Team:Sweden/Mathematical Modelling

From 2009.igem.org

(Difference between revisions)
(The Linguistic Cell)
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[[Image:pathway.jpg|600px|left|Model Structure.]]
[[Image:pathway.jpg|600px|left|Model Structure.]]
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<font color="blue">Here the mathematical modeling data and results will be published soon. The picture on the left represent the model that we are currently following to model our parsing cell.</font>
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<font color="blue">In this section you will find everything about the modeling of the sentence parsing cell.
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1 Design obstacles and Solutions
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Several problems needed to be taken care of when implementing a biological model.
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      i.  Repeated input (e.g. det det adj)
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    ii.  Wrong input (e.g. det adj det)
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For the repeatedly inputting the same PoS we used a counter (with courtesy of ETH iGEM 2005) which counts how many times an input occurs in a row. It uses an interval state and an intermediate input to move the automaton from one state to the other.
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For the wrong input we set inhibitions in the model in a specific way.
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 +
 
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2 The theory
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Several assumptions have been made to make the model work the way we want it to.
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      i.  I1 is always active at t=0,
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          meaning that every sentence begins with and intermediate input to activated I1.
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    ii.  The strength of every inhibition is the same
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    iii.  We introduce a stop signal, so we know when a sentence is finished
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 +
We used ODEs to model the system.  In general, it the ODEs look like this:
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      State i = synthesis * (every incoming activation and repression) – degradation of i
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:<math>\text{dydt}_i = syn_i \prod \limits_{j\in i} \begin{cases} act_j, & \text{if } j \text{ activates } i \\ rep_j, & \text{if } j \text{ inhibits } i \end{cases} - \text{deg}_i</math>
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Here the mathematical modeling data and results will be published soon. The picture on the left represent the model that we are currently following to model our parsing cell.</font>

Revision as of 07:27, 19 October 2009

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The Linguistic Cell


Model Structure.

In this section you will find everything about the modeling of the sentence parsing cell.

1 Design obstacles and Solutions

Several problems needed to be taken care of when implementing a biological model.

     i.   Repeated input (e.g. det det adj)
    ii.   Wrong input (e.g. det adj det)

For the repeatedly inputting the same PoS we used a counter (with courtesy of ETH iGEM 2005) which counts how many times an input occurs in a row. It uses an interval state and an intermediate input to move the automaton from one state to the other. For the wrong input we set inhibitions in the model in a specific way.


2 The theory Several assumptions have been made to make the model work the way we want it to.

     i.   I1 is always active at t=0, 
          meaning that every sentence begins with and intermediate input to activated I1.
    ii.   The strength of every inhibition is the same
   iii.   We introduce a stop signal, so we know when a sentence is finished

We used ODEs to model the system. In general, it the ODEs look like this:

      State i = synthesis * (every incoming activation and repression) – degradation of i
<math>\text{dydt}_i = syn_i \prod \limits_{j\in i} \begin{cases} act_j, & \text{if } j \text{ activates } i \\ rep_j, & \text{if } j \text{ inhibits } i \end{cases} - \text{deg}_i</math>


Here the mathematical modeling data and results will be published soon. The picture on the left represent the model that we are currently following to model our parsing cell.