An evolutionary Monte Carlo algorithm for predicting DNA hybridization.
Many DNA-based technologies, such as DNA computing, DNA nanoassembly and DNA biochips, rely on DNA hybridization reactions. Previous hybridization models have focused on macroscopic reactions between two DNA strands at the sequence level. Here, we propose a novel population-based Monte Carlo algorithm that simulates a microscopic model of reacting DNA molecules. The algorithm uses two essential thermodynamic quantities of DNA molecules: the binding energy of bound DNA strands and the entropy of unbound strands. Using this evolutionary Monte Carlo method, we obtain a minimum free energy configuration in the equilibrium state. We applied this method to a logical reasoning problem and compared the simulation results with the experimental results of the wet-lab DNA experiments performed subsequently. Our simulation predicted the experimental results quantitatively.
Kim
Joon Shik
JS
Department of Physics and Astronomy, Seoul National University, San 56-1, Shillim-Dong, Kwanak-Gu, Seoul 151-747, Republic of Korea.
Lee
Ji-Woo
JW
Noh
Yung-Kyun
YK
Park
Ji-Yoon
JY
Lee
Dong-Yoon
DY
Yang
Kyung-Ae
KA
Chai
Young Gyu
YG
Kim
Jong Chan
JC
Zhang
Byoung-Tak
BT
D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation; D004247:DNA / Q000235:genetics*; D006824:Hybridization, Genetic / Q000235:genetics*; D009010:Monte Carlo Method
D004247:DNA