Identifying new genetic and molecular targets to improve bioenergy crops
Rothamsted Research: Dr Angela Karp, Dr Steve Hanley
Renewable energy has been identified by the UK Government as a vital part of the wider sustainability agenda. This presents a formidable challenge and contributions from several renewable energy sources will need to be significantly boosted. Biomass has been identified as playing a significant role in this increase. Willows are among the most advanced biomass crops in temperate regions. The project aims to integrate in-house genetic and genomic willow resources (genetic maps, QTLs, ESTs) with publicly available data sets from model organisms (e.g. Arabidopsis, poplar) to begin the transition from willow QTLs to specific candidate genes for further study, with the ultimate aim of direct selection of beneficial alleles within the crop improvement pipeline.
Functional candidate genes that are implicated in key architecture and yield-related traits will be first identified from model organisms such as Arabidopsis and poplar using data integration and deep text mining techniques. In parallel, by running an Ondex workflow, available willow EST and genomic sequence previously generated at Rothamsted will be annotated by sequence comparison with publicly available databases. Sequence homology will then be used to identify willow orthologues of functional candidates identified from the willow genomic datasets. As a final stage, candidate gene sets that combine both positional and functional information for further study will be generated.
Integration augmentation and validation of yeast metabolome models
University of Manchester: Prof Douglas Kell
VIDEO of our ISMB 2009 technology track demo
Yeast is an important model organism for both its practical uses in fermentation and its genetic similarities to higher, more complex eukaryotes. Yeast is a single-celled eukaryotic organism which is widely used as a simple model for cell biology in humans and the study of human metabolic disease, because its internal signalling pathways are similar to those in human cells.
The project proposes to use the combined technologies of Ondex, data integration, Taverna, text mining and graph analysis to study the yeast metabolome and its response to different conditions. This will enable an increased understanding of yeast metabolism and also identify common metabolic processes for modelling in yeast and other systems.
Supporting research into the role of telomere function in ageing
University of Newcastle, CISBAN (Centre for Integrated Systems Biology of Ageing and Nutrition):
Prof David Lydall, Prof Anil Wipat
Dividing cells balance the conflicting needs of maintaining genetic stability with those of replicating and segregating their genomes. Double strand breaks (DSBs) in DNA are particularly dangerous to genetic stability. Accordingly, DSBs are potent activators of cell cycle arrest and DNA repair. Telomeres look very much like DSBs, yet in most normal cells they are "capped" and hence hidden from repair and cell cycle arrest (checkpoint) pathways. However, as fibroblasts age, telomeres become uncapped, DSB-like, and stimulate repair and checkpoint pathways.
This project in budding yeast, Saccharomyces cerevisiae, examines the interactions of repair and checkpoint pathways with uncapped telomeres. It uses some of the powerful approaches available only in budding yeast to study how the DDR (DNA damage response) responds to uncapped telomeres. Firstly, the system will be customised to incorporate the latest S. cerevisiae data sources, together with information gathered as part of the CISBAN project, and used, together with the literature extracted through text mining to review telomere biology in S. cerevisiae, to further understand how pathological responses to uncapped telomeres are regulated by DNA repair and checkpoint pathways and the identification of novel genes and interactions that occur.