Obaidul Malek, Anastasios Venetsanopoulos, Laila Alamgir, Javad Alirezaie and Sridhar Krishnan
The main challenge of stem cell biology is to characterize proliferation and differentiation processes; since, very little is known regarding the molecular stimuli responsible for their regulatory mechanisms. On the other hand, comprehensive molecular analysis is yet too complex to perform intuitively. Therefore, computational models are essential for the optimization of clinical understanding in order to enhance the therapeutic process.
In this article, a discrete-time convergence model for stem cell growth process based on clinical observations and engineering predictions has been proposed. Typically, stem cell populations are in quiescent; but in response to molecular stimuli they become activated and proliferate, and undergo divisional cycle before experiencing the terminal differentiation or disappearance process. The objective of this paper is to present a computational analysis of stem cell proliferation process, and standardize a model which converges with the experimental hypothesis. More importantly, it has also been shown that, inherent homogeneous and heterogeneous properties of stem cell populations are also the necessary conditions for this convergent theory. In addition, Kalman filter has been used for estimating the unknowns as well as effciency of the proposed model. Simulation results based on synthetic data are presented to illustrate the performance of the proposed technique.
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