The genetic transistor

Regulation of gene expression is accomplished by the binding of transcription factors to the operator regions of promoters. The operator-promoter combination plays a genetic role analogous to that of the transistor in electronic circuits. Analysis of genetic circuits can lead to complex stochastic simulations with many parameters that are difficult to obtain. Electronic transistor models can be similarly complex. However, electronic engineers have developed simplified abstract models that are sufficient for developing predictable designs. By invoking the transistor metaphor, it may be possible to obtain simple models of genetic circuits that offer high predictive ability. To develop such a model, the first step is to recognize the roles that cellular components play in genetic circuits and to identify their specific electronic analogs. While the operator-promoter is part of our genetic transistor, many of the other reactions are not. Consider that the ribosomes and tRNA's are shared by all the genetic circuits in the cell. Through them, each circuit affects the others, though typically in a minor way. This is similar to a shared power supply where resistance can lead to loading effects such as brownout. So, the electronic analog for ribosomes and tRNA's is a voltage source with some impedance. Similarly, the fluorescent reporter output can be modeled as a transducer that features a capacitor in parallel with a resistor. The charge on the capacitor reflects reporter protein concentration while the current in the resistor models the degradation by proteases. The genetic transistor controls the flow from the ribosome "power supply" to the fluorescent reporter "transducer". The transistor's gain depends on the mRNA's half-life and the number of ribosomes that may fit on each mRNA transcript. Other interactions are incorporated to define the genetic transistor. Using these component analogs, genetic circuit models have been constructed for sample circuits. These models are linear but include offsets to account for major nonlinearities. The predictions of these models have been compared to those of high-quality stochastic simulations with favorable results. Limited comparisons with real data from bacterial systems were similarly favorable. The practical use of the genetic transistor model in the design of synthetic genetic circuits is demonstrated. This new metaphor shows promise in addressing complex circuits with minimal effort.