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.

Overview: We propose a Centre for Doctoral Training in Data Science. Data science is an emerging discipline that combines machine learning, databases, and other research areas in order to generate new knowledge from complex data. Interest in data science is exploding in industry and the public sector, both in the UK and internationally. Students from the Centre will be well prepared to work on tough problems involving large-scale unstructured and semistructured data, which are increasingly arising across a wide variety of application areas. Skills need: There is a significant industrial need for students who are well trained in data science. Skilled data scientists are in high demand. A report by McKinsey Global Institute cites a shortage of up to 190,000 qualified data scientists in the US; the situation in the UK is likely to be similar. A 2012 report in the Harvard Business Review concludes: "Indeed the shortage of data scientists is becoming a serious constraint in some sectors." A report on the Nature web site cited an astonishing 15,000% increase in job postings for data scientists in a single year, from 2011 to 2012. Many of our industrial partners (see letters of support) have expressed a pressing need to hire in data science. Training approach: We will train students using a rigorous and innovative four-year programme that is designed not only to train students in performing cutting-edge research but also to foster interdisciplinary interactions between students and to build students' practical expertise by interacting with a wide consortium of partners. The first year of the programme combines taught coursework and a sequence of small research projects. Taught coursework will include courses in machine learning, databases, and other research areas. Years 2-4 of the programme will consist primarily of an intensive PhD-level research project. The programme will provide students with breadth throughout the interdisciplinary scope of data science, depth in a specialist area, training in leadership and communication skills, and appreciation for practical issues in applied data science. All students will receive individual supervision from at least two members of Centre staff. The training programme will be especially characterized by opportunities for combining theory and practice, and for student-led and peer-to-peer learning.