William A. C. Gendron Week 14

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GRNmap and Gene Regulatory Network Modeling

Purpose: This week I used Week 13's work to produce a GRN map. I created two optimizations; once where the threshold parameters, b, are not estimated and once where the threshold parameters 'are estimated. I then compared the estimated weights and the production rates between these models in addition to entering them onto GRNsight.

  1. I set the optimization_parameters sheet of my input workbook to these values: fix_b to 1, fix_P to 0, and iestimate to 1. I set alpha to 0.01. kk_max to 1, MaxIter and MaxFunEval to 1e08 (one hundred million in plain English), and TolFun and TolX to 1e-6. Sigmoid to 1. igraph to 1. simtime to 0, 5 <...fill by steps of 5...> 60, each number in a different cell. Strain, Sheet, Deletion were set to match their position in how my data was organized. I had dCIN5 so I matched it to the deletion strain sheet and to the number that aligned with CIN5. Deletion was set to 0 for the wild type. After prepping this sheet, I ran GRNmodel.
  2. The graphs were saved as jpegs and then added to a powerpoint. The graphs were labeled appropriately and sized the same to make them easily comparable.
    • There were some issues with the program so I had to make sure the last graph was saved.
  3. I made a new workbook for analyzing weights labelling the first worksheet estimated_weights. In this new worksheet, I created a column of labels of the form ControllerGeneA -> TargetGeneB, replacing these generic names with the standard gene names for each regulatory pair in my network.
  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. I saved my input workbook as a new file with "estimate-b" to the previous filename, and changed fix_b to 0 in the "optimization_parameters" worksheet, so that the thresholds will be estimated. I then reran GRNmodel with the new input sheet.
  6. Witht the results I repeated parts (2) through (4) with the new output.
  7. I copied these weights to the weight workbook.
  8. I then creaed a bar chart in order to compare the "fixed b" and "estimated b" weights.
  9. I repeated parts (7) and (8) with the production rates.
  10. I copied the two bar charts into your powerpoint.
  11. I then visualized the output of each of my model runs with GRNsight.
    • In order for this to work, I needed to alter my output workbook slightly. I changed the name of the sheet called "out_network_optimized_weights" to "network_optimized_weights"; i.e., delete the "out_" from that sheet name.
    • I arranged the genes in the same pattern to make visualization easier. I then took a screenshot and entered that into my powerpoint.
    • Note that GRNsight will display color coded and shape coded information.
  12. I then uploaded my powerpoint and the results to openwetware.
  13. Interpreting my results.
    • Results:
    • Examine the graphs that were output by each of the runs. Which genes in the model have the closest fit between the model data and actual data? Why do you think that is? How does this help you to interpret the microarray data?
    • 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?