The module will introduce you to our current understanding of genomes and transcriptomes. Mechanistic understanding of gene regulation and RNA expression remains the foundation, together with the ingenious experimental approaches by which we can probe them. Genomics and other ‘omic technologies have revolutionised the scope of the problems that we can attack: we now aim to investigate complete sets of components of biological systems, rather than just selected items; evolution is studied more precisely in terms of a wealth of DNA sequence information, rather than based on morphology; we are beginning to see the molecular mechanisms behind inter-individual differences. We are also starting to map out and model the interactions of the building blocks of life - DNA, RNA, proteins, metabolites and other types of molecules - in space and time. Diseases can now be characterised by their molecular profiles, and genomics and deepening biological understanding of individual disease and treatment are beginning to transform medicine.
Genomics is a fast-moving field that is driven by technology - by the reagents that we can construct and by the measurement instruments that we can build. Computational biology and its related disciplines statistics, computer science, mathematics are important to structure the massive amounts of data, to create maps of the system and eventually predictive models. Biology is becoming an information science, and the data production in biology and biomedicine now arguably exceeds that in physics, astronomy or geography in terms of complexity.
The content of the lectures is expanded in a set of practical afternoon lab sessions. Each student can take part in one of the labs. You will obtain your participation tickets to your lab in a raffle at the beginning of the module; you can trade those tickets between you before the start of the labs. After the practicals, all lab groups meet in the plenum and present their project. We will also have a workshop on scientific project design, where you will be asked to define a scientific project, including the definition of the question, feasibility, prior art, resources needed, and expected impact.