BioSystems 122 (2014) 1–6
Contents lists available at ScienceDirect
BioSystems journal homepage: www.elsevier.com/locate/biosystems
Towards plant wires Andrew Adamatzky ∗ Unconventional Computing Centre, University of the West of England, Bristol BS16 1QY, United Kingdom
a r t i c l e
i n f o
Article history: Received 10 May 2014 Received in revised form 9 June 2014 Accepted 10 June 2014 Keywords: Bio-wires Plants Bio-electronics Potential transfer function
a b s t r a c t In experimental laboratory studies we evaluate a possibility of making electrical wires from living plants. In scoping experiments we use lettuce seedlings as a prototype model of a plant wire. We approximate an electrical potential transfer function by applying direct current voltage to the lettuce seedlings and recording output voltage. We analyse oscillation frequencies of the output potential and assess noise immunity of the plant wires. Our findings will be used in future designs of self-growing wetware circuits and devices, and integration of plant-based electronic components into future and emergent bio-hybrid systems. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Since its inception in 1980s the field of unconventional computation (Calude et al., 1998) became dominated by theoretical research, including quantum computation, membrane computing and dynamical-systems computing. Just a few experimental laboratory prototypes are designed so far (Adamatzky and Teuscher, 2006), (Teuscher and Adamatzky, 2005), e.g. chemical reaction–diffusion processors (Adamatzky et al., 2005), extended analogue computers (Mills, 2008), micro-fluidic circuits (Fuerstman et al., 2003), gas-discharge systems (Reyes et al., 2002), chemo-tactic droplets (Lagzi et al., 2010), enzyme-based logical circuits (Katz and Privman, 2010; Privman et al., 2009), crystallisation computers (Adamatzky, 2009), geometrically constrained chemical computers (Sielewiesiuk and Gorecki, 2001; Motoike and Yoshikawa, 2003; Gorecki et al., 2009; Yoshikawa et al., 2009; Górecki and Górecka, 2006), molecular logical gates and circuits (Stojanovic et al., 2002; Macdonald et al., 2006). In contrast, there are hundreds if not thousands of papers published on quantum computation, membrane computing and artificial immune systems. Such a weak representation of laboratory experiments in the field of unconventional computation may be due to technical difficulties and costs of prototyping. In last few years we have developed a concept, architectures and experimental laboratory prototypes of living and hybrid computers made of slime mould Physarum polycephalum (Adamatzky, 2010).
∗ Tel.: +44 01173282662. E-mail address: [email protected] http://dx.doi.org/10.1016/j.biosystems.2014.06.006 0303-2647/© 2014 Elsevier Ireland Ltd. All rights reserved.
In course of our studies of the slime mould’s computational properties and designs of Physarum chips we found that the slime mould based sensors and processor are very fragile, highly dependent on environmental conditions and somewhat difficult to control and constrain. Thus we have started to look for living substrates that could complement, or even become an alternative to, computing devices made of Physarum’s protoplasmic tubes. Plant roots got our attention because they are mobile, growing, adaptive intelligent units (Brenner et al., 2006; Baluˇska et al., 2009, 2010; Ciszak et al., 2012; Burbach et al., 2012), similar in their behaviour to active growing zones of P. polycephalum. Plants are, in general, more robust and resilient, less dependent on environmental conditions and can survive in a hostile environment of bio-hybrid electronic devices longer than slime moulds do. A prospective computing device made from living plant would perform computation by means of electrical charge propagation, by travelling waves of mechanical deformation of immobile structures, and by physical propagation of structures. Conductive pathways would be a key component of any plant-based electrical circuit. Thus to achieve a realistic assessment of the potential of the living plants based hybrid processing technology we must evaluate the feasibility of living plant wires in a proof-of-concept device. Previous findings on of organic wires and using living substrates to grow conductive pathways: self-assembling molecular wires (Wang et al., 2006; Paul and Lapinte, 1998), DNA wires (Beratan et al., 1997), electron transfer pathways in biological systems (Willner and Katz, 2006), live bacteria templates for conductive pathways (Berry and Saraf, 2005), bio-wires with cardiac tissues (Cingolani et al., 2012), golden wires with templates of fungi (Sabah et al., 2012). We are not aware of any published results related to evaluation of living plants, or
2
A. Adamatzky / BioSystems 122 (2014) 1–6
Fig. 1. Experimental setup. (a) A photo of lettuce seedling resting on aluminium electrodes in drops of distilled water. (b) A scheme of the experimental measurement of potential transfer function.
their parts, as electrical wires. There are substantial experimental findings on electrical impedance of plants however mostly related to evaluation of the plants physiological state via their impedance characteristics, see e.g. (Zhang and Willison, 1993; Inaba et al., 1995; Mancuso, 1999, 2000). The paper is structured as follows. We describe experimental techniques in Section 2. Section 3 presents results on resistance of lettuce seedlings, approximation of direct current potential transfer
function and immunity of plant wires to noise. Outcomes of the research and future directions of study are discussed in Section 4. 2. Methods We experimented with lettuce (Lactuca sativa) seedlings 3–4 days after germination. Electrodes were made of a conductive aluminium foil, 0.07 mm thick, 8 mm wide, 50 mm (inclusive part
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Fig. 2. Lettuce resistance. (a) Exemplar dynamics of resistance. (b) Power frequency spectrum.
A. Adamatzky / BioSystems 122 (2014) 1–6
Vin , V 2 3 4 5 6 7 8 9 10 11 12
Vout , V 0.82 1.46 2.35 3.49 4.41 5.06 6.5 6.67 8.46 8.81 10.00
std(Vout ) 0.011 0.019 0.037 0.025 0.096 0.092 0.044 0.016 0.115 0.096 0.120
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˜ ,V Vout 0.82 1.45 2.34 3.75 4.44 5.08 6.50 6.68 8.46 8.84 10.02
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(b) Fig. 3. Potential transfer function implemented by lettuce seedling. (a) A table of experimental measurements: for each value of input potential Vin we show average output ˜ . (b) Experimental plot of a transfer function. potential Vout , its standard deviation std (Vout ) and median output potential Vout
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Time, sec (b) Fig. 4. Dynamics of output potential for input potential 5 V (a) and 12 V (b).
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protruding outside Petri dish) long. Distance between proximal sites of electrodes is always 10 mm. In each experiment an undamaged lettuce seedling was placed onto electrodes in drops of distilled water (Fig. 1a). In each experiment a resistance and potential (not at the same time indeed) were measured during 10 min with four wires using Fluke 8846A precision voltmeter, test current 1 A with 0.0013 A error. Direct current potential was applied using Gw Instek GPS1850D laboratory DC power supply. A scheme of potential transfer function measurement is shown in Fig. 1b. Each session of measurements lasted exactly 10 min. For each measurement a new seedling was used. We measured resistance of 20 seedlings, and potential transfer function of 5 seedlings for each value of input, applied, potential: 2 V, 3 V, . . ., 12 V.
A lettuce seedling acts as a potential divider. Potential Vout recorded on a lettuce wire is a fraction of a potential Vin applied to the lettuce wire. Results of 60 experiments, 5 experiments for each value of input potential, are summarised in Fig. 3a and illustrated in Fig. 3b. The transfer function is linear (Fig. 3b) Vout = 0.928 · Vin − 1.221, subject to oscillations of output potential (Fig. 4) caused by fluctuations of impedance (Fig. 2a). Resistance of lettuce seedlings changes during recording. This causes oscillations of output potential (Fig. 4). Values of two dominating frequencies, calculated by FFT, are shown in Fig. 5a. For most values of input potential dominating frequencies of output potential oscillations stay in a range between 0.05 Hz and 0.3 Hz (Fig. 5b). There is no correlation between frequency of output potential oscillations and values of input potential applies. Output potential oscillates in lower frequencies than resistance. For values of input potential studied frequencies of output potential oscillations lie in a range between 0.0062 Hz and 0.038 Hz (with average 0.0221 Hz and median 0.0197 Hz over all values of input potential). We studied lettuce seedling as a prototypes of plant wires. The wires are noisy because the output potential oscillates (Fig. 4). Level of noise can be evaluated via maximum amplitudes of oscillations. The maximum amplitudes of output potential oscillations are characterised in Fig. 6a. The maximum amplitudes stay in a range 0.15–0.45 V and just slightly increase (rough linear approximation 0.208 +0.0126·Vout ) with increase of a potential. The maximum
3. Results Average resistance of lettuce seedling is 2.76 M with standard deviation 0.18 M, and median 2.72 M. The seedlings show oscillations of resistance (Fig. 2a). The oscillations are more likely is a combinations of dynamical changes in resistance in root, stem and leaves. Thus the oscillations recorded have no clearly visible periodicity. Three most dominating frequencies observed are 0.043 Hz, 0.084 Hz, and 0.009 Hz (Fig. 2b). Vin , V 2
f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2
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average, Hz 0.0117 0.0310 0.0320 0.0295 0.0088 0.0208 0.0176 0.0149 0.0190 0.0163 0.0078 0.0247 0.0139 0.0180 0.0205 0.0260 0.0120 0.0251 0.0062 0.0241 0.0299 0.0385
st. deviation 0.00626 0.03535 0.03167 0.01056 0.00328 0.00700 0.02172 0.01236 0.01147 0.00578 0.00082 0.01680 0.00922 0.00697 0.01690 0.02271 0.00374 0.01915 0.00346 0.01290 0.03295 0.01487
average, sec 85.65 32.24 31.21 33.9 113.96 48.13 56.92 67.1 52.49 61.26 128.21 40.53 72.05 55.68 48.78 38.49 83.51 39.76 161.29 41.49 33.47 25.99
(a) Dominating frequency, Hz
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(b) Fig. 5. Two dominating frequencies of output potential oscillations for input potential applied 2 V, . . ., 12 V. (a) Table of first f1 and second f2 dominating frequencies, their values in Herz and seconds. (c) Average dominating frequencies for Vin = 2 V, . . ., 12 V: f1 is shown by circles, f2 is shown by discs.
Maximum amlitude of oscillations, V
A. Adamatzky / BioSystems 122 (2014) 1–6
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(b) Fig. 6. Maximum amplitudes of output potential oscillations. (a) Maximum amplitude of output potential oscillations, averaged over trials for each value of input potential, versus average output potential. Data points derived from laboratory experiments are shown by circle. Dotted line acts as an eye guide. Solid line is a linear fit 0.208 +0.0126·Vout . (b) Maximum amplitude as a percentage of average output potential. Experimental data are shown by circles. Dotted line guides eye.
amplitude makes 25% of output potential for input potential 2 V, then decreases to 19.6% (3 V), 9.5% (4 V), stays between 5% and 7% for 5–7 V applied potential, and then decreases to 4%–4.5% for input potential 8–12 V (Fig. 6). 4. Discussions In laboratory experiments with lettuce seedlings we found that they implement a linear transfer function of input potential to output potential. Roughly an output potential is 1.5–2 V less than an input potential, thus e.g. by applying 12 V potential we get 10 V output potential. Resistance of 3–4 day lettuce seedling is about 2.76 M. This is much higher than resistance of conventional conductors yet relatively low comparing to resistance of other living creatures (Geddes and Baker, 1967), e.g. a resistance of 10 mm long protoplasmic tube of P. polycephalum is near 3 M (Adamatzky, 2014). The lettuce resistance oscillates. The dominating frequencies of lettuce seedlings resistance oscillation roughly corresponds to periods of oscillation 23 s, 12 s and 111 s. These are compatible with periods of resistance oscillations recorded in our experiments with protoplasmic tubes of acellular slime mould P. polycephalum (Adamatzky, 2014): 73 s is an average period of oscillation. Resistance oscillation in P. polycephalum are caused by peristaltic oscillations (Sun et al., 2009) and cytoplasmic flow in the tubes (Adamatzky, 2014). The peristaltic and flow are governed by calcium waves travelling along the tubes where a calcium ion flux through membrane triggers oscillators responsible for dynamic
of contractile activity (Meyer and Stockem, 1979; Fingerle and Gradmann, 1982). Thus we can speculate that there are types of cytoplasmic flow in lettuce seedlings which might be reflected in changes of volume of leaves, roots or stem that are reflected in resistance changes. Lettuce seedling is somewhat a noisy wire. Its output potential oscillates. However, maximum amplitude of oscillations never exceeds 4% of an input potential for potential applied over 8 V. For example, a line held near 10 V (output voltage) has 9 V of noise immunity. To incorporate plant wires into bio-hybrid self-growing circuits we must develop techniques for reliable routing of the plant roots between living and silicon components of the circuits. A first step would be to find a way of navigating plant roots in labyrinths. We have undertook few scoping experiments, see two illustrations in Fig. 7, yet no reached no conclusive results. When seeds are placed in or near a central chamber of a labyrinth their routes somewhat grow towards exit of the labyrinth. However, they often become stuck midway and do not actually reach the exit. The inconclusive results are possibly due to the fact that we used gravity as the only guiding force to navigate the routes. In their recent paper Yokawa et al. (2014) demonstrated that by employing chemotaxis and using volatiles it is possible to navigate the roots in simple binary mazes. Their results gives us a hope it could be possible to route plant wires in the same efficient manner as slime mould based wires are routed (Adamatzky, 2012, 2013). Another line of future research would be to investigate suitability of live plants to be incorporated as sensors in bio-hybrid electronic circuits. Thermistors could be
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Fig. 7. Scoping experiments on routing plant roots in labyrinths.
the first port of call because there is an experimental evidence that plants resistance is changed during cooling (Mancuso, 2000). References Adamatzky, A., 2009. Hot ice computer. Phys. Lett. A 374 (2), 264–271. Adamatzky, A., 2010. Physarum Machines: Computers from Slime Mould, vol.74. World Scientific, London Singapour. Adamatzky, A., 2012. Slime mold solves maze in one pass, assisted by gradient of chemo-attractants. IEEE Trans. NanoBiosci. 11 (2), 131–134. Adamatzky, A., 2013. Physarum wires: self-growing self-repairing smart wires made from slime mould. Biomed. Eng. Lett. 3 (4), 232–241. Adamatzky, A., 2014. Slime mould electronic oscillators. Microelectron. Eng. 124, 58–65. Adamatzky, A., Costello, B.D.L., Asai, T., 2005. Reaction–diffusion Computers. Elsevier, Amsterdam. Adamatzky, A., Teuscher, C., 2006. From Utopian to Genuine Unconventional Computers. Luniver Press, Bristol London. Baluˇska, F., Lev-Yadun, S., Mancuso, S., 2010. Swarm intelligence in plant roots. Trends Ecol. Evol. 25 (12), 682–683. Baluˇska, F., Mancuso, S., Volkmann, D., Barlow, P.W., 2009. The root-brainhypothesis of charles and francis darwin. Plant Signal. Behav. 4 (12), 1121–1127. Beratan, D.N., Priyadarshy, S., Risser, S.M., 1997. DNA: insulator or wire? Chem. Biol. 4 (1), 3–8. Berry, V., Saraf, R.F., 2005. Self-assembly of nanoparticles on live bacterium: an avenue to fabricate electronic devices. Angew. Chem. 117 (41), 6826–6831. Brenner, E.D., Stahlberg, R., Mancuso, S., Vivanco, J., Baluˇska, F., Van Volkenburgh, E., 2006. Plant neurobiology: an integrated view of plant signaling. Trends Plant Sci. 11 (8), 413–419. Burbach, C., Markus, K., Zhang, Y., Schlicht, M., Baluˇska, F., et al., 2012. Photophobic behavior of maize roots. Plant Signal. Behav. 7 (7), 874–878. Calude, C.S., Casti, J.L., Dinneen, M.J., 1998. Unconventional Models of Computation. Springer-Verlag Singapore Pte. Limited, Singapore. Cingolani, E., Ionta, V., Giacomello, A., Marbán, E., Cheol Cho, H., 2012. Creation of a biological wire using cell-targeted paramagnetic beads. Biophys. J. 102 (3), 416a. Ciszak, M., Comparini, D., Mazzolai, B., Baluska, F., Arecchi, F.T., Vicsek, T., Mancuso, S., 2012. Swarming behavior in plant roots. PLoS ONE 7 (1), e29759. Fingerle, J., Gradmann, D., 1982. Electrical properties of the plasma membrane of microplasmodia of Physarum polycephalum. J. Membr. Biol. 68 (1), 67–77. Fuerstman, M.J., Deschatelets, P., Kane, R., Schwartz, A., Kenis, P.J., Deutch, J.M., Whitesides, G.M., 2003. Solving mazes using microfluidic networks. Langmuir 19 (11), 4714–4722. Geddes, L., Baker, L., 1967. The specific resistance of biological materiala compendium of data for the biomedical engineer and physiologist. Med. Biol. Eng. 5 (3), 271–293. Górecki, J., Górecka, J., 2006. Information processing with chemical excitations-from instant machines to an artificial chemical brain. Int. J. Unconv. Comput. 2 (4), 321–336. Gorecki, J., Gorecka, J.N., Igarashi, Y., 2009. Information processing with structured excitable medium. Nat. Comput. 8 (3), 473–492. Inaba, A., Manabe, T., Tsuji, H., Iwamoto, T., 1995. Electrical impedance analysis of tissue properties associated with ethylene induction by electric currents in cucumber (Cucumis sativus L.) fruit. Plant Physiol. 107 (1), 199–205.
Katz, E., Privman, V., 2010. Enzyme-based logic systems for information processing. Chem. Soc. Rev. 39 (5), 1835–1857. Lagzi, I., Soh, S., Wesson, P.J., Browne, K.P., Grzybowski, B.A., 2010. Maze solving by chemotactic droplets. J. Am. Chem. Soc. 132 (4), 1198–1199. Macdonald, J., Li, Y., Sutovic, M., Lederman, H., Pendri, K., Lu, W., Andrews, B.L., Stefanovic, D., Stojanovic, M.N., 2006. Medium scale integration of molecular logic gates in an automaton. Nano Lett. 6 (11), 2598–2603. Mancuso, S., 1999. Seasonal dynamics of electrical impedance parameters in shoots and leaves related to rooting ability of olive (olea europea) cuttings. Tree Physiol. 19 (2), 95–101. Mancuso, S., 2000. Electrical resistance changes during exposure to low temperature measure chilling and freezing tolerance in olive tree (Olea europaea L.) plants. Plant Cell Environ. 23 (3), 291–299. Meyer, R., Stockem, W., 1979. Studies on microplasmodia of Physarum polycephalum V: electrical activity of different types of microplasmodia and macroplasmodia. Cell Biol. Int. Rep. 3 (4), 321–330. Mills, J.W., 2008. The nature of the extended analog computer. Phys. D: Nonlinear Phenom. 237 (9), 1235–1256. Motoike, I.N., Yoshikawa, K., 2003. Information operations with multiple pulses on an excitable field. Chaos Solitons Fractals 17 (2), 455–461. Paul, F., Lapinte, C., 1998. Organometallic molecular wires and other nanoscale-sized devices: an approach using the organoiron (dppe) Cp*Fe building block. Coord. Chem. Rev. 178, 431–509. Privman, V., Pedrosa, V., Melnikov, D., Pita, M., Simonian, A., Katz, E., 2009. Enzymatic and-gate based on electrode-immobilized glucose-6-phosphate dehydrogenase: towards digital biosensors and biochemical logic systems with low noise. Bios. Bioelectron. 25 (4), 695–701. Reyes, D.R., Ghanem, M.M., Whitesides, G.M., Manz, A., 2002. Glow discharge in microfluidic chips for visible analog computing. Lab Chip 2 (2), 113–116. Sabah, A., Dakua, I., Kumar, P., Mohammed, W.S., Dutta, J., 2012. Growth of templated gold microwires by self organization of colloids on aspergillus niger. Dig. J. Nanomater. Biostruct. 7, 583–591. Sielewiesiuk, J., Gorecki, J., 2001. Logical functions of a cross junction of excitable chemical media. J. Phys. Chem. A 105 (35), 8189–8195. Stojanovic, M.N., Mitchell, T.E., Stefanovic, D., 2002. Deoxyribozyme-based logic gates. J. Am. Chem. Soc. 124 (14), 3555–3561. Sun, T., Tsuda, S., Zauner, K.-P., Morgan, F., 2009. Single cell imaging using electrical impedance tomography. I. In: 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, 2009 (NEMS, 2009). IEEE, pp. 858–863. Teuscher, C., Adamatzky, A., 2005. Proceedings of the 2005 Workshop on Unconventional Computing: From Cellular Automata to Wetware. Luniver Press. Wang, H., Wang, L.-J., Shi, Z.-F., Guo, Y., Cao, X.-P., Zhang, H.-L., 2006. Application of self-assembled molecular wires monolayers for electroanalysis of dopamine. Electrochem. Commun. 8 (11), 1779–1783. Willner, I., Katz, E., 2006. Bioelectronics: From Theory to Applications. John Wiley & Sons, Weinheim. Yokawa, K., Derrien-Maz, N., Mancuso, S., Baluˇska, F., 2014. Binary decisions in maize root behaviour: Y-maze system as tool for unconventional computation in plants. Int. J. Unconv. Comput.. Yoshikawa, K., Motoike, I., Ichino, T., Yamaguchi, T., Igarashi, Y., Gorecki, J., Gorecka, J.N., 2009. Basic information processing operations with pulses of excitation in a reaction–diffusion system. Int. J. Unconv. Comput. 5 (1), 3–37. Zhang, M., Willison, J., 1993. Electrical impedance analysis in plant tissues8. J. Exp. Bot. 44 (8), 1369–1375.
Contents lists available at ScienceDirect
BioSystems journal homepage: www.elsevier.com/locate/biosystems
Towards plant wires Andrew Adamatzky ∗ Unconventional Computing Centre, University of the West of England, Bristol BS16 1QY, United Kingdom
a r t i c l e
i n f o
Article history: Received 10 May 2014 Received in revised form 9 June 2014 Accepted 10 June 2014 Keywords: Bio-wires Plants Bio-electronics Potential transfer function
a b s t r a c t In experimental laboratory studies we evaluate a possibility of making electrical wires from living plants. In scoping experiments we use lettuce seedlings as a prototype model of a plant wire. We approximate an electrical potential transfer function by applying direct current voltage to the lettuce seedlings and recording output voltage. We analyse oscillation frequencies of the output potential and assess noise immunity of the plant wires. Our findings will be used in future designs of self-growing wetware circuits and devices, and integration of plant-based electronic components into future and emergent bio-hybrid systems. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Since its inception in 1980s the field of unconventional computation (Calude et al., 1998) became dominated by theoretical research, including quantum computation, membrane computing and dynamical-systems computing. Just a few experimental laboratory prototypes are designed so far (Adamatzky and Teuscher, 2006), (Teuscher and Adamatzky, 2005), e.g. chemical reaction–diffusion processors (Adamatzky et al., 2005), extended analogue computers (Mills, 2008), micro-fluidic circuits (Fuerstman et al., 2003), gas-discharge systems (Reyes et al., 2002), chemo-tactic droplets (Lagzi et al., 2010), enzyme-based logical circuits (Katz and Privman, 2010; Privman et al., 2009), crystallisation computers (Adamatzky, 2009), geometrically constrained chemical computers (Sielewiesiuk and Gorecki, 2001; Motoike and Yoshikawa, 2003; Gorecki et al., 2009; Yoshikawa et al., 2009; Górecki and Górecka, 2006), molecular logical gates and circuits (Stojanovic et al., 2002; Macdonald et al., 2006). In contrast, there are hundreds if not thousands of papers published on quantum computation, membrane computing and artificial immune systems. Such a weak representation of laboratory experiments in the field of unconventional computation may be due to technical difficulties and costs of prototyping. In last few years we have developed a concept, architectures and experimental laboratory prototypes of living and hybrid computers made of slime mould Physarum polycephalum (Adamatzky, 2010).
∗ Tel.: +44 01173282662. E-mail address: [email protected] http://dx.doi.org/10.1016/j.biosystems.2014.06.006 0303-2647/© 2014 Elsevier Ireland Ltd. All rights reserved.
In course of our studies of the slime mould’s computational properties and designs of Physarum chips we found that the slime mould based sensors and processor are very fragile, highly dependent on environmental conditions and somewhat difficult to control and constrain. Thus we have started to look for living substrates that could complement, or even become an alternative to, computing devices made of Physarum’s protoplasmic tubes. Plant roots got our attention because they are mobile, growing, adaptive intelligent units (Brenner et al., 2006; Baluˇska et al., 2009, 2010; Ciszak et al., 2012; Burbach et al., 2012), similar in their behaviour to active growing zones of P. polycephalum. Plants are, in general, more robust and resilient, less dependent on environmental conditions and can survive in a hostile environment of bio-hybrid electronic devices longer than slime moulds do. A prospective computing device made from living plant would perform computation by means of electrical charge propagation, by travelling waves of mechanical deformation of immobile structures, and by physical propagation of structures. Conductive pathways would be a key component of any plant-based electrical circuit. Thus to achieve a realistic assessment of the potential of the living plants based hybrid processing technology we must evaluate the feasibility of living plant wires in a proof-of-concept device. Previous findings on of organic wires and using living substrates to grow conductive pathways: self-assembling molecular wires (Wang et al., 2006; Paul and Lapinte, 1998), DNA wires (Beratan et al., 1997), electron transfer pathways in biological systems (Willner and Katz, 2006), live bacteria templates for conductive pathways (Berry and Saraf, 2005), bio-wires with cardiac tissues (Cingolani et al., 2012), golden wires with templates of fungi (Sabah et al., 2012). We are not aware of any published results related to evaluation of living plants, or
2
A. Adamatzky / BioSystems 122 (2014) 1–6
Fig. 1. Experimental setup. (a) A photo of lettuce seedling resting on aluminium electrodes in drops of distilled water. (b) A scheme of the experimental measurement of potential transfer function.
their parts, as electrical wires. There are substantial experimental findings on electrical impedance of plants however mostly related to evaluation of the plants physiological state via their impedance characteristics, see e.g. (Zhang and Willison, 1993; Inaba et al., 1995; Mancuso, 1999, 2000). The paper is structured as follows. We describe experimental techniques in Section 2. Section 3 presents results on resistance of lettuce seedlings, approximation of direct current potential transfer
function and immunity of plant wires to noise. Outcomes of the research and future directions of study are discussed in Section 4. 2. Methods We experimented with lettuce (Lactuca sativa) seedlings 3–4 days after germination. Electrodes were made of a conductive aluminium foil, 0.07 mm thick, 8 mm wide, 50 mm (inclusive part
Resistance, MOhm
2.4
2.3
2.2
2.1 0
50
100
150
200
250
300
350
400
450
500
550
600
650
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(a)
0.03
Power
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0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
Frequency, Hz
(b)
Fig. 2. Lettuce resistance. (a) Exemplar dynamics of resistance. (b) Power frequency spectrum.
A. Adamatzky / BioSystems 122 (2014) 1–6
Vin , V 2 3 4 5 6 7 8 9 10 11 12
Vout , V 0.82 1.46 2.35 3.49 4.41 5.06 6.5 6.67 8.46 8.81 10.00
std(Vout ) 0.011 0.019 0.037 0.025 0.096 0.092 0.044 0.016 0.115 0.096 0.120
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(b) Fig. 3. Potential transfer function implemented by lettuce seedling. (a) A table of experimental measurements: for each value of input potential Vin we show average output ˜ . (b) Experimental plot of a transfer function. potential Vout , its standard deviation std (Vout ) and median output potential Vout
Output potential, V
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Time, sec (b) Fig. 4. Dynamics of output potential for input potential 5 V (a) and 12 V (b).
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protruding outside Petri dish) long. Distance between proximal sites of electrodes is always 10 mm. In each experiment an undamaged lettuce seedling was placed onto electrodes in drops of distilled water (Fig. 1a). In each experiment a resistance and potential (not at the same time indeed) were measured during 10 min with four wires using Fluke 8846A precision voltmeter, test current 1 A with 0.0013 A error. Direct current potential was applied using Gw Instek GPS1850D laboratory DC power supply. A scheme of potential transfer function measurement is shown in Fig. 1b. Each session of measurements lasted exactly 10 min. For each measurement a new seedling was used. We measured resistance of 20 seedlings, and potential transfer function of 5 seedlings for each value of input, applied, potential: 2 V, 3 V, . . ., 12 V.
A lettuce seedling acts as a potential divider. Potential Vout recorded on a lettuce wire is a fraction of a potential Vin applied to the lettuce wire. Results of 60 experiments, 5 experiments for each value of input potential, are summarised in Fig. 3a and illustrated in Fig. 3b. The transfer function is linear (Fig. 3b) Vout = 0.928 · Vin − 1.221, subject to oscillations of output potential (Fig. 4) caused by fluctuations of impedance (Fig. 2a). Resistance of lettuce seedlings changes during recording. This causes oscillations of output potential (Fig. 4). Values of two dominating frequencies, calculated by FFT, are shown in Fig. 5a. For most values of input potential dominating frequencies of output potential oscillations stay in a range between 0.05 Hz and 0.3 Hz (Fig. 5b). There is no correlation between frequency of output potential oscillations and values of input potential applies. Output potential oscillates in lower frequencies than resistance. For values of input potential studied frequencies of output potential oscillations lie in a range between 0.0062 Hz and 0.038 Hz (with average 0.0221 Hz and median 0.0197 Hz over all values of input potential). We studied lettuce seedling as a prototypes of plant wires. The wires are noisy because the output potential oscillates (Fig. 4). Level of noise can be evaluated via maximum amplitudes of oscillations. The maximum amplitudes of output potential oscillations are characterised in Fig. 6a. The maximum amplitudes stay in a range 0.15–0.45 V and just slightly increase (rough linear approximation 0.208 +0.0126·Vout ) with increase of a potential. The maximum
3. Results Average resistance of lettuce seedling is 2.76 M with standard deviation 0.18 M, and median 2.72 M. The seedlings show oscillations of resistance (Fig. 2a). The oscillations are more likely is a combinations of dynamical changes in resistance in root, stem and leaves. Thus the oscillations recorded have no clearly visible periodicity. Three most dominating frequencies observed are 0.043 Hz, 0.084 Hz, and 0.009 Hz (Fig. 2b). Vin , V 2
f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2 f1 f2
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average, Hz 0.0117 0.0310 0.0320 0.0295 0.0088 0.0208 0.0176 0.0149 0.0190 0.0163 0.0078 0.0247 0.0139 0.0180 0.0205 0.0260 0.0120 0.0251 0.0062 0.0241 0.0299 0.0385
st. deviation 0.00626 0.03535 0.03167 0.01056 0.00328 0.00700 0.02172 0.01236 0.01147 0.00578 0.00082 0.01680 0.00922 0.00697 0.01690 0.02271 0.00374 0.01915 0.00346 0.01290 0.03295 0.01487
average, sec 85.65 32.24 31.21 33.9 113.96 48.13 56.92 67.1 52.49 61.26 128.21 40.53 72.05 55.68 48.78 38.49 83.51 39.76 161.29 41.49 33.47 25.99
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(b) Fig. 5. Two dominating frequencies of output potential oscillations for input potential applied 2 V, . . ., 12 V. (a) Table of first f1 and second f2 dominating frequencies, their values in Herz and seconds. (c) Average dominating frequencies for Vin = 2 V, . . ., 12 V: f1 is shown by circles, f2 is shown by discs.
Maximum amlitude of oscillations, V
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(b) Fig. 6. Maximum amplitudes of output potential oscillations. (a) Maximum amplitude of output potential oscillations, averaged over trials for each value of input potential, versus average output potential. Data points derived from laboratory experiments are shown by circle. Dotted line acts as an eye guide. Solid line is a linear fit 0.208 +0.0126·Vout . (b) Maximum amplitude as a percentage of average output potential. Experimental data are shown by circles. Dotted line guides eye.
amplitude makes 25% of output potential for input potential 2 V, then decreases to 19.6% (3 V), 9.5% (4 V), stays between 5% and 7% for 5–7 V applied potential, and then decreases to 4%–4.5% for input potential 8–12 V (Fig. 6). 4. Discussions In laboratory experiments with lettuce seedlings we found that they implement a linear transfer function of input potential to output potential. Roughly an output potential is 1.5–2 V less than an input potential, thus e.g. by applying 12 V potential we get 10 V output potential. Resistance of 3–4 day lettuce seedling is about 2.76 M. This is much higher than resistance of conventional conductors yet relatively low comparing to resistance of other living creatures (Geddes and Baker, 1967), e.g. a resistance of 10 mm long protoplasmic tube of P. polycephalum is near 3 M (Adamatzky, 2014). The lettuce resistance oscillates. The dominating frequencies of lettuce seedlings resistance oscillation roughly corresponds to periods of oscillation 23 s, 12 s and 111 s. These are compatible with periods of resistance oscillations recorded in our experiments with protoplasmic tubes of acellular slime mould P. polycephalum (Adamatzky, 2014): 73 s is an average period of oscillation. Resistance oscillation in P. polycephalum are caused by peristaltic oscillations (Sun et al., 2009) and cytoplasmic flow in the tubes (Adamatzky, 2014). The peristaltic and flow are governed by calcium waves travelling along the tubes where a calcium ion flux through membrane triggers oscillators responsible for dynamic
of contractile activity (Meyer and Stockem, 1979; Fingerle and Gradmann, 1982). Thus we can speculate that there are types of cytoplasmic flow in lettuce seedlings which might be reflected in changes of volume of leaves, roots or stem that are reflected in resistance changes. Lettuce seedling is somewhat a noisy wire. Its output potential oscillates. However, maximum amplitude of oscillations never exceeds 4% of an input potential for potential applied over 8 V. For example, a line held near 10 V (output voltage) has 9 V of noise immunity. To incorporate plant wires into bio-hybrid self-growing circuits we must develop techniques for reliable routing of the plant roots between living and silicon components of the circuits. A first step would be to find a way of navigating plant roots in labyrinths. We have undertook few scoping experiments, see two illustrations in Fig. 7, yet no reached no conclusive results. When seeds are placed in or near a central chamber of a labyrinth their routes somewhat grow towards exit of the labyrinth. However, they often become stuck midway and do not actually reach the exit. The inconclusive results are possibly due to the fact that we used gravity as the only guiding force to navigate the routes. In their recent paper Yokawa et al. (2014) demonstrated that by employing chemotaxis and using volatiles it is possible to navigate the roots in simple binary mazes. Their results gives us a hope it could be possible to route plant wires in the same efficient manner as slime mould based wires are routed (Adamatzky, 2012, 2013). Another line of future research would be to investigate suitability of live plants to be incorporated as sensors in bio-hybrid electronic circuits. Thermistors could be
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Fig. 7. Scoping experiments on routing plant roots in labyrinths.
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