In Vivo Information Processing Using RNA Interference

Abstract

Regulatory Pathways as ComputationsThe impact of synthetic biology on biological sciences has been compared to the effect of synthetic approaches on the hitherto descriptive science of chemistry [1]. While making new genetic material has been possible since the dawn of recombinant DNA technology [2], modern synthetic biology aspires to deliver systems of previously unseen complexity -from whole synthetic genomes [3] to complex pathways [4-6] to artificial cells [7]. In particular, fully or partially engineered biological pathways have been in the spotlight of this research due, on the one hand, to their central role of carrying out useful biological functions and, on the other, to their relative tractability as compared to entire cells or genomes. This latter effort comprises engineering enzymatic and metabolic pathways for bioproduction as well as regulatory and signaling pathways that trigger programmed physiological responses to environmental and internal molecular cues. These pathways have also emerged as the central object of systems biology research that draws the attention of engineers, computer scientists, physicists, and biologists alike.Regulatory pathways can be conceptualized as reactive systems, or automata, composed of sensors that interact with external stimuli, the processor or a computer that integrates the signals in a meaningful way, and the actuation module that executes the results of signal processing in the cell [8]. While this is a useful abstraction, in nature the pathway structure is less modular; often the same protein combines the role of a sensor with that of a processor and the actuator. Still, it is possible to characterize a pathway by systematically varying its inputs and measuring the response, in a way that will provide enough information about the exact input-output mapping [9] in this pathway. Unfortunately, the actual molecular mechanisms behind these computations are often quite intricate and not easily understood; as such, they may not always be useful guides for constructing pathways that perform new types of computation.