New approaches to developmental biology: landscapes, information and genetic network design.

Developing methods that provide insight into the complex dynamical processes that characterize developing tissues is essential if we are to understand embryonic development. The need for such approaches is becoming ever more urgent as the complexity and size of the data relating to development - such as single cell transcriptome analysis and high dimensional imaging -continues to increase. Moreover, the use of directed differentiation to generate specific cell types in vitro by exposing embryonic stem cells to developmental signals raises new questions about the control of cell fate.
Dimensionality reduction methods and frameworks that allow the analysis of complex dynamics cellular and molecular are central to this. While the 'landscape' model of Waddington, developed in the 1940s-50s, has been enormously influential to the field, it has largely been viewed as a metaphor and has lacked a rigorous interpretation, constrained by experimental data. The approach you are proposing will change this and provide a framework in which an intuitive picture of how signals and gene regulatory networks shape cellular decision-making to allocate multiple cell fates. These models will help us understand how to control the development of spinal cord and mesodermal tissue. More generally, the approach is relevant and applicable to a large number of developmental problems including those relevant to tissue engineering and regenerative medicine. As such, I anticipate that the outcome of this project will be of broad interest to the developmental biology and stem cell biology community.

The context of the research - In developmental biology, the goal is to understand what leads cells to organise into different types, shapes and quantities and to develop specialised functions in order to form tissues and organisms. The so-called epigenetic landscape is underpinned by the combination of external signals and the gene regulatory networks of the cells. If we are able to correctly identify and estimate the specific landscape of a system from some observed data, we will be able to provide insights about which signals lead to certain fates, what causes changes in the cells decision-making and when these changes take place during this process.
The aims and objectives of the research -
Aim 1: To extend the theory on model specification and parameter estimation methods to single-cells data.
Aim 2: To apply the landscape approach to the systems described in important published papers.
Aim 3: To use these approaches to analyse data coming from the Briscoe Lab in order to support their research questions.
The novelty of the research methodology - The epigenetic landscape paradigm is a relatively new approach to address the study of cell development. The main contribution will be to adapt the theory and use data which have higher granularity and to consider single-cells separately instead of studying summary statistics for clusters of cells.
The potential impact, applications, and benefits - Results of modelling will improve our understanding of cells development and differentiation and provide scientists at the Crick Institute with mathematical tools and insights that could be used for future experiments.
How the research relates to the remit - Research area of the PhD is under Mathematical Biology as we are going to develop new mathematical and statistical tools to investigate patterns in cells formation and differentiation.
Research area; Mathematical Sciences
External Partner - Francis Crick Institute

Impact from the MathSys CDT will arise from three separate mechanisms, each of which will generate a spectrum of academic, economic and societal impacts.

1) Most prominently, this CDT will create the next generation of quantitative researchers that are trained in the necessary skills and techniques to make substantial impact in academia, industry and government agencies. Creation of skilled researchers with a broad scientific outlook will have a number of beneficiaries. We expect that our students will be in high demand within academia and will be the researcher leaders of tomorrow. In addition, many of our brightest students post-PhD are now moving out of academia to research positions within industry or government agencies; such students are likely to generate substantial financial impact within industry and societal benefits within government agencies. By encouraging strong collaboration with our external partner organisations throughout their training, our PhD students will have a broad insight into the impact that mathematics can bring, and the routes through which academic excellence can be translated into meaningful applied outputs with impact. The assembled team of supervisors has an excellent track-record of supporting and training high calibre PhD students with skills that are in demand both within and outside of academia.

2) More immediate economic and societal benefits will accrue from the direct interaction of our students with external partners that is an integral part of their training. We anticipate that 4-6 students per cohort will undertake a PhD that is co-supervised by one of our external partner organisations; in addition all students during their MSc year will partake in one of several group projects led and supported by one of our external partners. In both cases, research will be focused towards real-world problems that are of current concern to the partners. It is anticipated that through these close interactions our students will develop methodologies and results that will address real-world problems. These new solutions to particular challenging real-world problems from external partners are likely to have substantial industrial, economic or societal benefits as they directly tackle prominent and pressing issues set by those with the greatest knowledge of the real-world challenges. Impact will therefore be generated through direct problem-solving research with a number of the UK's leading organisations.

3) Finally, we envisage that the mathematical techniques that are developed in the context of one real-world problem will have wider benefit to other academic fields. Although the immediate beneficiaries are likely to be other academics who will gain from an increased repertoire of tools and techniques, in the longer term these insights are likely to lead to new applications that feed back into industry, finance and society in general. The transdisciplinary nature of our MathSys CDT will facilitate such interactions, promoting the exchange of ideas between diverse subject areas. We firmly believe that such cross-fertilisation of ideas will be a feature of the MathSys CDT, where students are united by common goals of quantitative understanding and prediction and a common language of mathematics. We therefore expect rapid impact in a variety of applied areas, as novel techniques are introduced.