Project description
Aims
To understand the transition from healthy airways to diseased (asthmatic) airways and predict disease progression. This will be achieved by developing models that combine mechanistic and data-driven components, trained using in-vivo/in-vitro datasets, resulting in a multi-scale organ-level network model.
Introduction
Asthma is a chronic disease affecting ~300m people worldwide. The annual financial burden in the UK is £2.3billion, with 80% of this spent on the 20% of people with the most severe and poorly controlled asthma. The disease is characterized by inflammation, airway hyper-responsiveness (causing rapid bronchoconstriction) and airway remodelling (structural changes to the epithelium, extra-cellular matrix and airway smooth muscle within the airway wall). However, it is not clear how these mechanisms are linked, and whether the latter two are causes or symptoms of the disease.
Previously, we have undertaken a joint experimental and theoretical study to better understand the links. We developed multiphase morphoelastic-growth partial differential equation (PDE) models linking inflammation to bronchoconstriction and remodelling for the first time. In parallel, we performed in vitro and in vivo studies to obtain an unprecedented spatio-temporal dataset of airway geometry, mechanics and constituent changes in ~2000 airways from a chronic mouse model of asthma. However, the mechanistic models in their current form rely on empirical descriptions of how cell processes such as proliferation and apoptosis respond to tissue stress and inflammatory mediators, that are not fully informed by the animal data and fail to account for biological variability in vivo.
In this project, we will augment existing mechanistic models with data-driven components to develop a biophysically-informed machine learning/mechanistic model hybrid to reveal non-linear dependencies of cell processes on stress/inflammation. This will help us better understand the emergent system dynamics and identify underlying pathogenic processes .
Outcomes
Through multidisciplinary collaboration between applied mathematicians, statisticians, imaging experts and respiratory medicine clinicians, we will develop a biologically- and physically-informed model that will provide insight into disease pathogenesis and progression, as well as predict the effect of biological/mechanical interventions.