Modelling Telomere Dynamics in Ageing and Senescence
Dr Tamir Chandra, Dr Linus Schumacher & Dr Lucy Martin
About the Project
Through advancements in healthcare technology, the average longevity of individuals has steadily increased. Yet, ageing represents one of the greatest risk factors for a diverse range of pathologies including neurodegeneration, chronic inflammation, and cancer. Recently, many age-related diseases have been linked to a state of proliferative arrest, or senescence, in which cells no longer grow and divide but are still metabolically active. Senescence was first observed in cells which have undergone many divisions. This replicative senescence is due to the attrition of telomeres, a repetitive sequence of bases found at the end of each chromosome.
Numerous mathematical models of telomere attrition exist, but it has been difficult to verify these models through comparison to telomere lengths in humans due to a lack of appropriate data. As the technology required to measure telomere length in humans has only become prevalent in the last decade, it has not been possible to measure the telomere length of an individual over their lifetime. However, data from the Lothian Birth Cohort can now be used to determine the telomere length of individuals at several time points later in life. For this project, we will use this data, combined with telomere length measurements from a larger population from the Generation Scotland dataset.
The student will create mathematical and computational models for telomere attrition in ageing, using the available data to determine which models may describe the telomere shortening we see in vivo, and which models it is possible to distinguish between. Furthermore, as the student will be working as part of an interdisciplinary team there is the opportunity to design wet-lab experiments to observe telomere length in cell division to inform the modelling.
This project is a great opportunity for students with previous experience in physics, mathematics/statistics or bioinformatics and interest in both computational and experimental research. The student will benefit from integration in an active biomedical research environment with a cross-institutional network of collaborators.
Training in professional and research skills will be tailored to the individual student’s background and training needs. The student’s critical understanding of primary data and research literature will be advanced through regular group meetings and journal clubs. The student will also have the opportunity to engage with the mathematical and systems biology research community at other departments.