Up to this point, we have been talking largely about Watson–Crick double helical DNA. No variations in the base-pairing and no variations in the backbone. Here we are going to discuss just a little bit of the work that is going on with other DNA structures, work that entails non-DNA backbones, and other species organized by DNA nanoconstructs. This chapter is not meant to be a comprehensive review of variations on the theme of either DNA nor its interactions with other species, just a taste to stimulate the reader to pursue other materials on her own.

Paukstelis DNA structure. Perhaps the first place to start is DNA, but DNA that is not simply Watson–Crick. A robust motif discovered in a single-crystal structure is shown in Figure 13-1. This motif contains three conventional nucleotide pairs and three parallel pairs, consisting of one A–G pair and two G–G pairs. This overall motif is shown in stereo in Figure 13-2. A salient feature of the structure is that it crystallizes in a hexagonal space group with a cavity whose volume is 300 Å3, which is shown in stereo in Figure 13-3a. The robustness of the motif is demonstrated by the fact that the Watson–Crick portion of the motif can be extended by 10 nucleotide pairs, yet the space group remains the same. Although the resolution of the crystal decreases somewhat, the cavity is greatly expanded by this expansion, so its volume is now increased markedly, as shown in Figure 13-3b.

Triplex DNA. In addition to the simple double helical motif, there are other helical motifs that have been characterized. The earliest of these was the DNA triplex, wherein a pyrimidine–purine–pyrimidine structure was formed (see Figures 2-4 and 2-5). There are a number of utilities that one can imagine for such systems in DNA constructs. Without disrupting the double helix, it is possible to address specifically designed locations within the assembly by adding a triplex-forming oligonucleotide (TFO). One could imagine tethering both small molecules and macromolecules to DNA constructs by the use of triplex associations. An example of triplex DNA added to DNA tensegrity-triangle crystals (see Chapter 7, especially Figure 7-23) is shown schematically in Figure 13-4; crystals to which triplex molecules containing dyes have been attached are shown in Figure 13-5.

This book is not about biology, but it is hard to separate DNA from biology. Nature uses DNA to store and replicate the information for living organisms, so it is natural to ask whether DNA motifs can be used to replicate information and to select materials that are the most propitious for particular environments. Replication can be used to provide exponential growth in molecular or cellular populations. However, one can ask whether there is any merit to doing that, rather than just making a large batch of whatever you want. Certainly chemicals are not produced by replication; when a large amount of material is wanted, the synthesis methods are just scaled up, from, say, the millimole scale to the mole scale.

Evolution and selection. The key element here is the notion of selection and evolution. If there are many different species produced, then it is useful to be able to select for the best one, given particular selection criteria. If there is a given circumstance existent in the medium, then it is useful to be able to select those individuals, be they molecules, cells, or larger organisms, that are best suited to survive under the selection criteria. If only those particular individuals survive the selection criteria, or if they are better suited to replicate, then self-replication is useful: the best-fitted individuals can then dominate the succeeding populations within that environment. This is hardly new wisdom. The nature of natural selection was pointed out by Darwin and Wallace in the nineteenth century. The idea of applying this notion to molecules (viral genomes) was first suggested by Spiegelman in the last third of the twentieth century, and then was taken up by Ellington and Szostak and by Tuerk and Gold some years later. In the latter cases, the investigators made partially random linear RNA molecules thought likely to fold to give a specific phenotype, either enzymatic or binding activity. Those molecules best suited to have this phenotype were selected and amplified repeatedly until a few molecular species dominated the population and their sequences could be identified.

Can the same thing be done with unusual DNA motifs? It is certainly an open challenge to optimize detailed structural properties in the 1–4 Å range based on selection procedures involving conventional phosphoramidites.

Anybody who has reached this point in the book realizes that DNA and its congeners are special molecules because of their ability to encrypt information. We have seen a variety of examples here of how that information can be used to direct the folding of molecules to produce specifically shaped and organized molecular species. Likewise, everybody alive during the twenty-first century is aware that we live in an age of information. The information that we use is usually encrypted in electronic bits, rather than in DNA; nevertheless, some information has already been stored specifically in DNA molecules, as an alternative to electronic or print media. Of course, the key way in which we are exposed to information in our daily lives is through our computers and their variations: pads and smartphones. These circumstances lead one to ask if it might be useful to try doing computation with DNA. There is a large community of investigators who work in the field of molecular computation, and, more than any other, this community has contributed valuable ideas and workers to the field of structural DNA nanotechnology. The treatment of this topic here is only a simplified introduction to a few topics in an elementary form.

DNA in logical computations: the Adleman experiment. The first use of DNA in computation was done by Leonard Adleman. He solved a Hamiltonian path problem using DNA molecules. The problem he solved, as has been the case for much of the computation performed with DNA, was a toy problem, one that could be solved simply in one's head, but which also represented a class of problems that are potentially challenging to traditional computational techniques. The Hamiltonian path problem is related closely to the “traveling salesman” problem, the optimization of a route through a number of cities in a territory. If there are only a few cities, the problem is easy to work out, but the number of solutions becomes enormous if there are many cities. Thus, if there are 10 cities, the number of possible answers is proportional to 10!, or about 11 million, a large but tractable number. However, if there are 100 cities, the number of possible answers is proportional to 100!, which is not a number readily handled by a computer that examines each possibility in turn.

One of the central concepts in structural science is the notion of resolution. What we're talking about here is the extent of detail or precision with which we wish to work. Resolution is usually thought of as the detail level of analysis, particularly optical analysis, but it can also refer to the detail level of construction. For example, when I was a “small molecule” crystallographer, it was useful to have a mental picture of my structures where the rulings were approximately every 10 picometers. My structures themselves were built up from Fourier components whose crests were separated by about 80 picometers (their nominal resolution) or more, but I was able to find meaningful differences between things such as bond lengths that were about 10 picometers different from each other. It is usually more useful to think of macromolecular crystals (resolution typically 200–400 picometers) more crudely, say in a world where the rulings are around 50 picometers apart. Resolution should fit the subject appropriately: if we were building a house with 5 × 10 × 15 cm bricks, this type of thinking would be far too detailed; we would be wasting out time thinking in a world with rulings closer than about a centimeter. Biology is replete with phenomena that take place on multiple distance scales, everywhere from the sub-nanometer scale (e.g., simple enzymatic reactions) to the nanometer scale (e.g., transcription, translation, recombination, and other aspects of nucleic acid metabolism) to the micron scale (cellular phenomena) to the macroscopic scale (organs like muscles and nerves).

DNA origami. In the aspects of structural DNA nanotechnology we have discussed so far, our basic unit has been the DNA double helix, with a diameter of 2 nm. 2D DX arrays should in principle have a repeat perpendicular to the helix axis of about 4–5 nm, but we usually find that the actual repeat is about 6 nm. Thus, these structures are usually designable to within ±1 nm. If we loosen up our design criteria a bit, say to 6 nm, it is possible to design larger structures readily. This is the technique known as DNA origami, first published by Paul Rothemund in 2006. He took the single-stranded form of the M13 virus, ~7500 nt, which is commercially available, and combined it with around 250 “staple” strands to get it to fold into a series of shapes containing parallel helix axes. These shapes are all based on extended versions of the DAO motif.

We have been talking so far about static DNA structures: we make them with some expectation of their 3D geometry, or perhaps of their topology, and then they sit there, perhaps changing shape as a result of thermal fluctuations. However, it is certainly possible to do more with DNA than just make static species. It is also possible to make molecules that can change their shapes, producing multi-state devices that can produce mechanical action on the nanoscale. The key DNA devices operate on two different principles: either DNA structural transitions, or programmed sequence-dependent devices whose states can be addressed or created individually. DNA structural transitions can be based on the contents of the solution, but they are only individually addressable by nuanced chemistry, such as the formation of the various knots shown in Figure 4-4, or by the action of protein molecules.

Robust devices. As all of you reading this book are aware, things can happen in chemical systems that do not happen on the macroscopic scale. If we are making a DNA-based nanomechanical device, we term it a “robust” device if all of the molecules undergo the same transition, looking the same after the transition as they did before. If the strands of its framework can recombine and perhaps form another species (a dimer or a breakdown product), then we would say that the device is not robust. Exchange of strands while in an intermediate state is another example of non-robust behavior. Sometimes the conditions will define the robustness of the device. If the device is based on a DNA structural transition, a partial transition of the ensemble of molecules will be the result if the transition trigger is not present in an overwhelming quantity; such conditions could be corrected, if necessary. The basic notion of a robust device is that it behaves like a macroscopic device made of simple machines: a lever is either up or down, a torsional element is either rotated or not, a movement occurs or it doesn't, throughout the ensemble of molecules in the system.

Shape-shifters

A key category of devices can be thought of as shape-shifters, molecules that have different structures under different conditions. Clearly a molecule at equilibrium will have a fixed shape or ensemble of shapes, and they won't change.

Let's look again at the molecule shown in Figure 3-5a. It looks like a cube built of DNA. However, as we noted earlier, the molecular geometry has really not been characterized, because the 3-arm junctions on its vertices are floppy units. Thus, it could look as we have drawn it, or it could look like a rhombohedron (a cube-like structure where one of the body diagonals has been stretched or squashed somewhat). The things we can say for sure about the molecule are that each of the edges is two turns long (the sequence was designed that way) and that each face of the object corresponds to a cyclic single strand of DNA. For example, the front face corresponds to the red strand. Because DNA is a double helix, every turn of the double helix results in the strands being interwound. Since each edge is two turns long, that means that the red strand is linked twice to the four strands of the four faces that flank the front: the green strand on the right, the cyan strand on top, the magenta strand on the left, and the dark-blue strand on the bottom. It is only indirectly linked to the yellow strand at the back. When cyclic molecules are linked together like the links of a chain, they form what is known as a catenane. Of course the hexacatenane corresponding to the cube is a much more complex object than just a simple chain. The molecule in Figure 3-5b, with the connectivity of a truncated octahedron, is a 14-catenane. It is even more complex than the cube-like molecule shown in Figure 3-5a.

Catenanes and knots. It turns out that catenanes are closely related to knots. This relationship is indicated in Figure 4-1. The upper left image is of a knot with five nodes and an arbitrary strand polarity. Look at the lower right node in this knot. It is made up of a strand that passes over another strand. You can think of it as four strands: the first half of the strand on top, connected to the second half of the strand on top, the first half of the strand on the bottom, connected to the second half of the strand on the bottom. Now, let's imagine breaking those connections, and reconnecting the strands so that we maintain the same polarity.

This chapter describes retinal prosthetic devices that are used withcomplementary metal oxide semiconductor (CMOS) technologies or large-scaleintegration (LSI) circuit technologies. The introduction of CMOStechnologies has made retinal prosthesis systems compact and versatile.Moreover, CMOS technology is particularly effective for increasing thenumber of stimulus electrodes with limited wiring. The remainder of thischapter is organized as follows. In Section 25.1, the principle of theretinal prosthesis and its basic components are discussed. In Section 25.2,various types of retinal prostheses are described. In Section 25.3, multiplemicrochip architectures that can realize a large number of stimuluselectrodes are introduced and demonstrated in detail. In Section 25.4integration of a photosensing function in a retinal stimulator is discussed.Finally, Section 25.5 proposes a smart electrode as an avenue for futureresearch in retinal prostheses. A preliminary demonstration isdescribed.

Principle and basic components of the retinal prosthesis

The front end of visual information is the retina. The human retina is athin, layered tissue with a thickness ranging from 0.1 to 0.4 mmattached to the inner surface of the eyeball [1], as shown in Figure 25.1.The retina has a layered structure with photoreceptor cells for lightdetection in the bottom layer and ganglion cells for output in the toplayer. The retina plays an important role in visual information collectionand processing, and so its dysfunction can result in blindness. Amongblindness diseases, retinitis pigmentosa (RP) and age-related maculardegeneration (AMD) have no effective remedies at present. In both cases, thephotoreceptors gradually become dysfunctional, so that the patienteventually becomes blind. However, some portion of ganglion cells remainsalive [2]. Consequently, by stimulating the remaining retinal cells, visualsensation or phosphenes can be evoked. This is the principle of the retinalprosthesis or artificial vision. Based on this principle, a retinalprosthetic device stimulates retinal cells with a patterned electricalsignal so that a blind patient can sense a phosphene pattern, or somethinglike an image. Stimulating the optic nerve and the visual cortex can alsorestore visual sensation, but this would require more complicated surgicaloperations.

Introduction

Rapid development of novel electrochemical systems was achieved in the1970–80s when a new concept of chemically modified electrodes wasintroduced [1,2]. Application of organic chemistry methods to thefunctionalization of electrode surfaces [3] and later pioneering of novelself-assembly methods [4–6] fostered the development of numerousmodified electrodes with properties unusual for bare conducting surfaces.While attempts were made to harness enhanced catalytic properties ofelectrodes and their selective responses to different redox species[1–6], the modified electrodes rapidly became important components ofvarious electroanalytical systems [7] and fuel cells [8]. Novelbioelectrochemical systems [9,10], particularly used in biosensors [11,12]and biofuel cells [13,14], have emerged upon introduction of modifiedelectrodes with bioelectrocatalytic properties. To continue this remarkablesuccess, electrodes functionalized with various signal-responsive materials(including molecular [15], supramolecular [16], and polymeric species [17])attached to electrode surfaces as monolayers or thin films were pioneered toallow switchable/tunable properties of the functional interfacescontrolled by external signals [18].

Over the past two decades, sustained advances in chemical modification of theelectrodes have given us a large variety of electrodes, switchable byvarious physical and/or chemical signals between electrochemicallyactive and inactive states [15–18]. However, very few of them wereused in biofuel cells [19,20]. Different mechanisms were involved in thetransition of the electrode interfaces between the active and inactivestates depending on the properties of the modified surfaces and the natureof the applied signals. The activity of the switchable electrodes wasusually controlled by physical signals (optical [21–23], electrical[24,25] or magnetic [26–29]) which failed to provide directcommunication between the electrodes and their biochemical environment inbiofuel cells. Switchable electrodes controlled by biochemical rather thanphysical signals are needed to design a biofuel cell adjustable to itsbiochemical environment according to the presence or absence of biochemicalsubstances. A new approach became possible when a polymer-modified electrodeswitchable between ON/OFF states by pH values [30] was coupled tobiochemical reactions generating pH changes in situ [31].This allowed transduction of biochemical input signals (e.g. glucoseconcentration) to the pH changes governing the electrochemical activity ofthe switchable electrode [32].

One of the main advances brought about by the advent of synthetic biology isthe design of artificial gene regulation networks that mimic the behavior ofnatural ones. This simplifies the analysis of cell behavior by isolating therelevant network motifs in a modular way. Consequently they can be studiedindependently of other complex cellular processes that in the natural caseinterfere with the motif of interest. This has been the main motivationbehind the design of certain synthetic gene networks, such as oscillatorsand switches. In spite of their advantages, experimental studies of thesesynthetic systems are still difficult, owing both to the inherent complexityof molecular biology experiments and to our lack of knowledge of the kineticparameters of the specific network components. Here we present a differentmodeling approach that makes sense in the context of electrical andelectronic engineering. Genetic and electronic circuits share many aspectsthat can be interesting for the design and the analysis of syntheticnetworks.

Introduction

The climax of the genome project, of which the most notable success has beenthe complete sequencing of the human genome, is the knowledge of all thegenes that compose the genetic material of an organism. The second phase ofthe project consists of the identification of its functional elements, whichis the aim of the ENCODE project [1].

Introduction

Every year we see a constant increase in the need for energy. Whereas most ofthis is supplied by large, fixed location units, i.e. power stations anddistribution networks, there is a market for smaller, units which can befixed, portable, or even self-propelling. Much of this need is supplied byproducts such as generators and vehicle engines, but this places a constantdemand on the fossil fuel supply. One potential alternative is the fuelcell.

Conventional fuel cells offer a possible (and partial) solution to thisproblem, allowing the direct production of electricity from the chemicalreaction of a suitable fuel with oxygen obtained from the atmosphere. Themost common type of fuel cell uses hydrogen, but this is of course a gas andhighly explosive, with the storage and transport challenges this entails.Another common series of fuel cells utilize methanol, and other cells havebeen developed that run on other fuels such as hydrocarbons [1,2]. Thesestill, however, utilize highly flammable liquids and are often reliant on afossil fuel supply. Moreover, many of these cells use expensive materialssuch as platinum as catalysts.

There is a wide range of common organic products, often waste products, thatcontain large amounts of stored energy which could potentially be used tosupply energy. Common methods include incineration, but a much moreattractive approach would be to use these materials as fuels in fuelcells.

Older readers of this chapter might remember the American TV series“The Bionic Woman”, aired in the late 1970s by NBC and ABC.The main character is nearly killed in a sky-diving accident only to berescued and receive various surgical implants. As the result of theimplanted bionics, she receives amplified hearing in her right ear, agreatly strengthened right arm, and stronger and enhanced legs that enableher to run at speeds exceeding 100 kilometers per hour. Who would not wantto have super-human capabilities like these?

Forty years later, bionics has still not delivered on this promise, but atthe same time the technological achievements have been amazing. Most of ushave watched Oscar Pistorius as the first double leg amputee to participatein the summer Olympics in 2012. He nearly won a medal. Some able-bodiedathletes are now arguing that prostheses should be banned in regularcompetition as it offers unfair advantage.

Prosthetic legs are the simplest example, as they are “merely” a mechanicalproduct. Much more complex devices with sophisticated electronics and signalprocessing have been developed to restore vision and contact brain tissuedirectly. One can envision that one day it will be possible to implant manybionic devices to restore, control or enhance many bodily functions, evenproviding direct connection to medical services through our personalcommunication systems (Figure 22.1); see [1] for some examples.

Rapid development in micro- and nanotechnologies in recent years has createdopportunities for the technology to connect to individual cells, bacteriaand viruses (Figure 8.1). The ability to sense biological properties createsamazing opportunities to improve human lives through advances in earlydisease detection, health monitoring, and new biology-based products. Evenmore exciting is the technology’s ability to sense DNA and proteins.The exploration of bio-organic device functionality and sensing in thefuture will require interfacing to traditional electronic materials andstructures [1,2].

An example of one such interface was recently considered in the context ofthe resonant sensing of biomolecules [3]. Resonant far-infrared (IR)spectroscopy is a common technique for the characterization of biologicalmolecules. The lower portion of the THz spectrum of DNA and proteins is alsobeing actively studied using both experimental and computational methods. Todate, good progress has been made in the detection and identification ofbiomaterials, and interest is rapidly increasing across the scientific andtechnology communities.

Biosensors (Figure 8.2) are defined as analytical devices incorporating abiological material (tissue, microorganisms, organelles, cell receptors,enzymes, antibodies, or nucleic acids), a biologically derived material(recombinant antibodies, engineered proteins, aptamers) or a biomimic(synthetic receptors, biomimetic catalysts, combinatorial ligands, imprintedpolymers) intimately associated with or integrated within a physicochemicaltransducer or transducing microsystem, which may be optical,electrochemical, thermometric, piezoelectric, magnetic, or micromechanical[4, 5]. The generated electrical signal is related to the concentration ofanalytes through the biological reactions.

Most people are impressed, if not amazed, at the fantastic progress in thebiomedical field, which barely existed 50 years ago. There have been giantleaps not just in the manner in which technology is being used to treatpatients, but also in the way the electronics and sensors have diffused intosociety and resulted in paradigm shifts in health monitoring. Electronicmicrosystems can now be ingested (e.g. swallowable capsules) to explore thegastrointestinal tract and can transmit the acquired information to a basestation [1]. The march of electronic technologies to the atomic scale and tonon-planarity (i.e. three dimensions), and rapid advances in system, cell,and molecular biology will forge an increased synergy between electronicsand biology, and we can see more exciting opportunities in the near future.For example, in the next decade it may become possible to restore vision orreverse the effects of spinal cord injury or disease, or for a lab-on-a-chipto allow medical diagnoses without a clinic, or instantaneous biologicalagent detection. Some of these fields are discussed in detail in otherchapters of this book. Similarly, we may see new ways of recording neuralsignals or brain–machine interfaces if the electronics could becomeultra-thin, bendable, and stretchable, and thus integrate intimately withthe soft, curvilinear surfaces of biological tissues. Some of thesedevelopments are discussed in Chapters 22–27. Recent results in thisdirection are encouraging and make it a real possibility in the near future[2,3]. This chapter is about this key enabler, i.e. epidermal electronics,which will lead to further convergence of biology and electronics. The termepidermal electronics here also refers to electronic skin or e-skin (Figure19.1), which is an ultra-thin and lightweight structure with electronicand/or sensing components on flexible/bendable substrates.