Biomolecular Information Processing E-Book
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- Herausgeber: Wiley-VCH
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Edited by a renowned and much cited chemist, this book covers the whole span of molecular computers that are based on biomolecules. The contributions by all the major scientists in the field provide an excellent overview of the latest developments in this rapidly expanding area. A must-have for all researchers working on this very hot topic. Perfectly complements Molecular and Supramolecular Information Processing, also by Prof. Katz, and available as a two-volume set.
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Seitenzahl: 721
Veröffentlichungsjahr: 2013
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Table of Contents
Related Titles
Title Page
Copyright
Preface
List of Contributors
Chapter 1: Biomolecular Computing: From Unconventional Computing to “Smart” Biosensors and Actuators – Editorial Introduction
References
Chapter 2: Peptide-Based Computation: Switches, Gates,and Simple Arithmetic
2.1 Introduction
2.2 Peptide-Based Replication Networks
2.3 Logic Gates within Ternary Networks
2.4 Symmetry and Order Requirements for Constructing the Logic Gates
2.5 Taking the Steps toward More Complex Arithmetic
2.6 Experimental Logic Gates
2.7 Adaptive Networks
2.8 Peptide-Based Switches and Gates for Molecular Electronics
2.9 Summary and Conclusion
Acknowledgments
References
Chapter 3: Biomolecular Electronics and Protein-Based Optical Computing
3.1 Introduction
3.2 Biomolecular and Semiconductor Electronics
3.3 Bacteriorhodopsin as a Photonic and Holographic Material for Bioelectronics
3.4 Fourier Transform Holographic Associative Processors
3.5 Three-Dimensional Optical Memories
3.6 Genetic Engineering of Bacteriorhodopsin for Device Applications
3.7 Future Directions
Acknowledgments
References
Chapter 4: Bioelectronic Devices Controlled by Enzyme-Based Information Processing Systems
4.1 Introduction
4.2 Enzyme-Based Logic Systems Producing pH Changes as Output Signals
4.3 Interfacing of the Enzyme Logic Systems with Electrodes Modified with Signal-Responsive Polymers
4.4 Switchable Biofuel Cells Controlled by the Enzyme Logic Systems
4.5 Biomolecular Logic Systems Composed of Biocatalytic and Biorecognition Units and Their Integration with Biofuel Cells
4.6 Processing of Injury Biomarkers by Enzyme Logic Systems Associated with Switchable Electrodes
4.7 Summary and Outlook
Acknowledgments
References
Chapter 5: Enzyme Logic Digital Biosensors for Biomedical Applications
5.1 Introduction
5.2 Enzyme-Based Logic Systems for Identification of Injury Conditions
5.3 Multiplexing of Injury Codes for the Parallel Operation of Enzyme Logic Gates
5.4 Scaling Up the Complexity of the Biocomputing Systems for Biomedical Applications — Mimicking Biochemical Pathways
5.5 Application of Filter Systems for Improving Digitalization of the Output Signals Generated by Enzyme Logic Systems for Injury Analysis
5.6 Conclusions and Perspectives
Acknowledgment
Appendix
References
Chapter 6: Information Security Applications Based on Biomolecular Systems
6.1 Introduction
6.2 Molecular and Bio-Molecular Keypad Locks
6.3 Antibody Encryption and Steganography
6.4 Bio-Barcode
6.5 Conclusion
Acknowledgment
References
Chapter 7: Biocomputing: Explore Its Realization and Intelligent Logic Detection
7.1 Introduction
7.2 DNA Biocomputing
7.3 Aptamer Biocomputing
7.4 Enzyme Biocomputing
7.5 Conclusions and Perspectives
References
Chapter 8: Some Experiments and Models in Molecular Computing and Robotics
8.1 Introduction
8.2 From Gates to Programmable Automata
8.3 From Random Walker to Molecular Robotics
8.4 Conclusions
Acknowledgments
References
Chapter 9: Biomolecular Finite Automata
9.1 Introduction
9.2 Biomolecular Finite Automata
9.3 Biomolecular Finite Transducer
9.4 Applications in Developmental Biology
9.5 Outlook
References
Chapter 10: In Vivo Information Processing Using RNA Interference
10.1 Introduction
10.2 RNA Interference-Based Logic
10.3 Building the Sensory Module
10.4 Outlook
References
Chapter 11: Biomolecular Computing Systems
11.1 Introduction
11.2 DNA as a Tool for Molecular Programming
11.3 Birth of DNA Computing: Adleman's Experiment and Extensions
11.4 Computation Using DNA Tiles
11.5 Experimental Advances in Purely Hybridization-Based Computation
11.6 Experimental Advances in Enzyme-Based DNA Computing
11.7 Biochemical DNA Reaction Networks
11.8 Conclusion: Challenges in DNA-Based Biomolecular Computation
Acknowledgment
References
Chapter 12: Enumeration Approach to the Analysis of Interacting Nucleic Acid Strands
12.1 Introduction
12.2 Definitions and Notations for Set and Multiset
12.3 Chemical Equilibrium and Hybridization Reaction System
12.4 Symmetric Enumeration Method
12.5 Applying SEM to Nucleic Acid Strands Interaction
12.6 Conclusions
References
Chapter 13: Restriction Enzymes in Language Generation and Plasmid Computing
13.1 Introduction
13.2 Wet Splicing Systems
13.3 Dry Splicing Systems
13.4 Splicing Theory: Its Original Motivation and Its Extensive Unforeseen Developments
13.5 Computing with Plasmids
13.6 Fluid Memory
13.7 Examples of Aqueous Computations
13.8 Final Comments about Computing with Biomolecules
References
Chapter 14: Development of Bacteria-Based Cellular Computing Circuits for Sensing and Control in Biological Systems
14.1 Introduction
14.2 Cellular Computing Circuits
14.3 Conclusion
Acknowledgment
References
Chapter 15: The Logic of Decision Making in Environmental Bacteria
15.1 Introduction
15.2 Building Models for Biological Networks
15.3 Formulation and Simulation of Regulatory Networks
15.4 Boolean Analysis of Regulatory Networks
15.5 Boolean Description of m-xylene Biodegradation by P.putida mt-2: the TOL logicome
15.6 Conclusion and Outlook
Acknowledgments
References
Chapter 16: Qualitative and Quantitative Aspects of a Model for Processes Inspired by the Functioning of the Living Cell
16.1 Introduction
16.2 Reactions
16.3 Reaction Systems
16.4 Examples
16.5 Reaction Systems with Measurements
16.6 Generalized Reactions
16.7 A Generic Quantitative Model
16.8 Approximations of Gene Expression Systems
16.9 Simulating Approximations by Reaction Systems
16.10 Discussion
Acknowledgments
References
Chapter 17: Computational Methods for Quantitative Submodel Comparison
17.1 Introduction
17.2 Methods for Model Decomposition
17.3 Methods for Submodel Comparison
17.4 Case Study
17.5 Discussion
Acknowledgments
References
Chapter 18: Conclusions and Perspectives
References
Index
Related Titles
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The Editors
Prof. Dr. Evgeny Katz
Clarkson University
Department of Chemistry
and Biomolecular Science
8, Clarkson Avenue
Potsdam, NY 13699-5810
USA
Cover
The cover page picture was designed by Dr. Vera Bocharova (Clarkson University) and represents artistic vision of the chapter “Bioelectronic Devices Controlled by Enzyme-Based Information Processing Systems” by Evgeny Katz.
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Preface
The use of biomolecular systems for processing information, performing logic operations, computational operations, and even automata performance is a rapidly developing research area. The entire field was named with the general buzzwords, “biomolecular computing” or “biocomputing.” Exciting advances in the area include the use of various biomolecular systems including proteins/enzymes, DNA, RNA, DNAzymes, antigens/antibodies, and even whole biological (usually microbial) cells operating as “hardware” for unconventional computing. The most important feature of the biocomputing systems is their operation in biochemical and even biological environment. Many different applications of these systems, in addition to unconventional computation, are feasible; while their biosensor/biomedical use is obviously one of the most important applications. Interfacing of biological systems with “smart” biosensors, signal responsive materials, and bioelectronic devices is of highest importance for future developments in the area of biomolecular computing. The various topics covered highlight key aspects and the future perspectives of biomolecular computing. The book discusses experimental work done by biochemists and biologists and theoretical approaches developed by physicists and computer scientists. The different topics addressed in this book will be of high interest to the interdisciplinary community active in the area of unconventional biocomputing. It is hoped that the collection of the different chapters will be important and beneficial for researchers and students working in various areas related to biochemical computing, including biochemistry, materials science, computer science, and so on. Furthermore, the book is aimed to attract young scientists and introduce them to the field while providing newcomers with an enormous collection of literature references. I, indeed, hope that the book will spark the imagination of scientists to further develop the topic.
Finally, the Editor (E. Katz) and Publisher (Wiley-VCH) express their thanks to all authors of the chapters, whose dedication and hard work made this book possible, hoping that the book will be interesting and beneficial for researchers and students working in various areas related to unconventional biocomputing. It should be noted that the field of biocomputing extends to the fascinating area of chemical molecular and supra-molecular synthetic systems which consideration was outside the scope of the present book. This complementary area of molecular computing (based on synthetic rather than natural biological molecules) is covered in another new book of Wiley-VCH: Molecular and Supramolecular Information Processing: from Molecular Switches to Logic Systems – E. Katz, Editor. Both the books are must for the shelves of specialists interested in various aspects of molecular and biomolecular information processing.
Potsdam, NY, USA
Evgeny Katz
October 2011
List of Contributors
Chapter 1
Biomolecular Computing: From Unconventional Computing to “Smart” Biosensors and Actuators – Editorial Introduction
Evgeny Katz
Chemical computing [1] as a subarea of unconventional computing [2] has achieved tremendous development in the past two decades, driven mostly by the idea of making revolutionary changes in computing technology. While the conventional silicon-based electronic technology comes to the physical limit of miniaturization [3], chemical systems might operate at the level of single molecules, bringing information processing systems from the present microsize to novel nanosize [4]. Even more importantly, chemical systems can perform massively parallel computational operations with involvement of as many as 1023 molecules, resulting in a speed of information processing presently impossible in silicon-based computers [5]. Motivated by these ideas from computer science, chemists designed sophisticated switchable molecules and supramolecular complexes to perform logic operations and mimic computing systems [6]. Complex chemical reactions with unusual kinetics (e.g., oscillating diffusional systems – Belousov–Zhabotinsky reactions) [7] were suggested as media performing computing operations [8]. Extensive research in the area of reaction–diffusion computing systems [9] resulted in the formulation of conceptually novel circuits performing information processing with the use of subexcitable chemical media [10]. Novel conceptual approaches required for the usage of new chemical “hardware” were designed, resulting in algorithms potentially capable of solving “hard-to-solve” computational problems, thus demonstrating potential advantages of the novel unconventional chemical computing systems over classic silicon-based systems. The present state of the art of the unconventional chemical computing was summarized in the recent Wiley-VCH book: “Molecular and Supramolecular Information Processing: From Molecular Switches to Logic Systems,” E. Katz – Editor.
It should be noted, however, that chemical systems designed for information processing usually suffer from two major problems: (i) They are very difficult to prepare – in other words – the synthetic processes required for their preparation are so complex that only a few laboratories are able to prepare and study the switchable molecules operating as the chemical computing “hardware.” This problem is technical rather than conceptual, and it could be solved at the present level of technology if the molecular computing elements find real applications. (ii) The main challenge in further development of chemical information processing systems is scaling up their complexity assembling individual logic gates in logic networks [11]. Impressive results have recently been achieved in this direction [6]. Combination of chemical logic gates in small groups or networks resulted in simple computing devices performing basic arithmetic operations such as half-adder/half-subtractor or full-adder/full-subtractor [12]. Integration of several functional units in a molecular structure resulted in multisignal responses to stimuli of various chemical or physical natures, thus allowing different logic operations or even simple arithmetic functions to be performed within a single multifunctional molecule [13]. Despite the progress achieved, assembling complex systems from individual chemical components is very limited and presently achieved only for very small networks incomparable with silicon-based electronic chips. The chemical computing units performing logic operations [6] and functioning as auxiliary “devices” (e.g., memory units [14], multiplexers/demultiplexers [15]) are very difficult for integration in functional networks. In other words, each chemical unit might be a perfect computing element, but the integration of this element with other similar elements for their concerted operation is extremely difficult. The difficulty in the interconnectivity of chemical elements in computing networks mostly originates from incompatibility of the chemical input and output signals. The product of the preceding chemical reaction frequently cannot be used as a reagent for the following chemical step. Even more problematic is the use of chemical switchable systems activated by physical signals (such as light [16], magnetic [17] or electrical field [18]) since these signals operating as inputs cannot be reproduced by the chemical reactions and cannot be used for interconnecting several chemical steps in a functional network. This is already a conceptual problem that limits the practical application of chemical systems, keeping them mostly at the level of single units, being scientific “toys” rather than practical devices. It is not surprising that these kinds of molecules were not used by Nature in living systems, where interconnectivity between chemical steps is critically important for their concerted operation, being the base of life.
Many of the problems hardly addressable by synthetic chemical systems can be solved naturally by utilization of biomolecular systems [19, 20]. The emerging research field of biocomputing, based on application of biomolecular systems for processing chemical information, has achieved higher complexity of information processing while using much simpler chemical tools, because of the natural specificity and compatibility of biomolecules [21]. Different biomolecular tools, including proteins/enzymes [20, 22], DNA [19, 23], RNA [24], and whole cells [25], were used to assemble computing systems processing biochemical information. Arithmetic functions, for example, full-adder, were realized using RNA as the information processing biomolecular tool [26]. Deoxyribozymes with various catalytic abilities toward DNA assemblies were applied to extend the computing options provided by DNA-based systems [27]. RNA-based computing systems exploit the biological regulatory functions of RNA in cells, thus allowing operation of cells as “biocomputers” programed by artificially designed biomolecular ensembles [28]. Recently pioneered DNA molecules with biocatalytic properties mimicking enzyme functions, called DNAzymes [29], were extensively used to carry out logic operations [30]. These briefly mentioned biomolecular computing systems represent a rapidly developing research field, and they are already covered by comprehensive review articles, for example,on DNA [31], RNA [32], and DNAzyme [33] biocomputing.
The present book summarizes the diverse subareas of biomolecular computing including (i) various aspects of protein/enzyme information processing systems – Chapters 2–7 (ii) DNA/RNA-based computing systems – Chapters 8– 13; (iii) application of whole biological (mostly microbial) cells for biocomputing – Chapters 14–16; as well as (iv) general computational aspects of biomolecular computing – Chapter 16. Chapter 18 offers conclusions and perspectives for the biomolecular computing research area summarized by the Editor.
The variety of the systems described in the book and their possible applications are really impressive. While some of the biochemical systems, particularly represented by DNA computing, follow the general trend of unconventional computing, pretending to bring up novel computational chemical “devices” and algorithms competing with conventional silicon-based computers [34], other systems, mostly represented by enzyme-based assemblies, are directed to “noncomputational” applications, which are more related to “smart” biosensors [35] and bioactuators [36]. Biomolecular systems can perform various automata operations [37], particularly illustrated by the tic-tac-toe game [38]. Much more complex robotic functions of biocomputing systems are also feasible [39]. However, the main expected shorter term practical benefit of biomolecular computing systems is their ability to process biochemical information received in the form of chemical inputs directly from biological systems, offering the possibility to operate in biological environments [40], for biomedical/diagnostic [35] and homeland security applications [41]. Biomolecular logic gates and their networks can recognize various biomarkers associated with diseases [42] or injuries [43] and generate a biomedical conclusion in the binary form “YES”/“NO” upon logic processing of the biomarker concentration patterns. The produced binary output can be extended to a chemical actuation resulting in drug release or bioelectronic system activation controlled by logic conclusions derived from the information processed by biomolecular systems [44]. This research direction will certainly result in tremendous contribution to future personalized medicine [45]. Biochemical systems activated by several chemical input signals processed via logic circuitry implemented in the biochemical assembly can activate/inactivate various bioelectronic devices [46], for example, electrodes [47], biofuel cells [48], and field-effect transistors [49] (Figure 1.1), thus contributing to the next level of sophistication of bioelectronics [50] (Figure 1.2). Chemical signal processing through biocatalytic or biorecognition reactions might be applicable in information security systems performing encoding and encrypting operations as well as providing hiding of information in steganography applications [51] (Figure 1.3). Biocomputing systems can also be used as a part of signal-responsive “smart” materials with functions controlled by logically processed biochemical signals. Various nanostructured materials, including switchable membranes [52] (Figure 1.4), can benefit from built-in logic implemented via biocomputing gates and networks [52, 53].
Figure 1.1 A cartoon illustrating biocomputing control over a switchable biofuel cell producing electrical power on demand upon receiving signals processed through an enzyme-based logic system (see [48] for details).
Figure 1.2 An artistic vision of the integration of biomolecular systems with bioelectronic devices (see [46] for details).
Figure 1.3 A cartoon outlining application of a biorecognition information processing system for data security, encoding, and steganography (see [51c] for details).
Figure 1.4 The signal-responsive membrane associated with an indium tin oxide (ITO) electrode and coupled with the enzyme-based AND (dark gray bars)/OR (light gray bars) logic gates. (A) Atomic force microscope (AFM) topographic images (10 × 10 μm2) of the membrane with closed (a) and open (b) pores. (B) The electron transfer resistance, Ret, of the switchable interface derived from the impedance spectroscopy measurements obtained upon different combinations of the input signals.
(Adapted from 52, with permission; Copyright American Chemical Society, 2009.)
The variety of systems inspired by biology and their possible applications are really unlimited, and the combination of computer science, biomolecular science, material science, and electronics will result in novel scientific and technological advances in this multidimensional research area. The present book aims at summarizing the achievements in this rapidly developing multifaceted research area providing background for further progress and helping in understanding of various aspects in this complex scientific field.
References
1. Katz, E. (Guest ed.) (2011) Isr. J. Chem., 51 (1), 13–14, and review articles wherein.
2 (a) Calude, C.S., Costa, J.F., Dershowitz, N., Freire, E., and Rozenberg, G. (eds) (2009) Unconventional Computation, Lecture Notes in Computer Science, Vol. 5715, Springer, Berlin; (b) Adamatzky, A., De Lacy Costello, B., Bull, L., Stepney, S., and Teuscher, C. (eds) (2007) Unconventional Computing 2007, Luniver Press.
3 (a) Freebody, M. (2011) Photonucs Spectra, 45, 45–47; (b) Rupp, K. and Selberherr, S. (2010) Proc. IEEE, 98, 351–353; (c) Rupp, K. and Selberherr, S. (2011) IEEE Trans. Semicond. Manufact., 24, 1–4; (d) Powell, J.R. (2008) Proc. IEEE, 96, 1247–1248; (e) Choi, C. (2004) New Scientist, 182, 12–12.
4 (a) Stadler, R., Ami, S., Joachim, C., and Forshaw, M. (2004) Nanotechnology, 15, S115–S121; (b) De Silva, A.P., Leydet, Y., Lincheneau, C., and McClenaghan, N.D. (2006) J. Phys. Condens. Mater., 18, S1847–S1872.
5. Adamatzky, A. (2004) IEICE Trans. Electron., E87C, 1748–1756.
6 (a) de Silva, A.P., Uchiyama, S., Vance, T.P., and Wannalerse, B. (2007) Coord. Chem. Rev., 251, 1623–1632; (b) de Silva, A.P. and Uchiyama, S. (2007) Nat. Nanotechnol., 2, 399–410; (c) Szacilowski, K. (2008) Chem. Rev., 108, 3481–3548; (d) Credi, A. (2007) Angew. Chem. Int. Ed., 46, 5472–5475; (e) Pischel, U. (2007) Angew. Chem. Int. Ed., 46, 4026–4040; (f) Pischel, U. (2010) Aust. J. Chem., 63, 148–164; (g) Andreasson, J. and Pischel, U. (2010) Chem. Soc. Rev., 39, 174–188.
7 (a) Belousov, B.P. (1959) Collection of Abstracts on Radiation Medicine in 1958, Medicine Publishing, Moscow, pp. 145–147; Field, R.J. and Burger, M. (translated eds) (1985) Oscillations and Traveling Waves in Chemical Systems, John Wiley & Sons, Inc., New York. (in Russian); (b) Epstein, I.R. (2006) Proc. Natl. Acad. U.S.A., 103, 15727–15728.
8 (a) Lebender, D. and Schneider, F.W. (1994) J. Phys. Chem., 98, 7533–7537; (b) Tóth, A., Gáspár, V., and Showalter, K. (1994) J. Phys. Chem., 98, 522–531; (c) Tóth, A. and Showalter, K. (1995) J. Chem. Phys., 103, 2058–2066.
9. Adamatzky, A. (2011) J. Comput. Theor. Nanosci., 8, 295–303.
10 (a) Adamatzky, A., Costello, B., Bull, L., and Holley, J. (2011) Isr. J. Chem., 51, 56–66; (b) Costello, B.D. and Adamatzky, A. (2005) Chaos Solitons Fractals, 25, 535–544.
11. Wagner, N. and Ashkenasy, G. (2009) Chem. Eur. J., 15, 1765–1775.
12 (a) Pischel, U. (2007) Angew. Chem. Int. Ed., 46, 4026–4040; (b) Brown, G.J., de Silva, A.P., and Pagliari, S. (2002) Chem. Commun., 2461–2463.
13 (a) Liu, Y., Jiang, W., Zhang, H.-Y., and Li, C.-J. (2006) J. Phys. Chem. B, 110, 14231–14235; (b) Guo, X., Zhang, D., Zhang, G., and Zhu, D. (2004) J. Phys. Chem. B, 108, 11942–11945; (c) Raymo, F.M. and Giordani, S. (2001) J. Am. Chem. Soc., 123, 4651–4652.
14 (a) Chatterjee, M.N., Kay, E.R., and Leigh, D.A. (2006) J. Am. Chem. Soc., 128, 4058–4073; (b) Baron, R., Onopriyenko, A., Katz, E., Lioubashevski, O., Willner, I., Wang, S., and Tian, H. (2006) Chem. Commun., 2147–2149; (c) Galindo, F., Lima, J.C., Luis, S.V., Parola, A.J., and Pina, F. (2005) Adv. Funct. Mater., 15, 541–545; (d) Bandyopadhyay, A. and Pal, A.J. (2005) J. Phys. Chem. B, 109, 6084–6088; (e) Pina, F., Lima, J.C., Parola, A.J., and Afonso, C.A.M. (2004) Angew. Chem. Int. Ed., 43, 1525–1527.
15 (a) Andreasson, J., Straight, S.D., Bandyopadhyay, S., Mitchell, R.H., Moore, T.A., Moore, A.L., and Gust, D. (2007) J. Phys. Chem. C, 111, 14274–14278; (b) Amelia, M., Baroncini, M., and Credi, A. (2008) Angew. Chem. Int. Ed., 47, 6240–6243; (c) Perez-Inestrosa, E., Montenegro, J.M., Collado, D., and Suau, R. (2008) Chem. Commun., 1085–1087.
16 (a) Katz, E. and Shipway, A.N. (2005) in Bioelectronics: from Theory to Applications, Chapter 11 (eds I. Willner and E. Katz), Wiley-VCH Verlag GmbH, Weinheim, pp. 309–338; (b) Bonnet, S. and Collin, J.P. (2008) Chem. Soc. Rev., 37, 1207–1217; (c) Ashton, P.R., Ballardini, R., Balzani, V., Credi, A., Dress, K.R., Ishow, E., Kleverlaan, C.J., Kocian, O., Preece, J.A., Spencer, N., Stoddart, J.F., Venturi, M., and Wenger, S. (2000) Chem. Eur. J., 6, 3558–3574; (d) Thanopulos, I., Kral, P., Shapiro, M., and Paspalakis, E. (2009) J. Mod. Opt., 56, 1–18.
17 (a) Hsing, I.M., Xu, Y., and Zhao, W.T. (2007) Electroanalysis, 19, 755–768; (b) Katz, E., Baron, R., and Willner, I. (2005) J. Am. Chem. Soc., 127, 4060–4070; (c) Katz, E., Sheeney-Haj-Ichia, L., Basnar, B., Felner, I., and Willner, I. (2004) Langmuir, 20, 9714–9719.
18 (a) Zheng, L. and Xiong, L. (2006) Colloids Surf. A, 289, 179–184; (b) Riskin, M., Basnar, B., Katz, E., and Willner, I. (2006) Chem. Eur. J., 12, 8549–8557; (c) Riskin, M., Basnar, B., Chegel, V.I., Katz, E., Willner, I., Shi, F., and Zhang, X. (2006) J. Am. Chem. Soc., 128, 1253–1260.
19 (a) Xu, J. and Tan, G.J. (2007) J. Comput. Theor. Nanosci., 4, 1219–1230; (b) Soreni, M., Yogev, S., Kossoy, E., Shoham, Y., and Keinan, E. (2005) J. Am. Chem. Soc., 127, 3935–3943; (c) Stojanovic, M.N. and Stefanovic, D. (2003) Nat. Biotechnol., 21, 1069–1074.
20. Katz, E. and Privman, V. (2010) Chem. Soc. Rev., 39, 1835–1857.
21 (a) Saghatelian, A., Volcker, N.H., Guckian, K.M., Lin, V.S.Y., and Ghadiri, M.R. (2003) J. Am. Chem. Soc., 125, 346–347; (b) Ashkenasy, G. and Ghadiri, M.R. (2004) J. Am. Chem. Soc., 126, 11140–11141.
22 (a) Sivan, S. and Lotan, N. (1999) Biotechnol. Prog., 15, 964–970; (b) Sivan, S., Tuchman, S., and Lotan, N. (2003) Biosystems, 70, 21–33; (c) Deonarine, A.S., Clark, S.M., and Konermann, L. (2003) Future Generation Comput. Syst., 19, 87–97; (d) Ashkenazi, G., Ripoll, D.R., Lotan, N., and Scheraga, H.A. (1997) Biosens. Bioelectron., 12, 85–95; (e) Unger, R. and Moult, J. (2006) Proteins, 63, 53–64.
23. Ezziane, Z. (2006) Nanotechnology, 17, R27–R39.
24 (a) Win, M.N. and Smolke, C.D. (2008) Science, 322, 456–460; (b) Rinaudo, K., Bleris, L., Maddamsetti, R., Subramanian, S., Weiss, R., and Benenson, Y. (2007) Nat. Biotechnol., 25, 795–801; (c) Ogawa, A. and Maeda, M. (2009) Chem. Commun., 4666–4668.
25 (a) Simpson, M.L., Sayler, G.S., Fleming, J.T., and Applegate, B. (2001) Trends Biotechnol., 19, 317–323; (b) Li, Z., Rosenbaum, M.A., Venkataraman, A., Tam, T.K., Katz, E., and Angenent, L.T. (2011) Chem. Commun., 47, 3060–3062.
26. Lederman, H., Macdonald, J., Stefanovic, D., and Stojanovic, M.N. (2006) Biochemistry, 45, 1194–1199.
27. Stojanovic, M.N., Mitchell, T.E., and Stefanovic, D. (2002) J. Am. Chem. Soc., 124, 3555–3561.
28. Win, M.N. and Smolke, D.D. (2008) Science, 322, 456–460.
29. Schlosser, K. and Li, Y. (2009) Chem. Biol., 16, 311–322.
30 (a) Li, T., Wang, E., and Dong, S. (2009) J. Am. Chem. Soc., 131, 15082–15083; (b) Elbaz, J., Shlyahovsky, B., Li, D., and Willner, I. (2008) ChemBioChem, 9, 232–239; (c) Moshe, M., Elbaz, J., and Willner, I. (2009) Nano Lett., 9, 1196–1200.
31. Stojanovic, M.N., Stefanovic, D., LaBean, T., and Yan, H. (2005) in Bioelectronics: From Theory to Applications, Chapter 14 (eds I. Willner and E. Katz), Wiley-VCH Verlag GmbH, Weinheim, pp. 427–455.
32. Benenson, Y. (2009) Curr. Opin. Biotechnol., 20, 471–478.
33. Willner, I., Shlyahovsky, B., Zayats, M., and Willner, B. (2008) Chem. Soc. Rev., 37, 1153–1165.
34. Shapiro, E. and Benenson, Y. (2006) Scientific Am., 45–51.
35 (a) Wang, J. and Katz, E. (2011) Isr. J. Chem., 51, 141–150; (b) Wang, J. and Katz, E. (2010) Anal. Bioanal. Chem., 398, 1591–1603.
36. Strack, G., Bocharova, V., Arugula, M.A., Pita, M., Halámek, J., and Katz, E. (2010) J. Phys. Chem. Lett., 1, 839–843.
37 (a) Adar, R., Benenson, Y., Linshiz, G., Rosner, A., Tishby, N., and Shapiro, E. (2004) Proc. Natl. Acad. U.S.A., 101, 9960–9965; (b) Pei, R.J., Matamoros, E., Liu, M.H., Stefanovic, D., and Stojanovic, M.N. (2010) Nat. Nanotechnol., 5, 773–777.
38. Macdonald, J., Li, Y., Sutovic, M., Lederman, H., Pendri, K., Lu, W.H., Andrews, B.L., Stefanovic, D., and Stojanovic, M.N. (2006) Nano Lett., 6, 2598–2603.
39 (a) Stojanovic, M.N. and Stefanovic, D. (2011) J. Comput. Theor. Nanosci., 8, 434–440; (b) Stojanovic, M.N. (2011) Isr. J. Chem., 51, 99–105.
40. Kahan, M., Gil, B., Adar, R., and Shapiro, E. (2008) Phys. D, 237, 1165–1172.
41. Chuang, M.-C., Windmiller, J.R., Santhosh, P., Valdés-Ramírez, G., Katz, E., and Wang, J. (2011) Chem. Commun., 47, 3087–3089.
42 (a) May, E.E., Dolan, P.L., Crozier, P.S., Brozik, S., and Manginell, M. (2008) IEEE Sens. J., 8, 1011–1019; (b) von Maltzahn, G., Harris, T.J., Park, J.-H., Min, D.-H., Schmidt, A.J., Sailor, M.J., and Bhatia, S.N. (2007) J. Am. Chem. Soc., 129, 6064–6065.
43 (a) Pita, M., Zhou, J., Manesh, K.M., Halámek, J., Katz, E., and Wang, J. (2009) Sens. Actuat. B, 139, 631–636; (b) Manesh, K.M., Halámek, J., Pita, M., Zhou, J., Tam, T.K., Santhosh, P., Chuang, M.-C., Windmiller, J.R., Abidin, D., Katz, E., and Wang, J. (2009) Biosens. Bioelectron., 24, 3569–3574; (c) Windmiller, J.R., Strack, G., Chuan, M.-C., Halámek, J., Santhosh, P., Bocharova, V., Zhou, J., Katz, E., and Wang, J. (2010) Sens. Actuat. B, 150, 285–290; (d) Bocharova, V., Halámek, J., Zhou, J., Strack, G., Wang, J., and Katz, E. (2011) Talanta, 85, 800–803.
44. Privman, M., Tam, T.K., Bocharova, V., Halámek, J., Wang, J., and Katz, E. (2011) ACS Appl. Mater. Interfaces, 3, 1620–1623.
45 (a) Phan, J.H., Moffitt, R.A., Stokes, T.H., Liu, J., Young, A.N., Nie, S.M., and Wang, M.D. (2009) Trends Biotechnol., 27, 350–358; (b) Fernald, G.H., Capriotti, E., Daneshjou, R., Karczewski, K.J., and Altman, R.B. (2011) Bioinformatics, 27, 1741–1748.
46. Katz, E. (2011) Isr. J. Chem., 51, 132–140.
47. Privman, M., Tam, T.K., Pita, M., and Katz, E. (2009) J. Am. Chem. Soc., 131, 1314–1321.
48. Katz, E. and Pita, M. (2009) Chem. Eur. J., 15, 12554–12564.
49. Krämer, M., Pita, M., Zhou, J., Ornatska, M., Poghossian, A., Schöning, M.J., and Katz, E. (2009) J. Phys. Chem. C, 113, 2573–2579.
50. Willner, I. and Katz, E. (eds) (2005) Bioelectronics: from Theory to Applications, Chapter 14, Wiley-VCH Verlag GmbH, Weinheim, pp. 427–455.
51 (a) Halámek, J., Tam, T.K., Chinnapareddy, S., Bocharova, V., and Katz, E. (2010) J. Phys. Chem. Lett., 1, 973–977; (b) Strack, G., Ornatska, M., Pita, M., and Katz, E. (2008) J. Am. Chem. Soc., 130, 4234–4235; (c) Kim, K.-W., Bocharova, V., Halámek, J., Oh, M.-K., and Katz, E. (2011) Biotechnol. Bioeng., 105, 1100–1107.
52. Tokarev, I., Gopishetty, V., Zhou, J., Pita, M., Motornov, M., Katz, E. and Minko, S. (2009) ACS Appl. Mater. Interfaces, 1, 532–536.
53 (a) Minko, S., Katz, E., Motornov, M., Tokarev, I., and Pita, M. (2011) J. Comput. Theor. Nanosci., 8, 356–364; (b) Pita, M., Minko, S., and Katz, E. (2009) J. Mater. Sci.: Mater. Med., 20, 457–462; (c) Motornov, M., Zhou, J., Pita, M., Tokarev, I., Gopishetty, V., Katz, E., and Minko, S. (2009) Small, 5, 817–820; (d) Motornov, M., Zhou, J., Pita, M., Gopishetty, V., Tokarev, I., Katz, E., and Minko, S. (2008) Nano Lett., 8, 2993–2997.
Chapter 2
Peptide-Based Computation: Switches, Gates,and Simple Arithmetic
Zehavit Dadon, Manickasundaram Samiappan, Nathaniel Wagner, Nurit Ashkenasy, and Gonen Ashkenasy
This chapter compiles our recent studies directed at identifying Boolean operations in relatively large synthetic systems, as well as the de novo design of small networks that perform specific functions. We first describe the “recipe” for constructing peptide-based replication networks and then explain how to use that information for simulating their kinetics. It is shown that the networks can be manipulated to facilitate molecular replication through all Boolean logic operations, and furthermore that the catalytic pathways can be wired together to perform more complex computational modules. Finally, we discuss the formation of logic gates within adaptive networks that respond to changes in the environment (pH, salt, and light), and the first steps toward realization of switching and gating molecular electronic devices using the peptides.
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
Lesen Sie weiter in der vollständigen Ausgabe!
