| Biological Physics
The Oxford Biological Physics Group's research uses a range of biophysical techniques, in particular single-molecule methods, to study molecular machines, rotary motors, ion channels and other membrane proteins.
| Bioinformatics, Evolution and Genomics
Bioinformatics is an exciting field that is central in analyzing the large amounts of data produced in molecular genetics and biochemistry, and allows an unprecedented understanding of the architecture and dynamics of life. Such data includes genomes, the activity levels of individual genes and cellular metabolites, the structure of individual proteins and protein complexes, protein interaction data, and DNA sequence variation within a population and over evolutionary timescales. Bioinformatics is highly interdisciplinary and brings together researchers with a variety of interests and expertise.
The University of Oxford is centre for a wide range of initiatives within genomics, population variation, disease gene mapping, high throughput technologies and modeling in the biosciences. This gives the ideal background for writing a DPhil that either focused on a specific biological problem, or on a theoretical problem. The LSI Bioinformatics application area covers:
Bioinformatics, Evolution, Genomics and Proteomics
The large number of genomes and the falling prices of sequencing naturally make efficient analysis of genomes a central issue. A key component in successful analysis has been the comparison of genomes that allows the measurement of rates, selective constraints and analysis of function. Jotun Hein's Bioinformatics group focuses on models of sequence evolution; detecting recombination, producing statistical models of sequence alignment and research into the annotation of genomes. John Hancock directs the Bioinformatics group at MRC Harwell and analyses high throughput mutagenesis data and sequence data, while Chris Ponting's group analyses mammalian genomes comparatively. Richard Mott runs the Bioinformatics unit at the Wellcome Trust Centre for Human Genetics (WTCHG) and has focus on mapping using mouse strains, prediction of regulatory signals and analysis of expression data, while Chris Holmes works on a variety of throughput data (including metabolomics and proteomics) and their analysis.
Analysis has clearly moved beyond genomes to structures and networks. Charlotte Deane's protein informatics group looks at the prediction and manipulation of protein structures, while Gesine Reinert's research focus is the probability theory of networks.
Population Variation and Disease Gene Mapping
Oxford is an international centre for the study of these topics with large initiatives both within modelling, statistical inference, analysis and data generation. Research in this area is centred around the WTCHG and the Mathematical Genetics group, within the Department of Statistics. The MG group includes researchers such as Peter Donnelly, Gil McVean, Bob Griffiths, Simon Myers and Jonathan Marchini. Variation studies are presently characterizing differences between individuals at the genetic level and reconstructing the genetic history of the human population. Combining observations about the genomes of individuals and their phenotype (height, intelligence, disease status,) allows homing in on the genes responsible for these traits, which is of major medical importance.
The techniques underpinning Bioinformatics:
The methodology employed when studying in the field of Bioinformatics comes from a variety of disciplines. Computer Science and Algorithms are important in the analysis of large data sets and Bioinformatics has motivated algorithmic research in strings, trees, networks and other data structures. Key problems include searching, creating the consensus of a set, and measuring the distance between, for instance, strings. Devising algorithms and characterizing the difficulty (complexity theory) of these problems are prerequisites for the analysis of real data. All high‐throughput technologies create enormous amounts of data, that must represented in databases and obey certain standards to be useful and exchangeable between researchers. Increasingly integration of databases and representation of human knowledge is a challenge in highly integrated analysis.
Mathematics, Probability Theory and Statistics are central to Bioinformatics. Many objects in biology (molecules, strings, graphs etc.) are combinatorial objects and pose new and exciting problems. In addition, many biological phenomena are stochastic (i.e. have a random element), while others are deterministic and only observed with error. This creates a need for probabilistic modeling and statistical inference.
All biological phenomena are inherently based on Physics and Chemistry and all physical science disciplines have become increasingly important in modeling of networks and patterns in biology. Previous and current LSI students, undertaking DPhil projects in the area of Bioinformatics, come from a wide variety of physical science backgrounds including Physics, Mathematics, Chemistry, Computer Science and Engineering.
|Medical Images & Signals
Within the Medical Images and Signals application area, there is a focus on the development of clinically efficient IT systems for clinicians and researchers who have vast amounts of images and measurements at their disposal which they need to analyse effectively in order to diagnose and treat their patients in the best possible way.
| Computational Biology
The Computational Biology Research Group, led by Prof. Gavaghan, is a particularly vibrant group based at the Oxford University Computing Laboratory. It comprises 9 academic staff members, 13 post-doctoral researchers, and 10 DPhil students, and has strong collaborative links with internationally leading theoretical and experimental research groups. The group also plays a leading role within the UK e-Science Programme, which, within the last 5 years, has developed a Grid infrastructure necessary to conduct the research proposed here.
The key research focus within the group is the development of rigorous theoretical and computational approaches to important research questions in biology and physiology. Research themes include the modelling of the cardiac and respiratory systems, and of cancer. The group has extensive technical expertise in mathematical modelling, numerical analysis, software engineering, scientific computing, advanced visualisation techniques, high performance computing, and e-Science. All of this theoretical research is allied to very extensive collaborations with leading experimental groups within the University and elsewhere.