Abstract
We introduce a new Markovian Agents formalism, called Population Markovian Agent Models, a technique able to describe systems characterized by large populations of entities whose properties and interactions may depend on their position. We apply this approach to the analysis of the cancer evolution, and to comprehend the mechanisms underlying the Cancer Stem Cells hierarchy whose characterization is crucial in the study of tumor progression. We exploit the model to consider movement of cell populations in a bi-dimensional space, and use it to derive the system evolution.
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Cordero, F., Fornari, C., Gribaudo, M., Manini, D. (2014). Markovian Agents Population Models to Study Cancer Evolution. In: Sericola, B., Telek, M., Horváth, G. (eds) Analytical and Stochastic Modeling Techniques and Applications. ASMTA 2014. Lecture Notes in Computer Science, vol 8499. Springer, Cham. https://doi.org/10.1007/978-3-319-08219-6_2
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DOI: https://doi.org/10.1007/978-3-319-08219-6_2
Publisher Name: Springer, Cham
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