Modelling a synthetic genetic oscillator
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Transcript of Modelling a synthetic genetic oscillator
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Modeling a synthetic genetic oscillator
Part of an iGem project
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iGemGlobal synthetic biology competitionInternational Genetically Engineered
Machine8th year in a row, first time Wageningen UR
competesGenetic building blocks
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Projects
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Synchronized Oscillatory SystemNegative feedback loopsPositive feedback for signaling moleculeSignaling molecule synchronizes oscillations
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The Danino et. al scheme
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Equations from Danino et. al
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Advantages of this model4 differential equationsSimplified reaction schemeTakes the surrounding physics into account
Cell density
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Modeling resultsEquations introduced in MatlabThe P function is covered by dde23
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Disadvantages of the modelUnits of parametersSome biologically relevant information
missingNo useful result can be extracted
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Alternative modelMore biologically relevant and accurate
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Equations for this modelY1 : 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
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Disadvantages of this modelMany parametersA large number of them unknownDoes not (yet) take into account flow rates or
cell density
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The microsieve
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Modeling of the microsieveA more global approachUnits are more logicalA more widely applicable model
However:Many measurements are needed to validate the
modelMany physical units are required
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Measurement plansIntroduce different flow rates to the systemMeasure both the outflow and permeate flow
(under influence of pressure)
Introduce a cell suspension to the systemMeasure flow rates
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GoalProduce a model that can estimate a flow
rate to achieve:An appropriate cell densityA constant oscillation through AHL expression
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QuestionsIn 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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Questions?