Systems biology underpins our success in integrating the wealth of quantitative biological data generated from basic research and from studying complex diseases, including the UK’s major killers: cancer, cardiovascular and neurodegenerative diseases.
A class of features that are often ignored in systems models are physical and geometrical signatures, such as muscle function, stiffened and realigned extracellular matrix, alterations in intracellular forces and obstructions of blood flow. These occur in a broad range of conditions such as solid tumours, atherosclerosis, cardiac fibrosis liver cirrhosis or with ageing.
We have developed mathematical systems-biology models, mostly calibrated and validated with in-vitro data, for various age-related diseases, including cancer and cardiovascular diseases. In this project, we will use human data to investigate how models need to be adapted. Specifically, we will extract geometrical and physical information from biomedical imaging data (such as organ size, fibrotic scores, blood vessel features), and map this information to molecular biomarkers, e.g. from blood measurements. By analysing the evolution of these parameters with age, we will aim to infer how molecular changes cause physical and geometric changes, and likewise, how physical changes cause molecular changes. Our hypothesis is that molecular and physical features are inseparable features of one complex system, and maintenance of both is required for health. Our complex systems model will be used to predict how alterations in physical, geometrical and molecular features predict the emergence of major age-related diseases, such as cancer, cardiovascular and neurodegenerative diseases.