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Team:KU Leuven/Project/Ecological/Modelling
From 2013.igem.org
Secret garden
Congratulations! You've found our secret garden! Follow the instructions below and win a great prize at the World jamboree!
- A video shows that two of our team members are having great fun at our favourite company. Do you know the name of the second member that appears in the video?
- For one of our models we had to do very extensive computations. To prevent our own computers from overheating and to keep the temperature in our iGEM room at a normal level, we used a supercomputer. Which centre maintains this supercomputer? (Dutch abbreviation)
- We organised a symposium with a debate, some seminars and 2 iGEM project presentations. An iGEM team came all the way from the Netherlands to present their project. What is the name of their city?
Now put all of these in this URL:https://2013.igem.org/Team:KU_Leuven/(firstname)(abbreviation)(city), (loose the brackets and put everything in lowercase) and follow the very last instruction to get your special jamboree prize!
The Ecological Model
Ultimately our project aims to reduce crop loss because of aphid infestation. Given the time span of the competition, it is impossible to conduct a field experiment for this. Therefore we attempt to predict the effect of our pheromones on the ecology through a series of modeling steps.
Diffusion and convection of the pheromones
The first step is to calculate the concentration of the pheromones released in the environment. When a colony of E. coligy in what form so ever is placed in a field, the produced substances will be transported in the air by diffusion and convection. Diffusion is always present, whereas the source of the convection term is the wind. In order to establish a realistic model, certain parameters are needed. Therefore approximate diffusion coefficients and air velocity were searched. Production of the pheromones by the colony was coupled with the other modeling approach <link to Sander’s text>.
Wind speed
Because of friction and obstacles on the earth’s surface, wind speed varies with altitude. Generally, the velocity decreases with increasing altitude. For the part above the crops we can use the logarithmic wind profile is appropriate. The formula for this profile is
with u representing the velocity. Here d accounts for an upward shift above a vegetative cover. The relation d=0.63 x zc is suggested, where zc is the height of the crops. The length z0 is called the roughness length and is often supposed to be about one tenth of zc.
For the part inside the canopy, the profile is exponential.
As can be noticed, the wind speed decreases exponentially with extinction factor a when approaching ground level and thus going deeper into the canopy. uc is the speed at height zc and can easily be calculated by the formula of the logarithmic wind profile.
Evidently, the wind direction changes in time. Most regions however tend to have a dominant wind , at least during a certain time period. This is the reason our model only incorporates convection in one direction, which greatly simplifies calculation. Furthermore we can also find average wind speeds, measured by KMI in Belgium. These are measured at 10 m above the ground, making it possible to calculate u*/k used in the log law. Now, all parameters for convection are known when the crop height is given. The wind profile was entered in the software using a piecewise function and self-defined parameters, making it easy to change wind speed and crop height. An example of the profile is plotted in Figure 1.
Figure 1 ǀ Wind profile for a crop height of 2 m and a wind speed of 3.39 m/s at a height of 10 m.
Figure xǀ text.
Diffusion coefficients
Because we found no measured diffusion coefficients of the pheromones in literature, estimations were made with a calculator based on methods described in i. Using the average supplied by the calculator, the results are 4.62 x 10-6 m2/s for E-beta-farnesene and 6.33 x 10-6 m2/s for methyl salicylate. The conditions supplied were a pressure of 1 atm and a temperature of 15 °C.
Figure xǀ text.
Wind speed
Because of friction and obstacles on the earth’s surface, wind speed varies with altitude. Generally, the velocity decreases with increasing altitude. For the part above the crops we can use the logarithmic wind profile is appropriate. The formula for this profile is
with u representing the velocity. Here d accounts for an upward shift above a vegetative cover. The relation d=0.63 x zc is suggested, where zc is the height of the crops. The length z0 is called the roughness length and is often supposed to be about one tenth of zc.
For the part inside the canopy, the profile is exponential.
As can be noticed, the wind speed decreases exponentially with extinction factor a when approaching ground level and thus going deeper into the canopy. uc is the speed at height zc and can easily be calculated by the formula of the logarithmic wind profile.
Evidently, the wind direction changes in time. Most regions however tend to have a dominant wind , at least during a certain time period. This is the reason our model only incorporates convection in one direction, which greatly simplifies calculation. Furthermore we can also find average wind speeds, measured by KMI in Belgium. These are measured at 10 m above the ground, making it possible to calculate u*/k used in the log law. Now, all parameters for convection are known when the crop height is given. The wind profile was entered in the software using a piecewise function and self-defined parameters, making it easy to change wind speed and crop height. An example of the profile is plotted in Figure 1.
