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Dynamic Modeling and Parameter Identification for Biological Networks: Application to the DNA Damage and Repair Processes

Dynamic Modeling and Parameter Identification for Biological Networks: Application to the DNA Damage and Repair Processes

Fortunato Bianconi, Gabriele Lillacci, Paolo Valigi
Copyright: © 2011 |Pages: 33
ISBN13: 9781609604912|ISBN10: 1609604911|EISBN13: 9781609604929
DOI: 10.4018/978-1-60960-491-2.ch021
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MLA

Bianconi, Fortunato, et al. "Dynamic Modeling and Parameter Identification for Biological Networks: Application to the DNA Damage and Repair Processes." Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., IGI Global, 2011, pp. 478-510. https://doi.org/10.4018/978-1-60960-491-2.ch021

APA

Bianconi, F., Lillacci, G., & Valigi, P. (2011). Dynamic Modeling and Parameter Identification for Biological Networks: Application to the DNA Damage and Repair Processes. In L. Liu, D. Wei, Y. Li, & H. Lei (Eds.), Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications (pp. 478-510). IGI Global. https://doi.org/10.4018/978-1-60960-491-2.ch021

Chicago

Bianconi, Fortunato, Gabriele Lillacci, and Paolo Valigi. "Dynamic Modeling and Parameter Identification for Biological Networks: Application to the DNA Damage and Repair Processes." In Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., 478-510. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-491-2.ch021

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Abstract

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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