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Date of Publication:2008-01-01

Journal:大连理工大学学报

Issue:4

Page Number:496-502

ISSN No.:1000-8608

Abstract:An evaluating and predictive model for the ex-vivo expansion of hematopoietic stem cells (HSCs) was built up with artificial neural network (ANN) technology. 341 groups of data were summarized from literatures, in which 124, 90 and 86 data were employed to train the network and 17, 10 and 14 data were applied to predict respectively. Expansion folds of nuclear cells (NCs), colony-forming units (CFU-Cs) and CD34+cells were chosen as evaluation objectives and inoculated density, cytokines, cell resources, serum, stromal cells, bioreactor types and culture time were chosen as network inputs. The calculated results show that for the training of network, the interval accuracy of the expansion folds for the different cells is 85.5%, 86.7% and 86.1% respectively. While for the prediction of network, the interval accuracy can be up to 82.4%, 70.0% and 71.4% respectively. Therefore, this nonlinear modeling makes it possible to quantitatively describe the effects of the culture conditions on the HSCs expansion and to predict the optimal culture conditions for higher ex-vivo expansion of HSCs.

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