Convergence, Connectivity, and Continuity: Topological Perspectives for Mining Novel Biological Information from ‘Omics Data.

This interdisciplinary Lord Kelvin-Adam Smith studentship is based in Maths & Stats (College of Science & Engineering) with on-going interaction with the Evolutionary Analysis Group, Institute of Biodiversity, Animal Health & Comparative Medicine (College of Medical, Veterinary & Life Science).

The ideal candidate will have a strong background in mathematics and a diverse interest and some experience in the experimental and biological sciences (molecular biology, evolution). Additionally, the desire to engage with existing techniques in statistics and computer programming will be an asset. Applicants must hold a First Class degree (or equivalent) in a relevant discipline and demonstrate exceptional aptitude for interdisciplinarity. Informal inquiries to the PIs in advance of the deadline are welcome; please include a CV, recent academic transcript, and a statement of interest.

Project: The challenges posed by big data are the new reality across scientific disciplines. For example, recent advances in high throughput DNA sequencing technology for genomics have revolutionised our ability to quantifying how genes are expressed at cellular, biological, and evolutionary scales. However, the tools for analysing the resultant big data from such transcriptomics studies have not kept pace for biological and evolutionary perspectives on gene expression and co-expressed gene networks. The current project aims to advance the field by applying ideas from topology – a branch of mathematics that is specifically adapted to treat qualitative properties such as connectivity. Since the expression of genes in an organism are co-dependent, co-varying and continuous, treating data from a topological viewpoint can reveal new relationships by – perhaps paradoxically – deliberately ignoring structure from conventional modes of traditional analysis. This project will bridge mathematics and biology to adapt and develop new ways to analyse patterns in complex data, specifically transcriptome-wide gene expression in evolutionary, molecular, and biodiversity context.

Details on the project and application process are available at http://www.gla.ac.uk/services/postgraduateresearch/scholarships/kelvinsmith/shortlistedscholarshipprojects/#d.en.240419

The ideal candidate will have a strong background in mathematics and a diverse interest and some experience in the experimental and biological sciences (molecular biology, evolution). Additionally, the desire to engage with existing techniques in statistics and computer programming will be an asset. Applicants must hold a First Class degree (or equivalent) in a relevant discipline. Informal inquiries to the PIs in advance of the deadline are welcome; please include a CV, academic transcript and statement of interest.

Interested candidates are encouraged to contact the supervisors in advance of the deadline with a CV, recent academic transcript, and a statement of interest.