DNA Computing

Volume 4287 of the series Lecture Notes in Computer Science pp 428-438

Experimental Validation of the Statistical Thermodynamic Model for Prediction of the Behavior of Autonomous Molecular Computers Based on DNA Hairpin Formation

  • Ken KomiyaAffiliated withDept. of Computational Intelligence and Systems Science, Tokyo Institute of TechnologyDepartment of Computer Science, The University of Tokyo
  • , Satsuki YaegashiAffiliated withJapan Science and Technology Agency-CREST
  • , Masami HagiyaAffiliated withDepartment of Computer Science, The University of TokyoJapan Science and Technology Agency-CREST
  • , Akira SuyamaAffiliated withJapan Science and Technology Agency-CRESTDepartment of Life Sciences and Institute of Physics, The University of Tokyo
  • , John A. RoseAffiliated withDepartment of Computer Science, The University of TokyoJapan Science and Technology Agency-CRESTInstitute of Information Communication Technology, Ritsumeikan Asia Pacific University

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Due to the multi-state nature of autonomous computing systems, it is important to develop a simulation model which accounts for process coupling, and allows the precise prediction of the behavior of a composite system formed by a series of competing reactions, in which each intermediate step is difficult to probe. In this work, the statistical thermodynamic apparatus for predicting the efficiency of DNA hairpin-based computers is validated experimentally. The model system employed is a simple competitive folding system, formed by two competing hairpin structures (sub-optimal vs. optimal), with the intent of testing the ability to predict the efficiency of target structure formation in the presence of a non-target structure. System behavior was characterized via a set of fluorescence measurement experiments, to directly determine the fractional occupancy of target structures versus temperature. Predicted and experimental behaviors are compared for both the melting of each of the two isolated hairpin structures (control), and the efficiency of the competitive composite system. Results indicate that the applied equilibrium model provides predictions which consistently agree with experimental results, supporting design for the control and programming of DNA-based systems.