GRNmap Testing Report NEW 2016-10-26 WT MXL626123M

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Test Conditions

  • Date: 2016 Oct. 26
  • Test Performed by: [[User:Natalie Williams|Natalie Williams]], [[Natalie Williams: Electronic Notebook]]
  • Code Version: 1.4.4
  • MATLAB Version: R2014b
  • Computer on which the model was run: MXL626123M (boulardii2)


  • We are doing an initial run of our db-derived wt network. Degradation rates will be different between these runs and previous runs. The most up-to-date connections of the genes may also be used, causing variance in the network itself. If a specific gene is in the network and has the corresponding expression data from its deletion, it will be run through the model.
  • Link to Issue # on GRNmap @ GitHub #265.


  • Input sheet: Media:16-genes_36-edges_NW-wt-fam_Sigmoid_estimation_all_strains.xlsx
  • Output sheet: [[Media:]]
  • Output .mat file (zipped): [[Media:]]
    • LSE:
    • Penalty term:
    • Number of iterations (counter):
  • Figures (all expression graphs .jpg files zipped together): [[Media:]]
  • Save the progress figure containing the counts manually: [[Media:]]
  • analysis.xlsx containing bar graphs: [[Media:]]
  • GRNsight figure of unweighted network:
  • GRNsight figure of weighted network:
    File:*filename here*


  • Discuss the results of the test with regards to the stated purpose. Additionally, answer the relevant questions below:
    • 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. 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?