283614 Characterization of Coherent Feedforward Motifs in Mammalian Cells Using Synthetic Gene Circuits

Tuesday, October 30, 2012: 8:30 AM
Somerset East (Westin )
Richard Moore1, Li Yi1 and Leonidas Bleris1,2, (1)Bioengineering, The University of Texas at Dallas, Richardson, TX, (2)Electrical Engineeering, The University of Texas at Dallas, Richardson, TX

Characterization of Coherent Feedforward Motifs in Mammalian Cells using Synthetic Gene Circuits

It is by now appreciated that the behavior of large biochemical networks can often be understood through the analysis of relatively small network motifs. Synthetic circuit engineering gives us the ability to use only a handful of components to build a range of small-scale networks. We report the construction and characterization of three (well-studied in bacteria) feedforward motifs, which we introduce in mammalian cells via transient transfections. Each motif consists of three nodes which code for transcription factors that interact to form the following specific architectures: a type 1 feedforward loop with OR decision at the final node, a type 1 feedforward loop with AND decision, and a cascade style architecture. We selected components that are known to have minimum interference with the host, and designed all necessary control experiments. Additionally, the edges to the second and final node are controlled by ligands, giving us the flexibility to study these networks under a range of different conditions.

We monitor each node via the use of the three different fluorescent reporters, which have negligible crosstalk (microscopy and flow cytometer measurements). Using control experiments, we have established that the AND and OR decisions indeed operate as expected at the final node for the respective architectures. In addition, we have established that the cascade shows a clear delay from the initial node to the secondary node and from the secondary node to the final node, under saturating levels of ligands. We will present time-lapse microscopy and flow cytometry data, for a range of induction levels and perturbations, and will discuss the observed properties of these feedforward motifs. Furthermore, we use population and single-cell data to build quantitative models of the architectures' characteristics. To conclude, the topologies of the circuits that we study are found in many types of natural mammalian networks, including neural and transcriptional networks. We will discuss the relevance to endogenous networks and the implications to understanding and mapping biological pathways.

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