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Fabrication of Electrochemical-Based Bioelectronic Device and Biosensor Composed of Biomaterial-Nanomaterial Hybrid

  • Mohsen Mohammadniaei
  • Chulhwan Park
  • Junhong Min
  • Hiesang SohnEmail author
  • Taek LeeEmail author
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1064)

Abstract

The field of bioelectronics has paved the way for the development of biochips, biomedical devices, biosensors and biocomputation devices. Various biosensors and biomedical devices have been developed to commercialize laboratory products and transform them into industry products in the clinical, pharmaceutical, environmental fields. Recently, the electrochemical bioelectronic devices that mimicked the functionality of living organisms in nature were applied to the use of bioelectronics device and biosensors. In particular, the electrochemical-based bioelectronic devices and biosensors composed of biomolecule-nanoparticle hybrids have been proposed to generate new functionality as alternatives to silicon-based electronic computation devices, such as information storage, process, computations and detection. In this chapter, we described the recent progress of bioelectronic devices and biosensors based on biomaterial-nanomaterial hybrid.

Keywords

Biomaterial-nanomaterial hybrid Electrochemical bioelectronic device Electrochemical biosensor 

17.1 Introduction

With the rapid advances in biotechnology (BT), nanotechnology (NT) and information & communication technology (ICT), a new technology field called bioelectronics emerged (Christof and Chad 2004; Itamar and Eugenii 2005; Noy 2011). Since 2000, the bioelectronics has led to the development of biochips (Farzadfard and Lu 2014; Michael 2007; Qiu et al. 2013), biosensors (Lee et al. 2007; Sarkar et al. 2014), biomedical devices (Deng et al. 2014) and bioelectronic devices for computation (Nikitin et al. 2014). Specifically, bioelectronic devices have been investigated in several fields, such as electrical engineering, nanobiotechnology, mechanical engineering and chemistry (Offenhäusser and Rinaldi 2009; Ren et al. 2017; Strukov and Kohlstedt 2012). The bioelectronic computation system can be at the center of these advances, as the elements that control information storage (Choi et al. 2007), determination, process (Benenson et al. 2004; de Silva and Uchiyama 2007), and logical behavior (Baron et al. 2006a; Fujibayashi et al. 2008; Win and Smolke 2008) provide appropriate functions to constitute the integrated molecular circuit (IMC). The advantages of bioelectronic devices include miniaturization, new functions, implantable devices that will be able to replace silicon-based (digitalized) systems in the future. Several groups have proposed the biocomputation concept (Adleman 1794; Ausländer et al. 2012; Rinaudo et al. 2007; Weber et al. 2008; Yin et al. 2008).

Bioelectronic devices for computation are usually composed of biomolecules such as metalloprotein (Chen et al. 2012; Lee et al. 2014b), enzyme (Katz and Privman 2010), DNA (Okamoto et al. 2004) and RNA (Win and Smolke 2008). These biomaterials have intriguing properties in nature, and their original properties have been mimicked in order to use computation on to the chip. For the immobilization of those biomolecules onto the inorganic substrate, the self-assembly technique was suitable for this application (Lu and Suo 2002; Schwartz 2001). Various groups have used biomolecules to demonstrate the logic gate (Baron et al. 2006b; Hild et al. 2010; Willner and Katz 2000; Zhang et al. 2013; Zhou et al. 2009), information storage device (Min et al. 2010; Yagati et al. 2009a, b, 2010; Yoon et al. 2014), transistor (Artés et al. 2012; Keren et al. 2003; Meng et al. 2011), computation device (Liu et al. 2000; Xie et al. 2011). Usually, those biomolecules were combined with nanomaterials such as nanoparticle, grapheme and quantum dot that provide the multi-functionality (Luo et al. 2018) (Fig. 17.1).
Fig. 17.1

Schematic diagram shows biomaterials and nanomaterials combination allow their integration in nanobio hybrid materials for bioelectronic devices

Willner Group suggested several enzyme-based logic gates (Baron et al. 2006a; Itamar and Eugenii 2005; Katz and Privman 2010). They introduced a pair of enzymes, horseradish peroxidase (HRP) and glucose dehydrogenase (GDH), for gate performance. As an input material, hydrogen peroxide (H2O2) and glucose were used to show the AND, XOR Gate functions. Recently, they proposed that the DNA computing circuit comprised libraries of DNAzymes. The system operated the parallel logic gate that depends on input markers. This operation process intends to regulate the anti-sense molecules and aptamer, which inhibit the enzymes. They provided a potential biochemical computer for the therapeutic control of biomedical applications. Furthermore, various enzyme-based logic gates were established (Baron et al. 2006b; Willner and Katz 2000; Zhang et al. 2013; Zhou et al. 2009). Also, Smolke group proposed that the information processing devices consisted of an RNA aptamer and RNA ribozyme (Win and Smolke 2008). The information processor received, processed and transmitted the input materials to express the green fluorescent protein as an output. In this study, the RNA aptamer and ribozyme combination can be used as the activating materials for a biomolecule-based processor. Furthermore, some pioneering groups suggested the RNA or DNA molecule-based biocompuation systems for medical applications or cell system analysis (Liu et al. 2000; Weber et al. 2008; Xie et al. 2011). Huang’s group demonstrated the protein-based transistor. For transistor fabrication, Huang developed the antibody-two gold nanoparticle complex immobilized onto the molecular gap that connected to the source and drain electrodes. This protein-based transistor provides a versatile platform for studying single molecule-based electronic devices. The fabricated bioelectronic device has the following advantage (Fig. 17.2).
Fig. 17.2

Schematic diagram of main advantage of bioelectronics device

Biosensors are powerful analytical devices, usually composed of biological sensing materials (bioreceptors) and physiochemical transducers (Fig. 17.3). These devices are employed to recognize and detect the desired target molecules with high specificity, selectivity and sensitivity either at the trace amounts or in the complex environments (Hunt and Armani 2010; Tamayo et al. 2013). Amongst all the sensing devices, electrochemical biosensors have received intensive attention for the detection of clinical biomarkers and analytes due to their fast response, low fabrication cost, high sensitivity and selectivity as well as simplicity and miniaturization capability, which have made them considerable candidates for the point-of-care diagnostics (Liu et al. 2017; Thévenot et al. 2001).
Fig. 17.3

Schematic diagram of working mechanism of biosensor

Electrochemical method is fundamentally based on the process of electron transfer between the electrode surface and the electroactive substances in solution (electrolyte). The electrode surface could be composed of metal (such as gold, platinum, etc.), conducting polymers, carbon or composite materials. The electroactive materials which are meant to have redox properties (oxidation/reduction) generally consist of ions, organic/inorganic materials and enzymes (Grieshaber et al. 2008; Pingarrón et al. 2008; Ronkainen et al. 2010; Wang 2002). Electrochemical biosensors are normally set up in a three-electrode electrochemical cell composed of working electrode (Target substrate, where the reaction of studied species occurs), counter electrode (To detect the electrochemical current/signal) and standard electrode, possessing a stable and fixed potential (Usually Ag/AgCl, due to its construction simplicity) (Pumera et al. 2007). The recorded electrochemical signal is directly proportional to the concentration of the electroactive species and is defined as the detection signal. There are mainly two analytical techniques applied for illustrating the experimental data; current vs. potential (i–v) which is called voltammetry, and current vs. time (i–t) that is called chronoamperometry, in which, the working electrode is maintained at a constant potential (Kimmel et al. 2012).

As the major sensing element of biosensors, the biological sensing materials are substances with well immobilization properties to be attached strongly onto the electrode surface and high selectivity features to specifically detect the target analytes. They are mainly categorized in two groups: (1) Proteins and (2) Nucleic acids.

17.2 Protein-Based Electrochemical Bioelectronic Device

The protein-based biomolecular information storage device was proposed to alter the current silicon-based information storage device (Choi et al. 2007). Consequently, there are several types of biomemory devices proposed, for example, the WORM-type biomemory device (Yagati et al. 2009b), multi-bit biomemory device (Yagati et al. 2009a), multi-level biomemory device (Lee et al. 2010), multi-functional biomemory chip (Lee et al. 2011a), signal-enhanced biomemory device (Yuan et al. 2013), and the bioprocessor (Ko et al. 2011). Such devices could control and modulate the electrochemical signal due to the redox property of metalloprotein in order to achieve the store and release the electron by external potential. Usually, the bioelectronic devices were classified with six categories (Fig. 17.4). This article briefly introduced devise ranging from the basic concept of a biomolecular memory device to the various function validations of multi-functional biomemory devices and bioprocessors.
Fig. 17.4

(a) Schematic representation shows the expected structure of electrochemical bioelectronic device. (b) Classification of bioelectronics device constitution

In previous time, they proposed the bioelectronic device composed of metalloprotein. The purpose of this electronic device was to accomplish electronic functions of the information storage. For this reason, Choi group introduced the redox protein for making biomemory device by self-assembly technique and validating the electrochemical biomemory functions (Choi et al. 2007). Recently, the protein/DNA-based bioprocessor was demonstrated to show the multi-functionality in one defined devices corresponding to input materials (Lee et al. 2014b). Here, we review from the protein-based biomemory device, protein/DNA-based bioprocessor, also briefly survey DNA/RNA biologic gate and device which could be one of alternative standard device format in biocomputation system.

17.2.1 Protein-Based Information Storage Device

In the early stage, the metalloprotein-based biomemory device was proposed to overcome the limitation of inorganic molecule-based information storage device by Choi group (Choi et al. 2007). The metalloprotein contained the metal ion in the protein molecule that can be enabled to store the electron corresponding to input potentials. To fabricate the biomemory device, the immobilization and orientation of biomolecule technique should be required. To immobilize the biomolecule onto the inorganic substrate, a self-assembly (SA) method has been widely used to immobilize biomolecules on the substrate (Mitsumasa et al. 2010). The immobilization of biomolecules on the substrate needs an additional linker which anchors between the biomolecule and the substrate. Chemical linkers like (3-aminopropyl)triethoxysilane (APTES), 2-mercaptoacetic acid (2-MAA) 6-mercaptohexanoic acids (6-MHA) can be used to make connections between the gold substrate and the biomolecule (Chung et al. 2011; Robles-Águila et al. 2014; Yoo et al. 2011). However, direct immobilization of biomolecules on the substrate without chemical linkers is more effective for the fabricating a well-ordered biomolecular monolayer than the use of chemical linkers. To achieve direct immobilization, a cysteine-modified azurin was introduced as an electron storage element to fabricate a biomolecular memory device. On the basis of this technique, pseudomonas aeruginosa azurin was modified to possess cysteine residue for direct immobilization on the gold surface by covalent bonding. This recombinant azurin was immobilized directly on the gold substrate, and its orientation was investigated by atomic force microscopy (AFM) and surface plasmon resonance (SPR). Then, the redox property of azurin was investigated by cyclic voltammetry (CV).

The basic mechanism of the proposed biomolecular memory device is that electrons flow into the recombinant azurin. A reduced copper ion in azurin gives the ‘write’ state and outflowing of electron from the recombinant azurin, and the oxidized metal ion in azurin gives the ‘erase’ state. Like this process, based on metalloprotein, electrons can flow in and out of the recombinant azurin by applied voltage. The quantity of the stored charge can be calculated as the memory performance of the fabricated biomolecular memory device using chronoamperometry (CA). Also, the recombinant azurin showed unique redox potential peaks and memory functions. From experimental results, this proposed biomemory device indicates new conceptual approach to bioelectronic application. Furthermore, the biomolecular memory devices have been developed by several groups (Ko et al. 2011; Mitsumasa et al. 2010; Yuan et al. 2013). Yu group suggested the electrochemical based biomemory device based on living bacteria Shewanella oneidensis (Yuan et al. 2013). Furthermore, Cho group investigated the electrical bistable property of ferritin to develop a resistive memory device application (Ko et al. 2011). Among those biomolecules, metalloprotein was widely investigated to use a biomemory source due to its bistable redox properties (Choi et al. 2007).

The main advantage of biomemory is functionality compared to inorganic molecule-based memory device. Usually, the biomolecule can be easily tailored and conjugated with other biomolecule or nanoparticle that can provides the additional functionality such as signal-amplified current or dual-level information storage. It is hard to perform the inorganic molecule-based memory device and silicon-based memory device because of simple molecular structure. However, the biomemory can be extended to the various functionalities based on combination of biomolecule and nanoaprticle. The natural property of a biomolecule can be modified and extended by introduction of nanoparticles. Yang’s group conjugated the tobacco mosaic virus and quantum dot nanoparticles, and electrical investigation of that virus/nanoparticle conjugates for digital memory application (Tseng et al. 2006). Moreover, the functional benefits of a biomolecule are its original redox property and recombinant technique for feasible formation that can be applied to bioelectronics devices.

The gold nanoparticle on the recombinant azurin monolayer was developed an electrochemical signal enhanced biomemory device (Lee et al. 2011b) (Fig. 17.5). In this study, they introduced various gold nanoparticles to recombinant azurin monolayer (5 nm ~ 60 nm) to optimize the gold nanoparticle size which transfer the maximum electron transfer. For this reason, the recombinant azurin was immobilized directly on the gold substrate by cysteine residue, and 1-Octadecanethiol was used as a connecter between the recombinant azurin and gold nanoparticle. From the electrochemical results acquired by CV, in a small particle range, the electrochemical signal of recombinant azurin/gold nanoparticle decreased, but in a large particle range, the electrochemical signal only originated from a gold nanoparticle without recombinant azurin. Therefore, 5 nm size of gold nanoparticle was determined as the optimal size. After that, biomemory device composed of recombinant azurin and gold nanoparticle was fabricated on the gold substrate. The confirmation of recombinant azurin and gold nanoparticle immobilization was verified by SPR and AFM. Then, the electrochemical investigation of the recombinant azurin/gold nanoparticle was carried out to evaluate the electrochemical signal enhancing effect compared to a recombinant azurin monolayer without a gold nanoparticle by CV and CA. The electrochemical signal of the recombinant azurin/gold nanoparticle was five times greater than the recombinant azurin monolayer. Also, the stored charge amount of the recombinant azurin/gold nanoparticle, measured as memory performance, was 4.503 μC, and 1.14 μC in the case of the recombinant azurin monolayer. This signal enhanced biomemory device suggested the possibility of bioelectronic development at a single molecular level with subjugation of a signal detecting limitation. As seen in this research, biomolecules have a huge potential with limitless functional expansion through the combination of various materials (Jensen et al. 2009).
Fig. 17.5

(a) Scheme of the proposed signal-enhanced biomemory device composed of recombinant azurin and gold nanoparticle. (b) Cyclic voltamogram of recombinant azurin (green plot), recombinant azurin/gold nano particle (red plot). (Figures adapted from Lee et al. (2011b), with permission from © WILEY-VCH Verlag GmbH & Co. KGaA 2011)

In the conventional field of electronics, researchers have tried to improve memory density and circuit-integration efficiency to develop an advanced computing system. However, as the approach applies a scale of a less than 50 nm for fabricating semiconductor-based chip, economic and technical limitations have been identified. In terms of the field of bioelectronics, a Moreover, the biomemory can be developed to increase the memory density in the defined area. Biomolecular based electronic system might be an alternative option to overcoming this limitation. This combination can be used to realize a biomolecular memory device with improved memory density.

The multi-level biomolecular memory device composed of recombinant azurin and cytochrome c to increase memory density with multiple redox states (Lee et al. 2013) (Fig. 17.6). The heterolayer composed of recombinant azurin and cytochrome c was fabricated through self-assembled layer-by-layer formation on the gold substrate. First, recombinant azurin was immobilized directly on the gold substrate, and then cytochrome c was immobilized on the recombinant azurin layer through electrostatic interaction. At pH 7.0, the isoelectric point of recombinant azurin was 6.03 and that of cytochrome c was 9.59, so recombinant azurin had a negatively-charged surface and cytochrome c had a positively-charged surface. Confirmation of the heterolayer formation was operated by SPR and AFM. After biochip fabrication, the electrochemical properties of the heterolayer were investigated. Using CV, the heterolayer showed that both redox peaks of recombinant azurin and cytochrome c had an obvious shape. The oxidation potential peak and reduction potential peak of recombinant azurin and cytochrome c were 0.062 V, 0.131 V and 0.131 V, 0.294 V, respectively. These redox values coincided with copper ion in recombinant azurin and iron ion in cytochrome c. Two different metal ions in recombinant azurin and cytochrome c played key roles as storage for controlling the various data in defined memory sector. Those acquired redox potential values were used as oxidation potential for the ‘write’ and reduction potential for ‘erase’, and open circuit potential was applied as the ‘read’ step. Accordingly, the multi-level memory function was evaluated by open circuit potential amperometry (OCPA). From OCPA results, the fabricated heterolayer showed exceptional multi-level memory performance by applied potentials. This biomolecular memory device offered the potential of a biomolecular based memory device with high memory density. Recently, they proposed a new method to fabricate a multi-level biomolecular memory device (Lee et al. 2014c). As seen in this section, the combination of metalloproteins and nanoparticles can be applied to develop the functional biomemory devices.
Fig. 17.6

A schmatic diagram reprents (a) The electron transfer mechanism of a cytochrome c/recombinant azurin heterolayer on Au surface. (b) Cyclic voltammogram of cytochrome c/recombinant azurin heterolayers. (c). Multi-level biomemory performance by OCPA. (Figures adapted from Lee et al. (2010), with permission from © WILEY-VCH Verlag GmbH & Co. KGaA 2010)

17.2.2 Enzyme-Based Logic Gate

As seen in the part of the front section, biomolecules have been widely used for bioelectronic devices including information storage device. Furthermore, those materials also have been introduced to develop the biologic gate. In conventional electronic field, logic gates have been developed to acts as performing component in electronic device for logical operation such as Boolean function using field-effect transistor (FET), metal–oxide–semiconductor field-effect transistor (MOSFET) (Lee et al. 2015; Tomohiro et al. 2004).

However, with the miniaturization of electronic devices, scale down of logic gate has been researched in electronic researching areas. Thus, molecular-based logic gate or nanomaterial-based logic gate was developed to demonstrate logic function at molecular level (de Ruiter and van der Boom 2011; de Silva and Uchiyama 2007; Huang et al. 2001). Biomolecules also had been researched as a candidate to apply logic gate with miniaturization (Bychkova et al. 2010). However, in recent years, biomolecules have been recognized as a logic component because of the new advantages compared to conventional logic devices. The first advantage is that biomolecules can offer the possibility of homogeneous system fabrication to develop the uniform three-dimensional logic system compared to two-dimensional solid-state device which is widely used (Gdor et al. 2013). Thus, biomolecules-based system can provide the integration of complex reacting process for the development of high-order logic gate (Katz 2015). The second is that biomolecules-based logic gate can be operated by various input signals and output signals including light energy instead of electronic input signal (Prokup et al. 2012). Thus, biomolecules-based logic gate may not be bound to the electronic system for logic operation.

Among various biomolecules, enzyme and DNA have been widely used for biologic gate development. The advantage of enzyme used for logic gate is that various input signals can be used according to the used enzymes and chain reaction by related enzymes can be used for logic function. Thus, the complex logic systems have been demonstrated by enzyme (Baron et al. 2006a). In case of DNA, DNA can be used to build the complicated geometric structures by specific binding and conformational changes of DNA which is able to develop logic gate only using DNA itself (Okamoto et al. 2004; Seelig et al. 2006). In addition to these biomolecules, bacteria also have been investigated for logic gate fabrication (Arugula et al. 2012).

Enzyme has been used in bioelectronics field due to its unique properties like structure-folding and specific interaction with the substrate. Based on the property of enzyme, different output materials and signals can be induced by different input substrate injected into the enzyme. Thus, various combination of enzyme and the substrate have been used to develop logic gates like “AND” or “NAND” logic gate (Zhou et al. 2009). Katz group has been developed various functional enzyme-based logic gates. They developed the Boolean logic gate using enzymes including glucose oxidase, glucose dehydrogenase as input signals for logic operation (Strack et al. 2008b).

They developed the concatenated enzyme-based logic gate by highly specific recognition chain reactions using invertase, glucose oxidase and microperoxidase-11. Figure 17.7 shows the schematic mechanism of this logic gate. Sucrose, glucose and hydrogen peroxide were used as input materials. These input signals were considered “1” when they were existed and “0” when they were absent. To measure the output signal of this logic gate, the absorbance change of ABTS, biocatalytically oxidized dye, was used with defined threshold value (O.D = 0.3). Thus, the output signal with overthreshold was defined as “1”, otherwise “0”. According to the associated chain reaction of this logic system, only when all three inputs were injected, the output signal overpassed the defined threshold value. So, only (h), defined as “1,1,1”, showed the “1” as the output signal (Strack et al. 2008a). This logic system, where individual reactions were interrelating and input materials were compatible, could be used as an alternative solution for assembling complex logic process which is difficult to demonstrate with synthesized chemical molecules due to limitation of synthetic complexity and scale up.
Fig. 17.7

Schematic mechanism of the developed concatenated enzyme-based logic gate by highly specific recognition chain reactions using invertase, glucose oxidase and microperoxidase-11

In addition to the logic gate based on interaction of enzyme and input substrate, there exists the other type of logic gate using induced folding and unfolding of polypeptide chain in protein (Deonarine et al. 2003; Muramatsu et al. 2006). Looking in detail, structure of genetically and chemically engineered chaperonin azo-GroEL was changed by ATP and light. According to light as input signal, photomechanical gate of GroEL was induced to trans-to-cis isomerization and cis-to-trans isomerization by UV and visible lights, respectively. Also, its geometrical structure was changed by ATP. Using these two inputs, “AND” logic gate was developed.

Besides these logic gates, enzyme-based logic gates using interfacial pH change (Pita et al. 2009a), enzyme-functionalized nanoparticles (Pita et al. 2008) and supramolecular enzyme-hydrogel hybrids (Ikeda et al. 2014) were developed. These enzyme-based logic gates offer a huge potential for application in wide areas including clinical field for drug delivery and physiological conditioning assessment (Mailloux et al. 2014; Pita et al. 2009b; Radhakrishnan et al. 2013).

17.2.3 Protein-DNA-Based Bioprocessor

The conventional information processor is a programmable device that performs the various functions according to input data, then, the proper output produced-based on defined functions. The biomolecule can be applied to construct the bioprocessing device. The protein-DNA hybrid molecule-based information-processing device was developed for mimicking the information process in cellular signal (Lee et al. 2014b). The proposed bioprocessing device performed three functions: ‘information regulation’, ‘information reinforcement’, and ‘information amplification’. The information process system is based on the biomemory platform consisting of metalloprotein/DNA hybrids, and could be regulated by surrounding commands (metal ions, conducting nanoparticles and semiconducting nanoparticles) (Fig. 17.8). The core material (redox material) in the biomemory platform was re-engineered from a simple metalloprotein into protein/DNA hybrids to receive the surrounding’s commands and to store the information based on input.
Fig. 17.8

Schematic diagram of bioprocessing device comprised with recombinant protein/DNA hybrid corresponding to input materials. That shows the proper pre-defined functions

The azurin was rolled as the memory core and the ssDNA can be used as the processing receptor. When cDNA-nanoparticle or various metal ions are hybridized, the ssDNA can be hybridized or intercalated with metal ions for bioprocessing functions. The information reinforcement and information regulation functions were validated based on input materials by the chronoamperometry (CA) method. The ssDNA arm has a charged backbone that can bind to various heavy metal ions, such as Cu, Zn, Ni, Co, Fe, Mn.

To assess the information amplification function, a scanning tunneling spectroscopy experiment was carried out on the recombinant azurin/DNA hybrid when cDNA-quantum dot (QD: CdSe-ZnS) added to bioprocessor. In the case of the recombinant azurin/DNA hybrid, the I-V result shows the semiconductor behavior, as after 0.2 V of the applied bias, the recombinant azurin/DNA hybrid shows a non-ohmic behavior. However, in the case of the recombinant azurin/DNA-cDNA/QD complex, the result shows that the bi-electrical stability ranges from −2.0 to +2.0 V. In this case, the recombinant azurin/DNA hybrid-biotin-tagged cDNA/streptavidin-coated QD conjugate is initially in a low conducting state until it reaches about 0.8 V. After 0.8 V, the I-V curve drastically changed which indicates a transition of the recombinant azurin/DNA hybrid /biotin-tagged cDNA/ streptavidin-coated QD complex conjugate from a low conducting state to a high conducting state. This state change can be defined as ‘information amplification’. Thus, the protein/DNA-based bioprocessor has complex functionality with the suggested concept and this functionality can be extended with new input materials such as graphene, nanoparticles, proteins and RNA. This concept provides the possibility for biomolecular-based computing systems; analog style memorizing, fuzzy type determining, and the environment are affected.

17.3 Nucleic Acid-Based Bioelectronic Device

Nucleic acid has been received attention due to its functionality and programmability. By specific recognition and hybridization with complementary nucleic acid sequence, Especially, DNA has become an attractive biomolecule for bioelectronics application including biosensor and biologic gate (Campolongo et al. 2011). Widely studied DNA logic gate is based on the specific-sequence recognizing and binding property of DNA itself. For example, in DNA hybridizing mechanism, longer-specific complementary DNA can disrupt the hybridized DNAs formed with shorter-specific complementary DNA. Then, longer-specific complementary DNA replaces the shorter-specific sequence and shorter-specific complementary DNA is de-hybridized from interaction. This mechanism is fit to develop logic gate like DNA displacement-based logic gate (Frezza et al. 2007). In the field of DNA logic gate, Willner group and Winfree group have developed various logic gates based on functionality of DNA (Liu et al. 2012b; Seelig et al. 2006).

17.3.1 DNA-Based Logic Gate

Willner group designed and developed the aptamer-based DNA tweezer structure for “SET-RESET” logic demonstration (Elbaz et al. 2009a) (Fig. 17.9). This DNA tweezer, composed of four different nucleic acids, could be trans-shaped its structure, opened shape or closed shape, by insertion of specific materials such as adenosine monophosphate (AMP), adenosine deaminase (AD) and inosine monophosphate. This DNA tweezer possessed fluorescence dye (Cy5) and quencher (Iowa black RQ) located on tweezer frame nucleic acid. Thus, in the case of closed shape, fluorescence intensity was quenched, but in the case of opened shape, high fluorescence intensity was detected. By this mechanism, shape-change of DNA tweezer was verified through fluorescence intensity. Two different but related DNA tweezers which were called “Tweezer A” and “Tweezer B” respectively, possessing two different dyes and quenchers each other, were used to develop “SET-RESET’ logic gate in this paper, and these tweezers were trans-shaped in contrast to each other. By addition of AD, the system composed of two different DNA tweezers could become “state1” as defined “RESET”, and the system could become “state2” by addition of AMP as “SET”. Each state was confirmed by fluorescence intensity. Figure 17.9a, b show the schematic image and process of this logic system and “SET-RESET” logic data with exist of two different states.
Fig. 17.9

(a) Scheme of coherent activating logic gate of two tweezers using adenosine monophosphate and adenosine deaminase as input materials. (b) Schematic diagram of the SET-RESET system

In addition to this research, G-quadruplexes, one type of DNA sequence composed of stacks of guanine tetrads by Hoogsteen hydrogen bonding, have been wide used for DNA logic gate due to the easy modulation of DNA structure under benign conditions (He et al. 2013; Wang et al. 2012). For example, G-quadruplex-hemin complex was formed by the insertion of potassium ion and hemin into G-quadruplex, then specific structure was formed and hemin used as electrochemical probe was inserted inside that structure. That change could be detected by electrochemical technique. Using this mechanism, “AND” logic was demonstrated. Both potassium and hemin were added to G-quadruplex, then the output signal was passed over the defined threshold value, 1, however, addition of only one of them to G-quadruplex showed the under-threshold value, 0. Also, the output signal was under-threshold value without potassium ion and hemin (Wang et al. 2012).

In addition to these achievements, pH, nanoparticles have also used to fabricate DNA logic gates similar to enzyme-based logic gates (Elbaz et al. 2009b, 2012; Freeman et al. 2009). Also, there exists logic gate using mismatching of hybridized DNA, insertion of mercury ion in thymine–thymine (T–T) mismatch in hybridized DNA and insertion of iron ion in cytosine–cytosine (C–C) mismatch in DNA duplexes, for electrochemical logic outputs (Li et al. 2011).

These developed DNA logic gates can be applied for DNA computing development. Winfree group developed the digital circuit computation with multilayer circuits by DNA displacement cascades within all logical operation (Qian and Winfree 2011). Furthermore, using DNA displacement cascades, they developed the DNA computing system mimicking neural network computation which even showed the property of Hopfield networks associative memory (Qian et al. 2011). As seen in this chapter, biomolecules, especially enzyme and DNA, have been widely investigated for the development of logic gate. There is also the case of programmable DNA-enzyme conjugates fabrication for logic gate (Gianneschi and Ghadiri 2007). These biomolecular logic gates give a chance to develop the effective complex computing functions demonstration for biocomputer system development (Ogihara and Ray 2000), also enrich the life of mankind due to the expanded application of biomolecules in clinical field (Mailloux et al. 2014; Pita et al. 2009b; Radhakrishnan et al. 2013).

17.3.2 RNA-Based Biologic Gate

The RNA molecule is a powerful source for constructing molecular logic gate owing to its intriguing characteristics. Compared to DNA molecule, RNA has various functionality and applications (Haque et al. 2012; Jaeger and Chworos 2006). Those functionalities of RNA molecules were originated from the proper folding and assembly of RNA molecule tertiary structures using the formation of hairpin loops, dove-tail, bulges, and internal loops. Those tertiary structures of RNA give a unique functionality such as aptamers, ribozymes, and riboswitches (Grabow and Jaeger 2014). These functional RNA molecules can be easily designed to constitute the logic gate core for performing specific function such as gene expression, diagnosis, or cell signaling (Benenson et al. 2004; Rinaudo et al. 2007). The RNA-based logic gate is usually activated through the RNA hybridization or displacement that gives conformational change or ligand binding according to input molecule (Benenson 2009; Xie et al. 2010).

Benenson group reported the RNAi-based logic evaluator to perform Boolean logic behavior-based on to input molecules (Rinaudo et al. 2007). The constituted biological circuit was composed of a couple of mRNA species to produce the fluorescent protein in human kidney cell. Those mRNA species were constituted with the different non-coding region to perform the logic behavior. Then, the specific designed DNA contained plasmid was applied to control the gate function in the cell through transfection. The siRNA was used to regulate the mRNA degradation as the gate input. Then, the mRNA produced the fluorescence protein corresponding to input signal and this expression of fluorescence protein level was used to the output signal.

Recently, the field-effect transistor structure-based genetic RNA logic gate was suggested (Bonnet et al. 2012, 2013). The bacteriophage serine integrase was used to regulate the state of double stranded DNA. Interestingly, this study defined the input and output signals are the transcription rates of the flow of RNA polymerase according to DNA at the logic element boundaries (Fig. 17.10). The integrase-serine control invert or delete the DNA encoding transcription, thus terminating or promoting the transcription rates. With this mechanism, the AND, OR, XOR, NOR and XNOR gates have been fabricated with one/two asymmetric transcriptor. Those results demonstrated RNA molecule can be extended to construct new concept of logic gate which is hard to achieve in molecular logic gate.
Fig. 17.10

(a) Three-terminal transcriptor based gates use integrase (Int) control signals to modulate RNApolymerase (RNA Pol) flow between a separate gate input and output. (b) The logic element within a three-terminal Boolean integrase XOR gate such that gate output is high only if control signals are different

17.3.3 RNA-Based Bioprocessor

The RNA has a unique functionality such as catalytic property, recognition, self-folding, self-splicing and etc. (Grabow and Jaeger 2014; Haque et al. 2012; Jaeger and Chworos 2006). Those properties of RNA molecule can be extended to use of bioprocessor unit (Benenson 2009; Win and Smolke 2008). Usually, the bioprocessor composed of RNA molecule can be received the chemicals or RNA sequences and it process the information. The RNA molecule can be used to the molecular information processor operating in living systems with a biological environment (Rinaudo et al. 2007; Win and Smolke 2008), RNA-based biocomputation system would be powerful alternative to solve the current limitation of silicon-based computation, for example, (1) RNA molecule-based computation can be directly used to diagnostic system such as cancer or hereditary disease. (2) The combination of RNA-molecule can be used to detect RNA-related virus sensing system (3) it may provide new type of computation that gives the analogue-based result corresponding to RNA input material. (4) the silicon-based computation system is hard to operate in a living organism (Xie et al. 2010).

The synthetic RNA-based information processing devices was fabricated to perform the logic gates, signal filtering, and cooperativity functions (Win and Smolke 2008). The RNA-based bioprocessor that constituted with ribozymes and RNA aptamers was received the molecular input. Then, the processed input was transmitted to control the expression of the green fluorescent protein as output. The RNA aptamer that rolled as the sensor part was composed of a hammerhead ribozyme for cleaving of the aptamer. Also, the information transmitter part has the complementary RNA sequences for binding to RNA aptamer and ribozymes parts. Like this, RNA molecule can be used to bioprocessor module to perform the multi-functional information processor development (Fig. 17.11).
Fig. 17.11

Functional RNA device composition framework. The color scheme for all figures is as follows: brown, aptamer or sensor component; purple, catalytic core of the ribozyme or actuator component; blue, loop regions of the actuator component; green and red, strands within the transmitter component that participate in the competitive hybridization event. A functional composition framework for assembling RNA devices from modular components. Information in the form of a molecular input is received by the sensor and transmitted by the transmitter to a regulated activity of the actuator, which in turn controls the translation of a target transcript as an output

The interesting concept of RNA-based autonomous bioinformation processor was reported to program a biomolecular computing device to work inside a living cell (Ausländer et al. 2012). In this study, the autonomous biomolecular information processor was fabricated to control the disease-related gene expression for small-cell lung cancer and prostate cancer detection system. To operate the autonomous bioinformation processor, the regulation of specific mRNA level and chemicals level should be required to control the point mutation as the input material. Then, the bioprocessor gives a short ssDNA which control the gene expression level for anticancer effect as the output. The automaton bioprocessor regulates ‘positive state’ and ‘negative state’ corresponding to specific gene expression level. They demonstrated RNA-based computation system can be directly applied to the gene expression control system for future diagnostic detection. Like this, the advances in RNA-based information processing system demonstrate the promise for biocomputation with new functionality.

17.3.4 RNA-Based Biomemory

In recent years, Choi’s group reported the RNA and semiconductor nanoparticle hybrid can be used to the resistive memory device application (Lee et al. 2015). To construct the resistive memory device, the thermodynamically stable pRNA 3WJ from the phi29 DNA packaging motor was used and conjugated with quantum dot nanoparticle (CdSe-ZnS). The pRNA 3WJ was easily conjugated with the quantum dot using Sephadex G100 resin-recognized RNA aptamer-based site-specific conjugation method. The prepared pRNA 3WJ/QD hybrid was immobilized onto Au substrate by self-assembly technique. The pRNA 3WJ was rolled as the connector between QD nanoparticles and Au substrate for the resistive memory performance. Furthermore, the pRNA 3WJ rolled as the insulator between the QD and Au substrate. As a semiconductor, the QD was rolled to storing the electron for memory function. And, the metal Au substrate was rolled to m. The electrical bi-stability property (I-V curve) of pRNA 3WJ/QD hybrid was confirmed by scanning tunneling spectroscopy (STS). As a result, the pRNA 3WJ/QD hybrid exhibited the resistive memory property. The proposed resistive memory device using a combination of RNA and nanoparticles can be applied to bioinformation storage device.

17.4 Protein-Based Electrochemical Biosensor

Protein-based electrochemical biosensors (ECBs) can be divided into two classes of enzyme-based and antibody-based biosensors (Vestergaard et al. 2007). However, in comparison with immunosensors, enzymatic biosensors have been most regularly used in disease diagnosis and point-of-care applications. They are biological catalysts and can be exploited in the purified forms and be engineered for the particular reactions. Whereas, antibodies (used for immunosensors) are non-catalytic biological elements which are well capable to specifically bind with their corresponding antigens. Although, they have a very high specificity, their applications are limited and their handling needs a considerable experimental proficiency (Li et al. 2009; Ramanavičius et al. 2006; Rocchitta et al. 2016).

Enzymes are large macromolecules, mostly proteins, which usually harbor prosthetic groups (one or more metal ions). These metal ions enable the enzymes to undergo oxidation and reduction upon the reaction with their corresponding analytes. This redox action which is corresponding to the presence of the analyte, can be detected electrochemically as the function of enzymatic ECBs. The basic mechanism for enzyme catalysis is as follows:
$$ S+E\underset{k_{-1}}{\overset{k_1}{\rightleftharpoons }} ES\overset{k_2}{\to }E+P $$
(17.1)
Where, S is substrate, E is enzyme, ES is enzyme/substrate complex, and P is product.

The enzymatic ECBs are highly selective and fast with high sensitivity due to their catalytic activities and they can be effectively immobilized onto the substrate (transducer) due to their 3D structure. However, they are relatively expensive and still suffering from the loss of activity after a prolonged usage, due to the deactivation and/or substrate detachment (Rocchitta et al. 2016).

Basically, analytes can be directly oxidized/reduced at ordinary solid electrodes. However, employment of conventional electrodes have been restricted because of their slow electron transfer kinetics and high overpotentials, which reduce the sensing performance of the biosensors (Pumera et al. 2007). It has been reported that, incorporation of enzymes with nanostructured solid electrodes can enhance the electron-transfer rate between the modified electrode and the solution interface (Wang 2005). Moreover, the size and structure of enzymes can be engineered for the further amplification of sensing performances (Dolatabadi et al. 2011; Malekzad et al. 2017). On the other hand, enzyme immobilization onto the electrode surface is a very important issue to be studied. The successful immobilization normally requires; (i) enzyme stability and specific affinity towards the surface, (ii) surfaces uniformity, (iii) maintenance of the natural enzymes’ biological functions, and (iv) controlling the enzyme orientation for the achievement of maximum surface density (Zhang et al. 2009).

There are usually two methods of immobilization: (1) Indirect immobilization; (2) Direct immobilization. The indirect immobilization technique is based on the modification of the electrode surface with suitable linkers for the establishment of functional groups to be further used for the enzyme binding reaction. Whereas, the direct immobilization method does not require extra chemical linkers and involves either physical adsorption or specific interaction between the enzyme and the electrode, such as immobilization by means of Au-thiol (sulfhydryl) interaction (Rao et al. 1798; Singh et al. 2016; Wong et al. 2009). Although, the enzyme-based ECBs have been utilized for the detection of various toxins and analytes, such as glucose (Wang 2008), urea (Chen et al. 2011; Cho and Huang 1798), nitric oxide (Nagase et al. 1797; Yoon et al. 2017b) and hydrogen peroxide, here we demonstrate some examples of enzyme-based ECBs for the detection of hydrogen peroxide (H2O2).

17.4.1 Protein-Based Electrochemical Biosensor for H2O2 Detection

Hydrogen peroxide (H2O2) is an important biomarker of the major reactive oxygen species (ROS) whose mediated pathways have been related to various bodily disorders such as neurodegenerative diseases, Alzheimer, asthma, cancer and inflammatory arthritis (Andre et al. 2013; Giorgio et al. 2007; Rojkind et al. 2002; Schalkwijk et al. 1786). Therefore, detection of low concentration of H2O2 in a rapid and selective fashion is highly demanding. Up until now, many enzymes (redox-active proteins) have been exploited to develop various H2O2 ECBs, such as cytochrome c (cyt c), Myoglobin (Mb), Hemoglobin (Hb), Ferredoxin (Fdx) and Horseradish Peroxidase (HRP). Their corresponding electrocatalytic reactions towards H2O2 is shown as follows:
$$ {\displaystyle \begin{array}{l}\mathrm{Mb}\ \left({\mathrm{Fe}}^{3+}\right)+{\mathrm{e}}^{-}\to \mathrm{Mb}\ \left({\mathrm{Fe}}^{2+}\right)\\ {}2\ \mathrm{Mb}\ \left({\mathrm{Fe}}^{2+}\right)+{\mathrm{H}}_2{\mathrm{O}}_2\to 2\ \mathrm{Mb}\ \left({\mathrm{Fe}}^{3+}\right)+2{\mathrm{H}}_2\mathrm{O}\end{array}} $$
(17.2)
$$ {\displaystyle \begin{array}{l}\mathrm{c}\mathrm{yt}\ \mathrm{c}\ \left({\mathrm{Fe}}^{3+}\right)+{\mathrm{e}}^{-}\to \mathrm{cyt}\ \mathrm{c}\ \left({\mathrm{Fe}}^{2+}\right)\\ {}2\ \mathrm{c}\mathrm{yt}\ \mathrm{c}\ \left({\mathrm{Fe}}^{2+}\right)+2{\mathrm{H}}^{+}+{\mathrm{H}}_2{\mathrm{O}}_2\to 2\ \mathrm{c}\mathrm{yt}\ \mathrm{c}\ \left({\mathrm{Fe}}^{3+}\right)+2{\mathrm{H}}_2\mathrm{O}\end{array}} $$
(17.3)
$$ {\displaystyle \begin{array}{l}\mathrm{Hb}\ \left({\mathrm{Fe}}^{3+}\right)+{\mathrm{H}}_2{\mathrm{O}}_2\to \mathrm{Compound}\ \mathrm{I}\ \left({\mathrm{Fe}}^{4+}=\mathrm{O}\right)+{\mathrm{H}}_2\mathrm{O}\\ {}\mathrm{Compound}\ \mathrm{I}\ \left({\mathrm{Fe}}^{4+}=\mathrm{O}\right)+{\mathrm{e}}^{-}+{\mathrm{H}}^{+}\to \mathrm{Compound}\ \mathrm{I}\mathrm{I}\\ {}\mathrm{Compound}\ \mathrm{I}\mathrm{I}+{\mathrm{e}}^{-}+{\mathrm{H}}^{+}\to \mathrm{Hb}\ \left({\mathrm{Fe}}^{3+}\right)+{\mathrm{H}}_2\mathrm{O}\end{array}} $$
(17.4)
$$ {\displaystyle \begin{array}{l}\mathrm{Fdx}\ {\left(2\mathrm{Fe}-2\mathrm{S}\right)}^{2+}+{\mathrm{e}}^{-}\to \mathrm{Fdx}\ {\left(2\mathrm{Fe}-2\mathrm{S}\right)}^{+}\\ {}\mathrm{Fdx}\ {\left(2\mathrm{Fe}-2\mathrm{S}\right)}^{+}+{\mathrm{H}}_2{\mathrm{O}}_2\to \mathrm{Fdx}\ {\left(2\mathrm{Fe}-2\mathrm{S}\right)}^{2+}+2{\mathrm{O}\mathrm{H}}^{-}\end{array}} $$
(17.5)
$$ {\displaystyle \begin{array}{l}\mathrm{HRP}\ \left({\mathrm{Fe}}^{3+}\right)+{\mathrm{H}}_2{\mathrm{O}}_2\to \mathrm{Compound}\ \mathrm{I}\ \left({\mathrm{Fe}}^{4+}=\mathrm{O}\right)+{\mathrm{H}}_2\mathrm{O}\\ {}\mathrm{Compound}\ \mathrm{I}\ \left({\mathrm{Fe}}^{4+}=\mathrm{O}\right)+{\mathrm{e}}^{-}+{\mathrm{H}}^{+}\to \mathrm{Compound}\ \mathrm{I}\mathrm{I}\\ {}\mathrm{Compound}\ \mathrm{I}\mathrm{I}+{\mathrm{e}}^{-}+{\mathrm{H}}^{+}\to \mathrm{HRP}\ \left({\mathrm{Fe}}^{3+}\right)+{\mathrm{H}}_2\mathrm{O}\end{array}} $$
(17.6)
A typical experimental setup for the electrochemical detection of H2O2 is shown in Fig. 17.12. Basically, amperometric technique (i−t) is employed, from which, the applied voltage is kept constant at the value where all the species are in the reduced states. Then, an identical aliquot of H2O2 with various concentrations is injected inside the N2-saturated buffer solution with the constant time intervals, while the solution is continuously stirring. The current versus time is recorded for further analysis.
Fig. 17.12

Schematic diagram of the enzyme-based ECB experimental setup. WE, RE and CE stand for working, reference and counter electrode, respectively

The Mb, cyt c, Hb, Fdx and HRP as the class of metalloproteins are hemeproteins containing iron cation(s). Due to the redox capability of hemeproteins, these metalloproteins have been widely incorporated for enzyme-based biosensors, particularly H2O2 biosensors. However, as we mentioned earlier, to increase the electron transfer rate of the solid electrodes, the modification of electrode prior to the enzyme immobilization is necessary.

Gold nanoparticle-modified iridium tin oxide (Au NP/ITO) has been reported to enhance the electrochemical properties of the cyt c (Yagati et al. 2012). It was observed that, the Au NP provided not only conduction enhancement but also a very high surface to volume ratio for the effective and dense immobilization of cyt c. This led to a clear quasi-reversible redox current signals resulting from the Fe3+/2+ redox center, which showed a good electron exchange between the protein and solid electrode. Later, Cho’s group developed new electrode by coupling gold nanoparticles with graphene oxide as the precursor for the immobilization of HRP to be further used for the sensitive detection of H2O2 (Yagati et al. 2014). The electrode was fabricated using chronoamperometry method based on electrochemical co-reduction of graphene oxide/nanoparticle (ERGO-NP) composite films onto ITO electrode. The ERGO-NP/ITO electrodes demonstrated a very high conductivity (ca. 5 times higher than the unmodified electrodes) which was attributed to well-distribution of immobilized enzyme onto the surface as well as the high surface area and highly conductivity of the substrate. The sensor showed excellent sensitivity of 1808.9 μA mM−1 cm−2 and selectivity with a linear dynamic detection range and the detection limit of 0.6 μM.

Another report dealt with the Mb immobilization onto porous cerium dioxide (CeO2) which was priory electrodeposited onto the ITO glass (Yagati et al. 2013). The developed CeO2/ITO film offered a nanoporous structure with a large surface area for the direct immobilization of Mb without any chemical linker to hamper the electron transfer between the interfaces. The biosensor represented a good selectivity and a sound current response of 10 s. Recently, a very sensitive enzyme-based H2O2 ECB was reported which was based on the MoS2 nanoparticle encapsulated with graphene oxide (Fig. 17.13) (Yoon et al. 2017a). Owing to the unique electrochemical properties of topological insulator (MoS2) nanoparticle as well as the graphene oxide, the proposed biosensor exhibited high electrochemical signal which gave rise to the sensitive detection of H2O2 at 20 nm. A comprehensive report of different enzyme-based H2O2 ECBs is provided in Table 17.1.
Fig. 17.13

Schematic depicting the synthesis route of the biosensor (Upper panel) and constitution of biosensor towards EC signal enhancement and H2O2 detection improvement. (Figure reproduced with permission from Yoon et al. 2017a)

Table 17.1

List of protein-based electrochemical biosensor for H2O2 detection

Protein

Modified Electrode

LOD (μM)

Dynamic Range (μM)

References

Cyt c

Au NP/ITO

0.5

Yagati et al. (2012)

MPCE

0.146

0.02–24

Zhang (2008)

GNPs/RTIL/MWNTs/GCE

3.0

0.05–11.5

Xiang et al. (2008)

RTIL-PDDA-AuNPs/MUA-MCH/au

5.0

0.04–3.45

Song et al. (2013)

MPA/au

1.0

0–0.25

Suárez et al. (2013)

Mb

GO@MoS2

0.02

Yoon et al. (2017a)

CeO2/ITO

0.6

3.0–3000

Yagati et al. (2013)

Nafion/IL/GCE

0.14

1.0–180

Safavi and Farjami (2010)

GNRs@SiO2/RTIL-sol-gel/GCE

0.12

0.2–180

Zhu et al. (2009)

Clay-IL/GCE

0.73

3.9–259

Dai et al. (2009)

Hb

GNPs/MWNT/GC

0.08

0.21–3000

Jia et al. (2009)

Graphene/Fe3O4/GCE

6.0

0.25–1.7

Wang et al. (2013)

ZnO/MWCNT/GCE

0.02

Palanisamy et al. (2012)

SDS/TiO2/GCE

0.087

0.5–70

Wang et al. (2011)

HRP

ERGO-NP

0.6

Yagati et al. (2014)

Composite-3

0.009

0.01–0.22

Umasankar et al. (2012)

PTMSPA@GNR

0.06

10–1000

Komathi et al. (2013)

Au NAE

0.42

0.74–15,000

Xu et al. (2010)

Au NP/MPA/au

0.16

0.48–1200

Wan et al. (2013)

17.4.2 Protein/DNA-Based Electrochemical Biosensor for H2O2 Detection

The study of interaction between protein and DNA has become a very interesting topic in variety fields of biology, chemistry and biotechnology (Gromiha and Nagarajan 2013). The study on charge transfer between the redox proteins or enzymes and nucleic acids has attracted much attention, since it can provide deeper understandings of the electron transfer mechanism in real biological systems and establish a stepping stone for the fabricating of novel biosensors and biodevices (Gorton et al. 1799; Nowak et al. 2011). Here we review some examples of Protein/DNA-based ECBs for the detection of H2O2 and oligonucleotides.

There are mainly two methods for the DNA/protein conjugation: electrostatic bonding and covenant bonding. Taking the advantage of electrostatic bonding, ECBs composed of DNA and hemoglobin (Hb) dropletting onto the gold electrode to detect H2O2. The DNA helped the Hb to keep its native structure and to less aggregate giving rise to its reducibility enhancement (Kafi et al. 2006). Another approach was based on HRP/DNA–silver nanohybrids and poly(diallyldimethylammonium chloride) (PDDA)-protected gold nanoparticles (Ma et al. 2009). As shown in Fig. 17.14, at first, DNA–Ag + complex was electrochemically reduced onto the bare gold electrode to obtain negatively charged immobilization matrix (DNA–Ag) for the further immobilization of PDDA–Au particles. Then the process of A and B were repeated to achieve a more conductive bilayer structure. Next, the positively charged HRP (H2O2 reducing enzyme) was bond to the negative surface for the subsequent detection of H2O2. The reported biosensor represented a linear dynamic range over H2O2 concentrations from 7.0 μm to 7.8 mm and the detection limit of 2.0 μm (S/N = 3) with good selectivity and acceptable stability.
Fig. 17.14

Layer-by-layer formation of the DNA-Ag/PDDA-Au/DNA-Ag/Au

Using two different types of proteins (HRP and Cyt c) in conjugation with DNA, Yonghai et al. fabricated an H2O2 biosensor to mimic the charge transfer and electrocatalytic mechanism of two proteins in living organisms (Song et al. 2012). According to their results, a faster charge transfer rate was observed for the bi-protein bio-interphase than the single protein biointerphase, demonstrating a synergetic effect to better the electron transfer. The DNA role was to provide a network film as a biocompatible microenvironment for the proteins adsorption and an essential pathway for the charge transfer between the electrode and proteins (Fig. 17.15).
Fig. 17.15

Surface functionalization of the biosensor composed of DNA and HRP-Cyt c

17.4.3 Protein/DNA-Based Electrochemical Biosensor for Oligonucleotide Detection

Interaction between DNA and protein has been also studied to develop various ECBs for the detection of different genomic DNA/RNA strands. One of the well-known protein-ligand covalent conjugation techniques is the streptavidin (STV)-biotin. (Dundas et al. 2013; González et al. 1799) Making use of STV-biotin interaction, Shanlin et al. fabricated a chemical controllable electrode for EC detection of DNA using EIS method (Pan and Rothberg 2005). As depicted in Fig. 17.16, bare gold electrode was firstly modified with the mixed monolayer of 2-mercaptoethanol (ME) and 11-mercaptoundecanoic acid (11-MUA) to provide enough space for the conjugation of STV onto the free carboxyl group of the 11-MUA through amide bonding. A biotin-modified ssDNA was then bound to the STV through the robust STV-biotin chemistry. Using [Fe(CN)6]4−/3− redox reporter, the EIS measurement was performed. Before the target invasion, the ssDNAs hampered the redox probes to reach the gold electrode surface leading, whereas, after the target hybridization, the formation of the upright dsDNA facilitated approach of the redox probe to the surface. The resistance difference was monitored by EIS method to offer a very sensitive and selective ECB with the detection limit of 10 pm.
Fig. 17.16

Surface functionalization of the biosensor and further EIS measurement. (Figure reproduced with permission from Pan and Rothberg 2005)

The redox active proteins or enzymes have been also implemented for the nucleic acid detection. For instance, using HRP as the redox reporter by reducing the H2O2. Gang et al. reported a very sensitive enzyme-based E-DNA sensor consists of a stemloop DNA probe which was labeled with biotin and digoxigenin (DIG) at its each end (Liu et al. 2008). The probe was immobilized onto an avidin-modified electrode through the biotin-avidin conjugation (another strong conjugation technique). In the absence of the target DNA, the DIG was shielded from being approached by bulky AntiDIG-modified HRP because of the steric effect. After the target induction and hybridization, the dsDNA forced the DIG to be detached from the surface and be accessible by the AntiDIG-modified HRP for the enzymatic transduction via H2O2 reduction. The proposed biosensor exhibited a high sensitivity down to femtomolar with the ability of mismatch detection.

An alternative approach for the oligonucleotide detection using protein/DNA-based ECB was to make use of metalloproteins in conjugation with nucleic acids. Recently, a novel parallel dsDNA and recombinant azurin hybrid was developed to have higher conductance that of the canonical DNA and they conjugated it with recombinant Azurin protein (denoted as PSD/rAzu) for the general detection of various viral DNAs and miRNAs (Mohammadniaei et al. 2017). As depicted in Fig. 17.17, the immobilized rAzu onto the gold electrode provided Cu+/Cu2+ redox reaction and a stable anchoring site to remove the requirements of additional chemical linkers and rolled as a selective-arrayed molecule due its appropriate cross-sectional diameter (~5 nm) and capability to receive only one DNA strand at its N-terminus. The EC and scanning tunneling spectroscopy (STS) measurement confirmed higher electron conductivity of the PSD (resembling a parallel electrical circuit,) compared to the dsDNA. Silver ion bond between C-C mismatched base pairs on the top of each helix, functioned as the redox signal reporter for EC conductance measurement and sensing application. The single mismatch detection strategy was inspired by the short-circuit law in classical physics which illustrates that, in a parallel electrical circuit possessing two current flow paths, current migrates through the path with no electrical impedance. Therefore, the single mismatched duplex could be considered as the path with higher impedance, resulting a lower electrochemical signal. The developed biosensor could detect miR-155, miR-21, miR-141, miR-143 as well as genomic MERS-CoV and HIV-1.
Fig. 17.17

Schematic illustration of PSD/rAzu biosensor

17.5 DNA-Based Electrochemical Biosensor

DNA-based electrochemical biosensors have been mostly used as the hybridization assays for the genetic analysis, due to the ability of the single stranded DNA (ssDNA), as the sensor probe, to seek out and hybridize with the target gene (Paleček and Jelen 2002; Wang 2002; Zhai et al. 1797). However, DNA has been also incorporated with different organic/inorganic platforms to form various biosensors (Chowdhury et al. 2014; Gao et al. 2016; Liu et al. 2015a). In this section, we are going to review some examples of DNA-based ECBs towards H2O2 detection and oligonucleotide detection.

Conventionally, the nucleic acids possess weak enzymatic properties due to the lack of prosthetic groups, in order to exploit them for the H2O2 detection (catalytic reaction) the necessity for the incorporation of this biomolecule with metal ions is demanding.

17.5.1 DNA-Based Electrochemical Biosensor for H2O2 Detection

In 2006, researchers developed a dimension-controlled silver–DNA hybrid nanoparticles which was electrodeposited on a glassy carbon electrode based on the reduction of silver with the help of DNA (Wu et al. 2006). The DNA rolled to avoid aggregation of silver nanoparticles and enhanced the catalytic capability of the nanocomplex to further detect H2O2 at the low concentration of 0.6 μM and linear detection range of 2.0 μm–2.5 mm.

Another amperometric H2O2 biosensor was reported by Yasushi group, composed of DNA-Cu(II) and chitosan polyion (Gu et al. 2009). DNA/chitosan polyion complex membrane was employed as a precursor for entrapment of electrocatalytic copper ions, which could specifically bound to double stranded DNA (dsDNA) and further reduce the H2O2 on the glassy carbon electrode (GCE). The sensor exhibited good sensitivity and selectivity towards ascorbic acid with the linear range from 10 μm to 10 mm and the detection limit of 3 μm.

DNA in the form of G-quadruplex DNAzyme has been also integrated with hemin to electrochemically detect H2O2 in a very low concentration of 0.16 μm (Wu et al. 2015). Deoxyribozymes (DNAzymes), are ssDNAs with particular catalytic features. G-quadruplex DNA is a self-assembled G-rich DNA sequence, whereas the hemin/G-quadruplex is formed by coordination of hemin inside the G-quadruplex DNA (Li et al. 2016). Owing to the Fe ion of the hemin group, the hemin/G-quadruplex can effectively catalyze H2O2. As depicted in Fig. 17.18, the G-quadruplex was firstly self-assembled onto the gold particles modified heated copper disk electrode (Au-HCuDE), then the electrode was back-field with 6-mercapto-1-hexanol (MCH) in order to remove physical bindings of thiol-modified ssDNA to the surface. After that, the hemin was introduced to the structure to form the hemin/G-quadruplex. Enhancement of the electrode temperature to 50 °C, resulted in the amplification of the electrocatalytic activity in the developed biosensor.
Fig. 17.18

Schematic construction and performance of the biosensing platform for electrocatalytic reduction of H2O2 at the elevated electrode temperature

Recently, a highly sensitive H2O2 ECB with the ability of H2O2 detection in the sterilize milk has been reported based on a novel “on-off-on” switch system. The electrode consisted of methylene blue (MB) as the charge mediator, gold nanoparticle as the electrochemical signal enhancer and iridium (III)/G-quadroplex to provide a hydrophobic layer (switch off). After the introduction of H2O2, iridium (III)/G-quadroplex was cleaved into DNA fragments. Releasing the DNA fragments from the electrode surface led to the signal recovering (switch on), which enabled H2O2 detection.

17.5.2 DNA-Based Electrochemical Biosensor for Oligonucleotide Detection

It has been proved that, nucleic acid (DNA/RNA) detection and analysis is highly essential not only for obtaining genetic information but also for the sake of diagnosis, identification and classification of various diseases and genetic disorders (Abi et al. 2018; Zhai et al. 1797). Nucleic acid ECBs are usually based on the hybridization method. It involves monitoring the electrochemical signal response, resulting from the Watson–Crick base-pairing of the genomic DNA/RNA target with the sensor probe (Jolly et al. 2016).

In 1994, Millan et al. reported an electrochemical DNA sequence-selective biosensor which was a stepping stone for the development of various nucleic acid ECBs (Millan et al. 1794). Generally, in the nucleic acid ECBs, sensor probe (recognition platform) consists of ssDNA or ssRNA covalently self-assembled onto the electrode surface, which should be conductive, biocompatible and have a low over potential (Odenthal and Gooding 2007). The probe sensor is then immersed into the solution containing target ssDNA/ssRNA, which is complementary to the probe strand, to form a double stranded nucleic acid helix. Depending on the experimental design and whether the sensor is “signal-off” or “signal-on”, the hybridization process results in a notable change in the transduced electrochemical signal and further detection by the signal processor (Liu et al. 2012a). There have been usually two main techniques for the development of electrochemical nucleic acid biosensors: labeled and label-free approaches. In the labeled method, enzyme labels or redox labels are employed to bind to the nucleic acids. This binding can be specific, covalent or electrostatic. Whereas, the label-free approach is commonly based on the difference in the electrical properties of the single stranded and double stranded structures. Schematic diagram in Fig. 17.19 demonstrates the two mentioned approaches.
Fig. 17.19

General diagram for the labeled and label-free electrochemical nucleic acid biosensor; DPV and SWV stand for differential phase voltammetry and square wave voltammetry, respectively

Several challenges have remained to develop nucleic acid ECBs such as surface immobilization control, single mismatch detection and fast response. Fabrication of a highly reproducible and credible nucleic acid ECB to discover genetic disorders caused by base pair mutation is highly demanding for early-stage diagnosis of different types of cancers and diseases (Baker 2006; Drummond et al. 2003). An acceptable nucleic acid ECB should compete against the standard sensing methods such as quantitative real-time polymerase chain reaction (qRT-PCR) (Maddocks and Jenkins 2017), northern blotting (NB) (Schwarzkopf and Pierce 2016) and microarray (Dastjerdi et al. 2014). However, microarray technique is prone to high cost which makes it less approachable to every user. Also the NB method is restricted due to the requirements for radiolabeling leading to cross contamination and low efficiency. The major limitation of qRT-PCR, regardless to its high cost, would be the detection of short strand nucleic acids such as microRNAs, due to their low melting temperature following by the complicated primer design to causes contamination experimental errors (Lee et al. 2014a; Wu and Qu 2015).

In 2005, Masahiko group reported a great capability of DNA ECBs for the detection of single mutation inside the dsDNA (Inouye et al. 2005). The idea was simply based on the defining the dsDNA as an electrical wire whose one end was attached onto the gold electrode and the other end was modified with Ferrocene as the EC redox reporter (Fig. 17.20). The duplex with mismatch base pair resulted in the rupture in π- π orbital loss in the dsDNA to interrupt the charge transfer from the electrode surface to Ferrocene through the double strand helix leading to a significant electrochemical signal drop (SWV technique).
Fig. 17.20

Mechanism of the electrochemical detection of mismatched DNA

One of the famous methods for sensitive electrochemical detection of nucleic acids is hybridization chain reaction (HCR) (Trifonov et al. 2016). This biosensor was consisted of a gold electrode, fictionalized by thiol-modified ssDNA (1) which is partially complementary to the target oligonucleotide (2). Hybridization of the analyte (2), with the probe leads to the formation of a dsDNA containing a toehold sequence. In the presence of the two hairpins of HA (3) and HB (4), the HCR triggers. The HCR mechanism is as follows: The toehold sequence of stand (2) opens the hairpin (3), exposing a new single-stranded toehold (W) which opens hairpin (4). The results in another free toehold (X) to open the hairpin (3) and the process keeps going until the hairpins supply is exhausted. The electrochemical detection method was EIS that is based on the semicircular diameter of the Nyquist plot from which the higher frequencies explains the higher charge transfer resistance (Ret) corresponding to the more negative charge of the electrodes resulting from the layer-by-layer assembly of the electrode surface with oligonucleotides. The HEPES buffer (10 mM, pH = 7.2) was used which contained Fe(CN)63−/4− as the negatively charged redox probe to indicate the electrode surface modification based on its repulsion from the surface, associated with the sequential addition of oligonucleotides (negative charge) to the surface. For a fixed incubation time of 45 min, different concentration of the analyte (2) was added to the biosensor to achieve the dynamic detection range. This method offered a detection limit of 1.2 nm of the analyte.

More recently, a simply-designed single-step miRNA biosensor was fabricated using the combination of EC and surface enhanced Raman spectroscopy techniques (SERS) (Fig. 17.21). With the strength of EC method, they removed the weakness of SERS technique for the single-mismatch detection. Also, in a back-to-back supporting situation, the combining method gave rise to the extension dynamic detection range of the biosensor from 10 pM to 450 nM (SERS: 10 pm ~ 5 nm and EC: 5 nm ~ 450 nm) (Mohammadniaei et al. 2018). In this report, a single stranded 3′ methylene blue (MB) and 5′ thiol-modified RNA (MB-ssRNA-SH) was immobilized onto the spectroelectrochemical-active gold nanoparticle-modified ITO (ITO/GNP) to detect the target miR-155. As a signal-off biosensor, upon the addition of target strand, the dsRNA transformed to an upright position resulting in a considerable decrease in SERS and EC signals of the MB.
Fig. 17.21

Schematic diagram of the surface modification and microRNA detection of the MB-dsDNA-SH@ITO/GNP biosensor

In this section we tried to give the reader some insights into the different techniques for the detection of oligonucleotides based on nucleic acid-ECBs, although, there have been reported many outstanding DNA-based ECBs such as dichalcogenides based electrochemical biosensors (Wang et al. 2017), rolling cycle amplification-based methods (RCA) (Cheng et al. 2009), isothermal amplification, (Zhang and Zhang 2012) aloe-like gold micro/nanostructures (Shi et al. 2013), three-mode system (Labib et al. 2013), (details of previous reports are provided in Table 17.2).
Table 17.2

Oligonucleotide detection using nucleic acid-based ECBs

Detection method

Detection steps

LOD

Labeling

Detection time

References

Cleavage-based signal amplification

10

69.2 aM

G-quadruplex hemin

> 2 h

Zhao et al. (2013)

Amperometric magnetobiosensor

4

0.4 fM

Biotin–strep-HRP

~ 3 h

Campuzano et al. (2014)

Three-mode electrochemical sensor

2

5 aM

None

~ 2 h

Labib et al. (2013)

Carbon nanotube-bridged field-effect transistor assisted by p19

2

1 aM

None

~ 2 h

Ramnani et al. (2013)

Tandem polymerization and cleavage-mediated cascade system

7

5 fM

None

~ 4 h

Liu et al. (2016)

DNA tetrahedral scaffold

4

10 aM

Biotinylated probe-avidin-HRP, poly-HRP80

~ 8 h

wen et al. (2012)

Oxidized carbon nano tubes and nanodiamonds

2

1.95 fM

DNAzyme based hybrid structure

~ 2 h

Liu et al. (2015b)

17.6 Future Prospective

In spite of initial achievement, the investigation of bioelectronic devices and biosensors are still required. The discussed results are intriguing for the electrochemical bioelectronics devices including biomemory device, biologic gate, bioinformation, biosensors and processor for future biocomputer systems. The biomolecule can easily be tailored and modified with other biomolecule or nanoparticles to embody the specific functionality. Not only the biomolecule can combine the original property with various nanoparticles and other biomolecules to the development of various bioelectronics computation platforms, but also the proto-type biocomputer can operate in living organism with hybrid molecule-neural cell connection. Moreover, unlike that of the silicon-based device, the future biomolecular-based computer could easily be integrated with the input module and energy module that give the new concept of output combination for the disease diagnostics and cancer cell identifications. That kind of characteristic is quite intriguing and potentially useful as a new concept of computing for bioelectronic medicine devices. On the edge of bioelectronics, biomolecule-based electronic devices can be envisaged as a powerful alternative, once appropriate fabrication technique and integrating circuit are achieved, then, the nano-scale system can be achieved with a biomolecule-nanoparticle hybrid.

Notes

Acknowledgements

This research was supported by NRF-2018R1D1A1B07049407 and by the Research Grant of Kwangwoon University in 2018.

References

  1. Abi A, Mohammadpour Z, Zuo X, Safavi A (2018) Nucleic acid-based electrochemical nanobiosensors. Biosens Bioelectron 102:479–489.  https://doi.org/10.1016/j.bios.2017.11.019 CrossRefPubMedGoogle Scholar
  2. Adleman L (1994) Molecular computation of solutions to combinatorial problems. Science 266:1021–1024.  https://doi.org/10.1126/science.7973651 CrossRefPubMedGoogle Scholar
  3. Andre C, Kim SW, Yu X-H, Shanklin J (2013) Fusing catalase to an alkane-producing enzyme maintains enzymatic activity by converting the inhibitory byproduct H2O2 to the cosubstrate O2. Proc Natl Acad Sci 110:3191–3196.  https://doi.org/10.1073/pnas.1218769110 CrossRefPubMedGoogle Scholar
  4. Artés JM, Díez-Pérez I, Gorostiza P (2012) Transistor-like behavior of single Metalloprotein junctions. Nano Lett 12:2679–2684.  https://doi.org/10.1021/nl2028969 CrossRefPubMedGoogle Scholar
  5. Arugula MA, Shroff N, Katz E, He Z (2012) Molecular AND logic gate based on bacterial anaerobic respiration. Chem Commun 48:10174–10176.  https://doi.org/10.1039/C2CC35595G CrossRefGoogle Scholar
  6. Ausländer S, Ausländer D, Müller M, Wieland M, Fussenegger M (2012) Programmable single-cell mammalian biocomputers. Nature 487:123.  https://doi.org/10.1038/nature11149 CrossRefPubMedGoogle Scholar
  7. Baker M (2006) New-wave diagnostics. Nat Biotechnol 24:931–938CrossRefGoogle Scholar
  8. Baron R, Lioubashevski O, Katz E, Niazov T, Willner I (2006a) Elementary arithmetic operations by enzymes: a model for metabolic pathway based computing. Angew Chem Int Ed 45:1572–1576.  https://doi.org/10.1002/anie.200503314 CrossRefGoogle Scholar
  9. Baron R, Lioubashevski O, Katz E, Niazov T, Willner I (2006b) Logic gates and elementary computing by enzymes. Chem A Eur J 110:8548–8553.  https://doi.org/10.1021/jp0568327 CrossRefGoogle Scholar
  10. Benenson Y (2009) RNA-based computation in live cells. Curr Opin Biotechnol 20:471–478.  https://doi.org/10.1016/j.copbio.2009.08.002 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Benenson Y, Gil B, Ben-Dor U, Adar R, Shapiro E (2004) An autonomous molecular computer for logical control of gene expression. Nature 429:423.  https://doi.org/10.1038/nature02551 CrossRefPubMedGoogle Scholar
  12. Bonnet J, Subsoontorn P, Endy D (2012) Rewritable digital data storage in live cells via engineered control of recombination directionality. Proc Natl Acad Sci 109:8884–8889.  https://doi.org/10.1073/pnas.1202344109 CrossRefPubMedGoogle Scholar
  13. Bonnet J, Yin P, Ortiz ME, Subsoontorn P, Endy D (2013) Amplifying genetic logic gates. Science 340:599–603.  https://doi.org/10.1126/science.1232758 CrossRefPubMedGoogle Scholar
  14. Bychkova V, Shvarev A, Zhou J, Pita M, Katz E (2010) Enzyme logic gate associated with a single responsive microparticle: scaling biocomputing to microsize systems. Chem Commun 46:94–96.  https://doi.org/10.1039/B917611J CrossRefGoogle Scholar
  15. Campolongo MJ, Kahn JS, Cheng W, Yang D, Gupton-Campolongo T, Luo D (2011) Adaptive DNA-based materials for switching, sensing, and logic devices. J Mater Chem 21:6113–6121.  https://doi.org/10.1039/C0JM03854G CrossRefGoogle Scholar
  16. Campuzano S, Torrente-Rodríguez RM, López-Hernández E, Conzuelo F, Granados R, Sánchez-Puelles JM, Pingarrón JM (2014) Magnetobiosensors based on viral protein p19 for MicroRNA determination in cancer cells and tissues. Angew Chem Int Ed 53:6168–6171.  https://doi.org/10.1002/anie.201403270 CrossRefGoogle Scholar
  17. Chen Y-P, Liu B, Lian H-T, Sun X-Y (2011) Preparation and application of urea electrochemical sensor based on chitosan molecularly imprinted films. Electroanalysis 23:1454–1461.  https://doi.org/10.1002/elan.201000693 CrossRefGoogle Scholar
  18. Chen Y-S, Hong M-Y, Huang GS (2012) A protein transistor made of an antibody molecule and two gold nanoparticles. Nat Nanotechnol 7:197.  https://doi.org/10.1038/nnano.2012.7 CrossRefPubMedGoogle Scholar
  19. Cheng Y, Zhang X, Li Z, Jiao X, Wang Y, Zhang Y (2009) Highly sensitive determination of microRNA using target-primed and branched rolling-circle amplification. Angew Chem Int Ed 48:3268–3272.  https://doi.org/10.1002/anie.200805665 CrossRefGoogle Scholar
  20. Cho W-J, Huang H-J (1998) An Amperometric urea biosensor based on a polyaniline−Perfluorosulfonated ionomer composite electrode. Anal Chem 70:3946–3951.  https://doi.org/10.1021/ac980004a CrossRefGoogle Scholar
  21. Choi J-W, Oh B-K, Kim YJ, Min J (2007) Protein-based biomemory device consisting of the cysteine-modified azurin. Appl Phys Lett 91:263902.  https://doi.org/10.1063/1.2828046 CrossRefGoogle Scholar
  22. Chowdhury AD, Gangopadhyay R, De A (2014) Highly sensitive electrochemical biosensor for glucose, DNA and protein using gold-polyaniline nanocomposites as a common matrix. Sensors Actuators B Chem 190:348–356.  https://doi.org/10.1016/j.snb.2013.08.071 CrossRefGoogle Scholar
  23. Christof MN, Chad AM (2004) Nanobiotechnology: concepts, applications and perspectives. Wiley-VCH, Weinheim, p 491Google Scholar
  24. Chung Y-H, Lee T, Min J, Choi J-W (2011) Investigation of the redox property of a metalloprotein layer self-assembled on various chemical linkers. Colloids Surf B Biointerfaces 87:36–41.  https://doi.org/10.1016/j.colsurfb.2011.04.034 CrossRefPubMedGoogle Scholar
  25. Dai Z, Xiao Y, Yu X, Mai Z, Zhao X, Zou X (2009) Direct electrochemistry of myoglobin based on ionic liquid–clay composite films. Biosens Bioelectron 24:1629–1634.  https://doi.org/10.1016/j.bios.2008.08.032 CrossRefPubMedGoogle Scholar
  26. Dastjerdi A, Fooks AR, Johnson N (2014) Chapter nineteen – oligonucleotide microarray: applications for lyssavirus speciation. Current laboratory techniques in rabies diagnosis, research and prevention. Academic Press, Amsterdam, pp 193–203Google Scholar
  27. de Ruiter G, van der Boom ME (2011) Surface-confined assemblies and polymers for molecular logic. Acc Chem Res 44:563–573.  https://doi.org/10.1021/ar200002v CrossRefPubMedGoogle Scholar
  28. de Silva AP, Uchiyama S (2007) Molecular logic and computing. Nat Nanotechnol 2:399.  https://doi.org/10.1038/nnano.2007.188 CrossRefPubMedGoogle Scholar
  29. Deng H, Shen W, Ren Y, Gao Z (2014) A highly sensitive microRNA biosensor based on hybridized microRNA-guided deposition of polyaniline. Biosens Bioelectron 60:195–200.  https://doi.org/10.1016/j.bios.2014.04.023 CrossRefPubMedGoogle Scholar
  30. Deonarine AS, Clark SM, Konermann L (2003) Implementation of a multifunctional logic gate based on folding/unfolding transitions of a protein. Futur Gener Comput Syst 19:87–97.  https://doi.org/10.1016/S0167-739X(02)00110-3 CrossRefGoogle Scholar
  31. Dolatabadi JEN, Mashinchian O, Ayoubi B, Jamali AA, Mobed A, Losic D, Omidi Y, de la Guardia M (2011) Optical and electrochemical DNA nanobiosensors. TrAC Trends Anal Chem 30:459–472.  https://doi.org/10.1016/j.trac.2010.11.010 CrossRefGoogle Scholar
  32. Drummond TG, Hill MG, Barton JK (2003) Electrochemical DNA sensors. Nat Biotechnol 21:1192–1199CrossRefGoogle Scholar
  33. Dundas CM, Demonte D, Park S (2013) Streptavidin–biotin technology: improvements and innovations in chemical and biological applications. Appl Microbiol Biotechnol 97:9343–9353.  https://doi.org/10.1007/s00253-013-5232-z CrossRefPubMedGoogle Scholar
  34. Elbaz J, Moshe M, Willner I (2009a) Coherent activation of DNA tweezers: a “SET–RESET” logic system. Angew Chem Int Ed 48:3834–3837.  https://doi.org/10.1002/anie.200805819 CrossRefGoogle Scholar
  35. Elbaz J, Wang Z-G, Orbach R, Willner I (2009b) pH-stimulated concurrent mechanical activation of two DNA “tweezers”. A “SET−RESET” logic gate system. Nano Lett 9:4510–4514.  https://doi.org/10.1021/nl902859m CrossRefPubMedGoogle Scholar
  36. Elbaz J, Wang F, Remacle F, Willner I (2012) pH-programmable DNA logic arrays powered by modular DNAzyme libraries. Nano Lett 12:6049–6054.  https://doi.org/10.1021/nl300051g CrossRefPubMedGoogle Scholar
  37. Farzadfard F, Lu TK (2014) Genomically encoded analog memory with precise in vivo DNA writing in living cell populations. Science 346:1256272.  https://doi.org/10.1126/science.1256272 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Freeman R, Finder T, Willner I (2009) Multiplexed analysis of Hg2+ and Ag+ ions by nucleic acid functionalized CdSe/ZnS quantum dots and their use for logic gate operations. Angew Chem Int Ed 48:7818–7821.  https://doi.org/10.1002/anie.200902395 CrossRefGoogle Scholar
  39. Frezza BM, Cockroft SL, Ghadiri MR (2007) Modular multi-level circuits from immobilized DNA-based logic gates. J Am Chem Soc 129:14875–14879.  https://doi.org/10.1021/ja0710149 CrossRefPubMedGoogle Scholar
  40. Fujibayashi K, Hariadi R, Park SH, Winfree E, Murata S (2008) Toward reliable algorithmic self-assembly of DNA tiles: a fixed-width cellular automaton pattern. Nano Lett 8:1791–1797.  https://doi.org/10.1021/nl0722830 CrossRefPubMedGoogle Scholar
  41. Gao W, Wei X, Wang X, Cui G, Liu Z, Tang B (2016) A competitive coordination-based CeO2 nanowire-DNA nanosensor: fast and selective detection of hydrogen peroxide in living cells and in vivo. Chem Commun 52:3643–3646.  https://doi.org/10.1039/C6CC00112B CrossRefGoogle Scholar
  42. Gdor E, Katz E, Mandler D (2013) Biomolecular AND logic gate based on immobilized enzymes with precise spatial separation controlled by scanning electrochemical microscopy. J Phys Chem B 117:16058–16065.  https://doi.org/10.1021/jp4095672 CrossRefPubMedGoogle Scholar
  43. Gianneschi NC, Ghadiri MR (2007) Design of Molecular Logic Devices Based on a programmable DNA-regulated semisynthetic enzyme. Angew Chem Int Ed 46:3955–3958.  https://doi.org/10.1002/anie.200700047 CrossRefGoogle Scholar
  44. Giorgio M, Trinei M, Migliaccio E, Pelicci PG (2007) Hydrogen peroxide: a metabolic by-product or a common mediator of ageing signals? Nat Rev Mol Cell Biol 8:722.  https://doi.org/10.1038/nrm2240 CrossRefPubMedGoogle Scholar
  45. González M n, Argaraña CE, Fidelio GD (1999) Extremely high thermal stability of streptavidin and avidin upon biotin binding. Biomol Eng 16:67–72.  https://doi.org/10.1016/S1050-3862(99)00041-8 CrossRefPubMedGoogle Scholar
  46. Gorton L, Lindgren A, Larsson T, Munteanu FD, Ruzgas T, Gazaryan I (1999) Direct electron transfer between heme-containing enzymes and electrodes as basis for third generation biosensors. Anal Chim Acta 400:91–108.  https://doi.org/10.1016/S0003-2670(99)00610-8 CrossRefGoogle Scholar
  47. Grabow WW, Jaeger L (2014) RNA self-assembly and RNA nanotechnology. Acc Chem Res 47:1871–1880.  https://doi.org/10.1021/ar500076k CrossRefPubMedGoogle Scholar
  48. Grieshaber D, MacKenzie R, Vörös J, Reimhult E (2008) Electrochemical biosensors - sensor principles and architectures. Sensors 8:1400CrossRefGoogle Scholar
  49. Gromiha MM, Nagarajan R (2013) Chapter three - computational approaches for predicting the binding sites and understanding the recognition mechanism of protein–DNA complexes. In: Donev R (ed) Advances in Protein Chemistry Structural Biology. Academic Press, New York, pp 65–99Google Scholar
  50. Gu T, Liu Y, Zhang J, Hasebe Y (2009) Amperometric hydrogen peroxide biosensor based on immobilization of DNA-Cu(II) in DNA/chitosan polyion complex membrane. J Environ Sci 21:S56–S59.  https://doi.org/10.1016/S1001-0742(09)60037-1 CrossRefGoogle Scholar
  51. Haque F, Shu D, Shu Y, Shlyakhtenko LS, Rychahou PG, Mark Evers B, Guo P (2012) Ultrastable synergistic tetravalent RNA nanoparticles for targeting to cancers. Nano Today 7:245–257.  https://doi.org/10.1016/j.nantod.2012.06.010 CrossRefPubMedPubMedCentralGoogle Scholar
  52. He H-Z, Chan DS-H, Leung C-H, Ma D-L (2013) G-quadruplexes for luminescent sensing and logic gates. Nucleic Acids Res 41:4345–4359.  https://doi.org/10.1093/nar/gkt108 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Hild W, Pollinger K, Caporale A, Cabrele C, Keller M, Pluym N, Buschauer A, Rachel R, Tessmar J, Breunig M, Goepferich A (2010) G protein-coupled receptors function as logic gates for nanoparticle binding and cell uptake. Proc Natl Acad Sci 107:10667–10672.  https://doi.org/10.1073/pnas.0912782107 CrossRefPubMedGoogle Scholar
  54. Huang Y, Duan X, Cui Y, Lauhon LJ, Kim K-H, Lieber CM (2001) Logic gates and computation from assembled nanowire building blocks. Science 294:1313–1317.  https://doi.org/10.1126/science.1066192 CrossRefPubMedGoogle Scholar
  55. Hunt HK, Armani AM (2010) Label-free biological and chemical sensors. Nanoscale 2:1544–1559.  https://doi.org/10.1039/C0NR00201A CrossRefPubMedGoogle Scholar
  56. Ikeda M, Tanida T, Yoshii T, Kurotani K, Onogi S, Urayama K, Hamachi I (2014) Installing logic-gate responses to a variety of biological substances in supramolecular hydrogel–enzyme hybrids. Nat Chem 6:511.  https://doi.org/10.1038/nchem.1937 CrossRefPubMedGoogle Scholar
  57. Inouye M, Ikeda R, Takase M, Tsuri T, Chiba J (2005) Single-nucleotide polymorphism detection with “wire-like” DNA probes that display quasi “on–off” digital action. Proc Natl Acad Sci USA 102:11606–11610.  https://doi.org/10.1073/pnas.0502078102 CrossRefPubMedGoogle Scholar
  58. Itamar W, Eugenii K (2005) Bioelectronics: from theory to applications. Wiley-VCH, WeinheimGoogle Scholar
  59. Jaeger L, Chworos A (2006) The architectonics of programmable RNA and DNA nanostructures. Curr Opin Struct Biol 16:531–543.  https://doi.org/10.1016/j.sbi.2006.07.001 CrossRefPubMedGoogle Scholar
  60. Jensen PS, Chi Q, Zhang J, Ulstrup J (2009) Long-range interfacial electrochemical Electron transfer of Pseudomonas aeruginosa Azurin−gold nanoparticle hybrid systems. J Phys Chem C 113:13993–14000.  https://doi.org/10.1021/jp902611x CrossRefGoogle Scholar
  61. Jia N, Lian Q, Wang Z, Shen H (2009) A hydrogen peroxide biosensor based on direct electrochemistry of hemoglobin incorporated in PEO–PPO–PEO triblock copolymer film. Sensors Actuators B Chem 137:230–234.  https://doi.org/10.1016/j.snb.2008.10.011 CrossRefGoogle Scholar
  62. Jolly P, Estrela P, Ladomery M (2016) Oligonucleotide-based systems: DNA, microRNAs, DNA/RNA aptamers. Essays Biochem 60:27–35.  https://doi.org/10.1042/ebc20150004 CrossRefPubMedPubMedCentralGoogle Scholar
  63. Kafi AKM, Fan Y, Shin H-K, Kwon Y-S (2006) Hydrogen peroxide biosensor based on DNA–Hb modified gold electrode. Thin Solid Films 499:420–424.  https://doi.org/10.1016/j.tsf.2005.06.073 CrossRefGoogle Scholar
  64. Katz E (2015) Biocomputing – tools, aims, perspectives. Curr Opin Biotechnol 34:202–208.  https://doi.org/10.1016/j.copbio.2015.02.011 CrossRefPubMedGoogle Scholar
  65. Katz E, Privman V (2010) Enzyme-based logic systems for information processing. Chem Soc Rev 39:1835–1857.  https://doi.org/10.1039/B806038J CrossRefPubMedGoogle Scholar
  66. Keren K, Berman RS, Buchstab E, Sivan U, Braun E (2003) DNA-templated carbon nanotube field-effect transistor. Science 302:1380–1382.  https://doi.org/10.1126/science.1091022 CrossRefPubMedGoogle Scholar
  67. Kimmel DW, LeBlanc G, Meschievitz ME, Cliffel DE (2012) Electrochemical sensors and biosensors. Anal Chem 84:685–707.  https://doi.org/10.1021/ac202878q CrossRefPubMedGoogle Scholar
  68. Ko Y, Kim Y, Baek H, Cho J (2011) Electrically Bistable properties of layer-by-layer assembled multilayers based on protein nanoparticles. ACS Nano 5:9918–9926.  https://doi.org/10.1021/nn2036939 CrossRefPubMedGoogle Scholar
  69. Komathi S, Gopalan AI, Kim S-K, Anand GS, Lee K-P (2013) Fabrication of horseradish peroxidase immobilized poly(N-[3-(trimethoxy silyl)propyl]aniline) gold nanorods film modified electrode and electrochemical hydrogen peroxide sensing. Electrochim Acta 92:71–78.  https://doi.org/10.1016/j.electacta.2013.01.032 CrossRefGoogle Scholar
  70. Labib M, Khan N, Ghobadloo SM, Cheng J, Pezacki JP, Berezovski MV (2013) Three-mode electrochemical sensing of ultralow MicroRNA levels. J Am Chem Soc 135:3027–3038.  https://doi.org/10.1021/ja308216z CrossRefPubMedGoogle Scholar
  71. Lee SW, Chang W-J, Bashir R, Koo Y-M (2007) “Bottom-up” approach for implementing nano/microstructure using biological and chemical interactions. Biotechnol Bioprocess Eng 12:185.  https://doi.org/10.1007/bf02931092 CrossRefGoogle Scholar
  72. Lee T, Kim SU, Min J, Choi JW (2010) Multilevel biomemory device consisting of recombinant Azurin/cytochrome c. Adv Mater 22:510–514.  https://doi.org/10.1002/adma.200902288 CrossRefPubMedGoogle Scholar
  73. Lee T, Min J, Kim S-U, Choi J-W (2011a) Multifunctional 4-bit biomemory chip consisting of recombinant azurin variants. Biomaterials 32:3815–3821.  https://doi.org/10.1016/j.biomaterials.2011.01.072 CrossRefPubMedGoogle Scholar
  74. Lee T, Yoo SY, Chung YH, Min J, Choi JW (2011b) Signal enhancement of electrochemical biomemory device composed of recombinant Azurin/gold nanoparticle. Electroanalysis 23:2023–2029.  https://doi.org/10.1002/elan.201100182 CrossRefGoogle Scholar
  75. Lee HJ, Oh JH, Oh JM, Park JM, Lee JG, Kim MS, Kim YJ, Kang HJ, Jeong J, Kim SI, Lee SS, Choi JW, Huh N (2013) Efficient isolation and accurate in situ analysis of circulating tumor cells using detachable beads and a high-pore-density filter. Angew Chem Int Ed 52:8337–8340.  https://doi.org/10.1002/anie.201302278 CrossRefGoogle Scholar
  76. Lee H, Park J-E, Nam J-M (2014a) Bio-barcode gel assay for microRNA. Nat Commun 5(5):3367.  https://doi.org/10.1038/ncomms4367 CrossRefPubMedGoogle Scholar
  77. Lee T, Yagati AK, Min J, Choi JW (2014b) Bioprocessing device composed of protein/DNA/inorganic material hybrid. Adv Funct Mater 24:1781–1789.  https://doi.org/10.1002/adfm.201302397 CrossRefGoogle Scholar
  78. Lee T, Chung Y-H, Yoon J, Min J, Choi J-W (2014c) Fusion protein-based biofilm fabrication composed of recombinant azurin–myoglobin for dual-level biomemory application. Appl Surf Sci 320:448–454.  https://doi.org/10.1016/j.apsusc.2014.09.020 CrossRefGoogle Scholar
  79. Lee T, Yagati AK, Pi F, Sharma A, Choi J-W, Guo P (2015) Construction of RNA–quantum dot chimera for nanoscale resistive biomemory application. ACS Nano 9:6675–6682.  https://doi.org/10.1021/acsnano.5b03269 CrossRefPubMedPubMedCentralGoogle Scholar
  80. Li H, Liu S, Dai Z, Bao J, Yang X (2009) Applications of nanomaterials in electrochemical enzyme biosensors. Sensors 9:8547CrossRefGoogle Scholar
  81. Li X, Sun L, Ding T (2011) Multiplexed sensing of mercury(II) and silver(I) ions: a new class of DNA electrochemiluminescent-molecular logic gates. Biosens Bioelectron 26:3570–3576.  https://doi.org/10.1016/j.bios.2011.02.003 CrossRefPubMedGoogle Scholar
  82. Li W, Li Y, Liu Z, Lin B, Yi H, Xu F, Nie Z, Yao S (2016) Insight into G-quadruplex-hemin DNAzyme/RNAzyme: adjacent adenine as the intramolecular species for remarkable enhancement of enzymatic activity. Nucleic Acids Res 44:7373–7384.  https://doi.org/10.1093/nar/gkw634 CrossRefPubMedPubMedCentralGoogle Scholar
  83. Liu Q, Wang L, Frutos AG, Condon AE, Corn RM, Smith LM (2000) DNA computing on surfaces. Nature 403:175.  https://doi.org/10.1038/35003155 CrossRefPubMedGoogle Scholar
  84. Liu G, Wan Y, Gau V, Zhang J, Wang L, Song S, Fan C (2008) An enzyme-based E-DNA sensor for sequence-specific detection of Femtomolar DNA targets. J Am Chem Soc 130:6820–6825.  https://doi.org/10.1021/ja800554t CrossRefPubMedGoogle Scholar
  85. Liu A, Wang K, Weng S, Lei Y, Lin L, Chen W, Lin X, Chen Y (2012a) Development of electrochemical DNA biosensors. TrAC Trends Anal Chem 37:101–111.  https://doi.org/10.1016/j.trac.2012.03.008 CrossRefGoogle Scholar
  86. Liu X, Aizen R, Freeman R, Yehezkeli O, Willner I (2012b) Multiplexed Aptasensors and amplified DNA sensors using functionalized graphene oxide: application for logic gate operations. ACS Nano 6:3553–3563.  https://doi.org/10.1021/nn300598q CrossRefPubMedGoogle Scholar
  87. Liu B, Sun Z, Huang P-JJ, Liu J (2015a) Hydrogen peroxide displacing DNA from Nanoceria: mechanism and detection of glucose in serum. J Am Chem Soc 137:1290–1295.  https://doi.org/10.1021/ja511444e CrossRefPubMedGoogle Scholar
  88. Liu L, Song C, Zhang Z, Yang J, Zhou L, Zhang X, Xie G (2015b) Ultrasensitive electrochemical detection of microRNA-21 combining layered nanostructure of oxidized single-walled carbon nanotubes and nanodiamonds by hybridization chain reaction. Biosens Bioelectron 70:351–357.  https://doi.org/10.1016/j.bios.2015.03.051 CrossRefPubMedGoogle Scholar
  89. Liu S, Gong H, Wang Y, Wang L (2016) Label-free electrochemical nucleic acid biosensing by tandem polymerization and cleavage-mediated cascade target recycling and DNAzyme amplification. Biosens Bioelectron 77:818–823.  https://doi.org/10.1016/j.bios.2015.10.056 CrossRefPubMedGoogle Scholar
  90. Liu H, Weng L, Yang C (2017) A review on nanomaterial-based electrochemical sensors for H2O2, H2S and NO inside cells or released by cells. Microchim Acta 184:1267–1283.  https://doi.org/10.1007/s00604-017-2179-2 CrossRefGoogle Scholar
  91. Lu W, Suo Z (2002) Symmetry breaking in self-assembled monolayers on solid surfaces: anisotropic surface stress. Phys Rev B 65:085401CrossRefGoogle Scholar
  92. Luo Z, Weiss DE, Liu Q, Tian B (2018) Biomimetic approaches toward smart bio-hybrid systems. Nano Res 11:3009.  https://doi.org/10.1007/s12274-018-2004-1 CrossRefGoogle Scholar
  93. Ma L, Yuan R, Chai Y, Chen S (2009) Amperometric hydrogen peroxide biosensor based on the immobilization of HRP on DNA–silver nanohybrids and PDDA-protected gold nanoparticles. J Mol Catal B Enzym 56:215–220.  https://doi.org/10.1016/j.molcatb.2008.05.007 CrossRefGoogle Scholar
  94. Maddocks S, Jenkins R (2017) Chapter 4 – Quantitative PCR: things to consider. In: Understanding PCR. Academic, Boston, pp 45–52CrossRefGoogle Scholar
  95. Mailloux S, Halamek J, Katz E (2014) A model system for targeted drug release triggered by biomolecular signals logically processed through enzyme logic networks. Analyst 139:982–986.  https://doi.org/10.1039/C3AN02162A CrossRefPubMedGoogle Scholar
  96. Malekzad H, Sahandi Zangabad P, Mirshekari H, Karimi M, Hamblin Michael R (2017) Noble metal nanoparticles in biosensors: recent studies and applications. Nanotechnol Rev 6(3):301–329.  https://doi.org/10.1515/ntrev-20160014
  97. Meng F, Jiang L, Zheng K, Goh CF, Lim S, Hng HH, Ma J, Boey F, Chen X (2011) Protein-based Memristive Nanodevices. Small 7:3016–3020.  https://doi.org/10.1002/smll.201101494 CrossRefPubMedGoogle Scholar
  98. Michael CP (2007) Molecular electronics: from principles to practice. Wiley, West SussexGoogle Scholar
  99. Millan KM, Saraullo A, Mikkelsen SR (1994) Voltammetric DNA biosensor for cystic fibrosis based on a modified carbon paste electrode. Anal Chem 66:2943–2948.  https://doi.org/10.1021/ac00090a023 CrossRefPubMedGoogle Scholar
  100. Min J, Lee T, Oh S-M, Kim H, Choi J-W (2010) Electrochemical biomemory device consisting of recombinant protein molecules. Biotechnol Bioprocess Eng 15:30–39.  https://doi.org/10.1007/s12257-009-3074-4 CrossRefGoogle Scholar
  101. Mitsumasa I, Young-Soo K, Takhee L (2010) Nanoscale Interface for organic electronics. World Scientific Publisher, SingaporeGoogle Scholar
  102. Mohammadniaei M, Lee T, Yoon J, Lee D, Choi J-W (2017) Electrochemical nucleic acid detection based on parallel structural dsDNA/recombinant azurin hybrid. Biosens Bioelectron 98:292–298.  https://doi.org/10.1016/j.bios.2017.07.005 CrossRefPubMedGoogle Scholar
  103. Mohammadniaei M, Lee T, Yoon J, Choi J-W (2018) Spectroelectrochemical detection of microRNA-155 based on functional RNA immobilization onto ITO/GNP Nanopattern. J Biotechnol 274:40CrossRefGoogle Scholar
  104. Muramatsu S, Kinbara K, Taguchi H, Ishii N, Aida T (2006) Semibiological molecular machine with an implemented “AND” logic gate for regulation of protein folding. J Am Chem Soc 128:3764–3769.  https://doi.org/10.1021/ja057604t CrossRefPubMedGoogle Scholar
  105. Nagase S, Ohkoshi N, Ueda A, Aoyagi K, Koyama A (1997) Hydrogen peroxide interferes with detection of nitric oxide by an electrochemical method. Clin Chem 43:1246–1246PubMedGoogle Scholar
  106. Nikitin MP, Shipunova VO, Deyev SM, Nikitin PI (2014) Biocomputing based on particle disassembly. Nat Nanotechnol 9:716.  https://doi.org/10.1038/nnano.2014.156 CrossRefPubMedGoogle Scholar
  107. Nowak C, Schach D, Gebert J, Grosserueschkamp M, Gennis RB, Ferguson-Miller S, Knoll W, Walz D, Naumann RLC (2011) Oriented immobilization and electron transfer to the cytochrome c oxidase. J Solid State Electrochem 15:105–114.  https://doi.org/10.1007/s10008-010-1032-x CrossRefGoogle Scholar
  108. Noy A (2011) Bionanoelectronics. Adv Mater 23:807–820.  https://doi.org/10.1002/adma.201003751 CrossRefPubMedGoogle Scholar
  109. Odenthal KJ, Gooding JJ (2007) An introduction to electrochemical DNAbiosensors. Analyst 132:603–610.  https://doi.org/10.1039/B701816A CrossRefPubMedGoogle Scholar
  110. Offenhäusser A, Rinaldi R (2009) Nanobioelectronics – for electronics, biology, and medicine. Springer, New YorkCrossRefGoogle Scholar
  111. Ogihara M, Ray A (2000) DNA computing on a chip. Nature 403:143.  https://doi.org/10.1038/35003071 CrossRefPubMedGoogle Scholar
  112. Okamoto A, Tanaka K, Saito I (2004) DNA logic gates. J Am Chem Soc 126:9458–9463.  https://doi.org/10.1021/ja047628k CrossRefPubMedGoogle Scholar
  113. Palanisamy S, Cheemalapati S, Chen S-M (2012) Highly sensitive and selective hydrogen peroxide biosensor based on hemoglobin immobilized at multiwalled carbon nanotubes–zinc oxide composite electrode. Anal Biochem 429:108–115.  https://doi.org/10.1016/j.ab.2012.07.001 CrossRefPubMedGoogle Scholar
  114. Paleček E, Jelen F (2002) Electrochemistry of nucleic acids and development of DNA sensors. Crit Rev Anal Chem 32:261–270.  https://doi.org/10.1080/10408340290765560 CrossRefGoogle Scholar
  115. Pan S, Rothberg L (2005) Chemical control of electrode functionalization for detection of DNA hybridization by electrochemical impedance spectroscopy. Langmuir 21:1022–1027.  https://doi.org/10.1021/la048083a CrossRefPubMedGoogle Scholar
  116. Pingarrón JM, Yáñez-Sedeño P, González-Cortés A (2008) Gold nanoparticle-based electrochemical biosensors. Electrochim Acta 53:5848–5866.  https://doi.org/10.1016/j.electacta.2008.03.005 CrossRefGoogle Scholar
  117. Pita M, Krämer M, Zhou J, Poghossian A, Schöning MJ, Fernández VM, Katz E (2008) Optoelectronic properties of nanostructured ensembles controlled by biomolecular logic systems. ACS Nano 2:2160–2166.  https://doi.org/10.1021/nn8004558 CrossRefPubMedGoogle Scholar
  118. Pita M, Tam TK, Minko S, Katz E (2009a) Dual Magnetobiochemical logic control of electrochemical processes based on local interfacial pH changes. ACS Appl Mater Interfaces 1:1166–1168.  https://doi.org/10.1021/am900185c CrossRefPubMedGoogle Scholar
  119. Pita M, Zhou J, Manesh KM, Halámek J, Katz E, Wang J (2009b) Enzyme logic gates for assessing physiological conditions during an injury: towards digital sensors and actuators. Sensors Actuators B Chem 139:631–636.  https://doi.org/10.1016/j.snb.2009.03.001 CrossRefGoogle Scholar
  120. Prokup A, Hemphill J, Deiters A (2012) DNA computation: a photochemically controlled AND gate. J Am Chem Soc 134:3810–3815.  https://doi.org/10.1021/ja210050s CrossRefPubMedGoogle Scholar
  121. Pumera M, Sánchez S, Ichinose I, Tang J (2007) Electrochemical nanobiosensors. Sensors Actuators B Chem 123:1195–1205.  https://doi.org/10.1016/j.snb.2006.11.016 CrossRefGoogle Scholar
  122. Qian L, Winfree E (2011) Scaling up digital circuit computation with DNA strand displacement cascades. Science 332:1196–1201.  https://doi.org/10.1126/science.1200520 CrossRefPubMedGoogle Scholar
  123. Qian L, Winfree E, Bruck J (2011) Neural network computation with DNA strand displacement cascades. Nature 475:368.  https://doi.org/10.1038/nature10262 CrossRefPubMedGoogle Scholar
  124. Qiu M, Khisamutdinov E, Zhao Z, Pan C, Choi J-W, Leontis NB, Guo P (2013) RNA nanotechnology for computer design and in vivo computation. Philos Trans R Soc A Math Phys Eng Sci 371:20120310.  https://doi.org/10.1098/rsta.2012.0310 CrossRefGoogle Scholar
  125. Radhakrishnan K, Tripathy J, Raichur AM (2013) Dual enzyme responsive microcapsules simulating an “OR” logic gate for biologically triggered drug delivery applications. Chem Commun 49:5390–5392.  https://doi.org/10.1039/C3CC42017E CrossRefGoogle Scholar
  126. Ramanavičius A, Ramanavičienė A, Malinauskas A (2006) Electrochemical sensors based on conducting polymer—polypyrrole. Electrochim Acta 51:6025–6037.  https://doi.org/10.1016/j.electacta.2005.11.052 CrossRefGoogle Scholar
  127. Ramnani P, Gao Y, Ozsoz M, Mulchandani A (2013) Electronic detection of MicroRNA at Attomolar level with high specificity. Anal Chem 85:8061–8064.  https://doi.org/10.1021/ac4018346 CrossRefPubMedGoogle Scholar
  128. Rao SV, Anderson KW, Bachas LG (1998) Oriented immobilization of proteins. Microchim Acta 128:127–143.  https://doi.org/10.1007/bf01243043 CrossRefGoogle Scholar
  129. Ren X, Yan J, Wu D, Wei Q, Wan Y (2017) Nanobody-based apolipoprotein E Immunosensor for point-of-care testing. ACS Sensors 2:1267–1271.  https://doi.org/10.1021/acssensors.7b00495 CrossRefPubMedGoogle Scholar
  130. Rinaudo K, Bleris L, Maddamsetti R, Subramanian S, Weiss R, Benenson Y (2007) A universal RNAi-based logic evaluator that operates in mammalian cells. Nat Biotechnol 25:795.  https://doi.org/10.1038/nbt1307 CrossRefPubMedGoogle Scholar
  131. Robles-Águila MJ, Pérez KS, Stojanoff V, Juárez-Santiesteban H, Silva-González R, Moreno A (2014) Design of molecular devices based on metalloproteins: a new approach. J Mater Sci Mater Electron 25:1354–1360.  https://doi.org/10.1007/s10854-014-1734-4 CrossRefGoogle Scholar
  132. Rocchitta G, Spanu A, Babudieri S, Latte G, Madeddu G, Galleri G, Nuvoli S, Bagella P, Demartis M, Fiore V, Manetti R, Serra P (2016) Enzyme biosensors for biomedical applications: strategies for safeguarding analytical performances in biological fluids. Sensors 16:780CrossRefGoogle Scholar
  133. Rojkind M, Domínguez-Rosales J-A, Nieto N, Greenwel P (2002) Role of hydrogen peroxide and oxidative stress in healing responses. Cell Mol Life Sci 59:1872–1891.  https://doi.org/10.1007/pl00012511 CrossRefPubMedGoogle Scholar
  134. Ronkainen NJ, Halsall HB, Heineman WR (2010) Electrochemical biosensors. Chem Soc Rev 39:1747–1763.  https://doi.org/10.1039/B714449K CrossRefPubMedGoogle Scholar
  135. Safavi A, Farjami F (2010) Hydrogen peroxide biosensor based on a myoglobin/hydrophilic room temperature ionic liquid film. Anal Biochem 402:20–25.  https://doi.org/10.1016/j.ab.2010.03.013 CrossRefPubMedGoogle Scholar
  136. Sarkar D, Liu W, Xie X, Anselmo AC, Mitragotri S, Banerjee K (2014) MoS2 field-effect transistor for next-generation label-free biosensors. ACS Nano 8:3992–4003.  https://doi.org/10.1021/nn5009148 CrossRefPubMedGoogle Scholar
  137. Schalkwijk J, van den Berg WB, van de Putte LBA, Joosten LAB (1986) An experimental model for hydrogen peroxide–induced tissue damage. Effects of a single inflammatory mediator on (peri)articular tissues. Arthritis Rheum 29:532–538.  https://doi.org/10.1002/art.1780290411 CrossRefPubMedGoogle Scholar
  138. Schwartz DK (2001) Mechanisms and kinetics of self-assembled monolayer formation. Annu Rev Phys Chem 52:107–137.  https://doi.org/10.1146/annurev.physchem.52.1.107 CrossRefPubMedGoogle Scholar
  139. Schwarzkopf M, Pierce NA (2016) Multiplexed miRNA northern blots via hybridization chain reaction. Nucleic Acids Res 44:e129.  https://doi.org/10.1093/nar/gkw503 CrossRefPubMedPubMedCentralGoogle Scholar
  140. Seelig G, Soloveichik D, Zhang DY, Winfree E (2006) Enzyme-free nucleic acid logic circuits. Science 314:1585–1588.  https://doi.org/10.1126/science.1132493 CrossRefPubMedGoogle Scholar
  141. Shi L, Chu Z, Liu Y, Jin W, Chen X (2013) Facile synthesis of hierarchically aloe-like gold micro/nanostructures for ultrasensitive DNA recognition. Biosens Bioelectron 49:184–191.  https://doi.org/10.1016/j.bios.2013.05.012 CrossRefPubMedGoogle Scholar
  142. Singh P, Pandey SK, Singh J, Srivastava S, Sachan S, Singh SK (2016) Biomedical perspective of electrochemical Nanobiosensor. Nano-Micro Lett 8:193–203.  https://doi.org/10.1007/s40820-015-0077-x CrossRefGoogle Scholar
  143. Song Y, Wan L, Wang Y, Zhao S, Hou H, Wang L (2012) Electron transfer and electrocatalytics of cytochrome c and horseradish peroxidase on DNA modified electrode. Bioelectrochemistry 85:29–35.  https://doi.org/10.1016/j.bioelechem.2011.11.007 CrossRefPubMedGoogle Scholar
  144. Song Y, Liu H, Wan L, Wang Y, Hou H, Wang L (2013) Direct electrochemistry of cytochrome c based on poly(diallyldimethylammonium chloride)- graphene Nanosheets/gold nanoparticles hybrid nanocomposites and its biosensing. Electroanalysis 25:1400–1409.  https://doi.org/10.1002/elan.201200524 CrossRefGoogle Scholar
  145. Strack G, Ornatska M, Pita M, Katz E (2008a) Biocomputing security system: concatenated enzyme-based logic gates operating as a biomolecular keypad lock. J Am Chem Soc 130:4234–4235.  https://doi.org/10.1021/ja7114713 CrossRefPubMedGoogle Scholar
  146. Strack G, Pita M, Ornatska M, Katz E (2008b) Boolean logic gates that use enzymes as input signals. Chembiochem 9:1260–1266.  https://doi.org/10.1002/cbic.200700762 CrossRefPubMedGoogle Scholar
  147. Strukov DB, Kohlstedt H (2012) Resistive switching phenomena in thin films: materials, devices, and applications. MRS Bull 37:108–114.  https://doi.org/10.1557/mrs.2012.2 CrossRefGoogle Scholar
  148. Suárez G, Santschi C, Martin OJF, Slaveykova VI (2013) Biosensor based on chemically-designed anchorable cytochrome c for the detection of H2O2 released by aquaticcells. Biosens Bioelectron 42:385–390.  https://doi.org/10.1016/j.bios.2012.10.083 CrossRefPubMedGoogle Scholar
  149. Tamayo J, Kosaka PM, Ruz JJ, San Paulo A, Calleja M (2013) Biosensors based on nanomechanical systems. Chem Soc Rev 42:1287–1311.  https://doi.org/10.1039/C2CS35293A CrossRefPubMedGoogle Scholar
  150. Thévenot DR, Toth K, Durst RA, Wilson GS (2001) Electrochemical Biosensors: recommended definitions and classification. Biosensors Bioelectron 16:121–131.  https://doi.org/10.1016/S0956-5663(01)00115-4 CrossRefGoogle Scholar
  151. Tomohiro M, Satoshi S, Masaaki T (2004) Novel reconfigurable logic gates using spin metal–oxide–semiconductor field-effect transistors. Jpn J Appl Phys 43:6032CrossRefGoogle Scholar
  152. Trifonov A, Sharon E, Tel-Vered R, Kahn JS, Willner I (2016) Application of the hybridization chain reaction on electrodes for the amplified and parallel electrochemical analysis of DNA. J Phys Chem C 120:15743–15752.  https://doi.org/10.1021/acs.jpcc.5b11308 CrossRefGoogle Scholar
  153. Tseng RJ, Tsai C, Ma L, Ouyang J, Ozkan CS, Yang Y (2006) Digital memory device based on tobacco mosaic virus conjugated with nanoparticles. Nat Nanotechnol 1:72.  https://doi.org/10.1038/nnano.2006.55 CrossRefPubMedGoogle Scholar
  154. Umasankar Y, Unnikrishnan B, Chen S-M, Ting T-W (2012) Graphene impregnated with horseradish peroxidase multimer for the determination of hydrogen peroxide. Anal Methods 4:3653–3660.  https://doi.org/10.1039/C2AY25276G CrossRefGoogle Scholar
  155. Vestergaard M, Kerman K, Tamiya E (2007) An overview of label-free electrochemical protein sensors. Sensors 7:3442CrossRefGoogle Scholar
  156. Wan Q, Song H, Shu H, Wang Z, Zou J, Yang N (2013) In situ synthesized gold nanoparticles for direct electrochemistry of horseradish peroxidase. Colloids Surf B Biointerfaces 104:181–185.  https://doi.org/10.1016/j.colsurfb.2012.12.009 CrossRefPubMedGoogle Scholar
  157. Wang J (2002) Electrochemical nucleic acid biosensors. Anal Chim Acta 469:63–71.  https://doi.org/10.1016/S0003-2670(01)01399-X CrossRefGoogle Scholar
  158. Wang J (2005) Carbon-nanotube based electrochemical biosensors: a review. Electroanalysis 17:7–14.  https://doi.org/10.1002/elan.200403113 CrossRefGoogle Scholar
  159. Wang J (2008) Electrochemical glucose biosensors. Chem Rev 108:814–825.  https://doi.org/10.1021/cr068123a CrossRefPubMedGoogle Scholar
  160. Wang R, Zhang J, Hu Y (2011) Liquid phase deposition of hemoglobin/SDS/TiO2 hybrid film preserving photoelectrochemical activity. Bioelectrochemistry 81:34–38.  https://doi.org/10.1016/j.bioelechem.2011.01.003 CrossRefPubMedGoogle Scholar
  161. Wang Z, Ning L, Duan A, Zhu X, Wang H, Li G (2012) A set of logic gates fabricated with G-quadruplex assembled at an electrode surface. Chem Commun 48:7507–7509.  https://doi.org/10.1039/C2CC33088A CrossRefGoogle Scholar
  162. Wang Y, Zhang H, Yao D, Pu J, Zhang Y, Gao X, Sun Y (2013) Direct electrochemistry of hemoglobin on graphene/Fe3O4 nanocomposite-modified glass carbon electrode and its sensitive detection for hydrogen peroxide. J Solid State Electrochem 17:881–887.  https://doi.org/10.1007/s10008-012-1939-5 CrossRefGoogle Scholar
  163. Wang Y-H, Huang K-J, Wu X (2017) Recent advances in transition-metal dichalcogenides based electrochemical biosensors: a review. Biosens Bioelectron 97:305–316.  https://doi.org/10.1016/j.bios.2017.06.011 CrossRefPubMedGoogle Scholar
  164. Weber W, Schoenmakers R, Keller B, Gitzinger M, Grau T, Daoud-El Baba M, Sander P, Fussenegger M (2008) A synthetic mammalian gene circuit reveals antituberculosis compounds. Proc Natl Acad Sci 105:9994–9998.  https://doi.org/10.1073/pnas.0800663105 CrossRefPubMedGoogle Scholar
  165. Wen Y, Pei H, Shen Y, Xi J, Lin M, Lu N, Shen X, Li J, Fan C (2012) DNA nanostructure-based interfacial engineering for PCR-free ultrasensitive electrochemical analysis of microRNA. Sci Rep 2(2):867.  https://doi.org/10.1038/srep00867 CrossRefPubMedPubMedCentralGoogle Scholar
  166. Willner I, Katz E (2000) Integration of layered redox proteins and conductive supports for bioelectronic applications. Angew Chem Int Ed 39:1180–1218. https://doi.org/10.1002/(SICI)1521-3773(20000403)39:7<1180::AID-ANIE1180>3.0.CO;2-E CrossRefGoogle Scholar
  167. Win MN, Smolke CD (2008) Higher-order cellular information processing with synthetic RNA devices. Science 322:456–460.  https://doi.org/10.1126/science.1160311 CrossRefPubMedPubMedCentralGoogle Scholar
  168. Wong LS, Khan F, Micklefield J (2009) Selective covalent protein immobilization: strategies and applications. Chem Rev 109:4025–4053.  https://doi.org/10.1021/cr8004668 CrossRefPubMedGoogle Scholar
  169. Wu L, Qu X (2015) Cancer biomarker detection: recent achievements and challenges. Chem Soc Rev 44:2963–2997.  https://doi.org/10.1039/C4CS00370E CrossRefPubMedGoogle Scholar
  170. Wu S, Zhao H, Ju H, Shi C, Zhao J (2006) Electrodeposition of silver–DNA hybrid nanoparticles for electrochemical sensing of hydrogen peroxide and glucose. Electrochem Commun 8:1197–1203.  https://doi.org/10.1016/j.elecom.2006.05.013 CrossRefGoogle Scholar
  171. Wu S-H, Tang Y, Chen L, Ma X-G, Tian S-M, Sun J-J (2015) Amplified electrochemical hydrogen peroxide reduction based on hemin/G-quadruplex DNAzyme as electrocatalyst at gold particles modified heated copper disk electrode. Biosens Bioelectron 73:41–46.  https://doi.org/10.1016/j.bios.2015.05.039 CrossRefPubMedGoogle Scholar
  172. Xiang C, Zou Y, Sun L-X, Xu F (2008) Direct electron transfer of cytochrome c and its biosensor based on gold nanoparticles/room temperature ionic liquid/carbon nanotubes composite film. Electrochem Commun 10:38–41.  https://doi.org/10.1016/j.elecom.2007.10.030 CrossRefGoogle Scholar
  173. Xie Z, Liu SJ, Bleris L, Benenson Y (2010) Logic integration of mRNA signals by an RNAi-based molecular computer. Nucleic Acids Res 38:2692–2701.  https://doi.org/10.1093/nar/gkq117 CrossRefPubMedPubMedCentralGoogle Scholar
  174. Xie Z, Wroblewska L, Prochazka L, Weiss R, Benenson Y (2011) Multi-input RNAi-based logic circuit for identification of specific Cancer cells. Science 333:1307–1311.  https://doi.org/10.1126/science.1205527 CrossRefPubMedGoogle Scholar
  175. Xu J, Shang F, Luong JHT, Razeeb KM, Glennon JD (2010) Direct electrochemistry of horseradish peroxidase immobilized on a monolayer modified nanowire array electrode. Biosens Bioelectron 25:1313–1318.  https://doi.org/10.1016/j.bios.2009.10.018 CrossRefPubMedGoogle Scholar
  176. Yagati AK, Kim S-U, Min J, Choi J-W (2009a) Multi-bit biomemory consisting of recombinant protein variants, azurin. Biosens Bioelectron 24:1503–1507.  https://doi.org/10.1016/j.bios.2008.07.080 CrossRefPubMedGoogle Scholar
  177. Yagati AK, Kim S-U, Min J, Choi J-W (2009b) Write-once–read-many-times (WORM) biomemory device consisting of cysteine modified ferredoxin. Electrochem Commun 11:854–858.  https://doi.org/10.1016/j.elecom.2009.02.014 CrossRefGoogle Scholar
  178. Yagati AK, Kim S-U, Min J, Choi J-W (2010) Ferredoxin molecular thin film with intrinsic switching mechanism for biomemory application. J Nanosci Nanotechnol 10:3220–3223.  https://doi.org/10.1166/jnn.2010.2229 CrossRefPubMedGoogle Scholar
  179. Yagati AK, Lee T, Min J, Choi J-W (2012) Electrochemical performance of gold nanoparticle–cytochrome c hybrid interface for H2O2 detection. Colloids Surf B Biointerfaces 92:161–167.  https://doi.org/10.1016/j.colsurfb.2011.11.035 CrossRefPubMedGoogle Scholar
  180. Yagati AK, Lee T, Min J, Choi J-W (2013) An enzymatic biosensor for hydrogen peroxide based on CeO2 nanostructure electrodeposited on ITO surface. Biosens Bioelectron 47:385–390.  https://doi.org/10.1016/j.bios.2013.03.035 CrossRefPubMedGoogle Scholar
  181. Yagati AK, Min J, Cho S (2014) Electrosynthesis of ERGO-NP nanocomposite films for Bioelectrocatalysis of horseradish peroxidase towards H2O2. J Electrochem Soc 161:G133–G140.  https://doi.org/10.1149/2.1001414jes CrossRefGoogle Scholar
  182. Yin P, Choi HMT, Calvert CR, Pierce NA (2008) Programming biomolecular self-assembly pathways. Nature 451:318.  https://doi.org/10.1038/nature06451 CrossRefPubMedGoogle Scholar
  183. Yoo S-Y, Lee T, Chung Y-H, Min J, Choi J-W (2011) Fabrication of biofilm in nanoscale consisting of cytochrome f/2-MAA bilayer on Au surface for bioelectronic devices by self-assembly technique. J Nanosci Nanotechnol 11:7069–7072.  https://doi.org/10.1166/jnn.2011.4845 CrossRefPubMedGoogle Scholar
  184. Yoon J, Chung Y-H, Yoo S-Y, Min J, Choi J-W (2014) Electrochemical-signal enhanced information storage device composed of cytochrome c/SNP bilayer. J Nanosci Nanotechnol 14:2466–2471.  https://doi.org/10.1166/jnn.2014.8542 CrossRefPubMedGoogle Scholar
  185. Yoon J, Lee T, Bapurao GB, Jo J, Oh B-K, Choi J-W (2017a) Electrochemical H2O2 biosensor composed of myoglobin on MoS2 nanoparticle-graphene oxide hybrid structure. Biosens Bioelectron 93:14–20.  https://doi.org/10.1016/j.bios.2016.11.064 CrossRefPubMedGoogle Scholar
  186. Yoon J, Shin J-W, Lim J, Mohammadniaei M, Bharate Bapurao G, Lee T, Choi J-W (2017b) Electrochemical nitric oxide biosensor based on amine-modified MoS2/graphene oxide/myoglobin hybrid. Colloids Surf B Biointerfaces 159:729–736.  https://doi.org/10.1016/j.colsurfb.2017.08.033 CrossRefPubMedGoogle Scholar
  187. Yuan Y, Zhou S, Yang G, Yu Z (2013) Electrochemical biomemory devices based on self-assembled graphene-Shewanella oneidensis composite biofilms. RSC Adv 3:18844–18848.  https://doi.org/10.1039/C3RA42850H CrossRefGoogle Scholar
  188. Zhai J, Cui H, Yang R (1997) DNA based biosensors. Biotechnol Adv 15:43–58.  https://doi.org/10.1016/S0734-9750(97)00003-7 CrossRefPubMedGoogle Scholar
  189. Zhang L (2008) Direct electrochemistry of cytochrome c at ordered macroporous active carbon electrode. Biosens Bioelectron 23:1610–1615.  https://doi.org/10.1016/j.bios.2008.01.022 CrossRefPubMedGoogle Scholar
  190. Zhang Y, Zhang C-y (2012) Sensitive detection of microRNA with isothermal amplification and a single-quantum-dot-based Nanosensor. Anal Chem 84:224–231.  https://doi.org/10.1021/ac202405q CrossRefPubMedGoogle Scholar
  191. Zhang J, Dong S, Lu J, Turner APF, Fan Q, Jia S, Yang H, Qiao C, Zhou H, He G (2009) A label free electrochemical Nanobiosensor study. Anal Lett 42:2905–2913.  https://doi.org/10.1080/00032710903201941 CrossRefGoogle Scholar
  192. Zhang YM, Zhang L, Liang RP, Qiu JD (2013) DNA electronic logic gates based on metal-ion-dependent induction of oligonucleotide structural motifs. Chem Eur J 19:6961–6965.  https://doi.org/10.1002/chem.201300625 CrossRefPubMedGoogle Scholar
  193. Zhao Y, Zhou L, Tang Z (2013) Cleavage-based signal amplification of RNA. Nat Commun 4:1493.  https://doi.org/10.1038/ncomms2492 http://www.nature.com/articles/ncomms2492#supplementary-information CrossRefPubMedGoogle Scholar
  194. Zhou J, Arugula MA, Halámek J, Pita M, Katz E (2009) Enzyme-based NAND and NOR logic gates with modular design. J Phys Chem B 113:16065–16070.  https://doi.org/10.1021/jp9079052 CrossRefPubMedGoogle Scholar
  195. Zhu W-L, Zhou Y, Zhang J-R (2009) Direct electrochemistry and electrocatalysis of myoglobin based on silica-coated gold nanorods/room temperature ionic liquid/silica sol–gel composite film. Talanta 80:224–230.  https://doi.org/10.1016/j.talanta.2009.06.056 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Chemical and Biomolecular EngineeringSogang UniversitySeoulSouth Korea
  2. 2.Department of Chemical EngineeringKwangwoon UniversitySeoulSouth Korea
  3. 3.School of Integrative Engineering Chung-Ang UniversitySeoulSouth Korea

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