The most common cause of deaths worldwide is cardiovascular disease, including coronary heart disease (CHD) which is responsible for over 7 million deaths per year. CHD often leads to myocardial infarction (MI), more commonly referred to as a heart attack. MI restricts blood flow to the heart, causing death of a large number of muscle and blood vessel cells in the surrounding cardiac tissue. A collagen-rich scar is formed post-MI to patch up the area of injury and compensate for the lost tissue. However, the scarring can be excessive (a condition termed fibrosis), which can itself cause further complications such as ventricular remodelling and ultimately heart failure. A relatable agent-based cardiac healing model was developed to improve the understanding of the complex cardiac healing process and predict the effects of pharmacological interventions on scarring. The model focuses on the interplay between fibroblast cells present in the cardiac tissue and the collagen matrix that constitutes the scar. The dynamics of these interactions dictate the remodelling outcomes post-MI. The model simulates a wide range of cell behaviours thought to play a role in cardiac remodelling, including migration, proliferation, apoptosis, and differentiation. Crucially, the implementation of these behaviours was informed by in-vitro experiments carried out using primary cardiac fibroblasts cells in a setting mimicking a cardiac wound. Local collagen alignment, a property differentiating healthy and scar tissue, was systematically and quantitatively determined using a novel metric that could be applied to both simulation outcomes and fluorescence microscopy images of collagen in tissue sections from intact and post-MI murine hearts. This metric enabled the comparison between model predictions and in-vivo experimental data. The model was used to simulate cardiac wound healing in a wide range of conditions, including those mimicking the administration of pharmacological compounds aiming at manipulating scarring by targeting various cell behaviours
