Alyssa N Gomes Week 14 Journal

Introduction to GRNmap and Gene Regulatory Network Modeling

This week's procedure involves running the GRNmap model on the input workbacks from last week using two different parsmeters for the threshold. We will use these two separate parameters in order to compare the runs and production rates.

1. In the optimization parameters of the input workbook, let fix_b=1 and fix_P=0 and iestimate=1. Let alpha be 0.01 and kk_max be 1. Let Maxlter and MaxFunEVal equal 1e08. Let TolFun and TolX be 1e-6 MaxIter and MaxFunEval should be 1e08 (one hundred million in plain English), and TolFun and TolX should be 1e-6. Sigmoid should be 1. igraph should be 1. simtime should be 0 5 10 15 20 25 30 35 40 45 50 55 60, each number in a different cell. After prepping this sheet, run GRNmodel by setting all the files in the matlab file folder and then type in 'GRNmodel' and click the input file. .
2. save all graphs as jpegs and put in the powerpoint, labeling. Save all your graphs as jpegs and paste them into a powerpoint file.
3. make a new workbook in excel and name the sheet estimated_weights. Create a new workbook for analyzing the weight data. create a column "ControllerGeneA->TargetGeneB". And then in the rows below, input the control->target genes given in your network sheet. Then create columns next to it, using the non-zero optimized weights and save the worksheet. change fix_b to 0 in the optimization parameters worksheet. rerun the GRNmodel. re-prep the sheet by putting in new weights.
4. Extract the non-zero optimized weights from their worksheet and put them in a single column next to the corresponding ControllerGeneA -> TargetGeneB label.
5. create a new worksheet and copy the weights into it. create a chart that compares the two weights.
6. do the same with the production rates. and put the charts into the powerpoint.
7. use GRNsight to visualize this,
8. use GRNsight to model these genes and put into the powerpoint, with similar positioning for the GRNsight as previously.
9. update your journal with the new excel sheets and grnsight photos.
10. interpret the results, examining the graphs and answering several questions.
    • 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?
    • Which genes showed the largest dynamics over the timecourse? Which genes showed differences in dynamics between the wild type and the other strain your group is using? 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?

Data and Observations

• Figures with fix_b at 1, fix_P at 0, iestimate at 1, and alpha at 0.01
• The experiment was run by Alyssa and Tessa together (same excel documents)

fix_b to 1
Input
Output
fix_b to 0
Input
Output
Powerpoint
Excel Summary Media:TM AG Summary Workbook.xlsx

• Our GRNsight images are included in the powerpoint

• upon doing this, Tessa and I ran into a couple issues. For the first output file, we had forgotten to save the numbers for the weights even though we had saved the photos. Upon us re-doing this, looking at the weights, we saw that all of the output weights came out either as 0 or 1. We attempted to go back and examine our errors, re-editing the original files to double check that we were correct.
• upon re-running it in matlab on several different computers, we continued to find the weights of 0 and 1and certain files kept on retracting back to the original numbers and inputs for the cin5 template. we decided eventually to upload all images and output files anyways, in order to seek future help for our final presentation.
• Which genes showed the largest dynamics over the timecourse? Which genes showed differences in dynamics between the wild type and the other strain your group is using? Given the connections in your network (see the visualization in GRNsight), does this make sense? Why or why not?
  • Upon doing this week's assignment, we had a hard time interpreting and discovering the dynamics over the timecourse. Our information was very scattered and amongst re-trials, we had found no significant trend or method in finding an accurate difference between wild type and dgln3.
• 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?
  • Again, due to error and all output rates equaling 0 or 1 when we set the fix_b to 0, we saw indescrepancies for the output answers. Our connections in the GRNsight also gave no insight as to a conclusion
• Future plans: This experiment definitely seemed like it was going fine and like we would have completed our assignment much much earlier today, given we had worked on it previously in the week and had an idea of what we were doing. However, now, although we put in the images for the first trial where b=0 , we cannot be sure that those images are correct or follow our error that we discovered upon realizing that we needed the weights again. However, we must note that our graphs had, for some of them, just the wild type information gathered and displayed.

____________________________________________________

• Figures for the Original Parameters: Figures

• Figures for the Updated Parameters Figures with fix_b at 1, fix_P at 0, iestimate at 1, and alpha at 0.01
  • • PPT: Tessa Morris and Alyssa Gomes PPT

4/28

• Upon getting to class Tuesday, we were told that we would be given an extension until Thursday due to numerous errors
• After re-checking our input file for the b=0 estimate we realized that the iestimate value was set to 0 rather than 1. We are currently re-running it but have hopes it will work because the counter image came up, unlike before, because there was actually something to estimate.

• Figures with fix_b at 1, fix_P at 0, iestimate at 1, and alpha at 0.01
• The experiment was run by Alyssa and Tessa together (same excel documents)

fix_b to 1
Input
Output
fix_b to 0
Input
Output
Powerpoint
Updated Powerpoint
Excel Summary
Summary Workbook