2016 Conference on Computational Modelling with COPASI
Manchester Institute of Biotechnology, 12th – 13th May, 2016

Computationally modelling the dynamics of cholesterol metabolism and ageing

Amy Morgan1, Kathleen Mooney2, Steve Wilkinson1, Neil Pickles1, Mark Mc Auley1

1 - University of Chester, UK; 2 - Edge Hill University, UK

Keywords: Cholesterol, Ageing, Cardiovascular Disease


Cardiovascular disease (CVD) accounted for 27% of all deaths in the United Kingdom in 2014, and was responsible for 1.7 million hospital admissions in 2013/14. This condition becomes increasingly prevalent with age, affecting 34.1 and 29.8% of males and females over 75 years of age respectively in 2011 (1). The dysregulation of cholesterol metabolism with age, often observed as a rise in low density lipoprotein cholesterol (LDL-C), and decline in high density lipoprotein cholesterol (HDL-C), has been associated with the pathogenesis of CVD (2). To compound this problem it is estimated that by 2050, 22% of the world’s population will be over 60 years of age, while resistance to pre-existing cholesterol regulating drugs such as statins has also been observed (3). Therefore, it is apparent that research into additional therapies for CVD prevention is a growing necessity. However it is imperative to recognise that this complex system cannot be studied using a reductionist approach, rather its biological uniqueness necessitates a more integrated methodology. The systems biology paradigm provides a more holistic framework for conducting investigations of this nature (4). Therefore, we have adopted this approach, and used COPASI to investigate the dysregulation of whole-body cholesterol metabolism with age. This kinetic model builds on our previous work in this area by including a more mechanistic representation of cholesterol absorption and biosynthesis (5). Using our model we were able to investigate the impact of intrinsic aging on cholesterol metabolism and were able to determine how dietary perturbations affect LDL-C and HDL-C levels (6). In the future it is hoped that the findings from our approach will inform novel nutrient and pharmacological based interventions which may help prevent CVD.

  1. Townsend N, Bhatnagar P, Wilkins E, Wickramasinghe K, Rayner M. Cardiovascular disease statistics 2015: BHF; 2015.
  2. Morgan AE, Mooney KM, Wilkinson SJ, Pickles NA, Mc Auley MT. Cholesterol metabolism: A review of how ageing disrupts the biological mechanisms responsible for its regulation. Ageing Research Reviews. 2016;27:108-24.
  3. WHO. Aging and Life Course: Facts about Aging: WHO; 2014. Available from: http://www.who.int/ageing/about/facts/en/.
  4. Mooney KM, Morgan AE, Mc Auley MT. Aging and computational systems biology. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. 2016;8(2):123-39.
  5. Mc Auley MT, Wilkinson DJ, Jones JJL, Kirkwood TBL. A whole-body mathematical model of cholesterol metabolism and its age-associated dysregulation. BMC Systems Biology. 2012;6:130-.
  6. Morgan AE, Mooney KM, Wilkinson SJ, Pickles NA, Mc Auley MT. Mathematically Modelling the Dynamics of Cholesterol Metabolism and Ageing Biosystems. Under Peer Review.

Conference Program