DNA and RNA Nanotechnology 2015; 2: 13–22

occurring molecular computer is a living cell. Cells continuously process an enormous amount of signals (inputs) generated by a broad range of environmental factors such as temperature, pH, pressure, nutrients, signaling chemicals, macromolecules, etc. [1-5]. Once the signal is processed, the appropriate response (output) is effected. Examples of such a response can include gene silencing, enzymatic activity, cell proliferation, migration, and apoptosis. In the field of synthetic biology, there is tremendous interest in the fabrication of artificial “smart” nano-devices that can autonomously perform functions similar to the physiological behavior of living cells. These functions, which utilize macromolecules such as DNA, RNA, and proteins, include the storage, retrieval, and processing of inputs.

Molecular computers use inputs of materials and energy to achieve a specific purpose by repeatedly cycling them through certain states. They differ from conventional computers in fundamental ways. Most molecular computers are nanoscale objects, thus are subject to manifestations of universal physical laws which differ greatly from the macroscopic world of our senses [6-8]. For example, molecular computers have a small mass-to-volume ratio and move in viscous media rather than in a vacuum or air. As a result, they cannot store momentum or kinetic energy as usefully as macroscopic devices do. In addition, they cannot store thermal energy for a significant period of time due to their extremely small size, and must operate isothermally.

Conformational changes in the moving parts of molecular machines, driven by thermal agitation, create, modify and disrupt binding and catalytic sites for substrates, and after the free-energy landscapes that govern their motions. This characteristic gives molecular computers a distinct advantage, although harnessing this capability of nanoscale soft matter is proving to be one of the most challenging aspects of molecular design. Molecular devices can, however, store elastic potential energy with spring-like conformational distortions of their macromolecular parts or substrates [8]. They consist of soft, conformationally-flexible matter that can function

DOI 10.1515/rnan-2015-0003 Received July 21, 2015; accepted September 11, 2015

Abstract: Molecular computers have existed on our planet for more than 3.5 billion years. Molecular computing devices, composed of biological substances such as nucleic acids, are responsible for the logical processing of a variety of inputs, creating viable outputs that are key components of the cellular machinery of all living organisms. We have begun to adopt some of the structural and functional knowledge of the cellular apparatus in order to fabricate nucleic-acid-based molecular computers in vitro and in vivo. Nucleic acid computing is directly dependent on advances in DNA and RNA nanotechnology. The field is still emerging and a number of challenges persist. Perhaps the most salient among these is how to translate a variety of nucleic-acid-based logic gates, developed by numerous research laboratories, into the realm of silicon-based computing. This mini-review provides some basic information on the advances in nucleic-acid-based computing and its potential to serve as an alternative that can revolutionize silicon-based technology.

Keywords: Molecular computer, RNA nanotechnology, DNA nanotechnology, Logic Gates, Biocomputing, Silicon based computing

1 IntroductionMolecular computers are natural and/or artificial devices in which macromolecules, including proteins and nucleic acids, mediate necessary functions. These functions usually include three basic operations: sensing inputs, processing the inputs, and generating specific outputs. The best-known example of a naturally

Research Article Open Access

© 2015 Seth G. Abels, Emil F. Khisamutdinov, licensee De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.

Seth G. Abels and Emil F. Khisamutdinov*

Nucleic Acid Computing and its Potential to Transform Silicon-Based Technology

*Corresponding authors: Emil F. Khisamutdinov, Department of Chemistry, Ball State University, 3401 N Tillotson Ave, Muncie, Indiana 47306, USA, E-mail: kemil@bsu.eduSeth G. Abels, Department of Chemistry, Ball State University, Muncie, IN 47304, USA

14    S.G. Abels, E.F. Khisamutdinov

as ratchet-and-pawl devices to bias molecular motions of their substrates or flexible subsystems in desired directions [6].

In bionics, many engineering principles have been adopted from the study of biological machineries (6-8). A few historical examples of bionics include fixed-wing aircraft, whose design was both inspired and informed by the study of birds in flight, and Velcro, which was based on the hook-and-loop mechanism by which burs cling to clothing and fur. As the bionic approach to engineering ventures more into the micro- and nanoscale, many fields, from computer science to medicine to robotics, could be transformed. For instance, consider ribosomal RNA. The study, incorporation, and even flat-out mimicry of the features of this naturally-occurring molecular computer may help us clear hurdles and uncover answers (as well as more questions) related to the design and behavior of nanocomputers. Current research in the fields of DNA [9-13] and RNA [14-18] nanotechnologies, as well as in molecular engineering [19-21], support the model of nucleic acid molecules as promising candidates to fabricate molecular devices or bio-computers. DNA and RNA molecules possess the programmable folding properties necessary to carry out various applications ranging from a drug carrier [22, 23] to nano-chips [24].

This mini-review focuses on the recent advances in nucleic acid computing. It is organized as follows: an introduction to the simple operations carried out by computer microchips, including their limitations; a review of DNA and RNA computing as an alternative to silicon-based computing technology; and a summary of, and an outlook for, nucleic-acid based computing.

2 Silicon-Based Technology and its LimitationsIn computing devices, the input information is mathematically processed into a digital signal. For example, in the case of a binary code, the basic unit of information is written as a series of “0’s” and “1’s,” indicating the two states of the logic circuit. To gain better insight into molecular computer operations, we need to look at the core of a computer – its microprocessor. The microprocessor consists of different modules, each one performing different operations, including adding and storing numbers. The modules are made of numerous transistors, which are widely used in electronics, from calculators to spacecrafts. A transistor is an electrically driven switch that permits or denies the passage of electrons, as demonstrated in Figure 1. In brief, transistors

made of semiconductor materials like silicon are tasked with the primary role of amplifying and switching electrical power. The semiconductor has positively- and negatively-charged areas. Electricity will not flow between these areas unless the conducting channel is open, as shown in Figure 1B. The channel opens when a conductor, such as an insulated metal plate, is electrified. For electrons to flow though the conducting channel, there must be a source (input) and drain (output) made of metal. Even if the input is charged, it cannot flow to the output unless the gate (metal plate in the middle) is also charged. When the gate is charged, it opens a conducting channel that allows the electrons to flow from the negative to positive areas, or from the source to the drain (Figure 1b, right panel). The most fascinating quality about this device is that there are no moving parts, in the mechanical sense. Electricity alone is used to perform the computer’s functions, toggling between ON and OFF. This turns out to be ideal for the assembly of logic gates - digital circuits which alternatively permit or deny the passage of an electrical signal. The signal can pass only if certain LOGICAL conditions are satisfied. As exemplified in Figure 2 A, an OR gate can be constructed by connecting two transistors to different power supplies (inputs) and allowing a current (output) to flow from either or both of these supplies directly to a light bulb. The ON and OFF states, in the case of the switches, can be represented as “1” and “0,” respectively. If at least one of the switches is ON, the light bulb will be powered and in its ON (or “1”) state. An AND gate can be assembled using similar principles. For this, the output wire of the first transistor needs to be connected to the input wire of the second transistor, as shown in the Figure 2B . In this case, both switches need to be ON for the bulb to be lit. The basic, binary language of modern computers lies in the realm of Boolean logic, which is a mathematical system featuring just two variables, “1” and “0”. The common logic gate functions (AND and OR, as touched upon, but also XOR, NAND, NOR, and XNOR) employed in Boolean algebra, as well as the truth tables which define each function, are summarized in Figure 3. Note that some gates, such as NAND, NOR, and XNOR, are in their ON state by default, unlike the AND, OR, and XOR gates. In fact, NAND, NOR, and XNOR gates can be viewed as inverses of AND, OR, and XOR gates, respectively.

The overall computer performance (e.g. system availability, response time, latency, throughput etc.) roughly depends on the numbers of transistors embedded within the microchip or integrated circuit (IC). The capabilities of digital electronic devices have increased in lock-step with Moore’s Law, which posits that the number

Nucleic Acid Nanotechnology: Promising Path to Altering Silicon Technology    15

of transistors in a microchip will double approximately every two years [25]. This trend is poised to reach a limit, though, as the unending quest to miniaturize transistors is expected to come to a halt due to the quantum tunneling effect[26]. When the distance of a gate is scaled down about 10 nm, its electrons will jump spontaneously from source to drain, and the control over the flow of electricity will be lost. To overcome this problem, it is possible to increase the size of microchips or fabricate them layer by layer (forming a stack of microchips). Nevertheless, this will not solve the problem of keeping up with Moore’s Law; it will only delay the stalemate. In anticipation of this stalemate, new technologies offering alternatives to silicon-based computing are in high demand.

3 Molecular TechnologyOne of the most promising alternatives to silicon-based computing is molecular computing; in particular, computing based on nucleic acids. Molecular computing has an enhanced ability to provide parallel computation. Harnessing this ability may be the most effective means of keeping up with Moore’s Law. The nature of the molecules used to perform the computations (their complexity,

Figure 1. Representation of a transistor - the core of any compu-ting device. (A) Example of a microprocessor chip showing variety or colored modules. Each module or area of the chip contains multiple transistors, as demonstrated on the right. The transistor is composed of a semiconductor material (base), a negatively charged area (source), a positively charged area (drain), and a gate build of conducting material like metal as shown in 3D view. (B) Different transistors states, ON and OFF. The electricity will not pass from source to drain until the gate is open (ON), even though there is a high potential on the source.

Figure 2. Exemplified view of (A) an OR logic gate and (B) an AND logic gate, both emloying transistors.

Figure 3. Summary of the common Boolean logic gates with symbols and truth tables.

variability, versatility, etc.) are just as important as, and perhaps more important than, the quantity of molecules used. This may obviate the problem of trying to fit an increasing number of transistors into a decreasing volume of space.

There are many reports available in which researchers have directly or indirectly used nucleic acids, both DNA

16    S.G. Abels, E.F. Khisamutdinov

probed the possibility of developing a new generation of molecular logic gates and molecular computers based on the advantages of DNA molecules [20, 30-40]. DNA molecules are attractive from two main reasons - they are amenable to well-regulated, programmable folding, and they have the unique ability to store genetic information. As such, DNA-based computing has been intensively used for solving a variety of computational problems [41-44], e.g. satisfiability problems, in which the computing time can grow exponentially with the problem size. The principle behind the use of a DNA duplex as computing material is based on Watson-Crick (WC) base pair formations, as shown in Figure 4A. Here, a set of DNA sequences can be used as inputs, and the output is a duplex formation which can be monitored using fluorescence, e.g. fluorescence resonance energy transfer (FRET) [45-49], or molecular beacons [35, 50-52] (Figure 4B). The formation of a DNA duplex requires that the inputs be perfectly matched to the set of solution molecules. The so-called strands hybridization approach can serve as an in vitro option for engineering a feasible logic-based network [49]. Two-input AND, OR and NOT logic gates were previously constructed using a branch-migration

and RNA, to construct artificial architectures for a variety of applications. These include “smart” devices capable of doing simple and complex molecular computations, similar to those of transistors. Below, we summarize some of the papers that demonstrate the successful employment of DNA and RNA biomolecules to generate logic gates, which are the elementary building blocks of a digital circuit.

Computing using nucleic acids is an integration of biochemistry and molecular biology with the goal of designing and creating algorithmic processes. To date, a variety of nucleic-acid-based logic gates have been built as defined counterparts of a biocomputer system, (see references [16, 27, 28] for review articles). We briefly describe them here, starting with DNA biomolecules:

4 Computing with DNA molecules The concept of DNA molecular computation was proven in 1994 when Leonard Adleman demonstrated the ability of synthetic DNA oligonucleotides to solve a seven-point Hamiltonian path problem by performing a sequence of logical operations [29]. Since then, many studies have

Figure 4. Demonstration the basic principles behind the use of a DNA molecules in logic gate operations. (A) Simplified view of DNA two strand hybridization, in which single-stranded DNA can be used as inputs and the duplex as an output. (B) An example of an AND gate demonstrating the molecular-beacon approach for monitoring DNA hybridization based on fluorophores and quencher pairs, see Ref # 35 for more details. (C) AND logic operation in vivo by DNA origami “nanorobot”. The construct opens only when Key A and Key B are both present in a cell, Ref 63. (D) Schematic design principle of AND logic gate utilizing reengineered Deoxyribozyme, Ref #68.

Nucleic Acid Nanotechnology: Promising Path to Altering Silicon Technology    17

scheme with a mechanism built on strand recognition and strand replacement [37, 53]. Single-stranded nucleic acids were used as inputs and outputs in this work. The gate function was created by sequential base pairing, triggered by toehold-toehold binding between single strands and subsequent breaks. The combination of such molecular events is similar to parallel computing, in which processes are carried out simultaneously rather than sequentially. The massive parallel computation power and colossal memory capacity [54, 55] are among the most attractive qualities of DNA computation. Much progress has been made in the performance and reliability of DNA computing [56, 57], though improvement in specificity of interaction and chemical stability are likely to be necessary to make these devices robust enough.

DNA self-assembly properties through WC base interactions can lead to the formation of a variety of structures, including an arrangement of tiles [58-60]. In principle, the macroscopic self -assembly of different DNA-based tiles can be used to perform DNA-based computations. This was demonstrated by the one-dimensional algorithmic self-assembly of DNA triple-crossover molecules for executing four steps of a logical XOR operation [61]. The value of a tile, 0 or 1, could be modulated by the presence of a restriction site (e.g. Pvu II represents 0, and EcoR V represents 1). To relay the answer after self–assembly, each molecular tile was embedded with a reporter strand. The answer produced a barcode display on a PAGE gel.

DNA aptamers [35, 62, 63] can be used to build logic operations in vitro and in vivo. As demonstrated by Yoshida et al., AND gates can be fabricated by fusing an adenosine-binding DNA aptamer to a thrombin-binding DNA aptamer in one construct [35]. The individual aptamer sequence binds to partially complementary fluorescence-quencher-modified nucleotides, QDNA1 and 2 respectively. When the two inputs, adenosine and thrombin, are bound simultaneously, the QDNAs release, causing an increase in fluorescence. Other input combinations, e.g. 0 + 0, 0 + 1, and 1 + 0, lead to the presence of “0” or “1” QDNA and a weaker fluorescence. Similarly, other logic gates can be created, if the positions of the fluorophore and QDNA are modified.

DNA nanorobots that can exist in open and closed conformations are another example of the successful use of DNA aptamers [63]. The principle of a DNA aptamer lock mechanism is based on the binding of DNA strands to their antigen keys (Figure 4C). This lock functions as an AND gate, in which the aptamer-antigen activation state is the input and the conformation of the DNA nanorobot (open or closed) is the output.

Other examples of in vitro DNA-Logic-Gate engineering utilize the properties of deoxyribozymes [64-67] (Figure 4D). To engineer, for example, an AND gate, two different oligonucleotide inputs were hybridized with corresponding controlling elements [66, 68]. This caused the cleavage of the substrate in the presence of both inputs, leading to the subsequent conformational change of controlling elements. NOT and XOR gates were constructed using a similar idea [68].

Many studies have focused on engineering a logic gate using DNA in vivo. To this end, a core machinery is usually selected based on gene expression regulation [66, 69-73]. One of the many examples use two inputs - beta-D-thiogalactopyranoside and anhydrotetracycline (aTC) - with a green fluorescent protein (GFP) as the output [74]. To construct this complex logic system, a DNA plasmid was designed consisting of three transcription-factor-encoding genes (LacI, TetR, and lambda cI) with their corresponding promoters. The binding states of LacI and TetR were modulated with the input molecules. This system was composed of five additional promoters that were regulated by the three transcription factors. Two of the promoters were repressed by LacI, one was repressed by TetR, and the remaining two were regulated (positively or negatively) by lambda cI, resulting in 125 possible networks. Various GFP-expressing systems were formed using a combination of different promoters, input molecules and host strains, e.g. E.coli. A set of functional networks was fabricated with the logic operations NOR, NOT, and NAND. A novel in vivo system, called the transcriptor, has been used to build permanent amplifying AND, NAND, OR, XOR, NOR, and XNOR gates to control transcription rates [75].

5 Computing with RNA molecules The progression in the field of RNA nanotechnology makes RNA molecules perhaps the most promising candidate in the construction of bio-computers [76-78]. The reason for this is twofold: RNA (i) possesses the structural properties of DNA and (ii) mimics the functional properties of proteins [14-17, 79]. At the beginning of its exploration, RNA molecules were viewed as little more than an intermediate (albeit a crucial one) with the main role of passing information from genome to proteome in all living creatures. However, the discovery of non-coding RNAs revealed that RNA performs more versatile functions, including gene expression and regulation. This broadened the view of RNA’s traditional role as a genetic intermediate. Indeed, the famous Adleman molecular computing

18    S.G. Abels, E.F. Khisamutdinov

approach has been expanded to RNA molecules [80]. A molecular algorithm was developed and applied to solve a chess problem, using specific ribonuclease digestion to manipulate strands of a 10-bit binary RNA library. There are many examples involving RNA molecules that can be viewed as computational algorithmic processes, including RNA editing (RNA sequence alternation) [29, 81] and RNA-based regulatory networks [82, 83]. The latter have been described as normal forms of logic function in the form of input, logic gate and output [84, 85]. In most RNA computational systems, the inputs are small RNA elements or motifs, and the output is mRNA [82, 86, 87]. There are various classes of RNA functional molecules, such as ribozymes, RNA aptamers, riboswitches, miRNA and siRNA, and orthogonal ribosomes [88-90] that enable the fabrication of RNA-based nanoparticles to advance modern technology [91-96].

Ribozymes are catalytic RNA molecules that have been shown to perform different functions in computing processes [33, 97] For example, the hammerhead ribozyme, which site-specifically cleaves RNA strands [98], can function as an actuator in an RNA computing device. In this case, the input-binding is translated to a change in the activity of the actuator structure and the functional (active) state results in self-cleavage of the ribozyme [99]. The RNA device is placed at the 3′-untranslated region (3’-UTR) of the target gene, where ribozyme self-cleavage results in the inactivation of the transcript and therefore lowers gene expression [99]. Different signal integration schemes act as various logic gates. Logic gates have been engineered with ribozymes in vivo [100, 101] and in vitro [102, 103]. Hybridization of two oligonucleotide inputs with the ribozyme leads to its activation, demonstrating an example of AND gate [102]. Using the ribozyme system, Chen et al. were able to design a YES gate (an input of “0” produces an output of “0” and an input of “1” produces an output of “1”) in vivo. The system was inserted into the 3′-UTR of a target transgene where the ribozyme was inactivated in the presence of theophylline, resulting in expression of transgene [100].

Another important class of functional RNA candidates for computing are aptamers, artificial RNA sequences that have been selected in vitro through a SELEX (systematic evolution of ligands by exponential enrichment) process to tightly bind their ligands, such as DNA, proteins, other RNA and small organic molecules [104]. This aptamer property has been widely used to engineer input sensors in biological computing devices [99, 105]. For example, RNA-aptamer-binding β-catenin has been used in the engineering of an AND gate in vivo [106]. To accomplish this, the aptamer sequence was embedded into an intron

between a protein-coding exon and an alternatively spliced exon (Ex) containing a stop codon. These sequences were followed by another intron, another protein-coding exon, and the herpes simplex virus-thymidine kinase (HSV-TK) gene. The product of this gene was an activator of ganciclovir (GCV). Binding of β-catenin with the RNA aptamer led to mature mRNA which lacked Ex, leading to the expression of HSV-TK. Early translation termination occurred only when Ex was included in the mature mRNA, resulting in the synthesis of a nonfunctional peptide. For the induction of apoptosis as output, both the expression of HSV-TK and the presence of GCV were required. Later, the same group produced AND, NOR, NAND, or OR gates based on RNA aptamers by constructing a high-ordered RNA device which included an RNA aptamer sensor, ribozyme and a connecting component [99].

Riboswitches are RNA elements that act by binding a small molecule, which switches gene expression on or off [107-110]. Ligand-binding that can cause suppression or activation of transcription and/or translation has been demonstrated by Sudarsan et al. using a tandem riboswitch core machinery in vivo [111]. This machinery facilitates sophisticated control of gene expression discovered in the 5’ untranslated region of Bacillus clausii metE. Two naturally occurring RNA riboswitches bind independently to two different metabolites, one to S-adenosylmethionine (SAM) and the other to coenzyme B12 (AdoCbl). This binding induces the transcriptional termination of a gene of interest through cis-acting corresponding riboswitches. This serves as a NOR gate, as the absence of both inputs (SAM and AdoCbl) is required for the complete length of transcript produced as output.

Recently, particular interest in computing with RNA molecules has been sparked by RNA interference (RNAi). RNAi is natural process that stimulates post-transcriptional gene silencing in higher eukaryotes and utilizes a class of short RNA duplexes and RNA hairpins [small interfering RNAs (siRNA) and micro RNA (miRNA) respectively [112, 113]]. With the introduction of synthetic siRNAs, the RNAi machinery can be harnessed [114, 115]. In recent studies, it is been shown that it is possible to design a synthetic gene network that implements general Boolean logic to make certain decisions based on endogenous molecular inputs [116, 117]. Using inputs in the form of two groups of miRNAs and expression of the hBAX protein as output, it is possible to build an AND-type logic gate, as demonstrated by Xei, et. al. [116]. Binding of miRNA, along with the consequent repression of translation, makes miRNA suitable to serve as sensory modules for RNA- and DNA-based digital logic circuits [53, 87, 116, 118].

Nucleic Acid Nanotechnology: Promising Path to Altering Silicon Technology    19

Collectively, advances in RNA biochemistry, specifically in RNA nanotechnology, offer precise control over the programmability of the size and shape of RNA nanoparticles, as well as over the composition and stoichiometry of the delivered inputs [23, 79, 119-121]. This field is emerging and has contributed novel engineering concepts for biological systems, as it has been utilized to generate RNA-based algorithmic operations within a cell, within a test tube, or on a surface.

6 Conclusion Biological computers possess several distinct advantages over silicon computers [122, 123] as they follow very different principles than their macroscopic analogs. Biomolecules, including DNA, RNA and proteins are major elements in logic gates operations. Their enzymatic selectivity (they often process a specific chemical function) gives them an advantage over silicon-based computing in both specificity and usability in an intracellular environment. As most of the biological reactions controlled by specific enzymes are interconnected with other functional inputs, it is possible to fabricate DNase- and/or RNase-based informational processing units. This processing unit can be scaled-up to fabricate artificial biocomputing networks possessing variety of logic functions.

As touched upon earlier, molecular computers cannot store kinetic energy or momentum. Consequently, they rely mostly on thermal energy from the environment for the delivery and removal of substrates and other large-scale motions, including significant motions of their own moveable parts. These features of the nano-world pose new and unique challenges to nano-molecular engineers. Nature, however, has provided us with examples of remarkably sophisticated and highly evolved algorithmic processes from which we can learn and implement for our own advantage. Cells can be engineered to sense and respond to environmental signals or inputs such as temperature, pressure, radioactivity, or toxic chemicals. Biological systems have the ability to adapt to new information from an altered environment. They can self-assemble and self-reproduce, which might provide some economic advantages. Thus, the ultimate goals of biocomputing are the monitoring and control of biological systems. Although some solutions have been discussed above, many natural limitations to the engineering of biological computers remain. For example, how do we eliminate or reduce noise in nucleic-acid based

computing? An increase in the number of elements in RNA- and DNA-based logic operations is accompanied by a drastic increase in noise [124, 125]. The common approaches for noise reduction may include following: (i) better optimization of individual logic gates, (ii) utilization of network topology [125, 126], and (iii) for even larger networks, the introduction of new network elements to suppress the redundancy of the elements, thereby improving the signal-to-noise ratio. We still have a long way to go before we can claim to understand nano-molecular computers in sufficient detail to be able to “reverse engineer” an existing molecular computer or design an entirely new one.

Aknowledgements: We thank Ball State University for providing Start-Up fund to establish Dr. Khisamutdinov’s laboratory as well as BSU Chemistry Department’s CRISP program for supporting undergraduate researchers.

Conflict of interest: Authors state no conflict of interest

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