GRNmap Testing Report: Strain Run Comparisons 2015-05-27: Difference between revisions

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==wt + dCIN5 + dZAP1==
==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.xlsx]]
*output.xlsx
*[[Media:GJ Input 21 Gene Network Sigmoid Model Estimate wt dCIN5 dZAP1s estimation output.xlsx]]
*zipped figures
*zipped figures
*analysis.xlsx containing bar graphs
*analysis.xlsx containing bar graphs

Revision as of 10:38, 28 May 2015

Code Version: "current" version from Dr. Fitzpatrick 2014-09-18

MATLAB Version: 2014a

Computer on which the model was run: SEA120-03

  • Last two test categories (wt+dCIN5+dZAP1 and wt+dCIN5+dZAP1+dGLN3) cannot be run on SEA120-03 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 BIOL398-04/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).
    • New code version: from BIO398 (Note: file zipped with code was used to test wt)
    • MATLAB version: 2014b
    • Computer on which the model was run: SEA201-03
      • Note: for these older versions of the code, the input file must be in the same folder as the code itself.


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 re-run 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.

wt alone, 2014a

wt alone, 2014b

dCIN5 alone

  • Input sheet: [[Media:]]
  • Output sheet: [[Media:]]
  • Figures: [[Media:]]
  • analysis.xlsx containing bar graphs
  • LSE:
  • Penalty term:

dGLN3 alone

  • Input sheet: [[Media:]]
  • Output sheet: [[Media:]]
  • Figures: [[Media:]]
  • analysis.xlsx containing bar graphs
  • LSE:
  • Penalty term:

dHMO1 alone

  • Input sheet: [[Media:]]
  • Output sheet: [[Media:]]
  • Figures: [[Media:]]
  • analysis.xlsx containing bar graphs
  • LSE:
  • Penalty term:

dZAP1 alone

  • Input sheet: [[Media:]]
  • Output sheet: [[Media:]]
  • Figures: [[Media:]]
  • analysis.xlsx containing bar graphs
  • LSE:
  • Penalty term:

All Strains

wt vs. dCIN5

wt vs. dGLN3

wt vs. dHMO1

wt vs. dZAP1

wt + dCIN5 + dZAP1

wt + dCIN5 + dZAP1 + dGLN3

  • input.xlsx
  • output.xlsx
  • zipped figures
  • analysis.xlsx containing bar graphs
  • LSE:
  • Penalty term:

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?

Other Links

To view K. Grace Johnson's Notebook: here

To view Natalie Williams' Notebook: here

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