Modeling a synthetic genetic oscillator

Part of an iGem project

iGemGlobal synthetic biology competitionInternational Genetically Engineered

Machine8th year in a row, first time Wageningen UR

competesGenetic building blocks

Projects

Synchronized Oscillatory SystemNegative feedback loopsPositive feedback for signaling moleculeSignaling molecule synchronizes oscillations

The Danino et. al scheme

Equations from Danino et. al

Advantages of this model4 differential equationsSimplified reaction schemeTakes the surrounding physics into account

Cell density

Modeling resultsEquations introduced in MatlabThe P function is covered by dde23

Disadvantages of the modelUnits of parametersSome biologically relevant information

missingNo useful result can be extracted

Alternative modelMore biologically relevant and accurate

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

Disadvantages of this modelMany parametersA large number of them unknownDoes not (yet) take into account flow rates or

cell density

The microsieve

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

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

GoalProduce a model that can estimate a flow

rate to achieve:An appropriate cell densityA constant oscillation through AHL expression

QuestionsIn which way do we model this most

efficiently?Which of these models is actually feasible?Is it possible to combine the models?

Questions?