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.2014 May;9(5):353-357.
doi: 10.1038/nnano.2014.58. Epub 2014 Apr 6.

Universal computing by DNA origami robots in a living animal

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Universal computing by DNA origami robots in a living animal

Yaniv Amir et al. Nat Nanotechnol.2014 May.

Abstract

Biological systems are collections of discrete molecular objects that move around and collide with each other. Cells carry out elaborate processes by precisely controlling these collisions, but developing artificial machines that can interface with and control such interactions remains a significant challenge. DNA is a natural substrate for computing and has been used to implement a diverse set of mathematical problems, logic circuits and robotics. The molecule also interfaces naturally with living systems, and different forms of DNA-based biocomputing have already been demonstrated. Here, we show that DNA origami can be used to fabricate nanoscale robots that are capable of dynamically interacting with each other in a living animal. The interactions generate logical outputs, which are relayed to switch molecular payloads on or off. As a proof of principle, we use the system to create architectures that emulate various logic gates (AND, OR, XOR, NAND, NOT, CNOT and a half adder). Following an ex vivo prototyping phase, we successfully used the DNA origami robots in living cockroaches (Blaberus discoidalis) to control a molecule that targets their cells.

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Figures

Fig. 1
Fig. 1. Robots emulating AND, OR and XOR gates in a living animal.
A, schematic architecture representations. E robots (first from left) are equivalent to an AND gate, requiring both X and Y (in this study PDGF and VEGF, respectively) to open. EP1P2 architecture (second from left) functions as an OR gate, requiring either X, Y, or both to open. EP1P2N architecture (third from left) functions as a XOR gate, opening with either X or Y, but closing with both X and Y. EFP1P2N (right) emulates a half adder encoding the sum bit in E and carry bit in F.B, Top panels, AFM images (bars in nm) of robot architectures. Below every image is the corresponding flow cytometric analysis of insect cells isolated fromB. discoidalis several hours following the injection of robots and appropriate keys. The table on the left shows key combination corresponding to each row of the histograms. E robots (FL1 channel) were tagged with FAM. Blue/red peaks represent negative/positive signals, respectively.C, flow cytometric analysis of the EFP1P2N architecture, emulating a half adder. E robots representing the sum bit (FL1 channel) and F robots (FL4 channel) representing the carry bit of the half adder were measured simultaneously in-vivo using FAM and Cy5 tagged robots. 1,000 hemocytes were collected for each experiment.
Fig. 2
Fig. 2. Robots emulating NAND and NOT in a living animal.
A, The EopenN architecture emulating the logically complete NAND gate.B, The EopenNX architecture emulating an inverter (NOT). Histograms are flow cytometric analysis of hemocytes extracted fromB. discoidalis following injection of the proper robot architectures with keys. The table on the left shows key profile corresponding to each row of the histograms. Below are schematic representations of robot architectures. 1,000 hemocytes were collected for each experiment.
Fig. 3
Fig. 3. Robots emulating the reversible CNOT gate in a living animal.
Flow cytometric analysis ofB. discoidalis cells extracted following injection of the EFP1P2P3N architecture with various key profiles. E robots (FAM-tagged, FL1) and P1/P2 robots (Cy5-tagged, FL4) represent the state of the first bit entering the gate; F robots (Alexa546-tagged, FL2) and P3 robots (Alexa594-tagged, FL3) represent the state of second bit which remains unchanged. Slanting patterns of double-positive labeled cells indicate the presence of EP and FP complexes (Fig. S17). 1,000 hemocytes were collected for each experiment.
Fig. 4
Fig. 4. A hypothetic system capable of simultaneously controlling 3 therapeutic molecules.
This system serves as a processor for an imaginary medical task where a therapeutic response should be tailored per each disease state from a selection of 3 drugs. The system consists of 8 robot types: three effector robots E, F, and G, each carrying a different drug; four positive regulators, P1 and P2 keying F, P3 and P4 keying G; and a negative regulator N inactivating G. Together they form two first layer gates, AND and OR, each controlling its own drug while relaying its output state to a second layer XOR gate, which controls a third drug. This example lists 4 distinct drug combination outputs that could be generated by this system.
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