TY - GEN
T1 - Regulating gene expression using optimal control theory
AU - Liu, Yunlong
AU - Sun, Hui Bin
AU - Yokota, Hiroki
N1 - Publisher Copyright:
© 2003 IEEE.
PY - 2003
Y1 - 2003
N2 - We described development of a novel genome-based model-driven strategy useful for regulating eukaryotic gene expression. In order to extract biologically meaningful information from a large volume of mRNA expression data, we built previously a PROmoter-Based Estimation (PROBE) model. The PROBE model allowed us to establish a quantitative relationship between transcription-factor binding motifs in regulatory DNA sequences and mRNA expression levels. Here, we extended PROBE formulation to derive an optimal control law for gene regulation. The responses to shear stress in human synovial cells were chosen as a model biological system, and the system dynamics was identified from the expression pattern of the genes involved in degradation and maintenance of extracellular matrix. In order to suppress the responses to mechanical stimuli, a Ricatti equation was solved and an admissible control law was derived. The approach presented here can be implemented in any biological process, and it would be useful to develop a transcription-mediated strategy for gene therapies and tissue engineering.
AB - We described development of a novel genome-based model-driven strategy useful for regulating eukaryotic gene expression. In order to extract biologically meaningful information from a large volume of mRNA expression data, we built previously a PROmoter-Based Estimation (PROBE) model. The PROBE model allowed us to establish a quantitative relationship between transcription-factor binding motifs in regulatory DNA sequences and mRNA expression levels. Here, we extended PROBE formulation to derive an optimal control law for gene regulation. The responses to shear stress in human synovial cells were chosen as a model biological system, and the system dynamics was identified from the expression pattern of the genes involved in degradation and maintenance of extracellular matrix. In order to suppress the responses to mechanical stimuli, a Ricatti equation was solved and an admissible control law was derived. The approach presented here can be implemented in any biological process, and it would be useful to develop a transcription-mediated strategy for gene therapies and tissue engineering.
UR - http://www.scopus.com/inward/record.url?scp=39549103486&partnerID=8YFLogxK
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U2 - 10.1109/BIBE.2003.1188968
DO - 10.1109/BIBE.2003.1188968
M3 - Conference contribution
AN - SCOPUS:39549103486
T3 - Proceedings - 3rd IEEE Symposium on BioInformatics and BioEngineering, BIBE 2003
SP - 313
EP - 318
BT - Proceedings - 3rd IEEE Symposium on BioInformatics and BioEngineering, BIBE 2003
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE Symposium on BioInformatics and BioEngineering, BIBE 2003
Y2 - 10 March 2003 through 12 March 2003
ER -