial 1: Introduction to SciPy – SciPy_intro.ipynb

PSC file available in download section.
Save in $HOME/Pysces/psc on Linux and macOS.
Save in C:\Users\<USERNAME>\Pysces\psc on Windows.
Alternatively save in the same folder as the notebook.

Tutorial 2: Chemical kinetics and energetics (PDF)

PSC files, data files and skeleton notebook available in download section.
Memo notebook available in download section.

Tutorial 3: Enzyme-catalysed kinetics (PDF)

Data files and skeleton notebook available in download section.
Memo available in download section.

Tutorial 4: Structural analysis of reaction networks (PDF)

PSC files available in download section.
Memo (PDF)
Memo Jupyter notebook available in download section.

Tutorial 5: Metabolic control analysis (PDF)

PSC file and skeleton notebook available in download section.
Memo available in download section.

Tutorial 6: The kinetic model (PDF)

PSC file available in download section.
Memo available in download section.

File Downloads

Download section

Assessment

Assessment 1 — Tutorial test

Instructions available here.
Answer the questions in a Jupyter notebook. Hand in the notebook via the dedicated assignment link on SUNLearn.
Time: 2.5 hours (150 minutes)

Assessment 2 — Practical report

Instructions for installing and setting up your own Python environment

The computational Python environment can be set up either using Anaconda, or using the pip installer program if you already have Python installed on your computer.

Anaconda

This is by far the easiest method if you don't yet have Python installed.

Download and install Miniconda3 for your operating system from here. Get the latest version.
Download environment.yml and save to a local folder on your computer.
Open an Anaconda prompt, navigate to the folder where you downloaded environment.yml, and execute the following command:
> conda env create -f environment.yml
This creates an environment called minicourse.
To activate the environment and launch JupyterLab, open an Anaconda prompt, and execute the following commands:
> conda activate minicourse
> jupyter-lab

pip-based install

This assumes you already have Python installed, then you can use Python's pip utility to install the required packages:

First, create a separate Python environment (e.g. minicourse) for installing the packages, so they don't interfere with the rest of your system. Open up a Windows command prompt or macOS/Linux terminal, and create and activate the environment:
- For macOS or Linux:
  > python3 -m venv minicourse
  > source minicourse/bin/activate
- For Windows:
  > python3 -m venv minicourse
  > minicourse\Scripts\activate.bat
Once activated, this is indicated by a changed prompt:
(minicourse) > (or similar)
Download requirements.txt and save to a local folder on your computer.
In the Windows command prompt / macOS terminal, execute the following command (be sure the virtual environment is activated, see above):
> python3 -m pip install -r requirements.txt
As above, launch JupyterLab with the following command:
> jupyter-lab

Python resources and documentation

Scientific Python Lecture Notes (focus on Chapter 1, Sections 1.1-1.4)
Beginner's Python Tutorial (focus on Lessons 1–6)
Official Python Tutorial (focus on Chapters 3 and 4)
pandas
Jupyter Notebook
IPython Tutorial
NumPy Quickstart Tutorial
PySCeS documentation
Matplotlib user's guide
Markdown quick reference
lmfit documentation