Difference between revisions of "Team:Nanjing-China/Modeling"
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</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >tc1</th> |
<td>Transcription rate of tasA-AP in vegetative state</td> | <td>Transcription rate of tasA-AP in vegetative state</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >tc2</th> |
<td>Transcription rate of cotC-AP in spore state</td> | <td>Transcription rate of cotC-AP in spore state</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >deg1</th> |
<td>Degradation rate of tasA-AP mRNA in vegetative state</td> | <td>Degradation rate of tasA-AP mRNA in vegetative state</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >deg2</th> |
<td>Degradation rate of tasA-AP in nutrient state</td> | <td>Degradation rate of tasA-AP in nutrient state</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >deg3</th> |
<td>Degradation rate of cotC-AP mRNA in spore state</td> | <td>Degradation rate of cotC-AP mRNA in spore state</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >deg4</th> |
<td>Degradation rate of cotC-AP in spore state</td> | <td>Degradation rate of cotC-AP in spore state</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >fl1</th> |
<td>Folding efficiency of tasA-AP in vegetative state</td> | <td>Folding efficiency of tasA-AP in vegetative state</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >fl2</th> |
<td>Folding efficiency of cotC-AP in spore state</td> | <td>Folding efficiency of cotC-AP in spore state</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >tl1</th> |
<td>Translation rate of tasA-AP in vegetative state</td> | <td>Translation rate of tasA-AP in vegetative state</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >tl2</th> |
<td>Translation rate of cotC-AP in spore state</td> | <td>Translation rate of cotC-AP in spore state</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >ou1</th> |
<td>Extracellular transport rate of tasA-AP</td> | <td>Extracellular transport rate of tasA-AP</td> | ||
</tr> | </tr> | ||
<tr> | <tr> | ||
− | <th | + | <th >ou2</th> |
<td>Extracellular transport rate of cotC-AP</td> | <td>Extracellular transport rate of cotC-AP</td> | ||
</tr> | </tr> |
Revision as of 16:59, 17 September 2015
Modeling may seem to be a little bit alien and unfamiliar to students who major in Biology solely. However, modeling plays an increasingly vital role in the research of synthetic biology. Then, what is modeling? Modeling aims to build and use a model which provides a theoretical way to explain a phenomenon in a simpler fashion than the real situation. The procedure of modeling consists of observing a phenomenon, identifying what parameters may influence if and sorting them out so that the decisive ones remain. According to the results of modeling, you may predict what would happen under different conditions. Modeling also takes different forms, and the following is some examples of models:
Modeling also takes different forms, and the following is some examples of models:
This model is used widely. In theory, we can build the model of almost every problem if certain parameters are sorted out.
It seems more accessible than mathematical equations but the building of it depends on certain software to some extent.
Sometimes, a simple drawing is understandable enough to clarify biological processes, even without captions!
We use Matlab to do the modeling work.
Matlab is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis and numeric computation. Using matlab, we can solve technical computing problems much faster.
Matlab has a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology.
In our modeling, we use matlab to find out the influences imposed on protein expression by certain parameters, such as pH, ion concentration, etc.
pbrR:
tasA:
The 3D structures of GolB, SUP and CotC are not included in the database of NCBI.
To find out the growth tendency and best conditions for bacillus subtilis to grow in different states, the models of growth are discussed separately.
Part1 Growth
Considering the fact that proliferation is unhindered when resources are sufficient and minimal when resources have run out, our model couples growth (N) with resources (R).
As f(R) is the Hill function, where
f(R)=Rα/(1+Rα)
the growth of cells is modelled as:
dN/dt=k*N*f(R)
dR/dt=-α*N*f(R)
Spore formation is usually triggered by a lack of nutrients, and usually occurs in gram-positive bacteria. In spore state, bacillus subtilis is dormant and with strong resistance to the unfavorable conditions. According to the previous research, PH and the resources play crucial roles on the growth of bacillus subtilis.[reference1] Referred to Logistic model, here are our equations.
dN/dt=k*N*(1-N/Nm)
N | the number of cells |
---|---|
k | cell growth rate |
Nm | the maximum of cell number |
The experiment data shows that the number growth rate increases with PH increase when PH is lower than 7.0, but decreases with PH increase (>7.0). Therefore, optimal PH for growth is 7.0.
Combining the experimental data with modelling, we found that sucrose is the best carbon resource for the high growth rate and maximum cell number.
Combining the experimental data with modelling, we found that using corn flour as the nitrogen resource contributes to a high growth rate of bacillus subtilis, while yeast cream yields to the maximum in cell number.
Part2 Transcription-Translation
Assumptions:
- The functional genes of the plasmid are successfully recombined with DNA of bacillus subtilis.
- Concentration of RNA polymerase is constant.
- The concentration of the ions to be absorbed outside the cell has little influence on the transcription of the absorptive proteins.
Variables:
[mtasA-AP] [mcotC-AP] |
Concentration of mRNA of tasA and absorptive protein Concentration of mRNA of cotC and absorptive protein |
---|---|
[tasA-AP] [cotC-AP] |
Concentration of conjugates of tasA and absorptive protein Concentration of conjugates of cotC and absorptive protein |
Parameters:
Parameter | Description |
---|---|
tc1 | Transcription rate of tasA-AP in vegetative state |
tc2 | Transcription rate of cotC-AP in spore state |
deg1 | Degradation rate of tasA-AP mRNA in vegetative state |
deg2 | Degradation rate of tasA-AP in nutrient state |
deg3 | Degradation rate of cotC-AP mRNA in spore state |
deg4 | Degradation rate of cotC-AP in spore state |
fl1 | Folding efficiency of tasA-AP in vegetative state |
fl2 | Folding efficiency of cotC-AP in spore state |
tl1 | Translation rate of tasA-AP in vegetative state |
tl2 | Translation rate of cotC-AP in spore state |
ou1 | Extracellular transport rate of tasA-AP |
ou2 | Extracellular transport rate of cotC-AP |
Ordinary differential equations as follows:
d[mtasA-AP]/dt=tc1-deg1*[mtasA-AP]
d[tasA-AP]/dt=tl1*[mtasA-AP]*fl1-deg2*[tasA-AP]-ou1*[tasA-AP]
d[mcotC-AP]/dt=tc2-deg3*[mcotC-AP]
d[cotC-AP]/dt=tl2*[mcotC-AP]*fl1-deg4*[cotC-AP]-ou1*[cotC-AP]
In the 16 kinds of parallel constructs we designed, our model chose one of them to show the specific modelling results. The instanced construct
Part3 Bioabsorption
Based on a set of differential equations describing the kinetics of absorptive protein absorbing specific ions, the model is designed to predict the efficiency and affinity of the binding process. Given a certain initial concentration of absorptive protein, the model calculates the time necessary for a certain proportion of absorptive protein to be occupied, simulating the efficiency of the heavy ion-capturing process.
- Pb2+
Pb2++PbrR⇋Pb2+-PbrR
d[Pb(II)-PbrR]/dt=ka
- Au+
Au++GolB⇋Au+-GolB
- UO22+
UO22++UBP⇋UO22+-UBP
References
https://2014.igem.org/Team:INSA-Lyon/Modeling
http://www.mathworks.nl/products/matlab
http://www.ncbi.nlm.nih.gov/Structure/mmdb