Abstract
Myocardial Infarction (MI), also known as a heart attack, is the irreversible death of heart muscle caused by a prolonged lack of oxygen supply. It is the leading cause of death in the United States, occurring approximately every 40 seconds with about 805,000 cases each year. This injury initiates a wound-healing response in the myocardium. Unlike many other tissues in the body, adult cardiomyocytes have a very limited capacity to proliferate, meaning the cells lost after injury are not effectively replaced. Instead of regenerating new muscle, the injured region is reinforced with fibrotic scar tissue produced largely by activated cardiac fibroblasts. Over time, excessive scar tissue can stiffen the heart, reduce its ability to pump efficiently, and increase the risk of developing heart failure.
There are currently no effective therapies that reliably induce proliferation in adult cardiomyocytes, largely because we do not fully understand how these cells progress through and exit the cell cycle. Traditional proliferation markers such as Ki67 and DNA content measurements can indicate cell cycle activity, but they cannot distinguish between true division, polyploidization, or binucleation, nor do they quantify how cells transition between specific phases.
To address this gap, we developed CycloTran, an integrated experimental and computational framework that fits single-cell measurements of DNA content and Ki67 expression to a minimal ordinary differential equation model of the cardiomyocyte cell cycle. Using high-content imaging and automated image analysis, CycloTran classifies nuclei into defined cell cycle and ploidy states and estimates transition rates between phases. By quantifying both phase distributions and transition kinetics, CycloTran allows us to identify where cardiomyocytes accumulate or arrest under different perturbations. This provides a mechanistic foundation for designing targeted therapies that promote productive cell cycle progression and enhance the regenerative potential of the adult heart.
Although CycloTran is a computational model, it is ultimately embedded in a clinical and social context. Cardiovascular disease disproportionately affects certain populations, including older adults, low-income communities, and racial minorities. The datasets used to develop and validate computational tools can reflect these disparities, which may influence how models are interpreted or translated into therapies. It is therefore important to consider who is represented in the data, who benefits from resulting treatments, and who might be excluded.
One relevant STS framework is the Social Construction of Technology (SCOT), which examines how technological development is shaped by the values and assumptions of different social groups. CycloTran reflects definitions of what counts as “productive” proliferation and what constitutes meaningful regeneration. Actor-Network Theory (ANT) is also applicable because the project depends on a network of human and nonhuman actors, including imaging systems, segmentation software, mathematical models, experimental biologists, and funding structures. The model’s outputs are shaped by this network, and its authority emerges from the alignment of these components.
I will use a case study approach based on the workflow we use to study cardiomyocyte proliferation after myocardial infarction. We collect mouse and human heart tissue samples, stain them for DAPI and Ki67, train segmentation models in Ilastik, and measure signal intensities in CellProfiler. For my STS research, I will analyze how decisions made during staining, image training, segmentation, and intensity measurement shape how we interpret proliferation. I will also draw on scientific literature to compare how other researchers define and measure cell cycle activity. This will allow me to examine how technical tools and human judgment work together to produce conclusions about heart regeneration.
I expect to find that tools like CycloTran can more clearly distinguish between different stages of the cardiomyocyte cell cycle than traditional markers alone. By better identifying where cells arrest or fail to progress, we can gain a clearer understanding of why adult cardiomyocytes do not proliferate after injury. Ultimately, I hope this knowledge supports the development of targeted therapeutic strategies that encourage productive cell cycle progression and improve heart regeneration.
Together, the capstone project and the STS research show that advancing heart regeneration requires both strong technical tools and careful reflection on how those tools are developed and interpreted. CycloTran provides a more precise way to study the cell cycle, while the STS research helps ensure that the technology is applied thoughtfully and responsibly. Combined, they contribute to a more informed and realistic path toward regenerative therapies.
