Inverse RNAFolding

RNA molecules, the fundamental building blocks of life, are chains of nucleotides that can bond to parts of themselves. This allows them to form complex structural motifs that can be used as building blocks for self assembling nano-scale machines. One of bioinformatics ’ challenges is to design an implementation that, given an input structure, would predict a sequence that would most likely fold into this structure at a given temperature. This is known as the Inverse RNA folding problem for single structures. We have developed a genetic algorithm based solution to this problem. Some RNA molecules have the property of folding into different structures at different tem-peratures. This can be exploited in applications for synthetic biology. For example, one can de-sign a simple latching mechanism with an RNA stand at the binding site where simple changes in temperature can allow/block binding. We extended our software to predict a sequence which folds into several different structures at differing temperatures. Flow of the Algorithm Genetic algorithms are part of a wider class of evolutionary algorithms which mimic natural evolution to obtain solutions to an optimization problem