Solving the bio-machine interface—a synthetic biology approach

Solving the bio-machine interface—a synthetic biology approach

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O. Yarkoni, D.J. Frankel

3.1

Introduction

Interfacing biological cells/tissue with man-made machines has the potential to revolutionize robotics and medical technology. There are a number of biohybrid robots in development with this aim in mind (Ricotti et al., 2012), for example, one device that adheres bacteria to solid substrates to create bacteria-propelled rafts (Kim et al., 2012). However, there are usually insurmountable obstacles to their success. A good illustration to such obstacles is attempts to control a robot with explanted frog muscles. These hybrid devices lack an appropriate mechanism for controlling muscle contraction (Herr and Dennis, 2004), meaning that the robot body could thrash around a water tank but was unable to navigate a path defined by the controller. Similarly, there has been enormous interest in the development of biohybrid machines created from cultured muscle, but again these methods lack any mechanism for controlling contraction (Kim et al., 2008). This chapter has been written to familiarize the reader with the difficulties of cell/electronics communication and introduce some of the many approaches taken to overcome the difficulties. It will be loosely divided into three parts: an introduction to biosensors and the bio-electronic interface, approaches to using cells in devices, and the use of synthetic biology to improve cell/system compatibility. A wide variety of cells and systems designed to facilitate the aforementioned communication will be discussed, including bacterial, mammalian, and functional tissue.

3.2

Definition of the bio-machine interface

Interfacing biological tissue or cells with nonbiological materials does not necessarily produce a bio-machine interface. In the case that no dynamic mechanical or electronic function is to be performed by the device, the interface can be considered as biomaterial. When the interface is between biological systems such as tissue or cells and a functioning device, that is a device that can perform tasks, the interface is biomachine. This definition can be further expanded to include biosensors that themselves have a bio-machine interface, but one which usually involves individual cells, or groups of cells, rather than tissues. Although these definitions encompass some Biomimetic Technologies. http://dx.doi.org/10.1016/B978-0-08-100249-0.00003-3 © 2015 Elsevier Ltd. All rights reserved.

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gray areas, one can generally assume that the bio-machine interface requires overcoming some natural barriers between the biological and nonbiological components. A biosensor is a device that utilizes a mechanism of biological origin to detect a species and subsequently converts the signal into an interpretable reading. It can also include mechanisms that are nonbiological but detect biological species. This can be achieved using enzymes, receptors, or cells as the biological component. The bio-electronic interface is a subset area within biosensors. That is, a biosensor does not necessarily convert biological signal to an electronic output. For example, a much used biosensor is based on the quartz crystal microbalance which can convert specific receptor/ligand interactions into a change in resonant frequency of a quartz crystal (Ogi et al., 2010; Saitakis et al., 2010). The frequency change is subsequently processed via electronics but the signal of interest is the frequency shift. In contrast, the bio-electronic interface involves either a direct conversion of biological signal to electrical current flow or a more indirect route using intermediates but still with the result of trying to measure a difference in electronic signal. We can also open up the bio-electronic interface to include the interfacing of optical devices with biological cells and tissue. The reasoning here is that optical stimuli can be programmed into well-defined patterns, from pulses to continuous emission, thus allowing computer-generated algorithms to be transmitted to biological systems.

3.3

Historical perspective

When discussing the history of the bio-machine interface, it is appropriate to start with biosensors as many of the developments in this technology were later employed in other biohybrid devices. The earliest biosensor was developed in 1962 by Clark and Lyons (1962), an amperometric enzyme reaction-based electrochemical sensor capable of detecting glucose levels from blood samples. This was accomplished using the enzyme glucose oxidase and combining it with an electrode sensitive to dissolved oxygen. In this way, the oxidation of glucose could be monitored via a change in current from the electrode. This allowed the amount of glucose present in the sample to be detected directly. To this day, glucose sensors utilize this principle, a medical application of biosensors which has improved the quality of life for millions of diabetes patients. Although the field has developed significantly since this seminal discovery in the 1960s, the major principles involved remain identical. A historical perspective on the bio-machine interface would not be complete without a discussion of electrodes/microelectrodes used to stimulate and record from neurons. Microelectrode arrays for in vitro interfacing with neural tissue have led to implantable microelectrode arrays for the treatment of blindness caused by macular degeneration. One of the pioneering implantable devices for neural interfacing was the Utah microelectrode array developed in the mid 1980s. These devices are not after single-cell resolution; rather they stimulate many neurons at a time. Consisting of up to 100 spike-like electrodes fabricated from silicon (Figure 3.1), they can be implanted in either the cerebral cortex or the peripheral nerves.

Solving the bio-machine interface

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Figure 3.1 Multielectrode arrays used to record and stimulate neuronal activity. (a) Cyberkinetics silicon-based 100 channel multielectrode array. (b) View of the spike electrodes that interface with the tissue. Published with permission from Elsevier Ward et al. (2009).

From the early experiments with electrode stimulation of neurons to the fully implantable visual prosthesis, the progress to usable technology has demonstrated the progression from in vitro to in vivo devices. In much the same vain, new bioelectronic interfacing technology starts in vitro. One of the most exciting examples of this is work using novel nanotechnologies to stimulate and take electrical measurements from a single neuron (Patolsky et al., 2006). This was achieved by building arrays of field-effect transistors from silicon nanowires and connecting them to individual neurons. These were then able to record neuronal signals and stimulate the cells at precisely defined locations. For example, the transistors were able to address the neurons at user-defined positions along the cell dendrites (neuronal projections) and along the cell body. The potential of these nanoscale devices lies in the ability to address single cells precisely and also the possibility of integrating these tiny devices into larger electronic systems. A recent breakthrough in fabricating bioelectronic materials was reported in 2012 and involves integrating electronics with tissue (Tian et al., 2012). This “cyborg” tissue uses passivated nanoelectric scaffolds, which demonstrate biocompatibility and the ability to record electrical signals (Figure 3.2). Their fabrication technology offers potential for the wiring of bionic devices within wet and living biological tissue.

3.4

Cells as biosensors

Cell-based biosensors use living cells to monitor the immediate environment and detect changes in physiological conditions. This change in condition could be the introduction of a pathogen, toxins, or a subtle increase in the concentration of an analyte (Stenger et al., 2001). It is also feasible to manipulate the preexisting cellular machinery with the ultimate goal of using cells as sensors in their own right. However, progress is currently limited to subtle manipulation of the cellular machinery transforming the cells into transducers for the conversion of an input into a chemical,

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Biomimetics

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Figure 3.2 General principle of nanoelectric scaffold/tissue hybrid. (a) Fabrication of single nanowire field-effect transistors (FETs). (b) Formation of 3D nanowire FET matrices and hybridization with the extracellular matrix. (c) Integration with cells and tissue through biological processes. Reprinted by permission from Macmillan Publishers Ltd: [Nature Materials] Tian et al. (2012).

optical, or electrical signal (Aravanis et al., 2001; Feng et al., 2007; Moschopoulou and Kintzios, 2006). Nature has provided the cell with an extremely specific and subtle detection capability. Combined with the means to process information and parallel pathways, the cell has the potential to be harnessed as the ultimate biosensor. Some sensors use this principle directly, by redirecting the output signal to a stimulus that the cell is already programmed to interpret. It is now also possible, albeit within limits, to adapt preexisting cellular machinery to detect certain changes in extracellular environment. This is achieved using the principles of genetic engineering and the techniques of synthetic biology. One successful approach to the conversion of cells into sensors utilized cryoimmobilized Escherichia coli cells that had been genetically modified to produce organophosphate hydrolase (Owicki et al., 1994). This enzyme is capable of cleaving P–CN, P–F, PO, and P–S bonds, generating two protons per cleavage reaction. Due to the direct relationship between the amount of organophosphate that is hydrolyzed and the increase of protons in solution, the change of pH is proportional to the amount of organophosphate in solution, thus the system could be monitored with a pH sensor. Sensitivities achieved were high at 0.001–1 mM with a response time of 10–20 min. In addition, the cellular component of the sensor was found to be suitably active for over two months when stored appropriately.

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A contrasting approach for using cells as transducers can be found in the work of Rider et al. (2014). B cells were genetically modified to produce the luminescent protein Aequorin within their cytosol. This protein is both bioluminescent and sensitive to calcium. The Aequorin is released when the pathogen/antibody-binding event occurs. Detection limits were found to be as low as 20 colony forming units of the chosen pathogen, although higher densities produced more reliable results. However, its potential applications are limited as it requires complex machinery to detect/transduce the cellular signal making the technology only suitable for clinical laboratories. A more direct approach to the conversion of cells into sensor components is seen in the work of Smutok et al. (2007). In this work, Hansenula polymorpha yeast was engineered to overproduce Flavocytochrome b2 and was successfully used to detect L-lactate. The cells were immobilized directly onto a graphite electrode surface, either by use of a dialysis membrane or via electrodeposition of a polymer. Cells were also permeabilized using cetyltrimethylammonium bromide to increase diffusion rates.

3.5

Difficulties in addressing the bio-electronic interface

A major bottleneck to overcome is how to convert a biological input into an electrical output, which is both measurable and accurate. In other words, if a cell senses a molecule by its binding to a receptor, how can this biomolecular event be converted into an electronic signal that is to be read by a computer? What are the obstacles for cells to communicate and/or be stimulated directly with electronics? Some cells present an easily detectable electrical signal, i.e., neurons; however, many events at the cellular level do not present a signal which can be directly communicated to machinery. Cells can function as transducers themselves, transforming a stimulus into a detectable signal such as a photon, a chemical analyte, or a change in cell wall potential. Neurons are optimal cells for signal transfer and transduction. Their capability of electrical signaling renders their signal directly interpretable by electronics. The ability of neurons to communicate with each other has been widely reported and attempts to harness this trait have already been made. An example can be found in the work of Thewes et al., where neurons were grown into networks on top of a chip (Eickenscheidt et al, 2012). The strategy relies on the precise immobilization of neurons on field-effect transistors. This is accomplished by trapping the neurons within a ring of polyamide pillars so as to ensure contact with the two transistors, a stimulation and an open field-effect transistor. Neuronal connections occur between cells as axons grow and communication signals between cells can be detected and monitored using a CMOS system. Although there is an untold amount of potential in using neurons, their method of operation is still not fully understood and growing them selectively onto electrodes remains a challenge. In addition, this type of cell is highly sensitive to contamination and requires a very specific environment. There are some additional limitations to interfacing cells with electronics. It has been demonstrated that close proximity to a high potential (such as those generated by electrode surfaces) causes irregular cell growth, hysteresis, and irregular cellular

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behavior (Oni et al., 2004). This poses a significant problem to the cell-electronic interface as distance between cells and electrodes limits their mode of operation. If cells cannot be in direct contact with the electronic component’s surface, then certain methods of signal generation become incompatible, i.e., cell wall potential changes. In their work, the aforementioned group designed a possible solution to this discovered effect. It was proposed that implementing a cellagen# layer separating the cells from the electrode surface would most likely do away with the observed effects of cellular proximity to high potential. A unique approach to improving the cell-electronics interface was taken by Valderrama et al. (1995). A successful interface between nerve cells and a microelectrode array allowed in vivo signal detection and recording. To achieve this difficult feet, the bio-electronic interface was solved using an implantable microelectrode array on silicon-doped chip with via holes. It was shown that axons can regenerate through the via holes and that communication is resumed in a fashion that becomes measurable. Platinum electrodes were optimal for this task due to their low electrode– electrolyte impedance and good charge delivery. Using via holes as an electrode surface greatly increases the interface surface area, which can therefore allow for a greater accuracy of measurement. With this approach, the total exposed surface area is relatively large even though the electrode itself is quite small. Another step to improve the cell-sensor interface was accomplished by including a peptidefunctionalized surface to improve cell adhesion and promote neurite growth. Sensors that are designed to enter the environment of the human body must be nontoxic and not interfere with cell behavior. Achieving the latter can be a complicated task. In the case of electrochemical detection, cells need to remain at a certain distance from the electrode and its operating potential, in order to remain undisturbed (Oni et al., 2004). Conversely as well as the sensor harming the environment, the environment can harm the sensor. In vivo, the bodies multiple defense mechanisms can interfere with sensor function. This type of detrimental interaction between the sensor and the host is called biofouling and is one of the major causes of in vivo sensor failure. It occurs via the accumulation of cells, compounds, and proteins on the sensor surface and causes a marked decrease in sensor performance and lifetime (Wisniewski and Reichert, 2000).

3.6

Synthetic biology applied to the bio-electronic interface

Over the last decade, the new field of synthetic biology has evolved. It strides to apply engineering principles to biological systems, modifying living cells to perform unnatural functions. By doing this, synthetic biology has the potential to harness the power of biological systems to perform useful tasks. In particular, cells are being engineered to produce high value chemicals, to treat disease, and to produce biologically derived energy sources. It is recent technological advances in DNA sequencing and genetic manipulation that has made this new discipline possible. One of the underlying principles of synthetic biology is the creation of biological “parts” that are interchangeable between organisms, rather like standardized mechanical components in an engineered

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system. These are built by manipulating the regulation machinery of genetic expression, that is, by controlling the levels of specific genetic products (proteins). In this way, genetic circuits can be built including genetic AND, OR, and NOR gates. These gates can be connected to create cells that can take a specific input and manipulate it to produce the desired output. Genetic circuits have great potential in their ability to address the bio-electronic and bio-machine interface. A variety of genetic circuits have been implemented a variety of functions, including logic gates (e.g., AND or NOR), switches, oscillators, and time delays (Purnick and Weiss, 2009). The circuits are created by combining genetic parts such as promoters, inducers, repressors, and terminators to control the temporal and spatial expression of the target genes. There are unique obstacles which must be considered when constructing genetic circuits when applied to the bio/electronic interface. One such obstacle is the potential for toxicity. As many substances (or enzymatic metabolites) can be toxic when present in sufficient quantities, it becomes important to consider about gene expression levels, how active is the protein, and how toxic is the metabolite. Another possible undesirable outcome can arise via the interference of any of the products/elements in the genetic circuit with the endogenous activity of the cell.

3.7

Genetic programs that perform signal processing

An example of a genetic program whose function can be confirmed visually can be found in the work of Tabor and coworkers (Tabor et al., 2009), where E. coli bacteria were modified to be capable of photographic edge detection (Figure 3.3). This was

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Figure 3.3 Bacterial edge detection via a synthetic biology approach and engineering genetic circuits. (a) Selective illumination of bacteria in a petri dish via use of a mask. (b) Those bacteria in the dark produce signaling molecules (green circles) which diffuse across the dark–light interface. Only bacteria in the light can respond to the signal and are programmed to express a visible reporter gene with the sum of this activity over the two-dimension space corresponding to the edges of the input image. (c) The NOT logic gate represented the lightning bold with the adjacent triangle instigates cell–cell communication, represented by the green X and an inverter represented by the red Y and adjacent triangle. The output of these two circuit elements combine as inputs for an AND gate, the semicircle, which produces the final output Z. Published with permission from Elsevier Tabor et al. (2009).

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accomplished by incorporating a combination of an X AND (NOT Y) logic gates in a genetic circuit. As the terminology implies, the signal is only carried forth when X is present and Y is absent. By having cells produce a pigment as a form of signal, the circuit was made capable of defining edges of light incidence. The engineered cells produce a chemical signal upon light stimulus at the specific wavelength of 650 nm, while having the pigment-producing gene under a restriction that makes it unable to produce in the presence of light. Once switched on, the chemical signal diffuses across the cell population, and over time it activates pigment production in neighboring cells which have not been directly exposed to light. Quantitative genetic responses are well documented throughout the natural world. This is particularly true in developmental genetics. Gradients of proteins have been found to be at least partially responsible for stem cell division and mother/daughter cell differentiation (Discher et al., 2009). Such processes are part of a complex sequence of expression, transcription, and translation as well as protein migration. Effectively, stem cells are controlling spatiotemporal expression of genes and their products in order to determine which cells will undergo differentiation and which ones will simply undergo self-renewal. It is important to note, however, that this is mostly thought to be dependent on threshold values rather than specific concentrations. Examining these natural systems has been a key to developing synthetic biology methods of controlling spatiotemporal protein expression. An example of a synthetically engineered quantitative response can be found in the work of Weiss et al. (Basu et al., 2005; Weiss et al., 2003). Cells were engineered to respond to multiple gradients of AHL, with two different responses depending on the concentration of the analyte. Engineering of these mutant cells involved incorporating many of the parts used within the previously described edge detection system, further demonstrating the versatility of the genetic “parts” used. Synthetic gene circuits have been harnessed to enable cell-based electrochemical detection. A bacterial whole cell sensor was constructed based on cell wall damage reporting for the detection of water toxicity (Neufeld et al., 2006). This was achieved using the fabA promoter and fabR repressor. The fabA gene is responsible for the expression of unsaturated fatty acids (cell wall constituents) and is induced by interruptions in the biosynthesis of fatty acids. This promoter was fused to a promoterless lacZ gene so that expression of lacZ was controlled by the actions of fabA and fabR. The resulting recombinant bacteria were then placed into electrochemical cells containing screen-printed carbon electrodes coupled to a Ag/AgCl reference electrode. The resulting bio-electronic system was then tested with varying concentrations of phenol and found to be successful, with a detection time frame of approximately 20 min and sensitivity of up to 1.6 ppm of aminophenol. Aminophenol is the result of the cleavage of p-aminophenylgalactoside by lacZ and is the agent responsible for electrochemical detection within this system. Due to the system being DNA damage dependant, several other toxic compounds were found to activate the system, including toluene, hydrazine, and ethanol; however, it was unable to detect methanol or the organophosphate DDVP(2,2-dichlorovinyl dimethyl phosphate). This work showed an improvement on a previous system designed to detect cell wall damage using the same promoter/repressor system (Bechor et al., 2002). The new approach

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also increased lower detection limits as well as decreasing operating time by using electrochemical methods rather than fluorescence. An additional factor to consider when designing genetic circuits to work with electronics is the time response. Many of the systems based on synthetic biology work over a relatively slow time frame, with response times ranging from tens of minutes to several hours (Tabor et al., 2009; Basu et al., 2005). This is governed by the elements used, as it takes considerably longer to go from an inactive gene to a fully functional protein than to go from an inactive protein to an active one. An example of this would be phosphorylation. Phosphorylation is a post-translational modification common to a host of proteins, and as the process is very fast, a protein can be can activated or inactivated nearly on demand. It is also important to note that there is a major limitation to the use of logic gates for building more complex engineered cells. Only a few can actually be introduced into a host system. In E. coli, three gates is the described limit (Weiss et al., 2003).

3.8

Optogenetics for interfacing cells/tissue with machines

Use of light to control biological tissue can allow precisely programmed stimuli to be applied, anything from ultrafast pulses, to slow delay exposure. Thus, by using light in the form of inexpensive light-emitting diodes and optical fibers, it is possible to put biological systems under computer control. Moreover, there is a major advantage of using light to interface with biological tissue as opposed to direct electrical connection. This is that light stimulation allows “action at a distance.” That is there doesn’t need to be a direct contact between the machine and the biological component, thereby removing the inherent difficulties in terms of resistive losses, and electrical fieldinduced cell damage. The emerging field of optogenetics has been particularly successful in coupling light activation to neuronal activity via the expression of light-sensitive ion channels. These light-activated channels called Channelrhodopsins originate from algae. Using genetic engineering, it is possible to express these proteins into the cells of choice, a task made relatively simple by the fact that the single gene coding for Channelrhodopsin is less that 1 kb in size. When exposed to blue light, these transmembrane proteins undergo a conformational change, opening the channel pore, and allowing an influx of cationic ions. The influx of ion will initiate an action potential in the excitable cell (Figure 3.4). As well as Channelrhodopsin, there are a variety of light-sensitive ion channels, excitable by different wavelengths of light, and with different switching characteristics. Moreover, commonly used genetic engineering techniques can be used to vary the expression levels, thus altering the light-sensitive properties of the transfected cells. Stimulation can be via a variety of sources including light emitting diode, laser, or lamp. A variety of light-sensitive ion channels and advanced genetic engineering strategies have been utilized to photostimulate neurons (Zhang et al., 2006).

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Figure 3.4 (a) Light-sensitive channelrhodopsin-2 (ChR2) and the halorhodopsin (NpHR) pump. Upon illumination with blue light, the light-sensitive ion channel allows the entry of cations into the cell. NphR is activated by yellow light. (b) Adsorption spectra for the two lightactivated proteins have sufficient separation so that they can both be excited without interference. Reprinted by permission from Macmillan Publishers Ltd: [Nature] Hausser and Smith (2007).

Furthermore, mutant Channelrhodopsins have been engineered which are 70 times more light-sensitive than the wild-type variety, and particularly conductive to Ca2+, thus presenting a range of synthetic biology building blocks for the interfacing of electrically excitable cells with light (Kleinlogel et al., 2011). Although the field of optogenetics has been built around applications in neuroscience, more recent work has explored the possibility of using optogenetics to control muscle contraction. Success in this area would allow the interfacing of muscle tissue actuators to control robots, via the programmed flashing of light-emitting diodes. Furthermore, it could even be possible to engineer organs in vivo to respond to light impulses. A major breakthrough in this field has been made whereby cardiac function could be controlled by light impulses inside a living zebrafish (Arrenberg et al., 2010). Light-sensitive ion channels, including channelrhodopsin, were expressed in cardiomyocytes. These genetically engineered pacemaker cells were located within the zebrafish heart and illuminated with light. In this way, the heart could be controllably sped up and slowed down. It was even possible to initiate cardiac arrest. It is with this approach one could envisage the development of biohybrid implantable organs, capable of being controlled using flashing light-emitting diodes, and ultimately controlled by a microprocessor. Engineered light-activated cells have been taken one step further to create an implantable device capable of aiding blood glucose levels in mice (Ye et al., 2010). Implants containing genetically engineered cells were able to produce a peptide resulting in the attenuation of glycemic excursions in diabetic mice. The novelty of this technique lies in the manipulation and optical control of a complete signaling pathway using transgenic techniques. This could be a starting point for the optically controlled release of drugs via a patient’s own cells. In terms of the bio-machine interface, the light source itself can be programmed to maximize genetic expression of the proteins/peptides by either pulsing the light or using continuous exposure.

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An optogenetic strategy has been successfully applied to the light-activated control of muscle contraction (Sakar et al., 2012). Muscle cells were genetically engineered to express light-activated ion channels, which upon incidence of blue light would allow an influx of ions, initiating an action potential. The individual cells were encouraged to form muscle tissue and their contraction could be measured by force sensors (Figure 3.5). Response to optical stimulation in terms of dynamic tension in the muscle construct is on the timescale of seconds (Figure 3.6), and the constructs were able to achieve multiple degree of freedom movement. With such a quick response, this technique offers the possibility of biological actuation of mechanical devices, in particular, robots. A more direct approach to navigating the cell-machine interface has been taken by exploiting biological transduction of an optical input to produce a machine-readable output (Yarkoni et al., 2012). A synthetic biology approach was applied, resulting in cells that can quickly respond to light with a machine-readable signal. Upon light incidence, the mutant mammalian cells release gaseous nitric oxide resulting in a sizeable current increase recorded by the electrode. Furthermore, the cells were able to exhibit switching behavior, which is a current response due to switching on and off

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Figure 3.5 Muscle tissue constructs tethered to force sensors. (a) Images representing time course of a tissue construct. (b) CAD model of a single construct. (c) Fluorescence image of construct with nuclei stained red and membrane bound GFP. (d) Fluorescence image of F-actin (red) demonstrating alignment of myoblasts in the direction of stress gradients. (e) Remodeling of actin (red) with multinucleated myotubes. Reproduced from Sakar et al (2012) with permission of the Royal Society of Chemistry.

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Figure 3.6 Properties of muscle constructs with local stimulation. (a) Recording of the static and dynamic tension of a construct. The tissue is stimulated with a blue light pulse series (indicated by the blue bars). (b) Dynamic and static tension, average of 50 constructs with the error bars being SEM. (c–e) Multidegree of freedom actuation. Reproduced from Sakar et al (2012) with permission of the Royal Society of Chemistry.

the light source. Mutants were created with two different fusion sites of the LOV (light-capturing domain) with the nitric oxide synthase enzyme. Both mutants show the same magnitude of photosensitivity.

3.9

Conclusions

With the emergence of synthetic biology techniques, cells have a new role in biohyrbid devices that are no longer passive and reliant on natural biological pathways. Cells and biological tissue can be manipulated in order to put biological systems under optical control and to produce outputs that are either machine-readable or useful for driving a device. In particular, genetic circuits allow the “hacking” of biological signaling pathways to allow cells to work in device-like functions. When combined with tissue engineering, synthetic biology can produce muscle constructs capable of device actuation in response to optical stimuli. Development of part biological, part machine devices, which have in the past been out of reach due to the difficulties of traversing the bio-electronic interface, are now a realistic possibility as the limit of electronic probes is no longer the limiting factor.

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References Aravanis, A.M., Debusschere, B.D., Chruscinski, A.J., Gilchrist, K.H., Kobilka, B.K., Kovacs, G.T.A., 2001. A genetically engineered cell-based biosensor for functional classification of agents. Biosens. Bioelectron. 16, 571–577. Arrenberg, A.B., Stainier, D.Y.R., Baier, H., Huisken, J., 2010. Optogenetic control of cardiac function. Science 330 (2010), 971–974. Basu, S., Gerchman, Y., Collins, C.H., 2005. A synthetic multicellular system for programmed pattern formation. Nature 434, 1130–1134. Bechor, O., Smulski, D.R., Dyk, T.K.V., Larossa, R.A., Belkin, S., 2002. Recombinant microorganisms as environmental biosensors: pollutants detection by Escherichia coli bearing fabA’∷lux fusions. J. Biotechnol. 94, 125–132. Clark, L.C., Lyons, C., 1962. Electrode systems for continuous monitoring in cardiovascular surgery. Ann. N. Y. Acad. Sci. 102 (1), 29–45. Discher, D.E., Mooney, D.J., Zandstra, P.W., 2009. Growth factors, matrices, and forces combine and control stem cells. Science 324, 1673. Eickenscheidt, M., Jenkner, M., Thewes, R., Fromherz, P., Zeck, G., 2012. Electric stimulation of retinal neurons in epiretinal and subretinal configuration using a multicapacitor array. J. Neurophysiol. 107, 2742–2755. Feng, X., Castracane, J., Tokranova, N., Gracias, A., Lnenicka, G., Szaro, B.G., 2007. A living cell-based biosensor utilizing G-protein coupled receptors: principles and detection methods. Biosens. Bioelectron. 22, 3230–3237. Hausser, M., Smith, S.L., 2007. Neuroscience: controlling neural circuits with light. Nature 446, 617–618. Herr, H., Dennis, R.G., 2004. A swimming robot actuated by living muscle tissue. J. Neuroeng. Rehabil. 1 (1), 6. Kim, J., Park, J., Lee, J., Yoon, E., Park, J., Park, S., 2008. Biohybrid microsystems actuated by cardiomyocytes: microcantilever, microrobot, and micropump. In: Robotics and Automation, IEEE International Conference on, IEEE, pp. 880–885. Kim, D., Liu, A., Diller, E., Sitti, M., 2012. Chemotactic steering of bacteria propelling microbeads. Biomed. Microdevices 14, 1–9. Kleinlogel, S., Feldbauer, K., Dempski, R.E., Fotis, H., Wood, P.G., Bamann, C., Bamberg, E., 2011. Ultra light-sensitive and fast neuronal activation with the Ca++ permeable channelrhodopsin catch. Nat. Neurosci. 14, 513–518. Moschopoulou, G., Kintzios, S., 2006. Application of “membrane-engineering” to bioelectric recognition cell sensors for the ultra-sensitive detection of superoxide radical: a novel biosensor principle. Anal. Chim. Acta 574, 90–96. Neufeld, T., Biran, D., Popovtzer, R., Erez, T., Ron, E.Z., Rishpon, J., 2006. Genetically engineered pfabA pfabR bacteria: an electrochemical whole cell biosensor for detection of water toxicity. Anal. Chem. 78 (14), 4952–4956. Ogi, H., Nagai, H., Fukunishi, Y., Yanagida, T., Hirao, M., Nishiyama, M., 2010. Multichannel wireless-electrodeless quartz-crystal microbalance immunosensor. Anal. Chem. 82 (9), 3957–3962. Oni, J., Pailleret, A., Isik, S., Diab, N., Radtke, I., Bl€ ochl, A., Jackson, M., Bedioui, F., Schuhmann, W., 2004. Functionalised electrode array for the detection of nitric oxide released by endothelial cells using different NO-sensing chemistries. Anal. Bioanal. Chem. 378 (6), 1594–1600. Owicki, J.C., Bousse, L.J., Hafeman, D.G., Kirk, G.L., Olson, J.D., Wada, H.G., Parce, J.W., 1994. The light-addressable potentiometric sensor: principles and biological applications. Annu. Rev. Biophys. Biomol. Struct. 23, 87–113.

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Patolsky, F., Timko, B.P., Yu, G., Fang, Y., Greytak, A.B., Zheng, G., Lieber, C.M., 2006. Detection, stimulation, and inhibition of neuronal signals with high-density nanowire transistor arrays. Science 313, 1100–1104. Purnick, P.E.M., Weiss, R., 2009. The second wave of synthetic biology: from modules to systems. Nat. Rev. Mol. Cell Biol. 10, 410–422. http://dx.doi.org/10.1038/nrm2698. Ricotti, L., Menciassi, A., Morishima, K., 2012. Guest editorial introduction to the special issue on bio-hybrid systems and living machines. Biomed. Microdevices 14, 965–967. Rider, T.H., Blanchard, D.J., Bortolin, L.T., Young, A.M., 2014. A B cell-based sensor for rapid identification of pathogens. Science 301 (2003), 213–215. http://dx.doi.org/10.1126/ science.1084920. Saitakis, M., Tsortos, A., Gizeli, E., 2010. Probing the interaction of a membrane receptor with a surface-attached ligand using whole cells on acoustic biosensors. Biosens. Bioelectron. 25 (7), 1688–1693. Sakar, M.S., Neal, D., Boudou, T., Borochin, M.A., Li, Y., Weiss, R., Kamm, R.D., Chen, C.S., Asada, H.H., 2012. Formation and optogenetic control of engineered 3D skeletal muscle bioactuators. Lab Chip 12, 4976–4985. Smutok, O., Dmytruk, K., Gonchar, M., 2007. Permeabilized cells of flavocytochrome b 2 overproducing recombinant yeast Hansenula polymorpha as biological recognition element in amperometric lactate biosensors. Biosens. Bioelectron. 23, 599–605. Stenger, D.A., Gross, G.W., Keefer, E.W., Shaffer, K.M., Andreadis, J.D., Ma, W., Pancrazio, J. J., et al., 2001. Detection of physiologically active compounds using cell-based biosensors. Trends Biotechnol. 19 (8), 304–309. Tabor, J.J., Salis, H., Simpson, Z.B., Chevalier, A.A., Marcotte, E.M., Voigt, C.A., Ellington, A. D., 2009. A synthetic genetic edge detection program. Cell 137 (7), 1272–1281. Tian, B., Liu, J., Dvir, T., Jin, L., Tsui, J.H., Qing, Q., Suo, Z., Langer, R., Kohane, D.S., Lieber, C.M., 2012. Macroporous nanowire nanoelectronic scaffolds for synthetic tissues. Nat. Mat. 11, 986–994. Valderrama, E., Garrido, P., Cabruja, E., Heiduschka, P., Harsch, A., Gopel, W., 1995. Microfabrication and characterisation of microelectrode arrays for in vivo nerve signal recording. In: The Ninth International Conference on Solid-State Sensors and Actuators, and Eurosensors IX. Stockholm, Sweden, June 25–29, 1995, pp. 63–66. Ward, M., Rajdev, P., Ellison., Irazoqui, P., 2009. Toward a comparison of microelectrodes for acute and chronic recordings. Brain Res. 1282, 183–200. Weiss, R., Basu, S., Hooshangi, S., Kalmbach, A., Karig, D., 2003. Genetic circuit building blocks for cellular computation, communications, and signal processing. Nat. Comput. 2, 1–40. Wisniewski, N., Reichert, M., 2000. Methods for reducing biosensor membrane biofouling. Colloids Surf., B 18, 197–219. Yarkoni, O., Donlon, L., Frankel, D., 2012. Creating a bio-hybrid signal transduction pathway: opening a new channel of communication between cells and machines. Bioinspir. Biomim. 7, 046017. Ye, H., Baber, M.D., Peng, R., Fussenegger, M., 2010. A synthetic optogenetic transcription device enhances blood-glucose homeostasis in mice. Science 332, 1565–1568. Zhang, F., Wang, L.P., Boyden, E.S., Deisseroth, K., 2006. Channelrhodopsin-2 and optical control of excitable cells. Nat. Methods 3, 785–792.