Artificial RNA molecules with novel functionality have many applications in synthentic biology, pharmacy and white biotechnology. The de novo design of such devices using computational methods and prediction tools is a resource-efficient alternative to experimental screening and selection pipelines. In this review we describe a common pipeline , thoroughly dissect the applied methods and highlight open questions for the individual steps. An initial investigation of the biological target system and the desired components as well as the definition of design objectivesincluding the functional mechanism are fundamental prerequisites. The subsequent computational design is needed to combine the selected components and to obtain novel functionality. It can be usually split into constrained sequence sampling, the formulation of an optimization problem and an in silico analysis step to narrow down the number of candidates with respect to secondary goals. Finally, experimental analysis  to check whether the defined design objectives are indeed met in the target environment and detailed characterization experiments are performed to improve the mechanistic models and detect missing design goals .
Artificial RNA molecules with novel functionality have many applications in synthentic biology, pharmacy and white biotechnology. The de novo design of such devices using computational methods and prediction tools is a resource-efficient alternative to experimental screening and selection pipelines. In this review , we describe methods common to many such computational approaches , thoroughly dissect these methods and highlight open questions for the individual steps. Initially, it is essential to investigate the biological target system , the regulatory mechanism that will be exploited, as well as the desired components in order to define design objectives. Subsequent computational design is needed to combine the selected components and to obtain novel functionality. This process can usually be split into constrained sequence sampling, the formulation of an optimization problem and an in silico analysis to narrow down the number of candidates with respect to secondary goals. Finally, experimental analysis is important to check whether the defined design objectives are indeed met in the target environment and detailed characterization experiments should be performed to improve the mechanistic models and detect missing design requirements .
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