List of Figures

Chapter 1: Conformation-Based Computing—A Rationale and a Recipe

Figure 1.1: Schematic illustration of signal fusion mediated by conformational dynamics.

Figure 1.2: Empirical response surface of MDH with respect to CaCl₂ and MgCl₂. The dots are at concentrations where measurements were made. The surface is obtained by interpolation. (Reprinted with permission from Biotechnol. Prog. 2001, 17, 553–559. 2001 American Chemical Society/AIChE.)

Figure 1.3: Signal strengths for the XOR operation under different signal encoding schemes. The contour lines indicate areas of positive signal strengths, therefore concentrations that make the XOR feasible. Bold contour lines indicate an increase in signal strength of 0.1, the outermost line being 0. (A) Input line 1 releases MgCl₂ when a 1-signal arrives on this line. Input line 2 releases CaCl₂ under the same condition. When the input is 0 no ions are released. Encoding the input lines by different signal substances makes it possible to utilize the whole concentration range of the response surface. (B) Here both signal lines are encoded the same way, with MgCl₂ representing the 1-signal and CaCl₂ representing the 0-signal. (C) Input lines 1 and 2 have the same encoding. The 0- and 1-signals are both encoded with CaCl₂ concentrations that conse- quently must be different in order to obtain a positive signal strength. The symmetry of the graph reflects the symmetry of the XOR operation with respect to negation of the input signals (cf. table 1.2). (D) In this case the 1-signal is encoded by a mixture of MgCl₂ and CaCl₂ for both signal lines. The 0-signal is encoded by the absence of these ions. (Reprinted in part with permission from Biotechnol. Prog. 2001, 17, 553–559. 2001 American Chemical Society/AIChE.)

Figure 1.4: Experimental setup for first version of the tabletop XOR module. ( 2001 Zauner.)

Figure 1.5: Flow diagram for direct injection version of the XOR module. Figure 1.4 shows an earlier version utilizing a mixing chamber separate from the cuvette. (Reprinted with permission from Biotechnol. Prog. 2001, 17, 553–559. 2001 American Chemical Society/AIChE.)

Figure 1.6: Experimental run illustrating repeated operation of the XOR module. The absorbance output separates the 01/10 inputs from the 00 and 11 inputs.

Figure 1.7: Schematic of interactions supported by the CKSD simulator (for simplicity limited to a three enzyme system). The enzymes (labeled by e₁, e₂, and e₃) have from one to three states (labeled by the q_i). States represent conformations. Arrows connecting states represent conformational transitions. These are typically influenced by the milieu components (dashed arrows) and also may be influenced by direct interactions between two enzymes (dashed arrow from e₂ to e₁). Specific conformational states catalyze milieu reactions (indicated by bent arrows). Enzymes in complementary conformational states may self-assemble to form quaternary structures (indicated by the double arrow between e₁ and e₂). Note that the transitions of distant enzymes may be coupled through their catalytic effect on the milieu.

Figure 1.8: Conformational transition used to simulate enzyme e₁. The diagram is not based on any actual enzyme. The numbers below the state name indicate the relative catalytic activity of the state. Capital letters on the transitions refer to metabolites and signal molecules. The transition probabilities in the presence of these molecules is specified by superscripts.

Figure 1.9: Simulated response surface for enzyme e₁ with respect to signaling substances R and S. The product D is used as the output value. The values in the diagram show the actual number of molecules present in the simulation space. The latter contained 200 e₁ enzymes distributed on a 61 61 21 lattice.

Figure 1.10: Conformational transition diagram for enzyme e₂. See caption of figure 1.8 for explanation.

Figure 1.11: Simulated response surface for enzyme e₂. The space contained 300 e₂ enzymes; cf. figure 1.9.

Figure 1.12: Combined response surface resulting from interaction between enzymes e₁ and e₂.

Figure 1.13: Hypothetical molecular coprocessor combining microfluidics and integrated optoelectronics. (Reproduced with permission from Optical Memory and Neural Networks 1997, 6: 157–173. 1997 Allerton Press, Inc.)

Chapter 2: Molecular Recognition—Storage and Processing of Molecular Information

Figure 2.1: Some macropolycyclic structures. (From Lehn, 1973. With permission from Springer-Verlag 1973.)

Figure 2.2: Valinomycin with K⁺. (From Ionophores and Their Structures, M. Dobler. 1981, John Wiley. Reprinted by permission of John Wiley & Sons, Inc.)

Figure 2.3: Three different examples of macrobicyclic ligands. As the size of the internal cavity gradually increases from left to right, the most strongly bound ion changes from Li⁺ to Na⁺ to K⁺.

Figure 2.4: Ammonium cryptate. (Reprinted with permission from Graf et al. 1982. 1982 American Chemical Society.)

Figure 2.5: Ammonia in macrobicyclic receptor molecule. (Reprinted with permission from Dietrich et al. 1987. 1987 American Chemical Society.)

Figure 2.6: Binding of adenine in a cleft. (From Rebek, 1990. With permission from Accounts Chem. Res., 1987.)

Figure 2.7: (a) Hexaprotonated form of an ellipsoidal polyammonium bis-tren macrobicycle, in this case binding a spherical halide ion. (from Dietrich et al. 1984. With permission from Helv. Chim. Acta, 1984.) (b) Hexaprotonated form of an ellipsoidal polyammonium bis-tren macrobicycle, binding the linear triatomic anion N3⁻. (Reprinted with permission from Lehn, Sonveaux, and Willard 1978. 1978 American Chemical Society.)

Figure 2.8: Cascade-type dinuclear copper (II) cryptate formed with a macrocyclic polyamine as the ligand. The copper ions bind first, followed by the imidazole groups. (With permission from Supramolecular Chemistry, J.-M. Lehn. 1995 VCH Verlagsgesellschaft.)

Figure 2.9: Hypothetical structure of the ATP complex in the catalysis of ATP hydrolysis. (Reprinted with permission from Hosseini, Blacker, and Lehn, 1990. 1990 American Chemical Society.)

Figure 2.10: Cocatalysis cycle, with final products either being the transfer of a subunit or the ligation of two subunits. (With permission from Supramolecular Chemistry, J.-M. Lehn. 1995 VCH Verlagsgesellschaft.)

Figure 2.11: Enhancement of ligation of DNA as effected by an imidazole-functionalized spermine binding in the minor groove of the double helix. (Reprinted with permission from Zuber, Sirlin, and Behr. 1993 American Chemical Society.)

Figure 2.12: Schematic showing the four steps of a transport cycle.

Figure 2.13: Eu(III) cryptate showing the three steps (absorption, energy transfer, and emission) in light conversion. (Reprinted with permission from Supramolecular Chemistry, J.-M. Lehn. 1995 VCH Verlagsgesellschaft.)

Figure 2.14: Self-recognition in the self-assembly of double and triple helices from a mixture of two different oligobipyridine strands and of Cu(I) and Ni(II)ions (ClO₄⁻ counter-ions not shown) (Kr mer, Lehn, and Marquis-Rigault. 1993 Proc. Natl. Acad. Sci. USA.)

Chapter 3: Computing in Reaction-Diffusion and Excitable Media—Case Studies of Unconventional Processors

Figure 3.1: Voronoi diagram, constructed in experimental reaction-diffusion processor. Bisectors are seen as light (uncolored) segments. Original data points are represented by light discs. (With permission of Benjamin de Lacy Costello, Bristol, UK.)

Figure 3.2: Skeleton of a planar shape constructed in experimental reaction-diffusion processor. Data shape is shown in black color, segments of the skeleton are uncolored. Photo of the original chemical processor designed by D. Tolmachev in 1996.

Figure 3.3: A sketch of a light-sensitive chemical controller. Target-light stimulates wave generation in a reactor. Wave dynamic determines local repulsive vector field, inverted global vector guides a robot to the target.

Figure 3.4: A ciliate sheet: a micro-array of oscillating gel actuators is coupled with Belousov-Zhabotinsky reaction. (After Tabata et al. 2002.)

Figure 3.5: Architecture-based chemical gates. (a) A mass-transfer chemical device for computation of basic logical functions: x, y and z are acidity levels in the reactors; A and B are feeding tubes for supply of reagent A and B solutions with an indicator; α and β are coupling coefficients, and γ is a flow rate. Modified after (Blittersdorf et al 1995) (b) A wave realization of x OR y OR z gate. Waves in output chambers are asynchronous when all three input variables take the value FALSE. If one of the inputs is TRUE, represented by an excitation wave, the output chambers exhibit synchronous waves, that is represent TRUE. (Modified after Steinbock, Kettunen, and Showalter 1996.)

Figure 3.6: A palladium-based xor gate (colored zones represent precipitating palladium-iodine species). (a) The gate architecture, this also reflects the situation x = FALSE and y = FALSE. (b) Reaction development in the gate for x = TRUE and y = FALSE. (c) Formation of a bisector (uncolored strip) when two diffusive fronts "collide": this happens when x = TRUE and x = TRUE.

Figure 3.7: Catalog of spatio-temporal gates implemented in a model of a DNA molecule in the collisions between two breathers (1G-A, 1G-B, 1G-C) or between one (1G-D, 1G-E, 1G-F, 1G-G) or two (1G-G) breathers and an impurity. Time goes downward. Positions of nucleotides are indexed rightward.

Figure 3.8: Exemplar gates realized in collision of two-dimensional self-localized excitations.

Figure 3.9: Morphology of interval classification.

Figure 3.10: Julian Miller's field programmable matter array. (After Miller 2000.)

Figure 3.11: Computation with spreading and reacting crowds. (a) Collision of two fronts of virtual combatants. Simulated in ISAAC software (Ilachinski 1997). (b) Pebble-built Voronoi diagram of five lattice nodes, constructed in a lattice swarm model; data nodes are marked by pins; closed Voronoi cells are indicated by pins with hollow heads. (Modified after Adamatzky 2001.)

Figure 3.12: Diffusion of a doubt d in a one-dimensional layer of knowledge (shown by dots). The doubt reacts with the knowledge and a misbelief (μ) is produced. Ignorance (ι) and delusion (ε) emerge as byproducts of the diffusion and reaction. Certain norms are applied, see Adamatzky, 2001a for details.

Chapter 4: Chemical-Based Computing and Problems of High Computational Complexity—The Reaction-Diffusion Paradigm

Figure 4.1: Two modes of a Belousov-Zhabotinsky reaction in thin (pseudo two dimensional layers of the reagent. (A) A process of trigger wave spreading corresponding to the switching of the medium from one stable state to another; (B) an emission of circular waves from point-wise sources and their further evolution. Here and in the following figures gray arrows show steps of the image transformation by the reaction-diffusion medium, black arrows correspond to input of an initial image into medium.

Figure 4.2: Schematic representation of a biomolecular reaction-diffusion processor.

Figure 4.3: Schematic representation of neural network architecture of reaction-diffusion medium.

Figure 4.4: Schemes of neural networks (A) Pozin's feedback network containing excitatory (white circles) and inhibitory (black circles) neurons. (B) Grossberg's shunting on-center off-surround feedback network.

Figure 4.5: Responses of a neural network to external excitation calculated based on Pozin's model and representing: broadening of an input image (A), its sharpening (B), and contour enhancement (C). The upper level of the figure shows the shape of an input distribution, responses of the network are shown at the lower level, shapes of g(x) function (see text) are between them.

Figure 4.6: Responses of an on-center off-surround feedback neural network to arbitrary chosen external excitation depending on the shapes of f(w) function (see details in text).

Figure 4.7: Scheme of basic periodic process of image transformation performed by reaction-diffusion medium.

Figure 4.8: Temporal evolution of simple images in thin layers of a light-sensitive Belousov-Zhabotinsky medium depending on the state of the medium (A1–A3 correspond to different acidities of the media) and on the character of the medium illumination (A and B are positive and negative images). Initial images are at the left side of the figure.

Figure 4.9: Schematic representation of optical and digital video system for investigation of information processing by reaction-diffusion media.

Figure 4.10: Processing positive (A) and negative (B) black and white pictures by a Belousov-Zhabotinsky medium. Here and in following figures black arrows correspond to input of the initial picture, grey ones show transformation of the image by the medium. The sequences of images (A and B) in this figure and in figures 4.3 and 4.5 correspond to one period of Belousov-Zhabotinsky medium oscillation. This period was about 20–30 sec. Time intervals between separate images throughout of each period were 3–5 sec.

Figure 4.11: Schematic representation of image processing mechanism inherent in a light-sensitive BelousovZhabotinsky medium functioning in oscillating mode. Null clines and mechanisms of image transformation are given for cases of black and white (A) and half-tone (B) pictures. See also explanations in the text.

Figure 4.12: Processing positive (A) and negative (B) half-tone pictures by a Belousov-Zhabotinsky medium. See also caption to figure 4.10.

Figure 4.13: Different variants of half-tone picture processing: c1, contour enhancement (low contrast of the initial picture); c2, contour enhancement (high contrast of the initial picture); d1, enhancement of the basic shape of the initial picture; d2, enhancement of details of the initial picture.

Figure 4.14: Enhancement of picture fragments having different brightness by a Belousov-Zhabotinsky medium. A and B are the initial picture and subsequent results. Numbers in the initial picture correspond to relative optical densities of the image fragments. See also caption to figure 4.10.

Figure 4.15: The restoration of individual components of the picture where components overlap. Initial picture (A), evolution of the overlapped picture in the reaction-diffusion medium (B), and image processing operations that restore an overlapped component (C).

Figure 4.16: How many spots are on the skin of a cat. Original initial picture (p), initial picture together with additional spots statistically distributed (p1), and the negative image of the total picture (n1) are at the top of the figure. Below are: the whole overlapped picture (f1), the whole assembly of spots (f2), and spots introduced from outside (f3).

Figure 4.17: Labyrinth structures of different complexity. (A) Simple tree-type labyrinth. (B) Tree-type labyrinth containing cycles (filled with gray color). (C) Complicated labyrinth containing cycles and arbitrary numbers of starting and target points.

Figure 4.18: Principal scheme explaining the initiation of a light-induced phase wave. Non-uniform light background I(x) is in the upper part of the figure. Temporal evolution of catalyst concentrations at different points of the medium is in the basic part of the figure.

Figure 4.19: The initial labyrinth image (I), a non uniform background superimposed on the initial labyrinth image (IW), and first stages of the labyrinth evolution (phase wave spreading) in a Belousov-Zhabotinsky medium (L1–L3).

Figure 4.20: Finding the shortest path in a simple tree-type labyrinth. L0 is an initial image of the labyrinth in the Belousov-Zhabotinsky medium, L1–L4, some consecutive steps of its evolution in the process of the wave spreading; LF, the image of the shortest path in the labyrinth; L0-1, the pathway which the wave has passed during the first step of its spreading; L1-1, the result of Paint Bucket operations for L1 image; L1-2, the result of subtraction from the L1 image of parts unconnected with the target point (see details in text).

Figure 4.21: Basic steps of the procedure for finding the shortest paths in a labyrinth. (A) Wave propagation. (B) Check for connectedness. (C) Removal of deadlocks (subtraction of B and A).

Figure 4.22: Finding the shortest path in an arbitrary labyrinth. L0 is the initial image of the labyrinth in the BelousovZhabotinsky medium; (A) stages of its evolution; LF1 and LF2 are images of the shortest paths (see details in text); (B) results of Paint Bucket operations.

Figure 4.23: Different cases of nonlinear dynamics in distributed one-dimensional systems.

Figure 4.24: Physical configuration of the continuous flow stirred tank reactor. Black arrows correspond to feeding of each reactor, gray arrows show reactor coupling. See details in the text.

Figure 4.25: Scheme of stationary structures in assembly of 16 coupled reactors obtained under symmetrical boundary conditions. Black blocks correspond to high iodine concentrations, grey blocks to low iodine concentrations. Exchange rates are shown in each plate ("+" means that the structure was obtained when exchange rate increased, "−" corresponds to a decrease of the exchange rate.)

Figure 4.26: Matrix of connections for a chemical network having recognition capabilities (A; see text) and the process of recognition of a 10110001 pattern beginning from a 01110001 distribution (B).

Figure 4.27: Numerical simulation of image processing operations based on the reaction-diffusion equation. Initial halftone picture is shown in upper part of the figure. Results of simulation which can be different due to diverse choice of coupling functions (see text): enhancement of thin and thick contours, contrast enhancement, enhancement of lines having different slope and corners of the image, and skeleton of the image.

Figure 4.28: Different realizations of light-sensitive polymer based reaction-diffusion media: a thin flat layer of liquid medium (A), a liquid medium in a polymer matrix, for instance in thin flat layer of polyacrylamide gel (B), a system where a catalyst is immobilized on a solid support (C).

Figure 4.29: Brain [☺] machine [] reaction-diffusion processor [?] analogies-disanalogies.

Chapter 5: DNA Computing and Its Frontiers

Figure 5.1: In order to connect strand y to strand x a "glue strand" is used to bring the two strands into proximity through annealing. Then a ligase enzyme is added to fuse the two strands together. Here x indicates the Watson-Crick complement of sequence x.

Figure 5.2: Polymerase extension. A polymerase enzyme attaches to the 3′ end of a short primer sequence and constructs the complement (x) of the longer sequence.

Figure 5.3: Polymerase chain reaction (PCR) follows in cycles of three steps. (1) The double-stranded templates are melted apart. (2) The primers anneal to both strands. (3) A polymerase enzyme extends the primers into replicas of the templates. This is repeated, causing an exponential growth in the number of templates, as long as there are enough primers in the solution to catalyse the reaction. Note that because polymerase attaches to the 3′ end of a primer we need to use the subsequences x and z to get the desired reaction.

Figure 5.4: Lipton's graph for constructing binary numbers. The vertices and edges are constructed using Adleman's algorithm so that the longest path through the graph will represent an n-bit binary number. If the path goes through an x then it has a 0 at that position, otherwise it has a 1 at that position.

Figure 5.5: An example of a 5-node graph with a 3-node clique shown in black.

Figure 5.6: Full sequences can be constructed through the combination of short sequences that anneal together and polymerase to fill in the gaps. The grey arrows indicate the activity of polymerase while the numbers and their complements serve to label the sub-sequences. The black lines indicate the input sequences for the process of parallel overlap assembly. PCR will rapidly amplify the fully constructed sequences.

Figure 5.7: Addition proceeds from least significant to most significant bit. The numbers in parentheses represent subsequences encoding partial sums at each bit position. The numbers without parentheses are the bits of the answer. The sub-sequences are all assumed to include patterns that label the bit position, so that (0) at bit position 0 is a different sequence than (0) at bit position 1. Multiple fragments represent the operands n1 and n2. The addition for bit position 0 is simpler than addition for the other positions because there is no carry bit from a previous computation. Thus at the start, the least significant bit of the "answer" is the same as the least significant bit of n1. This bit is then added to the least significant bit of n2 and the result includes both the least significant bit of the sum of n1 and n2 as well as a pattern representing the presence (or absence) of a carry-over bit. Here there are two options for n2, corresponding to the cases where the least significant bit is either (0) or (1). By introducing (1) to the solution, we create an answer sequence (1) 0 (1), indicating that the least significant bit of the answer is 0 and there is a carry-over bit (1).

Figure 5.8: For all bit positions other than the least significant bit, addition has to follow in two steps. In step A, the carry-over bit is added to the bit of n1. Then, in step B, the result is added to the bit of n2. Note again that n1 and n2 are both represented as a set of DNA fragments, only one of which will bind to the answer strand in the chain reaction. The 3′ ends of these fragments have been altered to prevent the polymerase extending them.

Figure 5.9: Finally, a strand representing the carry-over bit anneals to the answer and completes the process of constructing the answer: 110 (in reverse order), or 6, encoded in binary.

Figure 5.10: An example of three Borromean rings. Removing any one of the three rings will disconnect the other two.

Figure 5.11: The migration of a branch in dsDNA can be controlled through the application of ethidium. In this case the ethidium applies torque to this cruciform and causes intrusion, a reduction of the hairpin bulges.

Figure 5.12: Three steps in the programmed mutagenesis of a counter. In the upper strand, the sequence XZZZZZ (representing 1) has an XY rewrite rule annealed to it (there is only a slight mismatch between Z and Y), as well as a standard PCR primer that catalyzes the construction of the template strand shown in the middle. The middle template strand now represents 2. It has the YX rewrite rule annealed to it. Again, X includes only a slight mismatch for annealing to Z. This causes the polymerization of the third template XYXZZZ which represents 3. The chain reaction will continue until the strand XYXYXY (6) has been constructed.

Figure 5.13: Three steps of a whiplash PCR reaction. The top strand shows the starting state (S1) with the 3′ end of the strand annealing to S1 and being extended by polymerase. In the middle strand, polymerase has added S2 to the 3′ end and halts at the stop sequence (shown in grey) because there are no available bases in the solution to construct the complement of the stop sequence. At some point the 3′ end melts away from the strand and then anneals to the beginning of the next transition (S3S2). This continues until there are no more possible transitions.

Figure 5.14: The function describing an idealized inverter's mapping of input values to output values. Note that a wide range of low input values maps to a narrow range of high output values. This allows a digital circuit to incrementally remove noise from the system.

Chapter 6: Bioelectronics and Protein-Based Optical Memories and Processors

Figure 6.1: A schematic representation of a semiconductor transistor currently in production (0.18-mm minimum dimension). A scanning capacitance image of a 0.05-mm transistor fabricated at Bell Labs (: 2000 Lucent Technology) is superimposed over this schematic with the same scale factor. The 0.05-mm transistor is considered to be about the minimum dimension for room temperature CMOS device operation because the "off" state will not be separable from the "on" state due to noise in smaller devices. Transistors of this size are to be fabricated in production around the year 2012 (Sematech). Also, inset in the figure is a small white square. The square represents the relative size of a bacteriorhodopsin molecule, which by itself is a complete optically activated switching device.

Figure 6.2: Analysis of the area in square microns required to store a single bit of information as a function of the evolution of computer technology in years. The data for magnetic disk, magnetic bubble, thin-film and silicon DRAM memories are taken from (Keyes 1992). These data are compared to the cross-sectional area per bit (neuron) for the human brain as well as anticipated areas and implementation times for optical three-dimensional memories and molecular memories (Birge 1994). Note that the optical 3D memory, the brain and the molecular memories are three-dimensional and therefore the cross-sectional area (A) per bit is plotted for comparison. The area is calculated in terms of the volume per bit, V/bit, by the formula A = (V)^2/3.

Figure 6.3: The propagation delay and power dissipation of selected molecular systems and semiconductor devices. HBT, hetero-junction bipolar transistor; HEMT, high electron mobility transistor; RTD, resonant tunneling device; OCNAND, optically coupled NAND gate; JJ, Josephson junction; bR, bacteriorhodopsin primary photochemical event; Rhod, visual rhodopsin primary photochemical event. Feature sizes of the semiconductor devices are indicated in parentheses. Propagation delay of photonic molecular devices are defined in terms of the time necessary for the absorption spectrum to reach 1/e of the final photoproduct absorption maximum.

Figure 6.4: Spectra of select intermediates during the bacteriorhodopsin photocycle. The outlined arrows indicate photochemical transitions, and the solid arrow represent thermal transitions. The insets represent the conformation of the retinal in that state. N, nitrogen; X, Schiff base nitrogen (in P) or carbonyl oxygen (in Q).

Figure 6.5: Schematic diagram of a Fourier transform holographic (FTH) associative memory with read/write FTH reference planes using thin polymer films of bacteriorhodopsin to provide real-time storage of the holograms. Note that a partial input image can select and regenerate the entire associated image stored on the reference hologram. Although only four reference images are shown, an optical associative memory can store many hundreds or thousands of images simultaneously. This memory can also work on binary data by using redundant binary representation logic, and a small segment of data can be used to find which page has the largest association with the input segment. Selected components are labeled as follows: FL, Fourier lens; FVA, Fresnel variable attenuator; H1,H2, holographic films; PHA, pin-hole array; SF, spatial filter; SP, beam stop.

Figure 6.6: Data storage based on the branched photocycle reactions of bacteriorhodopsin.

Figure 6.7: Schematic diagram of the branched-photocycle three-dimensional memory. The top diagram shows the write process, which is based on a page operation followed a few milliseconds later by an orthogonal write operation.

Figure 6.8: A close-up picture (a) and a schematic (b) of the current Level II prototype of the branched-photocycle volumetric memory. Key components of the prototype are shown and labeled in the schematic (b). This prototype uses a global erasure of the entire data cuvette via activation of a pair of cylindrical UV lamps. The lamps are located at the focus of two cylindrical hyperbolic mirrors positioned so that the entire memory cuvette can be irradiated (insert c). Dual data lasers coupled to the active matrix liquid crystal spatial light modulator (AMLC SLM) via a holographic diffuser are used to generate non-coherent light so that the imaged data beam does not contain diffracted artifacts (see text). (Reproduced from Birge et al. 1999.)

Figure 6.9: General scheme for cassette mutagenesis. The double circles represent a double-stranded plasmid, and the gray region indicates a gene. Restriction sites unique to the plasmid flank the region to be mutated. The distance from site A to site B should not be more than 80 nucleotides. In step 1, enzymes A and B are added to digest the plasmid at Sites A and B only, producing two linear pieces of DNA. The large fragment is then purified by gel electrophoresis, and added to a synthetic piece of DNA which contains the desired mutation (denoted by a M in a circle) (step 2). In the final step (step 3), the small synthetic fragment (containing the desired mutation) is ligated onto the large fragment. One end of the fragment then ligates with the other end to produce a circular mutant plasmid. The plasmid can then be expressed in bacteria to produce protein.

Figure 6.10: General schematic for mismatched primer mutagenesis. Although figure 6.9 is based on the ExSite^TM Mutagenesis kit (Stratagene, LaJolla, CA), the overall strategy used by this kit is similar to many PCRbased methods. In the first step, primers are designed to copy the wild type, template DNA. One of the primers is the complement to the wild type DNA, while the second primer contains the mutation. The example shown here is for a point mutation, but in fact, insertions and deletions can also be done with this method. In the first step, the template DNA is amplified with the new mutation incorporated. An enzyme that recognizes the template DNA cuts it into multiple fragments. In step III, another enzyme, ligase, circularizes the new, mutant DNA allowing it to transform into bacteria efficiently in step IV. Once in bacteria, the gene can be amplified or expressed (resulting in the production of mutant protein).

Chapter 7: Bioelectronics and Biocomputers

Figure 7.1: Electron transport path in a respiratory chain reaction.

Figure 7.2: Reaction of NAD⁺ (reactive part) with electrons. R⁻ represents the other nonreactive part of the molecule.

Figure 7.3: Reaction mechanism of the reactive parts of FAD and FADH₂.

Figure 7.4: Scheme of a glucose sensor. Glucose is oxidized into gluconolactone at the membrane and O₂ is consumed. H₂O₂ is produced at the same time. Both O₂ and H₂O₂ can be measured by the electrode.

Figure 7.5: Typical response curve of a glucose sensor. Sensor is placed in a buffer solution and stirred using a magnetic stirrer. (A) Standard glucose solution is dropped into the solution. (B) Sensor is washed using a buffer solution. Enough of the buffer solution is added so that the glucose is totally washed away.

Figure 7.6: Scheme of biosensor. A target molecule is recognized by a molecular recognition element and then the result of recognition is detected by the transducer. Finally, an electric signal is produced from the transducer.

Figure 7.7: Enzyme immobilization methods.

Figure 7.8: Glucose sensor using an enzyme and oxygen electrode.

Figure 7.9: Response pattern obtained using a fish-freshness sensor. Fish freshness can be evaluated from the pattern obtained from the concentration pattern of three compounds.

Figure 7.10: Enzyme sensor using thermistors. Two thermistors are used for the measurement.

Figure 7.11: Schematic diagram of an enzyme sensor using chemiluminescence.

Figure 7.12: Schematic of an immunosensor. Antigen and an enzyme-labeled antigen (fixed concentration) are added to an antibody-immobilized substrate. If the concentration of antigen is low, the enzyme reaction product is high. Conversely, if the concentration of antigen is high, the amount of enzyme reaction product is low. Due to this, measuring the amount of the product gives the amount of antigen. The validity of this measurement depends on whether or not nonspecific protein adsorption occurs, i.e., adsorption of other nondesired proteins to the sensor surface. Some sticky materials in the sample often stick to the metal surface or antibody or enzyme of the sensor. Such interaction occurs due to electrostatic interaction. As a result, the antibody or enzyme on the sensor surface will be blocked.

Figure 7.13: Scheme of a QCM sensor system. The antibody is immobilized on the gold electrode surface of a QCM chip.

Figure 7.14: Principle of an immunosensor using an SPR device. Interaction between antibody and antigen results in a change in θr.