Cell to Machine Communication

To create a means of communication from cell to machine, we chose the red fluorescence protein KillerRed as a reporter protein. The first condition that our device needs to fulfill is to record light intensity. Then it needs to generate fluorescence thanks to a light source and a couple of excitation and emission filters. Firstly we will explain the choice of the different components, then the several experiences we did to find the most accurate parameters for each part of the device – the photodiode, Arduino and the optic.

The components

To record light intensity, we were inspired by the E. glometer of the Cambridge team of iGEM 2010.

The E. glometer of Cambrige team (iGEM 2010)
Device built by Cambrige team in 2010 to measure the light intensity of their LuxBrick
Source:https://2010.igem.org/Team:Cambridge/Tools/Eglometer

We use quite the same photodiode (TSL230RD) – same as the TSL230RP-LF but in surface mounted device (SMD) – and an Arduino Uno. Arduino is a single-board microcontroller created to make electronics more accessible. The main asset of the photodiode is that the output can be either a pulse train or a square wave (50% duty cycle) with frequency directly proportional to light intensity. Since we are using a microcontroller, it is easy to calculate a frequency with the digital input of the microchip thanks to high or low level and we will have a better resolution because low frequencies are easier to measure than low voltages. For the optic part we use a domestic LED lamp and a cube filter from a microscope with excitation and emission filters and an adjustable lens. A domestic LED lamp was chosen to allow us not to buy several high-power LEDS and built a card with a heat sink. This lamp illuminate with 520 lumens in a cone of 40° under 12V and 6W. The low voltage was chosen as a safety condition and the small angle to avoid losing to much light. The excitation filter is a green interferential filter to excite the red fluorescent protein and the red emission filter is only a colored filter to collect all the red light in order to have a more efficient measure. In the cube there is also a dichroic mirror that reflects all the green light and transmits all the red light. This mirror enables us to separate completely the photodiode from the light source.

The photodiode

Before measuring with the photodiode, we need to know if the photodiode works as indicated in the datasheet. The photodiode was plug on a 5V stabilized power supply. For the same amount of light, we measure the frequency at the output of the photodiode for a pulse train or a square wave (50% duty cycle). According to the datasheet, when using a pulse train the linear relation between the frequency and the irrandiance is given by 1kHz=1µW/cm². When using a square wave (50% duty cycle) it is 1kHz=2µW/cm². This is what we can see on the figure #.

Since this frequency will be calculated by the Arduino controller, it may cause some trouble to the program to use a pulse train because the duration of the pulse is always 500ns and can be missed by the controller. The square wave (50% duty cycle) seems to be a better solution because of the 50% duty cycle. It means that the pulse duration depend on the frequency. Its duration is equal to 1/2f and since the light intensity we want to measure will be low this type of signal can be easily detected by Arduino.

Arduino

Arduino is used to translate the frequency given by the photodiode in irrandiance that gives us the light intensity. The algorithm is quite simple. It counts the number of high levels (samples) and the duration of the measurement (length) and with these two elements it makes this calculation: Irradiance=frequency/(frequency scaling)= samples/(frequency scaling × length) To know if this program works, a function generator was plug in one of the digital input of Arduino instead of the photodiode. By changing the frequency of the square signal sent by the generator and measuring several times the frequency with Arduino and compare the measure to the frequency given by an oscilloscope, we can calculate the accuracy of the program. If the algorithm is right, the curve should follow the equation x=y, which means that Arduino and the oscilloscope measure the same frequencies. According to the experience, the pulse train is not a good option since the curve doesn’t follow at all the x=y curve. Only three points are shown here because the others are worst. On the other hand, the 50% duty cycle seems to work better, at least for the low frequency. For frequencies under 35kHz the curve fits the equation y=x. However after these critical frequency, the algorithm seems to break down and follows the equation y=1/2x before being useless for frequencies over 100kHz. This phenomenon may be explain by the time of the while loop. In the end of this loop the program needs to get back to the beginning of the loop, it is fast but since the frequency is higher it may be not fast enough that’s why the program misses one pulse out of two and shed light on the curves y=1/2x. The curve of the standard deviation divided by the average shows us that around the critical frequency the program is not efficient at all, that makes us believe this is the right explanation. But this graph displays something else, the program for low frequencies and afterwards for low light intensities is very precise. The errors are under 0.5%. And the number of pulse measured is 100. That means that the device can make a measure every 5 min and this is absolutely enough for measuring a biological system. Now we need to know if the device is efficient enough to measure low light intensity like fluorescence.

The optic

TALK'E coli: C2M part
on the left: the real device, on the right: functional schematic
The light from the LED lamp goes through the green excitation filter and illuminate the sample thanks to a dichroic mirror. Then the red fluorescent protein is now excited and re-emits red light that goes through a lens that concentrate it on the photodiode.