Molecular programming: evolving genetic programs in a test tube

Authors:
Byoung-Tak Zhang;Ha-Young Jang
Affiliations:
Seoul National University, Seoul, Korea;Seoul National University, Seoul, Korea
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

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Abstract

We present a molecular computing algorithm for evolving DNA-encoded genetic programs in a test tube. The use of synthetic DNA molecules combined with biochemical techniques for variation and selection allows for various possibilities for building novel evolvable hardware. Also, the possibility of maintaining a huge number of individuals and their massively parallel manipulation allows us to make robust decisions by the "molecular" genetic programs evolved within a single population. We evaluate the potentials of this "molecular programming" approach by solving a medical diagnosis problem on a simulated DNA computer. Here the individual genetic program represents a decision list of variable length and the whole population takes part in making probabilistic decisions. Tested on a real-life leukemia diagnosis data, the evolved molecular genetic programs showed a comparable performance to decision trees. The molecular evolutionary algorithm can be adapted to solve problems in bio-technology and nano-technology where the physico-chemical evolution of target molecules is of pressing importance.