Contribution

Introduction

During the course of our project, we faced challenges especially in discovering and utilizing necessary resources for modeling. So, we wanted future iGEM teams to save time and energy by having a look at our project. Our contribution is not only limited to the results we have produced but also includes the process we have been through that is documented in this page. We believe this documentation would be especially useful for future teams that want to conduct dry lab experiments that involve RNA. The contributions we made to the future teams are described in this page.

Model

We created four models this year: (1) heat conduction model (2) RNA behavior according to temperature (3) RNA behavior according to time (4) molar concentration of molecules required for visibility. These results should be useful for future iGEM teams when creating a model of their own. By looking at our modeling examples, they may get an idea on how modeling is done and what kind of results it may produce. For more information, please click the following link

1.Heat distribution : We created a heat distribution model with an equation shown below and using Matlab code. We hope this model gives future teams an idea on how heat transfer model should be constructed by code. But for the teams that do not need extensive heat distribution modeling we recommend them to use transient heat conduction application in Matlab since it is more conveinient to use.

\( {{\delta T} \over {\delta t}} = k \nabla ^2 T \)

2. RNA behavior according to temperature: If you want to predict the hybridization behavior of RNA, we recommend Mfold (Available on web). Some advices on how to use the software to model the hybridization behavior according to temperature is described below. We hope our document helps you reduce your time discovering and learning to use this program. Additional python code is also given so that you can process data easily.

3. RNA behavior according to time: You may want to predict the behavior of RNA hybridization according to time, but unfortunately, as far as we searched, we couldn’t find modeling resources that predicts its behavior just from the sequence information. So, what we did was to simplify the system of ours by only considering the RDS(rate determining step) of the system and predicting its behavior. In our case, based on published results, the rate determining step seems to be the binding of the fluorophore to broccoli. By identifying the rate determining step of our system our system was simplified and we no longer had to be concerned about the hybridization rate of RNAs. So, we recommend future teams to identify the RDS if the system is too complicated to model according to time

4. Visibility: We predicted the amount of each type of molecule required to make the fluorescence visible. It turns out that evaluating brightness is difficult since it is heavily affected by the surroundings. Some physical concepts are involved quantum yield, wavelength and luminance etc. It is also briefly described so you can make use of the more eligible method. We anticipate our modeling to be useful for teams that are dealing with fluorescent reporters and want to evaluate their reporting systems.

If you can’t measure other fluorescence rate and the concentration of them, then you must use the bottom-up method. If you can, then you better use the relative induction.

Easy Mfold guide

This is a Mfold software [Link]. You can see that they are dealing with computational biology software mainly on nucleic acids.

At the left bar, you can see some applications. You can easily understand what they are for. They are only treating a single sequence, not an interaction between two sequences. Modeling secondary structures can be done with those sections.

But to see the hybridization you should click the DINAMelt Application menu above.

Then you can see the page above. What we used is Hybridization of two different strands.

Then the page above appears. You type in Job Name, Sequences, Temperature range etc. Keep in mind that if you want to model RNA behavior you should not change the salt conditions. It is not offered with the software. So we just qualitatively considered the salt effect.

This is a page where data about candidate 1 – modified sequence C1 is inserted in. Then I clicked ‘Submit’ button below.

The result includes not only a plot in the figure numbered 1 but also Text where exact numbers are in the figure numbered 2. With the text, you can see exact numerical data. If you want to plot the text then you can use a python code below that we made.

3D modeling

For the future teams who want to make a progress with our project, or the teams who want to make some systems with patch-like materials. We made a 3D model of our patch. The file is attached below so if you want to make use of it, you can freely use it.

Click this to download KUAS_Korea's thermal sticker STL file.

Python code

import matplotlib.pyplot as plt

def text_reading(f, mole) : #read the file, arrange them into a list, return the list
lines=[]
f.readline()
while True:
lines.append(f.readline())
if lines[-1] == '':
break
else:
A = lines[-1].split()
j = 0
for i in mole.keys() :
if i == 'temp' :
mole[i].append(int(A[j]))
else :

mole[i].append(float(A[j]))
j+=1
return mole

def plot_each_molecule(mole) : #plot each molecule in a single figure
for i in mole.keys() :
if i != 'temp' :
plt.plot(mole['temp'], mole[i], label=i)
plt.legend()

def mfoldplot(str1) : #receive a name of a data text from Mfold
f = open(str1, 'r')
data = {'temp' : [], 'Af' : [], 'Bf' : [], 'A' : [], 'B' : [], 'AA' : [], 'BB' : [], 'AB' : []}
data = text_reading(f, data)

plt.figure(num=str1[:-4])
plot_each_molecule(data)
plt.title(str1[:-4] + ', by Mfold')
plt.xlabel('Temperature (Celsius)')
plt.ylabel('Molar Concentration')
plt.suptitle(str1[:-4])
plt.show()

mfoldplot('H1H2.txt') #plot Mfold data named 'H1H2.txt'