The 2005 Autumn Meeting of the British Region of the International Biometric Society will be a full-day meeting, incorporating the Annual General Meeting. This meeting will start at 11am on Wednesday 23rd November 2005 at the Wellcome Trust Conference Centre, Cambridge, CB10 1RQ. Directions to reach the Wellcome Trust Conference Centre are included later in this newsletter.
This meeting brings together speakers from a range of different disciplines and application areas associated with these exciting and relatively new research areas, and is being held at one of the main UK sites for developments in these areas. The full programme for the meeting is given below, together with short abstracts for the papers to be given during the meeting. The Agenda for the Annual General Meeting follows at the end of the programme.
Advance registration is essential for this meeting – your registration form and payment (cheques made payable to “Biometric Society”) must be received by Monday 7th November. The registration fee includes morning coffee before the meeting, a two-course buffet lunch, and afternoon tea. For members of the IBS the registration fee is £30, whilst for non-members registration costs £45.
Non-members might be interested to know that Associate and Student Membership of the IBS British Region costs just £15 per year at present. Membership forms can be obtained from the British Region web-site. Non-members attending this meeting will be able to convert their non-members registration fee into Associate or Student membership of the IBS-British Region for 2006.
| 10.30 | Registration and Coffee | |
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| 11.00 | The Robot Scientist Project Ross King (The University of Wales, Aberystwyth |
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A Robot Scientist is a physically implemented active learning system for scientific discovery. A Robot Scientist can automatically: originate hypotheses to explain data, devise experiments to test these hypotheses, physically run the experiments using a laboratory robotics, interpret the results, and then repeat the cycle. In previous work we have demonstrated that such a Robot Scientist can automatically rediscover known science. We now demonstrate that the approach can be scaled up to the discovery of novel biology. To achieve this we have combined our logical reasoning approach with bioinformatics. We have built a logical model of all known Saccharomyces cerevisie metabolism. In this model there are still reactions where the gene encoding the enzyme is unknown. We demonstrate that we can automatically hypothesise these genes, and generate "wet" biological evidence that either confirms or contradicts this hypothesis. This approach can also be used to automatically test biological genome annotations. |
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| 11.40 | Statistical Methods in Metabonomics Maria De Iorio and David A. Stephens (Imperial College London) |
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Metabonomics is a rapidly developing field in biomedical science
that combines the application of spectroscopic techniques with
multivariate statistical analysis in studies of the composition of
biofluids, cells and tissues. It is formally defined as
‘the quantitative measurement of the multiparametric
metabolic response of living systems to pathophysiological stimuli
or genetic modification’; see for example Nicholson et
al. (1999). In particular nuclear magnetic resonance (NMR)
spectroscopy has emerged as a key experimental tool in this area for
understanding metabolic processes in living systems. The spectra
generated are information rich containing
signals from 100s to 1000s of molecular species (metabolites), and therefore
require sophisticated analysis techniques.
The enormous amounts of data generated, the complexity of the spectral
signals and the inter-individual variability within, as well as between,
patient subgroups pose considerable problems for classical methods of
statistical analysis and require the
development of more sophisticated statistical and computational
techniques. This
talk will review the main chemometric and statistical
methods used for the identification of structures contained
in such spectra and
will focus in particular on the NMR peak alignment problem, looking at methods
such as dynamic time warping.
Nicholson, J.K., Lindon, J.C. and Holmes, E. (1999) 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data, Xenobiotica, 29, 1181. |
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| 12.20 | International Biometric Society, British Region AGM | |
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(IBS British Region members only) Agenda |
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| 12.40 | Lunch | |
| 13.40 | Mathematical models as tools to probe
interaction networks Nick Monk (University of Sheffield) |
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Interaction studies, whether assaying functional or physical association, provide information on the topology of biological interaction networks. However, network function depends critically on the dynamical properties of the network. Mathematical models are important tools for helping to understand network dynamics, and for exploring the relationships between network topology and dynamics. I will give examples of the ways in which models can be used in combination with experimental data to deepen our understanding of cellular interaction networks. |
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| 14.20 | Metabonomics: the challenge of modelling
metabolic interactions, processes and diseases in complex organisms. Elaine Holmes (Imperial College London) |
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Metabonomics is a rapidly emerging field of research combining sophisticated analytical tools such as NMR spectroscopy and mass spectrometry with multivariate statistical analysis to generate complex metabolic profiles of and tissues. This approach measures dynamic biochemical responses of organisms to pathological stimuli or genetic modification and operates by profiling the metabolic responses of key intermediary biochemical pathways1-3. Such analysis has been shown to be of considerable value in providing detailed information regarding the metabolic status of an organism, in characterizing the metabolic phenotype of genetically modified organisms and in discerning and predicting a wide range of pathological conditions1-4. Moreover, this approach has proven value in assessing the efficacy of therapeutic interventions in animals and man. Models of metabolic phenotype can be constructed for the evaluation of pathological processes and predictive expert systems for disease diagnosis can be formed. However, the complexity and interactive nature of biological systems introduce confounding variation into the metabonomic data. Various chemometric and bioinformatic strategies for optimizing the characterization and prediction of pathological conditions can be adopted in order to increase the sensitivity of metabonomic analysis by reducing the influence of confounding random and systematic noise, accommodating the presence of large dynamic range in the measurement variables and/or incorporating the temporal dependence of pathological lesions. Using a similar multivariate analytical strategy, metabonomic data can be integrated with gene expression and proteomic data to provide a more holistic vision of biological processes at a whole systems level. Gene-metabolite interactions can be probed using a range of chemometric tools and the metabolic signature used to direct appropriate sampling points for genomic/proteomic analysis. Examples of this integrative approach will be drawn from a number of fields including dysmetabolic syndrome and insulin resistance, pharmacology and toxicology, functional genomics and nutritional intervention. In addition, several strategies for data optimization will be discussed and the potential role of Metabonomics in 21st century medicine considered. 1 Nicholson, J.K.; Connelly, J. ; Lindon, J.C.; Holmes. E., Nature Reviews Drug Discovery 2002, 1(2), 153-161. 2 Brindle, J.T.; Antti, H.; Holmes, E.; Tranter, G.; Nicholson, J.K.; Bethell, H.W.; Clarke, L.S.; Schofield P.M.; McKilligin, E.; Mosedale, D.E.; Grainger D.J., Nature Medicine 2002, 8(12), 1439-1444. 3 Nicholson, J.K.; Lindon, J.C.; Holmes E. Xenobiotica 1999, 11, 181-1189. 4 Gavaghan, C.L.; Holmes, E.; Lenz, E.; Wilson, I.D.; Nicholson, J.K.; Febs Lett 484 (2000), 169-174. |
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| 15.00 | Tea | |
| 15.20 | Detecting genotype changes in yeast, tumours and mice using
NMR-based metabolomics Kevin Brindle (University of Cambridge) |
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| We have used multivariate statistical analysis of NMR-determined metabolite profiles in the yeast Saccharomyces cerevisiae to look for patterns of metabolite changes that are correlated with changes in gene expression. We proposed that for a gene of unknown function, the function could be inferred if the metabolite profile of a yeast strain deleted for that gene is grouped, in the multivariate analysis, with those of known function. Although NMR detects relatively few metabolites, as compared to techniques such as GC-MS or HPLC-MS, it nevertheless would appear to sample highly connected metabolites whose concentrations reflect the state of the metabolic network as a whole. We have tested this hypothesis with a systematic study of the effects of deleting genes in the proline and pyrimidine biosynthetic pathways, which has provided support for this proposal. However, there were two gene deletions affecting the pyrimidine biosynthetic pathway that were distinct outliers in the multivariate analysis. Subsequent flux coupling analysis of the metabolic network has provided a potential explanation as to why this was the case. Therefore we propose that NMR-based metabolite profiling, in conjunction with flux coupling analysis, provides an empirical tool with which to investigate the structure and behaviour of cellular metabolic networks. Studies on the effects of gene modification in mice and in tumours will also be discussed. | ||
| 16.00 | Systems biology for parasites:
metabolic networks and pathway expression
David Westhead (University of Leeds) |
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Parasites, such as malaria and Eimeria, represent significant challenges for metabolic systems biology, not least because our knowledge of their metabolic networks is currently very limited by comparison with yeast or bacteria. Nevertheless, the need to use these networks in the design of new drug therapies has never been greater. The metaSHARK software for metabolic genome annotation will be presented, along with some interesting results of comparative metabolism in the Apicomplexa. Preliminary analysis of network gene expression will also be presented. | ||
| 16.40 | Panel Discussion (chaired by Joe Perry) | |
| 17.00 | Close |
By car:
From the South: Leave the M11 at junction 9 (signed A11 Newmarket) and
take the first exit signed A1301 towards Cambridge.
From the Stump Cross roundabout system take the first left (about 800
m). At the next small roundabout follow the signpost for 'Genome Campus'.
From the North: Leave the M11 at junction 10 (note that M11 junction 9 is restricted and only accessible from the South). Travel a mile or so (straight on at the first small roundabout) on the A505 in the direction of Saffron Walden. At the second roundabout take the third exit (signed A1301 towards Saffron Walden). Pass two turnings to Hinxton (these go to the village only), and take the next right, at a small roundabout, signposted to the Genome Campus.
By train:
Cambridge is on the main line from London King's Cross.
Whittlesford, Great Chesterford and Audley End
stations are on the line from London Liverpool Street to Cambridge.
The journey time from London is under an hour.
Please note that Whittlesford and Great Chesterford stations do not have a taxi rank or telephone. Please use either Cambridge or Audley End stations if a taxi has not been pre-booked.
More information is available from the Conference Centre web-site at http://www.wtconference.org.uk/