Many crucial cellular processes such as growth, death, cell division, as well as immune recognition and response to foreign pathogens crucially depend on receptor–ligand binding and subsequent cellular signaling [1]. However, such processes also tend to be very complex, typically involving a large number of components. It is not easy to develop a clear mechanistic understanding of such complex biological processes from experiments alone, and computational modeling can play a significant role in such an endeavor.
Mathematical and computational models of receptor–ligand dynamics may broadly be divided into two classes: (i) deterministic, differential equations-based models where molecular concentrations are treated as continuous functions, and (ii) Monte Carlo models where molecules are treated as discrete particles. Additionally, models may differ as to whether they consider spatial inhomogeneity in the distribution of receptor and signaling molecules. Thus, models may be classified into (i) continuous/nonspatial, (ii) continuous/spatial, (iii) discrete/nonspatial, and (iv) discrete/spatial, as shown in Figure 3.1.
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Raychaudhuri, S., Raychaudhuri, P. (2009). Computational Modeling of Receptor-Ligand Binding and Cellular Signaling Processes. In: Jue, T. (eds) Fundamental Concepts in Biophysics. Handbook of Modern Biophysics. Humana Press. https://doi.org/10.1007/978-1-59745-397-4_3
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