The aim of the study was to compare scenarios of numerical modeling of the operation of a heart valve bioprosthesis, identifying their advantages and limitations. Material and methods. Numerical modeling was conducted in the Abaqus/ CAE (Dassault Systèmes, France) engineering analysis environment, simulating two cycles of the valve apparatus’s operation. In total, three different computer models were studied, each providing different levels of detail and complexity of the “UniLine” bioprosthesis. Model No.1 was the most simplified and considered only the geometry of the flap; Model No. 2 incorporated elastic connectors with variable stiffness; Model No. 3 included a composite support frame. Qualitative validation of the modeling results was conducted by comparing with the bench tests data obtained on the hydrodynamic stand (ViVitro Labs, Canada) during tests of the corresponding clinical model of the “UniLine” bioprosthesis. Results. One of the setups, Model No. 2, displayed an artificial stress concentration according to Von Mises in the connector attachment area, reaching 2.695 MPa, which is close to the material’s strength limit. Other setups showed a more moderate stress distribution – up to 0.803 and 0.529 MPa. Moreover, it was demonstrated that only Model No. 2 and Model No. 3 reproduce the key effect of the bioprosthesis operation, the mobility of the commissural posts, ensuring a qualitative match with the work in bench conditions. Conclusions. A methodology is proposed that may be useful for conducting further in silico studies of heart valve bioprostheses. Boundary conditions, methods for linking prosthetic components, and opportunities for large-scale “exploratory” studies based on using simplified models are described. The study results confirm the necessity of including all prosthesis components in the numerical model for a more comprehensive and realistic representation of its biomechanics. Such detail contributes to a more accurate safety and effectiveness assessment of the device and can also serve as a foundation for its further optimization.