The modules shown below are those running in the 2017-18 academic year. The CDT is committed to providing broad training across these key areas, but the structure of this training and the teaching personnel involved may change in future years. The course structure allows some flexibility to meet individual needs.

Cells and Systems

Course Leaders: Dr Gail Preston and Esther Becker

This module introduces core concepts in molecular and cell biology for graduate students with a background in physical sciences. The module will be streamed for students with a background in chemistry and those without a strong background in chemistry. Over the course of the module we introduce the building blocks of life and discuss how they interact to support processes such as replication, metabolism, signal transduction and the immune system. The course is taught through a combination of lectures, problem-solving exercises, discussions and independent study. Topics covered* include: cell biology, DNA, replication, transcription and translation, protein structure and trafficking, signal transduction, metabolism, molecular genetics, epigenetics, neurobiology, immunology, cardiovascular science, cancer, and methods used in molecular biology and structural biology research. The course also incorporates a brief introduction to organic chemistry for students who have little or no academic background in organic chemistry. (Duration: two-and-a-half weeks)

*NB: Some advanced topics are covered for one stream only.

Bioinformatics and Statistical Genetics

Course Leader: Jotun Hein

This module will take you through exciting topics with many research opportunities within Oxford centred around following research areas: Large number of genomes have given us an unprecedented knowledge of molecular evolution, the nature of selection and the relationship of organisms. This revolution is not over. Data sets are still growing and even existing data is filled with opportunities for analysis and hypothesis testing. The mere size of these data sets pose new challenges to both models and algorithms. International collaborations such as the 1000 genomes (now much more) project has allowed the investigation of demographics, signatures of selection and not least recombination. Recombination is currently being elucidated in terms of signals, average rates and hotspots. Fast sequencing techniques gives incomplete information of the two diploid genomes and efficient algorithms are needed to handle the missing information. If sequence data is complemented with information about phenotypes, one is presented with finding the causal variants and their positions (Mapping), which is of tremendous value in moving toward functional dissection of diseases. Mapping is only a two-type data problem, but is being further enhanced by a series of other data types such as expression levels, concentration of small molecules (metabolomics) and information on proteins (proteomics) that would be fall into the category of Big Data and Integrative Genomics. Traditionally Bioinformatics have had a focus on sequences, but structures are following suit. Structures are closer to biological function and much more complex to analyse. This has led to the fields of Structural Genomics and Structural Bioinformatics. Each day will typically contain about 50% lecturing covering concepts, models and computational issues before moving on to the result and challenges from recent large scale projects. The remaining 50% will have exercises, practicals and hands-on analysis of data. (Duration: one week)


Course Leader: Jotun Hein

This Module is taught by the Department of Statistics and takes you through following topics: Basic Concepts, Markov Chains and Processes, Bayesian Statistics, Generalized Linear Modelling, Hidden Markov Models, and BIG DATA.  The first 8 days are generally organised in 4 groups of 90 minutes to 1. Overview Lecture, 2. Exercise, 3. Practical and 4. Hands-on projects. One or two lecturers might depart slightly from this format. Lectures and exercises covers models and algorithms, while Practicals and Projects are more application oriented. The 9th day has student preparing talks in the morning and the afternoon is dedicated to their presentation.. The students are assigned projects in advance to work on 90 minutes of each afternoon typically in groups of three. The projects illustrate key principles of statistical inference and since all projects are discussed by all, there should be some additional cross learning from this activity. (Duration: two weeks) 

Data Science and High Performance Computing 

Course Leader: Garrett M Morris

This module will give ample opportunity to explore practical aspects of modern data science. The focus will be on biomedical applications: in small molecule drug discovery, genomic analysis, and imaging in the life sciences. Topics covered include: noisy data; machine learning; data analysis and visual programming in KNIME; artificial intelligence & deep learning; scalable analysis of big data; cloud computing; and containerization. Most of the lectures and practicals will be provided and run by industrial scientists, so this module will give great insights into how data science is practiced in current, high-impact projects. (Duration: one week)

Structure-Based Drug Discovery

Course Leader: Garrett M Morris

This module will give students an opportunity to go deeper into the many fascinating aspects of structural biology, and to explore its computational aspects. (Duration: one week)


Course Leader: Martin Robinson

Introduction to MATLAB and data analysis; Basic calculus in MATLAB; linear algebra; ordinary differential equations; GUIs and writing good programs. (Duration: one week)

Essential Mathematics

Course Leader: Martin Robinson

Basic algebra revision, vectors, graphs; sequences & series; differentiation; integration; single differential equations; systems of differential equations; complex numbers; permutations; combinations; basic probability; matrix algebra; eigen-values & eigen-vectors; Students can choose between two parallel streams: the introductory stream is for those who are new to the above material, while the refresher stream is for those who have had previous exposure to the majority of the material. The refresher stream will also cover the use of programming tools in Python for the above topics, and the analysis of differential equations, including stability analysis. (Duration: two-and-a-half weeks)

Organic Chemistry

Course Leader: John Kiappes Jr

The module provides an introduction to theoretical and practical organic chemistry; This includes lectures, problem sessions, revision workshops and practicals in the teaching labs. The course covers the basics of organic chemistry including chemical formulae, molecular structure, isomerism, conformational analysis, reaction mechanisms, organic functional groups and their reactivity, energetics (thermodynamics and reaction kinetics), spectroscopy as well as acids and bases. Building on this understanding the following reaction types will be examined in more detail: nucleophilic substitution, elimination reactions, electrophilic addition and carbonyl chemistry. (Duration: two weeks)

High Performance Computing

Course Leader: Brian Marsden

Cutting-edge techniques for working with large datasets and computational problems in chemistry and systems biology. Topics include the efficient parallelisation of calculations across local large computational clusters; the cost-effective use of cloud computing platforms and strategies for aggregating, managing, mining and visualising datasets. Areas taught by SGC, ARC, AWS and Evotec. (Duration: one week)

Drug Discovery

Course Leader: Garrett M Morris

The Drug Discovery module will introduce both chemists and non-chemists to the principles and practice of modern drug discovery, including small molecules, biologics, and computational methods. Invited talks from industry will form a key part of this module. (Duration: two weeks)

Structural Biology

Course Leader: Charlotte Deane

Introduces techniques used to explore macromolecular structure. Topics include: structure determination, visualisation, classification and validation; protein structure prediction and folding; protein-protein, protein-ligand and protein-drug interactions. Includes visit to Diamond and laboratory work at the SGC. (Duration: two weeks)


Course Leader: Eoin Malins

Computer architecture; "Hello World": compiling and running your first program; Typed variables, numerical calculations and comparison operators; Loops; Arrays; Reading from the command line and type conversions; Functions and subroutines; Libraries; File I/O; Multidimensional arrays and graphics; Data structures; Individual programming project (Duration: two-and-a-half weeks)

Introduction to Experimental Bioscience

Course Leaders: Esther Becker and Gail Preston

Introduction to Experimental Bioscience: This course is intended for students with a background in physical sciences, who have little experience with wet-lab biological and/or biochemical research. The course provides a hands-on introduction to experimental techniques used in molecular and cellular biology, biochemistry and chemical biology, including DNA and protein extractions, gel electrophoresis, DNA sequencing including Nanopore sequencing, Western blotting and enzyme kinetics. The course will offer insight into the biological processes underlying the different techniques, training in experimental record-keeping, intellectual property, insight into the factors affecting experimental design, data generation and analysis, bioinformatics, and data presentation (Duration: two weeks)

Object-Oriented Programming in C++

Course Leader: Joe Pitt-Francis

C++ programming fundamentals (variables and expressions; input and output; flow of control; pointers and references; arrays; and functions). Object orientation in C++ (concept of a class; private, public and protected members; constructors; operator and function overloading; templates; and exceptions). Extra topics on software engineering, debugging and MATLAB Mex functions. (Duration: one week)


Mathematical and Computational Biology

Course Leaders: Helen Byrne and Ruth Baker

This course will provide an introduction to the theoretical approaches used to model complex systems in health and disease. The main goal will be to understand how simple, mathematical and computational models are developed, the techniques that are used for their solution, and how to interpret results. The initial focus will be on deterministic models, formulated as systems of ordinary and delay differential equations (eg signalling pathways, predator-prey systems, electrophysiology).  Attention will then switch to stochastic models and techniques for parameter estimation and inference. The course will conclude with an introduction to discrete and hybrid models (eg tumour growth). (Duration: two weeks)