Synthetic biology currently is a rapidly emerging discipline, where innovative and interdisciplinary work has led to promising results. Synthetic design of RNA requires novel methods to study and analyze known functional molecules, as well as to generate design candidates that have a high likelihood of being functional. This thesis is primarily focused on the development of novel algorithms for the design of synthetic RNAs. Previous strategies, such as RNAinverse, NUPACK-DESIGN, etc. use heuristic methods, such as adaptive walk, ensemble defect optimization (a form of simulated annealing), genetic algorithms, etc. to generate sequences that minimize specific measures (probability of the target structure, ensemble defect). In contrast, our approach is to generate a large number of sequences whose minimum free energy structure is identical to the target design structure, and subsequently filter with respect to different criteria in order to select the most promising candidates for biochemical validation. In addition, our software must be made accessible and user-friendly, thus allowing researchers from different backgrounds to use our software in their work. Therefore, the work presented in this thesis concerns three areas: Create a potent, versatile and user friendly RNA inverse folding algorithm suitable for the specific requirements of each project, implement tools to analyze the properties that differentiate known functional RNA structures, and use these methods for synthetic design of de-novo functional RNA molecules.
