Scaffolded DNA Computing for Thermodynamically Stable Outputs
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Solution Overview
Problem
Prior art DNA computing systems are not thermodynamically favoured, leading to unstable results due to algorithmic errors and off-seeded growth, with target outputs often not being the desired outcome and suffering from leaks in strand displacement circuits.
Innovation Solution
A method and arrangement that utilizes a scaffolded DNA computer (SDC) with a set of computing tiles designed to ensure correct compute domain bindings are enthalpically favoured over incorrect ones, allowing the system to naturally evolve to the target output at equilibrium, eliminating the need for error-correction subsystems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prior art DNA computing systems are used, then molecular computations can be performed, but the results are unstable due to algorithmic errors and off-seeded growth
Solution Approach 1:
The patent changes the thermodynamic parameters of the DNA binding interactions by designing compute domains with specific binding affinities. Correct bindings are designed to be enthalpically favoured with higher binding affinity, while incorrect bindings have lower affinity. This parameter change ensures that the target output configuration is thermodynamically stable without requiring external error correction mechanisms.
Solution Approach 2:
The system achieves self-correcting behavior where mismatched computing tiles are automatically replaced through thermodynamically driven strand displacement. The correct compute domain bindings naturally outcompete incorrect bindings, allowing the system to self-correct algorithmic errors and off-seeded growth without external intervention or complex error-correction subsystems.
2Reliability
If prior art molecular computing systems are used, then computations can proceed, but the target output is out-of-equilibrium leading to leaks in strand displacement circuits
Solution Approach 1:
The patent fundamentally changes the thermodynamic parameter landscape by ensuring that the target output configuration represents the global minimum free energy state. This is achieved by designing the compute domains such that correct bindings accumulate sufficient enthalpic favorability to overcome entropic costs, making the target output the thermodynamically stable equilibrium state rather than an out-of-equilibrium configuration.
3Reliability
If computing tiles are designed with strong compute domain bindings, then correct bindings are favoured, but mismatched tiles may still bind initially causing algorithmic errors
Solution Approach 1:
The system employs self-correcting kinetics where initially bound mismatched tiles are automatically displaced by correctly matched tiles through thermodynamically driven strand displacement reactions. The excess concentration of computing tiles ensures that correct tiles continuously compete for binding, and the enthalpic favorability of correct bindings ensures that once a correct tile binds, it is stable. This self-service mechanism eliminates algorithmic errors without requiring external error correction, thereby avoiding time loss.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The SDC achieves robust, fast, and renewable molecular computations by favouring the target output thermodynamically, outcompeting other configurations, and enabling multi-level fluorescence detection for outcome analysis.
Implementation Method 1
Watson-Crick base pairing, with guanine (G) forming a base pair with cytosine (C), and adenine (A) forming a base pair with thymine (T), allows a combinatorically large set of nucleotide sequences to be used when designing binding interactions.
Implementation Method 2
Each computing tile is comprised of a short information-encoding molecule or strand, such as e.g. a short polynucleotide or a short amino acid sequence, and comprises a bottom position domain and at least one compute domain, wherein for each computing tile, the bottom position domain is arranged to bind directly to a matching scaffold position on the scaffold with a first binding strength, and to the compute domains of other computing tiles with a set of second binding strengths, which are each weaker than the first binding strength.
Implementation Method 3
allowing replacement of all mismatched computing tiles, based on correct compute domain bindings being enthalpically favoured over incorrect compute domain bindings (algorithmic errors)
Data Source
AI summary
A method 800 for making molecular computations comprises: i) providing a mixture 100 comprising a scaffold 110 comprising N scaffold positions 120; ii) designing a set of computing tiles to drive a desired computation, the set of computing tiles comprising at least N different computing tile types, wherein the computing tile types are selected to be used for the computation, in such a way that a target output has a higher probability of being reached than the probability for any other potential output, wherein for each computing tile 140, a bottom position domain 160 is arranged to bind directly to a matching scaffold position 120 with a first binding strength, and to the compute domains 150 of other computing tiles 140 with a set of second binding strengths, which are each weaker than the first binding strength; iii) adding the designed set of computing tiles to the mixture 100; iv) allowing computing tiles 140 to bind to scaffold positions 120 until all scaffold positions 120 required for the computation have been filled; v) allowing replacement of all mismatched computing tiles 140, based on correct compute domain bindings being enthalpically favoured over incorrect compute domain bindings; and vi) reaching an output configuration.