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Excitation-contraction coupling (ECC) is a process linking the electrical excitation of the muscle cell (myocyte) membrane to the contraction of the cell. In this study the possibilities of mathematical modeling were studied in current ECC research. Mathematical modeling was employed in two distinct ECC research areas, the enzymatic regulation of ECC and ECC during cardiac myocyte development. Despite the distinction, both of these are extremely complex biological systems characterized by diverse and partly contradictory reported experimental results, with a large part based on genetically engineered animal models. Novel mathematical models were developed for both of these research areas. The model of ventricular myocyte ECC with calmodulin-dependent protein kinase II (CaMKII)-mediated regulation faithfully reproduced the heart-rate dependent regulation of ECC. This regulation is thought to be the major effect of CaMKII-mediated regulation. The model of the embryonic ventricular myocyte provided the first comprehensive system analysis of how the embryonic heartbeat is generated at the cellular level. A similar type of model was also developed to show the notable differences between neonatal and adult ventricular myocyte ECC. The mathematical models of ECC presented in this study were further used to simulate ECC in genetically engineered myocytes. The cellular mechanisms of genetically engineered animal models could be better understood by employing mathematical modeling in parallel to experimental characterization of the animal model. It was found in simulations that the indirect consequences and the compensatory mechanisms induced by genetic modification may have a more significant effect on ECC than the direct consequences of the modification. To understand the overwhelming complexity of biological systems including ECC, competent system analysis tools, such as mathematical modeling, are required. The purpose of mathematical modeling is not to replace the experimental studies, but to provide a more comprehensive system analysis based on the experimental data. This system analysis will help in planning subsequent experiments needed to gain the most relevant information about the studied biological system.