Systems

SYSTEMS

Aging is the most important risk factor for many chronic degenerative diseases, including dementia, atherosclerosis, kidney failure, and macular degeneration. However, the mechanisms underlying these pathologies are diverse, and no single biological theory of ageing accounts for the wide variation in the trajectory of human aging at the level of the individual. Nevertheless, many studies and theories of aging suggest that DNA damage and mitochondrial dysfunction are key drivers of aging in many species including humans.

System biology concerns the integration of knowledge and data to further our understanding and to generate testable hypotheses that can be addressed with computational models and laboratory study. Here we build computational models with sufficient molecular detail to enable us to simulate ageing at the cellular and tissue level. These models are informed by time dependent changes in data generated in our own laboratories together with other valuable datasets.

As a first example, we have developed a computational model of mito-nuclear communication for the molecular mechanisms that link genomic integrity and mitochondrial function with a focus on skin ageing and the role of senescent cells. This model is informed by a wide body of knowledge and multiple datasets including our published and newly generated data. We use qualitative model fitting software that enhances established systems modelling tools to explore a wide range of predicted behaviour. The analysis of time series omic-data, particularly multi-omic data, is currently not well supported and we have invested in developing our own bioinformatics software.

Our approach allows us to extract and synthesise data and knowledge generated in focused molecular biology experiments on cells in the laboratory to large omic-studies with data generated from tissue samples collected from people of different ages. An important aspect of our work is that the models are extensible meaning that new data and knowledge can be readily incorporated, and can be interconnected to other areas of study within the CHALLENGE platform.

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For further information, contact us at info@dataforgood.science.