Enzyme-Based Computing Systems - Evgeny Katz - ebook

Enzyme-Based Computing Systems ebook

Evgeny Katz

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This systematic and comprehensive overview of enzyme-based biocomputing is an excellent resource for scientists and engineers working on the design, study and applications of enzyme-logic systems.

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Table of Contents

Cover

Preface

References

Acknowledgment

Professor Vladimir Privman's Works on Biomolecular and Enzymatic Computing

List of Abbreviations

1 Introduction

1.1 Motivation and Applications

1.2 Enzyme‐Based Logic Gates and Short Logic Circuits

References

2 Boolean Logic Gates Realized with Enzyme‐Catalyzed Reactions: Unusual Look at Usual Chemical Reactions

2.1 General Introduction and Definitions

2.2 Fundamental Boolean Logic Operations Mimicked with Enzyme‐Catalyzed Reactions

2.3 Modular Design of NOR and NAND Logic Gates

2.4 Majority and Minority Logic Gates

2.5 Reconfigurable Logic Gates

2.6 Conclusions and Perspectives

References

3 Optimization of Enzyme‐Based Logic Gates for Reducing Noise in the Signal Transduction Process

3.1 Introduction

3.2 Signal Transduction Function in the Enzyme‐Based Logic Systems: Filters Producing Sigmoid Response Functions

3.3 Summary

References

4 Enzyme‐Based Short Logic Networks Composed of Concatenated Logic Gates

4.1 Introduction: Problems in Assembling of Multistep Logic Networks

4.2 Logic Network Composed of Concatenated Gates: An Example System

4.3 Logic Networks with Suppressed Noise in the Presence of Filter Systems

4.4 Logic Circuits Activated with Biomolecular Signals and Magnetic Field Applied

4.5 The Summary: Step Forward from Single Logic Gates to Complex Logic Circuits

References

5 Sophisticated Reversible Logic Systems

5.1 Introduction

5.2 Feynman Gate: Controlled NOT (CNOT) Gate

5.3 Double Feynman Gate (DFG) Operation

5.4 Toffoli Gate Operation

5.5 Peres Gate Operation

5.6 Gates Redirecting Output Signals

5.7 Advantages and Disadvantages of the Developed Approach

5.8 Conclusions and Perspectives

References

6 Transduction of Signals Generated by Enzyme Logic Gates

6.1 Optical Analysis of Output Signals Generated by Enzyme‐Based Logic Systems

6.2 Electrochemical Analysis of Output Signals Generated by Enzyme‐Based Logic Systems

6.3 Macro/Micro/Nano‐mechanical Transduction of Chemical Output Signals Produced by Enzyme‐Based Logic Systems

6.4 Conclusions and Perspectives

References

7 Circuit Elements Based on Enzyme Systems

7.1 Enzyme‐Based Multiplexer and Demultiplexer

7.2 Biomolecular Signal Amplifier Based on Enzyme‐Catalyzed Reactions

7.3 Biomolecular Signal Converter Based on Enzyme‐Catalyzed Reactions

7.4 Utilization of a Fluidic Infrastructure for the Realization of Enzyme‐Based Boolean Logic Circuits

7.5 Other Circuit Elements Required for the Networking of Enzyme Logic Systems and General Conclusions

References

8 Enzyme‐Based Memory Systems

8.1 Introduction

8.2 Enzyme‐Based Flip‐Flop Memory Elements

8.3 Memristor Based on Enzyme Biocatalytic Reactions

8.4 Enzyme‐Based Associative Memory Systems

8.5 Enzyme‐Based Memory Systems: Challenges, Perspectives, and Limitations

References

9 Arithmetic Functions Realized with Enzyme‐Catalyzed Reactions

9.1 Molecular and Biomolecular Arithmetic Systems: Introduction and Motivation

9.2 Half‐Adder

9.3 Half‐Subtractor

9.4 Conclusions and Perspectives

References

10 Information Security Applications Based on Enzyme Logic Systems

10.1 Keypad Lock Devices as Examples of Electronic Information Security Systems

10.2 Keypad Lock Systems Based on Biocatalytic Cascades

10.3 Other Biomolecular Information Security Systems

10.4 Summary

References

11 Enzyme Logic Digital Biosensors for Biomedical, Forensic, and Security Applications

11.1 Introduction: Short Overview

11.2 From Traditional Analog Biosensors to Novel Binary Biosensors Based on the Biocomputing Concept

11.3 How Binary Operating Biosensors Can Benefit Biomedical Analysis: Requirements, Challenges, and First Applications

11.4 Binary (YES/NO) Analysis of Liver Injury Biomarkers: From Test Tube Probes to Animal Research

11.5 Further Examples of Injury Biomarker Analysis Using AND/NAND Logic Gates

11.6 Multienzyme Logic Network Architectures for Assessing Injuries: Aiming at the Increased Complexity of the Biocomputing–Bioanalytic Systems

11.7 New Approach in Forensic Analysis: Biomolecular Computing‐Based Analysis of Forensic Biomarkers

11.8 Logic Analysis of Security Threats (Explosives and Nerve Agents) Based on Biocatalytic Cascades

11.9 Integration of Biocatalytic Cascades with Microelectronics and Wearable Sensors

11.10 Conclusions and Perspectives

References

12 Release of Molecular Species Stimulated by Logically Processed Biomolecule Signals

12.1 Motivation and Experimental Background

12.2 Fe

3+

‐Cross‐Linked Alginate Hydrogel is a Good Example of Matrix for Signal‐Stimulated Release

12.3 DNA Release as an Example of Signal‐Stimulated Biomolecule Release

12.4 Bioelectrochemical Systems with Sensing and Releasing Electrodes

12.5 Fe

3+

‐Cross‐Linked Alginate Hydrogel Decomposition and Entrapped Molecule Release Triggered by Enzymatically Produced H

2

O

2

12.6 Conclusions and Perspectives

References

13 Biofuel Cells Controlled by Biocomputing Systems

13.1 Introduction: Biofuel Cells, Their Applications, and Motivation for Designing Adaptive, Signal‐Controlled Devices

13.2 Biofuel Cells Controlled by Logically Processed Biochemical Signals

13.3 Biofuel Cells Controlled by Biomolecular Keypad Lock Systems

13.4 Conclusions and Perspectives

References

14 Bioelectronic Interface Between Enzyme‐Based and DNA‐Based Computing Systems

14.1 Introduction: Interfacing Enzyme‐Based and DNA‐Based Computing Systems Is a Challenging Goal

14.2 Bioelectronic Interface Transducing Logically Processed Signals from an Enzymatic System to a DNA System

14.3 The Bioelectronic Interface Connecting Enzyme‐Based Reversible Logic Gates and DNA‐Based Reversible Logic Gates: Realization in a Flow Device

14.4 Conclusions and Perspectives

Oligonucleotides Used in the System Mimicking Feynman Gate (See Section 14.3.2)

References

15 What Is Next? Mimicking Natural Biological Information Processes

15.1 Motivation and Goals

15.2 Example and Discussion of Feed Forward Loops

15.3 Enzymatic Feed‐Forward Loops

15.4 Process Design and Kinetic Modeling

15.5 Simpler Biocatalytic Systems: Not a Feed‐Forward Loop Yet

15.6 Conclusion

References

16 Conclusions and Perspectives: Where Are We Going?

16.1 Conclusions

16.2 Perspectives

16.3 Final Comments

References

Index

End User License Agreement

List of Tables

Chapter 11

Table 11.1 Concentrations of the inputs activating the biocatalytic cascade for ...

List of Illustrations

Chapter 1

Figure 1.1 Moore's law – exponential increase of transistors on integrated...

Figure 1.2 Biomolecular computing systems mimicking operation of different...

Figure 1.3 Enzyme‐based Boolean logic gates – artistic vision.

Chapter 2

Figure 2.1 (A) The enzyme logic system with the non‐variable “machinery” p...

Figure 2.2 (A) The biocatalytic reaction mimicking the Identity (YES) gate...

Figure 2.3 (A) The biocatalytic reaction mimicking the Inverted Identity (...

Figure 2.4 (A) Two parallel reactions catalyzed by glucose dehydrogenase (...

Figure 2.5 (A) Two parallel reactions catalyzed by glutathione reductase (...

Figure 2.6 (A) Two reactions catalyzed by NADH peroxidase (NADH‐POx) and l...

Figure 2.7 The system outlined mimics the features of the NXOR logic gate....

Figure 2.8 (A) Two consecutive reactions catalyzed by lactate oxidase (LOx...

Figure 2.9 (A) Two consecutive reactions catalyzed by alanine transaminase...

Figure 2.10 (A) The biocatalytic cascade operated with two input signals (...

Figure 2.11 Modular design of the enzyme‐based NAND and NOR gate. (A) The ...

Figure 2.12 Absorbance spectra corresponding to the output signal of NADH ...

Figure 2.13 Absorbance spectra corresponding to the output signal of NADH ...

Figure 2.14 The 3‐input majority gate mimicked with three parallel reactio...

Figure 2.15 (A) Time‐dependent absorbance changes corresponding to NADH pr...

Figure 2.16 The enzyme‐based 3‐input minority gate mimicked with three par...

Figure 2.17 (A) Time‐dependent absorbance changes corresponding to the NAD...

Figure 2.18 (A) Time‐dependent absorbance changes corresponding to NADH pr...

Figure 2.19 Switchable operation of the 3‐input gate. The gate operates as...

Figure 2.20 (A) The biocatalytic cascade mimicking various Boolean logic g...

Figure 2.21 The assembled flow system used for the realization of the bioc...

Figure 2.22 (A) Time‐dependent outputs produced by the biocatalytic system...

Figure 2.23 (A) Time‐dependent outputs produced by the biocatalytic system...

Figure 2.24 (A) Time‐dependent outputs produced by the biocatalytic system...

Chapter 3

Figure 3.1 Different response functions of the Identity logic gate: (a) li...

Figure 3.2 The map of a

cis

‐regulatory input function in living

Escherichi

...

Figure 3.3 (A) The biocatalytic system mimicking the Identity (YES) Boolea...

Figure 3.4 (A) A biocatalytic reaction mimicking an AND logic gate where t...

Figure 3.5 (A) The biocatalytic cascade for processing biomolecular input ...

Figure 3.6 (A) The biocatalytic cascade mimicking an OR logic gate with th...

Figure 3.7 (A) Schematic of enzyme‐catalyzed reactions for the realized bi...

Chapter 4

Figure 4.1 (A) Schematics of the biocatalytic cascade mimicking operation ...

Figure 4.2 Operation and analysis of the concatenated biocatalytic gates i...

Figure 4.3 (A) Schematics of the biocatalytic cascade mimicking three conc...

Figure 4.4 Schematics of the biocatalytic cascade realized in the presence...

Figure 4.5 (A) Absorbance increase corresponding to the biocatalytic forma...

Figure 4.6 (A) Logic circuit corresponding to the biocatalytic cascade ope...

Figure 4.7 Schematics of the biocatalytic cascade realized in the presence...

Chapter 5

Figure 5.1 Scientists greatly contributed to the formulation of reversible...

Figure 5.2 (A) A single flow cell used as a component of the system (the r...

Figure 5.3 The truth table (A), block diagram (B), logic circuitry (C) and...

Figure 5.4 The biocatalytic cascade mimicking the CNOT gate operation (Glc...

Figure 5.5 The experimental setup for realization of the Feynman (CNOT) ga...

Figure 5.6 (A) and (B) Optical responses of the Identity and XOR parts of ...

Figure 5.7 The truth table (A), block diagram (B), logic circuitry (C), an...

Figure 5.8 Experimental realization of the biocatalytic Double Feynman gat...

Figure 5.9 Experimental realization of DFG (photo of the flow cell circuit...

Figure 5.10 Optical responses of the Double Feynman gate (DFG) system to v...

Figure 5.11 The truth table (A), block diagram (B), logic circuitry (C), a...

Figure 5.12 Experimental realization of the biocatalytic Toffoli gate in t...

Figure 5.13 Experimental realization of Toffoli gate (photo of the flow ce...

Figure 5.14 Optical responses of the Toffoli gate system to various combin...

Figure 5.15 The truth table (A), block diagram (B), logic circuitry (C), a...

Figure 5.16 Experimental realization of the biocatalytic Peres gate in the...

Figure 5.17 Experimental realization of Peres gate (photo of the flow cell...

Figure 5.18 Optical responses of the Peres gate system to various combinat...

Figure 5.19 The truth table (A), block diagram (B), and equivalent electro...

Figure 5.20 Experimental realization of the biocatalytic Switch gate in th...

Figure 5.21 Experimental realization of the Switch gate (photo of the flow...

Figure 5.22 Optical responses of the Switch gate system to various combina...

Figure 5.23 The truth table (A), block diagram (B) and equivalent electron...

Figure 5.24 Experimental realization of the biocatalytic Fredkin gate in t...

Figure 5.25 Experimental realization of the Fredkin gate (photo of the flo...

Figure 5.26 Optical responses of the Fredkin gate system to various combin...

Chapter 6

Figure 6.1 Transduction of chemical output signals generated by enzyme‐bas...

Figure 6.2 Optical absorbance measurements used for the analysis of output...

Figure 6.3 Optical absorbance measurements used for the analysis of output...

Figure 6.4 Optical absorbance measurements used for the analysis of output...

Figure 6.5 Optical absorbance measurements used for the analysis of output...

Figure 6.6 Bioluminescence measurements used for the analysis of output si...

Figure 6.7 SPR measurements used for the analysis of the output signals pr...

Figure 6.8 Electrochemical transduction of the enzyme generated output sig...

Figure 6.9 Electrochemical transduction of the enzyme generated output sig...

Figure 6.10 Electrochemical transduction of the enzyme generated output si...

Figure 6.11 (A) The biocatalytic cascade activated with four input signals...

Figure 6.12 Experimental results corresponding to the system shown schemat...

Figure 6.13 The use of DC conductivity measurements for transducing output...

Figure 6.14 (A) Water‐in‐oil (W/O) and oil‐in‐water (O/W) emulsions obtain...

Figure 6.15 Electronic transduction of the enzyme generated output signals...

Figure 6.16 Mechanical transduction of the output signals produced by an e...

Figure 6.17 Microgravimetric transduction of the output signals produced b...

Figure 6.18 AFM transduction of the output signal produced by an enzyme lo...

Figure 6.19 AFM transduction of the output signal produced by an enzyme lo...

Chapter 7

Figure 7.1 (A) Schematics of the 2‐to‐1 digital multiplexer. (B) Operation...

Figure 7.2 (A) The biocatalytic system mimicking the 2‐to‐1 digital multip...

Figure 7.3 Spectra generated by the biocatalytic 2‐to‐1 multiplexer upon d...

Figure 7.4 The electronic equivalent circuitry of the 2‐to‐1 digital multi...

Figure 7.5 (A) The biocatalytic system mimicking the 1‐to‐2 digital demult...

Figure 7.6 Spectra generated by the biocatalytic 1‐to‐2 digital demultiple...

Figure 7.7 The electronic equivalent circuitry of the 1‐to‐2 digital demul...

Figure 7.8 Biocatalytic realization of the 1 : 2 demultiplexer in the flow...

Figure 7.9 (A) The experimental realization of the 1 : 2 demultiplexer (ph...

Figure 7.10 The experimental realization of the 1 : 2 demultiplexer (whole...

Figure 7.11 (A) Absorbance spectra measured as the result of the biocataly...

Figure 7.12 The cyclic voltammogram of the PQQ‐modified graphite electrode...

Figure 7.13 The self‐promoted biocatalytic system for the amplification of...

Figure 7.14 Absorbance changes (

λ

max

 = 340 nm) corresponding to the...

Figure 7.15 The scheme illustrating that the enzyme logic gates (e.g., AND...

Figure 7.16 The two‐step biocatalytic system converting the final output s...

Figure 7.17 Conversion of the final output signal of NADH produced by the ...

Chapter 8

Figure 8.1 (A) The truth table for the set/reset (SR) flip‐flop memory. (B...

Figure 8.2 Absorbance spectra corresponding to the SR flip‐flop system com...

Figure 8.3 Absorbance spectra corresponding to the SR flip‐flop system com...

Figure 8.4 The cyclic voltammogram of K

4

[Fe(CN)

6

] obtained in the presence...

Figure 8.5 Chronoamperometric readout of the state changes in the enzyme‐b...

Figure 8.6 Words encoded using ASCII character codes upon application of d...

Figure 8.7 Schematics of the D flip‐flop used in electronics. Note that th...

Figure 8.8 (A) Truth table of the D flip‐flop based on the enzyme reaction...

Figure 8.9 Schematics of the D flip‐flop realized with the enzyme system....

Figure 8.10 Optical responses of the enzyme D flip‐flop system measured at...

Figure 8.11 (A) The truth table corresponding to the T flip‐flop operation...

Figure 8.12 Illustration of the memristor in electrical network theory. Th...

Figure 8.13 (A) pH‐controlled restructuring of the P4VP brush at the ITO e...

Figure 8.14 Schematics of the bioelectrochemical memristor composition and...

Figure 8.15 Schematics of the electrochemical cell. Note that the ITO elec...

Figure 8.16 (A) Photo and SEM image of the buckypaper used as the conducti...

Figure 8.17 (A) The impedance spectrum obtained on the “closed” state of t...

Figure 8.18 Changes of the solution pH generated

in situ

upon connecting t...

Figure 8.19 Cyclic voltammogram demonstrating the hysteresis loop in the c...

Figure 8.20 (A) An example of a biocatalytic cascade that can be used to c...

Figure 8.21 (A) Ivan Petrovich Pavlov (1849–1936). (B) One of Pavlov's dog...

Figure 8.22 Pavlov's experiments with a dog, leaning to associate a “wrong...

Figure 8.23 The biocatalytic process steps of the associative memory syste...

Figure 8.24 Optical measurements at 340 nm of the solution after the react...

Figure 8.25 (A) Experiments demonstrating that the system can be “reset,” ...

Chapter 9

Figure 9.1 The block diagram (A) and equivalent electronic circuitry (B) f...

Figure 9.2 Experimental realization of the biocatalytic half‐adder system ...

Figure 9.3 Optical responses of the half‐adder system to various combinati...

Figure 9.4 The block diagram (A) and equivalent electronic circuitry (B) f...

Figure 9.5 Experimental realization of the biocatalytic half‐subtractor sy...

Figure 9.6 Optical responses of the half‐subtractor system to various comb...

Chapter 10

Figure 10.1 (A) Conventional keypad lock system shown here as a part of an...

Figure 10.2 (A) Biocatalytic cascade activated with three input signals re...

Figure 10.3 (A) Biocatalytic cascade activated with three input signals re...

Figure 10.4 (A) pH changes generated at the final step of the reaction cas...

Figure 10.5 Steganography based on the immuno‐specific interactions result...

Figure 10.6 Photo image of the ELISA plate with text “CLARKSON UNI” encode...

Figure 10.7 Generation of a barcode from direct amperometric output of imm...

Chapter 11

Figure 11.1 Different approaches to biosensing: (A) a single‐analyte biose...

Figure 11.2 Simultaneous presence of biomarkers with limited specificity i...

Figure 11.3 (A) The biocatalytic cascade for analysis of LI, activated by ...

Figure 11.4 Electrochemical transduction of the signals generated by the A...

Figure 11.5 Absorbance changes corresponding to the consumption of NADH up...

Figure 11.6 Receiver operating characteristic (ROC) empirical (red) and sm...

Figure 11.7 (A) The biocatalytic cascade realized for the logic analysis o...

Figure 11.8 Optical (A) and electrochemical (B) responses generated by the...

Figure 11.9 (A) The biocatalytic cascade realized for the logic analysis o...

Figure 11.10 Optical (A) and electrochemical (B) responses generated by th...

Figure 11.11 (A) The biocatalytic reaction realized for the logic analysis...

Figure 11.12 Optical (A) and electrochemical (B) responses generated by th...

Figure 11.13 (A) The biocatalytic cascade realized for the logic analysis ...

Figure 11.14 Optical (A) and electrochemical (B) responses generated by th...

Figure 11.15 (A) The biocatalytic reaction realized for the logic analysis...

Figure 11.16 Optical (A) and electrochemical (B) responses generated by th...

Figure 11.17 Electrochemical analysis of various biological reduced specie...

Figure 11.18 (A) Cyclic voltammograms obtained with a graphene‐functionali...

Figure 11.19 Biocomputing approach to simultaneous sensing of various biol...

Figure 11.20 (A) The biocatalytic cascade realized for the logic analysis ...

Figure 11.21 Optical detection of the output signal generated by the logic...

Figure 11.22 Multienzyme biocatalytic cascade for the analysis of STI and ...

Figure 11.23 Equivalent logic schemes for the concatenated logic gates ana...

Figure 11.24 (A) Optical detection of the output signal (NADH) generated b...

Figure 11.25 (A) Optical detection of the output signal (TMB

ox

) generated ...

Figure 11.26 (A) The biocatalytic cascade for the two‐enzyme CK–LDH assay ...

Figure 11.27 (A) Biocatalytic cascade used to perform NOR logic operation ...

Figure 11.28 (A) Chronoamperograms measured upon operation of the enzyme‐b...

Figure 11.29 Gate mapping illustrating the functional dependence between t...

Figure 11.30 (A–C) Flexible and stretchable bioelectronic devices allow in...

Figure 11.31 (A) The three‐input electrochemical sensor packed in a wearab...

Figure 11.32 Electrochemical analysis of DNT shown schematically (A) and c...

Figure 11.33 A wearable tattoo‐based biosensing system for noninvasive alc...

Chapter 12

Figure 12.1 Photograph represents reversible formation of alginate hydroge...

Figure 12.2 (A) Schematic structure of the Fe

3+

‐cross‐linked alginate ...

Figure 12.3 Electrochemical system composed of two electrically connected ...

Figure 12.4 (A) Immobilization of PQQ‐GDH enzyme on surface of the buckypa...

Figure 12.5 Biocatalytic cascades logically processing multiple biomolecul...

Figure 12.6 Electrochemically stimulated reductive dissolution of Fe

3+

Figure 12.7 Different activity of the released substances. (A) Antibacteri...

Figure 12.8 Functionalization of the SiO

2

‐NPs used in the enzyme logic gat...

Figure 12.9 Realization of the enzyme‐based OR logic gate in a solution wi...

Figure 12.10 Realization of the enzyme‐based AND logic gate in a solution ...

Figure 12.11 The scheme showing the signal‐triggered release of the DNA‐FA...

Figure 12.12 (A) Time‐dependent fluorescence corresponding to DNA‐FAM meas...

Figure 12.13 (A) Time‐dependent fluorescence corresponding to DNA‐FAM meas...

Figure 12.14 (A) The scheme showing operation of the INHIB (inhibition) lo...

Figure 12.15 (A) Time‐dependent fluorescence corresponding to DNA‐FAM meas...

Figure 12.16 Photos showing the alginate‐modified electrodes after the INH...

Figure 12.17 (A) The scheme illustrating the DNAzyme operation. The releas...

Figure 12.18 (A) The biocatalytic cascade shown schematically. The enzymes...

Figure 12.19 Microphotograph (×10 magnification, front view) of typical mo...

Figure 12.20 (A) Fluorescence response (

λ

 = 517 nm) corresponding to ...

Chapter 13

Figure 13.1 Various implantable/wearable medical microelectronic devices –...

Figure 13.2 Schematic futuristic vision of an enzyme‐based biofuel cell im...

Figure 13.3 Schematics of a signal‐switchable biofuel cell. The switchable...

Figure 13.4 (A) Schematics of the pH‐switchable laccase‐catalyzed O

2

reduc...

Figure 13.5 (A) The biocatalytic reactions operating in parallel and resul...

Figure 13.6 (A) The kinetics of the pH changes upon realizing the biocatal...

Figure 13.7 The polarization function,

V

vs.

i

, (A) and power density depe...

Figure 13.8 The bar chart showing the maximum power density obtained from ...

Figure 13.9 (A) The logic network composed of OR/AND gates realized with a...

Figure 13.10

V

i

polarization curves obtained for the biofuel cell with di...

Figure 13.11 The immune‐recognition system producing

in situ

pH changes co...

Figure 13.12 Schematics of the pH‐switchable biofuel cell with the power r...

Figure 13.13 (A) Polarization curves of the biofuel cell shown in Figure 1...

Figure 13.14 (A) The multicomponent immune recognition system assembled up...

Figure 13.15 (A) The biofuel cell implanted in a snail. (B) The biofuel ce...

Chapter 14

Figure 14.1 General scheme of the system operation: the enzyme computing s...

Figure 14.2 Two enzyme systems used in this study and their corresponding ...

Figure 14.3 (A) Potential measurements on the sensing electrode – general ...

Figure 14.4 (A) Optical analysis of the DNA released (note that the DNA wa...

Figure 14.5 The general scheme illustrating the DNA‐based 3‐AND logic gate...

Figure 14.6 Principal scheme of a three‐input deoxyribozyme AND gate. Stra...

Figure 14.7 Digital performance of the DNA logic gate. (A) General scheme....

Figure 14.8 The block scheme of the entire system including (A) the enzyme...

Figure 14.9 Operation of the electrochemical interface between the enzyme ...

Figure 14.10 (A) Logic scheme (including the ID and XOR gates operating in...

Figure 14.11 Schematic representation of the DNA reactions mimicking XOR f...

Chapter 15

Figure 15.1 Feed‐forward system with

activation

in all the signal transduc...

Figure 15.2 Experimental probe of the feasibility of processes needed for ...

Figure 15.3 Feed‐forward system with

repression

of all the signal processi...

Figure 15.4 Illustration of the described feed‐forward designs in terms of...

Figure 15.5 (A) The biocatalytic process realized through two different pa...

Chapter 16

Figure 16.1 (A) The Boolean AND logic gate realized in

P. aeruginosa

bacte...

Figure 16.2 Logic‐controlled self‐powered “sense–act–treat” system that is...

Figure 16.3 (A) The fusion protein composed of a biocatalytic PQQ‐GDH unit...

Figure 16.4 The enzyme operation controlled by many different chemical/phy...

Figure 16.5 (A) Catalase‐modified nanorod self‐propulsion in the presence ...

Guide

Cover

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Enzyme-Based Computing Systems

Evgeny Katz

Copyright

Author

Dr. Evgeny Katz

Clarkson University

Department of Chemistry

Clarkson Avenue 8

NY

United States

Cover

Image was kindly provided by Dr. Evgeny Katz

All books published by Wiley‐VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.

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applied for

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© 2019 Wiley‐VCH Verlag GmbH & Co. KGaA, Boschstr. 12, 69469 Weinheim, Germany

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Print ISBN: 978‐3‐527‐34570‐0

ePDF ISBN: 978‐3‐527‐81996‐6

ePub ISBN: 978‐3‐527‐81998‐0

oBook ISBN: 978‐3‐527‐81999‐7

Cover Design Adam-Design, Weinheim, Germany

Dedication

In the memory of Professor Vladimir Privman, my good friend and collaborator.

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 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.

Evgeny Katz

Potsdam, NY, USA

January 2019

References

1 Katz, E. (ed.) (2012).

Molecular and Supramolecular Information Processing: From Molecular Switches to Logic Systems

. Weinheim: Wiley‐VCH.

2 Katz, E. (ed.) (2012).

Biomolecular Information Processing – From Logic Systems to Smart Sensors and Actuators

. Weinheim: Wiley‐VCH.

Acknowledgment

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. 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. M.L. Wood, S. Domanskyi, V. Privman, Design of high quality chemical XOR gates with noise reduction.

ChemPhysChem

2017

,

18

, 1773–1781.

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. K. MacVittie, J. Halámek, V. Privman, E. Katz, A bioinspired associative memory system based on enzymatic cascades.

Chem. Commun

.

2013

,

49

, 6962–6964.

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. 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. 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. 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. 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. 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. 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. 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. 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. V. Privman, Control of noise in chemical and biochemical information processing.

Israel J. Chem

.

2011

,

51

, 118–131.

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. 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. 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. V. Privman, Error‐control and digitalization concepts for chemical and biomolecular information processing systems.

J. Comput. Theor. Nanosci

.

2011

,

8

, 490–502.

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. 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. 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. 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. 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. E. Katz, V. Privman, Enzyme‐based logic systems for information processing.

Chem. Soc. Rev

.

2010

,

39

, 1835–1857.

34. V. Privman, Biomolecular computing: Learning through play.

Nature Nanotechnol

.

2010

,

5

, 767–768.

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. 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. 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. 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. 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. L. Fedichkin, E. Katz, V. Privman, Error correction and digitalization concepts in biochemical computing.

J. Comput. Theor. Nanoscience

2008

,

5

, 36–43.

Vladimir Privman

List of Abbreviations

αAmy

α‐amylase (enzyme)

βAmy

β‐amylase (enzyme)

α‐KTG

α‐ketoglutaric acid

Δ

f

oscillation frequency change measured by QCM

λ

wavelength

λ

max

wavelength of maximum absorbance in optical spectra

Θ

angle of incident light beam (in SPR measurements)

2‐OG

2‐oxoglutarate

2‐PGA

2‐phosphoglyceric acid (or salt form)

3‐oxo‐C12‐HSL

3‐oxododecanoyl homoserine lactone (QS signaling molecule)

AA

African American (ethnic origin)

Abs

optical absorbance

ABT

abdominal trauma

ABTS

2,2′‐azino‐

bis

(3‐ethylbenzothiazoline‐6‐sulfonic acid) (chromogenic substrate used to follow peroxidase activity)

ABTS

ox

oxidized ABTS (colored product)

Ac

acetic acid

AChE

acetylcholinesterase (enzyme)

AcP

acetyl phosphate

AcidP

acid phosphatase (enzyme)

ADH

alcohol dehydrogenase (enzyme)

ADP

adenosine 5′‐diphosphate

AFM

atomic force microscope (microscopy)

Ala

alanine (amino acid)

Ald

acetaldehyde

ALT

alanine transaminase (enzyme)

AMG

amyloglucosidase (enzyme)

AND

AND Boolean logic gate

anti‐DNP

anti‐dinitrophenyl IgG polyclonal antibody

anti‐NT

anti‐nitrotyrosine IgG polyclonal antibody

AOx

alcohol oxidase (enzyme)

AP

alkaline phosphatase (enzyme)

APTES

(3‐aminopropyl)triethoxysilane (silanizing agent for modification of electrodes and nanoparticles)

ArNHOH

oxidizable hydroxylamine (product of TNT biocatalytic reduction)

ArNO

nitroso compound (product of ArNHOH biocatalytic oxidation)

Asc

ascorbate

ASCII

American Standard Code for Information Interchange

ATM

automated teller machine (as an example of an electronic device with a keypad lock system)

ATP

adenosine 5′‐triphosphate

BHQ2

Black Hole Quencher® (fluorescence quencher)

Bo

borrow digit (output signal in a half‐subtractor)

BSA

bovine serum albumin

Bu

butyric acid

Bu‐O‐Et

ethyl butyrate ester

Bu‐O‐Me

methyl butyrate ester

C

carry digit (output signal in a half‐adder)

C4‐HSL

N

‐butanoyl‐l‐homoserine lactone (QS signaling molecule)

CA

Caucasian (ethnic origin)

CA

chronoamperometry

CaM

calmodulin

cAMP

cyclic adenosine monophosphate (a second messenger important in biological processes)

ChOx

choline oxidase (enzyme)

CK

creatine kinase (enzyme)

CoA

coenzyme A

CN

4‐chloro‐1‐naphthol

CNOT

Controlled NOT (reversible logic gate)

CN‐ox

CN insoluble oxidized product

CNT(s)

carbon nanotube(s)

Crt

creatine

CrtP

creatine phosphate

CSWAP

Controlled‐Swap (logic gate)

D

delay (flip‐flop memory)

D

difference digit (output signal in a half‐subtractor)

DC

direct current

DCPIP

dichlorophenolindophenol (DCPIP

red

and DCPIP

ox

are reduced and oxidized forms of DCPIP, respectively; DCPIP also corresponds to the oxidized form)

DDC

diethyldithiocarbamate (product of DS reduction)

DFG

double Feynman gate (reversible logic gate)

DHA

dehydroascorbate (product of Asc oxidation)

Diaph

diaphorase (enzyme)

dmo‐bpy

4,4′‐dimethoxy‐2,2′‐bipyridine (ligand in the redox active complex: Os(dmo‐bpy)

2

Cl)

DNA

deoxyribonucleic acid

DNAzyme

deoxyribozyme (catalytically active DNA)

DNP

2,4‐dinitrophenyl (used as an antigen for anti‐DNP)

DNT

2,4‐dinitrotoluene

DS

disulfiram

DTT

dithiothreitol

Dz

another abbreviation for DNAzyme

E

potential applied or measured in electrochemical experiments

E

°

standard redox potential (derived from electrochemically reversible cyclic voltammogram)

EDC

1‐ethyl‐3‐(3‐dimethylaminopropyl)carbodiimide (carbodiimide coupling reagent)

EIS

electrolyte–insulator–semiconductor

ELISA

enzyme‐linked immunosorbent assay

EN

enolase (enzyme)

Est

esterase (enzyme)

Et‐O‐Ac

ethyl acetate ester

EtOH

ethanol

f

oscillation frequency measured with QCM

FAM

fluorescein derivative used for labeling biomolecules

FET

field‐effect transistor

FITC

fluorescein isothiocyanate (fluorescent label)

Frc

fructose

G6PDH

glucose 6‐phosphate dehydrogenase (enzyme)

GDH

glucose dehydrogenase (enzyme)

Glc

glucose

Glc1P

glucose‐1‐phosphate

Glc6P

glucose‐6‐phosphate

Glc6PA

gluconate‐6‐phosphate acid (product of Glc6P oxidation)

GlcA

gluconic acid (product of glucose oxidation)

Glu

glutamate (amino acid, salt form)

GluOx

glutamate oxidase (enzyme)

GlutOx

glutathione oxidase (enzyme)

GOx

glucose oxidase (enzyme)

GR

glutathione reductase (enzyme)

GSH

glutathione (reduced form)

GSSG

glutathione (dimeric oxidized form)

HEPES

(4‐(2‐hydroxyethyl)‐1‐piperazineethanesulfonic acid) (buffer)

HK

hexokinase (enzyme)

HPLC

high‐performance liquid chromatography

HRP

horseradish peroxidase (enzyme)

HRP‐Ab

antibody labeled with HRP enzyme

HS

hemorrhagic shock

HAS

human serum albumin

i

current density produced by a biofuel cell on an external ohmic resistance

ID

Identity (YES) gate

INHIB

Inhibited Boolean logic gate

Inv

invertase (enzyme)

INV

Inverter (logic element)

I

p

peak current (measured with cyclic voltammetry)

IPTG

isopropyl β‐D‐thiogalactoside (artificial inducer in cellular regulating processes).

IR

infrared (light)

i

sc

short circuit current density produced by a biofuel cell on an external ohmic resistance

ITO

indium tin oxide (electrode)

JK

Jack Kilby (flip‐flop memory)

Lac

lactate

LDH

lactate dehydrogenase (enzyme)

LI

liver injury

LOx

lactate oxidase (enzyme)

LSPR

localized surface plasmon resonance

Luc

luciferase (enzyme)

Lucif

luciferin

M13

calmodulin‐binding peptide

Maj

majority logic gate

Mal

malate

Malt

maltose

MB

methylene blue (electron transfer mediator operating with GOx); MB

ox

and MB

red

are oxidized and reduced forms of MB, respectively

MDH

malate dehydrogenase (enzyme)

MHC I

MHC class I molecules are one of two primary classes of major histocompatibility complex molecules and are found on the cell surface of all nucleated cells in the bodies of jawed vertebrates

Min

minority logic gate

MMP2 and MMP7

matrix metalloproteinases (cancer biomarkers)

MNP(s)

magnetic nanoparticle(s)

MP‐11

microperoxidase‐11

MPh

maltose phosphorylase (enzyme)

MPAX

methyl paraoxon (acetylcholinesterase inhibitor; model nerve agent)

MWCNT(s)

multiwalled carbon nanotube(s)

NAD

+

nicotinamide adenine dinucleotide (oxidized form)

NADH

nicotinamide adenine dinucleotide (reduced form)

NADH‐POx

NADH peroxidase (enzyme)

NADP

+

β‐nicotinamide adenine dinucleotide phosphate oxidized

NADPH

β‐nicotinamide adenine dinucleotide phosphate reduced

NAD(P)H

represent either NADH or NADPH

NAND

NOT–AND Boolean logic gate

NE

norepinephrine (catecholamine hormone neurotransmitter)

NHS

N

‐hydroxysuccinimide

NOR

NOT–OR Boolean logic gate

NOT

Inverted Identity Boolean logic gate

NP(s)

nanoparticle(s)

NRd

nitroreductase (enzyme)

NT

3‐nitro‐

L

‐tyrosine (used as an antigen for anti‐NT)

NXOR

NOT‐Exclusive‐OR Boolean logic gate

O.D.

optical density (in optical absorbance measurements)

QCM

quartz crystal microbalance

Q‐F

oligonucleotide labeled with a fluorescent dye at one end and with a quencher at another end; F is a fluorescent dye; Q is a quencher

QS

quorum sensing

OPH

organophosphorous hydrolase (enzyme)

OR

OR Boolean logic gate

OS

oxidative stress

Q

t

initial (present) state of a flip‐flop device

Q

t

+1

next state of a flip‐flop device

Q

t

+2

next, next state of a flip‐flop device

OxAc

oxaloacetate

O/W

oil‐in‐water Pickering emulsion

Qz6

Quasar 670 (fluorescent dye)

P2VP

poly(2‐vinyl pyridine)

P4VP

poly(4‐vinyl pyridine)

PAX

paraoxon (acetylcholinesterase inhibitor; model nerve agent)

PB

Prussian blue

PBSE

1‐pyrenebutanoic acid succinimidyl ester (heterobifunctional reagent)

P.D.

power density produced by a biofuel cell on an external ohmic resistance

P.D.

max

maximum power density produced by a biofuel cell on an external optimized ohmic resistance

PDH

pyruvate dehydrogenase (enzyme)

PDI

protein disulfide‐isomerase (enzyme)

PEI

polyethyleneimine

PEO

poly(ethylene oxide)

PEP

phospho(enol)pyruvic acid (or phosphoenol pyruvate in the form of salt)

Pi

inorganic phosphate

PK

pyruvate kinase (enzyme)

p

K

a

acid dissociation constant

PNP

p

‐nitrophenol

PNPP

p

‐nitrophenyl phosphate

POx

pyruvate oxidase (enzyme)

Ppy

polypyrrole

Ppy‐ox

polypyrrole oxidized state

Ppy‐red

polypyrrole reduced state

PQQ

pyrroloquinoline quinone

PQQ‐GDH

PQQ‐dependent glucose dehydrogenase (enzyme)

PS

polystyrene

Pyr

pyruvate

R

reset signal

R

reflectance measured by SPR

R

external load resistance connected to a biofuel cell

R

cell

ohmic resistance measured in a bulk solution in an electrochemical cell

RE

reference electrode

R

et

electron transfer resistance (measured by Faradaic impedance spectroscopy)

RI

radiation injury

RNA

ribonucleic acid

RNS

reactive nitrogen species

ROC

receiver operating characteristic

ROS

reactive oxygen species

S

set signal

S

sum digit (output signal in a half‐adder)

SAND

single inversion AND (logic gate equivalent to NOT–AND operation, where inversion NOT is applied to one of the inputs)

SEM

scanning electron microscopy

SPE

screen‐printed electrode

SPR

surface plasmon resonance

SR

set/reset (flip‐flop memory)

STI

soft tissue injury

SWV

square wave voltammetry

T

toggle (flip‐flop memory)

TBI

traumatic brain injury

t

g

gate time (time of reaction after which the gate response is measured)

TMB

3,3′,5,5′‐tetramethylbenzidine (chromogenic substrate used to follow peroxidase activity)

TMB

dox

TMB double‐oxidized product

TMB

ox

oxidized colored form of TMB

TMB

red

TMB reduced original state (the same as TMB)

TMB

sox

TMB single‐oxidized product (the same as TMB

ox

)

TNT

trinitrotoluene (explosive)

Tris

2‐amino‐2‐(hydroxymethyl)propane‐1,3‐diol (buffer)

UV

ultraviolet (light)

Ure

urease (enzyme)

V

voltage produced by a biofuel cell on an external ohmic resistance

V

a

alternative voltage applied between the conducting support and reference electrode of the EIS devise

V

bias

constant (bias) voltage applied between the conducting support and reference electrode of the EIS devise

V

FB

flat band voltage of the EIS device

V

oc

open circuit voltage produced by a biofuel cell

W/O

water‐in‐oil Pickering emulsion

XOR

Exclusive‐OR Boolean logic gate

YES

Identity (ID) Boolean logic gate

1Introduction

1.1 Motivation and Applications

Exponential development of computing systems based on silicon materials and binary algorithms formulated as the “Moore's law” [1] (Figure 1.1) is coming to the end being limited by further component miniaturization and by the speed of operation. Conceptually novel ideas are needed to break through these limitations. The quest for novel ideas in the information processing has resulted in several exciting directions in the general area of unconventional computing [2–4], including research in quantum computing [5] and biologically inspired molecular computing [6–9]. Molecular computing systems, generally motivated by mimicking natural biological information processing [10, 11], are not necessarily based on biomolecules and could be represented by synthetic molecules with signal‐controlled switchable properties. Synthetic molecular systems and nano‐species have been designed to mimic the operation of Boolean logic gates and demonstrate basic arithmetic functions and memory units. However, despite progress achieved in assembling synthetic molecular systems performing basic Boolean operations and simple computations, these systems have limited complexity, and further increase of their complexity is very challenging. A new advance in the development of molecular information systems has been achieved with the use of biomolecular species [12] (Figure 1.2) such as DNA/RNA [13–16], oligopeptides [17], proteins [18], enzymes [2, 19, 20], antigens/antibodies [21], and even whole biological cells/organisms [22–24] capable of operating in a biological environment [25], borrowing some ideas from systems biology [26]. The advantage of the biomolecular computing systems is their ability to be integrated in artificially designed complex reacting processes mimicking multistep information processing networks. These systems are still far away from the natural information processing in cells but are already much more complex than pure synthetic molecular systems. In fact, biochemical reactions are at the core of the mechanism of life itself, and therefore one could set rather ambitious expectations for how far can (bio)chemical reaction systems be scaled up in complexity, if not speed, for information processing. While in a long perspective a “biocomputer” might become a reality [27], particularly for some special applications, e.g., for solving complex combinatorial problems [28], potentially promising to have an advantage over silicon‐based electronic computers due to parallel computing performed by numerous biomolecular units, the present level of technology does not allow any practical use of biomolecular systems for real computational applications. For achieving any practical result soon, some other applications, different from making a biocomputer, should be considered using the (bio)molecular systems with a limited complexity. One of the immediate possible applications for molecular logic systems is a special kind of biosensing [29–31] where the multiple input signals are logically processed through chemical reactions resulting in YES/NO decisions in the binary (0,1) format. In this subarea of biomolecular logic systems, practical results are already possible at the present level of the system complexity, particularly for biomedical applications [32, 33]. Overall, the research in molecular/biomolecular information processing, which has been motivated originally to progress unconventional computing applications, is broadly developing to areas not directly related to computing in its narrow definition. This research is bringing us to novel areas in sensing/biosensing [29–31], switchable “smart” materials controlled by logically processed signals [34–36], bioelectronic devices (e.g., biofuel cells) controlled by external signals [37, 38], signal‐controlled release processes [39–43], etc.

Figure 1.1 Moore's law – exponential increase of transistors on integrated circuit chips. (The plot shown in the figure is based on the data provided by Wikipedia: https://en.wikipedia.org/wiki/Moore%27s_law.)

Source: Katz 2018 [2]. Adapted with permission of John Wiley and Sons.

Figure 1.2 Biomolecular computing systems mimicking operation of different Boolean logic gates and circuitries can be based on various species including oligopeptides, enzymes/proteins, DNA/RNA, antibodies, and even whole biological (e.g., microbial) cells.

Source: Katz 2018 [2]. Adapted with permission of John Wiley and Sons.

1.2 Enzyme‐Based Logic Gates and Short Logic Circuits

While the major research efforts have been directed to the DNA‐based computing systems [10, 13–15], mostly aiming at computing applications in their direct narrow definition [27, 44–50] and expecting acceleration of the computing process due to massively parallel data processing [28, 51], enzyme logic systems [19, 20] received smaller attention since they are less promising for real large‐scale computational applications. Growing interest to the enzyme logic systems is based on their activation with physiologically relevant biomolecular signals (metabolites) appearing at physiological concentrations [52–54] allowing low‐scale information processing for biomedical applications, such as binary (YES/NO format) biosensing [32, 33], signal‐controlled materials, and implantable bioelectronic devices [55].

Enzyme‐based logic gates are usually realized through relatively simple enzyme‐catalyzed reactions [19, 20] (Figure 1.3). Rapid progress in enzyme‐based information processing systems has resulted in the design of biocatalytic cascades mimicking various Boolean logic gates, including AND [52, 56–59], OR [59, 60], NAND [61], NOR [57, 61], CNOT [62], XOR [57, 59, 63–65], INHIB [57, 59], Identity [57], and Inverter [57] gates. In order to digitalize chemical processes, the reacting species considered as logic input signals were applied at two levels of their concentrations: their physical absence (zero concentration) was defined as logic 0 input, while logic 1 input was defined as experimentally optimized and conveniently high concentration, thus allowing significant separation in the produced output signals when inputs 0 and 1 were applied in different combinations. Depending on specific needs set by applications, the input signals were defined as variable concentrations of substrates and/or cofactors reacting with enzymes [57] or different concentrations of the biocatalytic enzymes added to a “soup” of substrates/cofactors being ready to react with the enzymes [59]. The non‐variable part of the system was considered as a “machinery” operating with the variable input signals applied in various combinations. Multistep biocatalytic cascades activated by several (more than two) input signals have been assembled to mimic logic circuits composed of concatenated logic gates [66–69]. Various reaction cascades have been designed to mimic different combinations of concatenated logic gates; however, they usually do not include more than three or four logic steps. Due to noise formation and amplification through the reaction steps, the number of logic steps is limited, and theoretical estimation limits the system complexity by approximately 10 logic steps (which have never been realized experimentally in those enzyme‐biocatalyzed reactions) [70]. Complex branched biocatalytic reactions realized in flow cell systems have been used to mimic operation of reversible logic gates, such as Feynman gate, Double Feynman gate, Toffoli gate, Peres gate, and Fredkin gate [71–74].

Figure 1.3 Enzyme‐based Boolean logic gates – artistic vision.

Source: Katz 2018 [2]. Adapted with permission of John Wiley and Sons.