The recent tumor research has lead scientists to recognize the central role played by cancer stem cells in sustaining malignancy and chemoresistence. A model of cancer presented by [44] describes the mechanisms that give rise to the different kinds of cancer cells like-stem cells and the role of these cells in cancer diseases. The model implies a shift in the conceptualization of the disease from reductionism to complexity theory. By exploiting the link between the agent-based simulation technique and the theory of complexity, the medical view is here translated into a corresponding computational model. Two main categories of agents characterize the model: 1) cancer stem cells and 2) differentiation factors. Cancer cells agents are then distinguished based on the differentiation stage associated with their malignancy. Differentiation factors interact with cancer cells and cen, with varying degrees of fitness, induce differentiation or cause apoptosis. The model inputs are then fitted to experimental data and numerical simulations carried out. By performing virtual experiments on the model’s choice variables a decision-maker (physician) can obtains insights on the progression of the disease and on the effects of a choice of administration frequency and or dose. The model also paves the way to future research, whose perspectives are discussed.
Cancer cell reprogramming, 2010: stem cell differentiation stage factors and an agent based model to optimize cancer treatment
BORGONOVO, EMANUELE;
2011
Abstract
The recent tumor research has lead scientists to recognize the central role played by cancer stem cells in sustaining malignancy and chemoresistence. A model of cancer presented by [44] describes the mechanisms that give rise to the different kinds of cancer cells like-stem cells and the role of these cells in cancer diseases. The model implies a shift in the conceptualization of the disease from reductionism to complexity theory. By exploiting the link between the agent-based simulation technique and the theory of complexity, the medical view is here translated into a corresponding computational model. Two main categories of agents characterize the model: 1) cancer stem cells and 2) differentiation factors. Cancer cells agents are then distinguished based on the differentiation stage associated with their malignancy. Differentiation factors interact with cancer cells and cen, with varying degrees of fitness, induce differentiation or cause apoptosis. The model inputs are then fitted to experimental data and numerical simulations carried out. By performing virtual experiments on the model’s choice variables a decision-maker (physician) can obtains insights on the progression of the disease and on the effects of a choice of administration frequency and or dose. The model also paves the way to future research, whose perspectives are discussed.
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