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JenAge Research

The continuing increase in life expectancy poses demanding challenges to society in general and public health systems in particular. Promotion of health in old age requires intensive research efforts into ageing processes and age-related diseases. Ageing is currently considered a stochastic process with genetic contributions. Studies in model organisms have revealed a conserved phenotypic signature of ageing which includes, among others, neuronal degeneration, reduced stress resistance, altered expression of a number of genes or altered metabolic processes. A unifying molecular model which explains ageing is however still missing.

It is now being increasingly recognised that a purely reductionist approach is inadequate to explain all functional changes associated with ageing. Therefore strategies suited to study complex stochastic systems are required. In the recent years, a multidisciplinary approach known as systems biology has emerged that analyses the interactions between the components of biological systems in a systemic way. We expect that age research will greatly benefit from such a systems biology perspective.

The objective of the JenAge Centre is to join forces between age research and systems biology. Previous work on model organisms demonstrated that mild stress can increase lifespan and delay ageing. The generally favorable biological response of an organism to low dose exposure of stressors, called hormesis, has been repeatedly suggested to be the biological mechanism underlying the effects of caloric restriction and other life-extending treatments.

The JenAge Centre aims to identify conserved transcriptional and metabolic networks activated by mild stress by adopting a comparative cross-species approach to investigate their role in preserving functional integrity in old age.

This approach will characterise the network response to experimental perturbations (environmental, pharmacological, exercise, genetic) and age (i.e. old versus young animals). In an iterative process, experimental data will be delivered to the analysis and modelling groups. These groups will apply methods of network inference and modelling. New hypotheses obtained from this analysis will be embedded in predictive mathematical and computational models that represent (i) the available expert knowledge, (ii) facts extracted from biological databases and (iii) the hypotheses generated by the analysis of the experimental data. The modellers will then explore validation procedures together with the experimental groups. Automatic text mining will be used to cope with the plethora of scientific documents relevant to ageing in a systematic way and, in particular, to generate additional hypotheses on ageing and age-related diseases. This information will be used to set up new databases on molecular, cellular and organismic aspects of ageing.