Minimal genetic device with multiple tunable functions

S Bagh, M Mandal, DR McMillen - … Review E—Statistical, Nonlinear, and Soft …, 2010 - APS
S Bagh, M Mandal, DR McMillen
Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 2010APS
The ability to design artificial genetic devices with predictable functions is critical to the
development of synthetic biology. Given the highly variable requirements of biological
designs, the ability to tune the behavior of a genetic device is also of key importance; such
tuning will allow devices to be matched with other components into larger systems, and to be
shifted into the correct parameter regimes to elicit desired behaviors. Here, we have
developed a minimal synthetic genetic system that acts as a multifunction, tunable biodevice …
The ability to design artificial genetic devices with predictable functions is critical to the development of synthetic biology. Given the highly variable requirements of biological designs, the ability to tune the behavior of a genetic device is also of key importance; such tuning will allow devices to be matched with other components into larger systems, and to be shifted into the correct parameter regimes to elicit desired behaviors. Here, we have developed a minimal synthetic genetic system that acts as a multifunction, tunable biodevice in the bacterium Escherichia coli. First, it acts as a biochemical AND gate, sensing the extracellular small molecules isopropyl -1-thiogalactopyranoside and anhydrotetracycline as two input signals and expressing enhanced green fluorescent protein as an output signal. Next, the output signal of the AND gate can be amplified by the application of another extracellular chemical, arabinose. Further, the system can generate a wide range of chemically tunable single input-output response curves, without any genetic alteration of the circuit, by varying the concentrations of a set of extracellular small molecules. We have developed and parameterized a simple transfer function model for the system, and shown that the model successfully explains and predicts the quantitative relationships between input and output signals in the system.
American Physical Society
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