The past decade has witnessed the rise of an exciting new field of engineering: synthetic biology. Synthetic biology is the application of engineering principles to the fundamental components of biology with the aim of programming cells with novel functionalities for utilization in the health, environment, and energy industries. Since its beginnings in the early 2000s, control design principles have been used in synthetic biology to design dynamics, mitigate the effects of uncertainty, and aid modular and layered design. In this review, we provide a basic introduction to synthetic biology, its applications, and its foundations and then describe in more detail how control design approaches have permeated the field since its inception. We conclude with a discussion of pressing challenges in this field that will require new control theory, with the hope of attracting researchers in the control theory community to this exciting engineering area.

No abstract

The design of genetic circuits typically relies on characterization of constituent modules in isolation to predict the behavior of modules' composition. However, it has been shown that the behavior of a genetic module changes when other modules are in the cell due to competition for shared resources. In order to engineer multi-module circuits that behave as intended, it is thus necessary to predict changes in the behavior of a genetic module when other modules load cellular resources. Here, we introduce two characteristics of circuit modules: the demand for cellular resources and the sensitivity to resource loading. When both are known for every genetic module in a circuit library, they can be used to predict any module's behavior upon addition of any other module to the cell. We develop an experimental approach to measure both characteristics for any circuit module using a resource sensor module. Using the measured resource demand and sensitivity for each module in a library, the outputs of the modules can be accurately predicted when they are inserted in the cell in arbitrary combinations.1 These resource competition characteristics may be used to inform the design of genetic circuits that perform as predicted despite resource competition.

No abstract

No abstract