Revision as of 17:58, 22 October 2017

   Modeling.|
   E. coli and M13 titer and fitness|
   https://static.igem.org/mediawiki/2017/a/ae/T--Heidelberg--2017_Background_Tiger.jpg%7C

   



  

    

      

        

          

            

        Modeling
            

              
            
            

        With Interactive Modelling iGEM Heidelberg provides a comprehensive set
        of tools that not only help to facilitate the implementation of PACE but
        also give an intuitive understanding of underlying mechanisms. To control
        highly complex processes such as PACE or PALE in a near-ideal way enables
        to exploit as much of it's potential as possible. The most important
        parameters were determined and examined with ODE systems, solved
        analytically or numerically, [stochastic and
        distributional] models. As far as possible the models are available
        online to make them accessible to anyone interested. When useful, a [tool
        for comparison of experimental data and the model] is available.
        In addition the Interactive modelling helps to monitor parameters that
        cannot be easily be interpreted from raw data, such as [] and combines
        different parameters to make useful statements about an experiment.
          
        
    

    
  

       
            

                Introduction
            
        iGEM Heidelberg provides a comprehensive set of models that allows for
        both control and evaluation of continuous and discontinuous direction
        evolution. The interactive models facilitate regular use of the models
        in everyday lab work and are easier to understand as they provide an
        intuitive understanding by enabling the user to observe how the model
        behaves when parameters are changed.
        Predictions from the models helped to design the novel method Predcel to
        be both reliable and time efficient.
        To get accurate modelling results for the used setup, a selection of
        parameters was determined experimentally and included in the models.
        As models for different levels of abstraction were needed, a variety of
        approaches from ordinary differential equations, delayed
        differential equations over stochastic simulations to molecular dynamics
        was applied to obtain valuable information on the different aspects of
        directed evolution.