A stochastic model of proliferation of cancer stem cells and its estimation by particle filtering

In this paper, we propose a model for proliferation of cancer stem cells and a procedure for estimating the unknowns of the model. Understanding the proliferation of cancer stem cells is critical for the development of anti-cancer therapies. We propose to use a nonlinear and non-Gaussian state-space model for studying the proliferation process. For estimation of the unknowns we apply particle filtering, which is particularly appropriate given the nature of the model. In addition, we deal with a very large dimension of the state-space and very sparse time series of measurements. Computer simulations show promising results in a simple scenario generated with synthetic data.