Model-Driven Engineering of RNA Devices to Quantitatively Program Gene Expression

Abstract

The models and simulation tools available to design functionally complex synthetic biological devices are very limited. We formulated a design-driven approach that used mechanistic modeling and kinetic RNA folding simulations to engineer RNA-regulated genetic devices that control gene expression. Ribozyme and metabolite-controlled, aptazyme-regulated expression devices with quantitatively predictable functions were assembled from components characterized in vitro, in vivo, and in silico. The models and design strategy were verified by constructing 28 Escherichia coli expression devices that gave excellent quantitative agreement between the predicted and measured gene expression levels (r = 0.94). These technologies were applied to engineer RNA-regulated controls in metabolic pathways. More broadly, we provide a framework for studying RNA functions and illustrate the potential for the use of biochemical and biophysical modeling to develop biological design methods.

Synthetic RNA devices to expedite the evolution of metabolite-producing microbes

“…1d,f). As the binding sensitivity of aptamers towards chemicals, as well as the activation ratio of a riboswitch module, can be customized during the riboselector construction process 15,[26][27][28] , the selection cutoff levels could be further modulated according to the physiological conditions of the target strain.…”

“…Then, synthetic riboselectors based on these in vitro-selected aptamers can be easily constructed by applying any of several highthroughput screening methods as shown in this study [14][15][16] . Considering a recent advance in engineering RNA devices that enables such devices to quantitatively programme gene expression based on mathematical models 27 , it should be practicable to expand the use of the synthetic RNA devices developed here to various other metabolites. Moreover, by subjecting the synthetic riboselector to a series of in vitro and in vivo selection processes, it is possible to control kinetic components of the device, such as binding affinity and dynamic range, to obtain an RNA device that is functional under the physiological conditions of the parental strain 15 .…”

Design-driven, multi-use research agendas to enable applied synthetic biology for global health

“…2) for engineering RNAbased genetic control devices with quantitatively-predictable activities (Carothers et al 2011). We used mechanistic modeling and kinetic RNA folding simulations to design RNA devices that can program gene expression in pathways engineered to produce industrial chemicals, establishing a conceptual and experimental framework for developing computer-aided design (CAD) platforms for complex RNA-based genetic control systems.…”

“…As shown, metabolic pathways constructed to produce industrially-relevant aromatics (p-aminostryene, p-AS) could be reengineered to producelow cost drugs, such as painkillers (opiate analgesics), experimental type II diabetes treatments (dipeptidyl peptidase IV inhibitors) and antibiotics (pristinamycin antibiotics). Gene products and molecule names are as described (Carothers et al 2011) Design-driven, multi-use research agendas 81…”

“…We formulated a model-driven approach for engineering RNA-based genetic control devices to program pathway and circuit gene expression, with a 94 % correlation observed between the predicted and measured outputs. This approach could be used to engineer sets of static and dynamic RNA devices and implement novel designs derived from systemslevel models (after Carothers et al 2011). More broadly, this work established generalizable frameworks for integrating biochemical and biophysical modeling and developing biological CAD tools that reduce the time and resources needed to engineer functionallycomplex devices, pathways and systems (Carothers et al 2011) could be designed for dessicated (Billi et al 2000) engineered biosensor hosts to function as point-of-care diagnostics that produce genetically-encoded outputs upon detecting target compounds, such as pathogen biosensors or nutritional states pathways and systems should be a general procedure for improving the efficiency of biosynthetic production platforms (Zhang and Keasling 2011).…”