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| − | <h2 style="padding-top:5%;">Main circuit</h2>
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| − | <p>
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| − | As the circuit showed below, we first tested the oscillator and sensor with GFP as reporter. Then we replaced GFP with MtrB to test the expression of MtrB will also oscillate.
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| − | <br><br>
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| − | <img src="https://static.igem.org/mediawiki/2015/0/05/Nymu-FYVE-FYVE_circuit.jpg" style=" max-width: 70%;">
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| − | </p>
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| − | <h1 id="cure">Cure</h1>
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| − | <h2>Overview</h2>
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| − | <p>Our goal is to kill P. infestans without harming the potatoes and environment.
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| − | Fungicides used to kill P. infestans nowadays contain lots of heavy metal, such as Cu2+ that will do harm to both the plant and environment. At first we try to use some antimicrobial peptides; however this peptide will not only kill P. infestans but do harm to potatoes as well. Therefore, we try to look for the defensin that can weaken or even kill P. infestans from other plants. Although there are other chemicals that might be effective in inhibiting the growth of P. infestans, such as 2, 6-dichlorobenzonitrile, but some of these may also do harm to bacteria itself. Based on the above reasons, we finally choose lm-defensin, a defensing from maca that can effectively weaken and inhibit the growth of P. infestans
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| − | </p>
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| − | <h2>Circuit design</h2>
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| − | <img src="https://static.igem.org/mediawiki/2015/7/7d/Nymu-Cure_curcuit.jpg" style="padding-left:15%;max-width:150%;">
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| − | <p>To purify defensin, we put a His tag on the N-terminal of the defensin. Also to produce lots of defensin, we choose T7 promoter and E.coli BL21 (DE3) which is used to express huge amount of proteins.
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| − | </p>
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| − | <h1 id="system">System</h1>
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| − | <p>Our defense systen contains three different: prevention, detection, and cure. So how do we connect this three part together to form an impeccable defense system? First, we plant the genetically modified potato in the farm to prevent the disease from infecting the whole farm in a short period of time. Secondly, we implement the soil based microbial fuel cell that can detect and report whether the potato is infected or not immediately, in case that the potato can’t fend of the disease. When the SMFC detects the disease, it will send an electric signal that can trigger the spraying system. The spraying system will then spray the environmentally friendly defensin that we produced automatically. Man power is not required in the whole process except planting the potatoes.
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| − | <br>
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| − | Also, the defense system will also connect to a phone app that we created, so that we can know whether the potatoes are healthy or not. The defense system is localized, socialized, and mobilized.
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| − | </p>
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| − | <a class="anchor" id="modeling"></a>
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| − | <h1 id="inhibition" >FYVE inhibition</h1>
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| − | <p>This model was designed to investigate the competitive binding between FYVE protein domain and PI3P. Before we construct the circuit with FYVE, we have to determine whether the affinity between FYVE and PI3P is strong enough to compete with Avr3 effector protein secreted by P. infestans. We characterize this agonist-antagonist competition model by Gaddum-Schild equation.
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| − | This model consists of two parts: monomeric FYVE and dimeric FYVE. According to paper research, monomeric FYVE is not so stable but the affinity of both monomeric and dimeric FYVE is quite high. The model is used to determine how much FYVE protein domain we should link with a linker so that the affinity of FYVE will be high enough to compete with Avr3.
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| − | <br>
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| − | <img src="https://static.igem.org/mediawiki/2015/5/54/Nymu-Fyve_equation.png" >
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| − | </p>
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| − | <h2>Result</h2>
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| − | <img src="https://static.igem.org/mediawiki/2015/d/da/Nymu-Fyve-mono_mod.jpg" style="padding-left:15%;max-width:40%; display:inline-block;">
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| − | <img src="https://static.igem.org/mediawiki/2015/2/2d/Nymu-Fyve-dimer_mod.jpg" style="max-width:40%;display:inline-block;">
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| − | <p>
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| − | <h2>Conclusion</h2>
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| − | <p>
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| − | As we can see in the models dimeric FYVE is not only more stable but also have higher affinity to compete with avr3, the effector protein secreted by p.infestans. To create FYVE protein domain with higher affinity, we can link as much FYVE domain artificially as we want. However, it takes a lot of time to add a linker protein between FYVE and connect two FYVE together. Moreover, the longer the FYVE is, the longer it takes for the palnt to degrade the protein we created, which might have a negative impact to the potato. By constructing this model, we can find out the simplest way to create a competitive inhibitor and save the time for trial and error.
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| − | </p>
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| − | <h2>Parameters</h2>
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| − | <div id='t' >
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| − | <table class="wikitable">
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| − | <tbody> <tr>
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| − | <col width="160"><td> K<sub>d monomeric FYVE</sub> </td> <td> Dissociation constant of monomeric FYVE </td> <td> 420 </td> <td> nM </td> <td> Structural Basis for Endosomal Targeting by FYVE Domains </td> </tr> <tr> <td> K<sub>d dimeric FYVE</sub> </td> <td> Dissociation constant of dimeric FYVE </td> <td> 38 </td> <td> nM </td> <td> Phosphatidylinositol 3-Phosphate Induces the Membrane Penetration of the FYVE Domains of Vps27p and Hrs </td> </tr> <tr> <td> K<sub>d Avr3</sub> </td> <td> Dissociation constant of Avr3 </td> <td> 210 </td> <td> nM </td> <td> External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effectors into Plant and Animal Host Cells </td> </tr></tbody></table>
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| − | </div>
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| − | <h2>Reference:</h2>
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| − | <ol>
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| − | <li>Structural Basis for Endosomal Targeting by FYVE Domains</li>
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| − | <li>Phosphatidylinositol 3-Phosphate Induces the Membrane Penetration of the FYVE Domains of Vps27p and Hrs</li>
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| − | <li>External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effectors into Plant and Animal Host Cells</li>
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| − | </ol>
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| − | <h1 id="oscillation" style="margin-top:5%;">Oscillation</h1>
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| − | <p>We construct a Lux/Aiia quorum-sensing oscillator so that S. oneidensis-MR1 will generate oscillating current which can help farmers tell if the potatoes are infected. We build two models to characterize the outcome of the genetic oscillator. The first model is used to predict whether the genetic oscillator will work. Since it is possible that some of the engineered S. oneidensis we inoculated on anode won’t survive, the second model is used to predict whether the gene expression will oscillate when there are only a few bacteria left on the anode.
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| − | </p>
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| − | <h2>Population simulation with delay differential equation</h2>
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| − | <img src="https://static.igem.org/mediawiki/2015/2/2b/Nymu-Oscillation_equation_1.jpg" style="float:right;margin-left:5%;padding-right:20%;max-width:35%">
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| − | <p>Since our circuit design is based on the circuit published by Danino et al.[1] our first model is a slight modification of the equation from the supplementary information. This model consists of four delay differential equation. We added one more delay differential equation to this model to simulate the expression of MtrB gene.
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| − | </p>
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| − | <h2 style="padding-top:8%;clear:both">Parameters</h2>
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| − | <div id='t' >
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| − | <table class="wikitable">
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| − | <tbody><tr>
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| − | <td> Description </td> <td> Parameter </td> <td> Value </td> </tr> <tr> <td> C<sub>A</sub> </td> <td> Synthesis constant of Aiia </td> <td> 1 </td> </tr> <tr> <td> C<sub>I</sub> </td> <td> Synthesis constant of LuxI </td> <td> 4 </td> </tr> <tr> <td> C<sub>mtrB</sub> </td> <td> Synthesis constant of MtrB </td> <td> 1(assume) </td> </tr> <tr> <td> δ </td> <td> Hill function constant </td> <td> 10<sup>-3</sup> </td> </tr> <tr> <td> α </td> <td> Hill function constant </td> <td> 2500 </td> </tr> <tr> <td> k<sub>1</sub> </td> <td> Hill function constant </td> <td> 0.1 </td> </tr> <tr> <td> τ </td> <td> Time delay of the production of LuxR::AHL </td> <td> 10 </td> </tr> <tr> <td> k </td> <td> Kinetic constant of AHL synthesis </td> <td> 1 </td> </tr> <tr> <td> b </td> <td> Synthesis rate of AHL </td> <td> 0.06 </td> </tr> <tr> <td> γ<sub>A</sub> </td> <td> Enzymatic degradation rate of Aiia </td> <td> 15 </td> </tr> <tr> <td> γ<sub>I</sub> </td> <td> Enzymatic degradation rate of LuxI </td> <td> 24 </td> </tr> <tr> <td> γ<sub>H</sub> </td> <td> Enzymatic degradation rate of AHL </td> <td> 0.01 </td> </tr> <tr> <td> γ<sub>mtrB</sub> </td> <td> Enzymatic degradation rate of MtrB </td> <td> 0.007(assume,[3]) </td> </tr> <tr> <td> D </td> <td> AHL membrane diffusion </td> <td> 2.5 </td> </tr> <tr> <td> f </td> <td> 0.3 </td> </tr> <tr> <td> g </td> <td> Kinetics constant of AHL degradation </td> <td> 0.01 </td> </tr> <tr> <td> d<sub>0</sub> </td> <td> Maxium cell density </td> <td> 0.88 </td> </tr></tbody></table>
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| − | </div>
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| − | <h2 style="padding-top:8%;">Conclusion</h2>
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| − | <p>
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| − | As you can see in the graph, the production of Aiia and LuxI will oscillate as the time goes by. However, the production of MtrB will build up instead of oscillating. This is because MtrB is too stable and it is not totally degraded before the next period of oscillation. Thus, it is necessary to be targeted for proteolysis by adding a degradation tag in MtrB coding sequence.
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| − | </p>
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| − | <img src="https://static.igem.org/mediawiki/2015/0/0f/Nymu-Oscillation_graph_2.png" style="padding-left:15%;max-width:40%;">
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| − | <img src="https://static.igem.org/mediawiki/2015/f/f0/Nymu-Oscillation_graph_3.png" style="max-width:40%;">
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| − | <h2 style="padding-top:8%">Single cell simulation with ODE</h2>
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| − | <p>
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| − | If we inoculate the engineered S. oneidensis MR-1 into the anode of our SMFC, chances are that some of the bacteria will not survive in soil. Thus, it is necessary to simulate if the oscillator will work with only a single bacteria or a small population of bacteria.
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| − | <br><br>
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| − | With a small amount of bacteria, there will not have a significant time delay, and that’s why we chose a set of ODE to simulate the genetic oscillator.
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| − | <br>
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| − | <img src="https://static.igem.org/mediawiki/2015/8/82/Nymu-Oscillation_equation.png" style="max-width:70%;">
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| − | <img src="https://static.igem.org/mediawiki/2015/7/7d/Nymu-Oscillation_graph.png" style="max-width:80%">
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| − | </p>
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| − | <h2 style="margin-top:10%">Conclusion</h2>
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| − | <p>
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| − | As we can see in this model, even if there is only a small amount of bacteria left on the anode, the oscillator can still work. However, the current output will be much lesser than that of a large population of bacteria.
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| − | </p>
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| − | <h2 style="padding-top:5%;">Parameters</h2>
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| − | <div id='t' >
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| − | <table class="wikitable">
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| − | <tbody> <tr>
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| − | <td> Descriptions </td> <td> Parameter </td> <td> Units </td> <td> Value </td> </tr> <tr> <td> Basal production rate LuxI AiiA </td> <td> a<sub>0LI</sub> a<sub>0A</sub> </td> <td> μM/min μM/min </td> <td> 7.79x10<sup>-6</sup> 6.18x10<sup>-6</sup> </td> </tr> <tr> <td> Active production rate LuxI AiiA mtrB </td> <td> k<sub>pLI</sub> k<sub>pLA</sub> k<sub>pmtrB</sub> </td> <td> μM μM μM </td> <td> 0.9 0.84 0.33(assume[4]) </td> </tr> <tr> <td> Cell reaction rate AHL production rate LuxR AHL association rate LuxR AHL dissociation rate AHL::Aiia catalytic rate AHL conc. adjustsment for environment AHL membrane diffusion constant </td> <td> k<sub>p2</sub> k<sub>r1+</sub> k<sub>r1−</sub> k<sub>cataA</sub> η<sub>env</sub> η<sub>cell</sub> </td> <td> 1/min μM/min 1/min 1/min 1/min 1/min </td> <td> 16 5.99x10<sup>-5</sup> 6x10<sup>-6</sup> 7x10<sup>3</sup> 3x10<sup>-5</sup> 3 </td> </tr> <tr> <td> Michaelis-Menten constants LuxR::AHL complex Aiia complex </td> <td> K<sub>mLA</sub> K<sub>maA</sub> </td> <td> μM μM </td> <td> 1.00x10<sup>-2</sup> 1.4977x10<sup>3</sup> </td> </tr> <tr> <td> Degradation related parameters LuxR::AHL complex AHL </td> <td> τ<sub>LA</sub> τ<sub>A</sub>/τ<sub>A</sub>˜ </td> <td> 1/min 1/min </td> <td> 2.40x10<sup>-2</sup> 2.76x10<sup>-3</sup> </td> </tr> <tr> <td> Enzymatic degradation Inverse of KMclx Vmax × ET OT / KMclx for LuxI Vmax × ET OT / KMclx for Aiia </td> <td> f δ1 δ2 δ3 </td> <td> 1/μM 1/μM.min 1/μM.min 1/μM.min </td> <td> 4.12x10<sup>-2</sup> 7.16x10<sup>-1</sup> 2.50x10<sup>-2</sup> 0.99x10<sup>-2</sup> (assume[4]) </td> </tr></tbody></table>
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| − | </div>
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| − | <h2>Reference</h2>
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| − | <ol>
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| − | <li>Danino. T, Mondragon-Palomino, O, Tsimring, L. & Hasty, J. “A synchronized quorum of genetic clocks.” Nature 463, 326-330 (2010).</li>
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| − | <li>Petros Mina, Mario di Bernardo, Nigel J. Savery, Krasimira Tsaneva-Atanasova. “Modelling emergence of oscillations in communicating bacteria: a structured approach from one to many cells.” Published 7 November 2012</li>
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| − | <li>Danino. T, Mondragon-Palomino, O, Tsimring, L. & Hasty, J. “A synchronized quorum of genetic clocks.” Nature 463, 326-330 (2010).</li>
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| − | </ol>
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| − | <h1 id="epidemic">Epidemic</h1>
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| − | <p>P. infestans is an oligate parasite, which means it can’t complete its life cycle without exploiting potato. However, if a potato is infected by P. infestans, the disease will spread through the potato farm within 20 days.</p>
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| − | <p>In this model, we try to show the difference between planting the potato we engineered and planting potato that is susceptible or moderately resistant to potato late blight. Traditionally, SEIR model will be the perfect model to characterize epidemiology. However, we made some changes to the assumption of the traditional SEIR model so that the model can reflect the reality of potato late blight epidemiology much better. </p>
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| − | <img src="https://static.igem.org/mediawiki/2015/9/9b/Nymu-drylab-epidemic_1.jpg" style="padding-left:15%">
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| − | <div id="t">
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| − | <table class="wikitable">
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| − | <td rowspan="5">Epidemology</td><td> Symbol </td> <td> Meaning </td> </tr> <tr> <td> Suspected </td> <td> The potato that is slightly or moderately resistant to P.infestans </td> </tr> <tr> <td> Exposed </td> <td> Potatoes that are infected by a low amount of P.infestans, but they are still curable andwill not spread the disaese </td> </tr> <tr> <td> Infected </td> <td> Potatoes that are infected by a large amount of P.infestans. Potatoes infected will spread the disease and jeopardize the whole potato farm </td> </tr> <tr> <td> Immunized </td> <td> Potatoes that has been transformed by us and is resistant against P.infestans </td> </tr></table>
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| − | </div>
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| − | <p>According to our assumptions, we developed a set of ordinary differential equations that can characterize the epidemic of potato late blight.</p>
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| − | <img src="https://static.igem.org/mediawiki/2015/2/2b/Nymu-drylab-epidemic_2.jpg" style="padding-left:15%">
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| − | <h2>Parameters</h2>
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| − | <div id="t">
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| − | <table class="wikitable">
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| − | <tbody> <tr>
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| − | <td rowspan="6">SEIR MODEL(COEFFICIENTS)</td><td> γ </td> <td> transmission rate(1/days) </td> <td> 0.25 </td> </tr> <tr> <td> α </td> <td> Promoter activation rate(1/days) </td> <td> 1.0 </td> </tr> <tr> <td> ε </td> <td> Latent rate(1/days) </td> <td> 0.2 </td> <td> British Columbia Ministry of Agriculture[3] </td> </tr> <tr> <td> μ </td> <td> Infection rate(1/days) </td> <td> 0.2 </td> </tr> <tr> <td> k </td> <td> Rate of resistance to late blight </td> <td> 0.75 </td> <td> External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effectors into Plant and Animal Host Cells[2] </td> </tr> <tr> <td> beta </td> <td> Daeth rate of normal potato(1/days) </td> <td> 1/365 </td> </tr> <tr> <td rowspan="5">SEIR MODEL(VARIABLE)
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| − | </td> <td> S </td> <td> The amount of all potatoes </td> <td> X(1) </td> </tr> <tr> <td> E </td> <td> The amount of suspected individual </td> <td> X(2) </td> <td> Global analysis of an SEIR model with varying population size and vaccination </td> </tr> <tr> <td> I </td> <td> The amount of infected individual </td> <td> X(3) </td> <td> [1] </td> </tr> <tr> <td> R </td> <td> The amount of potato that survive </td> <td> X(4) </td> </tr> <tr> <td> V </td> <td> The amount of potato being immunized </td> <td> X(5) </td> </tr></tbody></table>
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| − | </div>
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| − | <h2>Result</h2>
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| − | <p>
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| − | As we can see from the model,with our genetically modified potatoes can prevent 80% of the potatoes from being infected, while only 20% of the moderately resistant potatoes can survive during the epidemic of potato late blight.
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| − | Also as we can see in the graphs above, the survival rate do fit our sassumption the the potatoes under the destruction of late blight only end in two consequences----death(once the potatoes exposed to small amount of p.infestans are seriously infected ) or survive (once the “exposed potato” truned into “immunized potato”).
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| − | This graphs not only show that our potatoes might be effective in fighting potato late blight in the real world but also show that potato late blight is a disease that needs to be take care of seriously and a impeccible system is necessary to fight against the disease.
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| − |
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| − | </p>
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| − | <img src="https://static.igem.org/mediawiki/2015/4/46/Nymu-drylab-epidemic_3.jpg" style="padding-left:15%;width:40%">
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| − | <img src="https://static.igem.org/mediawiki/2015/f/fd/Nymu-drylab-epidemic_4.jpg" style="width:40%">
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| − | <h2>Conclusion</h2>
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| − | <p>As we can see from the model,with our genetically modified potatoes can prevent 80% of the potatoes from being infected, while only 20% of the moderately resistant potatoes can survive during the epidemic of potato late blight.
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| − | Also as we can see in the graphs above, the survival rate do fit our sassumption the the potatoes under the destruction of late blight only end in two consequences----death(once the potatoes exposed to small amount of p.infestans are seriously infected ) or survive (once the “exposed potato” truned into “immunized potato”).
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| − | This graphs not only show that our potatoes might be effective in fighting potato late blight in the real world but also show that potato late blight is a disease that needs to be take care of seriously and a impeccible system is necessary to fight against the disease.
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| − | </p>
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| − | <h2>Reference</h2>
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| − |
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| − | <ol>
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| − | <li>Chengjun Suna, Ying-Hen Hsiehc. “Global analysis of an SEIR model with varying population size and vaccination” doi:10.1016/j.apm.2009.12.005</li>
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| − | <li>Kale SD1, Gu B, Capelluto DG, Dou D, Feldman E, Rumore A, Arredondo FD, Hanlon R, Fudal I, Rouxel T, Lawrence CB, Shan W, Tyler BM. “External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effector into Plant and Animal Host Cells” Cell. 2010 Sep 17;142(6):981-3.</li>
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| − | <li>British Columbia Ministry of Agriculture</li>
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| − | </ol>
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| | | | |
Overview
From the very beginning, we have engaged the experts who have a deep understanding of late blight disease in order to ensure that our project is addressing a genuine need as define by those who know best.
Who are these experts? They are farmers, professor, researcher and some government officials who deal with the late blight disease or plant the potatoes every single day. This also meant that patients were acutely aware of the limitations of the project, which we believe reduces the chance of giving false hope to people with a very serious need.
Farmer
Concept
FYVE protein domain is well conserved PI3P binding domain in various organisms with only 141 amino acids. FYVE protein domain originally existed in EEA1 and Hrs proteins in human and mouse, respectively. However, EEA1 and Hrs protein are too large and may take long time for the plant to degrade, we then decided to extract the protein domain from Hrs protein. On the other hand, monomeric FYVE has far lower affinity to PI3P than Hrs and it is not so stable. Therefore, we decided to construct a dimeric FYVE which has a higher affinity and is much more stable than monomeric FYVE.
Varieties
1. Kennebec:
Kennebec is originally bred by the United States Department of Agriculture, and it is the most common processed potatoes in the United States and Canada. Kennebec can be used in making French fries, potato chips, while TainungNO.1 cannot. Kennebec has shallow and evenly distributed tuberogemmas, thin and yellow skin, green sprout, oval-shaped tubers and big and light leave color. Kennebec’s tubers size is big, which meets to the eating habit of Taiwanese. However, Kennebec can be easily infected by potato virus and potato late blight, causing to the crop failure. Recently, the best preventive method is to adopt healthy seed potatoes for planting, and it needs to renew the seed potatoes every year. It takes 80 to 100 days to harvest, which means that it has a short planting time. It can crop about 25 to 30 tons of potatoes per hectare. Owing to the short cultivation period, it will not affect farmers in Taiwan to plant a period of rice in summer. Besides, the cultivation type of Kennebec is most familiar to the farmers. Due to the above reasons, farmers prefer to plant Kennebec .
2. TainungNO.1:
TainungNO.1 is bred by Taiwan Agricultural Research Institute. It has light yellow and oval-shaped tubers, smooth skin, purple sprout, dark green and glossy leaves and smaller stem and leaves comparing with Kennebec. It has strong resistant to late blight and potato virus, but it is susceptible to common scab. It also has very weak thermo tolerance, so it is not available to be early planted. The best sowing time in Taiwan is during the bottom of October to the top of November. The storage of TainungNO.1 will cause a big question, which is the accumulation of reducing reductive sugar. What is the matter？ When it comes to processing, the reducing sugar will react with asparagine and produce acrylamide under the high temperature of roasting or frying. (a)Acrylamide is a chemical compound that studies have linked to the formation of cancer in animals, and the FDA has encouraged people to cut back on foods that contain the substance. (a)It takes about 120 days to harvest and it can crop about 32 to 39 tons of potatoes per hectare. Because it is not a good source of processed potatoes and the farmers do not adapt to the cultivation mode of TainungNO.1, including irrigation, fertilization, and medication and so on, the planting area and the production of TainungNO.1 are still at low percentage.
Growth and harvest of tainungNO.1:
- preparation:
- decide species
- selecting seed potatoes
- avoid cropping obstacles
- organize farm land into furrow
- sprouting
- planting period:
- the best planting time
- the amount of seed potatoes used
- the splicing size of seed potatoes
- sterilization
- control the distance between seed potatoes
- weeds management
- growing period:
- plow and recover the soil
- remove the weeds
- add fertilizer
- moisture and pest management
- remove the infected plant
- harvest and storage:
- grade the potatoes
- remove the weeds
- add fertilizer
- moisture and pest management
- fridge in 4∘C.
economic
To the farmers, in addition to the late blight disease, the marketing question is also an important issue. We need to prove that our improved potatoes are really benefit to the farmers.
Marketing situation
Potatoes selling are separated into two parts. One is for fresh potatoes, and the other is for processed potatoes. The latter one accounts for the most part of potatoes. Therefore, we need to find out how the potatoes are processed.
The potatoes production produced in Taiwan has over eighty percent of market share, and the output value is about six hundred million NT dollars. Additionally, there are three potatoes’ factory, which output about three thousand tons of potato production and three hundred million NT dollars. There are the backbones of potatoes’ industry.The price of the potatoes is about 25 NT dollars per kilogram on average. It will reach the climax in November and go to the bottom in February.
The planting period is about 90 to 120 days, and it can store in the fridge under 4 degrees of Celsius condition after harvest. It can store up to two years, and the farmers can sale the potatoes successively during this period. Therefore, selling the potatoes is a program of marketing.
In terms of the period of the planting time, we can simply separate the stage of the marketing situation into three parts. There are planting and growing phase, harvest phase and storage phase.
- Planting and growing phase:
It is about August to November, including some preparation of planting. In this phase, the potatoes produced in the previous years are almost sold out, and the fresh potatoes haven’t cropped. Therefore, it reaches the highest price in this phase.
- Harvest period:
It is about December to March the year afterwards. In this phase, the potatoes in the first period are successively cropped. Those potatoes will influx in the market, so the price is decreasing.
- Storage phase:
It is about April to August. In this phase, in addition to supply the demand of the market, the most parts of potatoes will be stored to be frozen vegetables in order to balance the price of vegetables responding to the insufficient supply of summer vegetables when we meets some typhoon or other disaster. The price in this phase will slightly increase back to the average.
conclusion
Our objective is to compare the traditional way with our project, intending to quantize the benefit that we can bring to the farmers.
So we need to help the farmers to calculate the costs of planting, the income and the net profit. We will need to check out the current market situation and the output value to caculate.
Education
For education, we make lots of effort on promoting the central idea of iGEM and also how our project are going to affect our society.We go to school from primary to senior high (and of course university) to tell them how we can use the biobricks to construct a new sequence. We even make a picture to let younger kids understand what we have done.Furthurmore We published some article on magazines to educate the public about potatoes issues and iGEM.
