SNIC
SUPR
SNIC SUPR
Systems biology of ageing
Dnr:

SNIC 2017/2-23

Type:

SNAC Small

Principal Investigator:

Barbara Schnitzer

Affiliation:

Göteborgs universitet

Start Date:

2017-10-24

End Date:

2018-11-01

Primary Classification:

10610: Bioinformatics and Systems Biology (methods development to be 10203)

Allocation

Abstract

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 neurodegeneration. However, most biological research focuses on individual disease. Using a systems biology approach, this project aims at understanding the connection between the ageing process and the cause of ageing-related diseases by addressing the following fundamental questions that cannot be answered by experimental approaches only: 1. How do ageing factors found in 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 use mathematical modeling and simulations on both the single cell and the population level, that help us to describe and analyze different molecular processes connected to the research questions. By changing the conditions or perturbing the system we aim to understand how the system reacts and eventually which effect it has on both the individual cell health and on the population size in the long term. Mathematically it corresponds to optimizing and numerically solving a set of differential equations over time, while its size grows exponentially as the number of cells increases. In addition, model parameters can be stochastic, which requires performing averages over many independent realizations. We therefore apply for computer resources to do large scale simulations and be able to efficiently deal with the stochasticity and explore different input parameter sets. The experiments are mostly completely independent and thus perfectly parallelizable.