40 research outputs found

    Effective design principles for leakless strand displacement systems

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    Artificially designed molecular systems with programmable behaviors have become a valuable tool in chemistry, biology, material science, and medicine. Although information processing in biological regulatory pathways is remarkably robust to error, it remains a challenge to design molecular systems that are similarly robust. With functionality determined entirely by secondary structure of DNA, strand displacement has emerged as a uniquely versatile building block for cell-free biochemical networks. Here, we experimentally investigate a design principle to reduce undesired triggering in the absence of input (leak), a side reaction that critically reduces sensitivity and disrupts the behavior of strand displacement cascades. Inspired by error correction methods exploiting redundancy in electrical engineering, we ensure a higher-energy penalty to leak via logical redundancy. Our design strategy is, in principle, capable of reducing leak to arbitrarily low levels, and we experimentally test two levels of leak reduction for a core “translator” component that converts a signal of one sequence into that of another. We show that the leak was not measurable in the high-redundancy scheme, even for concentrations that are up to 100 times larger than typical. Beyond a single translator, we constructed a fast and low-leak translator cascade of nine strand displacement steps and a logic OR gate circuit consisting of 10 translators, showing that our design principle can be used to effectively reduce leak in more complex chemical systems

    Simplifying Chemical Reaction Network Implementations with Two-Stranded DNA Building Blocks

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    In molecular programming, the Chemical Reaction Network model is often used to describe real or hypothetical systems. Often, an interesting computational task can be done with a known hypothetical Chemical Reaction Network, but often such networks have no known physical implementation. One of the important breakthroughs in the field was that any Chemical Reaction Network can be physically implemented, approximately, using DNA strand displacement mechanisms. This allows us to treat the Chemical Reaction Network model as a programming language and the implementation schemes as its compiler. This also suggests that it would be useful to optimize the result of such a compilation, and in general to find effective ways to design better DNA strand displacement systems. We discuss DNA strand displacement systems in terms of "motifs", short sequences of elementary DNA strand displacement reactions. We argue that describing such motifs in terms of their inputs and outputs, then building larger systems out of the abstracted motifs, can be an efficient way of designing DNA strand displacement systems. We discuss four previously studied motifs in this abstracted way, and present a new motif based on cooperative 4-way strand exchange. We then show how Chemical Reaction Network implementations can be built out of abstracted motifs, discussing existing implementations as well as presenting two new implementations based on 4-way strand exchange, one of which uses the new cooperative motif. The new implementations both have two desirable properties not found in existing implementations, namely both use only at most 2-stranded DNA complexes for signal and fuel complexes and both are physically reversible. There are reasons to believe that those properties may make them more robust and energy-efficient, but at the expense of using more fuel complexes than existing implementation schemes

    Probabilistic switching circuits in DNA

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    A natural feature of molecular systems is their inherent stochastic behavior. A fundamental challenge related to the programming of molecular information processing systems is to develop a circuit architecture that controls the stochastic states of individual molecular events. Here we present a systematic implementation of probabilistic switching circuits, using DNA strand displacement reactions. Exploiting the intrinsic stochasticity of molecular interactions, we developed a simple, unbiased DNA switch: An input signal strand binds to the switch and releases an output signal strand with probability one-half. Using this unbiased switch as a molecular building block, we designed DNA circuits that convert an input signal to an output signal with any desired probability. Further, this probability can be switched between 2^n different values by simply varying the presence or absence of n distinct DNA molecules. We demonstrated several DNA circuits that have multiple layers and feedback, including a circuit that converts an input strand to an output strand with eight different probabilities, controlled by the combination of three DNA molecules. These circuits combine the advantages of digital and analog computation: They allow a small number of distinct input molecules to control a diverse signal range of output molecules, while keeping the inputs robust to noise and the outputs at precise values. Moreover, arbitrarily complex circuit behaviors can be implemented with just a single type of molecular building block

    Effective design principles for leakless strand displacement systems

    Get PDF
    Artificially designed molecular systems with programmable behaviors have become a valuable tool in chemistry, biology, material science, and medicine. Although information processing in biological regulatory pathways is remarkably robust to error, it remains a challenge to design molecular systems that are similarly robust. With functionality determined entirely by secondary structure of DNA, strand displacement has emerged as a uniquely versatile building block for cell-free biochemical networks. Here, we experimentally investigate a design principle to reduce undesired triggering in the absence of input (leak), a side reaction that critically reduces sensitivity and disrupts the behavior of strand displacement cascades. Inspired by error correction methods exploiting redundancy in electrical engineering, we ensure a higher-energy penalty to leak via logical redundancy. Our design strategy is, in principle, capable of reducing leak to arbitrarily low levels, and we experimentally test two levels of leak reduction for a core “translator” component that converts a signal of one sequence into that of another. We show that the leak was not measurable in the high-redundancy scheme, even for concentrations that are up to 100 times larger than typical. Beyond a single translator, we constructed a fast and low-leak translator cascade of nine strand displacement steps and a logic OR gate circuit consisting of 10 translators, showing that our design principle can be used to effectively reduce leak in more complex chemical systems

    Computing Properties of Thermodynamic Binding Networks: An Integer Programming Approach

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    The thermodynamic binding networks (TBN) model was recently developed as a tool for studying engineered molecular systems. The TBN model allows one to reason about their behavior through a simplified abstraction that ignores details about molecular composition, focusing on two key determinants of a system's energetics common to any chemical substrate: how many molecular bonds are formed, and how many separate complexes exist in the system. We formulate as an integer program the NP-hard problem of computing stable configurations of a TBN (a.k.a., minimum energy: those that maximize the number of bonds and complexes). We provide open-source software that solves these formulations, and give empirical evidence that this approach enables dramatically faster computation of TBN stable configurations than previous approaches based on SAT solvers. Our setup can also reason about TBNs in which some molecules have unbounded counts. These improvements in turn allow us to efficiently automate verification of desired properties of practical TBNs. Finally, we show that the TBN's Graver basis (a kind of certificate of optimality in integer programming) has a natural interpretation as the "fundamental components" out of which locally minimal energy configurations are composed. This characterization helps verify correctness of not only stable configurations, but entire "kinetic pathways" in a TBN

    Finite-time regulation property of DNA feedback regulator

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    In dynamic DNA nanotechnology, the DNA strand displacement technique provides the cornerstone for the bottom-up design of a man-made DNA molecular system. Practically, a feedback controller for regulating the concentration of a target DNA strand to the desired level is indispensable for mediating the kinetic momentum of a molecular actuator. However, such a regulator system operates by consuming fuel strands and requires sufficient supplies of these consumables for its normal execution, indicating that, in practice, optimal controller design requires the period of time during which the regulator proceeds with normal operation to be as long as possible. The fact that the system is naturally high dimensional and nonlinear complicates the analysis of properties emerging during a finite-time period in terms of their theoretical aspects. In this paper, we first define the new concept of a “finite-time regulation property” of DNA systems in the regulation problem. Then, to theoretically analyze this regulation property, we present two-time-scale modeling based on the difference in the initial distribution of the abundance of DNA strands. Focusing on the fast mode as a subsystem with a positive quadratic structure, we propose a new method for analyzing the regulation property observed in a finite period of time

    Approximate Majority with Catalytic Inputs

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    Population protocols are a class of algorithms for modeling distributed computation in networks of finite-state agents communicating through pairwise interactions. Their suitability for analyzing numerous chemical processes has motivated the adaptation of the original population protocol framework to better model these chemical systems. In this paper, we further the study of two such adaptations in the context of solving approximate majority: persistent-state agents (or catalysts) and spontaneous state changes (or leaks). Based on models considered in recent protocols for populations with persistent-state agents, we assume a population with nn catalytic input agents and mm worker agents, and the goal of the worker agents is to compute some predicate over the states of the catalytic inputs. We call this model the Catalytic Input (CI) model. For m=Θ(n)m = \Theta(n), we show that computing the parity of the input population with high probability requires at least Ω(n2)\Omega(n^2) total interactions, demonstrating a strong separation between the CI model and the standard population protocol model. On the other hand, we show that the simple third-state dynamics of Angluin et al. for approximate majority in the standard model can be naturally adapted to the CI model: we present such a constant-state protocol for the CI model that solves approximate majority in O(nlogn)O(n \log n) total steps with high probability when the input margin is Ω(nlogn)\Omega(\sqrt{n \log n}). We then show the robustness of third-state dynamics protocols to the transient leaks events introduced by Alistarh et al. In both the original and CI models, these protocols successfully compute approximate majority with high probability in the presence of leaks occurring at each step with probability βO(nlogn/n)\beta \leq O\left(\sqrt{n \log n}/n\right), exhibiting a resilience to leaks similar to that of Byzantine agents in previous works
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