Apoptosis is a form of programmed cell death involving caspases, specialized cysteine proteases found in animal cells as inactive proenzymes. In recent years, dramatic progress has been made in identifying and determining the biochemical activities and cellular functions of biomolecules that regulate apoptosis and carry out its proteolytic program. However, current knowledge is largely qualitative and descriptive, and the complex circuits that integrate prosurvival and prodeath signals to control the fates of normal and diseased cells remain poorly understood. The successful establishment of a quantitative and predictive calculation model for apoptosis will be of great significance to basic research and clinical research.
A number of biological questions can be explained by the application of various mathematical formalisms to apoptotic signaling. Combining single-cell analysis with mathematical modeling can help understand the molecular mechanism of apoptosis signaling. For example, it is helpful to understand how the switching between life and death in the cell occurs. In particular, it is possible to understand the contribution of different apoptosis regulators to apoptosis induction. Mathematical modeling of apoptosis also allowed determining and supporting many molecular paradigms.
Creative Bioarray has a professional research team and a rich database. We can help you design a good apoptosis model and perform relevant analysis according to your research purpose, and we can develop a comprehensive research plan for you.
Models of cellular biochemistry can be based on different mathematical formalisms：
ODEs Most models of apoptosis have been encoded using ordinary differential equations (ODEs). It can describe the evolution of a system in continuous time. ODEs are the mathematical representation of mass-action kinetics, the familiar biochemical approximation in which reaction rates are proportional to the concentrations of reactants. |
PDEs Diffusion, spatial gradients, or transport can be modeled explicitly using partial differential equations (PDEs). It can represent biochemical systems in continuous time and space. |
Stochastic Models Stochastic models make it possible to represent reactions as processes that are discrete and random, rather than continuous and deterministic. Stochastic models are advantageous when the number of individual reactants of any species is small (typically fewer than ~100) or reaction rates very slow. |
Logic-based Models A less precise modeling framework is usually advantageous when sufficient time-resolved quantitative data are lacking. Boolean models, for example, are discrete two-state logical models in which each node in a network is represented as a simple on/off switch. Boolean models have been used to represent the interplay among survival, necrosis, and apoptosis pathways and predict the likelihood that each phenotype would result following changes in the levels of regulatory proteins. |
Creative Bioarray has established a complete cell culture platform, equipped with complete detection and analysis equipment, we can provide customers with a variety of experimental services. We can undertake detections related to the identification of cell death types, and can provide a full set of customized technical services from experimental design, cell culture, cell processing and film production, image collection, and data measurement.
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