Team:Leeds/Modeling

After attending YSB 1.0, we signed on with Manchester to help develop a modelling standard - this also interlinks with our work with Purdue. We hope that by being more tightly involved in these processes, we will better understand how to approach our project in terms of Characterisation and Modelling. Additional thanks to Newcastle for showing us BioNetGen.

Modelling The Cpx Pathway

The key to making MicroBeagle successful hinges on proper integration with the Cpx pathway. As such, it is essential we develop a god working model, not only to predict how much fluorescence we can expect in different environments, but also to prototype and test potential methods for controlling this; in turn reducing our false positive rate.
We are currently using [http://bionetgen.org/index.php/Main_Page BioNetGenLanguage] to write the model code, as this provides a rule-based coding language that is easier to use than SBML. Rule-based modelling allows mechanistic simulation of the pertinent reactants, reducing the need to know the exact kinetics of reactions, as sub-processes can be absorbed into rate constants. Additionally, adaptation of the model should be easier after a basic framework has been produced, as new processes simply require insertion of new rules into the code. Finally, BNGL offers easy integration with [http://www.ibiblio.org/virtualcell/ Virtual Cell], a graphical simulator of cell systems - ideal for outreach with the public and teaching.

Cpx Pathway Model

So far, left|600px|modeling of CPX|frameless has produced a simplified model of the Cpx Pathway, and plans to improve it through an iterative design process. The model currently describes the behaviour of CpxP, CpxA, CpxR and the phosphorylation processes these use, as well as the effect of misfolded protein sub-units (caused by membrane stress) and the expected relative production of GFP versus other species produced via CpxR promotion. This should provide a workable, skeletal basis for characterisation of our first Bio-Device as GFP concentration scales directly with fluorescence. As we take measurements, we will then feed this back into the model to fine tune it, and thus develop it further for characterisation of our second device. Beyond this, it is hoped the model source code can then be submitted as a beta-stream to the modelling database.