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Enzyme-Based Reversible Logic Gates Operated in Flow Cells

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Advances in Unconventional Computing

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 23))

Abstract

Reversible logic gates, such as Feynman gate (Controlled NOT), Double Feynman gate, Toffoli gate and Peres gate, with 2-input/2-output and 3-input/3-output channels, were realized using reactions biocatalyzed by enzymes and performed in flow systems. The flow devices were constructed using a modular approach, where each flow cell was modified with one enzyme that biocatalyzed one chemical reaction. Assembling the biocatalytic flow cells in different networks, with different pathways for transporting the reacting species, allowed the multi-step processes mimicking various reversible logic gates. The chapter emphasizes “logic” reversibility but not the “physical” reversibility of the constructed systems. Their advantages and disadvantages are discussed and potential use in biosensing systems, rather than in computing devices, is suggested.

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References

  1. Calude, C.S., Costa, J.F., Dershowitz, N., Freire, E., Rozenberg, G. (eds.): Unconventional Computation. Lecture Notes in Computer Science, vol. 5715. Springer, Berlin (2009)

    Google Scholar 

  2. Szacilowski, K.: Infochemistry. Wiley, Chichester (2012)

    Book  Google Scholar 

  3. de Silva, A.P.: Molecular Logic-Based Computation. Royal Society of Chemistry, Cambridge (2013)

    Google Scholar 

  4. Katz, E. (ed.): Molecular and Supramolecular Information Processing – From Molecular Switches to Unconventional Computing. Willey-VCH, Weinheim (2012)

    Google Scholar 

  5. de Silva, A.P.: Molecular logic and computing. Nat. Nanotechnol. 2, 399–410 (2007)

    Article  Google Scholar 

  6. Claussen, J.C., Hildebrandt, N., Susumu, K., Ancona, M.G., Medintz, I.L.: Complex logic functions implemented with quantum dot bionanophotonic circuits. ACS Appl. Mater. Interfaces 6, 3771–3778 (2014)

    Article  Google Scholar 

  7. Pischel, U.: Advanced molecular logic with memory function. Angew. Chem. Int. Ed. 49, 1356–1358 (2010)

    Article  Google Scholar 

  8. Szacilowski, K.: Digital information processing in molecular systems. Chem. Rev. 108, 3481–3548 (2008)

    Article  Google Scholar 

  9. Pischel, U., Andreasson, J., Gust, D., Pais, V.F.: Information processing with molecules - Quo Vadis? ChemPhysChem 14, 28–46 (2013)

    Article  Google Scholar 

  10. Katz, E. (ed.): Biomolecular Computing – From Logic Systems to Smart Sensors and Actuators. Willey-VCH, Weinheim (2012)

    Google Scholar 

  11. Benenson, Y.: Biomolecular computing systems: principles, progress and potential. Nat. Rev. Genet. 13, 455–468 (2012)

    Article  Google Scholar 

  12. Alon, U.: An Introduction to Systems Biology. Design Principles of Biological Circuits. Chapman & Hall/CRC Press, Boca Raton (2007)

    MATH  Google Scholar 

  13. Adleman, L.M.: Molecular computation of solutions to combinatorial problems. Science 266, 1021–1024 (1994)

    Article  Google Scholar 

  14. Stojanovic, M.N., Stefanovic, D., Rudchenko, S.: Exercises in molecular computing. Acc. Chem. Res. 47, 1845–1852 (2014)

    Article  Google Scholar 

  15. Stojanovic, M.N., Stefanovic, D.: Chemistry at a higher level of abstraction. J. Comput. Theor. Nanosci. 8, 434–440 (2011)

    Article  Google Scholar 

  16. Ezziane, Z.: DNA computing: Applications and challenges. Nanotechnology 17, R27–R39 (2006)

    Article  Google Scholar 

  17. Ashkenasy, G., Dadon, Z., Alesebi, S., Wagner, N., Ashkenasy, N.: Building logic into peptide networks: Bottom-up and top-down. Isr. J. Chem. 51, 106–117 (2011)

    Article  Google Scholar 

  18. Unger, R., Moult, J.: Towards computing with proteins. Proteins 63, 53–64 (2006)

    Article  Google Scholar 

  19. Katz, E., Privman, V.: Enzyme-based logic systems for information processing. Chem. Soc. Rev. 39, 1835–1857 (2010)

    Article  Google Scholar 

  20. Rinaudo, K., Bleris, L., Maddamsetti, R., Subramanian, S., Weiss, R., Benenson, Y.: A universal RNAi-based logic evaluator that operates in mammalian cells. Nat. Biotechnol. 25, 795–801 (2007)

    Article  Google Scholar 

  21. Arugula, M.A., Shroff, N., Katz, E., He, Z.: Molecular AND logic gate based on bacterial anaerobic respiration. Chem. Commun. 48, 10174–10176 (2012)

    Article  Google Scholar 

  22. Kahan, M., Gil, B., Adar, R., Shapiro, E.: Towards molecular computers that operate in a biological environment. Phys. D 237, 1165–1172 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  23. Baron, R., Lioubashevski, O., Katz, E., Niazov, T., Willner, I.: Elementary arithmetic operations by enzymes: a model for metabolic pathway based computing. Angew. Chem. Int. Ed. 45, 1572–1576 (2006)

    Article  Google Scholar 

  24. Stojanovic, M.N., Stefanovic, D.: Deoxyribozyme-based half-adder. J. Am. Chem. Soc. 125, 6673–6676 (2003)

    Article  Google Scholar 

  25. Benenson, Y.: Biocomputing: DNA computes a square root. Nat. Nanotechnol. 6, 465–467 (2011)

    Article  Google Scholar 

  26. Pei, R.J., Matamoros, E., Liu, M.H., Stefanovic, D., Stojanovic, M.N.: Training a molecular automaton to play a game. Nat. Nanotechnol. 5, 773–777 (2010)

    Article  Google Scholar 

  27. Qian, L., Winfree, E., Bruck, J.: Neural network computation with DNA strand displacement cascades. Nature 475, 368–372 (2011)

    Article  Google Scholar 

  28. MacVittie, K., Halámek, J., Privman, V., Katz, E.: A bioinspired associative memory system based on enzymatic cascades. Chem. Commun. 49, 6962–6964 (2013)

    Article  Google Scholar 

  29. Privman, V., Katz, E.: Can bio-inspired information processing steps be realized as synthetic biochemical processes? Phys. Status Solidi A 212, 219–228 (2015)

    Article  Google Scholar 

  30. Katz, E.: Biocomputing - Tools, aims, perspectives. Curr. Opin. Biotechnol. 34, 202–208 (2015)

    Article  Google Scholar 

  31. de Silva, A.P.: Molecular computing - A layer of logic. Nature 454, 417–418 (2008)

    Article  Google Scholar 

  32. Benenson, Y.: Biocomputers: from test tubes to live cells. Mol. BioSyst. 5, 675–685 (2009)

    Article  Google Scholar 

  33. Pérez-Inestrosa, E., Montenegro, J.-M., Collado, D., Suau, R., Casado, J.: Molecules with multiple light-emissive electronic excited states as a strategy toward molecular reversible logic gates. J. Phys. Chem. C 111, 6904–6909 (2007)

    Article  Google Scholar 

  34. Cervera, J., Mafé, S.: Multivalued and reversible logic gates implemented with metallic nanoparticles and organic ligands. ChemPhysChem 11, 1654–1658 (2010)

    Article  Google Scholar 

  35. Remón, P., Ferreira, R., Montenegro, J.-M., Suau, R., Perez-Inestrosa, E., Pischel, U.: Reversible molecular logic: a photophysical example of a Feynman gate. ChemPhysChem 10, 2004–2007 (2009)

    Article  Google Scholar 

  36. Remón, P., Hammarson, M., Li, S., Kahnt, A., Pischel, U., Andréasson, J.: Molecular implementation of sequential and reversible logic through photochromic energy transfer switching. Chem. Eur. J. 17, 6492–6500 (2011)

    Article  Google Scholar 

  37. Sun, W., Xu, C.H., Zhu, Z., Fang, C.J., Yan, C.H.: Chemical-driven reconfigurable arithmetic functionalities within a fluorescent tetrathiafulvalene derivative. J. Phys. Chem. C 112, 16973–16983 (2008)

    Article  Google Scholar 

  38. Fratto, B.E., Roby, L.J., Guz, N., Katz, E.: Enzyme-based logic gates switchable between OR, NXOR and NAND Boolean operations realized in a flow system. Chem. Commun. 50, 12043–12046 (2014)

    Article  Google Scholar 

  39. Sun, W., Zheng, Y.-R., Xu, C.-H., Fang, C.-J., Yan, C.-H.: Fluorescence-based reconfigurable and resettable molecular arithmetic mode. J. Phys. Chem. C 111, 11706–11711 (2007)

    Article  Google Scholar 

  40. Liu, D.B., Chen, W.W., Sun, K., Deng, K., Zhang, W., Wang, Z., Jiang, X.Y.: Resettable, multi-readout logic gates based on controllably reversible aggregation of gold nanoparticles. Angew. Chem. Int. Ed. 50, 4103–4107 (2011)

    Article  Google Scholar 

  41. O’Steen, M.R., Cornett, E.M., Kolpashchikov, D.M.: Nuclease-containing media for resettable operation of DNA logic gates. Chem. Commun. 51, 1429–1431 (2015)

    Article  Google Scholar 

  42. Semeraro, M., Credi, A.: Multistable self-assembling system with three distinct luminescence outputs: prototype of a bidirectional half-subtractor and reversible logic device. J. Phys. Chem. C 114, 3209–3214 (2010)

    Article  Google Scholar 

  43. Andréasson, J., Pischel, U., Straight, S.D., Moore, T.A., Moore, A.L., Gust, D.: All-photonic multifunctional molecular logic device. J. Am. Chem. Soc. 133, 11641–11648 (2011)

    Article  Google Scholar 

  44. Orbach, R., Remacle, F., Levine, R.D., Willner, I.: Logic reversibility and thermodynamic irreversibility demonstrated by DNAzyme-based Toffoli and Fredkin logic gates. Proc. Natl. Acad. USA 109, 21228–21233 (2012)

    Article  Google Scholar 

  45. Roy, S., Prasad, M.: Novel proposal for all-optical Fredkin logic gate with bacteriorhodopsin-coated microcavity and its applications. Opt. Eng. 49, Article ID 065201 (2010)

    Google Scholar 

  46. Klein, J.P., Leete, T.H., Rubin, H.: A biomolecular implementation of logically reversible computation with minimal energy dissipation. Biosystems 52, 15–23 (1999)

    Article  Google Scholar 

  47. Moseley, F., Halámek, J., Kramer, F., Poghossian, A., Schöning, M.J., Katz, E.: An enzyme-based reversible CNOT logic gate realized in a flow system. Analyst 139, 1839–1842 (2014)

    Article  Google Scholar 

  48. Katz, E., Wang, J., Privman, M., Halámek, J.: Multi-analyte digital enzyme biosensors with built-in Boolean logic. Anal. Chem. 84, 5463–5469 (2012)

    Article  Google Scholar 

  49. Wang, J., Katz, E.: Digital biosensors with built-in logic for biomedical applications. Isr. J. Chem. 51, 141–150 (2011)

    Article  Google Scholar 

  50. Landauer, R.: Irreversibility and heat generation in the computing process. IBM J. Res. Develop. 5, 261–269 (1961)

    Article  MathSciNet  MATH  Google Scholar 

  51. Toffoli, T.: Physics and computation. Int. J. Theor. Phys. 21, 165–175 (1982)

    Article  MathSciNet  Google Scholar 

  52. Fredkin, E., Toffoli, T.: Conservative logic. Int. J. Theor. Phys. 21, 219–253 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  53. Takeuchi, N., Yamanashi, Y., Yoshikawa, N.: Reversible logic gate using adiabatic superconducting devices. Sci. Rep. 4, Article ID 6354 (2014)

    Google Scholar 

  54. Bennett, C.H.: Logical reversibility of computation. IBM J. Res. Develop. 17, 525–532 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  55. Zhou, J., Arugula, M.A., Halámek, J., Pita, M., Katz, E.: Enzyme-based NAND and NOR logic gates with modular design. J. Phys. Chem. B 113, 16065–16070 (2009)

    Article  Google Scholar 

  56. Privman, V., Arugula, M.A., Halámek, J., Pita, M., Katz, E.: Network analysis of biochemical logic for noise reduction and stability: a system of three coupled enzymatic AND gates. J. Phys. Chem. B 113, 5301–5310 (2009)

    Article  Google Scholar 

  57. Halámková, L., Halámek, J., Bocharova, V., Wolf, S., Mulier, K.E., Beilman, G., Wang, J., Katz, E.: Analysis of biomarkers characteristic of porcine liver injury - From biomolecular logic gates to animal model. Analyst 137, 1768–1770 (2012)

    Article  Google Scholar 

  58. Halámek, J., Windmiller, J.R., Zhou, J., Chuang, M.-C., Santhosh, P., Strack, G., Arugula, M.A., Chinnapareddy, S., Bocharova, V., Wang, J., Katz, E.: Multiplexing of injury codes for the parallel operation of enzyme logic gates. Analyst 135, 2249–2259 (2010)

    Article  Google Scholar 

  59. Toepke, M.W., Abhyankar, V.V., Beebe, D.J.: Microfluidic logic gates and timers. Lab Chip 7, 1449–1453 (2007)

    Article  Google Scholar 

  60. Scida, K., Li, B.L., Ellington, A.D., Crooks, R.M.: DNA detection using origami paper analytical devices. Anal. Chem. 85, 9713–9720 (2013)

    Article  Google Scholar 

  61. Garipelly, R., Madhu Kiran, P., Santhosh Kumar, A.: A Review on reversible logic gates and their implementation. Int. J. Emerging Technol. Adv. Eng. 3, 417–423 (2013)

    Google Scholar 

  62. O’Brien, J.L., Pryde, G.J., White, A.G., Ralph, T.C., Branning, D.: Demonstration of an all-optical quantum controlled-NOT gate. Nature 426, 264–267 (2003)

    Article  Google Scholar 

  63. Monroe, C., Meekhof, D.M., King, B.E., Itano, W.M., Wineland, D.J.: Demonstration of a fundamental quantum logic gate. Phys. Rev. Lett. 75, 4714–4717 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  64. Siomau, M., Fritzsche, S.: Universal quantum Controlled-NOT gate. Eur. Phys. J. D 60, 417–421 (2010)

    Article  Google Scholar 

  65. Privman, V., Zhou, J., Halámek, J., Katz, E.: Realization and properties of biochemical-computing biocatalytic XOR gate based on signal change. J. Phys. Chem. B 114, 13601–13608 (2000)

    Article  Google Scholar 

  66. Fratto, B.E., Katz, E.: Reversible logic gates based on enzyme-biocatalyzed reactions and realized in flow cells – Modular approach. ChemPhysChem 1, 1 (2015, in press)

    Google Scholar 

  67. Privman, V., Zavalov, O., Halámková, L., Moseley, F., Halámek, J., Katz, E.: Networked enzymatic logic gates with filtering: new theoretical modeling expressions and their experimental application. J. Phys. Chem. B 117, 14928–14939 (2013)

    Article  Google Scholar 

  68. Halámek, J., Bocharova, V., Chinnapareddy, S., Windmiller, J.R., Strack, G., Chuang, M.-C., Zhou, J., Santhosh, P., Ramirez, G.V., Arugula, M.A., Wang, J., Katz, E.: Multi-enzyme logic network architectures for assessing injuries: digital processing of biomarkers. Molec. Biosyst. 6, 2554–2560 (2010)

    Article  Google Scholar 

  69. Guz, N., Halámek, J., Rusling, J.F., Katz, E.: A biocatalytic cascade with several output signals - Towards biosensors with different levels of confidence. Anal. Bioanal. Chem. 406, 3365–3370 (2014)

    Article  Google Scholar 

  70. Mailloux, S., Guz, N., Zakharchenko, A., Minko, S., Katz, E.: Majority and minority gates realized in enzyme-biocatalyzed systems integrated with logic networks and interfaced with bioelectronic systems. J. Phys. Chem. B 118, 6775–6784 (2014)

    Article  Google Scholar 

  71. Mailloux, S., Halámek, J., Katz, E.: A model system for targeted drug release triggered by biomolecular signals logically processed through enzyme logic networks. Analyst 139, 982–986 (2014)

    Article  Google Scholar 

  72. De Vos, A.: Reversible Computing: Fundamentals, Quantum Computing, and Applications. Willey-VCH, Weinheim (2010)

    Book  MATH  Google Scholar 

  73. Zhao, Y., Chakrabarty, K.: Digital microfluidic logic gates and their application to built-in self-test of lab-on-chip. IEEE Trans. Biomed. Circuits Syst. 4, 250–262 (2010)

    Article  Google Scholar 

  74. Konkoli, Z.: Phys. Rev. E 72, Article ID 011917 (2005)

    Google Scholar 

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Acknowledgments

This work was supported by National Science Foundation (award CBET-1403208).

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Appendix

Appendix

This section is addressed to the readers interested in technical details of the experimental realization of the reversible logic gates described above.

2.1.1 Chemicals and Materials

Enzymes and their substrates used in the biocatalytic reactions were specified in Sect. 2.2. Results and Discussion (second paragraph).

Other chemicals used for the immobilization procedure and being components of reacting solutions: pepsin (E.C. 232.629.3) from porcine gastric mucosa, glutaric dialdehyde, poly(ethyleneimine) solution (PEI) (average M\(_{\text {w}}\) ca. 750,000), 2-amino-2-hydroxymethyl-propane-1,3-diol (Tris-buffer), \(2,2'\)-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), K\(_{3}\)[Fe(CN)\(_{6}\)] and K\(_{4}\)[Fe(CN)\(_{6}\)]. The chemicals listed above and other standard inorganic/organic reactants were purchased from Sigma-Aldrich and used as supplied. Ultrapure water (18.2 M\(\Omega \cdot \) cm) from NANOpure Diamond (Barnstead) source was used in all of the experiments.

2.1.2 Instruments and Devices

Flow cells (\(\upmu \)-Slide III 3in1 Flow Kit; ibidi GmbH) were used for the biocatalytic reactions. A Shimadzu UV-2450 UV-Vis spectrophotometer with flow-through quartz cuvettes (1 cm optical pathway) connected to the tubing of the flow device was used for all optical measurements. The reacting solutions were pumped through the flow cells and spectrophotometer cuvettes with the help of a peristaltic pump (Gilson Minipuls 3) connected with polyethylene tubing, 1 mm internal diameter.

2.1.3 Immobilization of Enzymes in the Flow Cells

Before any experimental data were realized, the flow cells were flushed with concentrated sulfuric acid to remove residual physical adsorption of PEI left on the cell surface from previous experiments. After this initial preparatory step, all subsequent cleanings were conducted with the following method. The flow cells were washed with a minimum of 10 mL of deionized water and then reacted with pepsin solution, 0.5 mg/mL, in 0.1 M phosphate buffer, pH 2.0, for 1 h. Then, the cells were washed with a minimum of 10 mL of deionized water. These cleaning steps aimed at removing remnant enzymes from previous experiments and prepared the cell surface for adsorption of PEI. Then, the flow cells were treated with a PEI solution (2 % v/v) for 1 h and then, thoroughly washed with 5 mL of deionized water, resulting in physical adsorption of PEI on polystyrene and providing the amino groups needed for the enzyme immobilization. Then, the amino-functionalized surface was reacted with glutaric dialdehyde (5 % v/v) for 1 h; after that, the surface was washed with 5 mL of deionized water to remove non-reacted glutaric dialdehyde. The enzyme solutions were reacted with the flow cells and the wells that were activated with glutaric dialdehyde for 1 h and then, the cells were thoroughly washed with Tris-buffer (0.1 M, pH 7.1) to remove non-reacted enzymes from the cells. The following enzyme concentrations were used for preparing the logic gates: (i) Feynman gate: AP ca. 500 units/mL; G6PDH ca. 280 units/mL; LDH ca. 600 units/mL, (ii) Double Feynman gate: GDH ca. 46 units/mL; G6PDH ca. 14 units/mL; LDH ca. 30 units/mL, (iii) Toffoli gate: GDH ca. 140 units/mL; G6PDH ca. 280 units/mL; LDH ca. 340 units/mL; GOx ca. 140 units/mL; HRP ca. 120 units/mL, (iv) Peres gate: GDH ca. 190 units/mL; GOx ca. 665 units/mL; LDH ca. 343 units/mL; HRP ca. 400 units/mL; Diaph ca. 66 units/mL. This procedure resulted in the enzymes covalently bound to the adsorbed PEI through Schiff-base bonds. The flow cell devices with the immobilized enzymes demonstrated reproducible performance for at least two days allowing pumping of the input solutions over long period of time, thus proving stable immobilization of the enzymes and preserving their biocatalytic activity.

2.1.4 Optimization of the Input Concentrations

The input concentrations were experimentally optimized for the specific enzyme activity in the flow cells. The optimization was aimed at the output signals with the comparable intensity upon application of different combinations of the input signals. Balancing output signals for XOR gates, when optimizing the reversible gates, was particularly important.

The following optimized input concentrations were considered as logic 1 values:

Feynman gate: Input A (PNPP + Pyr) 10 mM \(+\) 1 mM, respectively, Input B (G6P) 6 mM.

Double Feynman gate: Input A (Pyr) 1.46 mM, Input B (G6P) 2.22 mM, Input C (Glc) 0.6 mM.

Toffoli gate: Input A (Glc) 0.6 mM, Input B (NAD\(^{+})\) 2.75 mM, Input C (Pyr) 1.1 mM.

Peres gate: Input A (NADH) 0.02 mM, Input B (Glc) 10 mM, Input C (H\(_{2}\)O\(_{2})\) 0.7 mM.

Logic 0 value for all input signals was defined as the absence of the corresponding chemicals (meaning their zero physical concentration in the background solutions).

2.1.5 Flow Cell Performance and the Output Signal Measurements

The enzyme-modified flow cells were activated with solutions containing the input signals applied in all possible logic combinations (4 variants for Feynman gate and 8 variants for all other gates). The solutions also included non-variable reacting components which had the same initial concentrations for all combinations of the inputs signals. The solution compositions used for different gate are listed below:

Feynman gate

The input signals (represented with PNPP + Pyr and G6P solutions also containing non-variable NADH (0.4 mM) and NAD\(^{+}\) (10 mM) cofactors were pumped through the flow system with the volumetric rate of 50 \(\upmu \)L/min. Optical absorbance measurements were performed for the Identity gate channel (Output P) at \(\lambda = 420\) nm characteristic of p-nitrophenol (PNP) and for the XOR gate channel (Output Q) at \(\lambda = 340\) nm characteristic of NADH. The reference channel (cuvette) of the spectrophotometer was filled with the background (“machinery”) solution containing NADH (0.4 mM) and NAD\(^{+}\) (10 mM), thus allowing the absorbance change measurements versus the composition of the background solution.

Double Feynman gate

The input signals (represented with Pyr, G6P and Glc solutions also containing non-variable NADH, 0.4 mM, and NAD\(^{+}\), 5.0 mM) were pumped through the flow system with the volumetric rate of 50 \(\upmu \)L/min. Optical absorbance measurements were performed for the Identity and two XOR gate channels at \(\lambda = 340\) nm characteristic of NADH. The reference channel (cuvette) of the spectrophotometer was filled with the background (“machinery”) solution containing NADH (0.4 mM) and NAD\(^{+}\) (5.0 mM), thus allowing the absorbance change measurements versus the composition of the background solution.

Toffoli gate

The input signals (represented with Pyr, Glc and NAD\(^{+}\) solutions also containing non-variable NADH, 0.375 mM, G6P, 0.4 mM, ABTS, 4.0 mM) were pumped through the flow system with the volumetric rate of 50 \(\upmu \)L/min. Optical absorbance measurements were performed for the GOx/HRP Identity gate at \(\lambda = 415\) nm characteristic of the oxidized form of ABTS (ABTS\(_{\text {ox}})\), while the G6PDH Identity gate and the AND/XOR gate channels were measured at \(\lambda = 340\) nm characteristic of NADH. The reference channel (cuvette) of the spectrophotometer was filled with the background (“machinery”) solution containing NADH (0.375 mM), G6P (0.4 mM) and ABTS (4.0 mM), thus allowing the absorbance change measurements versus the composition of the background solution.

Peres gate

The input signals (represented with NADH, Glc and H\(_{2}\)O\(_{2 }\)solutions also containing non-variable K\(_{4}\)[Fe(CN)\(_{6}\)] 2.0 mM, K\(_{3}\)[Fe(CN)\(_{6}\)] 1.0 mM and Pyr 0.065 mM) were pumped through the flow system with the volumetric rate of 25 \(\upmu \)L/min. Optical absorbance measurements were performed at \(\lambda = 420\) nm characteristic of K\(_{3}\)[Fe(CN)\(_{6}\)]. The reference channel (cuvette) of the spectrophotometer was filled with the background (“machinery”) solution containing K\(_{4}\)[Fe(CN)\(_{6}\)] 2.0 mM, K\(_{3}\)[Fe(CN)\(_{6}\)] 1.0 mM and Pyr 0.065 mM, thus allowing the absorbance change measurements versus the composition of the background solution.

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Katz, E., Fratto, B.E. (2017). Enzyme-Based Reversible Logic Gates Operated in Flow Cells. In: Adamatzky, A. (eds) Advances in Unconventional Computing. Emergence, Complexity and Computation, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-33921-4_2

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