DNA damage and repair processes are key cellular phenomena that are being intensely studied because of their implications in the onset and therapy of cancer. This chapter introduces a general dynamic model of gene expression, and proposes a genetic network modeling framework based on the interconnection of a continuous-time model and a hybrid model. This strategy is applied to a network built around the p53 gene and protein, which detects DNA damage and activates the downstream nucleotide excision repair (NER) network, which carries out the actual repair tasks. Then, two different parameter identification techniques are presented for the proposed models. One is based on a least squares procedure, which treats the signals provided by a high gain observer; the other one is based on a Mixed Extended Kalman Filter. Prior to the estimation phase, identifiability and sensitivity analyses are used to determine which parameters can be and/or should be estimated. The procedures are tested and compared by means of data obtained by in silico experiments.

Dynamic modeling and parameter identification for biological networks: application to the DNA damage and repair processes

BIANCONI, FORTUNATO;LILLACCI, GABRIELE;VALIGI, Paolo
2011-01-01

Abstract

DNA damage and repair processes are key cellular phenomena that are being intensely studied because of their implications in the onset and therapy of cancer. This chapter introduces a general dynamic model of gene expression, and proposes a genetic network modeling framework based on the interconnection of a continuous-time model and a hybrid model. This strategy is applied to a network built around the p53 gene and protein, which detects DNA damage and activates the downstream nucleotide excision repair (NER) network, which carries out the actual repair tasks. Then, two different parameter identification techniques are presented for the proposed models. One is based on a least squares procedure, which treats the signals provided by a high gain observer; the other one is based on a Mixed Extended Kalman Filter. Prior to the estimation phase, identifiability and sensitivity analyses are used to determine which parameters can be and/or should be estimated. The procedures are tested and compared by means of data obtained by in silico experiments.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/155547
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