7. EXPRESSION VARIABILITY FROM A SYNTHETIC MAMMALIAN POSITIVE FEEDBACK NETWORK

Department: Bioengineering
Faculty Advisor(s): Jeff Hasty

Primary Student
Name: Diane Marie Longo
Email: dlongo@ucsd.edu
Phone: 858-822-3858
Grad Year: 2009

Abstract
The inherent complexity of the gene regulatory networks found in mammalian cells makes it difficult to understand and predict the behavior of the many interacting components contained in such networks. Designing and constructing synthetic gene networks that are simpler than their natural counterparts and more amenable to mathematical modeling can provide insight into the mechanisms underlying the behavior of natural gene networks. Numerous synthetic gene networks have been engineered and analyzed in simple organisms such as E. coli and S. cerevisiae. The recent development of inducible mammalian transgene control systems has allowed for the construction of synthetic gene circuits in mammalian systems. Here, we use an integrated experimental-computational approach to examine the signaling properties of a synthetic mammalian positive feedback circuit in which a tetracycline-regulated transactivator (rtTA) induces its own synthesis in the presence of tetracycline. We explore the differences in the behavior of several clonal cell populations transduced with the positive feedback network. Finally, we develop a model of the positive feedback network and we utilize the model to predict the dynamic behavior of the network. Our results are important for gaining insight into biological processes such as cell cycle regulation and apoptosis which rely on positive feedback to generate switch-like responses and may also facilitate the development of engineered mammalian control systems.

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