The exploitation of genetic variation in gene network inference

Modern biology is becoming more and more multidisciplinary. This is especially the case for the area of 'Systems Biology', which aims to predict how the different biological processes interact to result in a functional organism. These processes include the transcription of DNA into RNA, which codes for amino acids that make up the proteins, as well as the levels of hormones and metabolites that affect the biological processes. In the proposed network, we address how variation at the DNA level affects the transcription of DNA into RNA and how this then affects the characteristics of the whole organism. The aim is to reconstruct the networks that describe how genes interact. While conceptually straightforward, the area of research requires integration between biology, computer science (bioinformatics) and mathematics. At present, there is already some level of integration between researchers in these areas, but a lot of work is done in isolation. In the proposed network we will bring together: 1) biological research in plants, animals and humans. 2) Bioinformatics research which covers databases that contain known information on gene networks but also translates novel statistical and mathematical models into user-friendly software. 3) Mathematical biology, focussed on the methods of reverse-engineering of gene regulatory network, from a variety of experiments. The network will achieve its goal of further integration by organising annual meetings. These meetings will consist of an interactive workshop followed by a scientific conference. The workshop will provide ample opportunity for training of young researchers, dissemination of 'best practise' and new software tools and initiation of new collaborative research. The Conference will disseminate the cutting edge of the research area to the wider community.

The combined study of gene-expression and marker genotypes in a segregating population (genetical genomics) is now making an important contribution to the dissection of complex traits. Genetical genomics methods detect genomic loci that control variation in gene expression, so called expression QTL or eQTL (to distinguish them from functional QTL that affect traits at the whole-organism level). A major promise of genetical genomics is that by examining the relationship between transcript location, location of eQTL and pleiotropic effects of eQTL, it might be possible to reconstruct genetic pathways that underlie phenotypic variation. The major challenge in reverse engineering gene regulatory networks is the successful integration of biological knowledge, (e)QTL mapping results, bioinformatics tools and mathematical modelling approaches. The goal of the proposed network is to promote the required integration between genetics, bioinformatics and mathematics to facilitate the reverse-engineering of regulatory networks using genetic mapping data and high throughput data, such as transcriptomics. This is an important step toward systems biology approaches where these regulatory networks will be refined and evolve into quantitative models of regulation. The network will organise three annual meetings for the duration of the grant. Each meeting will have an interactive workshop with a training component followed by a scientific conference. For the workshop part, attendance will be limited to 50 participants with a core participation of the network members and additional participants via invitation or application. The workshops will include a clear training aspect and will be structured to facilitate active participation of PhD students and post-docs. The conference part will be open to the research community with an expected number of attendees between 100 and 200. The conference will feature mainly invited contributions from key note speakers.