Bioinformatics Seminars

This table represents a bioinformatics meta-series of talks and seminars at the University of Bristol. They are funded and hosted under various different programmes, centred this term around the Intelligent Systems series. This page brings together information on related talks in one place as a resource for people interested in bioinformatics.

If you know of any talks coming up which might be relevant, or if you would like to join the mailing list for this meta-series, then please send an E-mail request.

Date	Speaker	Title	Time	Location	Series	Abstract
18th Oct 2007	Nick Goldman	Two Algorithms on Evolutionary Trees - Better Multiple Sequence Alignment and Bioconservation	3pm	MVB 1.06	Intelligent systems	The talk will look at two different problems which each require consideration of evolutionary trees. In each case, progress has been made with algorithms that use the information in the patterns of species' relatedness that the trees depict.
25th Oct 2007	Siarhei Maslau	Functional Evolution of Protein Domains in Metabolic Enzymes	3pm	MVB 3.36	Intelligent systems	Proteins consist of domains. The 700 metabolic enzymes of the bacterium Escherichia coli contain 2,600 domains, which come from 300 superfamilies (groups of homologous domains). How has the wide spectrum of enzymatic functions evolved from this limited number of domain superfamilies? How did domain combinations and gene duplications contribute to the evolution of novel enzymatic functions? Our recent research into these questions uncovered general principles of functional evolution of one-domain and multi-domain enzymes.
8th Nov 2007	Lorenz Wernisch	Reconstructing hidden protein activity by nonparameteric methods	3pm	QB 1.7	Intelligent systems	Most of the cellular processes are not accessible to direct measurements. For example, the level of protein activities can often only be assessed indirectly by their effects on gene expression or the modification of other proteins. The exact form of the functional relationships describing such interactions are unknown as well. Nevertheless, some progress can be made by combining factor analysis or dynamical models with nonparametric regression methods, which don't impose any form on reconstructed functions. On the other hand, identifiability becomes are major problem and needs to be balanced against flexibility. I will report on our experience with simulated and experimental data in exploring the limits of reconstructing hidden protein activities and interaction networks.
6th Dec 2007	Christine Orengo	Comparative Genomics to Predict Protein Functions	4pm	MVB 1.06	Computer Science dept. seminar	There are now more than 500 completed genomes for organisms from all kingdoms of life. Information on the sequences and structures from the genomes can be mined to understand how function evolves in protein families and to develop new methods for predicting functional networks.
24th Jan 2008	Mike Sternberg	Computational approaches for drug discovery - protein bioinformatics and logic-based chemoinformatics	4pm	QB 1.15	Engineering Colloquium	This talk will describe the development of novel computational methods to aid drug discovery and development. Developments in protein bioinformatics will be described including our protein structure prediction tool PHYRE and a novel approach (CONFUNC) to predict the function of a protein from its sequence. Both are available for use by the community via our web site. The next step along the drug discovery pipeline often is the identification of low molecular weight molecules with the required biological activity. In a series of studies in collaboration with Professor Stephen Muggleton (Dept of Computing, Imperial College London), we have developed a novel machine learning approach to direct the discovery of new potential drugs. This is known as Support Vector Inductive Logic Programming which generates logic-rules using inductive-logic programming and then obtained quantitative predictions using a support vector machine.
7th Feb 2008	Janet Thornton	Understanding enzyme catalysis using protein structures	4pm	CLT1	Biochemistry colloquia	Professor Janet Thornton now Director of the European Bioinformatics Institute on the Genome Campus at Hinxton, near Cambridge. Her lab is pursuing multiple avenues including deriving principles of protein structure and elucidation of enzyme mechanism through structural biology. In 1999 she was elected a Fellow of the Royal Society and a Member of EMBO in 2000, and in 2003 she was elected a Foreign Associate Member of the National Academy of Sciences.
21st Feb 2008	Alex Bateman	Non-coding RNAs: Genomic dark matter	3pm	QB ARR	Intelligent systems	Within the last few years a wide range of new non-coding RNAs have been discovered. We have been catalogueing them in the Rfam and miRBase databases. Recent work by ourselves and others surprisingly suggests that RNA genes may be more numerous that protein coding genes in the human genome.
24th April 2008	Cyrus Chothia	The evolution of eukaryote protein repertoires	5pm	B75 Biology	Special lecture	Cyrus Chothia is head of the Theoretical and Computational Molecular Biology group at the MRC laboratory for molecular biology in Cambridge where he has been since 1970. Cyrus began his pioneering work on molecular evolution in the 1970s with the first classification of protein structures and subsequent work using innovative methods of protein structure analysis to understand how proteins adapt to mutations and how this is linked to sequence divergence. More recently, since the emergence of the first whole genome sequences, Cyrus' group has produced leading work on the duplication and recombination of protein domains and the evolution of the repertoire of proteins within organisms.
1st May 2008	David Westhead	Predictions and data analysis: trying to make a difference in molecular biology using computers.	3pm	MVB 3.36	Intelligent systems	Molecular biology has become immensely data rich in the last 10-15 years and this trend is set to increase. We work on computational methods to analyse and extract useful biological information from large data sets, including genome sequences and DNA microarrays. The methods employed include statistical analysis and machine learning. The talk will cover recent work in a number of areas, including plant microarrays, mutigenome analysis of metabolic genes and horizontal transfers, and analysis of single nucleotide polymorphisms.