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 present book concentrates on enzymatic systems, which involve biocatalytic reactions utilized for information processing (biocomputing). Extensive ongoing research in the enzyme‐based biocomputing, mimicking Boolean logic gates, has been motivated by potential applications in biotechnology and medicine. Furthermore, novel biosensor concepts have been contemplated with multiple inputs processed biochemically before the final output is coupled to transducing electronic or optical systems. These applications have warranted recent emphasis on networking of enzyme logic gates. First few gate networks have been experimentally realized, including coupling, for instance, to signal‐responsive electrodes for signal readout. In order to achieve scalable, stable network design and functioning, considerations of noise propagation and control have been initiated as a new research direction. Optimization of single enzyme‐based gates for avoiding analog noise amplification has been explored, as were certain network optimization concepts. The book reviews and exemplifies these developments, as well as offers an outlook for possible future research foci. The latter include design and uses of non‐Boolean network elements, e.g., filters, as well as other developments motivated by potential novel biosensor and biotechnology applications. The most important feature of the enzyme 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 biosensors, “smart” 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 the enzyme‐based computing. The different topics addressed in this book will be of high interest to the interdisciplinary community active in the area of unconventional biocomputing. The readers can find additional complementary material on molecular [1] and biomolecular [2] computing published recently by Wiley‐VCH. It is hoped that the book 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.

The text was carefully proofread, and the figures were meticulously redrawn and checked to eliminate possible typos, mistakes, and unclear meaning. Still because of the large volume and big number (230) figures, some problems may appear. If this happens, the readers are advised to go to the original publications following the references provided.

A significant amount of the discussed material has originated from the studies to which I have personally contributed. I am very grateful to all scientists, researchers, and students who have participated in this research and have made the achieved results possible.

I would like to conclude this preface by thanking my wife Nina for her support in every respect in the past 47 years. Without her help and support, it would not have been possible to complete this work.

References

1. 1 Katz, E. (ed.) (2012). Molecular and Supramolecular Information Processing: From Molecular Switches to Logic Systems. Weinheim: Wiley‐VCH.
2. 2 Katz, E. (ed.) (2012). Biomolecular Information Processing – From Logic Systems to Smart Sensors and Actuators. Weinheim: Wiley‐VCH.

Professor Vladimir Privman (1955–2018), Director of the Center for Quantum Device Technology and Robert A. Plane Professor of Physics with joint appointments in the Department of Chemistry and Biomolecular Science and Department of Electrical and Computer Engineering (Clarkson University, NY, USA), was a great contributor to the research area of enzyme computing, and this book would not be possible without his work. The majority of the material presented in this book includes contributions by Professor Privman.

His research interests spanned broad areas of advanced technology, including bio‐inspired information processing, synthesis of colloids and nanoparticles, kinetics of surface processes at the nanoscale, physics of semiconductor devices, spintronics, quantum computing, statistical mechanics, chemical kinetics, and surface and polymer science.

Professor Privman began earning recognition early in his career, receiving the Petroleum Research Fund Young Investigator Award and the Clarkson University Graham Award for Young Faculty Excellence. He contributed to a wide range of scientific fields and was a lecturer or moderator at national and international conferences every year. He authored/coauthored over 280 research papers, major reviews, and books. He served on numerous boards of scientific journals, and national funding agencies, and received an American Physical Society Outstanding Referee Award. In 2005 he was named a fellow of the American Physical Society, which recognized his fundamental contributions and professional leadership in statistical physics; surface, colloid, and polymer science; and quantum information science. In 2010 he was named an International Academy, Research, and Industry Association (IARIA) fellow.

Over the past 10 years, Professor Privman has been among the key players in the unconventional computing field. Particularly noteworthy are his contributions to the integration of biomolecular computing and actuation, implementation of biochemical logical gates, biomolecular signal processing, networked enzymatic gates with filtering, associative memory based on enzymatic cascades, biochemical logic for drug release, biomolecular filters for signal separation, enzymatic systems for information processing, and digital biosensors. Professor Privman's contributions to quantum computing were in the evaluation of decoherence for quantum computing architectures, modeling of semiconductor spintronics, quantum control, nuclear spin‐based memory and logic in quantum hall semiconductors, Hamiltonians for quantum computing, and three‐spin XOR gate.

In 2005 Professor Privman edited the Special Issue containing papers from the 2004 IEEE Nanotechnology Council (NTC) Quantum Device Technology Workshop, which was held on 17–21 May 2004, in Clarkson University, Potsdam, NY. The contents of the issue demonstrated breakthroughs in several fields of novel materials and devices, including biochemical logical gates, styrene butadiene rubber nanocomposites, swarms of microscale nanorobots, robots for target therapies, biomolecular motors, magnetoresistive detection of nanoparticles, and self‐assembly of quantum dots. In 2017 the International Journal of Parallel, Emergent and Distributed Systems (vol. 32, issue 1) published a special issue Signal processing, biosensing, and computing with bio‐inspired and biochemical systems compiled and edited by Professor Privman. He presented the field of unconventional computing with diverse contributions such as reaction–diffusion chemistry implementation of neural networks, fluidic infrastructure for enzyme‐based Boolean logic circuits, architectures of nano‐biointerfaces, modeling of enzymatic signal processing, wireless sensor networks with biological cultures, biosensors and memristors in networks of plants, oscillator dynamics of slime mold, insulin biosensor, and biocomputing in forensic analysis.

Professor Privman was highly regarded by his peers and students. He was proud of his trainee's success and advancement and took an active role in mentoring undergraduate, graduate, postdoc, and senior researchers in several departments at Clarkson University. He enjoyed training and collaborating with scientists throughout the United States and internationally. His passing is a great loss to the scientific community.

Professor Vladimir Privman's Works on Biomolecular and Enzymatic Computing

1. 1. A.V. Okhokhonin, S. Domanskyi, Y. Filipov, M. Gamella, A.N. Kozitsina, V. Privman, E. Katz, Biomolecular release from alginate‐modified electrode triggered by chemical inputs processed through a biocatalytic cascade – Integration of biomolecular computing and actuation. Electroanalysis 2018, 30, 426–435.
2. 2. M.L. Wood, S. Domanskyi, V. Privman, Design of high quality chemical XOR gates with noise reduction. ChemPhysChem 2017, 18, 1773–1781.
3. 3. S. Domanskyi, V. Privman, Modeling and modifying response of biochemical processes for biocomputing and biosensing signal processing. Ch. 3 in: Advances in Unconventional Computing, Vol. 2: Prototypes, Models and Algorithms, pp. 61–83, edited by A. Adamatzky, Vol. 23 of Emergence, Complexity and Computation, Springer Nature, Basel, Switzerland, 2017.
4. 4. V. Privman, Theoretical modeling expressions for networked enzymatic signal processing steps as logic gates optimized by filtering. Int. J. Parallel Emergent Distrib. Syst. 2017, 32, 30–43.
5. 5. Y. Filipov, S. Domanskyi, M.L. Wood, M. Gamella, V. Privman, E. Katz, Experimental realization of high quality biochemical XOR gate. ChemPhysChem 2017, 18, 2908–2915.
6. 6. A. Verma, B.E. Fratto, V. Privman, E. Katz, Design of flow systems for improved networking and reduced noise in biomolecular signal processing in biocomputing and biosensing applications. Sensors (MDPI) 2016, 16, article No. 1042.
7. 7. V. Privman, E. Katz, Can bio‐inspired information processing steps be realized as synthetic biochemical processes? Physica Status Solidi A 2015, 212, 219–228.
8. 8. E. Katz, V. Privman, O. Zavalov, Structure of feed‐forward realizations with enzymatic processes. Proceedings of The Eighth International Conference on Quantum, Nano/Bio, and Micro Technologies (ICQNM 2014), ThinkMind Online Publishing, Wilmington, DE, 2014, pp. 22–27.
9. 9. V. Privman, S. Domanskyi, S. Mailloux, Y. Holade, E. Katz, Kinetic model for a threshold filter in an enzymatic system for bioanalytical and biocomputing applications. J. Phys. Chem. B 2014, 118, 12435–12443.
10. 10. V. Privman, S. Domanskyi, S. Mailloux, Y. Holade, E. Katz, Kinetic model for a threshold filter in an enzymatic system for bioanalytical and biocomputing applications. J. Phys. Chem. B 2014, 118, 12435–12443.
11. 11. V. Privman, O. Zavalov, L. Halámková, F. Moseley, J. Halámek, E. Katz, Networked enzymatic logic gates with filtering: New theoretical modeling expressions and their experimental application. J. Phys. Chem. B 2013, 117, 14928–14939.
12. 12. S. Bakshi, O. Zavalov, J. Halámek, V. Privman, E. Katz, Modularity of biochemical filtering for inducing sigmoid response in both inputs in an enzymatic AND gate. J. Phys. Chem. B 2013, 117, 9857–9865.
13. 13. K. MacVittie, J. Halámek, V. Privman, E. Katz, A bioinspired associative memory system based on enzymatic cascades. Chem. Commun. 2013, 49, 6962–6964.
14. 14. V. Privman, B.E. Fratto, O. Zavalov, J. Halámek, E. Katz, Enzymatic AND logic gate with sigmoid response induced by photochemically controlled oxidation of the output. J. Phys. Chem. B 2013, 117, 7559–7568.
15. 15. O. Zavalov, V. Bocharova, J. Halámek, L. Halámková, S. Korkmaz, M.A. Arugula, S. Chinnapareddy, E. Katz, V. Privman, Two‐input enzymatic logic gates made sigmoid by modifications of the biocatalytic reaction cascades. Int. J. Unconventional Computing 2012, 8, 347–365.
16. 16. V. Bocharova, O. Zavalov, K. MacVittie, M.A. Arugula, N.V. Guz, M.E. Dokukin, J. Halámek, I. Sokolov, V. Privman, E. Katz, Biochemical logic approach to biomarker‐activated drug release. J. Mater. Chem. 2012, 22, 19709–19717.
17. 17. O. Zavalov, V. Bocharova, V. Privman, E. Katz, Enzyme‐based logic: OR gate with double‐sigmoid filter response. J. Phys. Chem. B 2012, 116, 9683–9689.
18. 18. V. Bocharova, K. MacVittie, S. Chinnapareddy, J. Halámek, V. Privman, E. Katz, Realization of associative memory in an enzymatic process: Toward biomolecular networks with learning and unlearning functionalities. J. Phys. Chem. Lett. 2012, 3, 1234–1237.
19. 19. J. Halámek, O. Zavalov, L. Halámková, S. Korkmaz, V. Privman, E. Katz, Enzyme‐based logic analysis of biomarkers at physiological concentrations: AND gate with double‐sigmoid “filter” response. J. Phys. Chem. B 2012, 116, 4457–4464.
20. 20. S. Domanskyi, V. Privman, Design of digital response in enzyme‐based bioanalytical systems for information processing applications. J. Phys. Chem. B 2012, 116, 13690–13695.
21. 21. V. Privman, Approaches to control of noise in chemical and biochemical information and signal processing, Ch. 12 in: Molecular and Supramolecular Information Processing. From Molecular Switches to Logic Systems, E. Katz (Ed.), Wiley‐VCH, Weinheim, 2012, pp. 281–303.
22. 22. J. Wang, J.R. Windmuller, P. Santosh, M.‐C. Chuang, E. Katz, J. Halamek, V. Bocharova, M. Pita, V. Privman, Patent: Enzyme‐Logic Biosensing, WO/2011/116151 (September 22, 2011).
23. 23. V. Privman, Control of noise in chemical and biochemical information processing. Israel J. Chem. 2011, 51, 118–131.
24. 24. J. Halámek, J. Zhou, L. Halámková, V. Bocharova, V. Privman, J. Wang, E. Katz, Biomolecular filters for improved separation of output signals in enzyme logic systems applied to biomedical analysis. Anal. Chem. 2011, 83, 8383–8386.
25. 25. J. Halámek, V. Bocharova, M.A. Arugula, G. Strack, V. Privman, E. Katz, Realization and properties of biochemical‐computing biocatalytic XOR gate based on enzyme inhibition by a substrate. J. Phys. Chem. B 2011, 115, 9838–9845.
26. 26. M. Pita, V. Privman, M.A. Arugula, D. Melnikov, V. Bocharova, E. Katz, Towards biochemical filter with sigmoidal response to pH changes: Buffered biocatalytic signal transduction. PhysChemChemPhys 2011, 13, 4507–4513.
27. 27. V. Privman, Error‐control and digitalization concepts for chemical and biomolecular information processing systems. J. Comput. Theor. Nanosci. 2011, 8, 490–502.
28. 28. V. Pedrosa, D. Melnikov, M. Pita, J. Halámek, V. Privman, A. Simonian, E. Katz, Enzymatic logic gates with noise‐reducing sigmoid response. Int. J. Unconventional Computing 2010, 6, 451–460.
29. 29. V. Privman, J. Halámek, M.A. Arugula, D. Melnikov, V. Bocharova, E. Katz, Biochemical filter with sigmoidal response: Increasing the complexity of biomolecular logic. J. Phys. Chem. B 2010, 114, 14103–14109.
30. 30. V. Privman, J. Zhou, J. Halámek, E. Katz, Realization and properties of biochemical‐computing biocatalytic XOR gate based on signal change. J. Phys. Chem. B 2010, 114, 13601–13608.
31. 31. D. Melnikov, G. Strack, J. Zhou, J.R. Windmiller, J. Halámek, V. Bocharova, M.‐C. Chuang, P. Santhosh, V. Privman, J. Wang, E. Katz, Enzymatic AND logic gates operated under conditions characteristic of biomedical applications. J. Phys. Chem. B 2010, 114, 12166–12174.
32. 32. E. Katz, V. Privman, J. Wang, Towards biosensing strategies based on biochemical logic systems. The Fourth International Conference on Quantum, Nano and Micro Technologies (ICQNM 2010). February 10–16, 2010 ‐ St. Maarten, Netherlands Antilles. Proceedings. pp. 1–9.
33. 33. E. Katz, V. Privman, Enzyme‐based logic systems for information processing. Chem. Soc. Rev. 2010, 39, 1835–1857.
34. 34. V. Privman, Biomolecular computing: Learning through play. Nature Nanotechnol. 2010, 5, 767–768.
35. 35. V. Privman, V. Pedrosa, D. Melnikov, M. Pita, A. Simonian, E. Katz, Enzymatic AND‐gate based on electrode‐immobilized glucose‐6‐phosphate dehydrogenase: Towards digital biosensors and biochemical logic systems with low noise. Biosens. Bioelectron. 2009, 25, 695–701.
36. 36. M.A. Arugula, J. Halámek, E. Katz, D. Melnikov, M. Pita, V. Privman, G. Strack, Optimization of enzymatic logic gates and networks for noise reduction and stability. Second International Conference on Advances in Circuits, Electronics and Micro‐Electronics, Proceedings, IEEE Comp. Soc. Publ. (Los Alamitos, California) 2009, 1–7.
37. 37. D. Melnikov, G. Strack, M. Pita, V. Privman, E. Katz, Analog noise reduction in enzymatic logic gates. J. Phys. Chem. B 2009, 113, 10472–10479.
38. 38. V. Privman, M.A. Arugula, J. Halámek, M. Pita, E. Katz, Network analysis of biochemical logic for noise reduction and stability: A system of three coupled enzymatic AND gates. J. Phys. Chem. B 2009, 113, 5301–5310.
39. 39. V. Privman, G. Strack, D. Solenov, M. Pita, E. Katz, Optimization of enzymatic biochemical logic for noise reduction and scalability: How many biocomputing gates can be interconnected in a circuit? J. Phys. Chem. B 2008, 112, 11777–11784.
40. 40. L. Fedichkin, E. Katz, V. Privman, Error correction and digitalization concepts in biochemical computing. J. Comput. Theor. Nanoscience 2008, 5, 36–43.

Vladimir Privman