Team:Imperial College London/Drylab/Protein Production

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=Introduction=
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This module consists of producing the drug protein of interest. In order to describe the function of the module, two models have been developed.
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<font face='Calibri' size='5'><b>Protein Production</b></font><br><br>
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Based on the module 1 genetic circuit, a LacI-IPTG inducible promoter is responsible for kickstarting the production of the drug.  
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==Model for protein drug production:==
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Based on the [https://2009.igem.org/Team:Imperial_College_London/M1/Genetic Genetic circuit], a LacI-IPTG inducible promoter is responsible for kickstarting the production of the drug.  
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* In the absence of IPTG, LacI represses the production of the drug (Cellulase or PAH)
* In the absence of IPTG, LacI represses the production of the drug (Cellulase or PAH)
* When IPTG is introduced, the LacI repressing pathway is “de-repressed”, and some output protein is produced.
* When IPTG is introduced, the LacI repressing pathway is “de-repressed”, and some output protein is produced.
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[[Image:II09_NoIPTG_yesIPTG.jpg|350px|centre]]
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[[Image:II09_NoIPTG_yesIPTG.jpg|350px|right]]
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This module is an integral part of the design, as large-scale commercialization of the drug of interest depends on finding the optimal conditions for protein production. This model has also helped the Wetlab to plan some of the experiments.
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This module is an integral part of the design, as large-scale commercialization of the drug of interest depends on finding the optimal conditions for protein production. We implemented a system of differential equations, having made some assumptions and predictions about how the system will behave.
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More information on the model can be found in the [https://2009.igem.org/Team:Imperial_College_London/Drylab Drylab hub]
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<html><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Protein_production/Analysis"><img style="vertical-align:bottom;" width=50px align="left" src="http://i691.photobucket.com/albums/vv271/dk806/II09_Learnmore.png"></a></html>&nbsp; <b><i>About the model assumptions and predictions!
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</i></b><br><br>
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==Model for drug protein enzyme kinetics==
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===The System===
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[[Image:m1gci.jpg | 600px]]<br>
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Genetic circuits can be simplified using ODEs. A good introduction to modelling of genetic circuits is provided in [3]. By clicking on the link below we can see how genetic circuits were implemented in this system. <br>
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The protein drug of interest is an enzyme.  It will bind to specific substrates and increase the rate of their conversion into products.  Therefore, by monitoring either the substrate concentration or the product concentration, we can indirectly see the activity of the enzyme.  This is quantified by measuring by enzyme activity in the [https://2009.igem.org/Team:Imperial_College_London/Wetlab/Protocols/cellulase cellulase assay] and [https://2009.igem.org/Team:Imperial_College_London/Wetlab/Protocols/PAH PAH assay]. Enzyme activity is the rate of substrate utilisation /product formation per unit time.
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<html><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/M1/Protein_production/Analysis/Detailed"><img style="vertical-align:bottom;" width=50px align="left" src="http://i691.photobucket.com/albums/vv271/dk806/II09_Learnmore.png"></a></html>&nbsp; <b><i>about the equations and what they mean!
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</i></b><br><br>
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===Summary of simulation results===
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*When we introduce IPTG into the system, it temporarily removes LacI from the system. Hence, during this period of time, we produce the drug of interest.
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*When the effects of IPTG wear off, the system returns to equilibrium.
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*The more IPTG we add in, the higher the amount of output protein.
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[[Image:II09_SIm_main_prot.jpg]]<br><br>
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===Our Goals===
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<html><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Protein_production/Simulations"><img style="vertical-align:bottom;" width=50px align="left" src="http://i691.photobucket.com/albums/vv271/dk806/II09_Learnmore.png"></a></html>&nbsp; <b><i>about the simulations!
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</i></b><br><br>
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This model aims to better understand the enzymatic action of the drug by:
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*The effects of IPTG toxicity were investigated and we found that for these concentration ranges, IPTG is not toxic to cells. Click on the link to see the analyzed results:See [https://2009.igem.org/Team:Imperial_College_London/Wetlab/Results/Cheminduction/IPTG IPTG growth curves]
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*The constants in this model are arbitrary. We justify our usage of these values with a more detailed dynamical analysis of the system, which shows that it can only have fixed points[1]. [[Media:II09_Prot_stability analysis.pdf | System stability analysis (read about the equations before opening this file, it's intimidating!)]]
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* Characterizing its enzymatic activity
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===Conclusions===
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* Subsequently,by  modeling the relationship between the quantity of protein being produced and the enzyme activity using a simple Michaelis-Menten enzyme kinetics model will enable us to take into account factors that could impair enzymatic activity
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NOTE: These will be better understood once the reader has gone through the details ("Learn More").
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*The greater the strength of the Lac promoter, the greater the repressive action of LacI prior IPTG induction.
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*The greater the Lac promoter leakiness (k<sub>leak</sub>) the greater the basal amount of expression of protein of interest, prior IPTG induction.
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*The greater the amount of IPTG introduced, the greater the production of protein of interest.
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*Here we assumed that the range of IPTG we have introduced is non-toxic for our cells. Growth curves will tell us whether IPTG does limit cell growth at the ranges we are interested in.
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===References===
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[1]Steven H. Strogatz (1994). Nonlinear dynamics and chaos: with applications to physics, biology chemistry and engineering. Addison Wesley. ISBN 0-201-54344-3<br>
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[2]3.Kuhlman T, Zhang Z, Saier MH Jr, & Hwa T (2007) Combinatorial transcriptional control of the lactose operon of Escherichia coli. - PNAS 104 (14) 6043-6048 <br>
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[3]2.Alon, U (2006) An Introduction to Systems Biology: Design Principles of Biological Circuits - Chapman & Hall/Crc Mathematical and Computational Biology
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This model will allow an understanding of the activity of the enzymes and allow the WETLAB to have an idea of the magnitude of the enzymatic activity.  More importantly, after the results from enzyme assays are obtained, the model should provide a means of relating the activity output data to the concentration of the enzyme.
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<html><center><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Autoinduction"><img style="vertical-align:bottom;" width="20%" src="http://i691.photobucket.com/albums/vv271/dk806/II09_Drylabmainimage1.png"></a><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Protein_Production"><img style="vertical-align:bottom;" width="20%" src="http://i691.photobucket.com/albums/vv271/dk806/II09_Drylabmainimage2.png"></a><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Enzyme"><img style="vertical-align:bottom;" width="20%" src="http://i691.photobucket.com/albums/vv271/dk806/II09_Drylabmainimage3.png"></a>
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<a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Genome_deletion"><img style="vertical-align:bottom;" width="20%" src="http://i691.photobucket.com/albums/vv271/dk806/II09_Drylabmainimage5.png"></a></center></html>
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More information on the model can be found in the [https://2009.igem.org/Team:Imperial_College_London/M1/Drylab Drylab hub]
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<html><table border="0" style="background-color:transparent;" width="100%">
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<tr><td width="0%">&nbsp;</td>
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<td width="22%"><center><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Autoinduction"><b>Autoinduction</b></a></center></td>
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<td width="22%"><center><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Protein_Production"><b>Protein Production</b></a></center></td>
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<td width="22%"><left><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Enzyme"><b>Drug Kinetics</b></a></left></td>
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<td width="22%"><left><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Genome_deletion"><b>Genome Deletion</b></a></left></td>
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<td width="1%"></td>
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</tr></table></html>
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<br>
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{{Imperial/Box1|Module 1: Protein production|Two models are required to explain the functionality of M1:
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*[https://2009.igem.org/Team:Imperial_College_London/Drylab/M1/Protein_production/Analysis The Model]<br>
 
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*[https://2009.igem.org/Team:Imperial_College_London/Drylab/M1/Protein_production/Simulations Simulations]<br>
 
{{Imperial/09/TemplateBottom}}
{{Imperial/09/TemplateBottom}}

Latest revision as of 23:53, 13 October 2009



Protein Production

Based on the module 1 genetic circuit, a LacI-IPTG inducible promoter is responsible for kickstarting the production of the drug.

  • In the absence of IPTG, LacI represses the production of the drug (Cellulase or PAH)
  • When IPTG is introduced, the LacI repressing pathway is “de-repressed”, and some output protein is produced.
II09 NoIPTG yesIPTG.jpg


Contents

Our goals

The modelling aims to provide an overview and better understanding of the M1 system’s function by:

  • Characterizing the system.
  • Modeling to account for several factors that may reduce/hinder the production of the protein drug such as:
    • Lac promoter leakiness
    • IPTG toxicity
    • Stability of output protein


This module is an integral part of the design, as large-scale commercialization of the drug of interest depends on finding the optimal conditions for protein production. We implemented a system of differential equations, having made some assumptions and predictions about how the system will behave.

  About the model assumptions and predictions!

The System

M1gci.jpg
Genetic circuits can be simplified using ODEs. A good introduction to modelling of genetic circuits is provided in [3]. By clicking on the link below we can see how genetic circuits were implemented in this system.

  about the equations and what they mean!

Summary of simulation results

  • When we introduce IPTG into the system, it temporarily removes LacI from the system. Hence, during this period of time, we produce the drug of interest.
  • When the effects of IPTG wear off, the system returns to equilibrium.
  • The more IPTG we add in, the higher the amount of output protein.

II09 SIm main prot.jpg

  about the simulations!

Conclusions

NOTE: These will be better understood once the reader has gone through the details ("Learn More").

  • The greater the strength of the Lac promoter, the greater the repressive action of LacI prior IPTG induction.
  • The greater the Lac promoter leakiness (kleak) the greater the basal amount of expression of protein of interest, prior IPTG induction.
  • The greater the amount of IPTG introduced, the greater the production of protein of interest.
  • Here we assumed that the range of IPTG we have introduced is non-toxic for our cells. Growth curves will tell us whether IPTG does limit cell growth at the ranges we are interested in.

References

[1]Steven H. Strogatz (1994). Nonlinear dynamics and chaos: with applications to physics, biology chemistry and engineering. Addison Wesley. ISBN 0-201-54344-3
[2]3.Kuhlman T, Zhang Z, Saier MH Jr, & Hwa T (2007) Combinatorial transcriptional control of the lactose operon of Escherichia coli. - PNAS 104 (14) 6043-6048
[3]2.Alon, U (2006) An Introduction to Systems Biology: Design Principles of Biological Circuits - Chapman & Hall/Crc Mathematical and Computational Biology

 
Autoinduction
Protein Production
 Drug Kinetics Genome Deletion


Mr. Gene   Geneart   Clontech   Giant Microbes

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