Remote Cell Growth Sensing Using Self-Sustained Bio-Oscillations

A smart sensor system for cell culture real-time supervision is proposed, allowing for a significant reduction in human effort applied to this type of assay. The approach converts the cell culture under test into a suitable “biological” oscillator. The system enables the remote acquisition and management of the “biological” oscillation signals through a secure web interface. The indirectly observed biological properties are cell growth and cell number, which are straightforwardly related to the measured bio-oscillation signal parameters, i.e., frequency and amplitude. The sensor extracts the information without complex circuitry for acquisition and measurement, taking advantage of the microcontroller features. A discrete prototype for sensing and remote monitoring is presented along with the experimental results obtained from the performed measurements, achieving the expected performance and outcomes.


Introduction
Biological cell culture assays performed in biomedical research laboratories are of vital importance [1]. However, estimating the cell growth on test plates is time-consuming and requires intensive human supervision during the whole process, introducing a large variability factor to assays. Characterizing the number of cells in a culture at a specific time, as well as measuring the cell proliferation rate, have deep implications in biomedicine, both at the technical and biological levels. The cell density in the plate will indicate the success of many different techniques, including transfection, infection, chemical response, etc. Cells distributed too sparsely will have problems growing and will react strangely to all the treatments. More important from a biological point of view, cell proliferation is a significant figure of merit to characterize a cell line. Excessive proliferation is the hallmark of cancer cells, and any physical or chemical treatments slowing down cell growth may lead to potential oncological therapies. Lack of proliferation usually represents excessive cell death, either due to extrinsic or intrinsic mechanisms, or the activation of cellular aging pathways (cellular senescence). Thus, understanding the processes that slow down or accelerate the proliferation of a culture, including the dynamics of these processes, is a powerful research tool [1,2].
The sensor devices are custom electronic prototypes that are also capable of measuring humidity, temperature, battery, and the analog sensor electrical properties. As shown above, the sensor devices can also measure the amplitude and frequency of a biological oscillator (OBT approach). The gateway (Intel Edison SoC) manages the experimental measurement protocol according to a set of configurable settings that exist on an online database, provided by a data server through the internet. The final components, the data server and web interface, represent the services layer. These services would be used by the end-user (a biomedical researcher). The user selects measurement rates and experiment start and end times through the secure web interface to begin the experiment and can supervise ongoing experiments through the web interface.

The OBT Sensor
Our approach uses an electrical cell-substrate impedance spectroscopy (ECIS) sensor [14]. Implementation of an ECIS sensor usually requires complex excitation and acquisition circuits to measure the bioimpedance response of the sample under test. This work takes advantage of this noninvasive impedance-based cell culture technique to reduce both the cost and the human effort. Furthermore, it also decreases the measurement dispersion since it allows a single culture per experimental assay. The electrode solution implemented for this system is provided by Applied Biophysics [15], and is presented on Figure 2.
The biological sample under test is a cell culture formed from mouse neuroblastoma cell lines (the N2A cell line). The cells were cultured in medium consisting of 50% DMEM high glucose (Biowest, Nuaillé, France) and 50% Opti-MEM (Gibco, Alcobendas, Spain) supplemented with 10% (v/v) foetal bovine serum (FBS) (Gibco), 2 mM L-glutamine, 50 μg/mL streptomycin and 50 U/mL penicillin (Sigma-Aldrich, Madrid, Spain). The cells were maintained at 37 °C in a humidified atmosphere with 5% CO2 and they were routinely sub-cultured. Different initial numbers of cells were seeded at the well-sensors for our experiments: 2500, 5000 and 10,000. Two well-sensors were filled only with culture medium. Petri plate cultures were also seeded with the same cell densities to further match with the proposed bioimpedance test. The cell count was done over Petri plate The sensor devices are custom electronic prototypes that are also capable of measuring humidity, temperature, battery, and the analog sensor electrical properties. As shown above, the sensor devices can also measure the amplitude and frequency of a biological oscillator (OBT approach). The gateway (Intel Edison SoC) manages the experimental measurement protocol according to a set of configurable settings that exist on an online database, provided by a data server through the internet. The final components, the data server and web interface, represent the services layer. These services would be used by the end-user (a biomedical researcher). The user selects measurement rates and experiment start and end times through the secure web interface to begin the experiment and can supervise ongoing experiments through the web interface.

The OBT Sensor
Our approach uses an electrical cell-substrate impedance spectroscopy (ECIS) sensor [14]. Implementation of an ECIS sensor usually requires complex excitation and acquisition circuits to measure the bioimpedance response of the sample under test. This work takes advantage of this non-invasive impedance-based cell culture technique to reduce both the cost and the human effort. Furthermore, it also decreases the measurement dispersion since it allows a single culture per experimental assay. The electrode solution implemented for this system is provided by Applied Biophysics [15], and is presented on Figure 2.
The biological sample under test is a cell culture formed from mouse neuroblastoma cell lines (the N2A cell line). The cells were cultured in medium consisting of 50% DMEM high glucose (Biowest, Nuaillé, France) and 50% Opti-MEM (Gibco, Alcobendas, Spain) supplemented with 10% (v/v) foetal bovine serum (FBS) (Gibco), 2 mM L-glutamine, 50 µg/mL streptomycin and 50 U/mL penicillin (Sigma-Aldrich, Madrid, Spain). The cells were maintained at 37 • C in a humidified atmosphere with 5% CO 2 and they were routinely sub-cultured. Different initial numbers of cells were seeded at the well-sensors for our experiments: 2500, 5000 and 10,000. Two well-sensors were filled only with culture medium. Petri plate cultures were also seeded with the same cell densities to further match with the proposed bioimpedance test. The cell count was done over Petri plate preparations for the three initial number of cells: 2500, 5000 and 10,000. The fill factor was estimated considering a N2A cell area value of 184 µm. preparations for the three initial number of cells: 2500, 5000 and 10,000. The fill factor was estimated considering a N2A cell area value of 184 μm.

Oscillation Based Test Methodology
The cornerstone of our method is to incorporate the ECIS sensor within the feedback loop of an electronic oscillator, using the OBT methodology depicted in [1][2][3]. The proposed idea is a strategy for the conversion of the cell culture into oscillators, whose characteristic parameters (frequency, amplitude, phase, etc.) are related to the cell culture evolution and can be easily determined. Put simply, a band pass filter followed by the bioimpedance of the cell culture (this is the proposed cellelectrode interface) and a comparator altogether close a nonlinear feedback loop to establish the oscillations (see the inner box on Figure 3, OBT). The closed-loop system is optimized for cellelectrode impedance analysis. Variations on the electrical properties of the cell-electrode complex will generate variations on the oscillation parameters in the oscillation output signal. The cell amount is reflected on an intrinsic parameter of the system which is directly proportional to the number of cells located on the electrodes. This parameter is the fill-factor, which is defined as the percentage of the electrode area covered by cells (always less than one). The fill-factor (ff) or cell-to-electrode overlap area variation generates different values for the impedance being measured by the sensor. Figure 4 displays the typical behavior of an experimental cell-electrode interface bode, showing curves for several cell occupation areas, ff. These curves are directly related to the electrode size, technology and biological material. Thus, to calibrate the bio-oscillator, we must tune the peak frequency (fpeak) of the bandpass filter in the nonlinear feedback loop as indicated in Figure 4. When frequencies around this peak frequency are considered, the system has optimum ff sensitivity for magnitude and phase. This means that both the magnitude and phase response can be correlated to the ff parameter. Most ECIS techniques search for the best frequency response for optimum impedance characterization, and then perform the measurements knowing the ff dependence. Therefore, absolute magnitudes ( Figure 4) or normalized magnitudes (CI: cell index) can be used as sensitivity curves for this impedance sensor and assays.
On the other hand, the output of the biological oscillator (the input of the nonlinear element, the comparator in Figure 3a) is approximately sinusoidal due to the bandpass characteristics of the global structure. This fact allows us to use the linear approximation stated in the described function method [3,4] for the mathematical treatment of the nonlinear element [16,17]. An oscillatory solution can be found for each fill-factor, ff, which directly correlates the main oscillation parameters with the occupied cell culture area and the number of cells in the culture. Thus, in short, the output signal oscillation parameters, amplitudes and frequencies, are functions of the ff parameter (see [3,4] for mathematical details). Figure 5 illustrates the main harmonic frequency and the amplitude of the predicted oscillation signal for a selected bandpass peak frequency at 1 kHz in our experimental example or study case ( Figure 4). For each cell line or type of electrode, the sensitivity of the oscillation parameters regarding the bandpass filter parameters,

Oscillation Based Test Methodology
The cornerstone of our method is to incorporate the ECIS sensor within the feedback loop of an electronic oscillator, using the OBT methodology depicted in [1][2][3]. The proposed idea is a strategy for the conversion of the cell culture into oscillators, whose characteristic parameters (frequency, amplitude, phase, etc.) are related to the cell culture evolution and can be easily determined. Put simply, a band pass filter followed by the bioimpedance of the cell culture (this is the proposed cell-electrode interface) and a comparator altogether close a nonlinear feedback loop to establish the oscillations (see the inner box on Figure 3, OBT). The closed-loop system is optimized for cell-electrode impedance analysis. Variations on the electrical properties of the cell-electrode complex will generate variations on the oscillation parameters in the oscillation output signal. The cell amount is reflected on an intrinsic parameter of the system which is directly proportional to the number of cells located on the electrodes. This parameter is the fill-factor, which is defined as the percentage of the electrode area covered by cells (always less than one). The fill-factor (ff) or cell-to-electrode overlap area variation generates different values for the impedance being measured by the sensor. Figure 4 displays the typical behavior of an experimental cell-electrode interface bode, showing curves for several cell occupation areas, ff. These curves are directly related to the electrode size, technology and biological material. Thus, to calibrate the bio-oscillator, we must tune the peak frequency (fpeak) of the bandpass filter in the nonlinear feedback loop as indicated in Figure 4. When frequencies around this peak frequency are considered, the system has optimum ff sensitivity for magnitude and phase. This means that both the magnitude and phase response can be correlated to the ff parameter. Most ECIS techniques search for the best frequency response for optimum impedance characterization, and then perform the measurements knowing the ff dependence. Therefore, absolute magnitudes ( Figure 4) or normalized magnitudes (CI: cell index) can be used as sensitivity curves for this impedance sensor and assays.
On the other hand, the output of the biological oscillator (the input of the nonlinear element, the comparator in Figure 3a) is approximately sinusoidal due to the bandpass characteristics of the global structure. This fact allows us to use the linear approximation stated in the described function method [3,4] for the mathematical treatment of the nonlinear element [16,17]. An oscillatory solution can be found for each fill-factor, ff, which directly correlates the main oscillation parameters with the occupied cell culture area and the number of cells in the culture. Thus, in short, the output signal oscillation parameters, amplitudes and frequencies, are functions of the ff parameter (see [3,4] for mathematical details). Figure 5 illustrates the main harmonic frequency and the amplitude of the predicted oscillation signal for a selected bandpass peak frequency at 1 kHz in our experimental example or study case ( Figure 4). For each cell line or type of electrode, the sensitivity of the oscillation parameters regarding the bandpass filter parameters, that is, the peak frequency and the quality factor, has to be determined in order to maximize both the oscillation parameter changes and the dynamic range of the measurements. In Figure 5, the oscillation parameters increase when the cell-to-electrode area overlap (ff) increases from 0 to 1. For each cell line, this behavior will be similar. Thus, acquiring this oscillation output signal is enough to provide accurate measurements of the cell culture status and provide safe and secure data storage for cell dynamics in cell cultures. Moreover, the amount of data generated from this real-time monitoring process enables for further data analysis insight than traditional measurement methods allow [4]. that is, the peak frequency and the quality factor, has to be determined in order to maximize both the oscillation parameter changes and the dynamic range of the measurements. In Figure 5, the oscillation parameters increase when the cell-to-electrode area overlap (ff) increases from 0 to 1. For each cell line, this behavior will be similar. Thus, acquiring this oscillation output signal is enough to provide accurate measurements of the cell culture status and provide safe and secure data storage for cell dynamics in cell cultures. Moreover, the amount of data generated from this real-time monitoring process enables for further data analysis insight than traditional measurement methods allow [4].  that is, the peak frequency and the quality factor, has to be determined in order to maximize both the oscillation parameter changes and the dynamic range of the measurements. In Figure 5, the oscillation parameters increase when the cell-to-electrode area overlap (ff) increases from 0 to 1. For each cell line, this behavior will be similar. Thus, acquiring this oscillation output signal is enough to provide accurate measurements of the cell culture status and provide safe and secure data storage for cell dynamics in cell cultures. Moreover, the amount of data generated from this real-time monitoring process enables for further data analysis insight than traditional measurement methods allow [4].  The device was developed with discrete component blocks, and the full diagram is presented in Figure 3a. In addition to the bio-oscillator, the sensor device implements a microcontroller based on ARM Cortex-M7 technology, configured to manage the sensor control signals, to perform the acquisition using the embedded analogue to digital converter (ADC). For fast stabilization of the oscillation process, a trigger circuit is enabled and equipped with an external communication interface via a Bluetooth module. A smart design was considered to optimize power consumption since this device is running on Li-ion batteries inside a cell culture incubator to achieve full experiment autonomy. The current battery life estimation is also provided as part of the sensor measurements.
In addition to the capabilities depicted above, a set of environmental sensors is included to also measure the relative humidity and temperature during the whole experimental process. The device employed is the Sensirion SHT21 (Amidata S. A., Pozuelo de Alarcon, Madrid, Spain), which is connected to the microcontroller via an I2C (Inter Integrated Circuit) bus as it is depicted on Figure 3a. The device was developed with discrete component blocks, and the full diagram is presented in Figure 3a. In addition to the bio-oscillator, the sensor device implements a microcontroller based on ARM Cortex-M7 technology, configured to manage the sensor control signals, to perform the acquisition using the embedded analogue to digital converter (ADC). For fast stabilization of the oscillation process, a trigger circuit is enabled and equipped with an external communication interface via a Bluetooth module. A smart design was considered to optimize power consumption since this device is running on Li-ion batteries inside a cell culture incubator to achieve full experiment autonomy. The current battery life estimation is also provided as part of the sensor measurements.
In addition to the capabilities depicted above, a set of environmental sensors is included to also measure the relative humidity and temperature during the whole experimental process. The device employed is the Sensirion SHT21 (Amidata S. A., Pozuelo de Alarcon, Madrid, Spain), which is connected to the microcontroller via an I2C (Inter Integrated Circuit) bus as it is depicted on Figure 3a.

Signal Acquisition and Processing
The self-sustained oscillator provides a sinusoidal wave in which oscillation parameters are directly related to the physical magnitudes under analysis. The microcontroller embeds an analog to digital converter (ADC) with 12 bits of precision, which is enough for performing a precise determination of both frequency and amplitude on the input signal. The estimation of both parameters proposed here requires signal processing based on algorithms to infer their values. It is necessary to apply discrete frequency domain analysis via fast Fourier transform (FFT) [18,19] and perform a good estimation using appropriate interpolation techniques [20]. All processing is programmed and carried out in real-time at the microcontroller.
The signal processing implementation accounts for both time and performance optimization in terms of memory and processor resources. The architecture is depicted in Figure 6.

Signal Acquisition and Processing
The self-sustained oscillator provides a sinusoidal wave in which oscillation parameters are directly related to the physical magnitudes under analysis. The microcontroller embeds an analog to digital converter (ADC) with 12 bits of precision, which is enough for performing a precise determination of both frequency and amplitude on the input signal. The estimation of both parameters proposed here requires signal processing based on algorithms to infer their values. It is necessary to apply discrete frequency domain analysis via fast Fourier transform (FFT) [18,19] and perform a good estimation using appropriate interpolation techniques [20]. All processing is programmed and carried out in real-time at the microcontroller.
The signal processing implementation accounts for both time and performance optimization in terms of memory and processor resources. The architecture is depicted in Figure 6. Figure 6. Schema for the signal processing library. The inputs for the system include the analog signal acquired from the sensor (signal), the requested fast Fourier transform (FFT) order, n, the c-parameter for the Gaussian window (c), the sampling frequency (fs) and a calibration factor (k_ampl) for the amplitude. fn and an correspond to the n-harmonic frequency and amplitude values. Total harmonic distortion (THD), signal-to-noise ratio (SNR) and signal-noise ratio+distortion (SNR+D) are also obtained. Q is a quality factor for the signal.
The system computes and estimates the signal parameters, which are related to cell culture growth (frequency and amplitude). Finally, all data gathered are sent to the gateway using the Bluetooth link.

Gateway
The system has been designed to minimize sensor power consumption. Three operation modes are defined in the sensor which are controlled by the gateway: standby mode, low power mode and acquisition mode.
Standby mode is used when waiting for instructions from the gateway. All devices except the microcontroller and the Bluetooth interface are turned off to minimize power consumption. The second mode is low power mode. Within this mode, the system achieves the lowest power consumption rates. Everything is off except for a peripheral on the microcontroller; a real-time clock with an alarm established by the gateway for the next measurement time. Finally, the acquisition mode must perform the measurement acquisition from the sensors and send the information back to the gateway.
The trace of the system behavior is depicted on Figure 7. In Figure 7a, the flow diagram from the gateway control is depicted. The process begins with the search for a device waiting for instructions (standby mode) and a check for active experiments on the database. If all the conditions are met, then a measure is triggered (set the device to acquisition mode) and after completion, the device is set to sleep (low power mode). Figure 6. Schema for the signal processing library. The inputs for the system include the analog signal acquired from the sensor (signal), the requested fast Fourier transform (FFT) order, n, the c-parameter for the Gaussian window (c), the sampling frequency (fs) and a calibration factor (k_ampl) for the amplitude. fn and an correspond to the n-harmonic frequency and amplitude values. Total harmonic distortion (THD), signal-to-noise ratio (SNR) and signal-noise ratio+distortion (SNR+D) are also obtained. Q is a quality factor for the signal.
The system computes and estimates the signal parameters, which are related to cell culture growth (frequency and amplitude). Finally, all data gathered are sent to the gateway using the Bluetooth link.

Gateway
The system has been designed to minimize sensor power consumption. Three operation modes are defined in the sensor which are controlled by the gateway: standby mode, low power mode and acquisition mode.
Standby mode is used when waiting for instructions from the gateway. All devices except the microcontroller and the Bluetooth interface are turned off to minimize power consumption. The second mode is low power mode. Within this mode, the system achieves the lowest power consumption rates. Everything is off except for a peripheral on the microcontroller; a real-time clock with an alarm established by the gateway for the next measurement time. Finally, the acquisition mode must perform the measurement acquisition from the sensors and send the information back to the gateway.
The trace of the system behavior is depicted on Figure 7. In Figure 7a, the flow diagram from the gateway control is depicted. The process begins with the search for a device waiting for instructions (standby mode) and a check for active experiments on the database. If all the conditions are met, then a measure is triggered (set the device to acquisition mode) and after completion, the device is set to sleep (low power mode).
Assuming a device is in Bluetooth range in standby mode and an experiment is active on the database, the process shown in Figure 7b will occur. The microcontroller iterates over the eight different channels applying the following process depicted in Figure 7b. Firstly, the start-up signal is generated on the microcontroller and applied to the oscillation loop. This improves the stabilization period for the oscillations, allowing for faster measurements in all wells. Once the oscillator is self-sustaining, the measurement is acquired using the microcontroller ADC. Finally, relative humidity and temperature sensor data are acquired (as critical factors in cell culture experiments whose stability may affect the electrode electrical response). At the end, when the device is set to low power mode, the gateway communicates with the services layer (a data server) and stores the information gathered on a database which will allow for further processing, data analysis and experiment configuration and supervision.
Assuming a device is in Bluetooth range in standby mode and an experiment is active on the database, the process shown in Figure 7b will occur. The microcontroller iterates over the eight different channels applying the following process depicted in Figure 7b. Firstly, the start-up signal is generated on the microcontroller and applied to the oscillation loop. This improves the stabilization period for the oscillations, allowing for faster measurements in all wells. Once the oscillator is selfsustaining, the measurement is acquired using the microcontroller ADC. Finally, relative humidity and temperature sensor data are acquired (as critical factors in cell culture experiments whose stability may affect the electrode electrical response). At the end, when the device is set to low power mode, the gateway communicates with the services layer (a data server) and stores the information gathered on a database which will allow for further processing, data analysis and experiment configuration and supervision.

Services
The service layer for this system has been implemented bearing in mind two specific sections: a data storage service which stores all data received from the sensor in a secure database, and the user interface provided by a web application which the operator can access to configure and visualize experimental data.

Data Server
The data server is implemented on a server established in the Higher Technical School of Computer Engineering in Seville. This server employs secure authentication to access information on a MySQL database which will store data gathered by the gateway (frequency, amplitude, temperature, humidity and battery information for each sensor device registered). Additionally, the server will store sensor device information and identification, gateway IP addresses and identifiers, experiment configuration (assigned dates, acquisition period and gateway), and user information for the web interface.

Web Interface
The secure user interface is implemented on a web server via a web login interface which provides exclusive access to authorized researchers. The services offered via this interface are: • Experiment configuration: acquisition period and dates configurable at any time. The information will be stored on the database and accessed by the gateway.

Services
The service layer for this system has been implemented bearing in mind two specific sections: a data storage service which stores all data received from the sensor in a secure database, and the user interface provided by a web application which the operator can access to configure and visualize experimental data.

Data Server
The data server is implemented on a server established in the Higher Technical School of Computer Engineering in Seville. This server employs secure authentication to access information on a MySQL database which will store data gathered by the gateway (frequency, amplitude, temperature, humidity and battery information for each sensor device registered). Additionally, the server will store sensor device information and identification, gateway IP addresses and identifiers, experiment configuration (assigned dates, acquisition period and gateway), and user information for the web interface.

Web Interface
The secure user interface is implemented on a web server via a web login interface which provides exclusive access to authorized researchers. The services offered via this interface are: • Experiment configuration: acquisition period and dates configurable at any time. The information will be stored on the database and accessed by the gateway. The voltage applied to the cells is below 11 mV; this value is measured using discrete amplification for improving dynamic ranges. From Figure 8, we have selected three different points on well number six; these results will be analyzed in the following section. Environmental and battery measurements are provided in Figure 9. Relative humidity and temperature levels are stable over the whole experiment.

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Phone ready visualization: the interface was designed considering the layout of smart devices so that the researchers can easily explore, manage and follow the experiment from this kind of device.
The voltage applied to the cells is below 11 mV; this value is measured using discrete amplification for improving dynamic ranges. From Figure 8, we have selected three different points on well number six; these results will be analyzed in the following section. Environmental and battery measurements are provided in Figure 9. Relative humidity and temperature levels are stable over the whole experiment.   • Phone ready visualization: the interface was designed considering the layout of smart devices so that the researchers can easily explore, manage and follow the experiment from this kind of device.
The voltage applied to the cells is below 11 mV; this value is measured using discrete amplification for improving dynamic ranges. From Figure 8, we have selected three different points on well number six; these results will be analyzed in the following section. Environmental and battery measurements are provided in Figure 9. Relative humidity and temperature levels are stable over the whole experiment.    The batteries employed are a set of lithium ion battery packs of 5.2 Ah. According to standard discharge curves for lithium ion batteries [21], the voltage drop observed in Figure 9 corresponds to a 60% consumption in capacity (B % ) which can also be expressed in mA (B mA ): Finally, using the following equation, the power consumption expressed in mAh can be computed:

Experimental Results
From the system described above, experiments were performed using cell cultures in biomedical labs with the N2A cell line. This is a neuroblastom cell line from albino rats, employed in basic reseach on neural signaling, axon regeneration, nervous system connection repair [22], and also in chemotherapy [23]. For the purpose of illustration, a subset of the obtained output oscillation signals was selected considering different cell occupation rates. The fill factor (ff) evolution during the experiment considering sampling times of 72 h, 120 h and 144 h, corresponding to ff of 10%, 53% and 100%, is listed in Table 1. The graphic for captured points is depicted in Figure 10. For the sake of clarity, the frequency and amplitude values measured by this OBT sensor are presented normalized within the obtained experimental dynamic ranges (800-910 Hz and 5.2-8.0 mV). For the sake of validation, the points observed from the cell culture measurements with a traditional approach (employing Petri plates) in the biomedical laboratory are also presented in blue. Such time points are found on the experimental growth curves (blue points in Figure 10) by using similar cell densities in a laboratory experiment to define the characteristic growth curve for this cell line. On the other hand, the acquired waveform signals and the calculated frequency spectrums for the three cases described before are depicted in Figure 11. The batteries employed are a set of lithium ion battery packs of 5.2 Ah. According to standard discharge curves for lithium ion batteries [21], the voltage drop observed in Figure 9 corresponds to a 60% consumption in capacity (B%) which can also be expressed in mA (BmA): = % · 5200 = 3120 mA Finally, using the following equation, the power consumption expressed in mAh can be computed:

Experimental Results
From the system described above, experiments were performed using cell cultures in biomedical labs with the N2A cell line. This is a neuroblastom cell line from albino rats, employed in basic reseach on neural signaling, axon regeneration, nervous system connection repair [22], and also in chemotherapy [23]. For the purpose of illustration, a subset of the obtained output oscillation signals was selected considering different cell occupation rates. The fill factor (ff) evolution during the experiment considering sampling times of 72 h, 120 h and 144 h, corresponding to ff of 10%, 53% and 100%, is listed in Table 1. The graphic for captured points is depicted in Figure 10. For the sake of clarity, the frequency and amplitude values measured by this OBT sensor are presented normalized within the obtained experimental dynamic ranges (800-910 Hz and 5.2-8.0 mV). For the sake of validation, the points observed from the cell culture measurements with a traditional approach (employing Petri plates) in the biomedical laboratory are also presented in blue. Such time points are found on the experimental growth curves (blue points in Figure 10) by using similar cell densities in a laboratory experiment to define the characteristic growth curve for this cell line. On the other hand, the acquired waveform signals and the calculated frequency spectrums for the three cases described before are depicted in Figure 11.    Table 1. Experimental values acquired from the system (W6, 2500 N2A seeded cells in t = 0). Frequencies, amplitudes, measured voltage, sensor amplitude, total harmonic distortion (THD) and signal-to-noise ratio (SNR).   Figure 10 illustrates the normalized values for the experimental results acquired from the sensor using the N2A cell line, and a 2500/0.8 cells/cm 2 initial density. Additionally, the blue dots represent the characteristic growth curve for this cell line, measured using a traditional approach (seeding several Petri plates and periodic cell counting) performed by the biologist team. The resulting realtime measurement suits well to the expected growth along the frequency range, proving the validity of this technique to perform cell culture monitoring growth control. Furthermore, the researchers are provided with a powerful tool including real-time visualization of the growing parameters without interrupting the experiment. Hence, the technology presented in this paper has good potential for reducing human and material costs associated with the biomedical research process on cell culture.

Discussion
The experimental results presented in Figure 11 and detailed in Table 1 correspond respectively to the bio-oscillator waveform signals and frequency spectrums of the electrical response in the sample under test. The bio-oscillator signal parameters follow the growth observed on the Petri plate assay (both frequency and amplitude), proving good sensitivity for cell growth monitoring in realtime. The measured frequency better matched the predicted value compared to the amplitude, but both values provided by the sensor (depicted in Table 1) follows the predicted evolution. Also, it must be said that amplitude values (sensor amplitude in Table 1) are obtained by interfacing the cells to an operational amplifier to achieve higher dynamic range on the measurement. Other figures of merit, such as the secondary harmonics, THD or SNR, can also be used as parameters to improve measurement sensitivity.
An approached system sensitivity value (Hz/cell and mV/cell) can be estimated from the frequency and the amplitude curves in Figure 10 (Well 6). The dynamic range observed for the measured frequency and the amplitude are 79 Hz and 1.6 mV respectively, when the cell culture goes  Table 1. Experimental values acquired from the system (W6, 2500 N2A seeded cells in t = 0). Frequencies, amplitudes, measured voltage, sensor amplitude, total harmonic distortion (THD) and signal-to-noise ratio (SNR).   Figure 10 illustrates the normalized values for the experimental results acquired from the sensor using the N2A cell line, and a 2500/0.8 cells/cm 2 initial density. Additionally, the blue dots represent the characteristic growth curve for this cell line, measured using a traditional approach (seeding several Petri plates and periodic cell counting) performed by the biologist team. The resulting real-time measurement suits well to the expected growth along the frequency range, proving the validity of this technique to perform cell culture monitoring growth control. Furthermore, the researchers are provided with a powerful tool including real-time visualization of the growing parameters without interrupting the experiment. Hence, the technology presented in this paper has good potential for reducing human and material costs associated with the biomedical research process on cell culture.

Discussion
The experimental results presented in Figure 11 and detailed in Table 1 correspond respectively to the bio-oscillator waveform signals and frequency spectrums of the electrical response in the sample under test. The bio-oscillator signal parameters follow the growth observed on the Petri plate assay (both frequency and amplitude), proving good sensitivity for cell growth monitoring in real-time. The measured frequency better matched the predicted value compared to the amplitude, but both values provided by the sensor (depicted in Table 1) follows the predicted evolution. Also, it must be said that amplitude values (sensor amplitude in Table 1) are obtained by interfacing the cells to an operational amplifier to achieve higher dynamic range on the measurement. Other figures of merit, such as the secondary harmonics, THD or SNR, can also be used as parameters to improve measurement sensitivity.
An approached system sensitivity value (Hz/cell and mV/cell) can be estimated from the frequency and the amplitude curves in Figure 10 (Well 6). The dynamic range observed for the measured frequency and the amplitude are 79 Hz and 1.6 mV respectively, when the cell culture goes from ff = 0 to ff = 1. In this process, 0.8 cm 2 of the well surface has been fully covered (at the confluence phase). For a circular approximated cell radius of 8 µm, the changes expected in frequency and voltage amplitude due to a cell are 0.200 mHz/cell and 4.1 nV/cell, if only a linear approach for the sensitivity calculus is considered.
The feasibility of the oscillation-based test technique has been demonstrated to sense impedance changes due to cell number increment, to validate the impedance-based system for cell culture monitoring as a result of cell attachment, and to determine the resistive properties of cell-electrode systems at low frequencies, mainly due to cell membrane capacity. The information obtained corresponds to electric current lines travelling outside the cells, since at low frequencies, cell membrane impedance is high due to its capacitive performance. Alternative optical techniques can be also employed when looking for changes in cell shape, viability, or structural composition inside cells, providing complementary information.

Conclusions
The paper describes a real-time cell culture monitoring smart sensor built upon discrete components and commercial off-the-shelf devices. This robust smart system can extract information on the current growth state from the cell culture under test through transducing cellular proliferation into electrical variables (frequency and amplitude) of a self-generated bio-oscillator.
The system architecture proposed herein is organized following the Internet of Things paradigm: devices, gateway and services as components, and is extensible to similar real-time monitoring applications. The system supports multiple sensors on a single gateway connected to the internet, and a database that serves as a data storage and control system. Three main operation modes have been designed and optimized for reduced power consumption, sampling time selection and managing multiple assays, allowing for the real-time acquisition and signal processing of sensor responses to obtain the relevant amplitude and frequency values required for the cell culture report. Experiments described with a neuroblastoma cell line (N2A) enable the proposed sensing system to be recommended as an alternative to end-point protocols, delivering comparable and useful cell culture measurement.

Patents
The work presented in this paper has been protected by a patent included on the invention registered as "Bioimpedance measurement system for wirelessly monitoring cell cultures in real time, based on an oscillation test using integrated circuits", register number; WO2016020561A1.