Team:Prairie View/Modeling

From 2008.igem.org

(Difference between revisions)
Line 1: Line 1:
-
 
<!--- The Mission, Experiments --->
<!--- The Mission, Experiments --->
 +
 +
{| style="color:#1b2c8a;background-color:#0c6;" cellpadding="3" cellspacing="1" border="1" bordercolor="#fff" width="62%" align="center"
 +
!align="center"|[[Team:Prairie_View|Home]]
 +
!align="center"|[[Team:Prairie_View/Team|The Team]]
 +
!align="center"|[[Team:Prairie_View/Project|The Project]]
 +
!align="center"|[[Team:Prairie_View/Parts|Parts Submitted to the Registry]]
 +
!align="center"|[[Team:Prairie_View/Modeling|Modeling]]
 +
!align="center"|[[Team:Prairie_View/Notebook|Notebook]]
 +
|}
 +
 +
 +
 +
<h1 align="center">Artifical Neural Network Modeling of the Molecular Biosensor</h1>
 +
<br>
 +
<br>
 +
 +
<h3>Training using back propagation</h3>:<br>
 +
 +
Select  function in matlab library to fit data
 +
<br>
 +
 +
Find error, and compare to target error
 +
<br>
 +
 +
<h3>General error function</h3>:
 +
<br>
 +
 +
Finally select function that gives least error
 +
 +
<h3>Sigmoid function</h3>:
 +
 +
An enose is an analytic device originally used for detecting chemicals and their concentrations in vapors
 +
<br>
 +
<br>
 +
 +
CAn be applied to a metal sensor by finding the functional relationship, which is the response to the concentration and type of metal
 +
<br>
 +
<br>
 +
<br>
 +
<br>
 +
 +
 +
Now that we have built our Molecular Sensor , with E-Nose we can: <br>
 +
Use experimental data for individual metal ion protein sequence, and ligations
 +
<br>
 +
 +
Look at a wider range of variation in the concentrations
 +
<br>
 +
 +
Collect data from rejected samples to determine the reliability of network
 +
<br>
 +
 +
And use the completion of the final network to identify the ion as well as it’s corresponding concentration
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
{| style="color:#1b2c8a;background-color:#0c6;" cellpadding="3" cellspacing="1" border="1" bordercolor="#fff" width="62%" align="center"
{| style="color:#1b2c8a;background-color:#0c6;" cellpadding="3" cellspacing="1" border="1" bordercolor="#fff" width="62%" align="center"

Revision as of 21:39, 29 October 2008


Home The Team The Project Parts Submitted to the Registry Modeling Notebook


Contents

Artifical Neural Network Modeling of the Molecular Biosensor



Training using back propagation

:

Select function in matlab library to fit data

Find error, and compare to target error

General error function

:

Finally select function that gives least error

Sigmoid function

:

An enose is an analytic device originally used for detecting chemicals and their concentrations in vapors

CAn be applied to a metal sensor by finding the functional relationship, which is the response to the concentration and type of metal




Now that we have built our Molecular Sensor , with E-Nose we can:
Use experimental data for individual metal ion protein sequence, and ligations

Look at a wider range of variation in the concentrations

Collect data from rejected samples to determine the reliability of network

And use the completion of the final network to identify the ion as well as it’s corresponding concentration


















Home The Team The Project Parts Submitted to the Registry Modeling Notebook
Retrieved from "http://2008.igem.org/Team:Prairie_View/Modeling"