(210a) Characterization of Coherent Feedforward Motifs in Mammalian Cells Using Synthetic Gene Circuits

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.