GRNmap Testing Report: Non-1 Initial Weight Guesses 2015-05-28

This is a template page for various GRNmap model tests.

Code Version: from BIO398 Spring 2015

MATLAB Version: 2014b

Computer on which the model was run:

wt and dHMO, Initial Weight Guess: 0

• Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dHMO1 weight0test.xlsx
• Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dHMO1 weight0test estimation output.xlsx
• Media:GJ wt dHMO1 weightguess0.zip
• analysis.xlsx containing bar graphs
• LSE: 15.5341
• Penalty term:0.1893
  • both LSE and penalty term same as control (w=1) run, maybe the number of iterations is different? I did not save the counter image, but will do with future runs.
  • Initial quick comparisons show that estimated weights, b values, and production weights were slightly different (out to the third decimal place) between the w=1 and w=0 runs.

wt alone, all initial Weights 0

• Input sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model wt weights0.xlsx
• output sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model wt weights0 estimation output.xlsx
• Figures Media:Wt tester figures NW.zip
• analysis.xlsx containing bar graphs
• LSE: 6.8824

dCIN5, all initial weights 0

• Input sheet: Media:2014.10.23.Input 21 Gene Network Sigmoid Model dCIN5 estimation weights0.xlsx‎
• Output sheet: Media:2014.10.23.Input 21 Gene Network Sigmoid Model dCIN5 estimation weights0 estimation output.xlsx
• Figures: Media:DCIN5 weights0 figures NW.zip
• analysis.xlsx containing bar graphs
• LSE:7.4497

dGLN3, all initial weights 0

• Input sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model GLN3 estimation weights0.xlsx
• Output sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model GLN3 estimation weights0 estimation output.xlsx
• Figures: Media:DGLN3 weights0 figures NW.zip
• analysis.xlsx containing bar graphs
• LSE: 9.5367

dHMO1, all initial weights 0

• Input sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model dHMO1 estimation weights0.xlsx
• Output sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model dHMO1 estimation weights0 estimation output.xlsx
• Figures: Media:DHMO1 weights0 figures NW.zip
• analysis.xlsx containing bar graphs
• LSE: 6.9139

dZAP1, all initial weights 0

• Input sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model dZAP1 estimation weights0.xlsx
• Output sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model dZAP1 estimation weights0 estimation output.xlsx
• Figures: Media:DZAP1 weights0 figures NW.zip
• analysis.xlsx containing bar graphs
• LSE: 6.9793

Results and Discussion

• 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? Which genes have the worst fit between the model and actual data? Why do you think that is? (Hint: how many inputs do these genes have?) How does this help you to interpret the microarray data?
• Which genes showed the largest dynamics over the timecourse? In other words, which genes had a log fold change that is different than zero at one or more timepoints. The p values from the Week 11 ANOVA analysis are informative here. Does this seem to have an effect on the goodness of fit (see question above)?
• Which genes showed differences in dynamics between the wild type and the other strain your group is using? Does the model adequately capture these differences? 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?
• What other questions should be answered to help us further analyze the data?