Modelling a synthetic genetic oscillator

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Modeling a synthetic genetic oscillator Part of an iGem project

iGem Global synthetic biology competition International Genetically Engineered Machine 8th year in a row, first time Wageningen UR competes Genetic building blocks

Synchronized Oscillatory System Negative feedback loops Positive feedback for signaling molecule Signaling molecule synchronizes oscillations

Advantages of this model 4 differential equations Simplified reaction scheme Takes the surrounding physics into account Cell density

Modeling results Equations introduced in Matlab The P function is covered by dde23

Disadvantages of the model Units of parameters Some biologically relevant information missing No useful result can be extracted

Alternative model More biologically relevant and accurate

Equations for this model Y1 : lux-I mRNA Y2 : LUX-I protein Y3 : AHL Y4 : AHL-LUX-R complex Y5 : aiia mRNA Y6 : AiiA protein Y7 : AiiA-AHL complex Y8 : gfp mRNA Y9 : GFP

Disadvantages of this model Many parameters A large number of them unknown Does not (yet) take into account flow rates or cell density

Modeling of the microsieve A more global approach Units are more logical A more widely applicable model However: Many measurements are needed to validate the model Many physical units are required

Measurement plans Introduce different flow rates to the system Measure both the outflow and permeate flow (under influence of pressure) Introduce a cell suspension to the system Measure flow rates

Goal Produce a model that can estimate a flow rate to achieve: An appropriate cell density A constant oscillation through AHL expression

Questions In which way do we model this most efficiently? Which of these models is actually feasible? Is it possible to combine the models?

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Modelling a synthetic genetic oscillator

1. Modeling a synthetic genetic oscillator Part of an iGem project

2. iGem Global synthetic biology competition International Genetically Engineered Machine 8th year in a row, first time Wageningen UR competes Genetic building blocks

3. Projects

4. Synchronized Oscillatory System Negative feedback loops Positive feedback for signaling molecule Signaling molecule synchronizes oscillations

5. The Danino et. al scheme

6. Equations from Danino et. al

7. Advantages of this model 4 differential equations Simplified reaction scheme Takes the surrounding physics into account Cell density

8. Modeling results Equations introduced in Matlab The P function is covered by dde23

9. Disadvantages of the model Units of parameters Some biologically relevant information missing No useful result can be extracted

10. Alternative model More biologically relevant and accurate

11.

12. Equations for this model Y1 : lux-I mRNA Y2 : LUX-I protein Y3 : AHL Y4 : AHL-LUX-R complex Y5 : aiia mRNA Y6 : AiiA protein Y7 : AiiA-AHL complex Y8 : gfp mRNA Y9 : GFP

13. Disadvantages of this model Many parameters A large number of them unknown Does not (yet) take into account flow rates or cell density

14. The microsieve

15. Modeling of the microsieve A more global approach Units are more logical A more widely applicable model However: Many measurements are needed to validate the model Many physical units are required

16. Measurement plans Introduce different flow rates to the system Measure both the outflow and permeate flow (under influence of pressure) Introduce a cell suspension to the system Measure flow rates

17. Goal Produce a model that can estimate a flow rate to achieve: An appropriate cell density A constant oscillation through AHL expression

18. Questions In which way do we model this most efficiently? Which of these models is actually feasible? Is it possible to combine the models?

19. Questions?

Modelling a synthetic genetic oscillator

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Modelling a synthetic genetic oscillator