Synthetic biology is a combination of biology and engineering that attempts to gain new understanding and utilization of the natural world by applying engineering principles to design genetic elements for cellular control. Common examples of synthetic biology include memory switch  and oscillator  circuits that enable cells to switch between distinct states based on chemical or other input. This ability to sense an input from the environment enables cells to serve as a biosensor. A synthetically engineered biosensor [3–6] takes advantage of the versatility of living cells to detect and process inputs into analyzable outputs. Living cells, especially bacteria, can survive under many conditions [7,8] and can be engineered to detect and diagnose infectious biomarkers in clinical settings.
Genetically modified organisms (GMOs) are regulated by many laws and governmental bodies [9–13], making them difficult to deploy outside of laboratory settings. To allow engineered biosensors to be deployed for disease diagnosis in the field, these regulations must be satisfied. Additive manufacturing, in the form of three-dimensional (3D) printing, can serve as a solution to isolate GMOs from the environment when used in the field.
Critical additive manufacturing approaches were first commercialized three decades ago . Since then, additive manufacturing technology has evolved rapidly, making 3D printers widely available. The 3D printers utilized for this work function by successively depositing layers of extruded filaments to build a 3D structure . These types of 3D printers have already been proven to successfully design, print, and test medically relevant devices [16,17].
In this work, we chose to employ a robust approach for detecting the quorum signal of Pseudomonas aeruginosa (P. aeruginosa) that is frequently used in synthetic biology (Fig. 1(a)). The living biosensor consists of engineered Escherichia coli (E. coli) that are designed to detect the presence of this quorum signal, N-3- oxododecanoyl homoserine lactone (3O-C12). The living biosensor was constructed by introducing components of P. aeruginosa’s native 3O-C12 signaling pathway to E. coli. Here, a transcription factor, LasR, acts as a signal transducer by binding 3O-C12 and dimerizing. This complex then binds to a transcription activation domain found within its cognate promoter, PLas [6,18,19], which is also transplanted into E. coli. In our design, once bound, LasR promotes the expression of the gene for mCherry, a red fluorescent protein. We specifically chose 3O-C12 as the chemical to be detected due to its relevance in medical settings [20–25].
Fig. 1. Field detection of 3O-C12 through the use of a GMO and a 3D-printed enclosure. (a) The 3O-C12 biosensor was engineered to constitutively express a protein, LasR, which is able to bind a 3O-C12 molecule. Once bound to 3O-C12, the LasR protein dimerizes and binds to a DNA region contained in the PLas promoter, which promotes the expression of mCherry. (b) The biosensor is placed in a 3Dprinted enclosure. A biological sample can then be extracted and placed in a sample tube that is connected to the 3D-printed device. The device cover is then pressed, causing the biosensor to fall into the sample. (c) The biosensor turns red to report exposure to 3O-C12 in the sample; this signal can then be quantified using flow cytometry. Histograms represent data collected for 10 000 events for both the uninduced and the 100 nmol·L-1 3O-C12 samples (three samples).
As an example of the clinical relevance of this approach, in rural and less-developed areas, lung infections from the organism P. aeruginosa can be acquired in local clinics. Upon lung infection by P. aeruginosa (Fig. 1(b)), the organism may be found in lung sputum samples. This sputum can be collected and placed within commercially available sample tubes. As a result, an approach enabling these samples to be immediately analyzed in the field, using the tools of synthetic biology, would be ideal for early diagnosis.
In the solution presented here, the sample tube is mated with a unique cap containing the synthetic biological sensor (Fig. 1(b)). This unique 3D-printed device completely encases the living biosensor, allowing its transport from the laboratory, while also allowing the sensor to be introduced directly to a sample. When the sputum sample is ready to be analyzed, force is applied to the device by the user’s finger, causing the bacterial biosensor to break out of the housing and fall into the sample. Detection of 3O-C12, which will be produced by P. aeruginosa in the sample or is already present in the sample, causes the living biosensor to produce mCherry (Fig. 1(c)) and turns the sample red; this color can be detected through fluorescence analysis or visually with the human eye. Thus, a colorimetric change in the sample can indicate the presence of P. aeruginosa in the lungs.
P. aeruginosa functions as an opportunistic pathogen in patients with cystic fibrosis, causing recurring infections of the lungs and respiratory tract. Current diagnostic technology typically takes the form of enzyme-linked immunosorbent assays (ELISAs)  and real-time polymerase chain reaction (PCR) assays , which are either expensive or require extensive training to administer. The sensing approach described here could serve as a simple and inexpensive complement to current diagnosis techniques for detecting P. aeruginosa. Since the cellular biosensor can be grown within a secure, 3D-printed device for containment, this device can be used as a platform for the deployment of synthetically engineered biosensors in the field.
《2. Material and methods》
2. Material and methods
《2.1. Cell culture and molecular cloning》
2.1. Cell culture and molecular cloning
The E. coli strains used in this study were derived from the E. coli K12 parent strain and are listed in Table 1 . E. coli cells were grown in batch culture at 37°C in lysogeny broth (LB, Thermo Fisher Scientific Inc., USA) media . Resistant transformants were selected using 100 μg·mL-1 kanamycin (Thermo Fisher Scientific Inc., USA). For cloning, NEB® Turbo Competent E. coli (New England Biolabs® Inc., USA) was used as the host to propagate the plasmids. Cells were grown at 37°C in LB media supplemented with kanamycin. For growth on plates, LB media was supplemented with 2% agar (w/v) and kanamycin.
《Table 1 》
Table 1 List of plasmids, strains, relevant characteristics, and origins.
The plasmids used in this study are listed in Table 1 and were constructed through standard molecular cloning approaches [29,30]. The sensor plasmid was constructed by first amplifying an 843 bp PLtetO-1–lasR DNA region from a plasmid, pKDT17, which was ordered from Addgene.org, and originally came from the laboratory of Dr. Peter Greenberg. A 711 bp mCherry gene was amplified to produce an RBS–mCherry–t0 fragment, which was inserted into pKDT17 to obtain the LasR sensor plasmid, pDW065 (Fig. 2).
Fig. 2. 3O-C12 sensor plasmid map. The 3O-C12 sensor, also known as pDW065, is composed of three main elements: the plasmid backbone, the lasR cassette, and the mCherry cassette. The backbone consists of an ampicillin-resistance cassette and a pUC origin of replication. The lasR gene is placed under the control of a PLtetO-1 promoter, while the mCherry gene is under the control of the PLas promoter, which contains a binding site for the LasR protein.
《2.2. Dose–response characterization》
2.2. Dose–response characterization
To study this gene network’s dose response, starter cultures of E. coli with pDW065 were grown overnight in LB media supplemented with 50 lg·mL-1 kanamycin. The cultures were diluted to 0.2 OD600 (i.e., an optical density measured at 600 nm) and allowed to grow until reaching 0.6 OD600. The cultures were then diluted again, supplemented with 0–100 lmol·L-1 3O-C12 (Sigma-Aldrich Corporation, USA), and maintained at the exponential phase at 37°C until fully induced (about 8 h). Cultures at 0.6 OD600 were diluted to 1:100 in filtered phosphate-buffered saline (PBS, Thermo Fisher Scientific Inc., USA) and fluorescence was measured using an AccuriTM C6 flow cytometer (Becton, Dickinson and Company, USA).
《2.3. Design and fabrication of acrylonitrile butadiene styrene devices》
2.3. Design and fabrication of acrylonitrile butadiene styrene devices
The 3D-printed device was designed using Autodesk Inventor® (Autodesk Inc., USA), where it was rendered into a 3D model. The designed model was then exported as an STL file, which is a file format that can be used by Z-suite® (Zortrax Company, Poland), the software that was used to translate the 3D object into a set of printer instructions. The Z-suite® software was then used to export the file as a z-code file, which was used in a Zortrax M200 (Zortrax Company, Poland) to print the 3D design using an acrylonitrile butadiene styrene (ABS) variant, ABS ULTRAT. This proprietary ABS variant is composed of ABS (90%–100%), stabilizer (0%–5%), lubricants (0%–2%), mineral oil (0%–4%), tallow (0%–4%), wax (0%–4%), polycarbonate (PC, 0%–3%) and anti-oxidant (< 2%).
《2.4. Device surface polishing》
2.4. Device surface polishing
In order to smooth the surface roughness of the device components post-printing, the 3D-printed components were first placed on a sterile surface. Paper towels were attached to the inner surface of a 1000 mL beaker and saturated with acetone. The beaker was inverted over the device components, and care was taken to avoid direct contact between the liquid acetone and the components. After 1 h, the beaker was removed and the devices were exposed to ambient air for 2 h.
《2.5. Device use and operation》
2.5. Device use and operation
To encase the 3O-C12 living biosensor into the 3D-printed device, a 40 μL agar plug was added to the bacterial well in the housing; the engineered E. coli cells were then plated onto the agar surface. After overnight culture, the plunger was pressed into the housing and a press-fit seal was formed to isolate the bacterial cells from the environment. The cap containing the living biosensor could then be safely transferred to the field and stored in a refrigerator for up to 30 d before use. When ready for use, the sample to be examined was placed into a 14 mL culture tube. The culture tube cap was removed and the assembled device containing the living biosensor was then snapped into place onto the culture tube. To activate the biosensing, the cover was firmly pressed with a lateral pinch grip until the bacterial well fell into the culture tube. The cell culture was then incubated at 37°C for 8–9 h and subsequently detected with fluorescence spectroscopy, or simply with the human eye. For the flow cytometry analysis used in this study, samples were collected at an OD600 of 0.6 and diluted to 1:100 in deionized water. Fluorescence measurements were acquired using an AccuriTM C6 flow cytometer. Following the completion of experiments, the system can be sterilized in an autoclave; or, if an autoclave is not available, the components can be disassembled and immersed in sodium hypochlorite (i.e., bleach).
《2.6. Gene regulatory network modeling》
2.6. Gene regulatory network modeling
We engineered an E. coli biosensor capable of expressing mCherry when exposed to 3O-C12. The activator, LasR, was constitutively produced in an inactive state. When 3O-C12 is introduced to the cell, it binds to LasR. This complex activates a gene regulatory network. As we did not engineer any post-transcriptional regulation, the general behavior of the regulatory gene networks can be modeled by the dynamics of the transcriptional response site.
To capture the underlying dynamics of our system, we decided to employ the Michaelis–Menten formalism for the modeling transcription. As we used a high copy count plasmid, we can assume that stochastic effects are negligible for specific genetic components. This allows us to simplify our simulation with a set of ordinary differential equations.
By assuming that the inducer, 3O-C12, is well mixed within our reaction chamber and that transport of 3O-C12 into the cytosol is consistent among individual cells, we can model the transcriptional response to 3O-C12 as shown in Eq. (1). Note that this equation assumes that the majority of post-transcriptional events (translation and protein maturation) occur consistently and linearly in response to mRNA production. This is a fair assumption, if we also assume that the ribosome binding site strengths are constant and that there is an abundance of resources and ribosomes within the cell.
Eq. (1) relates the time rate of change of mCherry to a number of inputs and parameters that drive transcriptional events.
Inherent in this model is the assumption that LasR concentrations within the cells remain relatively constant. The first term on the right side of Eq. (1) is a Hill function  relating the intracellular concentration of 3O-C12, [3O-C12], to the rate of mCherry production. This term is a function of the concentration of arabinose but it includes parameters for the maximum transcription rate, V1max, and the kinetic coefficient, K1. In addition, the Hill coefficient, n, relates the degree of cooperativeness of the RNA polymerase binding to the promoter site.
The second term on the right side of Eq. (1), V1leak, describes the “leak” of the promoter site. In physical terms, this is the rate of mCherry produced in the absence of 3O-C12. The final term on the right side describes the rate of degradation of mCherry within the cell. This term relates a decay constant, d1, to the concentration of mCherry, [mCherryi]. This equation may be fit to experimental data in order to better predict and model underlying dynamics.
《2.7. Model derivation: doseresponse curve fitting, simulation, and plotting》
2.7. Model derivation: doseresponse curve fitting, simulation, and plotting
Experimental data were analyzed and fit according to Eq. (1) using the MATLAB® (MathWorks Inc., USA) optimization toolbox. Once parameter values were identified, fits were confirmed with the MATLAB curve-fitting toolbox. The goodness of fit for the simulated equations was evaluated using the R2 value. Simulations were coded in Python and numerically integrated using LSODE  in the FORTRAN library. Simulations, with experimental data, were plotted in Python using the Matplotlib library.
《2.8. Model derivation: finite element analysis of biosensor》
2.8. Model derivation: finite element analysis of biosensor
Finite element analysis was performed using COMSOL (COMSOL Multiphysics® v5.2, COMSOL Inc., Sweden). All simulation results represent a stationary study performed using the Solid Mechanics module. Autodesk Inventor solid model files were imported into COMSOL to define the geometry for these simulations. Material properties were then entered into COMSOL to define the properties of our ABS material for analysis. Boundary constraints and applied loads were then defined. All simulation plots shown here were generated using COMSOL’s integrated graphics.
The 3D-printed device was designed to attach to a common laboratory sample tube form factor (Fig. 3(a)) to ensure ease of use. The device consists of two components (Fig. 3(b)), referred to herein as the “housing” and “cover.” The cover serves to isolate the contents enclosed in the housing interior from the surrounding environment. In addition, the top of the cover is designed to be ergonomic for pressing with a thumb or forefinger. Once assembled, the device can be enclosed within a rectangular prism with dimensions of approximately 0.026 m × 0.026 m × 0.023 m. A cross-sectional schematic of the assembled device (Fig. 3(c)) shows the housing and cover held together by a press-fit. The housing holds a bacterial well, which contains rich agar with a small lawn of bacteria; this serves to sustain and contain the bacterial biosensor within the device. Next, we added small mechanical features to the device, the design of which was based on modeling with a finite element method software suite , COMSOL Multiphysics®. It is important to note that our models were based on construction with ABS, a thermoplastic. Thermoplastic polymers become pliable after heating, harden upon cooling, and are frequently used in 3D printing. ABS is often used because its glass transition temperature of 105°C makes it well-suited for 3D printing, as the extruder temperature in many 3D printers is set at 230°C . This high printing temperature also enhances the sterility of the printed device.
Fig. 3. Device details and schematic. (a) The 3D-printed device can be attached to a test tube, where it forms a seal. This device is separated into two parts: the cover and the housing. (b) The device cover is sealed onto the housing using a press-fit. (c) The device housing contains a bacterial culture well, which serves as a chamber in which culture nutrients, agar, and living biosensors can be deposited. The housing is designed with a stress-focusing cutout, which serves to concentrate the pressing force transmitted from the cover to separate the bacterial well from the housing. (d) The stresses concentrated by the stress-focusing cutout were modeled using COMSOL to ensure that the device is usable with a one-handed lateral pinch grip. Max: maximum; min: minimum.
In addition to the cover, housing, and bacterial well features discussed above, we designed a small mechanical feature that enables an axial force (i.e., a force applied directly downward on the cover, along the major axis of the sample tube) to cause the bottom of the housing to quickly separate from the housing, thus releasing the biosensor into the sample during a (designed) catastrophic failure. In order to cause this fast yielding, we created two depressed annuli features in the housing bottom (on both the interior and exterior, shown in Figs. 4 and 5 with additional detail). Herein, we refer to these annuli as “stress-focusing cutouts.” These cutout features serve to concentrate the stresses that develop from applied axial force on the cover, thus allowing simple activation of the device by means of a user’s thumb using a pinch grip.
Fig. 4. Device boundary constraints, loads, and mesh elements for stress simulation. (a) Boundary constraints. The purple highlighted boundary of the biosensor housing is the sole fixed boundary constraint used for all simulations. All other boundaries are free. (b) Boundary loads. The purple highlighted boundary shows the surface where the sensor-activating lid applies a distributed pressure load during use. All simulations used a total load of 100 N distributed across this highlighted area. (c) Mesh elements. COMSOL predefined ‘‘normal” meshing was used for all simulations.
Fig. 5. Cross-sections of three different device iterations with von Mises stress distribution. (a) Final design configuration and stress distribution of the stressfocusing cutout portion of the biosensor housing; (b) an alternative design with arcs coming to a point; (c) an alternative design with peaks. The largest stress and best distribution of high stress, as shown here, resulted in the final design in (a) being chosen. All simulations had the following material properties: a material density of 1150 kg·m-3 , Young’s modulus of 2 GPa, and Poisson’s ratio of 0.35. All simulations used a total load of 100 N distributed across the boundary load area. COMSOL predefined ‘‘normal” meshing was used for all simulations.
Finite element analysis was used to numerically evaluate the stresses that developed within the device when force was applied during its use. The stress profile through the stress-focusing cutout was modeled using COMSOL for different designs in order to arrive at the final version, which is presented in this paper . An evenly distributed pressure load was applied to the surface of the housing by the cover to generate stress in the cutout. Three different designs of the stress-focusing cutout were modeled in order to identify the stress magnitude and concentration in the cutout for a given applied force. Relative to other preliminary designs, the final design presented here (Fig. 3(d)) has both the highest maximum stress developed within the cutout for a given applied force and the best spatial distribution of the high stress concentration through the cutout. This design of the cutout allows our device to be constructed from a relatively high-strength material, while still requiring a relatively small force to activate the sensor by breaking the cutout.
The device was also designed to be simple to assemble and use, as shown in Fig. 6. A 40 μL agar plug was added to the bacterial well and the 3O-C12 living biosensor was plated on its surface. The device cover was then fit onto the housing and attached to a culture tube, which isolates the biological sample from the environment. The user’s thumb is then used to activate the device. This applied force on the cover develops stress in the stress-focusing cutout within the housing. The bacterial well then breaks away from the housing and falls into the sample. After analysis, the cover can be pushed into the housing to create the second stage of the press-fit seal, thus preventing the sample within the test tube from escaping during a leak, in the event that the device is inverted or agitated. This seal also allows the user to transfer the biohazardous waste back to a biosafety lab for proper disposal.
Fig. 6. Demonstration of device use. (a) Agar is added to the bacterial well within the bioassay device. (b) The device lid is aligned with the device base and pressed with the thumb until the press-fit seals. Using the thumb, force can be applied to the lid, which releases the press-fit and directs force onto the stress-focusing cutout. (c) The bacterial well then falls into the sample, where it inoculates the sample with the biosensor. (d) The liquid and bacteria within the test tube remain isolated from the environment.
Upon immersion in the sample, the biosensor was grown for 8 h. The 3O-C12-activated biosensor then produced mCherry protein. As shown in the flow cytometry analysis (Fig. 7(a)), the induced biosensor showed a shift in fluorescence emitted at 590 nm, which was distinctly separate from the un-induced biosensor control fluorescent peak. In our dose–response experiment (Fig. 7(b)), 0.1μmol·L-1 of 3O-C12 was sufficient to induce the living biosensor to produce visible fluorescence in the tube after 8 h growth. When quantified with a flow cytometer, the dynamic range of the sensor was observed to be in the range of 0.01–1 μmol·L-1 3O-C12.
Fig. 7. Biosensor dose–response curve. (a) When induced with 3O-C12, the biosensor goes from an un-induced state to an induced state. After being induced, the biosensor fluoresces by producing mCherry protein. The biosensor was exposed to varying concentration decades of 3O-C12 and maintained in exponential state. Data were collected for 10 000 events for the un-induced and the 100 nmol·L-1 3O-C12 samples (three samples). (b) After 8 h of induction, the biosensor reaches an equilibrium state of mCherry expression, which can be quantified. The system can then be fitted to a Hill function with an R2 value of 0.987. (c) With enough time, the sensor becomes a toggle switch and is able to output an on/off response.
Although synthetic biology-based biosensors can be versatile in the detection of chemical inputs, many are currently confined to a laboratory setting due to regulations that restrict the use and transport of GMOs. It is worth noting that a paper-based synthetic biosensor was recently developed by Pardee et al. . This paper-based biosensor was developed by freeze-drying cell-free synthetic gene networks on paper to create materials that had the fundamental transcription and translation properties of a cell yet remained abiotic. The paper-based approach offers faster detection than cell-based living biosensors and eliminates the use of living bacterial cells for detection, which makes it suitable for the safe deployment of engineered gene circuits beyond the lab. However, the paper-based biosensor requires different sample processing and a different set of specialized materials. Thus, the system we describe here allows the complementary use of living biosensors, which can potentially be integrated in parallel with cell-free detection methods in the future. We envision our system being especially beneficial in areas that lack significant medical infrastructure or access to healthcare.
The 3O-C12 biosensor is able to detect 3O-C12 concentrations ranging from 0.1 to 10 μmol·L-1 . It is well documented [36–39] that a P. aeruginosa infection in the respiratory tracts can cause tissue inflammation. Although the specific method of action of chemical 3O-C12 remains unknown, it is believed to occur through interactions with toll-like receptors on the cell’s surface , which produce an inflammatory response. This inflammation is triggered by a 3O-C12 concentration in the range from 10 to 100 μmol·L-1 . The range of concentrations found in infected lungs lies outside of the dynamic response range of our 3O-C12 biosensor. However, a straightforward series of dilutions (e.g., 1/2, 1/4, ...) with media—as is done for other quantification assays, such as the Bradford Assay —can be performed before analysis. Diluting the sputum collected in media will also allow the cells to divide faster with a more suitable environment for growth.
The bioassay device can be fabricated anywhere a 3D printer is available. The 3D printer that we used for this research is sold for less than 2000 USD, and many new 3D printers can be purchased for prices ranging from 200 to 500 USD. Our bioassay device can also be fabricated quickly, as needed, with devices ready after less than 2 h (Fig. 8(a)). The material cost to manufacture one of the 3D-printed devices described here is approximately 0.50 USD, using 8 g of ABS. In comparison with the biosensor housing and cover, the biosensor itself contributes only a small portion of the overall cost of the device. The printer can be used at any location, making the deployment of this device flexible. Biosensors can also be readily grown, freeze-dried, and shipped worldwide. With a small quantity of freeze-dried biosensor loaded into the devices (Fig. 8(b)), they can be sealed and be ready for use. The devices can then be directly transported to the point-of-care for use (Fig. 8(c)). In the case of an accident, in which a device comes into contact with the environment, it is important to quantify how well the device isolates the biosensor from the environment. An experiment using nutrient-rich media (Fig. 8(d)) was used to simulate a worst-case scenario. When shaken at 140 r·min-1 in 100 mL of media (Fig. 8(e)), the bacterial well that had been introduced into the media was able to propagate cells immediately. When the entire intact device was submerged in media, the sealed device was able to isolate the biosensor from the environment for a period of 14 h before the cells escaped and showed visible turbidity in 100 mL of media.
Fig. 8. From the printer, to the patient, to the lab. (a) A 3D printer is used to print the bioassay device. (b) The genetically engineered sensor is cultured within the 3D-printed bioassay device. (c) The device with the sensor is transported to the patient’s location. (d) The device is water-resistant, so in the event that the device falls into a liquid, there is time to remove it without the escape of GMOs. (e) When the device is used normally, and the bacteria are directly deposited into nutrient-rich media, they will immediately begin to grow. However, the sealed device could also prevent liquid from reaching the cells for a period of 14 h in the event of device mishandling resulting in emersion in environmental liquids. Data collected for 10 000 events for the sealed and unsealed three samples.
In the field, a sputum sample (Fig. 9(a)) can be collected from the patient and diluted into a sample tube with cell media. To ensure that no bacteria outcompete the kanamycin-tolerant living biosensor in the sample, a kanamycin antibiotic could be added, making the bacterial samples from the patient’s sputum unable to survive. The biosensor cover would then be pressed firmly and actuated, forcing the freeze-dried bacterial pellet to enter the sputum sample. Next, the tube could be incubated and analyzed in the field (Fig. 9(b)). The biosensor can simultaneously quantify specific levels of 3O-C12 exposure when maintained in exponential state or can switch into an on/off behavior by simply adding the biosensor to the sample (Fig. 9(c)). A 9 h incubation allows the biosensor to turn the liquid sample visibly red, thus serving as a two-state system that is useful to visually determine in the field whether a patient has a P. aeruginosa infection.
Fig. 9. Biosensor activity on contact with 3O-C12. (a) A sputum sample is taken from the patient and the 3D-printed device is collapsed to release the bacteria into the sample. (b) The biosensor is built to enable both quantification of 3O-C12 concentration and a more simple on/off behavior in which 3O-C12 at or above 10 nmol•L-1 will turn the sample visibly red. (c) The biosensor turns visibly red with a 98-fold fluorescence increase, compared with the initial fluorescence value, F/F0, after 10 h of exposure to the inducer. mCherry protein production continues as long as cells remain exposed to the inducer. Data collected for 10 000 events for the uninduced and the 100 nmol•L-1 3O-C12 samples (three samples).
The device was designed to be fabricated and used by personnel or volunteers with minimal training. After attachment to a culture tube, the device and culture tube can be gripped by the hand, and the thumb can be used in a lateral pinch grip to activate the sensor. The average lateral pinch strength of an adult using the dominant hand ranges from approximately 5.0–7.5 kg of force , which is sufficient for the use of this device. Due to the design of the stressfocusing cutout presented here, the lateral pinch force generated by an adult will allow the device to reliably function in the field.
The experimental results presented here indicate that the 3O-C12 biosensor is able to sense inducer concentrations when P. aeruginosa biofilms are present, as in the case of recurring infections. The living biosensor, while useful for the purpose of detection, has the potential to face fewer regulatory hurdles due to its enclosure within the bioassay device, which permits its contained deployment outside the lab. The 3D-printed device turns the GMO into a component of the overall device and ensures that the biosensor is isolated from the environment. Thus, 3D printing this device enables our living biosensor, with its versatility to process environmental input, to be field-deployed as a novel, costeffective diagnostic technology.
The authors acknowledge support from funding from federal agencies of the United States including, the National Science Foundation (1709238), as well as funding from Office of Naval Research (N00014-17-12306 and N00014-15-1-2502), and the Air Force Office of Scientific Research (FA9550-13-1-0108). No funding agencies played a significant role in the study design.
Wolozny and Ruder conceived the idea for the system described here. All authors designed and performed the experiments, analyzed the data, discussed the results, wrote this manuscript and commented on the paper. Wolozny, Lake, Long, and Ruder revised the manuscript.
《Compliance with ethics guidelines》
Compliance with ethics guidelines
Daniel Wolozny, John R. Lake, Paul G. Movizzo, Zhicheng Long, and Warren C. Ruder declare that they have no conflict of interest or financial conflicts to disclose.