Team:Imperial College London/Modelling/Experiments

From 2010.igem.org

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{{:Team:Imperial_College_London/Templates/Header}}
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|style="font-family: helvetica, arial, sans-serif;font-size:2em;color:#ea8828;"|Dry-Lab Wet-Lab Interaction
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</div><br/>
</div><br/>
<p><b>Engineering approach to the project</b></p>
<p><b>Engineering approach to the project</b></p>
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1. Influence of Specification on Design and vice versa:
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1. Influence of specification on the design and vice versa:
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*It was one of the longest steps as we were struggling to compromise the specifications with the viable designs.
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*This was one of the most time-consuming steps as we were struggling to compromise the specifications of the viable designs.
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2. Influence of Design on Modelling and vice versa:
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2. Influence of design on modelling and vice versa:
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*All modelling done was meant to give answers to questions that arose in the design phase.
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*All modelling was meant to give answers to questions that arose during the design phase.
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*Once designs were chosen, they were modelled. It was found that 2 step amplification is not likely to be effiecient, so it was decided that only 1step amplification will be taken forward to assembly. This was a significant conclusion as it would take weeks in the labroatory to find that out.
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*Once the designs were chosen, they were modelled. It was found that 3-step amplification is not likely to be efficient, so it was decided that only 2-step amplification will be taken forward to assembly. This was a significant conclusion as it would take weeks in the laboratory to reach this conclusion.
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*The doubt about big enough gradient of AIPs to be established in the extracellular space to set off receptor was rationalised by modelling. The model allowed to determine conditions for the system to work
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*The question whether a sufficient AIP gradient could be established in the extracellular space to set off receptor was answered by modelling. The model allowed to determine under which conditions the system would work.
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3. Influence of Modelling on Assembly and vice versa:
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3. Influence of modelling on assembly and vice versa:
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*The results from modelling allowed to progress with assembly  
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*The results from modelling allowed to progress with the assembly.
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4. Influence of Assembly on Testing and vice versa:
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4. Influence of assembly on testing and vice versa:
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*testing has been planned ahead, so assembly contructs have been modified to allow some testing methods like: purification or negative control.
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*Testing has been planned ahead, so assembly constructs have been modified to allow some testing methods, e.g. purification or negative control.
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5. Influence of Testing on Specifications and vice versa:
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5. Influence of testing on specifications and vice versa:
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*We did not get there yet. However, if the specifications would not be met by the results, we would need to try redesigning the system or, in case of no alternative, changing the specifications.
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*This step has not been implemented yet. However, if the specifications were not met by the results, we would need to try to redesign the system or change the specifications (in case there was no other alternatives).
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6. Influence of components not adjacent to each other in the cycle:
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6. Influence of components which are not adjacent to each other in the cycle:
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*Testing may influence modelling as the results of the two do not match.
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*Visibility threshold concentration of muconic acid was determined in the labs and fed back to modellers.  
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*Many experiments were diesgned specifically on the request of modellers in order to find parameters for the models. Obtaining those paramters would increase the reliability of the models.
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*Many experiments were designed specifically on the request of the modelling team in order to find the necessary parameters for the models. Obtaining these parameters would increase the reliability of the models.
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|style="font-family: helvetica, arial, sans-serif;font-size:2em;color:#ea8828;"|Wet-Lab for Dry-Lab
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|style="font-family: helvetica, arial, sans-serif;font-size:2em;color:#ea8828;"|Wet-Lab Experiments for Dry-Lab
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<b>Output Amplification Model</b><br />
<b>Output Amplification Model</b><br />
<ol>
<ol>
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<li>Determine the maximum concentration of sD that <br />cells can produce (in vivo):
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<li>Determine the maximum concentration of sD that cells can produce (in vivo):
Compare activity with 2.
Compare activity with 2.
</li>
</li>
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<li>Determine how much XylE we need to produce <br />prior to adding Catechol (in vitro first, maybe in vivo <br />afterwards). In vivo IPTG: Compare cultures that <br />have less of it.  
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<li>Determine how much XylE we need to produce prior to adding Catechol (in vitro first, maybe in vivo afterwards). In vivo IPTG: Compare cultures that have less of it.  
</li>
</li>
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<li>Determine XylE and TEV production (in vivo). <br />Use robot to induce production and measure <br />activities TEV (FRET pairs with TEV link).  
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<li>Determine XylE and TEV production (in vivo). Use robot to induce production and measure activities of TEV (FRET pairs with TEV link).  
</li>
</li>
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<li>Degradation of XylE (in vivo or in vitro if cell division <br />is not taken into account). Monitoring activity, then <br />approximate the concentration. Remove the IPTG.  
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<li>Degradation of XylE (in vivo or in vitro if cell division is not taken into account). Monitoring activity, then approximate the concentration. Remove the IPTG.  
</li>
</li>
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<li>Determine TEV kinetics (k<sub>cat</sub>, K<sub>m</sub>) on <br />XylE-GFP (in vitro).</li>
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<li>Determine TEV kinetics (k<sub>cat</sub>, K<sub>m</sub>) on XylE-GFP (in vitro).</li>
</ol><br />
</ol><br />
<b>Protein Display Model</b><br />
<b>Protein Display Model</b><br />
<ol>
<ol>
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<li>Kinetic constants (k<sub>cat</sub>, K<sub>m</sub>) of <br />TEV acting on the display peptide.</li>
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<li>Kinetic constants (k<sub>cat</sub>, K<sub>m</sub>) of TEV acting on the display peptide.</li>
<li>Production rate of peptide.</li>
<li>Production rate of peptide.</li>
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<li>Total number of proteins expressed <br />on the cell wall.</li>
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<li>Total number of proteins expressed on the cell wall.</li>
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<li>Number of free floating proteins <br />without cleaving by TEV (or the <br />ratio of the above).</li>
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<li>Number of free floating proteins without cleaving by TEV (or the ratio of the above).</li>
</ol>
</ol>

Latest revision as of 22:27, 26 October 2010

Modelling Overview | Detection Model | Signaling Model | Fast Response Model | Interactions
A major part of the project consisted of modelling each module. This enabled us to decide which ideas we should implement. Look at the Fast Response page for a great example of how modelling has made a major impact on our design!
Interaction between Dry-Lab and Wet-Lab
Diagram of engineering cycle
Diagram representing the engineering cycle.

Engineering approach to the project

1. Influence of specification on the design and vice versa:

  • This was one of the most time-consuming steps as we were struggling to compromise the specifications of the viable designs.

2. Influence of design on modelling and vice versa:

  • All modelling was meant to give answers to questions that arose during the design phase.
  • Once the designs were chosen, they were modelled. It was found that 3-step amplification is not likely to be efficient, so it was decided that only 2-step amplification will be taken forward to assembly. This was a significant conclusion as it would take weeks in the laboratory to reach this conclusion.
  • The question whether a sufficient AIP gradient could be established in the extracellular space to set off receptor was answered by modelling. The model allowed to determine under which conditions the system would work.

3. Influence of modelling on assembly and vice versa:

  • The results from modelling allowed to progress with the assembly.

4. Influence of assembly on testing and vice versa:

  • Testing has been planned ahead, so assembly constructs have been modified to allow some testing methods, e.g. purification or negative control.

5. Influence of testing on specifications and vice versa:

  • This step has not been implemented yet. However, if the specifications were not met by the results, we would need to try to redesign the system or change the specifications (in case there was no other alternatives).

6. Influence of components which are not adjacent to each other in the cycle:

  • Visibility threshold concentration of muconic acid was determined in the labs and fed back to modellers.
  • Many experiments were designed specifically on the request of the modelling team in order to find the necessary parameters for the models. Obtaining these parameters would increase the reliability of the models.
Wet-Lab Experiments for Dry-Lab
Output Amplification Model
  1. Determine the maximum concentration of sD that cells can produce (in vivo): Compare activity with 2.
  2. Determine how much XylE we need to produce prior to adding Catechol (in vitro first, maybe in vivo afterwards). In vivo IPTG: Compare cultures that have less of it.
  3. Determine XylE and TEV production (in vivo). Use robot to induce production and measure activities of TEV (FRET pairs with TEV link).
  4. Degradation of XylE (in vivo or in vitro if cell division is not taken into account). Monitoring activity, then approximate the concentration. Remove the IPTG.
  5. Determine TEV kinetics (kcat, Km) on XylE-GFP (in vitro).

Protein Display Model
  1. Kinetic constants (kcat, Km) of TEV acting on the display peptide.
  2. Production rate of peptide.
  3. Total number of proteins expressed on the cell wall.
  4. Number of free floating proteins without cleaving by TEV (or the ratio of the above).
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