A method of three-dimensional visualization of molecular processes of apoptosis

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Abstract

Apoptosis or programmed cell death plays an important role in many physiological states and diseases. Detection of apoptotic cells, tracing the development of apoptosis, drug development and regulation of apoptosis are an important parts of basic research in medicine. A large number of models have been developed that are based on the differential equations of the chemical kinetics, and can be expressed in a uniform notation using some XML-based languages, such as SBML and CellML. We describe the CellML and the simulation environment OpenCell herein. These tools can display models schematically and output results in the form of graphs showing time dependencies of component concentrations. However, at the present time we do not have a software that could represent the results of the modelling in a form of animations as well as in the form of 3-D models. Using descriptive as well as quantitative models we discuss approaches to visualize the biological processes described by the apoptosis models. The quantitative method was implemented using a 3-D visualization of the molecular biological processes modelled by chemical kinetic equations. The quantitative parameters in our visualization scheme are determined based on the kinetic equations governing the participating components, so our visualization is not only qualitative but also quantitative. To implement this visualization, the C# software and a database of 3-D forms that model molecular complexes are developed. We present 3-D visualization of the molecular processes described in the mathematical model for the mitochondriadependent apoptosis proposed by Bagci et al. [22] as a case study.

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Muhamedyev, R. I., Gladkikh, V., Gopejenko, V. I., Daineko, Y. A., Mansharipova, A. T., Muhamedyeva, E. L., & Gopejenko, A. V. (2014). A method of three-dimensional visualization of molecular processes of apoptosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8853, pp. 103–112). Springer Verlag. https://doi.org/10.1007/978-3-319-13969-2_8

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