Team:Imperial College London/Drylab/Autoinduction

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** The effects of catabolite repression and inducer exclusion [3-4]( see [https://2009.igem.org/Team:Imperial_College_London/Temporal_Control/Chemical_Induction chemical induction] for further explanation).   
** The effects of catabolite repression and inducer exclusion [3-4]( see [https://2009.igem.org/Team:Imperial_College_London/Temporal_Control/Chemical_Induction chemical induction] for further explanation).   
** The "lag effect" seen when we switch from glucose to a secondary source. In the case of lactose, this delay is caused by the production of permeases and b-galactosidase.  
** The "lag effect" seen when we switch from glucose to a secondary source. In the case of lactose, this delay is caused by the production of permeases and b-galactosidase.  
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<html><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Autoinduction/Model2"><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 Zhao Lu's model</i></b><br><br>
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** Furthermore, other carbon sources we have used in our [https://2009.igem.org/Team:Imperial_College_London/Wetlab/Protocols/Autoinduction autoinduction experiments] such as galactose, xylose, mannose... are also related to lactose (in structure) so we believe that the dynamics of the lac operon have an important part to play in the metabolism of secondary carbon sources.
** Furthermore, other carbon sources we have used in our [https://2009.igem.org/Team:Imperial_College_London/Wetlab/Protocols/Autoinduction autoinduction experiments] such as galactose, xylose, mannose... are also related to lactose (in structure) so we believe that the dynamics of the lac operon have an important part to play in the metabolism of secondary carbon sources.
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<html><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Autoinduction/Model2"><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 Zhao Lu's model</i></b><br><br>
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<html><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Autoinduction/Model3"><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 Keasling's model</i></b><br><br>
===References===
===References===

Revision as of 17:30, 17 October 2009




Autoinduction

II09 diauxi illust.jpg

Starting the encapsulation process is related to diauxic growth of bacteria under the presence of two carbon sources. 2 models from literature have been discussed and implemented in this section. More information about each can be found in the extra tabs. After analysis of the models, we picked one of them as the best representation for our data. The sections below will provide a summary about each model, and at the end, discuss which one was picked and why. The diauxic growth phenomenon is tricky to understand, and the models we have chosen illustrate different approaches that groups have taken to explain what happens when bacteria switch to feeding from a primary carbon source to a secondary carbon source.

Contents

Goal of the models

  • Provide understanding of the diauxic growth phenomenon
  • Find out the time taken to consume glucose in the medium to characterize the CRP promoter.
  • Quantify the switch point from glucose to a secondary carbon source.
  • Understand which is the best secondary carbon source to power the system after glucose is used up.

Model 1: Kompala et al cybernetic model

  • This is a cybernetic model developed by Kompala et al [1].
  • It is based on Michaelis-Menten assumptions for substrate consumption.
  • The substrates in this case are our carbon sources: glucose, maltose, xylose, galactose...

Rationale:
In a mixture of carbon sources, glucose is energetically more favorable to consume(more on this is discussed in the autoinduction section).
This model includes the effects of enzyme activation and inhibition, as well as competition for metabolism of sugars [1].II09 diax wolf.jpg
Figure 1 from [2]

The red and green lines represent the concentrations of glucose (S1) and a secondary substrate (S2). As we can see, S1 is used up before S2. During this phase, the population (X- blue trace) grows exponentially and saturates when S1 runs out. Once it runs out, there is a switch phase, followed by metabolism of a secondary carbon source and entering a second exponential growth phase.

  About Kompala's cybernetic model

Model 2: Keasling et al model

  • Models such as Zhao Lu et al [3] or Keasling et al [4] cover in more detail lactose transport into the cell and the dynamics of the lac operon.
  • Zhao Lu et al takes into account several factors that are not included in Kompala's model, such as:
    • The effects of catabolite repression and inducer exclusion [3-4]( see chemical induction for further explanation).
    • The "lag effect" seen when we switch from glucose to a secondary source. In the case of lactose, this delay is caused by the production of permeases and b-galactosidase.

  About Zhao Lu's model

    • Furthermore, other carbon sources we have used in our autoinduction experiments such as galactose, xylose, mannose... are also related to lactose (in structure) so we believe that the dynamics of the lac operon have an important part to play in the metabolism of secondary carbon sources.

  About Keasling's model

References

[1]Kompala, D.S., D. Ramkrishna and G.T. Tsao, "Cybernetic modeling of microbial growth on multiple substrates," Biotechnology and Bioengineering 26 :1272-1281 (1984).
[2] Wolfram: http://demonstrations.wolfram.com/DiauxicGrowthOfBacteriaOnTwoSubstrates
[3] Zhao Lu et al. "Dynamics of Glucose-Lactose Diauxic Growth in E.coli". arXiv:0708.1993v1 [q-bio.OT] 15 Aug 2007
[4] Wong, P. , Keasling et al. "Mathematical Model of the lac Operon: Inducer Exclusion, Catabolite Repression, and Diauxic Growth on Glucose and Lactose." Biotechnol. Prog. 1997, 13, 132-143



 
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