Difference between revisions of "Team:KUAS Korea/DryLab/Conclusion"

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<center><img src="https://static.igem.org/mediawiki/2018/5/53/T--KUAS_Korea--monoddsfassfsda.png" height="100px"  allowfullscreen>
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<center><img src="https://static.igem.org/mediawiki/2018/b/ba/T--KUAS_Korea--code_epsilon.png" height="200px"  allowfullscreen>
<br>[Fig 1] Monod Equation</center><br><br>
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<br>[Fig 1] Python code</center><br><br>
  
  
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<center><img src="https://static.igem.org/mediawiki/2018/0/08/T--KUAS_Korea--epsilon.png" height="240px" align="center" allowfullscreen>
<br>[Fig 2] Cooperator Equation<br><br></center>
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<br>[Fig 2] curve of epsilon is 0.002<br><br></center>
  
  
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<center><img src="https://static.igem.org/mediawiki/2018/d/db/T--KUAS_Korea--epsilon_two.png" height="240px" align="center" allowfullscreen>
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<br>[Fig 3] Curve of epsilon = 0.2 <br><br><br></center>
  
<center><img src="https://static.igem.org/mediawiki/2018/5/59/T--KUAS_Korea--Cheater.png" height="60px" align="center" allowfullscreen>
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<center><img src="https://static.igem.org/mediawiki/2018/9/9c/T--KUAS_Korea--epsilon_eight.png" height="240px" align="center" allowfullscreen>
<br>[Fig 3] Cheater Equation<br><br><br></center>
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<br>[Fig 4] Curve of epsilon = 0.8 <br><br><br></center>
  
  

Revision as of 00:25, 18 October 2018

Modeling Results & Conclusion

Modeling Equations

    Because we have to know about the relation of microbial growth rates in an aqueous environment with the concentration of a limiting nutrient, we used monod equation for design cheater and cooperators mathematical model. In our case, limiting nutrient will be cellobiose. Also, in our experiment we have to find out the quantificated information of the dependence of the growth rate on substance. Finally, we can compare the growth rates of two E.coli strains.


    [Fig 1] Python code


    This is basic monod Equation. S is glucose released from cellobiose by cooperators. S can be derived from multiplication of q, ρ, χ. q refers the glucose released from cellobiose by cooperators, ρ is the number of total cell density, and χ is the frequency of the cooperator.



    [Fig 2] curve of epsilon is 0.002


    [Fig 3] Curve of epsilon = 0.2



    [Fig 4] Curve of epsilon = 0.8


    Then, we modified monod equation for Cooperator and Cheater. _𝜇_0_ is specific growth rate without glucose and c is the cost of the cooperation. Cost of the cooperation will decrease the growth rate of the cooperator. And 𝛿 is an advantage of cooperation. The cheater will not get the benefit of the cooperation. So we should minus from the cheater’s growth rate. And, we think that disadvantage comes from the glucose capture efficiency. Cheater could not catch efficiently like cooperator. So we give disadvantage to cheater by multiplying (1 – 𝜀) .



    [Fig 4] relative fitness


    And we divided _𝜇_𝑐_ by _𝜇_𝐷_ to express the relative fitness of cooperator.



Glucose Capture Efficiency



    1. Calculation of the flux of glucose into a cooperator cell

    • Measurement of displayed β-glucosidase per cell using whole-cell activity
    -  Vmax = 2.49 x 10^7 glucose·s-1 per cell
    -  The specific growth rate = 0.61 h-1 in 0.004% (w/v) glucose (5.16 x 10^5 cells·μL-1)
    -  Glucose creation rate = 2.40 x 10^7 glucose·s-1 per cell
    -  Glucose consumption = 2.59 x 10^8 glucose per cell


    • The resulting flux of glucose into a single cell

    = the growth rate x the number of glucose molecules per cell = 4.39 x 10^4 glucose ·s-1 per cell



    [Fig 5] glucose capture efficiency


    And we esitmated the efficency of glucose capture with dividing glucose flux by glucose creation rate. We assumed that the glucose molecules produced by β-glucosidase directly diffuse into media because it is located in the outer membrane of E.coli. And this will cause temporary increase of glucose concentration by a local cloud of glucose at the surface. And this will benefit to influx rate of the glucose into the cell.

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