PUNCH: An Evolutionary Algorithm for Optimizing Bit Set Selection Adam J. Ruben, Stephen J. Freeland, Laura F. Landweber DNA7, 2001 Summarized by Dongmin Kim

© 2001 SNU CSE Artificial Intelligence Lab (SCAI) Introduction A randomly selected bit set may experience some physical problems during computation Currently no method exists for finding the “best” bit set. PUNCH (Princeton University Nucleotide Computing Heuristic):  It is compatible with various current DNA computing methods  It is useful in selecting bit sets  One should be able to test the limits of current methodology  One suggest new techniques for DNA computing that may works better

© 2001 SNU CSE Artificial Intelligence Lab (SCAI) Program Architecture (1) The primary functional unit is a three-dimensional array  N : the number of bits in the problem  B : the number of nucleotides in each bit  V : the number of variations on each bit set Algorithm  Initialize  Generate random integers between 0 and 3 and fills the array  Assess  Each bit set is assigned the maximum possible score  Make a one-dimensional string from a two dimensional array  A window (size w) fills itself with the first w nucleotides in the string and scans the entire string, each time it encounters an identical sequences, subtracts w from the current score  Repeat this scanning with window which filled by the reverse of their complements

© 2001 SNU CSE Artificial Intelligence Lab (SCAI) Program Architecture (2) Algorithm (cont’d)  Pick and fill with best  Select the bit set with the highest score, and fills entire three- dimensional array with that bit set  Mutate  m represents a mutation rate, mBN(V – 1) bases are randomly selected and reassigned  Repeat  Repeat this procedure until each generation fails to improve r times Modifications  For certain protocols, the bits are treated as one linear string. since ignoring inter-bit combinations, the maximum possible score becomes

© 2001 SNU CSE Artificial Intelligence Lab (SCAI) Mutation Rate and comparison to Monte Carlo Sequences Best mutation rate is m = 1 / BN Comparison with randomly generated sequences

© 2001 SNU CSE Artificial Intelligence Lab (SCAI) “ Computing on Surfaces ” Using PUNCH There are two ways to test the usefulness of the scoring function  Perform an experiment using the bits it suggests  Assess the bits used in an existing, successful experiment Perform three analysis  10,000 randomly generated sequence, the exact sequences Liu et al., sequences optimized by PUNCH

© 2001 SNU CSE Artificial Intelligence Lab (SCAI) Introducing other scoring routine Base Equality  Now, scoring strategy tends to minimize variation where it matters least for avoiding reverse-complement penalties.  Thus, add routine counts the number of each base in a given bit set, divides it by the total number of bases in the set, and get the absolute value of its deviance from Total the deviance of all four bases, subtracts the result from 1, and use this value for the coefficient of score. Thus, having equal amounts of each base is rewarded Folding Score  Similarity to the reverse complements can be replaced with the scores based on single-stranded DNA folding energy  Vienna RNA package

© 2001 SNU CSE Artificial Intelligence Lab (SCAI) Comparison to another Bit Set Perform three analysis again  10,000 random sequences, Faulhammer et al. sequences, PUNCH sequences.  Now, the base equality scoring routine penalizes three-base alphabet Omitting Base Equality

© 2001 SNU CSE Artificial Intelligence Lab (SCAI) Multi-Base System Introduce a new constant “bases” capable of being defined such that 2 < bases < 9. PUNCH can run as though there are only “bases” bases in existence It can work with more than four bases using unnatural residues as dipropylglycine and dibutylglycine

© 2001 SNU CSE Artificial Intelligence Lab (SCAI) Future Directions Modeling Sexual Evolution Flexibility  Adaptability to various criteria  Definable constants Scalability  PUNCH can help identify which experiments may not be realistic on a larger scale