To improve our understanding of cardiovascular biology, we investigate the role of proteins under defined disease conditions via the use of model systems. Heart disease is the leading cause of mortality and morbidity worldwide, with coronary heart disease (CHD) accounting for 52% of attributable deaths. Acute myocardial infarction (AMI) is directly linked to CHD and results from myocardial ischemia, where the heart receives an inadequate supply of oxygen. The extent of damage to the heart is proportionate to the duration of ischemia, whereby transient episodes are protective, brief periods cause reversible contractile dysfunction and extended insults result in cellular necrosis and ultimately organ failure. Given the broad range of functional responses resolvable on a relatively sort time scale (1-60 minutes), we hypothesize that subtle protein post-translational modifications play an essential role in these outcomes. Proteomic technologies have facilitated our understanding of the role of protein degradation, phosphorylation, acetylation, glycosylation and oxidation in response to myocardial ischemia. To model disease, we utilise ex-vivo induction of ischemia in rat (Rattus norvegicus) and rabbit (Oryctolagus cuniculus) myocardium as model organisms. This permits investigation of the cellular events, under otherwise ideal conditions prior to validation in clinical cohorts, which are ultimately more challenging given the heterogeneous nature of AMI patients whom present with diverse co-morbidities including diabetes. An essential component of proteomic PTM investigations are the observation of non-modified counterparts to ensure that changes in the modified species are independent of changes at the protein level and to improve our coverage of the cardiac proteome. We have identified over 4,000 unique myocardial proteins of which nearly 50% are modified at one point during the progression from protection to necrosis using our ex-vivo model. In our experience, to improve our understanding of myocardial I/R injury, model systems provide the ideal biological situations from which to generate hypotheses for testing in more complex clinical situations.