This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Feng, X.-j. Articles by Rabitz, H. Search for Related Content PubMed PubMed Citation Articles by Feng, X.-j. Articles by Rabitz, H.

Optimizing Genetic Circuits by Global Sensitivity Analysis

Xiao-jiang Feng ^*, Sara Hooshangi , David Chen , Genyuan Li ^*, Ron Weiss   and Herschel Rabitz ^*

^* Department of Chemistry, Department of Electrical Engineering, and Department of Molecular Biology, Princeton University, Princeton, New Jersey

Correspondence: Address reprint requests to Herschel Rabitz, 129 Frick Laboratory, Dept. of Chemistry, Princeton University, Princeton, NJ 08544. Tel.: 609-258-3917; Fax: 609-258-0967, E-mail: hrabitz{at}chemvax.princeton.edu.

Artificial genetic circuits are becoming important tools for controlling cellular behavior and studying molecular biosystems. To genetically optimize the properties of complex circuits in a practically feasible fashion, it is necessary to identify the best genes and/or their regulatory components as mutation targets to avoid the mutation experiments being wasted on ineffective regions, but this goal is generally not achievable by current methods. The Random Sampling—High Dimensional Model Representation (RS-HDMR) algorithm is employed in this work as a global sensitivity analysis technique to estimate the sensitivities of the circuit properties with respect to the circuit model parameters, such as rate constants, without knowing the precise parameter values. The sensitivity information can then guide the selection of the optimal mutation targets and thereby reduce the laboratory effort. As a proof of principle, the in vivo effects of 16 pairwise mutations on the properties of a genetic inverter were compared against the RS-HDMR predictions, and the algorithm not only showed good consistency with laboratory results but also revealed useful information, such as different optimal mutation targets for optimizing different circuit properties, not available from previous experiments and modeling.

This article has been cited by other articles:

				G. Rodrigo, J. Carrera, and A. Jaramillo Genetdes: automatic design of transcriptional networks Bioinformatics, July 15, 2007; 23(14): 1857 - 1858. [Abstract] [Full Text] [PDF]

				R. Gunawan, Y. Cao, L. Petzold, and F. J. Doyle III Sensitivity Analysis of Discrete Stochastic Systems Biophys. J., April 1, 2005; 88(4): 2530 - 2540. [Abstract] [Full Text] [PDF]