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

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

Robust Digital Molecular Design of Binarized Neural Networks

Molecular programming - a paradigm wherein molecules are engineered to perform computation - shows great potential for applications in nanotechnology, disease diagnostics and smart therapeutics. A key challenge is to identify systematic approaches for compiling abstract models of computation to molecules. Due to their wide applicability, one of the most useful abstractions to realize is neural networks. In prior work, real-valued weights were achieved by individually controlling the concentrations of the corresponding "weight" molecules. However, large-scale preparation of reactants with precise concentrations quickly becomes intractable. Here, we propose to bypass this fundamental problem using Binarized Neural Networks (BNNs), a model that is highly scalable in a molecular setting due to the small number of distinct weight values. We devise a noise-tolerant digital molecular circuit that compactly implements a majority voting operation on binary-valued inputs to compute the neuron output. The network is also rate-independent, meaning the speed at which individual reactions occur does not affect the computation, further increasing robustness to noise. We first demonstrate our design on the MNIST classification task by simulating the system as idealized chemical reactions. Next, we map the reactions to DNA strand displacement cascades, providing simulation results that demonstrate the practical feasibility of our approach. We perform extensive noise tolerance simulations, showing that digital molecular neurons are notably more robust to noise in the concentrations of chemical reactants compared to their analog counterparts. Finally, we provide initial experimental results of a single binarized neuron. Our work suggests a solid framework for building even more complex neural network computation

CRNs Exposed: A Method for the Systematic Exploration of Chemical Reaction Networks

Formal methods have enabled breakthroughs in many fields, such as in hardware verification, machine learning and biological systems. The key object of interest in systems biology, synthetic biology, and molecular programming is chemical reaction networks (CRNs) which formalizes coupled chemical reactions in a well-mixed solution. CRNs are pivotal for our understanding of biological regulatory and metabolic networks, as well as for programming engineered molecular behavior. Although it is clear that small CRNs are capable of complex dynamics and computational behavior, it remains difficult to explore the space of CRNs in search for desired functionality. We use Alloy, a tool for expressing structural constraints and behavior in software systems, to enumerate CRNs with declaratively specified properties. We show how this framework can enumerate CRNs with a variety of structural constraints including biologically motivated catalytic networks and metabolic networks, and seesaw networks motivated by DNA nanotechnology. We also use the framework to explore analog function computation in rate-independent CRNs. By computing the desired output value with stoichiometry rather than with reaction rates (in the sense that X ? Y+Y computes multiplication by 2), such CRNs are completely robust to the choice of reaction rates or rate law. We find the smallest CRNs computing the max, minmax, abs and ReLU (rectified linear unit) functions in a natural subclass of rate-independent CRNs where rate-independence follows from structural network properties

Enzyme-Free Nucleic Acid Dynamical Systems

An important goal of synthetic biology is to create biochemical control systems with the desired characteristics from scratch. Srinivas et al. describe the creation of a biochemical oscillator that requires no enzymes or evolved components, but rather is implemented through DNA molecules designed to function in strand displacement cascades. Furthermore, they created a compiler that could translate a formal chemical reaction network into the necessary DNA sequences that could function together to provide a specified dynamic behavior

Programmable chemical controllers made from DNA

Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language' and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents

Reversible Bond Logic

The field of molecular programming allows for the programming of the structure and behavior of matter at the molecular level, even to the point of encoding arbitrary computation. However, current approaches tend to be wasteful in terms of monomers, gate complexes, and free energy. In response, we present a novel abstract model of molecular programming, Reversible Bond Logic (RBL), which exploits the concepts of reversibility and reversible computing to help address these issues. RBL systems permit very general manipulations of arbitrarily complex "molecular" structures, and possess properties such as component reuse, modularity, compositionality. We will demonstrate the implementation of a common free-energy currency that can be shared across systems, initially using it to power a biased walker. Then we will introduce some basic motifs for the manipulation of structures, which will be used to implement such computational primitives as conditional branching, looping, and subroutines. Example programs will include logical negation, and addition and squaring of arbitrarily large numbers. As a consequence of reversibility, we will also obtain the inverse programs (subtraction and square-rooting) for free. Due to modularity, multiple instances of these computations can occur in parallel without cross-talk. Future work aims to further characterize RBL, and develop variants that may be amenable to experimental implementation

DNAコンピューティングシステムの設計支援 : DNAツールボックスとその拡張

学位の種別:課程博士University of Tokyo(東京大学