Difference between revisions of "Team:Bielefeld-CeBiTec/Model"

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The toxicity of copper ions on the cell is well characterized (Ning <i>et al.</i>, 2015) but for important parts of this project like the <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Hardware">crossflow reactor</a> we needed to know the exact point of time when our cells will die to achieve highest possible yields of copper.  
 
The toxicity of copper ions on the cell is well characterized (Ning <i>et al.</i>, 2015) but for important parts of this project like the <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Hardware">crossflow reactor</a> we needed to know the exact point of time when our cells will die to achieve highest possible yields of copper.  
 
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The residence time should not exceed the rate of dying and cell lysis in <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Hardware">the system</a>. If cell lysis kicks in copper gets released back again into the substrate media and the yield minimizes. The modeling started with the copper uptake in our cells containing the BioBrick BBa_K2638204, which expresses <i>oprC</i> under pAra​BAD (<a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Hardware">BBa_I0500)</a>) control and induction at 1.0 % arabinose in. The toxicity is calculated for a single cell.</div><div class="article">
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The residence time should not exceed the rate of dying and cell lysis in <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Hardware">the system</a>. If cell lysis kicks in copper gets released back again into the substrate media and the yield minimizes. The modeling started with the copper uptake in our cells containing the BioBrick BBa_K2638204, which expresses <i>oprC</i> un<script>der pAra​BAD (<a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Hardware">BBa_I0500)</a>) control and induction at 1.0 % arabinose in. The toxicity is calculated for a single cell.</div><div class="article">
 
The first step was to calculate the rate of expression of <i>oprC</i>. Therefore the characterization of  <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Hardware">BBa_I0500)</a> of <a href="https://2011.igem.org/Team:Groningen/project_characterisation_promoters_pbad">Groningen</a> was used to calculate the expression speed. At 1.0 % arabinose induction a raise of fluorescence of approx. <i>ΔF</i> = 82,000 within of t = 36,000 s was measurable.  
 
The first step was to calculate the rate of expression of <i>oprC</i>. Therefore the characterization of  <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Hardware">BBa_I0500)</a> of <a href="https://2011.igem.org/Team:Groningen/project_characterisation_promoters_pbad">Groningen</a> was used to calculate the expression speed. At 1.0 % arabinose induction a raise of fluorescence of approx. <i>ΔF</i> = 82,000 within of t = 36,000 s was measurable.  
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The conversion from fluorescence units to concentration in mol/L was calculated as <i>k</i> = 2.5 * 10-6 mol/L (Furtado and Henry, 2002) and the volume of the used capillaries was <i>V</i> = 3.14 * 10<sup>-9</sup> L The rate of protein expression with 1.0 % arabinose is:
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The conversion from fluorescence units to concentration in mol/L was calculated as <i>k</i> = 2.5 * 10<sup>-6</sup> mol*L<sup>-1</sup> (Furtado and Henry, 2002) and the volume of the used capillaries was <i>V</i> = 3.14 * 10<sup>-9</sup> L The rate of protein expression with 1.0 % arabinose is:
 
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$$\frac{3.14 * 10^{-9} L * 2.5 * 10^{-6} mol L^{-1} * 87,500}{36,000 s} = 7.85 * 10^{-14} mol*s^{-1} (1)$$
 
$$\frac{3.14 * 10^{-9} L * 2.5 * 10^{-6} mol L^{-1} * 87,500}{36,000 s} = 7.85 * 10^{-14} mol*s^{-1} (1)$$
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$$[C_t]- K_M * ln{[C_t]} = v_max * t + [C_0] - K_M * ln {[C_0]} (5)$$
 
$$[C_t]- K_M * ln{[C_t]} = v_max * t + [C_0] - K_M * ln {[C_0]} (5)$$
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<article>  
 
<article>  
 
Due to the fact that this only describes the kinetic of a single molecule of OprC the (3) and the Avogadro number <i>N<sub>A</sub></i> = 6.022 * 10<sup>23</sup> mol<sup>-1</sup> . That way the addition was possible of the both terms on the right side of (5) and the end equation results in:</article>
 
Due to the fact that this only describes the kinetic of a single molecule of OprC the (3) and the Avogadro number <i>N<sub>A</sub></i> = 6.022 * 10<sup>23</sup> mol<sup>-1</sup> . That way the addition was possible of the both terms on the right side of (5) and the end equation results in:</article>
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                       <img class="figure hundred" src="https://static.igem.org/mediawiki/2018/e/e9/T--Bielefeld-CeBiTec--JZ--Modelingwithcopper.png">
 
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                           <b>Figure 1:</b> The results of the copper uptake modeling. On the y axis the concentration in mol*L<sup>-1</sup> is depicted, on the x axis the time <i>t</i> in s. The concentration of 1*10<sup>-4</sup> is reached at 800 s.   
 
                           <b>Figure 1:</b> The results of the copper uptake modeling. On the y axis the concentration in mol*L<sup>-1</sup> is depicted, on the x axis the time <i>t</i> in s. The concentration of 1*10<sup>-4</sup> is reached at 800 s.   
 
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<article>This way the toxic copper concentration <i>c<sub>tox</sub></i> = 1 * 10<sup>-5</sup> mol/L (Ning, 2015) inside the cell is approx. reached 800 s after the induction with 1.0 % arabinose.</article>
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<article>This way the toxic copper concentration <i>c<sub>tox</sub></i> = 1 * 10<sup>-5</sup> mol/L (Ning, 2015) inside the cell is approx. reached 800 s (Figure 1) after the induction with 1.0 % arabinose.</article>
  
 
   <br/><h2>siRNA promoter model</h2>
 
   <br/><h2>siRNA promoter model</h2>
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The modeling for the point of time where the toxicity kicks in showed, that due to the troublesome way to find the right promoter strength a universal promoter library would be an important addition to the synthetic biology community. That way the promoter library in the <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Part_Collection">parts collection</a> represents an universal standard for the iGEM community to characterize their promoters.<br/>
 
The modeling for the point of time where the toxicity kicks in showed, that due to the troublesome way to find the right promoter strength a universal promoter library would be an important addition to the synthetic biology community. That way the promoter library in the <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Part_Collection">parts collection</a> represents an universal standard for the iGEM community to characterize their promoters.<br/>
  
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The <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Hardware">crossflow reactor</a> design and modeling was strongly influenced by the toxicity modeling.Another way to approach this problem were the BioBricks BBa_K2638100, BBa_K2638101, BBa_K2638103 BBa_K2638105, BBa_K2638106, BBa_K2638120 and BBa_K2638121 enrich now the parts registry.<br/>
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The <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Hardware">crossflow reactor</a> design and modeling was strongly influenced by the toxicity modeling.Another way to approach this problem were the BioBricks <a href="http://parts.igem.org/Part:BBa_K2638101">BBa_K2638101</a>, <a href="http://parts.igem.org/Part:BBa_K2638100">BBa_K2638100</a>, <a href="http://parts.igem.org/Part:BBa_K2638103">BBa_K2638103</a>, <a href="http://parts.igem.org/Part:BBa_K2638105">BBa_K2638105</a>, <a href="http://parts.igem.org/Part:BBa_K2638106">BBa_K2638106</a>, <a href="http://parts.igem.org/Part:BBa_K2638120">BBa_K2638120</a> and <a href="http://parts.igem.org/Part:BBa_K2638121">BBa_K2638121</a> enrich now the parts registry.<br/>
  
 
The <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Software">siRNA/RNAi</a> modeling went over into our software project because it showed way more potential like this.
 
The <a href="https://2018.igem.org/Team:Bielefeld-CeBiTec/Software">siRNA/RNAi</a> modeling went over into our software project because it showed way more potential like this.
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                   <h2>References</h2>
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                   <b>Barnes, D. J., & Chu, D. (2010)</b>. Introduction to modeling for biosciences. Springer Science & Business Media.<br/>
 
                   <b>Barnes, D. J., & Chu, D. (2010)</b>. Introduction to modeling for biosciences. Springer Science & Business Media.<br/>
 
                   <b>Delihas, N., & Forst, S. (2001)</b>. MicF: an antisense RNA gene involved in response of Escherichia coli to global stress factors. Journal of molecular biology, 313(1), 1-12.<br/>
 
                   <b>Delihas, N., & Forst, S. (2001)</b>. MicF: an antisense RNA gene involved in response of Escherichia coli to global stress factors. Journal of molecular biology, 313(1), 1-12.<br/>

Revision as of 02:14, 18 October 2018

https://static.igem.org/mediawiki/2018/e/e9/T--Bielefeld-CeBiTec--JZ--Modelingwithcopper.png
Modeling
„Any modeling project should be tempered by the morality of laziness.“ (Barnes et. al., 2010)

Short Summary

This phrase illustrates that lab results can be predicted before actually working in the lab, saving precious time.
Our project revolves around new discovered or theoretical biological systems like for example RNAi in prokaryotes or newly discovered copper transporters. Our project required four different modeling approaches:
  • toxicity modeling
  • reactor modeling
  • siRNA modeling
  • ferritin structure modeling

Toxicity modeling

The toxicity of copper ions on the cell is well characterized (Ning et al., 2015) but for important parts of this project like the crossflow reactor we needed to know the exact point of time when our cells will die to achieve highest possible yields of copper.
The residence time should not exceed the rate of dying and cell lysis in the system. If cell lysis kicks in copper gets released back again into the substrate media and the yield minimizes. The modeling started with the copper uptake in our cells containing the BioBrick BBa_K2638204, which expresses oprC un