Self-assembly of DNA-like structures in silico


Through evolution, biomolecules have resolved fundamental problems as a highly interactive parallel and distributed system that we are just beginning to decipher. Biomolecular Computing (BMC) protocols, however, are unreliable, inefficient and unscalable when compared to computational algorithms run in silico. An alternative approach is explored to exploiting these properties by building biomolecular analogs (eDNA) and virtual test tubes in electronics that would capture the best of both worlds. A distributed implementation is described of a virtual tube, Edna, on a cluster of PCs that does capture the massive asynchronous parallel interactions typical of BMC. Results are reported from over 1000 experiments that calibrate and benchmark Edna's performance, reproduce and extend Adleman's solution to the Hamiltonian Path problem for larger families of graphs than has been possible on a single processor or has been actually carried out in wet labs, and benchmark the feasibility and performance of DNA-based associative memories. The results required a million-fold less molecules and are at least as reliable as in vitro experiments, and so provide strong evidence that the paradigm of molecular computing can be implemented much more efficiently (in terms of time, cost, and probability of success) in silico than the corresponding wet experiments, at least in the range where Edna can be practically run. This approach also demonstrates intrinsic advantages in using electronic analogs of DNA as genomes for genetic algorithms and evolutionary computation.

Publication Title

Genetic Programming and Evolvable Machines