What do-you need to know in order to work with us

In order to make the most of an internship, a studentship, or a post-doctoral fellowship in the group, it is very important to be able to share a basic layer of common knowledge with the rest of its members. This will allow you to be productive on day 1, and also to profit from the assistance offered by others. Below is a list of resources to refresh your understanding of the biology we study and the method we use.

Basis of molecular and cellular biology

Common to all

These notions should be mastered by anyone wanting to work with us, regardless of the project. Anyone with a high-school level in biology should be fine. A quick read is nevertheless advised.

For phosphoinositide related projects

For sequencing related projects

If you want a more structured introduction, you can read "Essential Cell Biology", which will give you a very good and comprehensive background, without being too detailed.

Mathematical and computational biology

Common to all

We find the following books very useful as complete introduction for modelling in life sciences:

  • Dynamic Models in Biology covers all kinds of dynamical modelling. It is a good starting point since it does not only focus on "ODE-type" models. It also comes with many exercises in MatLab and R
  • Computational Cell Biology is more specialised and covers mostly biochemical models. It used to be a classic and contains many exercises (using XPPaut).

Attempting to define systems biology has been a notoriously tricky enterprise. However, you can consult:

A very good and comprehensive book on Systems Biology is ""Systems Biology: a textbook".

Systems pharmacology

Modelling kinetics

An introduction to chemical kinetics

If you are coming to pursue a project dealing with kinetics, you are likely going to use COPASI at some point. Even if this is not the case, the software is still a good way to learn the basis of kinetics modelling of biological processes.


Several of the projects pursued in the group involve analysis of large datasets. In particular, we are analysing RNA-Seq and lipidomics datasets.

The bioinformatics team of the Babraham Institute organises a series of trainings that might be very useful. Much of the work is done with R. We find that a good starting point to learn R is  the R code school from O'Reilly.