Sprecher
Beschreibung
DNA-based sensors have come a long way and are now capable of detecting ions, molecules, proteins, and nucleic acids, generating an output response. However, processing different inputs typically requires fuel for strand displacement reactions or purification steps, which makes DNA computing relatively slow.
In this talk, a novel approach to Brownian DNA computing is presented. It utilizes molecular balances on DNA origami structures as basic computational units that operate at the Brownian limit and does not bear on strand displacement reactions. Through Brownian motion, the system explores all possible states within the circuit. Computation is driven by input DNA strands that reduce the number of possible states.
By coupling multiple molecular balances, it becomes possible to construct circuits of greater complexity capable of performing all Boolean logic operations. These computations are read out using single-molecule fluorescence spectroscopy.
Beyond traditional DNA strand inputs, the molecular processing unit (MPU) can also respond to antibodies and protein–aptamer inputs. Additionally, the DNA-based system supports cooperative input design, enhancing sensitivity to small concentration changes—surpassing the typical linear input–response behavior.
The readout is not limited to binary true-or-false states but can also be designed as a decoder. Overall, Brownian DNA computing has the potential to serve as a foundational computational unit for molecular computing, sensing, and soft robotics.
