Understanding nature’s selection of genetic languages

Abstract

All living organisms use two universal genetic languages in their molecular biology machinery, one containing four nucleotide bases in its alphabet, and the other containing twenty amino acids in its alphabet. They can be understood as the optimal encodings of genetic information for the tasks they carry out, i.e. replication/transcription for DNA/RNA and translation for polypeptide chains. These tasks select needed letters of the alphabet by complementary nucleotide base-pairing, from a collection of molecules in the cell. The computer science paradigm for this process is database search; various algorithms for it can be constructed and compared according to number of attempts (or queries) they need to make to find the correct nucleotide base-pairing. Grover’s search algorithm based on oscillatory wave dynamics perfectly fits the number of queries needed to search the genetic alphabets, and it is more efficient than the best Boolean search algorithm (i.e. binary tree search) that needs a larger number of queries. This result strongly suggests that the universal genetic languages have been selected by evolution as the optimal alphabets for the tasks they carry out, and are not an accident of history. The outstanding challenge is to demonstrate how Grover’s search algorithm would be executed in vivo by the living organisms.