GRNmap Testing Report: Strain Run Comparisons 20150527
From OpenWetWare

Purpose
 The purpose of this test is to analyze how the model behaves when running the same network with several different combinations of strain data.
 Issue #10 on GitHub: [1]
Test Conditions
 Date: 20150527
 Test Performed by: User:Katherine Grace Johnson, Katherine Grace Johnson Electronic Lab Notebook and User:Natalie Williams, Natalie Williams: Electronic Notebook
 Code Version: from the class BIOL39804/S15 (previously, code given by Fitzpatrick in September 2014. See below.)
 MATLAB Version: 2014b (previously, 2014a. See below)
 Computer on which the model was run: SEA12003
 Last two test categories (wt+dCIN5+dZAP1 and wt+dCIN5+dZAP1+dGLN3) cannot be run with earlier code on SEA12003 because the computer only has MATLAB 2014b. Error displayed when Fitzpatrick's code version is run:
Undefined function 'max' for input arguments of type 'matlab.graphics.GraphicsPlaceholder'.
Error in GRNmodel (line 15) nfig = max(figHandles);
 For the first ten categories, we will upload data obtained from September 2014. To see if we can run the last two categories on 2014b without any glaring differences, we will first test wt alone on both 2014a (with Fitzpatrick's fall version of the code) and 2014b (with the code from the class BIOL39804/S15). If the differences in estimated parameters are negligible, we could move on to run the last two categories (wt+dCIN5+dZAP1 and wt+dCIN5+dZAP1+dGLN3).
 Note: for these older versions of the code, the input file must be in the same folder as the code itself.
 For the first ten categories, we will upload data obtained from September 2014. To see if we can run the last two categories on 2014b without any glaring differences, we will first test wt alone on both 2014a (with Fitzpatrick's fall version of the code) and 2014b (with the code from the class BIOL39804/S15). If the differences in estimated parameters are negligible, we could move on to run the last two categories (wt+dCIN5+dZAP1 and wt+dCIN5+dZAP1+dGLN3).
To get the LSE & the penalty term, type the following:
Code for LSE: GRNstruct.GRNOutput.lse_out Code for Penalty GRNstruct.GRNOutput.reg_out
Excluding the wt, running the individual deletion strains through MATLAB was done with an iestimate set to 0.00E00. That value means that there would not be any estimation of the parameters.
 Because of this observation, we had to first compare the wt from MATLAB versions 2014a vs. 2014b.
 Next, we had to analyze the threshold values and the optimized weights in order to see if the differences between the outputs were negligible
 If they were negligible, we would proceed to run estimations of the individual strains
 The comparisons of the individual strains were estimated, so those do not have to be rerun on MATLAB.
We have decided to standardize everything on the code from BIO398 with the 2014b version of MATLAB. All data below will be run on this model (excluding the wt alone, 2014a). We are standardizing because, although the difference was negligible, it could confound our results if they also have negligible differences in estimated parameters.
 Note: when using this version, ensure that "fix_b" is set to 0 (i.e. estimate b) and create a simtime row on the optimization_parameters worksheet.
Results, Individual Strains
wt alone, 2014a
 Input sheet: Media:2014.10.23.Input 21 Gene Network Sigmoid Model wt NW.xls
 Output sheet: Media:2014.10.23.Input 21 Gene Network Sigmoid Model wt estimation output NW.xls
 Figures: Media:WT figures NW.zip
 LSE: 7.0809
 Penalty term: 0.0814
wt alone, 2014b
 Input sheet: Media:GJ2 Input 21 Gene Network Sigmoid Model wt alone 2014b.xlsx
 Output sheet: Media:GJ2 Input 21 Gene Network Sigmoid Model wt alone 2014b estimation output.xlsx
 Figures: Media:Images wt alone 2014b.zip
 LSE: 6.8824
 Penalty term: 0.1794
 Bar graphs comparing estimated weights and estimated b's for wt alone runs on 2014a vs. 2014b. (Note: estimated production rates not included because the earlier version of the code did not estimate production rates). There appears to be negligible difference between the two runs.
dCIN5 alone
 Input sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model dCIN5 hard0 NW.xls
 Output sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model dCIN5 hard0 estimation output NW.xls
 Figures: Media:DCIN5 figures NW.zip
 analysis.xlsx containing bar graphs
 LSE: 7.4496
 Penalty term: 0.2174
 GRNSight
dGLN3 alone
 Input sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model GLN3 estimation NW.xls
 Output sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model GLN3 estimation output NW.xls
 Figures: Media:DGLN3 figures NW.zip
 analysis.xlsx containing bar graphs
 LSE: 9.5367
 Penalty term: 0.3792
 GRNSight Network
dHMO1 alone
 Input sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model dHMO1 estimation NW.xls
 Output sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model dHMO1 estimation output NW.xls
 Figures: Media:DHMO1 figures NW.zip
 analysis.xlsx containing bar graphs
 LSE: 6.9139
 Penalty term: 0.1292
 GRNSight
dZAP1 alone
 Input sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model dZAP1 estimation NW.xls
 Output sheet: Media:2015.05.28.Input 21 Gene Network Sigmoid Model dZAP1 estimation output NW.xls
 Figures: Media:DZAP1 figures NW.zip
 analysis.xlsx containing bar graphs
 LSE: 6.9793
 Penalty term: 0.3216
 GRNSight
Results, Multiple Strains
All Strains
 Input sheet: Media:GJ input 21 Gene Network Sigmoid Model Estimate all strains.xlsx
 Output sheet: Media:GJ input 21 Gene Network Sigmoid Model Estimate all strains estimation output.xlsx
 Figures: Media:GJ all strains Figures.zip
 analysis.xlsx containing bar graphs
 LSE: 45.7010
 Penalty term: 0.7328
wt vs. dCIN5
 Input sheet: Media:Input 21 Gene Network Sigmoid Model Estimate WTCIN5 NW.xlsx
 Output sheet: Media:Input 21 Gene Network Sigmoid Model Estimate WTCIN5 estimation output NW.xls
 Figures: Media:Wt dCIN5 figures NW.zip
 analysis.xlsx containing bar graphs
 LSE: 15.1196
 Penalty term: 0.4994
 Weighted Network visualized
wt vs. dGLN3
 Input sheet: Media:Input 21 Gene Network Sigmoid Model Estimate WTvdGLN3 NW.xlsx
 Output sheet: Media:Input 21 Gene Network Sigmoid Model Estimate WTvdGLN3 estimation output NW.xls
 Figures: Media:Wt dGLN3 figures NW.zip
 analysis.xlsx containing bar graphs
 LSE: 18.1196
 Penalty term: 0.3529
 Weighted Network
wt vs. dHMO1
 Input sheet: Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dHMO1.xlsx
 Output sheet: Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dHMO1 estimation output.xlsx
 Figures: Media:GJ wt dHMO1 Figures.zip
 analysis.xlsx containing bar graphs
 LSE: 15.5341
 Penalty term: 0.1893
 Weighted Network
wt vs. dZAP1
 Input sheet: Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dZAP1s.xlsx
 Output sheet: Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dZAP1s estimation output.xlsx
 Figures: Media:GJ wt dZAP1 Figures.zip
 analysis.xlsx containing bar graphs
 LSE: 18.1215
 Penalty term: 0.1377
 Weighted Network
wt + dCIN5 + dZAP1
 Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dCIN5 dZAP1s.xlsx
 Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dCIN5 dZAP1s estimation output.xlsx
 Media:Wt dCIN5 dZAP1.zip
 analysis.xlsx containing bar graphs
 LSE: 26.6846
 Penalty term: 0.1682
 Weighted Network
wt + dCIN5 + dZAP1 + dGLN3
 Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dCIN5 dZAP1 dGLN3.xlsx
 Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dCIN5 dZAP1 dGLN3 estimation output.xlsx
 Media:GJ wt dCIN5 dZAP1 dGLN3 Figures.zip
 analysis.xlsx containing bar graphs
 LSE: 38.0868
 Penalty term: 0.2310
 GRNsight figure of weighted network:
Discussion
 Excel sheet comparing output weights for CIN5, FHL1, PHD1 and SKN7 regulators, estimated b values, and estimated production rates for all the above strain combinations: Media:GJ Estimated weight output comparison all combinations.xlsx
 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?
 Production rate vs. degradation rate. How do these combine?
 ANOVA pvalue for within strain
 Magnitude (large dynamics)?
 Variance (spread of the data points)?
 Some combination of the two?
 Fit of the model vs. parameter value stability
 To view the analysis for selfregulating genes, please view the following document: Media:2015.06.08.AutoReg Investigation NW.docx
 Ppt analyzing genes with no inputs and genes that only regulate themselves: Media:GRNmap Testing Analysis.pptx
 Powerpoint containing some genes that have poor T60 fits to the provided data: Media:Poor Fitting T60 Model to Data Points.pptx