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Revision as of 16:40, 17 September 2015

Entrepreneurship

Why Startup?


Playgrounds, carpets and boots, although creative, together they do not represent not even 1% of scrap tire destination. [1] Every year, over 1 billion tires are manufactured all over the world which is equivalent a (equivalente a 100 milhoes de pessoas em peso). After approximately 3 years, these tires are discarded and new tires are produced, generating a huge amount of scrap tire accumulation. If stored above ground they may create fire hazards, harborage for pests, and aesthetic and property value impacts. However, when waste tires are buried, they consume valuable landfill space.[2] To properly address this residue, many countries, such as United States and Brazil, passed a law designates to tire manufactures to collect and manage the solid residue. [3] Unfortunately, major destination is incineration to generate tire-derived fuel (TFD) broadly used in cement industry for energy. [4] This process generates chemicals proven to be extremely detrimental to human health and environment. Polycyclic aromatic hydrocarbons dioxins, carbon monoxide and sulfur oxides are some examples of carcinogens and toxic released compounds. [5] Specially in Brazil, seventy percent of collected tire is directed to incineration and the other thirty percent are reintroduced in limited markets. In this scenario, our project aim to make up the residue demand through a synthetic biology technology capable of transforming a waste into a high valued product to feed others chemical segments and as a final main product, a clean and sustainable jet-fuel.

Using a wild microorganism isolated from soil, the first step is to remove the sulfur bonds that covers the tire with no pollutant gases emission, generating a more flexible and easy to recycle material. In the second step, we designed an optimized bacterial DNA based system specific for polyisoprene degradation, the main tire component. It is capable of breaking long chains into smaller units that can be used as raw material for other processes. In our third reactor will be performed a single catalytic reaction to produce the jet-fuel. We also intend to use natural rubber from Hevea brasiliensis as renewable source for our fuel production. The remaining products with economic value of the whole process are: high quality raw material to input other industrial segment, jet fuel derived from tire waste, jet fuel derived from natural rubber and devulcanized rubber from tires.


[1] Rubber Manufacturers Association, 2014.
[2] Legislative Environmental Policy Office Study Conducted - Status of and Alternatives for the Management of Waste Tires in Montana, 1998.
[3] Environmental Protection Agency- State Scrap Tire Programs, a quick reference guide, United States, 1993.
[4] United States Environmental Protection Agency - Tire-Derived Fuel, 2005.
[5] Saleh Tawfik A., Gupta VK, Processing Methods - Characteristics and Adsorption Behavior of Tires Derived Carbons: A Review, Advances in Colloid and Interface Science, 2014.

Executive Summary


In this section it will be highlighted the main points about our company.

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Summary

Rubber Biotechnologies is a under development company in recycling rubber waste field that is responding to the need for scrap tires addressment to decrease solid residue accumulation and pollutant gases release. The company project was consolidated in 2015, from Brasil-USP iGEM team and has a biotechnology startup profile. The technology consists on genetically engineered microorganisms based on synthetic biology, capable of degradating rubber.

The Products

Devulcanized rubber

ODTD

Jet-fuel raw material

Manufacturing

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The Market

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Figure 3 - Time evolution of the relative fluorescence per cell, normalized by the LB+Cam activity. All colonies are in their log-phase, suitable for evaluating their promoter strengths (see below). Notice that for all times, Device 1 is stronger than Device 2, while Device 3 is by far the weaker.

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Cytometry

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Negative control (no plasmid)

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Figure 4 - E. coli DFNSAKJNKA.

Negative control (with plasmid)

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Figure 5 - E. coli DH5-alpha cells with chloramphenicol resistant plasmid pSB1C3 without insert DNA. The cells were excited at 488 nm and detected using FL1-H (FITC channel). At the left, dot plots obtained by flow cytometry correspondent to cells fluorescence and, at the right, histograms analysis of fluorescence levels.

Positive control

For comparaison purposes, a positive control expressing GFP (BBa_I20270 - a GFP expression device in the pSB1C3 backbone (chloramphenicol resistant) was also measured.

Figure 6 - E. coli cells GFP expression controlled by BBa_J23151 promoter. GFP was excited at 488 nm and detected using FL1-H (FITC channel). At the left dot plots obtained by flow cytometry correspondent to BBa_I20270 and histograms on the right show analysis of GFP expression in biological triplicate

Device 1

Figure 7 - E. coli cells GFP expression controlled by BBa_J23101 promoter. GFP was excited at 488 nm and detected using FL1-H (FITC channel). At the left dot plots obtained by flow cytometry correspondent to Device 1 (J23101 + I13504 ) and histograms on the right show analysis of GFP expression in biological triplicate

Device 2

Figure 8 - E. coli cells GFP expression controlled by BBa_J23101 promoter. GFP was excited at 488 nm and detected using FL1-H (FITC channel). At the left dot plots obtained by flow cytometry correspondent to Device 2 (J23106 + I13504 ) and histograms on the right show analysis of GFP expression in biological triplicate

Device 3

Figure 9 - E. coli cells GFP expression controlled by BBa_J23101 promoter. GFP was excited at 488 nm and detected using FL1-H (FITC channel). At the left dot plots obtained by flow cytometry correspondent to Device 3 (J23117 + I13504 ) and histograms on the right show analysis of GFP expression in biological triplicate

Comparison of expression rates of J23101, J23106 and J23117

Using flow cytometry we also conclude that promoter strength decreases from J23101>J23106>J23117 (Device 1>Device2>Device3).

Figure 10 - Comparison of GFP expression controlled by BBa_J23101, BBa_J23106 and BBa_J23117 promoters. GFP was excited at 488 nm and detected using FL1-H (FITC channel).

Relative Promoter Units

According to the Anderson Promoter Collection [1], Device 1 should be stronger among the tested promoters in this experiment. Moreover, we can numerically measure a relative promoter strength and compare with the values available in the Anderson Collection. To measure then, we have to evaluate the following equation while our colonies are in the log-phase of their growth: \[ RPU_{\phi} = \frac{ \left\langle \frac{dF_{\phi}}{dt}\frac{1}{OD_{\phi}} \right\rangle }{ \left\langle \frac{dF_{J101}}{dt}\frac{1}{OD_{J101}} \right\rangle } \]

Measuring Promoter Strength using a Camera + Gimp

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Discussions

Our findings were consistent with the reported Anderson promoters strength, J23101 exhibited the stronger promoter activity followed by J23106 and J23117 was the weakest promoter and this result being confirmed fluorescence reading on a plate reader and flow cytometer. Additionally, using the plate reader we measured fluorescence over time (12 hours) at room temperature.

Protocols

Assemble protocol

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Figure 2 (click for larger view)NJASKNKANSKA

Sample preparation protocols

E. coli DH5α cells transformed with plasmids containing device 1, device 2 and device 3 were plated on LB agar (SIGMA) plates supplemented with chloramphenicol and grown for 18-20 hours at 37°C. Into independent sterile tubes with 5 ml of LB media (1:4) containing 34 μg mL-1 of chloramphenicol, three colonies of each device and control were tip from the LB agar plates and grown at 37 °C, 80 rpm overnight ( for 14-16 hours). All tests were performed in E. coli DH5α cells in high-copy plasmid pSB1C3.

Plate reader

Samples were prepared in a black, flat-bottomed, 96-well Costar plate by adding a volume of 200 µL per well of each sample (180 µL of LB media and 20 µL of pre-inoculum). We also measured optical density using a clear, round-bottomed, 96-well Greiner plate

Flow cytometer

Cultured bacteria were diluted to approximately 8 x 106 bacteria mL-1 or OD600 of ~ 0.01 (cell concentration must be in the range of 1x106 to 2 x 107 bacteria mL-1 according to the equipment manufacturer’s recommendation). At this concentration, we guarantee enough cells in the samples and avoid clusters formation. To reduce background fluorescence originated from components of the media, samples were washed twice with phosphate buffer saline (PBS) at centrifugation 5000 g for 5 minutes. The supernatant was removed and the pellet resuspended in 1 ml sheath fluid, same volume as used to obtain OD600 of 0.01.

Data acquisition protocol

Plate reader

Data acquisition was performed in a plate reader model SpectraMax® M3 (Molecular Devices), SoftMax Pro software. To prevent cross-talk and light scattering, samples were prepared in a black plate and top read was set to acquire fluorescence measurements.

Flow cytometer

Flow cytometry measurements were acquired in a BD FACSCaliburTM Flow Cytometer (Becton, Dickinson and Company, BD Biosciences, San Jose, CA, USA) equipped with BD FACSComp™ and BD CellQuest™ Pro software. First the equipment was calibrated to ensure optimal performance due to inherent instrument variability. As model calibrators we used BD Calibrite 3 Beads (BD Calibrite™) by running FACSComp program.

Table 2 - 3-Color Lyse/Wash FACSComp Report, Software FACSComp 6.0

Table 3 - Flow cytometer calibration

For each sample, ten thousand events were acquired and fluorescence was measured in FL1 and FL2 channels. Voltage and amplifier gain were adjusted in the Detector/Amps window according to negative control (no plasmid) (Figure negative control no plasmid) to ensure all the events are in the plot FSC vs. SSC and the same parameters were used to read GFP expressing cells and other controls. FL1 vs. FL2 plot was divided in 4 quadrants, and the lower left quadrant (LL) contained all events from the negative controls. The lower right quadrant (LR) should show up the GFP expressing cells and histogram FL1 vs. counts, the fluorescence levels. Although, the histogram do not represents the origin of fluorescence and populations, then FL1 vs. FL2 plot can show the origin of the fluorescence.


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