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Team:Imperial College London/Drylab/Protein Production
From 2009.igem.org
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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> | [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> | ||
[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 | [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 | ||
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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> | ||
| + | <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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| + | <html><table border="0" style="background-color:transparent;" width="100%"> | ||
| + | <tr><td width="0%"> </td> | ||
| + | <td width="22%"><center><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Autoinduction"><b>Autoinduction</b></a></center></td> | ||
| + | <td width="22%"><center><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Protein_Production"><b>Protein Production</b></a></center></td> | ||
| + | <td width="22%"><left><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Enzyme"><b>Drug Kinetics</b></a></left></td> | ||
| + | <td width="22%"><left><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Genome_deletion"><b>Genome Deletion</b></a></left></td> | ||
| + | <td width="1%"></td> | ||
| + | </tr></table></html> | ||
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{{Imperial/Box1|Module 1: Protein production|Two models are required to explain the functionality of M1: | {{Imperial/Box1|Module 1: Protein production|Two models are required to explain the functionality of M1: | ||
Revision as of 18:40, 8 October 2009
- Overview
- The model
- Simulations
Protein Production
Based on the 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.
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.
about the model assumptions and predictions!
The System
There are 6 differential equations that describe the behaviour of 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.
about the simulations!
- 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: IPTG growth curves
- 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]. System stability analysis (read about the equations before opening this file, it's intimidating!)
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 size of the bump in 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
