This thesis explores the intersection of two techniques: evolutionary algorithms (EAs) and allosteric ribozymes (ARs). EAs are population-based search heuristics inspired by natural evolution and ARs are catalytic non-coding RNA (ncRNA) whose activity can be modulated via shape changes induced by external molecules. We present two EAs that design biological devices based on ARs. The first, TruthSeqEr, designs ribogates, logic gates that take short RNA strands as inputs and produce a short RNA output strand as output. Compared to existing approaches, TruthSeqEr is easy-to-use and produces ribogates that are more versatile and that have a greater computational capacity. In silico results show that TruthSeqEr successfully designs ribogates implementing all instances from representative sets of 1, 2, and 3-input functions, including linearly inseparable functions. Motivated by a desire to understand these complex ribogates at an intuitive level, we developed an abstract model that represents each ribogate as a small graph. Analysis of these graphs showed that
ribogates can be classified into families of varying complexity based on shared structural motifs and that ribogates act as a more general version of an artificial neuron. Our second EA, TriCleaver, designs selective ribozymes (sRzs) that cleave the pathogenic mutant mRNA transcript associated with a trinucleotide repeat expansion disorder (TRED) while leaving the functional wild-type (WT) transcript intact. TREDs are debilitating genetic disorders
that result in a severe reduction in quality of life, and in many cases, death. In silico results reveal that compared to existing approaches, TriCleaver is more general, being able to design sRzs that target TREDs in which the mutant is much longer than the WT, as well as TREDs in which the mutant and WT are close in length. In addition, in vitro results in mammalian cells showed that two sRzs designed by TriCleaver selectively silenced the mutant transcript associated with a TRED called OPMD. Altogether, these results highlight the therapeutic promise of combining EAs and ARs.