Natalie Williams: Electronic Notebook

Natalie Williams: Electronic Notebook

Protocol for MATLAB

This page will help you input and run data sets from your document into an output.

• Dahlquist:GRNmap

Electronic Notebook

September 2014

September 18, 2014

Data Set Up
Openwetware familiarization: I became familiar with openwetware code and programing
MATLAB procedure: the MATLAB procedure that was written contains the instructions in using it to receive the output with the optimization network weights of the system.

Network
Ten random networks were made from the original network.

• The original network Excel file was used, and each cell on the network sheet had the following formula in it:
  • =IF(RAND()<0.1134,1,0)
• This procedure was done ten times to get these ten random networks
• Each network was saved as rand# (1 - 10)

September 25, 2014

The random network Excel files were put into MATLAB to be run to get the optimized weights of the network

• The file is saved as the final name with _output.xls

Opening the file, the weights of these networks was found on the optimized_network_weights sheet.

Visualization of the Networks
These files had to be re-saved as .xlsx in order to upload them to GRNSight. GRNSight visualizes the networks and because there are varying numbers that suggest how much one gene controls another, the resulting output has different colors. After each individual random network was visualized, it was compared to the original network. For better analysis, the same order of the proteins was used to see the different connectivities.

October 2014

October 2, 2014

Information that could have been gathered from comparing the Original network to the 10 Random ones was found. This information includes: Nodes: the positive and the negative
Frequencies: the In and Out degrees. These were how often one gene controlled other genes. It was found through the following equations:

• =COUNTIF(B2:B22,"<>"&0)
• =COUNTIF(B3:V3,"<>"&0)
• From this, the frequencies were found by looking at how often 0 appeared or 1 appeared, etc.
  • For example, =COUNTIF(B23:V23,"=0") for In Degree to see how often 0 occurred

Next, bar graphs were used to compare the weighted networks between a random network and the original network.
After that, the minimum and maximum values from each random network was found.

• The minimum was found using: =MIN(B2:V22)
• The maximum was found using: =MAX(B2:V22)

The sum was found of the entire worksheet of the optimized_network_weights.

• =SUM(B2:V22)

The average of the worksheet was also taken for the entire matrix.

• =AVERAGE(B2:V22)

We used this information to see if there were any key factors to what made the original network the one that we accepted. We hoped that it would shed some light on what key differences were between the random networks and the original one.

October 9, 2014

I was out of town, so there was nothing I needed to do specifically for this week.

October 16, 2014

I began the process of the forward simulations of the networks. I had to isolate the deletion strains and see if there was any resemblance between the wild type strain with the four deletion strains.

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