Tessa A. Morris Week 14

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Electronic Lab Notebook




Running the model prior to class on 4/23/2015

  • Run was done on the LMU library computer so the default folder options could be used.
  • Download GRNmap software zipFile on Lionshare and save to desktop.
  • Open Matlab
  • Download our model from Lionshare
  • Unzip GRNmap software zipFile and move to desktop
  • Move model excel file (TM_AG_expression_data_params) into unzipped GRNmap software zipFile
  • Open file in Matlab
  • Type the command GRNmodel, then select TM_AG_expression_data_params.xlsx when prompted.
  • Save images as jpegs into a folder "TM AG Model Figures RUn 1"
    • Compress this file
    • Upload to OpenWetWare

Data & Observations

  • The model ran successfully!
  • There were 24 figures generated.
  • Figures

Electronic Lab Notebook






Alyssa N Gomes


Run the GRNmap model, once where the threshold parameters, b, are not estimated and once where the threshold parameters 'are estimated and compare the estimated weight and production rate parameters outputted by these two runs with each other.


Introduction to GRNmap and Gene Regulatory Network Modeling

  1. In the optimization_parameters sheet of your input workbook, set fix_b to 1, fix_P to 0, and iestimate to 1. Set alpha to 0.01. Then run GRNmodel.
  2. Save all your graphs as jpegs and paste them into a powerpoint file. Please label things clearly, placing an appropriate number of graphs on each page for a readable visual. Take some care to make sure that the graphs are the same size and the aspect ratio has not been changed.
  3. Create a new workbook: call the worksheet estimated_weights. In this new worksheet, create a column of labels of the form ControllerGeneA -> TargetGeneB, replacing these generic names with the standard gene names for each regulatory pair in your network. Remember that columns represent Controllers and rows represent Targets in your network and network_weights sheets. (alphabetize by controller)
  4. Extract the non-zero optimized weights from their worksheet and put them in a single column next to the corresponding ControllerGeneA -> TargetGeneB label.
  5. Save your input workbook as a new file with a meaningful name (e.g. append "estimate-b" to the previous filename), and change fix_b to 0 in the "optimization_parameters" worksheet, so that the thresholds will be estimated. Rerun GRNmodel with the new input sheet.
  6. Repeat Parts (2) through (4) with the new output.
  7. Create an empty excel workbook, and copy both sets of weights into a worksheet.
  8. Create a bar chart in order to compare the "fixed b" and "estimated b" weights.
  9. Repeat (7) and (8) with the production rates.
  10. Copy the two bar charts into your powerpoint.
  11. Visualize the output of each of your model runs with GRNsight. Arrange the genes in the same order you used to display them in your Week 12 assignment for both of your model output runs. Take a screenshot of each of the results and paste it into your PowerPoint presentation. Clearly label which screenshot belongs to which run.

Note that GRNsight will display differently now that you have estimated the weights. For positive weights > 0, the edge will be given a regular (pointy) arrowhead to indicate an activation relationship between the two nodes. For negative weights < 0, the edge will be given a blunt arrowhead (a line segment perpendicular to the edge direction) to indicate a repression relationship between the two nodes. The thickness of the edge will vary based on the magnitude of the absolute value of the weight. Larger magnitudes will have thicker edges and smaller magnitudes will have thinner edges. The way that GRNsight determines the edge thickness is as follows. GRNsight divides all weight values by the absolute value of the maximum weight in the matrix to normalize all the values to between zero and 1. GRNsight then adjusts the thickness of the lines to vary continuously from the minimum thickness (for normalized weights near zero) to maximum thickness (normalized weights of 1). The color of the edge also imparts information about the regulatory relationship. Edges with positive normalized weight values from 0.05 to 1 are colored magenta; edges with negative normalized weight values from -0.05 to -1 are colored cyan. Edges with normalized weight values between -0.05 and 0.05 are colored grey to emphasize that their normalized magnitude is near zero and that they have a weak influence on the target gene.

Upload your powerpoint, your two input workbooks, and your two output workbooks and link to them in your individual journal.

Interpreting your results.

  • Examine the graphs that were output by each of the runs. Which genes showed the largest dynamics over the timecourse? Which genes showed differences in dynamics between the wild type and the other strain your group is using? Given the connections in your network (see the visualization in GRNsight), does this make sense? Why or why not?
  • Examine the bar charts comparing the weights and production rates between the two runs. Were there any major differences between the two runs? Why do you think that was? Given the connections in your network (see the visualization in GRNsight), does this make sense? Why or why not?

Data & Observations

  • Figures with fix_b at 1, fix_P at 0, iestimate at 1, and alpha at 0.01
  • The experiment was run by Alyssa and Tessa together (same excel documents)

fix_b to 1
fix_b to 0
Updated Powerpoint
Excel Summary
Summary Workbook
Interpreting results

  • Alyssa and I were confused by our Matlab output for the run which had b set to 0. Every value in the "out_network_optimized_weights" was 1 instead of a value. We were unable to find the source of the error, but we assume that this created inaccurate graphs.
  • The genes that showed the largest dynamics over the timecourse were MSN2 and MSN4. On the graphs only the wild type showed up on the the graph. This indicated that there may be something wrong with that excel document or the way it was run on Matlab. It makes sense that MSN2 and MSN4 showed the largest dynamics because they were the most connected on the GRNmap.
  • The bar charts were inconclusive when comparing the estimated weights. All of the weights fixed_b were 1 so it gave us no useful information to interpret. On the production rates were exactly the same for both, which once again is likely due to an error within the excel documents, rather than a reflection on the model.


  • Due to the large number of errors in class, the assignment was extended to Thursday. We will be trying to figure out what went wrong in class on 4/28/2015.
  • After talking to Dr. Fitzpatrick, we realized that we did not set iestimate equal to 1 for the b=0 trial
  • After correcting that error the Matlab output worked.
  • In order for GRNsight to display the outputs, change "out_network_optimized_weights" to just "network_optimized_weights"
    • >0.05 magenta -->
    • <0.05 cyan --|


  • Noticed an error with the input file. "MIG2 was called MIG1 in the dgln sheet"
  • We are running the corrected input files in Matlab
  • Analysis (done while updated input is running in Matlab, which takes a considerable amount of time)
    • dGLN3 did not control any genes and was only controlled by one gene
    • The results showed that there was little difference between the wild type and the deleted dgln3 strain.

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