Abstract
AbstractA spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in ‘cancer state’ or in ‘non-cancer state’. The model assigns probabilities for the non-reversible transition from ‘non-cancer’ state to the ‘cancer state’ that depend on the states of the neighbouring nodes. The likelihood of transition further depends on the life burden intensity of the UV-rays that the skin is exposed to. The probabilistic nature of the process and the uncertainty in the input data is assessed by the use of Monte Carlo simulations. A good fit between experiments on mice and our model has been obtained.
Funder
Delft University of Technology
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Agricultural and Biological Sciences (miscellaneous),Modeling and Simulation
Reference30 articles.
1. Adra S, Sun T, MacNeil S, Holcombe M, Smallwood R (2010) Development of a three-dimensional multiscale computational model of the human epidermis. PLoS ONE 5(1):e8511
2. Alarcon T, Byrne HM, Maini PK (2003) A cellular automaton model for tumor growth in heterogeneous environment. J Theor Biol 225:257–274
3. Boon WM, Koppenol DC, Vermolen FJ (2016) A multi-agent cell-based model for wound contraction. J Biomech 49(8):1388–1401
4. Butler J, Mackay F, Denniston C, Daley M (2014) Simulating cancer growth using cellular automata to detect combination drug targets. In: Ibarra OH, Kari L, Kopecki S (eds) Unconventional computation and natural computation. Lecture Notes in Computer Science. Springer, Cham
5. Ciarletta P, Foret L, Ben Amar M (2011) The radial growth phase of malignant melanoma: multi-phase modelling, numerical simulations and linear stability analysis. J R Soc Interface 8:345–368
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