# Team:ETH Zurich/labblog/20140829mod

(Difference between revisions)
 Revision as of 11:09, 10 July 2014 (view source)Clormeau (Talk | contribs) (Created page with "
== Integrase parameters == = Wednesday, July 9th =
=== Finding int...")← Older edit Revision as of 13:35, 16 July 2014 (view source)Eledieu (Talk | contribs) (→Finding integrase parameters)Newer edit → Line 7: Line 7: === Finding integrase parameters === === Finding integrase parameters === - We used MCMC method and least square method to fit curves from the paper ... to a model we have written, that contains parameters for integrases. + In order to predict accurately the experiments we designed, we defined two different strategies to retrieve parameters for the integrase system. - Results : + As we did not find these parameters on the web, we used the data given by the Bonnet paper "Amplifying genetic logic gates". The figure S4 is particularly interesting, even if it does not give any information on the dynamics of the system. We modeled and wrote the equations corresponding to Bonnet's experiment. We then considered the steady state behavior of the dynamic system. + To fit the parameters with the data, we first try to use a minimum search for the difference between the data and the parametric function. Then, using these first approximation, we tried to do a Markov Chain Monte Carlo approach to find a more suited curve. + + As these two methods are strongly prior dependent, we planned another step of screening in order to have meaningful results.

# Wednesday, July 9th

### Finding integrase parameters

In order to predict accurately the experiments we designed, we defined two different strategies to retrieve parameters for the integrase system.

As we did not find these parameters on the web, we used the data given by the Bonnet paper "Amplifying genetic logic gates". The figure S4 is particularly interesting, even if it does not give any information on the dynamics of the system. We modeled and wrote the equations corresponding to Bonnet's experiment. We then considered the steady state behavior of the dynamic system.

To fit the parameters with the data, we first try to use a minimum search for the difference between the data and the parametric function. Then, using these first approximation, we tried to do a Markov Chain Monte Carlo approach to find a more suited curve.

As these two methods are strongly prior dependent, we planned another step of screening in order to have meaningful results.