Lucia I. Ramirez Week 14

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Introduction to GRNmap and Gene Regulatory Network Modeling

For this week's assignment, I ran the GRNmap model on the input workbook I created for the Week 13 Assignment. I ran the optimization twice; once where the threshold parameters, b, are not estimated and once where the threshold parameters 'are estimated. I then compared the estimated weight and production rate parameters outputted by these two runs with each other.

Analyzing the weight data

  • In a new workbook, I created a new sheet: estimated_weights.
  • Extracted the non-zero optimized weights from my workbook named "Input dhmo1 forward correct params Lsquared bFIXED" and put them in a single column next to the corresponding ControllerGeneA -> TargetGeneB label.
  • Changed fix_b to 0 in the "optimization_parameters" worksheet from my new workbook named "Input dhmo1 forward correct params Lsquared bESTIMATE", so that the thresholds will be estimated. Reran GRNmodel with the new input sheet.
  • Results of both sets of weights with bar chart.
  • Repeated these steps with production rates. Results show that the production rates did not change from alternating fix_b optimization parameter.

Visualizing the output of each model with GRNsight

  • Since GRNsight grabs the information under the worksheet titled "network", we decided to copy the weights under "out_network_optimized_weights" worksheet that were in the output file after each run (b fixed and b estimated) and then pasted them in that same worksheet but under the "network" worksheet. Here are our modified spreadsheets, changing only the "network" worksheet": fixed b and estimated b.
  • Once opening the modified spreadsheets, I arranged the genes in the same order I used to display them in my Week 12 assignment. See results of weighted networks.

two input workbooks

input 1: fixed b

input 2: estimated b

two output workbooks

output 1: fixed b

output 2: estimated b

Comparing figures after running GRNmodel

Powerpoint Slides

comparison of b values

Interpreting 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?

The changes were too slim to realize which run had a closer fit, but it seemed that the figures from the "fixed b" run had just a slightly better fit between the model data and actual data. I am not sure why there wasn't much change in the data.

  • Which genes showed the largest dynamics over the timecourse? Which genes showed differences in dynamics between the wild type and dHMO1? Given the connections in your network (see the visualization in GRNsight), does this make sense? Why or why not?

YHP1, ROX1, CIN5, and GLN3 showed the largest dynamics over the timecourse. The dynamics bettween the wild type and dHMO1 were more or less similar. But after seeing the networks, YHP1, MSN2, CIN5 and HMO1 were the thickest arrows, which somewhat related to the genes that showed the largest dynamics.

  • 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?

There were minor differences between the two runs which was also the case after seeing the two different networks produced through GRNsight.

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