Description (Taken from the Systems Biology 2016 Handbook)




Cells and Systems

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. *NB: Some advanced topics are covered for one stream only or in week 3.

Course Leader: Gail Preston and Esther Becker

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Programming (Python and C)

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; file I/O; multidimensional arrays and graphics; web design; Data structures; binary file I/O; & Programming Project: cellular automata, microarray image analysis, or automatic crystal detection.

Course Leader: Eoin Malins

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Scientific Computing using MATLAB, Modelling and Design

(First Week)

Revision of core mathematical techniques relevant to modelling in the life sciences, and basic introduction to scientific computing; stochastic simulation, and numerical solution techniques in continuous mathematics; programming in the MATLAB environment; Applications in the interface between the physical sciences and biology. First week: introduction to MATLAB and data analysis; Basic calculus in MATLAB; linear algebra; ordinary differential equations; GUIs and writing good programs; Second week: Mathematical modelling of different types such as static network models, stoichiometric networks and dynamic modelling of gene, metabolic and signalling systems. It will also discuss the number and types of “biological dials” that can be designed to modify gene circuits to specification. Third week: profiling your code; Speeding up your code; and group projects.

Course Leader: Martin Robinson

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Introduction to Biological Physics

This module will introduce a range of current research questions in biological physics, including the underlying physics of the relevant biological systems and the methods that are used and under development for their study. Topics will include: single molecule biology; the molecular mechanism of ion channels, transcription and molecular machines; Forster Resonance energy transfer as a molecular ruler; biomolecular self- assembly; DNA nanostructures; Atomic Force Microscopy of cells; the dynamics of soft matter.

Course Leader: Achillefs Kapanidis

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Advanced Experimental Techniques

Shadowed researchers performing PALM, STORM, and other single molecule imaging techniques of HER family proteins at the Octopus Laser Facility as well as recearchers performing live confocal and light sheet imaging of calcium waves in the developing heart.

Course Leader: Esther Becker

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Introduction to Organic Chemistry

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.

Course Leader: John Kiappes Jr

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This Module is taught by the Department of Statistics and takes you through following topics: Basic Concepts, Markov Chains and Processes, Bayesian Statistics, Markov Chain Monte Carlo, Generalized Linear Modelling, Hidden Markov Models, Model Choice and Markov Random Fields. 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. 1 or 2 lecturers have departed slightly from this format. Lectures and exercises covers models and algorithms, while Practicals and Projects are more application oriented. The 9th day has 3 research lectures in the morning and the afternoon is dedicated to project polishing. The last day has student presentations and discussions. The students are assigned projects in advance to work on 90 minutes of each afternoon typically in groups of 3. 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.

Course Leader: Jotun Hein

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Structural Biology

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.

Course Leader: Charlotte Deane

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Bioinformatics and Statistical Genetics

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 10 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 lead 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.

Course Leader: Jotun Hein

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Mathematical and Computational Biology

This course will provide an introduction to the theoretical approaches used to model complex systems in health and disease. The main focus will be on understanding how mathematical and computational models are developed, the techniques that are used for their solution, and interpreting results. The initial focus will be on models of subcellular processes (e.g. the cell cycle and metabolism) and epidemics. Attention will then switch to models of more complex systems (e.g. pattern formation, disease spread) (Duration: four weeks of teaching and one week for projects – weeks 6-9 and week 10 respectively)

Course Leader: Helen Byrne/Sarah Waters/Ruth Baker

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Object-Oriented Programming in C++

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 11 overloading; templates; and exceptions). Extra topics on software engineering, debugging and MATLAB Mex functions.

Course Leader: Joe Pitt-Francis

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High Performance Computing and Data Management

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.

Course Leader: Brian Marsden

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