Team:MoWestern Davidson

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One of the challenges of modern computer programming is the limitation of silicon computers. NP-Complete problems are a class of mathematical problems that exceeds these limitations. We focused on the Boolean satisfiability problem (SAT problem), which links groups of true-false variables to form decision problems. The task is to assign true or false to each variable in such a way that the problem is satisfied and outputs true. As the problem size grows, the time needed for even the best computer algorithms to solve the problems rapidly increases. Our team was interested in advancing bacterial computing by harnessing the parallel processing capabilities of E. coli to tackle the SAT problem from a biological perspective. For our biological computer, we used 5-base anticodon tRNAs to encode the variables. These tRNAs suppress frameshift mutations on the 5’ end of mRNA transcripts of modified reporter parts. If a tRNA suppresses a mutation and a problem is satisfied, then the reporter gene will be expressed. We designed and constructed physical models of our 5 nucleotide anticodon tRNAs to understand and communicate their functions.

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