User talk:Lauren M. Magee

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Week 14 Feedback

  • I have some observations to make about your data as you prepare for the final paper/presentation (I'm copying both partners on this feedback).
    • I noticed on your PowerPoint that there were no differences in the production rates between runs. I looked at your output workbooks for both runs and saw that you had copied the initial guesses for the production rates instead of the optimized versions. The optimized versions can be found in the sheet called "out_production_rates". You will need to redo this bar chart. Also, it would be helpful to see the labels of the gene names on the x axis instead of numbers. It would also be helpful to put the genes in alphabetical order.
    • For your bar chart that contains the weights, not all of the labels for the genes are showing up on the x axis. You might need to split this up into two charts to make them show.
    • In your PowerPoint, it is a nice visual display to see the two plots for the genes side by side. However, make sure that the plots are the same size.
    • For your presentation, you may not want to show all of the genes, but instead pick out a few to highlight that have interesting properties (goodness/badness of fit, large dynamics in expression, divergence between strains).
    • 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?
      • I this case, the question is asking whether the line (the model) fits the experimental data. In other words, do the lines go through the average or center of the data points? When I look at your data, it seems that most genes have a pretty good fit, except for MSN4, ROX1, and YOX1. Do you agree? Why do you think that is?
    • 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?
      • To my eye, it looks like MSN4, ROX1, CIN5, HMO1, YOX1, and YLR278C showed the largest dynamics over the timecourse (nonzero log fold changes). You could look at the p values for the ANOVA you did in Week 11 to confirm this. A lot of your genes showed divergence between the wt and dHMO1 strain. HMO1 has an out-degree of 9, which means that it affects a lot of genes in the network both directly and indirectly.
    • 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?
      • Since the bar chart for the production rates needs to be corrected, I can't comment on this yet.
      • I agree that there aren't major differences between the weight values. Upon closer inspection, it seems that your labels are reversed. ROX1 has 4 incoming edges, but no outgoing edges. You need to label the weights from the controller to the target gene (i.e. outgoing to incoming).
    • Finally, as you prepare your slides, make sure that each slide has a meaningful title that gives the message for that slide.

Kam D. Dahlquist 17:15, 1 May 2015 (EDT)

Week 13 Feedback

  • Thank you for submitting your work on time.
  • Your spreadsheet is complete and correct, although you need to save it as ".xlsx" instead of ".xls" for it to work with GRNsight.
  • Your electronic notebook is good, but could be improved by the following:
    • Actually give the filename for your new file.
    • You need to state which transcription factors did not have degradation rates in the file provided and which you had to substitute the specified value.

Kam D. Dahlquist 18:47, 21 April 2015 (EDT)

Week 11 Feedback Part 1

  • I checked your spreadsheet and you need to make a couple of corrections before proceeding:
    • Your equation for column M is incorrect; it has an incorrect row number reference. (This is the source of the num errors you see, later).
    • You did not actually calculate the Bonferroni correction in Column R, but went straight to the IF statement. Also, your IF statement is incorrect.

Kam D. Dahlquist 01:47, 26 March 2015 (EDT)

Week 7 Feedback

  • Your Week 7 individual journal assignment was late by 1 hour and 36 minutes and your shared journal reflection was on time.
  • You fulfilled all of the hyperlinks required for the assignment (back and forth to user page, to assignment, category).
  • Your electronic notebook had no descriptive text at all. The intent of an electronic notebook is to record what you did so that you or somebody else could reproduce what you did based on the information there. In addition to the MATLAB files, you should have described what values you used for each run of the model and why you chose them. You should have had multiple plots and your interpretation of what they mean.
  • Your handwritten notes on the analysis of the steady-state were good.

Kam D. Dahlquist 12:48, 17 March 2015 (EDT)

Week 1 Redux

  • I reviewed your Week 1 assignment a second time and noted that you made no further changes to this assignment.
  • I noted that your usage of the summary field has improved slightly; you have written comments in the summary field for 74% of the last 50 contributions you made. Remember, we are aiming for 100%.

Kam D. Dahlquist 19:22, 10 February 2015 (EST)

Week 1 Feedback

Here is the feedback to your Week 1 journal assignment.

  • Thank you for submitting your Week 1 assignment on time.
  • The grade for this assignment is posted on the MyLMUConnect Grade Center for this course. You will be able to earn back the points you missed on this assignment by completing the requested revisions below by the Week 3 journal assignment deadline of midnight on Tuesday, February 3 (Monday night/Tuesday morning).
    • Every time you make a change to a wiki page, please type something in the summary field (found above the "Save page" button). I estimate you are doing this about 67% of the time. You want to aim for 100%.
    • For your e-mail address, please use either a "mailto" link or a link to e-mail you through OpenWetWare.
    • Please list your future career interests and goals.
    • When you give publication information, please be sure to use an approved citation style (like APA which we are using for this class), which includes all co-authors, year, title, journal, volume, and page numbers. It is OK to substitue "manuscript in preparation" for things in progress, and use the terminology submitted for things that have actually been submitted to a journal.
    • Your external link needs a label.
    • Please "comment out" a section of your code.
    • The format for the link to your uploaded file is incorrect. You don't want to use[[Image: Lauren Magee Resume.pdf]], you want to use [[Media: Lauren Magee Resume.pdf]] instead.
    • You can also remove the OpenWetWare automated text from the bottom of this talk page.

Kam D. Dahlquist 17:50, 29 January 2015 (EST)

I've answered your question on my User talk page. Kam D. Dahlquist 21:28, 29 January 2015 (EST)

Answer from Dr. Fitzpatrick

You asked What importance do you see in the interdisciplinary study of biology and mathematics? Why do you think it is becoming necessary to integrate the two fields and how do you imagine math being integrated into the study of biology in the future?

I think biology is starting to catch up with physics, which has been traditionally aligned with mathematics. Biology is sufficiently complex that the reductionist approach in physics really does not work well. It has taken mathematics longer to develop a rich enough tool set to be able to help with biology. The importance of mathematical modeling in biology involves many points. Modeling helps to identify key assumptions and examine their implications. Even if the model doesn't fit data exactly, the qualitative behavior can provide insights into the biological system under study. Physics provides an example of how mathematics can help with advancement, but physics is sort of the low-hanging fruit for mathematics: physics is much easier for modeling. I believe the mathematical modes of inquiry can help to clarify questions in biology, suggest experiments, and support treatment design for all kinds of diseases. Ben G. Fitzpatrick 01:51, 21 January 2015 (EST)

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