TY - GEN
T1 - Parameter identification for nonlinear biological phenomena modeled by S-systems
AU - Mansouri, Majdi
AU - Avci, Onur
AU - Nounou, Hazem
AU - Nounou, Mohamed
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/4
Y1 - 2015/12/4
N2 - For computational modeling of biological systems, one of the major challenges is the identification of the model parameters. It is very beneficial to use easily obtained measurements and estimate variables and/or parameters in such systems. For instance, time-series dynamic genomic data can be used to develop models representing dynamic genetic regulatory networks. These models can be used to design intervention strategies such as understanding the biological system behavior and curing major illnesses. The study shown in this paper focuses on the parameter identification of biological phenomena modeled by S-systems using Particle Filter (PF). While the nonlinear observed system is assumed to progress according to a probabilistic state space model, the results show that the PF has good convergence properties. It is concluded that the good convergence is due to PF's ability to deal with highly nonlinear process models.
AB - For computational modeling of biological systems, one of the major challenges is the identification of the model parameters. It is very beneficial to use easily obtained measurements and estimate variables and/or parameters in such systems. For instance, time-series dynamic genomic data can be used to develop models representing dynamic genetic regulatory networks. These models can be used to design intervention strategies such as understanding the biological system behavior and curing major illnesses. The study shown in this paper focuses on the parameter identification of biological phenomena modeled by S-systems using Particle Filter (PF). While the nonlinear observed system is assumed to progress according to a probabilistic state space model, the results show that the PF has good convergence properties. It is concluded that the good convergence is due to PF's ability to deal with highly nonlinear process models.
KW - Cad System in E. coli
KW - Parameter identification
KW - nonlinear biological systems
KW - particle filtering
UR - http://www.scopus.com/inward/record.url?scp=84962703299&partnerID=8YFLogxK
U2 - 10.1109/SSD.2015.7348187
DO - 10.1109/SSD.2015.7348187
M3 - Conference contribution
AN - SCOPUS:84962703299
T3 - 12th International Multi-Conference on Systems, Signals and Devices, SSD 2015
BT - 12th International Multi-Conference on Systems, Signals and Devices, SSD 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Multi-Conference on Systems, Signals and Devices, SSD 2015
Y2 - 16 March 2015 through 19 March 2015
ER -