Computing Handbook Set - Computer Science ( Volume I ) Chapter : DNA Computing

Molecular computing is computation done at the molecular scale. DNA computing is a class of molecular computing that does computation by the use of reactions involving DNA molecules. DNA computing has been far the most successful (in scale and complexity of the computations and molecular assemblies done) of all known approaches to molecular computing, perhaps due in part to the very well established biotechnology and biochemistry on which its experimental demonstration relies, as well as the frequent teaming of scientists in the field with multiple essential disciplines including chemistry, biochemistry, physics, material science, and computer science. This chapter surveys the field of DNA computing. It begins with a discussion of underlying principles, including motivation for molecular and DNA computations, brief overviews of DNA structures, chemical reaction systems, DNA reactions, and classes of protocols and computations. Then, the chapter discusses potential applications of DNA computing research. The main section overviews how DNA computation is done, with a discussion of DNA hybridization circuits, including both solution-based as well as localized hybridization circuits. It also discusses design and simulation software for the same. It overviews DNA detectors, DNA replicators, DNA nanorobotic devices, and DNA dynamical systems. The chapter then describes tiling assembly computations, including theoretical models and results, design and simulation software, experimental methods for assembly of tiling lattices in various dimensions, step-wise tiling assemblies, activatable tiles, and tiling error-correction methods. 1 Underlying Principles 1.1 Motivation: Why do Molecular Computation and Why use DNA for Computation and Self-Assembly ? In an era where electronic computers are powerful and inexpensive, why do we need molecular computation? One response to this question is that conventional electronic devices have been miniaturized to the point where traditional top-down methods for manufacturing these devices are approaching their inherent limits due to constraints in photolithographic techniques, and further miniaturization is not cost-effective. On the other hand bottom-up manufacturing methods such as molecular self-assembly have no such scale size limits. Another response is that molecular computation provides capabilities that traditional computers can not provide; there are computations that need to be done in environments and at scales where a traditional computer can not be positioned, for example within a cell or within a synthetic molecular structure or material. Why use Nucleic Acids such as DNA for Computation and Self-Assembly? DNA, and nucleic acids in general, are unique in multiple aspects: 1. First of all, they hold and can convey information encoded in their sequences of bases. Most of their key physical properties are well understood. 2. Their tertiary structure is much more predictable, compared to molecules such as proteins. 3. Their hybridization reactions, which allow for addressing of specific subsequences, are also well understood and productively controllable. 4. They allow for a large set of operations to be performed on them. Well-known enzymatic reactions for manipulation of DNA exist. 5. Finally, there is a well-developed set of methods such as gel-electrophloresis, FRET, plasmonics, AFM imaging, etc. for quantifying the success of experiments involving DNA and DNA nanostructures. Before we delve into how molecular computation is done, we will discuss DNA structure and function, how information may be stored in it, and what environment it needs to efficiently do computation. 1.2 Brief Overview of DNA Structure DNA is a polymer which can exist in either single or double stranded form. Each strand of DNA is made up of a repeating set of monomers called nucleotides. A nucleotide consists of three components, a 5 carbon sugar molecule, a nitrogenous base, and a phosphate group. The ...-phosphate-sugar-phosphate-... covalent bond forms the backbone of a DNA strand. The phosphate group is attached to carbon atom 5 (C5) on one end, and C3 on another end. This gives the DNA strand directionality, and the two ends of a DNA strand are commonly termed the 5’ (prime) and the 3’ ends. This can be seen in figure 1. 1.2.1 DNA Bases Nitrogenous bases are the component of nucleotides not involved in forming the backbone of a strand. There are 5 types of these bases, named Adenine(A), Guanine(G), Cytosine(C), Thymine(T) and Uracil(U). Only 4 of these A,G,C,T are present in DNA, while T is replaced by U in RNA. A and G belong to a class of bases called Purines, while C,T,U fall under Pyrimidines. Figure 2 shows the difference in structure of Purines and Pyrimidines. A purine has a pyrimidine ring fused to an imidazole ring, and contains 4 nitrogen atoms as opposed to 2 nitrogen atoms in a pyrimidine. Figure 1: DNA Backbone (on left) and DNA Bases involved in hydrogen bonding (middle) [10] Figure 2: Structure of DNA/RNA bases ( [35]) 1.2.2 ssDNA & dsDNA Structure: The Double Helix DNA can exist either in single stranded DNA (ssDNA) form, or as a result of two complementary ssDNA binding together via hydrogen bonds to form double-stranded DNA (dsDNA). The two ssDNA are always antiparallel when bound, i.e. one strand has 5’ to 3’ direction, while the other has a 3’ to 5’ direction. DNA exists as a double helix, as shown in figure 3. The nitrogen bases in each ssDNA bind with a complementary base in the other strand, to give rise to this structure: A binds with T, and G binds to C. This pairing of bases, is called the Watson-Crick bonding in DNA, as shown in detail in figure 4. An important note is that a purine always binds to a pyrimidine and this can be seen in figure 1. dsDNA can have different helical conformations, namely the A, B and Z forms, and it can transform from one to another based on the hydration conditions, the pH and the ionic concentration of the environment. The most common form of DNA is the B form, which it assumes when hydrated. A relative comparison of each of these conformations is shown in figure 5. In its ssDNA form, DNA exists as a long single thread, or in many cases, it forms a secondary structure, where the strand loops around itself, and forms hydrogen bonds with other bases on itself (called a random coil). Figure 3: Double Helix form of dsDNA (B-form) [42] Figure 4: Watson Crick Hydrogen Bonding [105] 1.2.3 DNA Hairpins DNA hairpins are a special secondary structure formed by an ssDNA, and contain a neck/stem double stranded region, and an unhybridized loop region, as seen in figure 6. Hairpins have been recognized as a useful tool in molecular computation because of 3 reasons: (1) Hairpins store energy in their unhybridized loop, and on hybridization, energy is released driving the reaction forward. (2) In their hairpin form, they are relatively unreactive with other DNA strands, and act as excellent monomers until an external entity (usually another DNA strand) causes the stem region to open and react with other DNA complexes. Hence, they can persist with low leaks for a long amount of time. (3) A common way to create DNA complexes is to anneal them. DNA complexes usually contain a large number of strands, and multiple different structures can be formed because of varied interactions between different strands. In low concentrations, DNA hairpins usually form without error, and are not involved in spurious structure formation. This is because their formation is not diffusion dependent, i.e. the two ends of a hairpin hybridize with each other before two ends of different hairpins hybridize. This property, is known as locality, and is a strong motivation for the use of hairpins. Hairpins [32], and metastable DNA hairpin complexes [82,95] have been used as fuel in chain reactions to form large polymers [20], in programming pathways in self-assembly [113], and in logic circuits [81]. A common technique to help open a DNA hairpin is via a process known as toehold mediated strand displacement, which we shall discuss in more detail in section 1.4.1. Figure 5: Comparison of A, B and Z forms of DNA Figure 6: Hairpin open and closed forms [9] 1.3 Brief Overview of Chemical Reaction Networks in DNA Computing Chemical reaction networks (CRNs) are becoming central tools in the study and practice of DNA computing and molecular programming. Their role is twofold as a model for analyzing, quantifying and understanding the behavior of certain DNA computing systems and as a specification/programming language for prescribing information processing (computational) behavior. The first of these roles is traditional and is analogous to the role played in Biology by CRNs in describing biochemical processes and genetic reaction networks. The latter role thinking of CRNs as a programming language is unique to the field of DNA computing and is a consequence of the ability of DNA to act as an information processing medium and emulate (with certain restrictions) any CRN set down on paper. We will discuss both these roles briefly in the following paragraphs. 1.3.1 CRNs Model DNA Strand Displacement Reaction Networks Enzyme-free DNA computing devices can execute i) Boolean circuits and linear threshold gate networks (the latter model neural networks) [66, 67, 80], (ii) Nucleic acid amplifiers [20, 113, 121] (iii) Finite state automata [30] and (iv) Molecular walkers [31,115]. All of these devices are examples of strand displacement reaction networks (SDRNs). In a toehold-mediated strand displacement reaction an incoming DNA strand displaces a competing DNA strand hybridized to a DNA substrate strand. The incoming strand first binds to a toehold a short single stranded portion of the substrate and then competitively displaces the outgoing strand from the substrate by a one-dimensional random walk process. A cascade (network) of such strand displacement reactions are called SDRNs. The modular design characteristics of SDRNs allow them to be modelled as CRNs. In particular, the types and rates of reactions are limited. We can infer them from prior experience and/or predict them from thermodynamic parameters [124]. This allows us to predict the CRN model and verify its predictions experimentally. 1.3.2 CRNs as a Programming Language [88] have shown that SDRNs can in theory closely approximate the dynamic behaviour of any CRN upto a time and concentration scaling. They illustrate how any CRN that we set down upon paper can be translated into a set of DNA molecules that when mixed together in the appropriate concentrations will emulate the behavior of the CRN. Certain CRNs seem hard to emulate in practice and no successful SDRN implementations currently exist for these but many others have been successfully implemented. CRNs are more abstract than SDRNs and can be thought of as a higher level programming language. The process of translating a CRN into its corresponding SDRN is then analogous to compiling a higher level programming language down to a lower level programming language. Programming in the CRN language has the advantages inherent in programming in higher level languages versus programming in lower level languages. How powerful is the CRN language? Quite powerful, it turns out. [87] prove that a finite CRN obeying stochastic dynamics can achieve efficient Turing-universal computation with arbitrarily small (non-zero) error. Error-free computation is impossible in this setting, only semilinear functions can be computed without errors [14].