# Lauren M. Magee Week 14

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### Important Files

- Media:both sets of weights_Lsquared.xlsx
- Media:both sets of production rates_Lsquared.xlsx
- Media:Input dhmo1 forward correct params Lsquared bFIXED.xlsx
- Media:Input dhmo1 forward correct params Lsquared bESTIMATE.xlsx
- Media:Input dhmo1 forward correct params Lsquared estimation output bFIXED.xlsx
- Media:Input dhmo1 forward correct params Lsquared estimation output bESTIMATE.xlsx
- Media:Matlab_results_and_weighted_networks_Lsquared.pptx

### Introduction to GRNmap and Gene Regulatory Network Modeling

For this week's assignment, we will run "Input_4_gene_forward_correct_params_Lsquared.xlsx". You will run the optimization twice; once where the threshold parameters, b, are **not** estimated and once where the threshold parameters '**are** estimated. You will compare the estimated weight and production rate parameters outputted by these two runs with each other.

- In the optimization_parameters sheet of your input workbook, set fix_b to 1, fix_P to 0, and iestimate to 1. Set alpha to 0.01. kk_max should be 1, 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 <...fill by steps of 5...> 60, each number in a different cell. Strain, Sheet, Deletion depend on how your data is organized in the workbook and what problem you are solving. You should have two strains, wild type and dHMO1. Deletion should be 0 for the wild type, 13 for dHMO1 (the position in your list of genes for that deleted gene). After prepping this sheet, run GRNmodel. From the output sheet from this run you will gather the "fixed_b" weights.
- Save all your graphs as jpegs.
- Create a new workbook for analyzing the weight data. In this workbook, create a new sheet: call it estimated_weights. In this new worksheet, create a column of labels of the form ControllerGeneA -> TargetGeneB, replacing these generic names with the standard gene names for each regulatory pair in your network. Remember that columns represent Controllers and rows represent Targets in your network and network_weights sheets.
- Extract the non-zero optimized weights from their worksheet and put them in a single column next to the corresponding ControllerGeneA -> TargetGeneB label.
- Save your input workbook as a new file with a meaningful name "Input_dhmo1_2ndrun_forward_correct_params_Lsquared.xlsx", and change fix_b to 0 in the "optimization_parameters" worksheet, so that the thresholds will be estimated. Rerun GRNmodel with the new input sheet. From the output sheet from this run you will gather the "estimated b" weights.
- Repeat Parts (2) through (4) with the new output.
- Create an empty excel workbook "both sets of weights_Lsquared.xlsx", and copy both sets of weights into a worksheet.
- Create a bar chart in order to compare the "fixed b" and "estimated b" weights.
- Repeat (7) and (8) with the production rates.
- Copy the two bar charts into your powerpoint.
- Visualize the output of each of your model runs with GRNsight. Arrange the genes in the same order you used to display them in your Week 12 assignment for both of your model output runs. Take a screenshot of each of the results and paste it into your PowerPoint presentation. Clearly label which screenshot belongs to which run.
- Note that GRNsight will display differently now that you have estimated the weights. For positive weights > 0, the edge will be given a regular (pointy) arrowhead to indicate an activation relationship between the two nodes. For negative weights < 0, the edge will be given a blunt arrowhead (a line segment perpendicular to the edge direction) to indicate a repression relationship between the two nodes. The thickness of the edge will vary based on the magnitude of the absolute value of the weight. Larger magnitudes will have thicker edges and smaller magnitudes will have thinner edges. The way that GRNsight determines the edge thickness is as follows. GRNsight divides all weight values by the absolute value of the maximum weight in the matrix to normalize all the values to between zero and 1. GRNsight then adjusts the thickness of the lines to vary continuously from the minimum thickness (for normalized weights near zero) to maximum thickness (normalized weights of 1). The color of the edge also imparts information about the regulatory relationship. Edges with positive normalized weight values from 0.05 to 1 are colored magenta; edges with negative normalized weight values from -0.05 to -1 are colored cyan. Edges with normalized weight values between -0.05 and 0.05 are colored grey to emphasize that their normalized magnitude is near zero and that they have a weak influence on the target gene.

- Upload your powerpoint, your two input workbooks, and your two output workbooks and link to them in your individual journal.
- Interpreting your 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 two runs that we completed for this weeks assignment did not seem to very to much from one another. With that being said, it is hard to determine which one is a better model.

- 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?
- YHP1, ROX1, CIN5, and GLN3 showed the largest dynamics over the timecourse.That being said, I guess the differences in dynamics between the wild type and dHMO1 were the change in ROX1 and GLN3 (thinner arrow connections in wild type) and MSN2 and HMO1 (thicker arrow connection in wild type). The thicker connection with the HMO1 gene makes since for the wild type, because it is deleted in the dHMO1 strain and should therefore show little to no connection. The other differences clearly mean something to the network change that occurs along with the deletion, but I'm not sure what this change means yet.

- 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 no major differences between the two runs, which may have been due to the minimal changes that were made to the data between the different runs. Maybe our network isn't affected by changes in "fixed_b" values.

- 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?

## Lauren M. Magee

- Week 1
- Week 2
- Week 3
- Week 4
- Week 5
- Week 6
- Week 7
- Week 8
- Assignment Cancelled

- Week 9
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