Ageing is heterogeneous process both at the individual and population level. As life expectancy is increasing there is a strong need to ensure a healthy old age. Ageing is by far the biggest risk factor for most of the common diseases like cancer and neurodegenerative diseases. Using a systems biology approach, this project aims at understanding the connection between the ageing process and the cause of age-related diseases by addressing the following fundamental questions that cannot be answered by experimental approaches only:
1. How do ageing factors found in cellular youth affect old-age behaviour?
2. How do metabolites change with age and how do they affect phenotype and function of the aging individual?
3. How can cells evolve and select strategies that will result in a viable population with increased healthspan?
We tackle these questions by using mathematical modelling and numerical simulations on both the single cell and the cell population level to describe molecular processes in the cells. By changing initial conditions or perturbing the system, we try to understand which parameters in the model influence the health of the cells in the population and eventually lead to cell death. We account for cell-to-cell variability by introducing stochasticity to the model parameters. With increasing number of cells in the population the model (usually a coupled set of non-linear differential equations) becomes increasingly difficult and computationally expensive to solve and analyse. To efficiently perform the large scale simulations and explore properties of cell populations with different parameter sets we apply for computer resources from C3SE. The simulations are mostly completely independent and thus perfectly parallelisable.