Modeling a synthetic genetic oscillator<br />Part of an iGem project<br />
iGem<br />Global synthetic biology competition<br />International Genetically Engineered Machine<br />8th year in a row, f...
Projects<br />
Synchronized Oscillatory System<br />Negative feedback loops<br />Positive feedback for signaling molecule<br />Signaling ...
The Danino et. al scheme<br />
Equations from Danino et. al<br />
Advantages of this model<br />4 differential equations<br />Simplified reaction scheme<br />Takes the surrounding physics ...
Modeling results<br />Equations introduced in Matlab<br />The P function is covered by dde23<br />
Disadvantages of the model<br />Units of parameters<br />Some biologically relevant information missing<br />No useful res...
Alternative model<br />More biologically relevant and accurate<br />
Equations for this model<br />Y1 : lux-I mRNA<br />Y2 : LUX-I protein<br />Y3 : AHL<br />Y4 : AHL-LUX-R complex<br />Y5 : ...
Disadvantages of this model<br />Many parameters<br />A large number of them unknown<br />Does not (yet) take into account...
The microsieve<br />
Modeling of the microsieve<br />A more global approach<br />Units are more logical<br />A more widely applicable model<br ...
Measurement plans<br />Introduce different flow rates to the system<br />Measure both the outflow and permeate flow (under...
Goal<br />Produce a model that can estimate a flow rate to achieve:<br />An appropriate cell density<br />A constant oscil...
Questions<br />In which way do we model this most efficiently?<br />Which of these models is actually feasible?<br />Is it...
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Modelling a synthetic genetic oscillator

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Modelling a synthetic genetic oscillator

  1. 1. Modeling a synthetic genetic oscillator<br />Part of an iGem project<br />
  2. 2. iGem<br />Global synthetic biology competition<br />International Genetically Engineered Machine<br />8th year in a row, first time Wageningen UR competes<br />Genetic building blocks<br />
  3. 3. Projects<br />
  4. 4. Synchronized Oscillatory System<br />Negative feedback loops<br />Positive feedback for signaling molecule<br />Signaling molecule synchronizes oscillations<br />
  5. 5. The Danino et. al scheme<br />
  6. 6. Equations from Danino et. al<br />
  7. 7. Advantages of this model<br />4 differential equations<br />Simplified reaction scheme<br />Takes the surrounding physics into account<br />Cell density<br />
  8. 8. Modeling results<br />Equations introduced in Matlab<br />The P function is covered by dde23<br />
  9. 9. Disadvantages of the model<br />Units of parameters<br />Some biologically relevant information missing<br />No useful result can be extracted<br />
  10. 10. Alternative model<br />More biologically relevant and accurate<br />
  11. 11.
  12. 12. Equations for this model<br />Y1 : lux-I mRNA<br />Y2 : LUX-I protein<br />Y3 : AHL<br />Y4 : AHL-LUX-R complex<br />Y5 : aiia mRNA<br />Y6 : AiiA protein<br />Y7 : AiiA-AHL complex<br />Y8 : gfp mRNA<br />Y9 : GFP<br />
  13. 13. Disadvantages of this model<br />Many parameters<br />A large number of them unknown<br />Does not (yet) take into account flow rates or cell density<br />
  14. 14. The microsieve<br />
  15. 15. Modeling of the microsieve<br />A more global approach<br />Units are more logical<br />A more widely applicable model<br />However:<br />Many measurements are needed to validate the model<br />Many physical units are required<br />
  16. 16. Measurement plans<br />Introduce different flow rates to the system<br />Measure both the outflow and permeate flow (under influence of pressure)<br />Introduce a cell suspension to the system<br />Measure flow rates<br />
  17. 17. Goal<br />Produce a model that can estimate a flow rate to achieve:<br />An appropriate cell density<br />A constant oscillation through AHL expression<br />
  18. 18. Questions<br />In which way do we model this most efficiently?<br />Which of these models is actually feasible?<br />Is it possible to combine the models?<br />
  19. 19. Questions?<br />
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