Statistical Modelling of biological periodic processes

The main part of this research addresses the problem of modelling a periodic interaction between two biological variables. We model the interaction of the two variables by a centred two-dimensional Gaussian process with Markovian structure, as suggested by Bart et al. (2005). We develop appropriate confidence regions for estimating periodicity and the strength of the interaction and we adapt the model to deal with data with missing observations. We also discuss how identifiability of the periodicity is related to the experimental design. We apply this model to identify pairs of genes in yeast that interact periodically. The approach is then applied to model periodicity in the disease progression of acute pancre- atitis (AP), a frequent and potentially fatal systemic inflammatory disease, where the disease progression is described by two opposing factors, comprising of putative 'protective' and 'damaging' variables. In particular, we show that the first two principal components in the AP data can be interpreted as the 'protective' and 'damaging' variables and have a clear clinical interpretation, estimate periodicity of the disease progression for observed data with missing observations and rationalise the timing and opportunities for optimal medical intervention. We then reformulate this model into a latent growth model, which can more easily handle non- regularly spaced observations and missing data, enabling the use of a wider range of data sets. The remaining part of the research involves developing inference for exponentially decaying periodic functions, in particular when there is asymmetry in the decay rates of the maxima and minima of the periodic function. The inference is developed on simulated data and then applied to circadian gene expression data obtained from murine brain slices.

MAC-MIGS develops computational modelling and its application to a range of economic sectors, including high-value manufacturing, energy, finance and healthcare. These fields contribute over £500 billion to the UK economy. The CDT involves collaborations with more than a dozen companies and organisations, including large corporations (AkzoNobel, IBM, Dassault, P&G, Aberdeen Standard Investments, Intel), mid-size firms, particularly in the engineering and power sectors (NM Group, which provides monitoring services to power grid operators in 30 countries, Artemis Intelligent Power, the world leader in digital displacement hydraulics, Leonardo, a provider of defense, security and aerospace services, and Oliver Wymans, a management consultancy firm) and startups such as Brainnwave, which develops data-modelling solutions, and Opengosim which designs state-of-the-art and massively parallel software for subsurface reservoir simulation. Government and other agencies involved will include the British Geological Survey, Forestry Commission, James Hutton Institute, and Scottish National Heritage. Engagement will be via internships, short projects and PhD projects. BIS has stated that "Organisations using computer generated modelling and simulations and Big Data analytics create better products, get greater insights, and gain competitive advantage over traditional development processes". Our partners share this vision and are keen to develop deeper collaborations with us over the duration of the CDT.

Our CDT will achieve the following:

- Produce 76 highly skilled mathematical scientists and professionals, ready to take up positions in academia or in companies such as our partners. The students will have exposure to projects, modelling camps and high-level international collaborations.

- Deliver economic and societal benefits through student research projects developed in close collaboration with our partners in industry, business and government and other agencies.

- Create pathways for impact on computer science, chemistry, physics and engineering by involving interdisciplinary partners from Heriot-Watt and Edinburgh Universities in the supervision and training of our students.

- Organise a large number of lectures and seminars which will be open to staff and students of the two universities. Such lectures will inform the wide university communities about the state-of-the-art in computational and mathematical modelling.

- Work with other CDTs both in Edinburgh and beyond to organise a series of workshops for undergraduates, intended to foster an increased uptake of PhD studentship places in technical areas by female students and those from ethnic minorities, with potential impact on the broader UK CDT landscape.

- Organise industrial sandpits and modelling camps which offer the possibility for our partners to present a challenge arising in their work, and to explore innovative ways to tackle that challenge, fully involving the CDT students. This will kick-start a change in the corporate mindset by exposing the relevant staff to new approaches.

- Develop a new course, "Entrepreneurship for Doctoral Students in the Mathematical Sciences" in conjunction with Converge Challenge (Scotland's largest entrepreneurial training programme) and UoE's School of Business. This and other support measures will develop an innovation culture and facilitate the translation of our students' ideas into commercial activities.