Abstract Tu071: Computational Modeling to Predict Mechanisms of DYRK1A-mediated Inhibition of Cardiomyocyte Proliferation

Abstract

Introduction and Background: Adult cardiomyocytes lack strong proliferative capability with only a small subset being able to undergo mitosis. This can lead to poor functional recovery after heart injuries such as myocardial infarctions (MI). We have previously demonstrated that inhibition of DYRK1A, a regulator of the DREAM complex and cell quiescence, increases cardiomyocyte cycling. However, the mechanism of DYRK1A inhibition that induces this increased cycling has yet to be elucidated. Therefore, we built a network model that encapsulates potential components that can lead to cardiomyocyte proliferation. Methods: We developed a logic-based differential equations model to simulate DYRK1A-mediated contributors of cardiomyocyte proliferation and G1/S phase transition. Various knock-down/knock-out and overexpression simulations were performed to predict targets of cell cycle regulation. Literature validation was performed to compare experiments across cancer, cardiomyocytes, and broader cell types to independent model simulations. A sensitivity analysis was used to identify the influence of particular proteins/genes on cardiomyocyte proliferation. Results: Our model simulations validated against (n = # of primary literature articles) 84.6% of literature experiments in cancer cell lines (n = 13), 88.8% in broader cell types (n = 9), and 100% in cardiomyocytes (n = 5). Simulated knockouts of DYRK1A and overexpression of Cyclin-D, Cyclin-E, and B-MyB increased DNA replication, indicating exit from G 0 phase. Sensitivity analysis showed that a DYRK1A knockout was the most influential in terms of increasing DNA replication. Conclusion: Our model’s predictions were successfully validated against literature in several different types of cell lines. Furthermore, it gives valuable insight on possible targets that can induce cardiomyocyte proliferation in addition to DYRK1A. Further in vitro and in vivo experiments are necessary to fully evaluate our simulations in cardiomyocytes.