阅读说明：本技术 脂质纳米片 (Lipid nanosheet ) 是由 南佐旻 金鲜基 徐进荣 朴夏亨 于 2019-05-23 设计创作，主要内容包括：根据本公开的脂质纳米片包括：支撑的脂质双层,具有组合在纳米颗粒单元中的多个纳米颗粒；多个纳米颗粒之中的固定的纳米受体,包括结合到纳米受体的表面的至少一个第一表面分子；以及多个纳米颗粒之中的可移动的纳米漂浮物,包括结合到纳米漂浮物的表面的至少一个第二表面分子。根据由至少一个第一表面分子和至少一个第二表面分子对输入的反应的结果来控制纳米受体与纳米漂浮物之间的相互作用,并且脂质纳米片基于相互作用提供逻辑结果。(Lipid nanoplatelets according to the present disclosure comprise: a supported lipid bilayer having a plurality of nanoparticles combined in nanoparticle units; an immobilized nano-receptor among a plurality of nanoparticles comprising at least one first surface molecule bound to a surface of the nano-receptor; and a plurality of movable nano-floats among the nano-particles, including at least one second surface molecule bound to a surface of the nano-floats. The interaction between the nano-receptors and the nano-floats is controlled as a result of a reaction to the input by the at least one first surface molecule and the at least one second surface molecule, and the lipid nanoplatelets provide a logical result based on the interaction.)

1. A lipid nanoplatelet comprising:

a supported lipid bilayer to which a plurality of nanoparticles are integrated in nanoparticle units;

an immobilized nano-receptor comprising at least one first surface molecule tethered to a surface and immobilized among the plurality of nanoparticles; and

a nano-float comprising at least one second surface molecule tethered to a surface and movable among the plurality of nanoparticles,

wherein the interaction between the nano-receptors and the nano-floats is controlled according to the reaction results of the at least one first surface molecule and the at least one second surface molecule with respect to the input, and interaction-based logical results are provided.

2. Lipid nanoplatelets according to claim 1 comprising a YES gate that produces a logical result based on an assembly reaction in which the at least one first surface molecule and the at least one second surface molecule are combined by an input and, thus, tethered nanoreceptors and nanostars.

3. Lipid nanoplatelet of claim 2 wherein input comprises a DNA input and the at least one first surface molecule and the at least one second surface molecule each comprise a surface DNA ligand, and

the surface DNA ligands of the nano-receptors and the surface DNA ligands of the nano-floats hybridize in response to DNA input.

4. Lipid nanoplatelets according to claim 1 comprising a YES gate that produces a logical result based on a decomposition reaction that removes tethering between the nanoreceptors and the nanostars by input when the nanoreceptors and the nanostars are tethered by a combination of the at least one first surface molecule and the at least one second surface molecule.

5. Lipid nanoplatelet of claim 4 wherein the input comprises a DNA input, and

DNA input removes DNA bonds through pivot-mediated strand displacement in the pre-dimerization of the nano-receptors and nano-floats, which are tethered by DNA bonds.

6. Lipid nanoplatelets according to claim 1 comprising an AND gate that produces a logical result by tethering of a nano-receptor AND a nano-float, the nano-receptor AND nano-float being tethered by a combination of a first input to the at least one first surface molecule AND a second input to the at least one second surface molecule.

7. A lipid nanoplatelet according to claim 6 wherein the at least one first surface molecule and the at least one second surface molecule comprise conformationally switchable first and second DNA hairpins, and

the first DNA hairpin opens by hybridization to the first input and thus exposes the first binding domain, the second DNA hairpin opens by hybridization to the second input and thus exposes the second binding domain, and the nano-receptors and nano-floats are tethered by hybridization of the first and second binding domains.

8. A lipid nanoplatelet according to claim 1 wherein the at least one first surface molecule comprises a third surface molecule and a fourth surface molecule,

the at least one second surface molecule includes a fifth surface molecule and a sixth surface molecule, and

the lipid nanoplatelets comprise an OR gate that produces a logical result from the tethering of the nanoreceptors and the nanostars via at least one of the first input in combination with the third surface molecule and the fifth surface molecule and the second input in combination with the fourth surface molecule and the sixth surface molecule.

9. A lipid nanoplatelet according to claim 8 wherein the third to sixth surface molecules are DNA ligands and comprise first to fourth binding domains, and

the first input hybridizes to the first binding domain and the third binding domain, and the second input hybridizes to the second binding domain and the fourth binding domain.

10. A lipid nanoplatelet according to claim 1 wherein the at least one first surface molecule comprises a third surface molecule and a fourth surface molecule,

the at least one second surface molecule comprises a fifth surface molecule and a sixth surface molecule,

combining the third surface molecule and the fifth surface molecule, combining the fourth surface molecule and the sixth surface molecule, and

the lipid nanoplatelets comprise an AND gate that generates a logical result by removing the combination of the third surface molecule AND the fifth surface molecule AND removing the combination of the fourth surface molecule AND the sixth surface molecule.

11. A lipid nanoplatelet according to claim 10 wherein a first DNA binding between the third surface molecule and the fifth surface molecule and a second DNA binding between the fourth surface molecule and the sixth surface molecule exposes the first and second branch domains,

the first branch domain is a recognition region of the first input, the first input removes the first DNA binding by strand displacement, the second branch domain is a recognition region of the second input, and the second input removes the second DNA binding by strand displacement.

12. Lipid nanoplatelet of claim 1 wherein the at least one first surface molecule and the at least one second surface molecule are combined and

the lipid nanoplatelets comprise an OR gate that generates a logical result by removing the at least one first surface molecule and the at least one second surface molecule via at least one of the first input and the second input.

13. A lipid nanoplatelet according to claim 12 wherein the combination of the at least one first surface molecule and the at least one second surface molecule is DNA binding comprising a first and a second branch domain,

the first input cleaves DNA binding by strand displacement with at least one first surface molecule when the first input is recruited by the first branch domain, or

The second input cleaves DNA binding by strand displacement with the at least one second surface molecule when the second input is recruited by the second pivot domain.

14. Lipid nanoplatelet according to claim 1 wherein the interaction between the nanoreceptor and the nanostat is a first logic gate controlled by a first input and a second input,

the interaction between another first nano-acceptor and another first nano-float of the plurality of nano-particles is a second logic gate controlled by a third input and a fourth input, and

the lipid nanoplate comprises a third logic gate that generates a logic result based on the first logic result of the first logic gate and the second logic result of the second logic gate.

15. Lipid nanoplatelets according to claim 14 wherein the nanoreceptors and nanofloats are tethered by at least one of a first input and a second input,

the tether of the first nano-receptor and the first nano-float is broken down by the third input and the fourth input, and

the first logic output is a logic OFF output of the third logic gate and the second logic output is a logic ON output of the third logic gate.

16. A lipid nanoplatelet according to claim 1 wherein the at least one first surface molecule comprises a third surface molecule and a fourth surface molecule,

the at least one second surface molecule includes a fifth surface molecule and a sixth surface molecule, and

the lipid nanoplatelets comprise an INHIBIT gate that generates a logical result by removing the combination between the third surface molecule and the fifth surface molecule.

17. A lipid nanoplatelet according to claim 16 wherein a second input combines fourth and sixth surface molecules.

18. A lipid nanoplatelet according to claim 16 wherein the first input removes DNA binding between the third surface molecule and the fifth surface molecule.

19. Lipid nanoplatelets according to claim 1 wherein the interaction of the nanoreceptors and the nanofloats is controlled when a third and a fourth surface molecule tethered to the surface of the at least one first surface molecule and the nanoreceptors and a fifth and a sixth surface molecule tethered to the surface of the at least one second surface molecule and the nanofloats interact with each other.

20. Lipid nanoplatelet of claim 19 wherein at least one of the first and second inputs removes a combination between the at least one first surface molecule and the at least one second surface molecule,

at least one of the third input and the fourth input removes a combination between the third surface molecules and the fifth surface molecules, and

at least one of the fifth input and the sixth input removes a combination between the fourth surface molecules and the sixth surface molecules.

21. Lipid nanoplatelet of claim 19 wherein at least one of the first and second inputs removes a combination between the at least one first surface molecule and the at least one second surface molecule,

the third input forms a combination between the third surface molecule and the fifth surface molecule, and

the fourth input forms a combination between the fourth surface molecule and the sixth surface molecule.

22. Lipid nanoplatelet according to claim 19 wherein a first input removes a combination between the at least one first surface molecule and the at least one second surface molecule,

the second input removes a combination between the third surface molecule and the fifth surface molecule, and

the third input forms a combination between the fourth surface molecules and the sixth surface molecules.

23. Lipid nanoplatelets according to claim 1 wherein a first interaction between the nanoreceptors and the nanostars is controlled by a first input and a second input,

a second interaction between another first nano-receptor and another first nano-float in the plurality of nano-particles is controlled by a third input and a fourth input, and

the first input and the third input are of the same type, and the second input and the fourth input are of the same type.

24. Lipid nanoplatelet according to claim 23 wherein the colour of the image signal detected according to the first interaction and the colour of the image signal detected according to the second interaction are different from each other.

25. Lipid nanoplatelets according to claim 23 wherein the tether between the nanoreceptor and the nanostat is broken down by a first input and a second input, and

the tether between the first nano-acceptor and the first nano-float is broken down by the third input and the fourth input.

26. A lipid nanoplatelet in a supported lipid bilayer, a plurality of nanoparticles integrated into the supported lipid bilayer in nanoparticle units, the lipid nanoplatelet comprising:

a first nano-receptor;

a second nano-receptor;

a first nano-float interacting according to at least one input for a first nano-receptor; and

a second nano-float interacting according to the first input for the second nano-receptor,

wherein routing between a first logic gate comprising a first nano-acceptor and a second logic gate comprising a second nano-acceptor is determined based on the first nano-float and the second nano-float.

27. Lipid nanoplatelets according to claim 26 wherein the first and second nano-floats are of the same type and

the first logic gate AND the second logic gate are AND wired.

28. The lipid nanoplatelet of claim 27 wherein the at least one input comprises two inputs, and

the lipid nanoplatelets output the results of the interaction of the first nanoreceptor and the first nanostat according to the two inputs and the results of the interaction of the second nanoreceptor and the second nanostat according to the first input.

29. Lipid nanoplatelet according to claim 28 wherein the tether between the first nano-acceptor and the first nano-float is broken down by the two inputs and thus the first nano-float is released and the first nano-float and the second nano-acceptor are tethered by the first input.

30. Lipid nanoplatelet according to claim 28 wherein the tether between the first nano-acceptor and the first nano-float is broken down by at least one of the two inputs and thereby releases the first nano-float, and

the first nano-float and the second nano-float are tethered by a first input.

31. Lipid nanoplatelet according to claim 28 wherein the tether between the first nano-acceptor and the first nano-float is broken down by one of the two inputs and thereby releases the first nano-float,

the first nano-float and the second nano-float are tethered by a first input, and

the first nano-receptor and the first nano-float are tethered by the other of the two inputs.

32. Lipid nanoplatelets according to claim 26 wherein the first and second nano-floats are of different types and

the first logic gate and the second logic gate are OR wired.

33. Lipid nanoplatelet of claim 32 wherein the at least one input comprises two inputs and

the lipid nanoplatelets output the results of the interaction of the first nanoreceptor and the first nanostat according to the two inputs and the results of the interaction of the second nanoreceptor and the second nanostat according to the first input.

34. Lipid nanoplatelet according to claim 33 wherein the tether between the first nano-receptor and the first nano-float is broken down by the two inputs and thus the first nano-float is released and

the second nano-sized receptor and the second nano-sized float are decomposed by the first input, and thus the second nano-sized float is released.

35. Lipid nanoplatelet according to claim 33 wherein the tether between the first nano-acceptor and the first nano-float is broken down by at least one of the two inputs and thus the first nano-float is released and

the second nano-sized receptor and the second nano-sized float are decomposed by the first input, and thus the second nano-sized float is released.

36. A lipid nanoplatelet in a supported lipid bilayer, a plurality of nanoparticles integrated into the supported lipid bilayer in nanoparticle units, the lipid nanoplatelet comprising:

a first logic gate, wherein the first nano-acceptor and the first nano-float interact with each other according to the selected input and the first input; and

a second logic gate, wherein the second nano-acceptor and the second nano-float interact with each other according to the selected input and the second input,

wherein one of the first logic gate and the second logic gate releases the nano-float corresponding to the input according to the correspondence among the first input and the second input according to the selected input.

37. Lipid nanoplatelet according to claim 36 wherein the first nano-acceptor and the first nano-float are broken down by a first input,

a first nano-acceptor and a first nano-float are assembled by the selected input, and

the second nano-receptors and second nano-floats are decomposed by the second input and the selected input.

38. Lipid nanoplatelets according to claim 37 further comprising:

a first surface molecule and a second surface molecule tethered to a surface of the first nanoreceptor; and

a third surface molecule and a fourth surface molecule tethered to the surface of the first nano-float,

wherein the combination between the first surface molecule and the third surface molecule is removed by the first input and the second surface molecule and the fourth surface molecule are combined by the selected input.

39. Lipid nanoplatelets according to claim 38 further comprising:

a fifth surface molecule and a sixth surface molecule tethered to the surface of the second nanoreceptor; and

a seventh surface molecule and an eighth surface molecule tethered to the surface of the second nanoreceptor,

wherein a combination of the fifth surface molecule and the seventh surface molecule is removed by the second input and a combination of the sixth surface molecule and the eighth surface molecule is removed by the selected input.

40. A nanobiotropic computing method in a supported lipid bilayer to which a plurality of nanoparticles are integrated in a nanoparticle unit, the nanobiotropic computing method comprising:

generating a plurality of interactions between a plurality of immobilized nano-receptors in the lipid bilayer and a plurality of mobile nano-floats in the lipid bilayer, according to the input;

generating a plurality of signals based on the plurality of interactions;

tracking signals of the plurality of signals that are generated only from the plurality of nano-receptors; and

a logical result is determined based on the tracking result.

41. The nanobiocalculation method of claim 40, wherein tracking comprises:

detecting a signal higher than the detection parameter in the generated image data by the dark field microscope;

generating a segmented signal by distinguishing boundaries of the detected signal;

providing the position of the nanoparticle by locating the center of the segmented signal;

identifying a nano-receptor by comparing locations of the nanoparticles via a plurality of frames; and

signals corresponding to the identified nano-receptors are sampled.

42. The nanobiocalculation method of claim 40, wherein an increase in the intensity of the signal in the tracking result indicates an assembly of a nanobody among the plurality of nanobodies corresponding to an input and a nanobody among the plurality of nanobodies corresponding to the input.

43. The nanobiocalculation method of claim 40, wherein a decrease in the intensity of the signal in the tracking result indicates a decomposition of a nanobody corresponding to the input among a plurality of nanobodies and a nanobody corresponding to the input among the plurality of nanobodies.

Technical Field

The present disclosure relates to calculations using nanoparticles. In particular, the present disclosure relates to nanoparticle calculations using biological information as input.

Background

On many length scales, substances have been combined with calculations, from micro-sized droplets and microparticles to molecular machines and biomolecules such as enzymes and nucleic acids. However, despite a wide range of potential applications that would benefit from controlling their potentially useful plasmonic, photonic, catalytic and material properties, the implementation of complex calculations in nanoscale objects (particularly in nanoparticles) has not been explored.

A common method of calculation using nanoparticles as substrates is to functionalize the "core" structure with stimuli-responsive surface ligands. A set of surface modified nanoparticles can then perform basic logical operations in response to various chemical and physical inputs, ideally, the individual nanoparticles should be used as modular "nano-components" and the desired calculations should be implemented in a plug-and-play manner. However, this prior approach has been limited to installing a small number of logical calculations suitable for controlling only simple outputs, such as aggregation/dispersion of particles and release of surface molecules. This limitation is due to the difficulty of modular wiring of multiple logic gates in the solution phase, where the inputs, logic gates, and outputs all diffuse uncontrollably in three-dimensional space.

In particular, the following constraints have imposed limitations on the calculations with nanoparticles.

First, the logically embedded particles change irreversibly after one operation and are indiscriminately mixed with unreacted input in one bulk solution (bulk solution). The lack of compartmentalization prevents more than one computational task per tube.

Second, it is difficult to control or characterize structural changes, dynamic interactions and output signals of individual particles that are freely diffusing in 3D space. In most cases, only the averaged signal is obtained as the final readout, where the calculated particle-by-particle responses are averaged.

Thus, these constraints impose limitations on the adoption of nanoparticles as modular and combinable components that can be reconfigured to perform desired calculations in a "plug-and-play" manner.

Disclosure of Invention

Technical problem

In order to build complex and reliable nanoparticle circuits, it is necessary to move beyond methods that once relied on solution steps to scalable integrated platforms with in-situ readout and control functions. In addition, the desired nanoparticle circuit should be systematically designed and built based on digital design principles.

Technical scheme

A nanoparticle system with computational power can implement complex functions that cannot be achieved in a simple set of individual nanoparticles. Such "nanosystems" autonomously perform complex tasks in response to stimuli. The nano-system may then be a system that can direct the flow of substances and information on a nano-scale.

Processing molecular information with nanoparticles allows for the incorporation of the rich and powerful functions of nanoparticles into algorithms and autonomous control over molecular computational processes. In the present disclosure, a platform named "lipid nanoplatelets" is provided to build a widely applicable system that can produce nanoparticle circuits. Lipid nanoplatelets are a technology that builds nanoparticle logic gates and circuits at the level of individual particles in a two-dimensional lipid bilayer. This lipid bilayer nanoparticle-like computing platform (with computer systems/software as the basis for use) is referred to hereinafter as a Lipid Nanosheet (LNT).

Lipid nanoplatelets use lipid bilayers as chemical/biological plates, on which surface-modified nanoparticles are placed to enable the lipid bilayers to react with molecular information to accommodate the nanoparticles, and use them as a unit for calculations. A single nanoparticle logic gate senses molecular input and triggers particle assembly or disassembly. With the present disclosure, a set of boolean logic operations such as AND, OR, AND NOT computations, fan-in/fan-out logic gates, AND circuits such as multiplexers may be provided. As described, implementing nanoparticle circuit modules on lipid nano-chips may open previously unknown opportunities in information processing nano-systems.

A lipid nanoplatelet according to one aspect of the present invention comprises: a supported lipid bilayer to which a plurality of nanoparticles are integrated in nanoparticle units; an immobilized nano-receptor comprising at least one first surface molecule tethered to a surface and immobilized among a plurality of nanoparticles; and a nano-float comprising at least one second surface molecule tethered to the surface and movable among the plurality of nanoparticles, wherein an interaction between the nano-receptor and the nano-float is controlled according to a result of a reaction of the at least one first surface molecule and the at least one second surface molecule with respect to the input, and provides a logical result based on the interaction.

The lipid nanoplatelets may comprise YES gates that produce a logical result based on an assembly reaction in which at least one first surface molecule and at least one second surface molecule are combined by an input, and thus the nanoreceptors and the nanostars are tethered.

The input may comprise a DNA input, and the at least one first surface molecule and the at least one second surface molecule comprise surface DNA ligands, respectively, and the surface DNA ligands of the nano-receptors and the surface DNA ligands of the nano-floats may hybridize in response to the DNA input.

The lipid nanoplatelets may comprise YES gates that produce a logical result based on a dissociation reaction that removes the tether between the nanoreceptors and the nanofillers by input when the nanoreceptors and nanofillers are tethered by a combination of at least one first surface molecule and at least one second surface molecule.

The input may include a DNA input, and the DNA input may remove the DNA bond through a fulcrum-mediated strand displacement in pre-dimerization of the nanobody and the nanobody tethered by the DNA bond.

The lipid nanoplatelets may comprise AND gates that produce logical results by tethering of the nano-receptors AND nano-floats by a combination of a first input to the at least one first surface molecule AND a second input to the at least one second surface molecule.

The at least one first surface molecule and the at least one second surface molecule may comprise first and second DNA hairpins that are conformationally switchable, and the first DNA hairpin may be opened by hybridization to a first input and thus the first binding domain is exposed, the second DNA hairpin may be opened by hybridization to a second input and thus the second binding domain is exposed, and the nano-receptors and nano-floats may be tethered by hybridization of the first and second binding domains.

The at least one first surface molecule may include a third surface molecule and a fourth surface molecule, the at least one second surface molecule may include a fifth surface molecule and a sixth surface molecule, and the lipid nanoplatelets may include an OR gate that produces a logical result from the tethering of the nanoreceptors and the nanofloats through at least one of the first input in combination with the third surface molecule and the fifth surface molecule and the second input in combination with the fourth surface molecule and the sixth surface molecule.

The third through sixth surface molecules may be DNA ligands and may include first through fourth binding domains, and the first input may hybridize to the first and third binding domains and the second input may hybridize to the second and third binding domains.

The at least one first surface molecule may include a third surface molecule AND a fourth surface molecule, the at least one second surface molecule may include a fifth surface molecule AND a sixth surface molecule, the third surface molecule AND the fifth surface molecule may be combined, the fourth surface molecule AND the sixth surface molecule may be combined, AND the lipid nanoplatelets may include an AND gate that produces a logical result by removing the combination of the third surface molecule AND the fifth surface molecule AND removing the combination of the fourth surface molecule AND the sixth surface molecule.

The first DNA binding between the third surface molecule and the fifth surface molecule and the second DNA binding between the fourth surface molecule and the sixth surface molecule may expose a first pivot domain and a second pivot domain, the first pivot domain may be a recognition region of the first input, the first input may remove the first DNA binding by strand displacement, the second pivot domain may be a recognition region of the second input, and the second input may remove the second DNA binding by strand displacement.

The at least one first surface molecule and the at least one second surface molecule may be combined, and the lipid nanoplatelets may comprise an OR gate that produces a logical result by removing the at least one first surface molecule and the at least one second surface molecule through at least one of the first input and the second input.

The combination of the at least one first surface molecule and the at least one second surface molecule may be DNA binding, the DNA binding may include a first branch domain and a second branch domain, and the first input may cleave DNA by strand displacement with the at least one first surface molecule when the first branch domain recruits the first input, or the second input may cleave DNA by strand displacement with the at least one second surface molecule when the second branch domain recruits the second input.

The interaction between the nano-receptor and the nano-float may be a first logic gate controlled by a first input and a second input, the interaction between another first nano-receptor and another first nano-float of the plurality of nanoparticles may be a second logic gate controlled by a third input and a fourth input, and the lipid nanoplate may include a third logic gate that generates a logic result based on a first logic result of the first logic gate and a second logic result of the second logic gate.

The nano-acceptor and the nano-float may be tethered by at least one of the first input and the second input, the tether of the first nano-acceptor and the first nano-float may be resolved by the third input and the fourth input, and the first logic output may be a logic OFF output of the third logic gate and the second logic output may be a logic ON output of the third logic gate.

The at least one first surface molecule may include a third surface molecule and a fourth surface molecule, the at least one second surface molecule may include a fifth surface molecule and a sixth surface molecule, and the lipid nanoplatelets may include an INHIBIT gate that produces a logical result by removing a combination between the third surface molecule and the fifth surface molecule.

The second input may combine the fourth surface molecule and the sixth surface molecule.

The first input may remove DNA binding between the third surface molecule and the fifth surface molecule.

The interaction of the nano-receptors and nano-floats may be controlled when the third and fourth surface molecules tethered to the surface of the at least one first surface molecule and nano-receptors and the fifth and sixth surface molecules tethered to the surface of the at least one second surface molecule and nano-floats interact with each other.

At least one of the first input and the second input may remove a combination between at least one first surface molecule and at least one second surface molecule, at least one of the third input and the fourth input may remove a combination between a third surface molecule and a fifth surface molecule, and at least one of the fifth input and the sixth input may remove a combination between a fourth surface molecule and a sixth surface molecule.

At least one of the first input and the second input may remove a combination between at least one first surface molecule and at least one second surface molecule, the third input may form a combination between a third surface molecule and a fifth surface molecule, and the fourth input may form a combination between a fourth surface molecule and a sixth surface molecule.

The first input may remove a combination between at least one first surface molecule and at least one second surface molecule, the second input may remove a combination between a third surface molecule and a fifth surface molecule, and the third input may form a combination between a fourth surface molecule and a sixth surface molecule.

A first interaction between the nano-sized receptor and the nano-sized float may be controlled by a first input and a second input among the plurality of nano-sized particles, a second interaction between another first nano-sized receptor and another first nano-sized float may be controlled by a third input and a fourth input, and the first input and the third input may have the same type, and the second input and the fourth input have the same type.

The color of the image signal detected according to the first interaction and the color of the image signal detected according to the second interaction may be different from each other.

The tether between the nano-receptors and the nano-floats may be broken down by the first and second inputs, and the tether between the first nano-receptors and the first nano-floats may be broken down by the third and fourth inputs.

According to another aspect of the present invention, a lipid nanoplatelet in a supported lipid bilayer into which a plurality of nanoparticles are integrated in nanoparticle units comprises: a first nano-receptor; a second nano-receptor; a first nano-float interacting according to at least one input for a first nano-receptor; and a second nano-float interacting according to the first input for the second nano-acceptor, wherein a wiring between the first logic gate including the first nano-acceptor and the second logic gate including the second nano-acceptor may be determined based on the first nano-float and the second nano-float.

The first nano-float AND the second nano-float may be of the same type, AND the first logic gate AND the second logic gate may be AND wired.

The at least one input may include two inputs, and the lipid nanoplatelets may output the results of the interaction of the first nano-receptor and the first nano-float according to the two inputs and the results of the interaction of the second nano-receptor and the second nano-float according to the first input.

The tether between the first nano-receiver and the first nano-float may be broken down by the two inputs, and thus the first nano-float may be released, and the first nano-float and the second nano-receiver may be tethered by the first input.

The tether between the first nano-acceptor and the first nano-float may be broken down by at least one of the two inputs, and thus the first nano-float may be released, and the first nano-float and the second nano-float may be tethered by the first input.

The tether between the first nano-receiver and the first nano-float may be broken down by one of the two inputs and thus the first nano-float may be released, the first nano-float and the second nano-float may be tethered by the first input, and the first nano-receiver and the first nano-float may be tethered by the other of the two inputs.

The first nano-float and the second nano-float may be of different types, and the first logic gate and the second logic gate may be OR wired.

The at least one input may include two inputs, and the lipid nanoplatelets may output the results of the interaction of the first nano-receptor and the first nano-float according to the two inputs and the results of the interaction of the second nano-receptor and the second nano-float according to the first input.

The tether between the first nano-receiver and the first nano-float may be broken down by two inputs and thus the first nano-float may be released, and the second nano-receiver and the second nano-float may be broken down by the first input and thus the second nano-float may be released.

The tether between the first nano-receiver and the first nano-float may be broken down by at least one of the two inputs, and thus the first nano-float may be released, and the second nano-receiver and the second nano-float may be broken down by the first input, and thus the second nano-float may be released.

According to another aspect of the present invention, a lipid nanoplatelet in a supported lipid bilayer into which a plurality of nanoparticles are integrated in nanoparticle units comprises: a first logic gate, wherein the first nano-acceptor and the first nano-float interact with each other according to the selected input and the first input; and a second logic gate, wherein the second nano-acceptor and the second nano-float interact with each other according to the selected input and the second input, wherein one of the first logic gate and the second logic gate may release the nano-float corresponding to the input according to a correspondence among the first input and the second input according to the selected input.

The first nano-receptor and the first nano-float may be decomposed by a first input, the first nano-receptor and the first nano-float may be assembled by a selected input, and the second nano-receptor and the second nano-float may be decomposed by a second input and a selected input.

The lipid nanoplatelets may further comprise: a first surface molecule and a second surface molecule tethered to a surface of the first nanoreceptor; and a third surface molecule and a fourth surface molecule tethered to the surface of the first nano-float, wherein a combination between the first surface molecule and the third surface molecule is removed by the first input and the second surface molecule and the fourth surface molecule are combined by the selected input.

The lipid nanoplatelets may further comprise: a fifth surface molecule and a sixth surface molecule tethered to the surface of the second nanoreceptor; and seventh and eighth surface molecules tethered to the surface of the second nanoreceptor, wherein the combination of the fifth and seventh surface molecules can be removed by the second input, and the sixth and eighth surface molecules can be removed by the selected input.

According to another aspect of the present invention, a nanobiotropic computing method in a supported lipid bilayer into which a plurality of nanoparticles are integrated in a nanoparticle unit includes the steps of: generating a plurality of interactions between a plurality of immobilized nano-receptors in the lipid bilayer and a plurality of mobile nano-floats in the lipid bilayer, according to the input; generating a plurality of signals based on the plurality of interactions; tracking a signal generated only from the plurality of nano-receptors from among the plurality of signals; and determining a logical result based on the tracking result.

The tracking may include the steps of: detecting a signal higher than the detection parameter in the generated image data by the dark field microscope; generating a segmented signal by discriminating a boundary of the detected signal; providing the position of the nanoparticle by locating the center of the segmented signal; identifying a nano-receptor by comparing the positions of the nano-particles through a plurality of frames; and sampling signals corresponding to the identified nano-receptors.

An increase in the intensity of the signal in the tracking result may indicate an assembly of a nano-receptor corresponding to the input among the plurality of nano-receptors and a nano-float corresponding to the input among the plurality of nano-floats.

A decrease in the intensity of the signal in the tracking result may indicate a decomposition of a nano-receptor corresponding to the input among the plurality of nano-receptors and a nano-float corresponding to the input among the plurality of nano-floats.

Advantageous effects

Lipid nanoplates can be provided that can systematically design and build circuits based on digital design principles using an extensible integration platform with real-time readout and control functions, while overcoming the limitations of existing methods that can only drive one calculated solution class at a time.

Drawings

Fig. 1A-1C show single nanoparticle logic calculations on the LNT.

FIGS. 2A and 2B are schematic diagrams depicting LNT platforms.

Fig. 3A to 3C show the characteristics of the DNA-modified nanoparticles.

Fig. 4A to 4C show relevant DFM-SEM images for analyzing single nanoparticle scattering signals.

Fig. 5A-5B show the diffusion kinetics of nanoparticles tethered to a supported lipid bilayer.

FIGS. 6A and 6B show the operation of a single input split YES gate based on pivot-mediated strand displacement.

Figure 7 shows a parallel single particle analysis in nanoparticle logic gate operation by dark field microscopy.

Fig. 8A and 8B show the logic gate of nanoparticles tethered to a supported lipid bilayer.

FIG. 9 is a graph showing the extent of multimer-forming reactions simulated by MATLAB-like.

Fig. 10A-10C illustrate algorithms for receptor-only digital image processing for clear and more quantitative visualization of nanoparticles for logic gates on LNTs.

Fig. 11A to 11C show scattering signal distributions of R nanoparticles, G nanoparticles, and B nanoparticles.

Fig. 12A-12D illustrate dual-input nanoparticle logic gates on LNTs.

Fig. 13 shows the principle of operation and DNA sequence of a two-input assembly gate.

Fig. 14 shows the principle of operation and DNA sequence of a two-input assembled OR gate.

FIG. 15 shows the principle of operation AND DNA sequence of a two-input decomposition AND gate.

Fig. 16 shows the principle of operation and DNA sequence of a two-input OR gate.

Fig. 17A to 17F show the design principle of the nanoparticle logic gate.

Fig. 18A-18C illustrate the modularity in two hairpin-like input assembly AND gates.

Fig. 19 shows the non-uniform response of a two input split OR gate.

Fig. 20A to 20B show a dual rail of a NAND gate.

Fig. 21A-21C illustrate a nanoparticle logic gate that handles INHIBIT operations and has multiple inputs (fan-in) and multiple outputs (fan-out).

Fig. 22A to 22B show the operation of the two-input decomposition INHIBIT gate.

Fig. 23 shows the operation of the six-input decomposition gate.

Fig. 24A to 24B show a multi-input decomposition gate.

Fig. 25A to 25C show the connection of nanoparticle logic gates programmed through a network.

FIG. 26 shows a reconstructed dark-field snapshot of a two-layer AND-AND cascade circuit.

Fig. 27 shows a two-layer OR-AND cascade circuit.

Fig. 28 shows a two-layer INHIBIT-AND cascade circuit.

FIG. 29 shows a dark-field snapshot of a two-layer AND-OR cascade circuit.

Fig. 30 shows a two-layer OR-OR cascade circuit.

Fig. 31A to 31C show the design and embodiments of a nanoparticle multiplexer circuit.

Fig. 32 shows the operation of the multiplexing circuit in the domain stage.

Fig. 33A to 33C show analysis of robustness against float diffusion.

Detailed Description

The invention disclosed in the present disclosure can be applied to all nanoparticle calculations based on lipid membranes to which nanoparticles are attached, and is not limited to DNA molecules. Molecules that can be attached to nanoparticles can include various chemical ligands such as DNA, RNA, proteins, polypeptides, metal chelators, and the like. That is, the present invention can be applied not only to biological calculations but also to nanoparticle lipid platform-like molecular calculations.

The cell membrane biologically functions the same as the circuit board of the electronic circuit. The cell membrane distinguishes the receptors from the cell's information-rich external fluid while accommodating various receptor proteins in the computational unit and performs complex functions by directing the receptors to interact laterally on the two-dimensional fluid surface. Each receptor, which is the active component of a biological circuit, takes as "input" chemical and physical cues, such as binding events to ligands and changes in membrane voltage, and produces "outputs" such as conformational changes and dimerization/dissociation reactions. The membrane may allow many different computational processes to occur in parallel.

Inspiration is derived from the cell membrane, the Lipid Nanoplatelets (LNTs) comprise a Supported Lipid Bilayer (SLB) to which the light scattering plasmonic nanoparticles are tethered, and logical calculations are performed using the SLB. To perform the calculations, nanoparticles that were programmed to be SLB tethered interact with each other using surface ligands.

SLB, which has been widely used as a synthetic mimetic for cell surfaces, is used herein as a "chemical circuit board" on which nanoparticles for performing calculations are placed. Tethering the nanoparticles to the lipid bilayer can achieve the following objectives.

First, particle-to-particle interactions are limited to occur only by lateral diffusion at the 2D reaction space. Second, because a large number of light scattering nanoparticles are confined in the focal plane of a Dark Field Microscope (DFM), parallel in situ tracking and analysis of nanoparticle interactions can be achieved with single particle resolution. Third, the nanoparticles are distinguished from solutions containing molecular inputs. By using this feature, it is possible to provide "nanobiotic" calculations as an unconventional way of performing calculations with individual nanoparticles. Nano-biometrics take place at the interface of the nanostructure and the biomolecule. To demonstrate this, in the present disclosure, nanoparticles are tethered to the lipid bilayer by using a strong biotin-streptavidin interaction, and DNA is used as a molecular import DNA and surface ligand. However, the surface ligand of the present invention is not limited to DNA, and molecules that can provide binding capable of controlling interaction between nanoparticles (e.g., antigen-antibody binding, ligand-receptor binding, chelate binding, covalent binding, hydrogen bonding, van der waals binding, binding by hydrophobic interaction, electrostatic binding, or binding by chemical reaction) by specifically reacting with molecular input, and binding molecules that can provide functional binding, electrostatic binding, or chemical binding may be applied.

Fig. 1A-1C show single nanoparticle logic calculations on the LNT.

FIGS. 2A and 2B are schematic diagrams depicting LNT platforms.

As shown in fig. 1A, 2A and 2B, the LNT consists of a flow cell whose bottom substrate is coated with an SLB chip that is coupled to nanoparticles whose optical signals, mobility and surface DNA ligands are easily tunable.

DNA sequences and experimental conditions associated with single nanoparticle logical calculations are summarized in table 1, table 2 and table 13.

In FIG. 1A, a schematic structure of an LNT platform is shown. Two types of nanoparticles, immobilized Nanoreceptors (NR) and mobile Nanofloats (NF), are tethered to a Supported Lipid Bilayer (SLB). Both types of nanoparticles have DNA as input and the output of the logical operation is used to alter the interaction of the nanoparticles. Then, an assembly reaction or a decomposition reaction occurs.

As shown in fig. 2A, lipid nanoplatelets use molecules as input and perform nanoparticle logical computations through a dynamic nanoparticle network connected to a supported lipid bilayer and provide in situ optical reading (optical reading) using output that can be easily read and analyzed with a dark field microscope. The nanoparticle logic units are linked to the lipid bilayer surface by a strong interaction between a biotinylated DNA linker (linker) on the nanoparticle surface and streptavidin tethered to biotinylated DOPE (1, 2-dioleoyl-sn-glycero-3-phosphoethanolamine) lipid. The bound nanoparticle receptors and floats serve as logic gates and process molecular information in solution by using programmable stimuli-responsive surface ligands.

As shown in fig. 2B, the supported lipid bilayer surface is a chemical circuit test plate for nanoparticle calculations. Nanoparticle logic gates are integrated into lipid bilayer chips by chemical tethering. Basically, many different nanoparticle logic gates are integrated into a single lipid bilayer surface, so each logic gate can perform logical computations in parallel while generating different (or unique) optical signals. When nanoparticle logic gates are integrated on a lipid chip, wash buffers, nanoparticle gates, or solutions including molecular inputs can exchange bound particles without exchanging tethered particles. Furthermore, dark field imaging may be performed in situ during solution exchange.

Fig. 3A to 3C show the characteristics of the DNA-modified nanoparticles.

In fig. 3A, gold nanorods (diameter 22.2 ± 1.2nm, length 55.9 ± 2.9nm, aspect ratio 2.5) with silver nanoshells mainly showing red (R) scattering signals are shown.

In fig. 3B, gold nanospheres (50.0 ± 1.8nm in diameter) that predominantly exhibited green (G) scattering signals are shown.

In fig. 3C, silver nanospheres (diameter 54.8 ± 3.1nm) on gold seeds that predominantly display blue (B) scattering signals are shown.

In fig. 3A to 3C, the first column shows transmission electron microscope images, the second column shows extinction spectra obtained from an ultraviolet-visible spectrophotometer, and the three spectra are normalized by 1 Optical Density (OD). The third column shows Dark Field Microscope (DFM) images.

As shown in fig. 3A to 3C, there are three types of core nanoparticles, for example, gold nanorods, gold nanospheres, and silver nanospheres having silver nanoshells in the order of fig. 3A to 3C, respectively serving as gold seeds showing red R scattering signals, green G scattering signals, and blue B scattering signals. The calculation of nanoparticle units on LNTs is directly visualized by DFM, enabling easy exchange of solutions using flow chambers without washing away the nanoparticle circuits in the system.

Fig. 4A to 4C show relevant DFM-SEM images for analyzing single nanoparticle scattering signals.

In fig. 4A to 4C, the left column is a DFM image of nanoparticles on a patterned glass substrate, the center column is an enlarged DFM image in a marked area in the left column of the image, and the right column is a Scanning Electron Microscope (SEM) image at the same position as the center column of the image.

Fig. 4A shows a DFM image and an SEM image of gold nanorods (R nanoparticles) having silver nanoshells, fig. 4B shows a DFM image and an SEM image of gold nanospheres (G nanoparticles), and fig. 4C shows a DFM image and an SEM image of gold nanospheres (B nanoparticles) having silver nanoshells.

It was determined that the Cr patterned glass substrate helped to find the same position in both images. The correlation of the two images indicates that the characterized scattering signals shown in fig. 3A to 3C are from a single nanoparticle.

When two plasmonic nanoparticles are in close proximity, the nanoparticles exhibit a plasmon binding effect (plasmon binding effect), and thus whether a bright particle is a single nanoparticle or an aggregated nanoparticle can be determined by analyzing the scattering intensity of the DFM image. It can be confirmed that the bright spots in the initial state in the DFM image are from a single nanoparticle, compared to the same position in the SEM image.

Two types of nanoparticles, the nano-acceptor (NR) and the nano-float (NF), are used as essential components of LNTs. The surface DNA ligands of each of the nano-receptors and nano-floats are designed with a method in which the receptor-float interaction is controlled (e.g., by assembly or disassembly) based on the results of logical calculations to obtain DNA molecules from solution.

Fig. 5A and 5B show the diffusion kinetics of nanoparticles tethered to a supported lipid bilayer.

In fig. 5A, Mean Square Displacement (MSD) with respect to time is shown to plot five representative diffusion trajectories. As shown in FIG. 5A, the MSD plots of three mobilities R, G and B and the nanoscopic R-NF, G-NF, and B-NF show the linear relationship of random 2D Brownian motion, and the MSD plots of the G receptor G-NR and the B receptor B-NR show the immobilization of the G receptor G-NR and the B receptor B-NR.

In fig. 5B, the average diffusion coefficients of R-NF (Ntot: 154), G-NF (Ntot: 194), and B-NF (Ntot: 247) diffused are shown.

As shown in fig. 5A and 5B, the receptor is immobilized on the SLB because lateral diffusion of the receptor is limited by a large number of biotinylated DNA linkers that interact with streptavidin on the SLB. Between 34% and 50% of the surface valency of the receptor is functionalized by the linker. The receptor is monitored throughout the calculation and serves as a reporter for nanoparticle calculations.

The floating matter is about 1.0 μm²The diffusion coefficient/s is highly mobile on SLB, with the biotin linker valency of the float in the range 0.4% to 0.5%.

Due to high mobility, NF can actively (or actively) interact with NR in space and time while serving as an active unit for computation. Surface DNA ligands mediate receptor-float interactions, taking DNA molecules as input, and inducing assembly or disassembly of receptor-float complexes as output.

The action of floats tethered to the SLB is binary at the level of individual particles. That is, for a given observation period, the float either discretely switches its state (ON) by assembly or disassembly, or does not switch its state. When boolean logic is used to control the digital action of each float, the receptor-float pair may be implemented as a single logic gate.

In fig. 1B, the nanoparticle YES gate is shown as an example of a logic gate implemented by a receptor-float pair.

In fig. 1B, a receptor-float pair is shown as YES logic gates. The green nano-receptors G-NR and green nano-floats G-NF, which exhibit a predominant green color, can be used in YES gates.

When DNA (X) is input_a) The float-receptor pair is defined as an assembly YES gate, which induces an assembly reaction between the receptor and the float. When DNA (X) is input_d) When dissociation is induced by strand displacement output disclosed by the fulcrum (toehold) domain T2 and thus receptor-float (R-F) pairs, the float-receptor pairs are defined as the dissociation YES gate. In the figure, "T" denotes the branch domain, "A" and "B" are binding domains, and the functional domains may be linked in the order of A-T-B or B-T-A. Arrows indicate 3' ends, asterisks "", indicate complementarity. Each logic gate may be shown in the reaction diagram shown in fig. 1B.

In the assembled YES gate, G-NF responds to a single stranded DNA input (X) that can hybridize to surface DNA ligands of both receptors and floats_a) Its conformational state is switched from a diffusible monomer ("0") to a fixed dimer ("1") by association with G-NR.

In the decomposition YES gate, G-NF initially passes through the oligonucleotide (X)_d*) Hybridization binds to G-NR. This step is termed pre-dimerization. When DNA is input (X)_d) Removal of pre-existing DNA linkages X by fulcrum-mediated Strand Displacement_d*When G-NF is subsequently released from G-NR and switches its state from a fixed dimer ("0") to a diffusible monomer ("1").

The DNA sequences and experimental conditions for assembling the YES gate and decomposing the YES gate shown in FIG. 1B are disclosed in tables 1and 13.

FIGS. 6A and 6B show the operation of a single input split YES gate based on pivot-mediated strand displacement.

In fig. 6A, the separation reaction using the perfect complement input and the mismatch input is shown at the sequence level. Two-point transitions and mismatched base pairs at the side ends of the branch domain (A to G, G to T) are emphasized (Xmut of FIG. 6A)^*"T" and "G" at opposite ends of "TTCGCTGACG" in (1).

In fig. 6B, a plot of dark field dynamics experiments obtained with respect to two inputs is shown. In fig. 6B, the outputs are shown with respect to the fully complementary input and the mismatched input, respectively. Due to the high concentration input of 500nM and the long branch domain, the equilibrium is pushed back into separation even in the presence of base mismatches. Thus, the reaction under both conditions was saturated after a sufficient period of operation. However, the full complement input induces a faster response of the split gate, indicating that the system can recognize the mismatch input under specific conditions by using kinetic information. In fig. 6A, EG represents an ethylene glycol unit.

For input, logical values of "0" and "1" indicate the absence and presence of input DNA in the solution. For the output, "1" denotes G-NF bound to G-NR (i.e., R-F dimer) for assembling the gate and diffusible, monomeric G-NF for decomposing the gate. "0" indicates that the floats remain in their initial state.

Information about the state-switching behavior of the float can be obtained by tracking the signal changes of the receptors. For example, when G-NF assembles to G-NR via input DNA resulting in a step-wise increase in G-strength of G-NR, the assembly YES gate produces an output of "1".

The "nanoparticle reaction network" abstraction can be used to represent the behavior of nanoparticles of logic gates. The abstraction is based on a directed graph, where nodes are represented by nanoparticles and edges are represented by logic, inputs, and reaction types. As shown in fig. 1B, the assembly reaction is marked by a solid arrow pointing from the float to the receptor, and the dissociation reaction is marked by a dashed arrow pointing from the receptor to the float. Nanoparticle logic gates can be designed to generate a characteristic, plasmon coupling induced signal specific to the combination of receptor-float pairs. Thus, multiple nanoparticle logic gates can be analyzed in parallel, as long as each gate provides a different optical readout.

Figure 7 shows a parallel, single particle analysis in nanoparticle logic gate operation by dark field microscopy.

As shown in fig. 7, each of the plurality of nanoparticle logic gates may be analyzed in parallel, as long as each gate produces a different optical signal as an output. In FIG. 7, B-NR is indicated by darker shading than G-NF in the magnified frame region of dark field microscopy imaging and parallel single particle analysis. Since the plasmon coupling induced changes in nanoparticle scattering signal depend on the receptor-float pair associated with the interaction, multiple logic gates that produce unique optical signals can be easily designed.

For example, when the assembly gate is composed of G-NR and G-NF and the decomposition gate is composed of B-NR and G-NF, both gates may be performed simultaneously. In both signals, an increase in the G intensity of G-NR and a decrease in the G intensity of B-NR are readily discernible.

A sufficiently high density of nanoparticles needs to be maintained to ensure that a large number of logic gate nanoparticle reactions can occur in a short time. For the calculation process, which usually lasts from 15 minutes to 30 minutes, the tethering to a unit area (180X 180 μm) is monitored²) About more than 4000 nanoparticles: (>3700 receptors and 300 floats).

Fig. 8A and 8B show the logic gate of nanoparticles tethered to a supported lipid bilayer.

In fig. 8A, a plot of the number of nanoparticle NPs tethered to the supported lipid bilayer versus incubation time is shown.

In FIG. 8A, the tethering process for 2.5pM G-NR at the left, 2.6pM G-NF at the center, and 2.2pM B-NR at the right is shown. At each time, the number of each nanoparticle was counted in a 90 μm by 90 μm region for four different locations. Error bars are standard deviations calculated based on the number of particles obtained at the four positions. As shown in the plot shown in fig. 8A, tethered particlesThe number of particles is linear with the incubation time of the lipid bilayer chamber and the solution comprising biotinylated nanoparticles (R)²>0.98). In addition, the plots show that receptors tether faster than floaters, as expected from the higher linker density. This linear relationship enables precise control of nanoparticle density in the lipid bilayer.

In fig. 8B, the number of tethered nanoparticles in three replicate lipid flow chambers (e.g., sheets 1-3) is shown. The combination of G-NR and B-NR is shown in the left side of FIG. 8B, and the combination of B-NR and G-NF is shown in the center of FIG. 8B. On each patch, after tethering, the number of nanoparticles was counted at four different locations in a 90 μm by 90 μm region. Error bars are the standard deviation calculated based on the number of particles obtained at the four positions. The chip-to-chip variability in particle counts is negligible.

In the right side of fig. 8B, the number of each of the three tethered nanoparticles, B-NR, G-NR and G-NF, measured at four different locations in the lipid compartment is shown. Error bars are standard deviations calculated based on the number of NPs obtained at four positions. These results indicate that the tethering of the nanoparticles in the lipid nanoplatelets can be controlled and stable over a large area of the lipid bilayer, independent of nanoparticle type and mobility.

The number of receptors is set higher than the number of floaters to minimize trimer formation and tetramer formation. This condition allows the float to switch only between the monomer and dimer phases.

FIG. 9 is a graph showing the extent of multimer-forming reactions simulated by MATLAB-like.

As shown in fig. 9, the NF ratio for polymer (trimer or tetramer) formation in the assembly reaction simulated in the lipid bilayer relative to a given NR/NF ratio can be estimated by using MATLAB-type simulations. At NR/NF ratios of 10:1, 8:1, 5: 1and 2:1, 6%, 15% and 34% of the floes formed polymers by assembly reactions. For example, two assembly reactions between one receptor and two floats produce trimers. The total amount of nanoparticles in the simulated system was setIs at 128X 128 μm²Is about 1800 a in the region of (a). In agreement with the experimental results, the diffusion coefficient distribution of NF was such that the average 0.9 μm was satisfied²Normal distribution of/s and 0.3 μm²Standard deviation in/s.

In fig. 1C, receptor-only digital image processing for clear and quantitative visualization of nanoparticle logic calculations is shown. The immobilized signal from the receptor is specifically recognized and digitized for visualization. This analysis provides dynamic reconstructed dark-field video with enhanced signal-to-noise ratio, signal distribution for individual receptors, and logic computation. Kinetic mapping can be obtained by counting receptors that produce accurate individual particle readouts.

As shown in fig. 1C and fig. 10A-10C, an image analysis pipeline capable of integrated detection, tracking, classification, and visualization of single nanoparticle signals is used to analyze a large number of nanoparticle logic gates.

Fig. 10A-10C illustrate algorithms for receptor-only digital image processing for clear and more quantitative visualization of nanoparticles for logic gates on LNTs.

In fig. 10A, the computational identification of nanoparticles and receptors from time-lapse DFM data is shown. After registration (step 1), pixels having a signal intensity higher than the detection parameter are detected, and pixels are marked with a yellow cross (step 2). The detection parameter d may be varied according to each dark field video. This is because the type and number of nanoparticles in each slice affect the background of the video. For example, d is defined by the equation "d ═ mbackground +0.5 × σ background", where mbackground and σ background denote the mean and standard deviation of pixels whose grayscale intensity is less than or equal to the selected threshold T. The boundaries of the detected signals (pixels) can be easily distinguished. The segmentation signal is assumed to come from the nanoparticle. The center of the segmented signal is positioned to provide the nanoparticle location. The particles located are marked with crosses (step 3). The recipient is determined by comparing the locations located by frames N, N + 1and N +2 (step 4). The particles with the resting signal are identified with the receptor and the particles are marked with triangular dots (step 5). For each grain indicated by the triangular dots, an area (3 × 3 pixels) for signal sampling is set, and a signal in a predetermined sampling area is sampled (step 6). Visualizing only the sampled receptor signals produces a reconstructed video with enhanced signal-to-noise ratio (step 7).

Fig. 10B shows the number of detected signals plotted as a function of the threshold T. The presence of a platform shows that the number of signals identified is not sensitive to the threshold T selected for analysis.

Figure 10C shows the number of receptors plotted as a function of the trace frame plot. The presence of the platform shows that the number of receptors identified is insensitive to the threshold T selected for analysis. For example, when 70(a.u.) is selected as the threshold T and 31 (frames) is selected as the tracking length, the receptors visually identified in the original dark field video match the receptors identified in the algorithm described above. This comparison can show that the algorithm stably distinguishes receptors from floats at a high density setting.

Since it is difficult to track a moving signal of a float in a high density setting, an image analysis pipeline method of specifically tracking a subject is used.

Blurred signals that do not fall into the scatter plot categories for red, green, or blue signal clusters are eliminated for analysis. In image analysis pipeline methods, tracking algorithms are used to reconstruct video that only visualizes receptor signals. Such video exhibits enhanced signal-to-noise ratio, providing a clear view of how the nanoparticle circuits operate in real-time at the single-particle level. First, analysis of scattering signals of R nanoparticles, G nanoparticles, and B nanoparticles for signal classification was performed by a corresponding analysis method.

Fig. 11A to 11C show scattering signal distributions of R nanoparticles, G nanoparticles, and B nanoparticles.

In fig. 11A and 11B, each cluster is displayed in a corresponding color, and thus the clusters for each color are easily distinguished. In fig. 11A, RGB intensity scatter plots for R nanoparticles (top), G nanoparticles (middle), and B nanoparticles (bottom) are shown in 3D signal space. In fig. 11B, two perspective views showing the R, G, and B clusters together in the 3D signal space are shown. The two perspective views are divided according to the viewing angle from which the 3D signal space is viewed.

In fig. 11C, the average values of the red, green, and blue scattering intensities for each of the R, G, and B nanoparticles and the background signal are shown as bars. As shown in fig. 11C, a bar marked by "/" indicates green, a bar marked by "\" indicates blue, and a bar marked by "x" indicates red. Error bars indicate standard deviation. Signal analysis of nanoparticle monomers can be used to confirm and classify nanoparticle reactions of logic gates in lipid nanoplatelets.

Fig. 12A-12D illustrate dual-input nanoparticle logic gates on LNTs.

Referring to fig. 12A to 12D, an assembly AND gate, an assembly OR gate, a decomposition AND gate, AND a decomposition OR gate will be described. To construct these gates, the DNA bonds are programmed in the receptor-float interface such that bonds are formed by assembly OR cleaved by disassembly only when two different DNA inputs satisfy AND logic OR logic. This method is called interface programming.

The DNA sequences and experimental conditions for the dual input nanoparticle logic gate are summarized in tables 3 and 14.

Fig. 13 shows the principle of operation AND DNA sequence of a two-input assembly AND gate.

In fig. 13, a sequence level diagram is shown for describing a method for assembling an AND gate in response to two inputs X1AND X2. EG represents an ethylene glycol unit.

As shown in fig. 12A AND 13, conformationally switchable DNA hairpins can be used as surface ligands in assembling the AND gate. G-NR (R)₁) G-NF (F) modified with DNA hairpin T2₁) Modified with DNA hairpins T1, each of the DNA hairpins concealed its binding domain (B1a 1and a 1B 1) in the stem (stem). Opening the hairpin by hybridization at its loops with the input strands X1and X2, exposing the G-NR (R)₁) Binding domains of B1A 1and G-NF (F)₂) The binding domain of (A1B 1), G-NR (R)₁) And G-NF (F)₂) Assembly by hybridization between the two binding domains. That is, the R acceptor is only present when both X1and X2 are present in solution₁And a float F₁Is assembled. In the graph of fig. 12A, R due to assembly is shown₁Increase in strength of (a).

Fig. 14 shows the principle of operation and DNA sequence of a two-input assembled OR gate.

In fig. 14, a sequence level diagram is shown for describing a method for assembling an OR gate in response to two inputs X3 and X4. EG represents an ethylene glycol unit.

In assembling the OR gate, G-NR R₂Modified with two types of DNA ligands (B2 and B3), B-NF F₂Modified with two types of DNA ligands (a2 and A3). Each DNA ligand exposes a different binding domain. Either input X3or X4 may be with F₂And R₂The half-complementary domains on (c) hybridize, resulting in dimerization. In the graph of FIG. 12B, R due to binding is shown₂The strength is increased.

FIG. 15 shows the principle of operation AND DNA sequence of a two-input decomposition AND gate.

In fig. 15, a sequence level diagram is shown for describing a method for a decomposition AND gate responsive to two inputs X5 AND X6. EG represents an ethylene glycol unit.

As shown in FIGS. 12C AND 15, in the decomposition AND gate, G-NF F₃Binding to B-NR R by two different DNA bonds₃Each of the DNA bonds exposes a branch domain within the interface between particles (T5 and T6). The two pivot domains serve as recognition regions, recruiting input strands X5 and X6, and each removing DNA bonds by strand displacement. The cleavage reaction starts when two different DNA bonds are removed by X5 and X6. In the graph of FIG. 12C, R due to decomposition is shown₃The strength is increased.

Fig. 16 shows the principle of operation and DNA sequence of a two-input OR gate.

In fig. 16, a sequence level diagram is shown for describing a method for decomposing an OR gate in response to two inputs X7 and X8. EG represents an ethylene glycol unit.

As shown in FIGS. 12D and 16, in the split OR gate, B-NF F₄Binding to B-NR R by one type of DNA bond₄DNA bond violenceTwo branch domains, T7 and T8, in the interface are exposed. One of the two pivot points domains can independently recruit an input chain. The sequence domain of each of the input strands X7 and X8 is semi-complementary to an existing DNA bond. For example, the sequence domain "T7 a 6" of the input strand X7 is linked to DNA "B6T 8T7A6"half-complementary, sequence Domain of input strand X8" B6T8 "is bonded to DNA"B6T8T7a6 "half complementary. The input chains X7 and X8 are linked via a C-NR R₄Strand displacement of the ligand (via X8) or with B-NF F₄Strand displacement of the ligand (via X7) cleaves the bond. In the graph of fig. 12D, R due to decomposition is shown₄The strength is reduced.

The design principles for interface programming are schematically outlined and shown in fig. 17A to 17F.

Fig. 17A to 17F show the design principle of the nanoparticle logic gate.

In fig. 17A, a graphical overview of the generalized concept is shown. The assembly/disassembly of YES gates of effector mediated nanoparticles (left) and the truth table for this concept (right) are shown. The configuration for assembling/disassembling logic gates requires selective effector-ligand and effector-chelator pairs. To build a logic gate by using two nanoparticles, the bond interaction needs to be programmed in the receptor-float interface such that a bond is formed by assembly OR cleaved by decomposition only when the two molecular inputs satisfy the AND logic OR the OR logic.

Fig. 17B shows a design of an assembled AND gate. Fig. 17C shows a design of an assembled OR gate.

When a keyed interaction requires serial activation by two inputs, the assembly reaction is controlled by AND logic, AND when a keyed interaction is controlled in parallel, the assembly reaction is controlled by OR logic.

FIG. 17D shows a design of a decomposition AND gate. Fig. 17E shows a split OR gate design.

The decomposition reaction is controlled by AND logic through the parallel disconnect paradigm AND OR logic through the serial disconnect scheme.

FIG. 17F illustrates the generalized concept of interface programming. Sequence recognition and strand displacement of DNA serve as mechanisms to implement logic. Specifically, single stranded DNA molecules are used as effectors, thiol oligonucleotides as ligands, and strand displacement as chelating mechanisms. The simplicity of the design makes it possible to design sequences with several constraints. This simple design rule can be applied to other ligand systems and core nanostructures.

The performance of the nanoparticle gate can be analyzed by counting the output response of the nanoparticle gate captured in dark field video. Whether a type of nanoparticle gate produces an accurate digital output can be determined by a quantization process.

All four logic gates produce a low output count under a logic FALSE condition and a high output count under a TURE condition. In particular, ON/OFF levels of more than 5, 88, 93 AND 42 times with fast response kinetics (t1/2<19 min, t1/2<5 min, t1/2<9 min AND t1/2<5 min) are provided for assembling AND gates, assembling OR gates, disassembling AND gates AND disassembling OR gates, respectively.

The ON/OFF level is evaluated by dividing the lowest output count obtained under the TURE condition by the highest output count obtained under the FALSE condition. In table 1, under the TURE conditions, the response (%) (number of floats reacted to input divided by total number of floats) is typically about 80%.

[ Table 1]

The response rate (defined as the number of floats reacted to the input divided by the total number of floats counted in the initial state) is typically over 80%. The assembled AND gate exhibits less output leakage at 1AND 0AND 1, presumably because the surface hairpin is in dynamic equilibrium between the closed AND open states.

Fig. 18A-18C illustrate the modularity of two hairpin-like inputs assembling an AND gate.

As shown in fig. 18A, the hairpin-like AND assembly process is controlled by the input-induced hairpin opening.

As shown in fig. 18B, a clip-like assembly process occurs without interference from other hybridization events.

In fig. 18A, the assembly AND gates are sequentially activated with respect to the sequentially introduced inputs, resulting in the response of the assembly AND gates. In fig. 18A, □ denotes a case where X1 is added after addition of X2, o denotes a case where X2 is added after addition of X1. As shown in fig. 18A, both hybridization events are required to induce nanoparticle assembly.

In FIG. 18B, the assembled AND gate operation is shown after hybridization by DNA input X3 interacting with non-hairpin ligands. As shown in fig. 18B, hairpin-like assembly was insensitive to other hybridization events in the same particle.

In fig. 18C, the assembly by normal DNA input X3 is shown. As shown in fig. 18C, assembly by simple hybridization (as in the assembly OR gate) is insensitive to the presence of hairpin ligands in the same particle. The DNA sequences and experimental conditions are summarized in table 7, table 8 and table 18.

Fig. 19 shows the non-uniform response of two input split OR gates.

As shown in fig. 19, the split OR gate exhibits a non-uniform response, where the fire output under 0OR 1 conditions is a 57% fraction of the other fire outputs under other input conditions. This result is due to the difference in surface ligand density between the receptor and the float.

In FIG. 19, since the density of surface ligands is at F₄Middle ratio in R₄Medium high, so the response rate under the 1OR 0 condition can be higher than the response rate under the 0OR 1 condition. Due to the high ligand density, F₄Ratio R₄Exposing a large number of single-stranded domains (B6-T8-T7). The exposed strand may interact with the incoming input strand. The interaction between input B6-T8 and exposed bonds under 0OR 1 conditions was more effective than the interaction between input a 6-T7 and bonds under 1OR 0 conditions. This is because the recognizable sequence is longer and easier to interact with previously. Thus, input B6-T8 was more easily captured by exposed strands without causing effective strand displacement that induces particle breakdown.

Additionally, LNT systems are compatible with the "dual rail" rule, where the Boolean value of a logic gate is represented by the presence of one signal ("0") or the other ("1"). This form is used for a system in which it is difficult to define the NOT function. With this representation, AND gates AND OR gates are sufficient to compute any boolean function.

Fig. 20A to 20B show a dual rail of a NAND gate.

In fig. 20A, a two-input, two-rail NAND gate is shown. For dual-rail input of XiAnd(logic OFF and logic ON are indicated, respectively) and the output is also marked with the same rule. Two-input assembled OR gate (OR) AND a two-input decomposition AND gate (AND) Implemented in parallel to handle dual-rail NAND logic.

Fig. 20B shows the dark field dynamics experiment results. Two gates produce accurate logic outputs while providing over 37 times (Y)⁰2 input Assembly OR) and 33 times (Y)¹2 input resolution AND) without interfering with each other. This result can demonstrate the modularity of the nanoparticle logic gate. The DNA sequences and experimental conditions are summarized in table 9 and table 19.

Interface programming can be extended to enable nanoparticle logic gates to handle INHIBIT logic (X1AND NOT X2) AND produce multiple outputs (fan-out) with multiple inputs (fan-in).

Fig. 21A-21C illustrate a nanoparticle logic gate that handles INHIBIT operations and has multiple inputs (fan-in) and multiple outputs (fan-out).

In fig. 21A to 21C, the Λ, v, AND ￢ represent logical signs with respect to AND, OR, AND NOT, respectively. Each plot contains a graph of the response corresponding to each gate. The DNA sequences and experimental conditions are summarized in Table 4-1, Table 4-2 and Table 15. Experiments were performed in 1x PBS buffer at 25 ℃.

First, in FIG. 21A, it is shown that R is₁-F₁The dual-input decomposition INHIBIT gate implemented in (1). To implement a logic function, ligands used to assemble and disassemble the YES gates are simultaneously used to control G-NR (R)₁) With G-NF (F)₁) To be decomposed. Assembly input X2 was used to form other DNA bindings, but breakdown input X1 was used to remove existing DNA bindings.

In the two-input decomposition INHIBIT gate shown in fig. 21A, the NOT logic required for the INHIBIT gate can be achieved by competition between DNA bond removal triggered by X1and DNA bond formation triggered by X2. INHIBIT gate release F if and only if there is an assembly input X1and there is no assembly input X2₁As an output.

Fig. 22A to 22B show the operation of the two-input decomposition INHIBIT gate.

In fig. 22A, a domain level map of the operation of the dual input decomposition INHIBIT gate and the reconstructed dark field image are shown. The reconstructed image provides only receptor signals. The decomposition reaction is observed only in the state of logic 1AND (NOT 0).

As shown in fig. 22A, the INHIBIT gate only generates an output count in the tune state having an ON/OFF level of more than 129 times. When there are two inputs, no output leakage is observed, indicating that key formation by X2 is faster than key removal by X1.

As shown in fig. 22B, the two competing reactions proceed without interfering with each other.

FIG. 22B shows the characteristics of the strand displacement reaction in the INHIBIT gate. According to this design, the gate combination changes from B1-T1-A1 to B2-T2-A2X 2 when two inputs X1and X2 are added. Conformational switching is effective in the receptor-float interface and the cleavage reaction should occur upon subsequent addition of X2. The dark field kinetic plot indicates that the INHIBIT gate operates as designed. The DNA sequences and experimental conditions are summarized in Table 4-1, Table 4-2 and Table 15.

The proof of the INHIBIT gate is important because the two-input AND, OR, AND INHIBIT operations constitute a functionally complete set of Boolean functions.

Second, the number of different DNA bonds in the receptor-float dimer that can be broken down is increased, enabling fan-in to the breakdown gate.

In FIG. 21B, R is shown₂-F₂The circuit diagram of the implemented six-input decomposition gate and the operation of the gate (left) and the kinetic experiment graph (right).

As shown in FIG. 21B, when from G-NR (R)₂) Releasing G-NF (F)₂) When three different DNA bonds need to be broken (each of the DNA bonds can be cleaved by two-input OR logic), the decomposition is controlled by six-input expressions (X3OR X4) AND (X5 OR X6) AND (X7 OR X8).

Fig. 23 illustrates the operation of the six-input decomposition gate.

In fig. 23, the reconstructed dark-field image AND domain level map (left) of the six-input decomposition gate that processes (X3OR X4) AND (X5 OR X6) AND (X7 OR X8) logic are shown. The DNA sequences and experimental conditions are summarized in Table 4-1, Table 4-2 and Table 15. As shown in fig. 23, dark field imaging confirms that the six-input logic gate produces an output only in the fire state with an ON/OFF level exceeding 88 times.

Fig. 24A to 24B show a multi-input decomposition gate.

In fig. 24A and 24B, by combining INHIBIT logic and the approach method used in implementing the fan-in, decomposition gates are designed that can implement more complex logic expressions.

In fig. 24A, a four-input decomposition gate is shown that processes (X1 OR X2) AND NOT (X3OR X4) logical expressions. In this design, X1 or X2 can cleave the DNA bond (at a preformed R)₁-F₁In a dimer), X3or X4 may form a bond in the dimer. In order for decomposition to occur, bond cleavage reactions must be performed in the absence of X3 and X4.

In fig. 24B, a three-input decomposition gate is shown that processes (X5 AND X6) AND (NOT X7) logic. For decomposition, X5 and X6 required removal of two different DNA bonds without formation of additional bonds through X7.

As shown in fig. 24A AND 24B, two strategies based on kinetic competition AND increased "key rank" can be used to yield decomposition logic gates with complex multi-input boolean logic, such as (X1 OR X2) AND NOT (X3OR X4) AND (X1AND X2) AND (NOT X3).

Third, a two-input decomposition AND gate with three outputs is shown in fig. 21C. The division gate processes two types of DNA inputs with AND logic AND generates three different movable floats F₃、F₄And F₅As an output. Three representative R-F pairs are labeled R₃-F₃、R₃-F₄And R₃-F₅。

As shown in fig. 21C, the fanout of the logic gate is demonstrated by implementing the same two-input split AND logic in three different receptor-float pairs, each with a different float signal. The decomposition reactions of the three floats can be easily analyzed due to the characteristic signal of each float. Dissociation of R-NF, G-NF, and B-NF from the respective receptors results in a gradual decrease in the R, G, and B intensities of the receptor signals. The splitting gate releases all three outputs F according to logic with more than 20 times the ON/OFF level₃、F₄And F₅。

As the complexity of the reactor-float internal reactions increases, incomplete reactions or spurious interactions may also occur. Thus, relying on designing a float-to-receptor surface interface is not an efficient and scalable strategy for constructing complex circuits. Thus, in the present disclosure, nanoparticle "net programming" is used, which can connect two single particle logic gates with AND logic OR logic.

Fig. 25A to 25C show the connection of nanoparticle logic gates programmed through a network.

The DNA sequences and experimental conditions are summarized in table 5 and table 16. Experiments were performed in 1x PBS buffer at 25 ℃.

In the network programming shown in fig. 25A, two nanoparticle gates may be modularly wired in a nanoparticle network level, with an R-F interface programmed with the desired logic in each of the two nanoparticle gates. The float, which is designed to participate sequentially in both the splitter gate 1AND the assembler gate 2, enables both gates to be wired with AND logic (top). When two split logic gates (gate 3 and gate 4) produce floats with the same signal, both gates are wired with OR logic (lower).

Fig. 25B and 25C show circuit diagrams of a net-level routing strategy using representative single-particle dark-field analysis (black background) in the left column, with the assembly and disassembly processes in the routing strategy circuit diagrams marked with solid and dashed arrows, respectively. Reactive receptor R₁、R₂、R₃And R₄Marked with a pre-assembly circle CR 1or a pre-disassembly circle CR2 and a post-assembly circuit CR 3or a post-disassembly circuit CR 4. Kinetic experimental results are shown in the lower end of fig. 25B and 25C, and the final output counts (left) and quantification of intermediate reactions (right) are shown. The Λ represents the AND logic AND the v represents the OR logic.

Fig. 25B shows the wiring of a logic gate having AND logic. Float F which in its initial state binds to a first receptor R1₁Designed to act as a split AND logic gate (X1AND X2) AND subsequently as a gate with a second receptor R₂Assembling YES door (X3). R₂-F₁Dimer production is the final output. In FIG. 25B, the receptor R before decomposition₁First receptor R after cleavage, marked with circle CR2₁Second receptor R after Assembly, marked with circle CR4₂Marked with a circle CR 3.

The logic gate shown in fig. 25C is wired with OR logic. Both the split AND gate (X4 AND X5) AND the split YES gate X6 are designed to release G-NF, so the generation of G-NF is controlled by a circuit that combines the two gates using OR logic. In FIG. 25C, the acceptor R before decomposition₃And R₄Receptor R after dissociation, marked with circle CR2₃And R₄Marked with a circle CR 4.

First, as shown in fig. 25B, network-level AND wiring can be demonstrated by being able to use floats in the breakdown AND assembly gates. For example, in the reaction of G-NR R₁And G-NF F₁Release of G-NF F in the formed decomposition AND gate₁G-NF F released later₁With another receptor B-NR R₂For assembling the YES door.

In this network level wiring scheme, B-NR (R)₂)-G-NF(F₁) The formation of the dimer becomes the output of the AND-AND cascade circuit (X1AND X2) AND X3.

FIG. 26 shows a reconstructed dark-field snapshot of a two-layer AND-AND cascade circuit.

From G-NR R₁Releasing G-NF F₁Controlled by AND logic (X1AND X2). F released only when the assembly input X3 is present₁Can be tethered to R₂. The final circuit output is controlled by the three-input logic equation (X1AND X2) AND X3. First Condition (1AND 1) Green intensity in AND 1 at receptor R₁Is reduced and at the receptor R₂Is increased and is therefore successfully treated by G-NF F₁And (4) cascading. The decrease in green intensity is only observed under the second condition (1AND 1) AND 0. Absence of signal increase indicates released float F₁Not tethered to another receptor. No reaction is seen in the two figures below.

The AND-AND cascade is depicted by a reaction diagram in which two receptors R₁And R₂Connected in series to a float F₁. The circuit provides an ON/OFF level of 36 times. As shown in fig. 26, the intermediate decomposition reactions can also be monitored and analyzed simultaneously due to their different optical signals. The upstream split AND gate results in an ON/OFF level that is over 89 times higher. By this parallel analysis, the flotage F over time in response to the combination of four inputs is estimated₁Population, floaters F released only when input X3 is TURE₁Can subsequently bind to the receptor R₂。

For the (1AND 1) AND 1 condition, over 92% of F₁In response to the assembly input X3. This result indicates that the sequential decomposition-assembly cascade is efficient. Other two-input decomposition gates (such as an OR gate AND an INHIBIT gate) may be re-routed modularly without optimization, resulting in an OR-AND cascade as shown in fig. 27 AND an INHIBIT-AND cascade as shown in fig. 28.

Fig. 27 shows a two-layer OR-AND cascade circuit.

In the two-layer OR-AND cascade circuit shown in fig. 27, a network-level AND wiring is applied in the configuration of a nanoparticle circuit that performs (X1 OR X2) AND X3 logic calculation. The circuit provides ON/OFF levels in excess of 11 times by producing an accurate output corresponding to the logic. The upstream decomposition gate shows an ON/OFF level that exceeds 128 times. As in the AND-AND cascade circuit shown in fig. 25B, the population dynamics of G-NF can be estimated in the circuit. According to the analysis, the released G-NF binds to B-NR only when the input X3 required for the assembly reaction is present. The DNA sequences and experimental conditions are summarized in table 10 and table 20.

Fig. 28 shows a two-layer INHIBIT-AND cascade circuit.

In the two-layer INHIBIT-AND cascade circuit shown in fig. 28, a network-level AND routing application is implemented to construct a nanoparticle circuit that performs (X1AND NOT X2) AND X3 logic computations. The circuit performs a logical calculation as designed so that the ON/OFF level exceeds 37 times. The upstream INHIBIT gate provides more than 70 times the ON/OFF level. The released G-NF binds to B-NR only when the input X3 is present. The DNA sequences and experimental conditions are summarized in table 11 and table 21.

Second, by designing two split gates, the OR wiring can be implemented to generate floats in parallel with the same optical signal.

As shown in FIG. 25C, for example, by B-NR (R)₃) And G-NF (F)₂) Formed decomposition AND gate is connected with the gate by G-NR (R)₄) And another G-NF (F)₃) The resulting disassembled YES doors are installed together. In the circuit ((X4 AND X5) OR X6), G-NF can be generated from an AND gate OR a YES gate, thus enabling two gates to be wired with OR logic.

FIG. 29 shows a dark-field snapshot of a two-layer AND-OR cascade circuit.

Referring to FIG. 29, a dark-field snapshot of a two-layer AND-OR cascade circuit is shown, from B-NR (R)₃) Releasing G-NF (F)₂) Controlled by AND logic (X4 AND X5) from G-NR (R)₄) Releasing another G-NF (F)₃) Controlled by YES logic (X6). The final circuit output is controlled by a three-input logic expression (X4 AND X5) OR X6. In the first condition (1AND 1) OR 1, the green intensity is at R₃And R₄And thus G-NF was successfully released from each receptor. In the second condition (1AND 1) OR 0, the decrease in green intensity is observed only in B-NR. That is, R is cracked as a result of AND logic calculations₃-F₂And (4) carrying out pairing.

As shown in fig. 29, the AND-OR cascade outputs an ON/OFF output level exceeding 55 times. The operation of the two upstream splitter gates is evaluated separately to indicate that each gate is calculating at a high ON/OFF level without interfering with each other.

Fig. 30 shows a two-layer OR-OR cascade circuit.

As shown in fig. 30, the split OR gates may also be connected to yield an OR-OR cascade circuit.

In the two-layer OR-OR cascade circuit shown in fig. 30, a network-level OR wiring scheme is applied to form a nanoparticle circuit that performs (X1 OR X2) OR X3 logic computation. The circuit performs the calculation as designed so that the ON/OFF level becomes more than 43 times. The upstream split OR gate and YES gate provide ON/OFF levels that exceed 37 times and 24 times, respectively. The DNA sequences and experimental conditions are summarized in table 12 and table 22.

Since the splitter gates support fanout, any upstream splitter gate should be able to easily generate multiple floats, each responsible for a different downstream computation. The released float may then be "plugged" into another layer of the assembly gate through network-level AND wiring. This approach enables complex multi-layer cascading.

To demonstrate the modularity of the circuit design on the LNT, multiplexers MUX 2 to 1 are implemented by network programming through routing previously introduced logic gates.

Fig. 31A to 31C show the design and embodiments of a nanoparticle multiplexer circuit.

In fig. 31A, a multiplexer circuit MUX 2 to 1 implemented with a network of nanoparticles is shown. The multiplexer is formed by connecting a split INHIBIT gate (X1AND NOT Sel) AND a split AND gate (Sel AND X2) through OR logic. Four nanoparticles R₁、R₂、F₁And F₂Forming a multiplexer circuit.

In FIG. 31BA modular embodiment of the nanoparticle multiplexer on the lipid bilayer is shown. The circuit constituent elements can be highly controlled and loaded in a modular manner. To prevent F₁And F₂Undesirable natural interaction between, in tether F₁Before, by means of a protective chain A₂Protection F₂。

In fig. 31C, the measured performance and truth table of the multiplexer circuit is shown. A domain level diagram for the operation of the circuit is depicted in fig. 32. In the analysis of the delayed dark field image recorded with the operation process of the circuit, the nanoparticle multiplexer yielded the expected response to eight different input combinations with ON/OFF levels exceeding 35 times. The DNA sequences and experimental conditions are summarized in table 6 and table 17. Experiments were performed in 1x PBS buffer at 25 ℃.

The circuit diagram of fig. 31A can be converted into a reaction graph that guides the design of surface DNA ligands for each nanoparticle. At the nanoparticle level, the multiplexer circuit consists of two R-F pairs: r₁-F₁(X1AND NOT Sel) AND R₂-F₂(Sel AND X2). The two split gates that produce G-NF as outputs are wired with OR logic. Specifically, in the multiplexer circuit, the selector Sel as the input of selection should be made of INHIBIT logic (R)₁-F₁Inner) AND AND logic (R)₂-F₂Inner) are processed simultaneously. Under this design constraint, each nanoparticle should expose a sequence domain (a2 or B2) that is fully complementary to the domain of another particle. Due to this condition, the nanoparticle circuit constituent elements can spontaneously form aggregates in the solution step. To prevent this spontaneous interaction, the nanoparticle circuit constituent elements were loaded in a specific order and protective chains a2 were introduced.

As shown in fig. 31B, the multiplexing circuit can be easily constructed in the LNT platform because undesirable spontaneous interactions between nanoparticles can be compartmentalized and controlled during the tethering and pre-dimerization processes. Because of the fixation of R1 and R2, R1 and R2 do not collide with each other, F₁-F₂The interaction is through the process of tetheringIntroducing a protective chain A2 for protecting F₂The a2 domains are temporarily blocked. After the four types of nanoparticles are loaded into the lipid bilayer, two decomposition logic gates are formed by forming the corresponding R₁-F₁And R₂-F₂And (4) preparing a dimer.

Fig. 32 shows the operation of the multiplexing circuit in the domain stage.

Nanoparticle surface ligands were designed to allow two different receptor-float pairs to simultaneously treat the selection strand (Sel). The multiplexer selects one of the two inputs X1and X2 by using a selector chain (Sel), and converts the selected input into a single output.

The demonstration of nanoparticle multiplexers shows that nanoparticle circuits can be designed and operated on LNT platforms in a modular and controllable manner.

The operating principle of the LNT is divided into the following three aspects.

First, the calculations were driven only by SLB-tethered nanoparticles, whose interparticle interactions were programmable and readable in situ. The dynamic network of individual nanoparticles is equivalent to a logic circuit.

Second, process d does not require signal recovery or amplification since the cascade is driven only by the float. That is, a float is a "wire" that carries information of an upstream gate into a downstream gate via lateral diffusion that is stable to external conditions.

Fig. 33A to 33C show analysis of stability against float diffusion.

As shown in fig. 33A to 33C, the diffusion kinetics of flotage G-NF were analyzed under various conditions. For example, in 1x PBS buffer, the float diffusion process can be analyzed under different conditions with a high concentration (500nM) of dummy DNA, a low concentration (20nM) of complementary DNA input, and a high concentration (500nM) of complementary DNA input.

There was no complementarity between the surface ligands of float G-NF and the dummy DNA5 '-GTTTAAGATTTATGGTTAAGCGTAGATTAAGTATTAAG-3'. The G-NF used in the single input assembly YES gate was used to analyze G-NF (see Table 1). The analysis in each solution was repeated at three different positions in the lipid nanoplatelets.

FIG. 33A is a bar graph of the diffusion coefficient of G-NF under each condition. FIG. 33B shows the diffusion coefficient of G-NF and FIG. 33C shows the mean square displacement versus time to plot four representative diffusion traces. The analysis results show that the overall diffusion behavior of G-NF is robust to the chemical environment of the solution.

Third, spatial constraints are used to control the flow of molecular information in nanoparticle "signal" networks. As shown in the multiplexer, undesired interactions can be controlled modularly. Complex digital positioning operations can then be performed using relatively small numbers of particle and ligand types. Through integration with the lipid bilayer, nanoparticles are programmed, controlled and visualized on a single particle level to design and implement the desired circuit according to digital principles. This functionality is not possible in prior methods that relied solely on nanoparticles "passively" polymerized in solution. The range of molecular information that can be processed on an LNT can be extended in several ways.

First, because only DNA molecules are needed to operate the nanoparticle circuits on the LNT, a solution-phase molecular circuit that releases single-stranded DNA as an output can be cooperatively linked with the LNT platform. In this case, the molecular circuit on the solution can additionally process the molecular information. The method may mediate communication between different nanoparticle circuit modules on the lipid bilayer and with the external environment.

Second, particle modifications based on different chemical ligands other than DNA can be easily introduced to process different chemical information. When introducing new surface chemistry components on the nanoparticles, design constraints (which may be caused by cross-talk between different surface ligands) may be reduced because the interaction between particles is spatially or temporarily controlled on the LNT.

Third, the integration of lipid bilayers with DNA nanostructures can provide a path towards new types of molecular circuits. For example, a dynamic network of interactions between origami can be exploited by tethering a DNA origami scaffold (origami scaffold) containing spatially localized DNA circuits to the SLB. The principles for building or connecting nanoparticle logic gates (interface programming and network programming) can be applied to build DNA origami support circuits. The method may enable more complex and even practical molecular calculations.

Despite this potential, the further expanded complexity of nanoparticle circuits on LNTs will lead to challenges because the input (DNA) and output (state-switching floats) have different forms. Currently, this inherent difference limits the construction of arbitrarily large circuits. This challenge can potentially be addressed in two ways.

First, because it allows control of nanoparticle interfaces and networks through more complex mechanisms, introducing new modes of nanoparticle reaction and ligand activation, such as dynamic reconfiguration, communication, DNA walking (DNA walker), and light-induced DNA release, would be able to provide a much wider design space for circuit design.

Second, increasing the number of different nanoparticle computing units per LNT will enhance the overall processing capacity of the LNT. Different nanoparticle calculation units can be operated in parallel or assembled as a combinational circuit by network programming. This approach is analogous to how an increase in integrated circuit density leads to an improvement in the computational power of silicon-based computers. Finally, each nanoparticle independently performs a different calculation itself by using parallel processing.

Tethering nanoparticles to lipid bilayers provides a systematic approach to building complex nanoparticle circuits due to spatial constraints (such as positioning and encapsulation) leading to modular implementation of molecular circuits. LNT-like approaches according to the present disclosure may play a key role in building highly functional "autonomous" nanostructures. Such devices can have a wide range of impacts on molecular diagnostics and smart sensors. The nanosystems in the device should be able to sense multiple stimuli and trigger the most appropriate response using internal computational algorithms.

In addition, the information processing nano system on the lipid bilayer may be applied to reconstruct the connection between artificial cells and used as a tool for studying membrane-related phenomena in living cells. Unlike existing methods that rely on invariable materials (such as patterned films), LNT-like methods can allow the network of nanostructures on the SLB to autonomously form clusters or patterns of structures and respond to signal molecule analysis from cells. This "active" SLB cell connection, which allows each nano-and cell system to communicate with each other, can also be used to test how a single theranostic nano-robot navigates in a complex and dynamic environment.

Can be manufactured as followsSupported Lipid Bilayers (SLBs) for use in the lipid nanoplatelets shown in FIG. 1A Chip "。

The lipid solution was mixed in a round bottom flask (in chloroform) to obtain a mixture comprising 97.2 mol% Dioleoylphosphatidylcholine (DOPC), 0.3 mol% biotinylated Dioleoylphosphatidylethanolamine (DOPE), and 2.5 mol% poly (ethylene glycol) (1K) -DOPE. Removing chloroform by rotary evaporator, and placing lipid membrane formed in the flask in N₂The flow was completely dried for 15 minutes. The dried mixture was resuspended in deionized water (DI water) so that the total concentration became 2 mg/mL. For the obtained lipid solution, freeze-thaw cycles were repeated 3 times at a temperature between 78 ℃ and 40 ℃. The lipid solution obtained as described above can be stored in a liquid nitrogen tank for up to 2 weeks. The lipid solution was extruded 11 times through a polycarbonate membrane with 100nm pores at 30 ℃, and then sonicated for 15 minutes to produce Small Unilamellar Vesicles (SUVs) from the lipid solution. The SUV solution obtained as described above was stored at 4 ℃ until use.

SLB was formed by vesicle fusion method in a glass chamber consisting of upper and lower glasses and a parafilm spacer (4 mm. times.50 mm. times.200 μm).

The operating volume of the glass chamber is less than 40uL (40 uL). After washing the upper slide with inflow and outflow ports (Paul Marienfeld GmbH) by sonication in deionized water for 5 minutes and piranha for 2 minutes, SLB formation was blocked by passivation with 10mg/mL Bovine Serum Albumin (BSA) dissolved in 150mM NaCl phosphate buffered saline (1x PBS). The lower coverslip (co.kg, germany) was sonicated in acetone and deionized water for 5 minutes, immersed in a piranha etching solution for 2 minutes, and washed, then rinsed with deionized water. Thereafter, a double paraffin spacer was placed between two slides and heat sealed at 100 ℃. The freshly extruded SUV solution was diluted to 1mg/mL in 1 XPBS solution and then sonicated for 15 minutes until used. SLB is formed by introducing a vesicle solution into the flow cell at 30 ℃. After 60 minutes, the flow cell was gently washed with deionized water (2 times) and 1x PBS. Then, defects on the SLB surface were blocked for 45 minutes using 100. mu.g/mL BSA in 1 XPBS. 17nM Streptavidin (STV) dissolved in 1 XPBS was injected into the flow chamber to convert biotinylated lipids for 45 minutes. After BSA blocking and streptavidin conversion, the flow cell in the flow cell was washed twice with 1x PBS. The flow cell with STV modified SLB can be stored in a humidified refrigerator at 4 ℃ for up to 3 days. Air bubbles must be avoided in all processes associated with the lipid compartment.

The synthesis of nanoparticles is as follows.

The gold nanorods were synthesized with silver nanoshells, gold nanospheres and silver nanospheres on gold seeds that exhibited primarily red R, green G and blue B scattering signals, and they were used throughout the following examples. First, gold nanorods with an aspect ratio of 4 were synthesized by a seed-mediated growth mechanism. Mixing the seeds with HAuCl₄·3H₂O solution (5mL, 0.5mM) and cetyl trimethylammonium bromide (CTAB) solution (5mL, 0.2M) were mixed and then ice-cooled NaBH was rapidly injected₄Solution (600. mu.l, 10 mM). After the reduction step, the seed solution was left for 2 hours. Adding HAuCl₄·3H₂O solution (5mL, 1mM) was mixed with CTAB solution (5mL, 0.2M) and AgNO was added₃After the solution (250. mu.l, 4mM), L-ascorbic acid solution (70. mu.l, 78mM) was added. To this 12. mu.l of the seed solution was added and mixed gently. The resulting solution was incubated at 60 ℃ for 4 hours, centrifuged, and redispersed in deionized water 3 times. Mixing gold nanorod solution (1ml, 100nm) with cetyltrimethylammonium chloride (CTAC) solution (1ml, 10mM), AgNO₃(1ml, 0.2mM) and ascorbic acid (1ml, 50mM) were mixed and then gold nanorods were coated with about 5nm silver shells. After 4 hours of incubation at 60 ℃ by centrifugationThe solution was washed with heart, the supernatant was removed, and then redispersed in deionized water three times to obtain red (R) nanoparticles. Spherical shaped gold nanoparticles (50nm) were purchased from BBI Solutions (Cardiff, UK) and used as green (G) nanoparticles. Blue (B) nanoparticles were prepared by growing a 17nm silver shell on a 20nm spherical shaped gold seed. A sodium ascorbate solution (100 μ l, 50mM) was rapidly injected into a mixture containing 150pM of 20nm gold nanoparticles, 0.2% polyvinylpyrrolidone (PVP) and 0.24mM nitric acid to form a silver shell on the gold seeds.

The nanoparticle receptor and float were produced as follows.

Synthetic DNA oligonucleotides with thiol functionality (Bioneer, Daejeon, Korea) were reduced with a solution of 100mM Dithiothreitol (DTT) dissolved in 100mM Phosphate Buffer (PB) pH 8.0 for 1 hour and then separated by using a NAP-5 column (GE Healthcare, Buckinghamshire, UK). The sequences, modifications and densities of the surface DNA ligands used in the following examples are summarized in tables 2 to 12. Tables 2 to 12 show the sequences of thiolated DNA strands for functionalizing nanoparticles (r: spacer used in ligand and interface with surface density of 5 'thiol groups of thiolated ENA strands: spacer used in ligand and interface with 5' -A₁₅To EG₆-ligands of 3'3' thiol groups and spacers used in the interface: and the sequence Nos. 5' -EG to A disclosed in the following tables₁₅3' corresponds to the sequence of the ligand and the interface without spacer).

[ Table 2]

[ Table 3]

[ Table 4-1]

[ tables 4-2]

[ Table 5]

[ Table 6]

[ Table 7]

[ Table 8]

[ Table 9]

[ Table 10]

[ Table 11]

[ Table 12]

The mixture of thiolated DNA and nanoparticles (final concentration: 15pM) was incubated at room temperature for 1 hour. The full concentration of thiolated DNA was used, resulting in 14,400-fold and 19,200-fold excess relative to R and G nanoparticles, respectively. The ratio of biotinylated DNA interface to total surface DNA ligand was 0.5% (w/v), 35% (w/v) and 50% (w/v) relative to red (R-NF), green (G-NF), blue (B-NF), green (G-NR) and blue (B-NR) nano floats, respectively. Thereafter, the solution was adjusted to 0.1% (w/v) PVP in 10mM PB in the case of gold nanorods (R nanoparticles), to 0.1% (w/v) Sodium Dodecyl Sulfate (SDS) in 10mM PB in the case of spherical shaped gold nanoparticles (G nanoparticles), and to spherical shape in the solution of 10mM PB for nanoparticles (B nanoparticles). Three samples of 1M NaCl, 0.1% SDS and 10mM PB salt solution were added at 1 hour intervals so that the final concentration of NaCl was 0.3M. After addition of each salt, the mixture was heated at 50 ℃ for 10 minutes and incubated at room temperature. 2 hours after the final concentration was reached, the nanorod solution was washed centrifugally and redispersed in 1x PBS. Another nanoparticle solution was incubated for 12 hours, centrifuged, washed, and redispersed in deionized water (spherical shaped gold nanoparticles) or 1x PBS (spherical shaped nanoparticles). The characteristics of the modified nanoparticles were viewed by using a transmission electron microscope (JEM-2100, JEOL Ltd, Japan), an ultraviolet-visible spectrophotometer (Agilent 8453, Agilent Technologies, USA) and a dark field microscope (Axiovert 200M, Carl Zeiss, Gottingen, Germany) (fig. 3A to fig. 3C). Single particle scattering signals from three nanoparticles were analyzed by correlated SEM-DFM images (fig. 4A to 4C). The nanoparticles were loaded onto a glass substrate and imaged with a dark field microscope. Thereafter, after Pt coating (Cressington 108auto, Cressington Scientific Instruments Ltd, UK), the same sites were imaged by field emission scanning electron microscopy (FE-SEM, JSM-7600F, JEOL Ltd, Japan). TEM and FE-SEM measurements were performed at the Inter-University Research facility National Center and the Advanced Materials Institute (National Center for Inter-University Research Facilities and Research Institute of Advanced Materials), both of Seoul National University, Seoul.

Experimental conditions for signature analysis of nanoparticle circuits on lipid nanoplates are as follows.

To fully assemble the lipid nanoplatelets, a solution comprising DNA-modified nanoparticles with biotinylated linkers (1 to 10pM) is introduced into a flow chamber, the lower glass substrate of which is coated with a streptavidin-modified lipid bilayer surface. The solution is incubated for 1 to 5 minutes to obtain the desired particle density. Under these conditions, the particle density is linearly proportional to the incubation time (fig. 8A and 8B). After loading the lipid nanoplates, they were washed twice with 1x PBS. The tethered particles then take the DNA input contained in 1x PBS buffer and serve as a logic gate. DNA input sequences and densities and experimental conditions for pre-dimerization (decomposition gates) are summarized in tables 13 to 22. Tables 13 to 22 show experimental conditions and DNA input sequences used in logic circuit calculations (DNA sequences are disclosed in the 5 'to 3' direction, N/A: not applicable).

[ Table 13]

[ Table 14]

[ Table 15]

[ Table 16]

[ Table 17]

[ Table 18]

[ Table 19]

[ Table 20]

[ Table 21]

[ Table 22]

During dark field imaging, 500 μ Ι of input solution was injected into the flow cell to test the performance of each nanoparticle circuit at 25 ℃. Dark field imaging was performed using a 40x objective (NA 0.6) and a dark field microscope (tail Systems, South Korea) equipped with an AxiCam HRC color camera on the optical bench. Prior to injection of the infusion solution, 31 images were taken with a 200ms imaging time step to confirm the acceptor nanoparticles. Circuit performance was recorded at 2.5s imaging intervals during and after infusion of the infusion solution. Two image sequences are acquired in a fixed position.

Dark field time delay data analysis methodAs follows.

Image data obtained from the delayed dark-field image is processed and analyzed to quantify the output of the nanoparticle circuit. The quantization of the logic gate output is based on three steps: signal identification, tracking and classification.

After time-lapse imaging with a dark-field microscope, the optical scattering signals from the nanoparticle logic gate were validated and tracked with ImageJ software and custom MATLAB code. In ImageJ, the image is first registered by the StackReg plug-in to correct for lateral drift during imaging. The drift corrected image sequence is processed by an image analysis algorithm capable of detecting and tracking particles. This process is described in fig. 10A to 10C. The analytical procedure was as follows: (1) performing a signal detection step capable of segmenting the spots (pixels) with signals well above the threshold intensity and each nanoparticle signal; (2) performing a particle localization step for determining a representative portion of each segmented signal; and (3) performing a particle tracking step to identify receptor nanoparticles and classify the signal. In order to stably confirm the acceptor position, a short video in the initial state (a video shot at a recording frame rate of 5fps before the input joining) was used. Subsequently, only the receptor nanoparticles were tracked because the high particle density used under typical experimental conditions (capturing more than 4000 nanoparticles in the field of view of the area (180 μm × 180 μm)) interfered with the reliable tracking of the mobile float nanoparticles. After imaging, a portion of the field of view (128 x 128 μm) is selected because non-uniform focus and illumination over the field of view (typically viewed along the image border) prevents acquisition of a consistent nanoparticle scatter signal distribution²) For analysis.

Because the change in receptor signal depends on the signal of the float interacting with the receptor, receptor signal tracking provides sufficient information for use in quantitative logic gates. (4) Finally, the scatter signals from 3 x3 pixels around the localized position of each receptor are sampled and averaged, and one averaged scatter signal is assigned to the receptor at a given time. This process is repeated for all receptors and frames, thus obtaining a trace of the scatter signal for all receptors identified in the field of view. The gradual change in receptor signal due to the increase in float assembly and the increase in float disassembly was identified as an assembly event and a disassembly event, respectively. Eliminating transient interactions (transient and transient increases or decreases in receptor signaling). Homemade codes exclude intensity variations that do not persist until imaging is complete. That is, only receptors that show a signal trace similar to a step function are identified as output product particles.

Each assembly/disassembly event corresponding to a desired logic gate is classified according to how the receptor signal changes over time. The signal distributions of R nanoparticles, G nanoparticles, and B nanoparticles were used for classification (fig. 11A to 11C). The classification process is as follows: (1) receptors representing individual signal changes are identified by signal traces of the receptors obtained from parallel imaging and analysis. A step-up in receptor signaling indicates particle assembly, and a step-down in receptor signaling indicates particle breakdown (from pre-dimerized nanoparticle pairs). (2) For the assembly reactions, the signals of the indicated receptors were compared to the signal profiles shown in fig. 11A to 11C. Based on its position in the 3D signal distribution, the receptor is classified as one of R nanoparticle monomer, G nanoparticle monomer, or B nanoparticle monomer. In the case of a breakdown reaction, the original identity of the pre-dimer receptor can be determined by comparing the final signal of the receptor displayed with the 3D signal profile. (3) Since the signal changes induced by R float, G float and B float are specific to the combination of particles involved in each reaction, the changes in signal tracking are used to classify the floats that interact with the identified receptors. (4) The type of reaction between the receptor-float pair is completely classified by using the type of receptor (obtained in step (2)) and the change in signal (obtained in step (3)).

In fig. 7, (i) the signal trace with discrete stepwise decreases represents the decomposition reaction, (ii) the signal after decomposition represents B-NR, and (iii) the change in signal represents a sharp decrease in G intensity (representing release of G-NF from B-NR), so the decomposition of G-NF from B-NR can be discerned. In the experiment, the receptor-to-float ratio was set to about 10 to minimize multimer formation.

For each circuit, the number of receptors producing the correct output is calculated over time. Recording was continued until the kinetic map became constant. The final number of events is normalized for each type of logic gate to minimize the impact of variability of the particle population. For the sectional door, the event count is normalized by the number of floats detected in the initial 31 images. In the case of the dissociation gate, the dissociation event count is normalized by the number of dimers formed in the pre-dimerization. The ON/OFF level is calculated by dividing the lowest output count obtained under the tune condition by the highest output count obtained under the FALSE condition (when the output count under the FALSE condition is 0, the output count is set to 1).

The diffusion behavior of the nanoparticle logic gate, specifically, the diffusion behavior of the float particles, was analyzed as follows. (1) The particle density of the float particles loaded on the SLB (200 particles per total field of view) is low enough, thus providing long-term tracking without rail overlap. (2) From each frame a signal is detected, the position of each float particle is located based on the same algorithm described above. (3) The determined positions are used to generate a trajectory for each float and then used for the calculation of the diffusion coefficient. For each particle, Mean Square Displacement (MSD) values are obtained as a function of time. MSD mapping of the trajectory was fitted to an equation<r²>4Dt, wherein<r²>Is MSD, D is the diffusion coefficient, and t is the time interval.

The simulation method of nanoparticle assembly reaction for lipid bilayer is as follows.

The assembly reaction of SLB-tethered nanoparticles was modeled and simulated by using MATLAB. This calculation was developed to assess how the NR/NF ratio affects the extent of dimer formation. In this model, a given number of NR and NF are randomly dispersed over 128X 128 μm with periodic boundary conditions²In the region of (a). The diffusion constant of NF was specified to have a normal distribution with 0.9 μm²Average value of/s and 0.3. mu.m²Standard deviation in/s. This approximation is shown in fig. 5A and 5B, and is based on the diffusion profile associated with the NF shown in fig. 33A-33CExperimental data of (2). The dispersion of the floats is affected by two-dimensional random walk, assuming a step size of 4Dt per float when t is 5 ms. The position of NR is fixed. To perform the simulation efficiently, the nanoparticles are set to diffuse at the grid points through the interplanar spacings of the nanoparticles (including the lengths of the surface ligands). For each collision, the binding event between NR and NF occurs with a probability of 0.3. In the simulation, "collision" is defined as an event that occurs when the profile of the float overlaps the profile of the receptor. In the formation of multimers, it is speculated that the addition of another float to the receptor-plotter dimer is not spatially preferred over the addition of a float to the receptor, and therefore a low binding potential is used (0.18 for trimer formation and 0.09 for tetramer formation). The scaling factor is introduced based on geometric constraints and the simulation results are shown in fig. 9.

The oligonucleotide design method is as follows.

The nanoparticles are functionalized with a single-stranded DNA strand comprising a thiol modification at the 3 'end or 5' end. The DNA sequence as a free native code was designed at the domain level by using DomainDesign ("D.Y.Zhang, A.J.Turberfield, B.Yurke, E.Wifree, Engineering Engineering control-drive Reactions and Networks catalysts by DNA.science.318, 1121-11252007" and "D.Y.Zhang in DNA Computing and Molecular Programming, Y.Sakakibara, Y.Mi Eds. (Springer Berlin Heidelberg,2010. left Notes in Computer Science, pp.162-175.) Thiolated DNA strands include (1) a" linker "DNA strand for tethering the nanoparticle to the surface of streptavidin-modified SLB, and (2) a ligand for direct hybridization with the" nanoparticle "by an input ligand to the surface and in the input solution of other nanoparticles on the SLB.

Linker chains with 5' -thiol modification include: (i) a poly a domain of 15 bases after the 5' -thiol group; (ii)6 ethylene glycol EG units (PEG moieties); and (iii) a linker domain of 34 bases followed by biotin modification.

The linker strand with the 3' -thiol modification (with the biotin modification at the 5' -end) is followed by (i) a 34 base linker domain, (ii) a PEG moiety and (iii) a 3' -thiol modification, followed by a 15 base poly a domain.

The surface ligand DNA strands used in the following examples were classified into the following two types: (1) a "normal" single-stranded DNA strand that does not form a hairpin loop; AND (2) hairpin DNA ligands used in assembling AND gates.

Normal ligand types are also classified into two groups: group with 3' -thiol modification; and groups with 5' -thiol modifications.

5' -thiol ligands include: (i) PEG moieties (after 5' -thiol); (ii) a 10 base spacer domain; and (iii) a 14 base binding domain. The 3' -thiol ligand includes: (i) a 14 base binding domain; (ii) a 10 base spacer domain; and (iii) a PEG moiety (including subsequent 3' -thiol modification). Unless otherwise indicated, 3 '-thiol ligands and 5' -thiol ligands are used for receptors and floats, respectively. Hairpin DNA ligands are thiolated at the 5' end. In the following example, a 10 base branch domain is used.

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