Electrochemical processes in biological systems andrzej lewenstam

ELECTROCHEMICAL PROCESSES IN BIOLOGICAL SYSTEMS WILEY SERIES ON ELECTROCATALYSIS AND ELECTROCHEMISTRY Andrzej Wieckowski, Series Editor Fuel Cell Catalysis: A Surface Science Approach, Edited by Marc T M Koper Electrochemistry of Functional Supramolecular Systems, Margherita Venturi, Paola Ceroni, and Alberto Credi Catalysis in Electrochemistry: From Fundamentals to Strategies for Fuel Cell Development, Elizabeth Santos and Wolfgang Schmickler Fuel Cell Science: Theory, Fundamentals, and Biocatalysis, Andrzej Wieckowski and Jens Norskov Vibrational Spectroscopy at Electrified Interfaces, Edited by Andrzej Wieckowski, Carol Korzeniewski and Bjorn Braunschweig ELECTROCHEMICAL PROCESSES IN BIOLOGICAL SYSTEMS Edited by ANDRZEJ LEWENSTAM LO GORTON Wiley Series on Electrocatalysis and Electrochemistry Copyright © 2015 by John Wiley & Sons, Inc All rights reserved Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be 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Congress Cataloging-in-Publication Data Electrochemical processes in biological systems / edited by Andrzej Lewenstam, Lo Gorton pages cm – (Wiley series on electrocatalysis and electrochemistry) Includes bibliographical references and index ISBN 978-0-470-57845-2 (cloth : alk paper) Bioenergetics Ion exchange I Lewenstam, Andrzej II Gorton, L (Lo) QP517.B54E44 2015 612 01421–dc23 2014049433 Set in 10/12pt Times by SPi Global, Pondicherry, India Printed in the United States of America 10 CONTENTS Contributors vii Preface ix Modeling of Relations between Ionic Fluxes and Membrane Potential in Artificial Membranes Agata Michalska and Krzysztof Maksymiuk Transmembrane Ion Fluxes for Lowering Detection Limit of Ion-Selective Electrodes 23 Tomasz Sokalski Ion Transport and (Selected) Ion Channels in Biological Membranes in Health and Pathology 61 Krzysztof Dołowy Electrical Coupling through Gap Junctions between Electrically Excitable Cells 83 Yaara Lefler and Marylka Yoe Uusisaari Enzyme Film Electrochemistry 105 Julea N Butt, Andrew J Gates, Sophie J Marritt and David J Richardson Plant Photosystem II as an Example of a Natural Photovoltaic Device 121 Wiesław I Gruszecki v vi CONTENTS Electrochemical Activation of Cytochrome P450 133 Andrew K Udit, Michael G Hill and Harry B Gray Molecular Properties and Reaction Mechanism of Multicopper Oxidases Related to Their Use in Biofuel Cells 169 Edward I Solomon, Christian H Kjaergaard and David E Heppner Electrochemical Monitoring of the Well-Being of Cells 213 Kalle Levon, Qi Zhang, Yanyan Wang, Aabhas Martur and Ramya Kolli 10 Electrochemical Systems Controlled by Enzyme-Based Logic Networks: Toward Biochemically Controlled Bioelectronics 231 Jan Halámek and Evgeny Katz Index 253 CONTRIBUTORS Julea N Butt, School of Chemistry and School of Biological Sciences, University of East Anglia, Norwich, UK Krzysztof Dołowy, Laboratory of Biophysics, Warsaw University of Life Sciences (SGGW), Warsaw, Poland Andrew J Gates, School of Biological Sciences, University of East Anglia, Norwich, UK Harry B Gray, Beckman Institute, California Institute of Technology, Pasadena, CA, USA Wiesław I Gruszecki, Department of Biophysics, Institute of Physics, Maria Curie-Skłodowska University, Lublin, Poland Jan Halámek, Department of Chemistry and Biomolecular Science, and NanoBio Laboratory (NABLAB), Clarkson University, Potsdam NY, USA David E Heppner, Department of Chemistry, Stanford University, Stanford, CA, USA Michael G Hill, Department of Chemistry, Occidental College, Los Angeles, CA, USA Evgeny Katz, Department of Chemistry, University at Albany, SUNY, Albany, NY, USA Christian H Kjaergaard, Department of Chemistry, Stanford University, Stanford, CA, USA vii viii CONTRIBUTORS Ramya Kolli, Department of Chemical and Biomolecular Engineering, New York University Polytechnic School of Engineering, Six Metrotech Center, Brooklyn, USA Yaara Lefler, Department of Neurobiology, The Institute of Life Sciences and Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University, Jerusalem, Israel Kalle Levon, Department of Chemical and Biomolecular Engineering, New York University Polytechnic School of Engineering, Six Metrotech Center, Brooklyn, USA Krzysztof Maksymiuk, Faculty of Chemistry, University of Warsaw, Warsaw, Poland Sophie J Marritt, School of Chemistry, University of East Anglia, Norwich, UK Aabhas Martur, Department of Chemical and Biomolecular Engineering, New York University Polytechnic School of Engineering, Six Metrotech Center, Brooklyn, USA Agata Michalska, Faculty of Chemistry, University of Warsaw, Warsaw, Poland David J Richardson, School of Biological Sciences, University of East Anglia, Norwich, UK Tomasz Sokalski, Laboratory of Analytical Chemistry, Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland Edward I Solomon, Department of Chemistry, Stanford University, Stanford, CA, USA Andrew K Udit, Department of Chemistry, Occidental College, Los Angeles, CA, USA Marylka Yoe Uusisaari, Department of Neurobiology, The Institute of Life Sciences and Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University, Jerusalem, Israel Yanyan Wang, Department of Chemical and Biomolecular Engineering, New York University Polytechnic School of Engineering, Six Metrotech Center, Brooklyn, USA Qi Zhang, Department of Chemical and Biomolecular Engineering, New York University Polytechnic School of Engineering, Six Metrotech Center, Brooklyn, USA ITO e– Off state (pH 6) e– NADH –Z ˝ 242 ELECTROCHEMICAL SYSTEMS CONTROLLED BY ENZYME-BASED LOGIC NETWORKS I E Sucrose Z´ Invertase Input A O N O N O2 Cl N O Glucose N O GOx O2 pH Gluconic acid NH3 pH Reset Input B H2O2 AND A B Output 0 0 1 0 1 A B Output 0 0 1 1 1 pH Butyrate acid Urease Ethyl butyrate and Glucose Urea e– OR Esterase Enable Input A GOx Input B O2 pH Gluconic acid H2O2 e– NADH NAD+ I –Z ˝ On state (pH 4) E Z´ FIGURE 10.7 Logic operations AND/OR performed by the enzyme-based systems resulting in the ON and OFF states of the bioelectrocatalytic interface followed by the Reset function to complete the reversible cycle Schematically shown cyclic voltammograms and impedance spectra correspond to the ON and OFF states of the bioelectrocatalytic electrode applied for the NADH oxidation Adopted from Ref [27] with permission © American Chemical Society, 2009 the biofuel cell power production [34] Higher-complexity biocatalytic system based on the concerted operation of four enzymes activated by four chemical input signals was designed (Fig 10.9a) to mimic a logic network composed of three logic gates (AND/OR connected in parallel generating two intermediate signals for the final AND gate) [35] (Fig 10.9b) The switchable biofuel cell was characterized by measuring polarization curves at its “mute” and active states A low voltage-current production was characteristic of the initial inactive state of the biofuel cell at pH ca (Fig 10.10a, curve a) Upon receiving an output signal in the form of a pH decrease from the enzyme logic network, the voltage-current production by the biofuel cell was dramatically enhanced when pH reached ca 4.3 (Fig 10.10a, curve b) When the activation of the biofuel cell was achieved, another biochemical signal (urea in the presence of urease) resulted in the increasing pH, thus resetting the cell to its inactive state with the low voltage-current production (Fig 10.10a, curve c) The cyclic operation of the biofuel cell upon receiving biochemical signals can be followed by the reversible changes of the current production (Fig 10.10a, inset) The biofuel cell switching from ELECTROCHEMICAL SYSTEMS CONTROLLED BY ENZYME-BASED LOGIC NETWORKS 243 V I R MB Nafion membrane e– Cathode O2 O2 Gluconic acid Lac O2 H 2O Glucose O2 – e Anode e– e– Ar GOx Ar Ar pH N e– e– H 3C – e e– N CH3 pH pH + N S CH3 – Cl CH3 MB FIGURE 10.8 The biofuel cell composed of the pH-switchable logically controlled biocatalytic cathode and glucose-oxidizing anode Adopted from Ref [35] with permission (a) CH3COH Input B NADH Input A Glucose CH3CH2OH NAD+ Gluconic acid ADH GDH Maltose Input C AGS Sucrose Input D ΔpH (b) NADH AND Maltose NADH NAD+ CH3COH INV pH AND OR Glucose Gluconic acid Sucrose FIGURE 10.9 (a) The cascade of reactions biocatalyzed by alcohol dehydrogenase (ADH), amyloglucosidase (AGS), invertase (Inv) and glucose dehydrogenase (GDH) and triggered by the chemical input signals NADH, acetaldehyde, maltose, and sucrose added in different combinations (b) The logic network composed of three concatenated gates and equivalent to the cascade of enzymatic reactions outlined in (a) Adopted from Ref [35] with permission 244 ELECTROCHEMICAL SYSTEMS CONTROLLED BY ENZYME-BASED LOGIC NETWORKS (b) (a) 0.4 b 0.1 c 0.0 0.0 a 1.5 1.0 0.5 0.0 Steps 0.5 1.0 i (μA cm–2) 200 150 100 50 1.5 0000 1000 0100 0010 0001 1100 1010 1001 0110 0101 0011 1110 1101 1011 1011 1011 0111 1111 0.2 i (μA cm–2) V (V) 0.3 P D (nW cm–2) 250 Inputs FIGURE 10.10 (a) V–i polarization curves obtained for the biofuel cell with different load resistances: (a) in the inactive state prior to the addition of the biochemical input signals (pH value in the cathodic compartment ca 6), (b) in the active state after the cathode was activated by changing pH to ca 4.3 by the biochemical signals, (c) after the Reset function activated by the addition of mM urea Inset: switchable isc upon transition of the biofuel cell from the mute state to the active state and back performed upon biochemical signals processed by the enzyme logic network (b) The bar diagram showing the power density produced by the biofuel cell in response to different patterns of the chemical input signals Dashed lines show thresholds separating digital 0, undefined, and output signals produced by the system Adopted from Ref [35] with permission the “mute” state with a low activity to the active state was achieved upon appropriate combination of the input signals processed by the enzyme logic network Only three combinations of the input signals—1,1,1,0; 1,1,0,1; and 1,1,1,1—from all 16 possible variants resulted in the solution pH change, thus switching the biofuel cell to its active state (Fig 10.10b) The studied biofuel cells exemplify a new kind of bioelectronic devices where the bioelectronic function is controlled by a biocomputing system Such devices will provide a new dimension in bioelectronics and biocomputing benefiting from the integration of both concepts The enzyme-based biochemical networks demonstrate robust error-free processing of biochemical signals upon appropriate optimization of their components and interconnections [36–38] However, the limit of the biocomputing network complexity is set by the cross-reactivity of the enzyme-catalyzed reactions Only enzymes belonging to different biocatalytic classes (oxidases, dehydrogenases, peroxidises, hydrolases, etc.) could operate in a homogeneous system without significant crossreactivity If chemical reasons require the use of cross-reacting enzymes in the system, they must be compartmentalized using patterning on surfaces or applied in microfluidic devices Application of more selective biomolecular interactions would be an advantage to make biocomputing systems more specific to various input signals and less cross-reactive in the chemical signal processing This aim can be achieved by the application of highly selective biorecognition (e.g., immune) interactions for biocomputing [39] One of the novel immune-based biocomputing systems was ELECTROCHEMICAL SYSTEMS CONTROLLED BY ENZYME-BASED LOGIC NETWORKS 245 already applied for switching the biofuel cell activity by the logically processed antibody signals [40] A surface functionalized with a mixed monolayer of two different antigens, 2,4dinitrophenyl (DNP) and 3-nitro-l-tyrosine (NT), loaded on human serum albumin (HSA) and bovine serum albumin (BSA), respectively, was used to analyze the input signals of the corresponding antibodies: anti-dinitrophenyl (anti-DNP IgG polyclonal from goat) and anti-nitrotyrosine (anti-NT IgG from rabbit) [40] After binding to the surface, the primary antibodies were reacted with the secondary antibodies: anti-goat IgG HRP and anti-rabbit IgG HRP (mouse origin IgG against goat immunoglobulin and mouse origin IgG against rabbit IgG, both labeled with horseradish peroxidise, HRP) to attach the biocatalytic HRP tag to the immune complexes generated on the surfaces (Fig 10.11a) The primary anti-DNP and anti-NT antibodies (signals A and B, respectively) were applied in four different combinations: 0,0; 0,1; 1,0; and 1,1, where the digital value corresponded to the absence of the antibody and value corresponded to their presence in the optimized concentrations The secondary antibody labeled with the HRP biocatalytic tag was bound to the surface only if the respective primary antibody was already there Since both secondary antibodies were labeled with HRP, the biocatalytic entity appeared on the surface upon application of 0,1; 1,0; and 1,1 signal combinations Only in the absence of both primary antibodies (signals 0,0) the secondary antibodies were not bound to the surface and the HRP biocatalyst did not appear there, thus resembling the OR logic operation The assembled functional interface was reacted with 2,2 -azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and H2O2 The biocatalytic oxidation of ABTS and concomitant reduction of H2O2 resulted in the increase of the solution pH only when the biocatalytic HRP tag was present on the surface (Fig 10.11b) This happened when the primary antibody signals were applied in the combinations 0,1; 1,0; and 1,1 The pH increase generated in situ by the enzyme reaction coupled with the immune-recognition system yielded the inactive shrunken state of the polymer brush-modified electrode, thus deactivating the entire biofuel cell It should be noted that for simplicity, the cathode was represented by a model redox system with a ferricyanide solution instead of the oxygen system (Fig 10.11c) The biofuel cell being active at pH 4.5 (Fig 10.12a, b, curves a) was partially inactivated (curves b) by the pH increase up to 5.8 generated by the immune-based logic system Since the output signal from the logic system resulted in the inactivation of the biofuel cell (operating as the inverter producing output for input 1, and vice versa), the system modeled an NOR logic gate (Fig 10.12b, inset) After the biofuel cell inactivation, the next cycle was started by the reset to the initial pH value activating the switchable electrode again To activate the biofuel cell, GOx and glucose were added to the cathodic compartment, resulting in the pH decrease to ca 4.2 due to the biocatalytic oxidation of glucose and formation of gluconic acid It should be noted however that all biomolecular logic systems described earlier produce bulk pH changes in order to switch the electrode interface between active and inactive states This cannot be used in many biomedical applications when bulk pH cannot be altered in physiological liquids, particularly in implantable switchable 246 ELECTROCHEMICAL SYSTEMS CONTROLLED BY ENZYME-BASED LOGIC NETWORKS (a) (b) HRP HRP Anti-goat lgG Anti-rabbit lgG 2ABTSOX 2ABTS Goat anti-DNP lgG Rabbit anti-NT lgG HRP 2H2O H2O2 HSA (c) HSA BSA pH < 4.5 ON Glucose e– NC NC +H+ CN Fe CN CN CN e– NC pH > 5.5 Gluconic acid 3– –H+ e– OFF + 2H+ BSA NC CN Fe CN 3– MBox MBred e– CN CN Nafion membrane pH = FIGURE 10.11 (a) The immune system composed of two antigens, two primary antibodies, and two secondary antibodies labeled with horseradish peroxidise (HRP) biocatalytic tag used for the OR logic gate (b) The biocatalytic reaction producing pH changes to control the biofuel cell performance (c) The biofuel cell controlled by the immune OR logic gate due to the pHswitchable [Fe(CN)6]3−-reducing cathode MBox and MBred are oxidized and reduced states of the mediator methylene blue Adapted from Ref [40] with permission © American Chemical Society, 2009 biosensors and bioactuators operating in vivo Thus, development of switchable electrode interfaces controlled by local pH changes is an important goal One of the first systems allowing switchable functioning of electrode interfaces upon pH changes localized at the electrode surface has been developed recently [41] Magnetic NPs ELECTROCHEMICAL SYSTEMS CONTROLLED BY ENZYME-BASED LOGIC NETWORKS 247 (a) 0.6 0.5 a V (V) 0.4 b 0.3 0.2 0.1 0.0 0.0 0.5 1.0 1.5 2.0 I (μAcm–2) (b) 3.0 P (μW cm–2) 0.8 0.7 a 0.6 0.5 P (μW cm–2) 2.5 0.4 0.0 0.4 0.3 0.0 0.1 1.0 1.1 Reset Inputs b 0.2 0.1 0.0 200 400 600 R (kΩ) 800 1000 FIGURE 10.12 (a) The polarization curves of the biofuel cell with the pH-switchable cathode obtained at different pH values generated in situ by the immune OR logic gate: (a) pH 4.5, (b) pH 5.8 (b) Electrical power density generated by the biofuel cell on different load resistances at different pH values generated in situ by the immune OR logic gate: (a) pH 4.5, (b) pH 5.8 Inset: the maximum electrical power density produced by the biofuel cell upon different combinations of the immune input signals Adapted from Ref [40] with permission © American Chemical Society, 2009 covalently modified with GOx (Fig 10.13a) were used to produce acidic pH value upon biocatalytic oxidation of glucose, resulting in the formation of gluconic acid The biocatalytic NPs suspended in a bulk solution were collected on an electrode surface functionalized with a pH-switchable polymer layer (P4VP brush) and produced local pH decrease when glucose was added to the solution (Fig 10.13b) It is very important to note that the local pH decrease was not accompanied by any pH changes in the bulk solution and the electrode interface was open for electrochemical reactions 248 ELECTROCHEMICAL SYSTEMS CONTROLLED BY ENZYME-BASED LOGIC NETWORKS (a) OH O OH OH TOA O GOx O O EDC O OH OH OH O O OH OH O H O O O O O O (b) Gluconic acid + H Glucose ITO-PVP ITO-PVP Magnet ABTS Bulk pH Local pH pH I (μA) (c) pH ITO-PVP Magnet E (V) FIGURE 10.13 (a) Functionalization of Au-shell/CoFe2O4-magnetic core NPs with GOx (b) Magneto-assisted concentration of the GOx NPs on the electrode surface modified with the P4VP brush to perform glucose oxidation at the interface (c) Opening of the P4VP brush for the electrochemical reaction at acidic pH generated at the interface upon the biocatalytic reaction Adopted from Ref [41] with permission © American Chemical Society, 2009 ELECTROCHEMICAL SYSTEMS CONTROLLED BY ENZYME-BASED LOGIC NETWORKS 249 (b) 1.0 20 200 c Steps 160 10 d a b 0.0 0.0 120 1.0 a 250 b 0.3 0.4 0.5 0.6 0.7 0.8 0.9 E (V) 0.5 Zre (kΩ) 500 80 40 –10 –20 0.5 Ret (kΩ) I (μA) 20 10 –Zim (kΩ) 30 –Zim (kΩ) 30 Ip (μA) (a) 0 Steps 100 200 300 Zre (kΩ) 400 FIGURE 10.14 (a) Cyclic voltammograms obtained on the P4VP-modified electrode: (a) GOx NPs in the solution in the absence of glucose, (b) GOx NPs confined at the electrode in the absence of glucose, (c) GOx NPs confined at the electrode in the presence of glucose, (d) GOx NPs redispersed in the solution in presence of glucose Inset: reversible ON–OFF electrode switching by adding and removing glucose, while the GOx NPs are confined at the electrode surface (b) Impedance spectra (Nyquist plots) obtained on the P4VP-modified electrode with the GOx NPs confined at the electrode: (a) in the absence of glucose, (b) in the presence of glucose (also shown at a smaller scale) Inset: reversible switching of the Ret by adding and removing glucose Solution: 0.1 mM ABTS in 0.1 M Na2SO4, pH 7; GOx NPs, 0.3 mg mL−1; glucose addition, 10 mM; cyclic voltammograms, 100 mV s−1; impedance bias potential, 0.62 V Adopted from Ref [41] with permission © American Chemical Society, 2009 when only local pH changes were achieved (Fig 10.13c) Reversible confinement– removal of the biocatalytic NPs to and from the electrode surface as well as addition and washing out of glucose resulted in the reversible activation and inactivation of the switchable electrode interface controlled exclusively by local interfacial pH changes The switchable behavior of the electrode interface was followed by cyclic voltammetry and Faradaic impedance spectroscopy (Fig 10.14) The present results demonstrate the possibility to control the activity of switchable surfaces by producing local pH changes upon biocatalytic reactions running at the interfaces The external signals activating the interfaces might be chemical (e.g., glucose) or physical (magnetic field) or both of them together (AND logic gate) The developed approach exemplified by several systems described in the present paper paves the way to the novel digital biosensors and bioelectronic devices processing multiple biochemical input signals and producing a combination of output signals dependent on the whole pattern of various input signals The biochemical signals are processed by chemical means based on the enzyme logic system prior to their electronic transduction, hence obviating the need for computer analysis of the biosensing information In addition to the analysis of the data, the output signals might be directed to chemical actuators (e.g., signal-responsive membranes) leading to an on-demand 250 ELECTROCHEMICAL SYSTEMS CONTROLLED BY ENZYME-BASED LOGIC NETWORKS drug release We anticipate that such biochemical logic gates connected with bioelectronic sensing and actuating devices will find numerous biomedical applications They will facilitate decision-making in connection to an autonomous feedback-loop drug delivery system and will revolutionize the monitoring and treatment of patients ACKNOWLEDGMENT This research was supported by the National Science Foundation (grants DMR0706209 and CCF-1015983), by ONR (grant N00014-08-1-1202), and by the Semiconductor Research Corporation (award 2008-RJ-1839G) REFERENCES E Katz, V Privman, Chem Soc Rev 2010, 39(5), 1835–1857 G Strack, M Pita, M Ornatska, E Katz, ChemBioChem 2008, 9, 1260–1266 K.M Manesh, J Halámek, M Pita, J Zhou, T.K Tam, P Santhosh, M.-C Chuang, J.R Windmiller, D Abidin, E Katz, J Wang, Biosens Bioelectron 2009, 24, 3569–3574 M Pita, J Zhou, K.M Manesh, J Halámek, E Katz, J Wang, Sens Actuat B 2009, 139, 631–636 R Baron, O Lioubashevski, E Katz, T Niazov, I Willner, Org Biomol Chem 2006, 4, 989–991 R Baron, O Lioubashevski, E Katz, T Niazov, I Willner, J Phys Chem A 2006, 110, 8548–8553 R Baron, O Lioubashevski, E Katz, T Niazov, I Willner, Angew Chem Int Ed 2006, 45, 1572–1576 M Pita, E Katz, J Am Chem Soc 2008, 130, 36–37 A.P de Silva, S Uchiyama, T.P Vance, B Wannalerse, Coord Chem Rev 2007, 251, 1623–1632 10 A.P de Silva, S Uchiyama, Nat Nanotechnol 2007, 2, 399–410 11 K Szacilowski, Chem Rev 2008, 108, 3481–3548 12 I Tokarev, S Minko, Soft Matter 2009, 5, 511–524 13 S.K Ahn, R.M Kasi, S.C Kim, N Sharma, Y.X Zhou, Soft Matter 2008, 4, 1151–1157 14 K Glinel, C Dejugnat, M Prevot, B Scholer, M Schonhoff, R.V Klitzing, Colloids Surf A 2007, 303, 3–13 15 P.M Mendes, Chem Soc Rev 2008, 37, 2512–2529 16 M Pita, S Minko, E Katz, J Mater Sci Mater Med 2009, 20, 457–462 17 I Willner, A Doron, E Katz, J Phys Org Chem 1998, 11, 546–560 18 V.I Chegel, O.A Raitman, O Lioubashevski, Y Shirshov, E Katz, I Willner, Adv Mater 2002, 14, 1549–1553 19 E Katz, L Sheeney-Haj-Ichia, B Basnar, I Felner, I Willner, Langmuir 2004, 20, 9714–9719 REFERENCES 251 20 X Wang, Z Gershman, A.B Kharitonov, E Katz, I Willner, Langmuir 2003, 19, 5413–5420 21 I Luzinov, S Minko, V.V Tsukruk, Prog Polym Sci 2004, 29, 635–698 22 S Minko, Polymer Rev 2006, 46, 397–420 23 I Tokarev, V Gopishetty, J Zhou, M Pita, M Motornov, E Katz, S Minko, ACS Appl Mater Interfaces 2009, 1, 532–536 24 M Motornov, J Zhou, M Pita, V Gopishetty, I Tokarev, E Katz, S Minko, Nano Lett 2008, 8, 2993–2997 25 M Pita, M Krämer, J Zhou, A Poghossian, M.J Schöning, V.M Fernández, E Katz, ACS Nano 2008, 2, 2160–2166 26 M Motornov, J Zhou, M Pita, I Tokarev, V Gopishetty, E Katz, S Minko, Small 2009, 5, 817–820 27 J Zhou, T.K Tam, M Pita, M Ornatska, S Minko, E Katz, ACS Appl Mater Interfaces 2009, 1, 144–149 28 M Krämer, M Pita, J Zhou, M Ornatska, A Poghossian, M.J Schöning, E Katz, J Phys Chem B 2009, 113, 2573–2579 29 D.A LaVan, T McGuire, R Langer, Nature Biotechnol 2003, 21, 1184–1191 30 J Ghajar, Lancet 2000, 356, 923–929 31 M Privman, T.K Tam, M Pita, E Katz, J Am Chem Soc 2009, 131, 1314–1321 32 T.K Tam, M Ornatska, M Pita, S Minko, E Katz, J Phys Chem C 2008, 112, 8438–8445 33 E Katz, M Pita, Chem Eur J 2009, 15, 12554–12564 34 L Amir, T.K Tam, M Pita, M.M Meijler, L Alfonta, E Katz, J Am Chem Soc 2009, 131, 826–832 35 T.K Tam, M Pita, M Ornatska, E Katz, Bioelectrochemistry 2009, 76, 4–9 36 D Melnikov, G Strack, M Pita, V Privman, E Katz, J Phys Chem B 2009, 113, 10472–10479 37 V Privman, M.A Arugula, J Halámek, M Pita, E Katz, J Phys Chem B 2009, 113, 5301–5310 38 V Privman, G Strack, D Solenov, M Pita, E Katz, J Phys Chem B 2008, 112, 11777–11784 39 G Strack, S Chinnapareddy, D Volkov, J Halámek, M Pita, I Sokolov, E Katz, J Phys Chem B 2009, 113, 12154–12159 40 T.K Tam, G Strack, M Pita, E Katz, J Am Chem Soc 2009, 131, 11670–11671 41 M Pita, T.K Tam, S Minko, E Katz, ACS Appl Mater Interfaces 2009, 1, 1166–1168 INDEX Note: Page numbers in italics refer to Figures; those in bold to Tables amiloride sensitive sodium channel (ENaC), 74, 76–8 anchorage, 216 anchorage dependence, 215 antenna complexes, 123, 127, 129 antibody, 245 apical, 73, 74, 76, 77 “artificial photosynthesis,” 128, 129 astrocyte, 87 ATP blocked potassium channel (KATP), 66–72 basolateral, 73–8 big conductance calcium activated potassium channel (BKCa), 66–72 bilirubin oxidase (BOD), 192 bioactuator, 246 biocatalysis, 134, 140, 159 biocatalytic system, 242 biochemical network, 244 bioelectronic device, 249 biofuel cell, 169–207 biosensor, 246 black lipid membrane (BLM), 61, 62, 64, 65, 69 Boolean operation, 234 boundary potential, brain, 86–91, 94–5 Butler–Volmer equation, 112 C×26, 86 C×30, 87 C×36, 83, 85, 87–8, 91 C×37, 88 C×43, 86–7 C×45, 86 calcium activated calcium channel (CaCC), 74, 76, 77 calcium preconditioning, 70, 71 catalysis, 134, 136, 138–41, 143, 159 catalytic voltammetry, 110–113 cell adhesion, 214, 215, 218, 223 Electrochemical Processes in Biological Systems, First Edition Edited by Andrzej Lewenstam and Lo Gorton © 2015 John Wiley & Sons, Inc Published 2015 by John Wiley & Sons, Inc 253 254 central nervous system (CNS), 86–8, 94 cerebellum, 93 chemical actuator, 237 chronoamperometry, 113–15 comparison of DL, 27 connexin, 83, 85–9, 92–4 connexon, 83, 85, 86, 88, 93 copper, 169–74, 180, 181, 183, 184, 201 coupling coefficient (CC), 84–5, 88–9, 93 cyclic voltammetry, 106, 109, 113, 114 cystic fibrosis, 67, 68 cystic fibrosis transport regulator (CFTR), 73–8 cytotoxicity, 215–18 denaturation, 218–21 detection limit, 23, 24, 26, 29, 50, 54 definition, 24–6 diabetes, 67, 69 diffusion layer model (DLM), 33, 35, 36, 38, 40 diffusion potential, dioxygen, 135–7, 139–45, 150, 153, 155–60 Donnan potential, 6–7 electrical coupling, 83, 86, 91–4 electrical synapse, 87, 89–90 electrochemical cell, 107 electrochemical transduction, 238 electrochemistry, 136–7, 141, 145, 153, 158–60, 188–99, 207 electrode graphite, 108 immobilization, 197, 200, 201, 207 interface, 247 rotating, 108, 111–12 working, 108 electronic structure, 172, 174, 184 electron transfer (ET), 134, 170, 179, 198 enzyme, 105–17 inhibition, 114 kinetics, 110, 113 logic system, 231, 234 equilibrium potential mode, 12 excitation energy transfer, 122–4 ferryl, 150, 155 Fick’s equation, films, 140–160 INDEX gap junction (GJ), 83–95 glial cell, 86–8 Goldman approximation, Goldman–Hodgkin–Katz equation, gold nanoparticles (AuNP), 217, 218, 221, 223 Grätzel-type cell, 129 heart, 66, 69–72 heme, 133, 135–7, 139, 141, 145–8, 151, 155–7, 159, 160 Henderson approximation, hierarchical genetic strategy method (HGS), 51 hydroxylation, 139, 145 imprinting, 224, 225, 227 inferior olive (IO), 90–93 interfacial electron transfer, 109, 112 inverse problem, 51 ion exchanger electrodes, 36–8 ion flux, ion-selective electrode, 11 ischemia, 70–72 ischemic preconditioning, 67, 71 Joliot-Kok mechanism, 126 Koutecky–Levich equation, 112 laccase, 171, 173, 175, 205 LHCII, 122, 123, 129 lipid bilayer, 61, 62, 64, 65 local equilibrium potential model, 13 Marcus theory, 112 Michaelis–Menten description of enzyme kinetics, 110, 113 mitochondrium, 68–72 mixed-polymer system, 233 model comparison, 46, 49 modified electrode, 241 molecular mechanism, 169–207 monolayer, 216–18, 224, 225, 227 multicopper oxidases (MCOs), 169–207 multisignal-responsive material, 232 nano cavity, 11 Nernst equation, 255 INDEX Nernst–Planck equation, 2, 15 Nernst–Planck–Poisson (NPP) model, 15, 35, 43 neuron, 84–95 neutral carrier electrodes, 31, 32, 39, 40 Nicolsky–Eisenman equation, 13 nitrate reductase, 110–113 nitrite reductase, 113–15 nonturnover voltammetry, 108 NPP-HGS, 51 photosystem I (PSI), 126 photosystem II (PSII), 121–30 pH-switchable material, 238 potentiometry, 216, 217, 218 protein adsorption, 108 protein film electrochemistry, 105–17 proton motive force, 70 pump, 64, 65, 73, 74 oligodendrocyte, 87 O–O bond cleavage, 186 oscillation, 89, 90, 92, 93 overpotential, 169, 188, 189, 192, 199, 201, 202, 207 oxidation, 133–7, 139, 145, 148, 150–155, 160 oxygen evolving center, 126, 127 oxygen reduction, 201, 207 redox potential, 136, 143, 147, 150, 159 rotated-disk electrode (RDE), 142–4, 155, 158 P450, 133–60 partial differential equations, 58 patch-clamp, 62–5, 69 phase boundary model, 29, 33, 35, 43, 52 photosynthesis, 121–2 photosynthetic ATP synthesis, 127 photosynthetic electron transfer, 124–8 photosynthetic primary charge separation, 122, 128 photosynthetic proton transfer, 125 photosynthetic water splitting, 122, 127 quinol dehydrogenase, 116 shunting conductance, 85 signal-responsive membrane, 249 solid state electrodes, 27, 33, 35–7 spectroscopy, 179, 180, 183, 184, 202, 204, 206, 207 steady-state, 33, 35, 37, 40 steady-state catalysis, 110 stroke, 67, 70 surface imprinting, 215, 216, 226 synchronization, 90–95 syncytium, 87 Teorell–Mayer–Sievers model, thiolation, 221, 223 time dependent, 23, 33, 35, 40, 41, 52 transporter, 64–6, 73–6 wiley end user license agreement Go to www.wiley.com/go/eula to access Wiley’s ebook EULA ... one-dimensional Electrochemical Processes in Biological Systems, First Edition Edited by Andrzej Lewenstam and Lo Gorton © 2015 John Wiley & Sons, Inc Published 2015 by John Wiley & Sons, Inc MODELING OF... analysis Electrochemical Processes in Biological Systems, First Edition Edited by Andrzej Lewenstam and Lo Gorton © 2015 John Wiley & Sons, Inc Published 2015 by John Wiley & Sons, Inc 23 24... Electrochemical processes in biological systems / edited by Andrzej Lewenstam, Lo Gorton pages cm – (Wiley series on electrocatalysis and electrochemistry) Includes bibliographical references and index